From e57ab5ceadc6b7b97767480d9b65a6ed5b74d542 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 10:55:24 +0100 Subject: [PATCH 001/216] feat: new pixi env setup file --- pixi.toml | 127 ++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 99 insertions(+), 28 deletions(-) diff --git a/pixi.toml b/pixi.toml index 381e8c4..0b3830b 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,40 +1,111 @@ [workspace] authors = ["energyls "] -channels = ["conda-forge", "bioconda", "gurobi"] +channels = ["conda-forge", "bioconda"] name = "shift" -platforms = ["linux-64"] +description = "Techno-economic optimization of global iron and steel supply chains under decarbonization" +platforms = ["linux-64", "win-64"] version = "0.1.0" [tasks] [dependencies] +# Core Python +python = ">=3.10" +# Inhouse packages - energy system modeling +pypsa = ">=0.32.1" +atlite = ">=0.3" +linopy = ">=0.4.4" +powerplantmatching = ">=0.5.15" -python = ">=3.11,<3.14" -pypsa = ">=0.25" -pandas = ">=2.0" -numpy = ">=1.24" -matplotlib = ">=3.5" -seaborn = ">=0.13" -xarray = ">=2023.9" -netcdf4 = ">=1.6" -cartopy = ">=0.23" -geopandas = ">=0.13" -shapely = ">=2.0" -pyproj = ">=3.0" -pyyaml = ">=6.0" -requests = ">=2.25" -tqdm = ">=4.64" -country_converter = ">=0.7" -linopy = ">=0.3" -gurobi = ">=12.0.3" -# gurobipy = "*" # Gurobi is required by some solver calls; version managed via gurobi channel/license -snakemake-executor-plugin-cluster-generic = ">=1.0.9" -snakemake-executor-plugin-slurm = ">=1.9.2" +# Workflow orchestration snakemake-minimal = ">=9" snakemake-storage-plugin-http = ">=0.3" -snakemake-storage-plugin-cached-http = ">=0.1.0" -pycountry = ">=24.6.1" -# Further requirements -ipykernel = ">=7.0.0" -coincbc = "*" \ No newline at end of file +snakemake-executor-plugin-slurm = "*" +snakemake-executor-plugin-cluster-generic = "*" + +# Data processing and I/O +dask = "*" +pandas = ">=2.1" +numpy = "*" +xarray = ">=2024.03.0" +pytables = "*" +netcdf4 = "*" +libgdal-netcdf = "*" +lxml = "*" +xlrd = "*" +openpyxl = "*" + +# Geospatial and mapping +geopandas = ">=1" +rioxarray = "*" +cartopy = "*" +rasterio = "*" +fiona = "*" +shapely = ">=2.0" +proj = "*" +descartes = "*" + +# Scientific computing +scipy = "*" +networkx = "*" +seaborn = "*" +matplotlib = "*" + +# Utilities +pyyaml = "*" +tqdm = "*" +country_converter = "*" +geopy = "*" +pytz = "*" +memory_profiler = "*" +jpype1 = "*" +pyxlsb = "*" +graphviz = "*" +geojson = "*" + +[pypi-dependencies] +# pip-installable packages (always included) +tsam = ">=2.3.1" +entsoe-py = "*" +pypsatopo = "*" + +[dependency-groups] +# Optional dependency sets usable with features/environments +test = [ + "pytest", + "pytest-cov", +] +docs = [ + "sphinx", +] +gurobi = [ + "gurobipy", +] +dev = [ + { include-group = "test" }, + { include-group = "docs" }, + { include-group = "solvers" }, + "ruff", + "pre-commit", + "pylint", + "jupyter", + "ipython", +] + +[tool.pixi.environments] +default = { solve-group = "default" } +test = { solve-group = "default", features = ["test"] } +docs = { solve-group = "default", features = ["docs"] } +dev = { solve-group = "default", features = ["dev"] } + +[tool.ruff] +# Ruff linter configuration +line-length = 88 +target-version = "py310" + +[tool.ruff.lint] +select = ["E", "F", "W", "I"] # Basic style, logical and import checks + +[tool.pytest.ini_options] +testpaths = ["tests"] \ No newline at end of file From f2a0410720da8ae4000dc647152b6ad05ceaab21 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 10:55:50 +0100 Subject: [PATCH 002/216] feat: Add pre-commit config with ruff + hygiene hooks --- .pre-commit-config.yaml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..f3369a6 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,22 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.0.261 + hooks: + - id: ruff + args: ["--fix"] + # This will run ruff in the current repository root (default) + # and fix formatting issues automatically. + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: mixed-line-ending + args: ["--fix=auto"] + - id: check-merge-conflict + - id: check-added-large-files + - id: check-yaml + exclude: ^conda/meta.yaml$ + - id: check-json + - id: check-xml From d509aa5f667a195abd0aa5ff5c207e1b32829ef4 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 11:59:41 +0100 Subject: [PATCH 003/216] fix: correct pixi.toml feature/environment configuration --- pixi.toml | 49 +++++++++++++++++++++---------------------------- 1 file changed, 21 insertions(+), 28 deletions(-) diff --git a/pixi.toml b/pixi.toml index 0b3830b..669eb9a 100644 --- a/pixi.toml +++ b/pixi.toml @@ -69,40 +69,33 @@ geojson = "*" tsam = ">=2.3.1" entsoe-py = "*" pypsatopo = "*" +gurobipy = "*" -[dependency-groups] -# Optional dependency sets usable with features/environments -test = [ - "pytest", - "pytest-cov", -] -docs = [ - "sphinx", -] -gurobi = [ - "gurobipy", -] -dev = [ - { include-group = "test" }, - { include-group = "docs" }, - { include-group = "solvers" }, - "ruff", - "pre-commit", - "pylint", - "jupyter", - "ipython", -] - -[tool.pixi.environments] -default = { solve-group = "default" } -test = { solve-group = "default", features = ["test"] } -docs = { solve-group = "default", features = ["docs"] } -dev = { solve-group = "default", features = ["dev"] } +[feature.test.dependencies] +pytest = "*" +pytest-cov = "*" + +[feature.docs.dependencies] +sphinx = "*" + +[feature.dev.dependencies] +ruff = "*" +pre-commit = "*" +pylint = "*" +jupyter = "*" +ipython = "*" + +[environments] +default = { features = [], solve-group = "default" } +test = { features = ["test"], solve-group = "default" } +docs = { features = ["docs"], solve-group = "default" } +dev = { features = ["dev", "test", "docs"], solve-group = "default" } [tool.ruff] # Ruff linter configuration line-length = 88 target-version = "py310" +exclude = ["**/*.ipynb"] [tool.ruff.lint] select = ["E", "F", "W", "I"] # Basic style, logical and import checks From fa090289e5ba476e51262bf4cb7c61a88520bb6e Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 12:00:24 +0100 Subject: [PATCH 004/216] feat: add pre-commit configuration --- .pre-commit-config.yaml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f3369a6..673f02f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,8 +4,7 @@ repos: hooks: - id: ruff args: ["--fix"] - # This will run ruff in the current repository root (default) - # and fix formatting issues automatically. + files: "\\.py$" # This will run ruff on Python source files only and fix formatting issues automatically. - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 From d5f892fdec9b5fbdb177c2903365b5835699d827 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 12:01:29 +0100 Subject: [PATCH 005/216] chore: lint Python scripts with ruff --- workflow/scripts/_helpers.py | 14 -------------- .../create_hydrogen_supply_curve_with_demand.py | 7 ++++++- workflow/scripts/model_lcoh.py | 5 ++--- workflow/scripts/model_lcox.py | 8 ++------ workflow/scripts/model_trade.py | 4 ++-- 5 files changed, 12 insertions(+), 26 deletions(-) diff --git a/workflow/scripts/_helpers.py b/workflow/scripts/_helpers.py index c2bcfaa..0626b03 100644 --- a/workflow/scripts/_helpers.py +++ b/workflow/scripts/_helpers.py @@ -1,25 +1,11 @@ -import calendar -import io import logging -import os -import shutil -import subprocess -import sys -import time -import zipfile -from datetime import datetime, timedelta from pathlib import Path -import country_converter as coco -import geopandas as gpd -import numpy as np -import pandas as pd import requests import yaml # from fake_useragent import UserAgent # from pypsa.components import component_attrs, components -from shapely.geometry import Point from tqdm import tqdm logger = logging.getLogger(__name__) diff --git a/workflow/scripts/create_hydrogen_supply_curve_with_demand.py b/workflow/scripts/create_hydrogen_supply_curve_with_demand.py index a1893fa..5e922ca 100644 --- a/workflow/scripts/create_hydrogen_supply_curve_with_demand.py +++ b/workflow/scripts/create_hydrogen_supply_curve_with_demand.py @@ -1,6 +1,11 @@ import pandas as pd import matplotlib.pyplot as plt +# Note: This script is intended to be executed via Snakemake. +# The `snakemake` object will be injected by the workflow. +# Ruff still needs a definition to avoid F821 in linting. +snakemake = None # noqa: F821 + def create_supply_curve_with_demand(): # input: "resources/lcoh/{region}/results_{demand_factor}.csv", @@ -15,7 +20,7 @@ def create_supply_curve_with_demand(): ]["demand"] ) # final_green is wrong, this is final_el - final_green = ( + ( final_demand * float( final_demand_data.loc[ diff --git a/workflow/scripts/model_lcoh.py b/workflow/scripts/model_lcoh.py index fe2f847..2877101 100644 --- a/workflow/scripts/model_lcoh.py +++ b/workflow/scripts/model_lcoh.py @@ -1,6 +1,5 @@ import pypsa import pandas as pd -import numpy as np # import matplotlib.pyplot as plt # from pyomo.environ import Constraint @@ -59,7 +58,7 @@ def building_model(ds, dw, dc, load, h_cost): battery_cost = calc_cap_cost(dc, "battery storage", interest_rate) # hydrogen cost can either be 0 or real cost - if h_cost == False: + if h_cost is False: hydrogen_storage_cost = 0 else: hydrogen_storage_cost = calc_cap_cost( @@ -178,7 +177,7 @@ def save_lcoh(solved_network): try: solved_network.objective - except: + except Exception: # if infeasible print("saving infeasible network") res.loc[res.shape[0]] = [ diff --git a/workflow/scripts/model_lcox.py b/workflow/scripts/model_lcox.py index 1bec15e..4d2196f 100644 --- a/workflow/scripts/model_lcox.py +++ b/workflow/scripts/model_lcox.py @@ -173,17 +173,13 @@ def building_model(n, ds, dw, dc, load, h_cost, iron_ore_cost): raise ValueError("product not recognized, choose steel, hbi, eaf, eaf-grid") # hydrogen cost can either be 0 or real cost. Real cost is the default of the imported network - if h_cost == False: + if not h_cost: n.stores.at[ "hydrogen storage tank type 1 including compressor (exp)", "capital_cost" ] = 0 - else: - pass - if iron_ore_cost == False: + if not iron_ore_cost: n.generators.at["iron ore DRI-ready (exp)", "marginal_cost"] = 0 - else: - pass # Remove trace shipping components n = remove_shipping_importer_components(n) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index e41023b..d7d23cf 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -127,7 +127,7 @@ def building_model( demands.loc[demands["region"] == region_name].loc[:, "demand"].values[0] * 1 ) # float(snakemake.wildcards["demand"]) print( - f"Load set via snakemake.wildcard to 100% of regional final energy demand." + "Load set via snakemake.wildcard to 100% of regional final energy demand." ) n.add( @@ -650,7 +650,7 @@ def solve_network(n, mga=None): n.optimize(n.snapshots, solver_name=solver_name, solver_options=options) - if mga == None: + if mga is None: pass else: From 365993c6288ac00b1b97eafd1e7b330aafc4970a Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 12:08:50 +0100 Subject: [PATCH 006/216] chore: notebook linting adjustments --- ruff.toml | 2 ++ workflow/notebooks/_helpers.py | 14 -------------- workflow/notebooks/analysis-basemodel.ipynb | 2 -- workflow/notebooks/analysis-coststructure.ipynb | 3 --- .../notebooks/analysis-globalsupplycurve.ipynb | 6 +----- workflow/notebooks/analysis-hourly.ipynb | 4 ---- workflow/notebooks/analysis-iron-ore.ipynb | 9 +-------- workflow/notebooks/analysis-re.ipynb | 9 +-------- workflow/notebooks/analysis-steel-hbi-split.ipynb | 5 +---- workflow/notebooks/analysis-trace.ipynb | 2 -- workflow/notebooks/compare-scenarios.ipynb | 7 +------ workflow/notebooks/conceptual-model.ipynb | 7 +------ workflow/notebooks/cost-transport-iron-ore.ipynb | 0 workflow/notebooks/global-iron-ore.ipynb | 2 +- workflow/notebooks/global-steel-production.ipynb | 2 +- workflow/notebooks/plot_countries.ipynb | 1 - workflow/notebooks/prepare-iron-ore.ipynb | 1 - workflow/notebooks/prepare-steel.ipynb | 1 - workflow/notebooks/validation.ipynb | 3 --- 19 files changed, 10 insertions(+), 70 deletions(-) create mode 100644 ruff.toml delete mode 100644 workflow/notebooks/cost-transport-iron-ore.ipynb diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000..b6a6604 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,2 @@ +line-length = 88 +exclude = ["**/*.ipynb"] diff --git a/workflow/notebooks/_helpers.py b/workflow/notebooks/_helpers.py index c2bcfaa..0626b03 100644 --- a/workflow/notebooks/_helpers.py +++ b/workflow/notebooks/_helpers.py @@ -1,25 +1,11 @@ -import calendar -import io import logging -import os -import shutil -import subprocess -import sys -import time -import zipfile -from datetime import datetime, timedelta from pathlib import Path -import country_converter as coco -import geopandas as gpd -import numpy as np -import pandas as pd import requests import yaml # from fake_useragent import UserAgent # from pypsa.components import component_attrs, components -from shapely.geometry import Point from tqdm import tqdm logger = logging.getLogger(__name__) diff --git a/workflow/notebooks/analysis-basemodel.ipynb b/workflow/notebooks/analysis-basemodel.ipynb index d45880b..4dca377 100644 --- a/workflow/notebooks/analysis-basemodel.ipynb +++ b/workflow/notebooks/analysis-basemodel.ipynb @@ -8,7 +8,6 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n", "import numpy as np\n", "import xarray as xr" ] @@ -225,7 +224,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots()\n", "n.statistics.energy_balance(aggregate_time=False).loc[:, :, \"el\"].droplevel(0).iloc[\n", " :, :4000\n", diff --git a/workflow/notebooks/analysis-coststructure.ipynb b/workflow/notebooks/analysis-coststructure.ipynb index 4e2f0fb..9ef9905 100644 --- a/workflow/notebooks/analysis-coststructure.ipynb +++ b/workflow/notebooks/analysis-coststructure.ipynb @@ -8,9 +8,6 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n", - "import numpy as np\n", - "import xarray as xr\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 30c078d..4c8aae0 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -9,12 +9,8 @@ "source": [ "import pypsa\n", "import pandas as pd\n", - "import numpy as np\n", - "import xarray as xr\n", "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re" + "import matplotlib.pyplot as plt" ] }, { diff --git a/workflow/notebooks/analysis-hourly.ipynb b/workflow/notebooks/analysis-hourly.ipynb index 6668e4b..9882546 100644 --- a/workflow/notebooks/analysis-hourly.ipynb +++ b/workflow/notebooks/analysis-hourly.ipynb @@ -9,12 +9,8 @@ "source": [ "import pypsa\n", "import pandas as pd\n", - "import numpy as np\n", - "import xarray as xr\n", "\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", "from _helpers import load_config" ] }, diff --git a/workflow/notebooks/analysis-iron-ore.ipynb b/workflow/notebooks/analysis-iron-ore.ipynb index 07d1a93..391a566 100644 --- a/workflow/notebooks/analysis-iron-ore.ipynb +++ b/workflow/notebooks/analysis-iron-ore.ipynb @@ -8,14 +8,7 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n", - "import numpy as np\n", - "import xarray as xr\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", - "from _helpers import load_config" + "import pandas as pd\n" ] }, { diff --git a/workflow/notebooks/analysis-re.ipynb b/workflow/notebooks/analysis-re.ipynb index 5941845..5bc574d 100644 --- a/workflow/notebooks/analysis-re.ipynb +++ b/workflow/notebooks/analysis-re.ipynb @@ -8,14 +8,7 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n", - "import numpy as np\n", - "import xarray as xr\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", - "from _helpers import load_config" + "import pandas as pd\n" ] }, { diff --git a/workflow/notebooks/analysis-steel-hbi-split.ipynb b/workflow/notebooks/analysis-steel-hbi-split.ipynb index 6add435..f5dfed5 100644 --- a/workflow/notebooks/analysis-steel-hbi-split.ipynb +++ b/workflow/notebooks/analysis-steel-hbi-split.ipynb @@ -9,10 +9,7 @@ "source": [ "import pandas as pd\n", "import pypsa\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "from _helpers import load_config" + "import matplotlib.pyplot as plt" ] }, { diff --git a/workflow/notebooks/analysis-trace.ipynb b/workflow/notebooks/analysis-trace.ipynb index 68b562b..3ba1d44 100644 --- a/workflow/notebooks/analysis-trace.ipynb +++ b/workflow/notebooks/analysis-trace.ipynb @@ -7,7 +7,6 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", "import pypsa" ] }, @@ -85,7 +84,6 @@ "outputs": [], "source": [ "import pypsatopo\n", - "import pandas as pd\n", "import pypsa" ] }, diff --git a/workflow/notebooks/compare-scenarios.ipynb b/workflow/notebooks/compare-scenarios.ipynb index a7d79ea..7c40345 100644 --- a/workflow/notebooks/compare-scenarios.ipynb +++ b/workflow/notebooks/compare-scenarios.ipynb @@ -8,13 +8,8 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n", - "import numpy as np\n", - "import xarray as xr\n", "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re" + "import matplotlib.pyplot as plt" ] }, { diff --git a/workflow/notebooks/conceptual-model.ipynb b/workflow/notebooks/conceptual-model.ipynb index e337a6b..e17de6c 100644 --- a/workflow/notebooks/conceptual-model.ipynb +++ b/workflow/notebooks/conceptual-model.ipynb @@ -7,9 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "import pypsa\n", - "import pandas as pd\n", - "import numpy as np" + "import pypsa" ] }, { @@ -355,11 +353,8 @@ "# Iron Ore and Steel Production Flow Sankey Diagram\n", "# This notebook creates a Sankey diagram from PyPSA optimization results\n", "\n", - "import pandas as pd\n", "import plotly.graph_objects as go\n", "import plotly.offline as pyo\n", - "from collections import defaultdict\n", - "import numpy as np\n", "\n", "# Enable offline plotting in Jupyter\n", "pyo.init_notebook_mode(connected=True)\n", diff --git a/workflow/notebooks/cost-transport-iron-ore.ipynb b/workflow/notebooks/cost-transport-iron-ore.ipynb deleted file mode 100644 index e69de29..0000000 diff --git a/workflow/notebooks/global-iron-ore.ipynb b/workflow/notebooks/global-iron-ore.ipynb index b61f8ca..8432e60 100644 --- a/workflow/notebooks/global-iron-ore.ipynb +++ b/workflow/notebooks/global-iron-ore.ipynb @@ -235,7 +235,7 @@ " missing_kwds={\"color\": \"lightgrey\"}\n", ")\n", "\n", - "ax.set_title(f\"Iron Ore Production by Country\")\n", + "ax.set_title(\"Iron Ore Production by Country\")\n", "ax.axis('off')\n", "plt.tight_layout()\n", "plt.savefig(iron_ore_map_fn)\n", diff --git a/workflow/notebooks/global-steel-production.ipynb b/workflow/notebooks/global-steel-production.ipynb index 999fa97..fb6f530 100644 --- a/workflow/notebooks/global-steel-production.ipynb +++ b/workflow/notebooks/global-steel-production.ipynb @@ -217,7 +217,7 @@ " missing_kwds={\"color\": \"lightgrey\"}\n", ")\n", "\n", - "ax.set_title(f\"Steel Production by Country\")\n", + "ax.set_title(\"Steel Production by Country\")\n", "ax.axis('off')\n", "plt.tight_layout()\n", "plt.show()\n" diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index a13567b..dd374f5 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -7,7 +7,6 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import cartopy.io.shapereader as shpreader\n", diff --git a/workflow/notebooks/prepare-iron-ore.ipynb b/workflow/notebooks/prepare-iron-ore.ipynb index 705a54c..eb3dd6c 100644 --- a/workflow/notebooks/prepare-iron-ore.ipynb +++ b/workflow/notebooks/prepare-iron-ore.ipynb @@ -8,7 +8,6 @@ "outputs": [], "source": [ "import pandas as pd\n", - "import pypsa\n", "import yaml\n", "import pycountry" ] diff --git a/workflow/notebooks/prepare-steel.ipynb b/workflow/notebooks/prepare-steel.ipynb index e3e06bb..c3edd7c 100644 --- a/workflow/notebooks/prepare-steel.ipynb +++ b/workflow/notebooks/prepare-steel.ipynb @@ -8,7 +8,6 @@ "outputs": [], "source": [ "import pandas as pd\n", - "import pypsa\n", "import yaml\n", "import pycountry" ] diff --git a/workflow/notebooks/validation.ipynb b/workflow/notebooks/validation.ipynb index 5518a5b..72959b6 100644 --- a/workflow/notebooks/validation.ipynb +++ b/workflow/notebooks/validation.ipynb @@ -8,7 +8,6 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import yaml" ] @@ -342,7 +341,6 @@ "metadata": {}, "outputs": [], "source": [ - "import yaml\n", "\n", "# Load config from YAML\n", "with open(\"../../config/config.yaml\", \"r\") as f:\n", @@ -370,7 +368,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import yaml\n", "\n", From 4468e9042af1547d2653800092e5ead66bb10e90 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 12:10:01 +0100 Subject: [PATCH 007/216] chore: add nbqa to pixi dev dependencies --- pixi.lock | 13371 ++++++++++++++++++++++++++++++++++++++++++++++------ pixi.toml | 1 + 2 files changed, 11996 insertions(+), 1376 deletions(-) diff --git a/pixi.lock b/pixi.lock index 7512093..34a23b8 100644 --- a/pixi.lock +++ b/pixi.lock @@ -4,529 +4,4615 @@ environments: channels: - 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vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + purls: [] + size: 388453 + timestamp: 1764777142545 diff --git a/pixi.toml b/pixi.toml index 669eb9a..3b45173 100644 --- a/pixi.toml +++ b/pixi.toml @@ -84,6 +84,7 @@ pre-commit = "*" pylint = "*" jupyter = "*" ipython = "*" +nbqa = "*" [environments] default = { features = [], solve-group = "default" } diff --git a/ruff.toml b/ruff.toml deleted file mode 100644 index b6a6604..0000000 --- a/ruff.toml +++ /dev/null @@ -1,2 +0,0 @@ -line-length = 88 -exclude = ["**/*.ipynb"] From 5dbe7af78da9923733d0c015a1b0c8ee29424f9e Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 13 Mar 2026 12:17:26 +0100 Subject: [PATCH 009/216] chore: add GitHub Actions CI for Pixi env setup --- .github/workflows/ci.yml | 46 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 .github/workflows/ci.yml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..a603886 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,46 @@ +name: Python 🐍 CI/CD tests + +on: + push: + branches: [main, develop] + paths-ignore: + - "**.md" + - "**.bib" + - "**.ya?ml" + - "LICENSE" + - ".gitignore" + pull_request: + branches: [main, develop] + paths-ignore: + - "**.md" + - "**.bib" + - "**.ya?ml" + - "LICENSE" + - ".gitignore" + workflow_dispatch: + +jobs: + build: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + py-version: ["3.11", "3.12"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.py-version }} + + - name: Install Pixi + run: python -m pip install --upgrade pip pixi + + - name: Install dependencies via Pixi + run: pixi install --environment default + + - name: Validate environment + run: pixi info From 8d79c3639eb48697b09a89a0fa5fcdb9b4a341c1 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 20 Mar 2026 15:54:01 +0100 Subject: [PATCH 010/216] feat: add new streamlined readme --- README.md | 79 ++++++++++++++++++++++++++----------------------------- 1 file changed, 38 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index 9a92543..d319651 100644 --- a/README.md +++ b/README.md @@ -2,78 +2,75 @@ This repository contains the **SHIFT model**, a spatially resolved techno-economic optimization of global iron and steel supply chains under decarbonization. It explores how hydrogen-based direct reduced iron (DRI) production and hot-briquetted iron (HBI) trade can shift value creation to regions with renewable energy, infrastructure, and capital availability. -The model identifies cost-optimal configurations for mining, hydrogen production, DRI processing, and steel trade, using the PyPSA framework. +The model identifies cost-optimal configurations for mining, hydrogen production, DRI processing, and HBI trade, using a two-stage optimization pipeline and open energy system libraries. ---- +## Introduction SHIFT +SHIFT evaluates global supply and trade of low-carbon iron and steel at high spatial resolution. The model quantifies where to produce, where to ship, and how to meet demand cost-effectively under decarbonization constraints. -## Prerequesite: TRACE model +Key features: +- 🚀 two-stage process: greenfield supply curves + cross-region LP trade +- 🌍 spatial renewable potentials: PyPSA-Earth wind/solar CF distributions +- 💰 integrated LCOX: region-level cost curves for H2, DRI, HBI +- 🛳️ global trade dispatch: route costs, flows, nodal prices, utilization +- ⚙️ configurable automation: regions, technologies, scenarios via YAML + Snakemake rules -The SHIFT model integrates the energy supply chain `shipping-steel` from a [fork](https://github.com/fneum/trace/tree/pypsa-eur-sec-imports-atlite) of the [TRACE model](https://github.com/euronion/trace). - -Therefore, clone the TRACE fork with `git`: +## Quick installation (PIXI) ```sh -git clone https://github.com/fneum/trace.git +git clone https://github.com/energyLS/shift.git && cd shift +python -m pip install --upgrade pip pixi +pixi install ``` -and switch to the branch `pypsa-eur-sec-imports-atlite` (commit [8bf0571](https://github.com/fneum/trace/commit/8bf057142d4e035926ffb084493462eff64fe188)) and follow these steps: -- delete `escs/shipping-steel/loads.csv`, -- delete `escs/shipping-steel/ships.csv`, -- in `data/efficiencies.csv` and `escs/shipping-steel/links.csv`, and `escs/shipping-hbi/links.csv`, add replace `direct iron reduction furnace` with `hydrogen direct iron reduction furnace` to match the latest technology-data version -- set `technology_data: "v0.12.0"` in the `config/config.default.yaml` (same as in SHIFT: `config/config.yaml`), -- run `snakemake -c1 resources/networks/default/2030/shipping-steel/DE-DE/network.nc`, -- run `snakemake -c1 resources/networks/default/2050/shipping-steel/DE-DE/network.nc`. - -This creates a steel supply chain for 2030 and 2050 without loads and shipping, those parameters will be added later in the SHIFT workflow. The resulting steel model will be stored in `resources/networks/default/2050/shipping-steel/DE-DE/network.nc` and automatically fetched by the SHIFT model. - +For details, see https://pixi.prefix.dev/latest/ (or your local PIXI docs). -## Download, Install, and Run the SHIFT model +## Run (core workflow) -Clone the repository with `git`: +In the workspace root: ```sh -git clone https://github.com/energyLS/shift.git +cd workflow +pixi run snakemake -call model_trade_all ``` -Create the environment with `conda`: +To collect all figures (under development): ```sh -conda env create -f environment.yaml +pixi run snakemake -call collect_figures ``` -Navigate to the `workflow/notebooks` and run the notebooks `global-iron-ore.ipynb`, `global-steel-production.iypnb`, `prepare-iron-ore.ipynb`, and `prepare-steel.ipynb` for preparation. Those steps will be included in the main workflow in a future version. -Run the trade model by navigating to the `workflow/` folder via `cd workflow` and then run +## Workflow overview -```sh -snakemake -call model_trade_all -``` +### Step 0: Renewable potentials (Atlite + GIS) -To plot the supply curves subtracted with demand, run +In this stage we generate the supply-side resource backbone. PyPSA-Earth assembles spatial inputs (country polygons, exclusion masks, weather datasets) and computes hourly capacity-factor series and maximum deployable potentials for wind and solar in each region. The workflow uses the [`build_renewable_profiles`](https://pypsa-earth.readthedocs.io/en/latest/user-guide/rules-reference/populate/build-renewable-profiles/) Snakefile rule, and it can produce .nc outputs for per-region, per-technology capacity factor distributions and installable potentials. -```sh -snakemake -c1 create_all_supply_curves_with_demand -``` +> **Note:** SHIFT may consume precomputed Step 0 datasets to avoid the long runtime of full GIS processing; this is the recommended default for day-to-day scenario work. +> +### Step 1: Greenfield supply curve generation (PyPSA) +With renewable profiles and [techno-economic assumptions](https://github.com/PyPSA/technology-data) in place, SHIFT builds regional PyPSA optimization models to size generation, storage, and process assets. It evaluates each candidate plant (H2 electrolyser, DRI furnace, HBI plant, steel mills) across resource quality and cost parameters to produce levelized cost curves (LCOX) as a function of capacity. The result is a fleet of supply curve elements (capacity buckets with marginal costs and metadata) for H2, DRI, HBI, and steel by region. -*Under development:* +Each greenfield run schemes the spot around: location selection, renewable share, process stack, cost adders, and available build option integration. The output is a harmonized set of offer curves used as input for the trade stage. -Run the whole workflow using `snakemake`: +### Step 2: Global trade optimization (LP) -```sh -snakemake -call collect_figures -``` +This stage takes regional supply curves and demand obligations, then runs a linear program over the regional network. It includes transport cost matrices, ore production constraints, and market compatibility. The solver decides how much each region should produce versus import/export, by product and route. - -## Licence - -This repository is licensed under the MIT License. See `LICENCE` for details. +The trade solution yields detailed outputs: regional production volume and shipped quantities. It can also be reconciled with scenarios for demand, policy constraints, and infrastructure availability. ## Acknowledgements Thanks to: +- Oda Agdal and her Master's Thesis on the [Investigation of Future Global Trade of Hydrogen from Renewable Energy Sources](https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3031513) +- TRACE? +- PYPSA-earth? -* Oda Agdal and her Master's Thesis on the [Investigation of Future Global Trade of Hydrogen from Renewable Energy Sources](https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3031513). + +## Licence + +This repository is licensed under the MIT License. See `LICENCE` for details. From b8ec17a5914659e75a6cf7510cd8d1489529bafb Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 20 Mar 2026 17:35:09 +0100 Subject: [PATCH 011/216] feat: add detailed workflow plan --- plan_shift.prompt.md | 275 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 275 insertions(+) create mode 100644 plan_shift.prompt.md diff --git a/plan_shift.prompt.md b/plan_shift.prompt.md new file mode 100644 index 0000000..0126915 --- /dev/null +++ b/plan_shift.prompt.md @@ -0,0 +1,275 @@ + +# SHIFT Workflow: High-Level Plan (Steps 0, 1, 2) + +## TL;DR + +Three-stage workflow: (0) Pre-compute renewable potentials externally → (1) Regional greenfield PyPSA optimization for **hydrogen with EAF + local demand as fixed competing loads**, yielding LCOH supply curves → (2) Global trade LP for **complete supply chain (H₂ → DRI → HBI → EAF → steel) + ore trade**. + +--- + +## Step 0: Renewable Potentials (External, Pre-computed) + +**Scope:** Data aggregation; largely handled outside SHIFT via PyPSA-Earth + +**Outputs from PyPSA-Earth:** +- Hourly capacity factors and potential power generation (solar, wind_on, wind_off): `profile_solar.nc`, `profile_wind_on.nc`, `profile_wind_off.nc` per region +- Weather year: 2013 + +**SHIFT Integration Point (Data Aggregation Only):** +- Ingest Step 0 outputs +- Aggregate regional, but keep hourly resolution for supply curve generation + +**File Locations:** +- Input: `data/renewable_profiles/profile_{technology}.nc` +- Output (Step 1 input): Regional renewable supply table + +--- + +## Step 1: Greenfield H₂ Supply Curve Generation (PyPSA per Region) + +**Purpose:** Generate **LCOH curves** (levelized cost of hydrogen) at each discrete renewable utilization scenario. **EAF and local electricity demand are fixed competing loads on the same electricity bus.** + +### Inputs + +1. **Renewable Potentials** (from Step 0) + - Annual CF and max [GW] per region, technology + +2. **Demand Data (Fixed, Exogenous)** + - Regional steel demand: `resources/steel_production_clustered.csv` → compute `EAF_el_demand = steel_demand_Mt × 5.25` [MWh/year] + - Regional non-steel electricity demand: `data/un_enerdata_demand_2050_final.csv` → `el_demand_TWh [TWh/year]` with `el_share [%]` + +3. **Tech-Economic Parameters** (`config/config.yaml`) + - Electrolyser efficiency: 75% + - `demand_factors: [0.001, 0.01, ..., 70%]` – Discrete renewable utilization scenarios for H₂ + - Interest rate, CAPEX/OPEX costs from technology-data v0.12.0 + +### Process (Per Region, Per Demand-Factor) + +``` +For region ∈ X_regions: + For demand_factor ∈ demand_factors: + + # Calculate fixed electricity loads (non-optimizable) + EAF_el_demand = steel_demand_Mt × 5.25 [MWh/year] + Local_el_demand = regional_el_demand × el_share [MWh/year] + Total_fixed_load = EAF_el_demand + Local_el_demand + + # Build PyPSA network + # - Generators: PV, Wind_on, Wind_off + # (capacity limited by max_potential × demand_factor) + # - Storage: Battery, + # - Loads (Fixed, constant p_set): + # → EAF (fixed): p_set = EAF_el_demand / 8760 [MW] + # → Local (fixed): p_set = Local_el_demand / 8760 [MW] + # - Links: Electrolyser (75% efficiency) + # → Output: H₂ at demand_factor × max_potential + # - Solve: minimize CAPEX + OPEX + # (supply EAF + Local + H₂_product simultaneously) + + # Extract cost + supply_curve_point = (demand_factor, LCOH_EUR_per_MWh_H2) +``` + +### Outputs (Per Region) + +- **H₂ Supply Curve (CSV):** `resources/supply_curves/cost_year~2030/{region}_hydrogen.csv` + ``` + demand_MWh | LCOH_EUR_per_MWh | demand_factor + 1000 | 125 | 0.1 + 2000 | 135 | 1.0 + ... + ``` + - Each row = discrete demand-factor solved + - Continuous interpolation for Step 2 + +### Key Design Features + +- **Electricity competition explicit:** EAF + local + hydrogen all compete for the same renewable supply +- **Higher EAF demand → higher LCOH:** Renewable capacity must first satisfy EAF + local; remainder available for H₂ +- **Output is hydrogen only:** No metallurgical conversions; Step 1 ends at H₂ product +- **Iron ore cost excluded:** Cannot cause circular dependency (ore cost is in Step 2 only) + +--- + +## Step 2: Global Supply Chain Optimization (LP) + +**Purpose:** Given H₂ supply curves, optimize complete supply chain (H₂ → HBI → steel) + ore sourcing and trade to minimize global cost. + +### Inputs + +1. **H₂ Supply Curves** (from Step 1) + - `resources/supply_curves/cost_year~2030/{region}_hydrogen.csv` for all 15 regions + +2. **Regional Demands (Fixed, Exogenous)** + - Steel demand: `resources/steel_production_clustered.csv` [Mt/year] + +3. **Ore Production Potentials** + - Baseline: `resources/ironore_production_clustered.csv` [Mt_ore/year per region] + - Limit: `production × 1.2` allowance + +4. **Transport Costs** + - Distance matrix: `data/transport_costs/{transport_cost}.csv` [km] + - Unit cost: `0.005` [€/t·km] + +5. **Tech-Economic Parameters** (`config/config.yaml`) + - DRI kiln efficiency: 90% (ore → DRI) + - Other CAPEX/OPEX from technology-data + +6. **Regional Locations** (for distance calculation) + - `data/bus_locations.csv`: `region_name, lat, long` + +7. **Trade Scenarios** + - `config/trade_scenarios.csv`: scenario modifiers + +### Process + +**Metallurgical Chain (inline in LP):** +``` +For each region: + H₂_input [MWh] + →[DRI 90%]→ DRI_output [t_DRI] + →[stoichiometry]→ HBI_production [t_HBI] + →[material balance]→ consumed for steel production + +Material balance constraints: + Ore_extracted [t_ore] × (1 / 1.59) = HBI_production [t_HBI] + (where 1.59 = ore_to_steel_ratio) + HBI_production [t_HBI] → Steel_production [t_steel] + +EAF electricity (external, fixed, zero cost): + EAF_el_supply = steel_demand_Mt × 5.25 [MWh/year] + (Pre-allocated; NOT optimized in Step 2) +``` + +**Decision Variables:** +- `h2_prod[region][curve_idx]` [MWh]: H₂ production on curve point +- `ore_prod[region]` [t_ore]: Iron ore extraction per region +- `hbi_trade[region_from][region_to]` [t_HBI]: HBI shipments +- `ore_trade[region_from][region_to]` [t_ore]: Ore shipments + +**Objective Function:** +``` +minimize Σ_region Σ_curve( LCOH[curve] × h2_prod ) + + Σ_region( DRI_cost_per_t × h2_to_dri[region] ) + + Σ_region( ore_extraction_cost × ore_prod ) + + Σ_route( transport_cost_ore × ore_trade ) + + Σ_route( transport_cost_hbi × hbi_trade ) +``` + +**Constraints:** +- Material balance (H₂ → DRI): h2_prod × efficiency_factor = DRI_output [t] +- Material balance (ore → HBI): ore_prod / 1.59 = HBI_production [t] +- Material balance (HBI demand): total_hbi_available ≥ sum(regional_steel_demand × ore_requirement) +- Supply limits: ore_prod ≤ regional_potential × 1.2 +- Non-negativity + +### Outputs + +- **Trade Solution (CSV):** `results/cost_year~2030/trade_{scenario}.csv` + ``` + region | H2_prod_MWh | DRI_output_t | HBI_prod_t | HBI_import_t | HBI_export_t | Ore_prod_t | Ore_import_t | Ore_export_t | Steel_prod_t | Cost_EUR + Europe | 50000 | 5500 | 5000 | 200 | 0 | 2000 | 800 | 0 | 3150 | 1.2e8 + N_W_Africa | 30000 | 3300 | 3000 | 0 | 2800 | 5000 | 0 | 3200 | 1890 | 8.5e7 + ... + ``` + +- **Detailed Results:** Marginal costs, shadow prices, trade flows, objective value + +- **Visualizations:** Regional production, trade routes, cost breakdown, sensitivity analysis + +--- + +## Critical Data Files (All Confirmed ✓) + +| File | Purpose | Source | Status | +|------|---------|--------|--------| +| `resources/steel_production_clustered.csv` | Regional steel demand [Mt/year] | OWID clustered | ✓ | +| `resources/ironore_production_clustered.csv` | Regional iron ore production [Mt_ore/year] | OWID clustered | ✓ | +| `data/un_enerdata_demand_2050_final.csv` | Regional final energy + electricity share [%] | External | ✓ | +| `data/bus_locations.csv` | Regional centroids (lat/long) | Hardcoded | ✓ | +| `data/transport_costs/{transport_cost}.csv` | Distance matrix [km] | Computed | ✓ | +| `config/trade_scenarios.csv` | Trade scenario definitions | Config | ✓ | +| `data/new_renewables/supply_*.nc` | Renewable CF + max potentials | PyPSA-Earth | ✓ | +| `config/config.yaml` | All parameters (costs, ratios, tech-data) | Config | ✓ | + +--- + +## Data Flow Architecture + +``` +┌──────────────────────────────────┐ +│ Step 0: Renewables (External) │ +│ Output: CF + max_GW per region │ +└────────────┬─────────────────────┘ + │ + ↓ +┌──────────────────────────────────────────────────┐ +│ Step 1: Regional PyPSA (H₂ Only) │ +│ │ +│ Electricity bus: │ +│ ├─ Renewables (PV, Wind) │ +│ ├─ EAF demand [fixed load] │ +│ ├─ Local demand [fixed load] │ +│ └─ Electrolyser → H₂ [variable] │ +│ │ +│ Output: LCOH curves {region}_hydrogen.csv │ +└────────────┬─────────────────────────────────────┘ + │ + ↓ +┌──────────────────────────────────────────────────┐ +│ Step 2: Global LP (Complete Supply Chain) │ +│ │ +│ H₂ (from Step 1) │ +│ →[DRI 90%]→ HBI │ +│ + Ore extraction & trade │ +│ + HBI trade │ +│ →[material balance]→ EAF (fixed)→ Steel │ +│ │ +│ Output: Trade solution, visualizations │ +└──────────────────────────────────────────────────┘ +``` + +--- + +## Key Design Decisions + +1. **Step 1 Scope: Electricity → H₂ Only** + - Benefit: Simpler PyPSA network, shorter solve time + - Electricity competition explicit: EAF + local are fixed loads + - LCOH already reflects competition for renewable capacity + +2. **Iron Ore Cost in Step 2 (Not Step 1)** + - Rationale: Avoids circular dependency (ore cost ↔ H₂ cost) + - Step 1 is purely renewable + H₂ electrolysis + - Step 2 integrates H₂ curves with metallurgical conversions + ore costs + +3. **EAF Electricity Fixed (External)** + - In Step 1: Acts as fixed competing load (competes with H₂ for renewables) + - In Step 2: Provided externally (zero cost, fixed quantity = steel_demand × 5.25) + - Rationale: EAF is exogenous constraint; not optimized + +4. **Trade Scope: HBI + Ore (Not H₂ or Steel)** + - H₂: Local production only (transport cost prohibitive) + - HBI: Tradeable intermediate (minimizes bulk of final product) + - Ore: Tradeable raw material (regions have different potentials) + - Steel: Manufactured locally from HBI + ore + +--- + +## Key Parameters (config.yaml) + +| Parameter | Value | Unit | Notes | +|-----------|-------|------|-------| +| `demand_factors` | [0.001, ..., 0.70] | fraction | Discrete scenarios for Step 1 | +| `electrolyser_efficiency` | 0.75 | — | H₂ output / electricity input | +| `DRI_efficiency` | 0.90 | — | Direct reduction iron yield | +| `electricity_steel_ratio` | 5.25 | MWh/t | EAF electricity per tonne steel | +| `ore_to_steel_ratio` | 1.59 | t_ore/t_steel | Ore requirement per steel output | +| `shipping_cost` | 0.005 | €/t·km | Transport cost for ore/HBI | + +--- + +## Next Steps + +1. **Detailed Plan 1:** Step 1 Snakemake rules, PyPSA network template, discrete scenario handling +2. **Detailed Plan 2:** Step 2 LP structure with piecewise-linear H₂ cost assembly \ No newline at end of file From 56855bd2bb05188c23f1be540cb6e3017b34e411 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 24 Mar 2026 14:00:36 +0100 Subject: [PATCH 012/216] chore: update plan with Step 1 policy assumptions --- plan_shift.prompt.md | 354 ++++++++++++++++++++++++++++--------------- 1 file changed, 231 insertions(+), 123 deletions(-) diff --git a/plan_shift.prompt.md b/plan_shift.prompt.md index 0126915..4f6b9b6 100644 --- a/plan_shift.prompt.md +++ b/plan_shift.prompt.md @@ -3,7 +3,7 @@ ## TL;DR -Three-stage workflow: (0) Pre-compute renewable potentials externally → (1) Regional greenfield PyPSA optimization for **hydrogen with EAF + local demand as fixed competing loads**, yielding LCOH supply curves → (2) Global trade LP for **complete supply chain (H₂ → DRI → HBI → EAF → steel) + ore trade**. +Three-stage workflow: (0) Pre-compute renewable potentials externally → (1) **Constrained regional steel supply curve optimization** under discrete renewable utilization fractions, with local demand as priority load → (2) Global trade LP for **complete supply chain optimization (ore sourcing + HBI/ore trading)** to minimize system cost. --- @@ -25,95 +25,139 @@ Three-stage workflow: (0) Pre-compute renewable potentials externally → (1) Re --- -## Step 1: Greenfield H₂ Supply Curve Generation (PyPSA per Region) +## Step 1: Constrained Steel LCO Optimization (PyPSA per Region) -**Purpose:** Generate **LCOH curves** (levelized cost of hydrogen) at each discrete renewable utilization scenario. **EAF and local electricity demand are fixed competing loads on the same electricity bus.** +**Purpose:** Generate **LCOSTEEL curves** under discrete renewable potential constraints (1%, 10%, 50%), with local electricity demand pre-served by the most efficient renewable potentials. + +**Key Innovation:** +1. Local demand satisfied FIRST using most efficient (highest CF) renewable resources → blocked from steel supply chain +2. Remaining potential available for steel production (H₂ → DRI → HBI → EAF → Steel) +3. Explicit renewable scarcity constraint per region + +**Policy Assumptions:** +- Local electricity demand is exogenous infrastructure (non-negotiable baseline; must be served first) +- `renewable_utilization_fractions` apply **only** to remaining capacity after local demand is allocated +- All regions assumed capable of meeting local demand from local renewables (or with grid imports) +- Steel production cost (LCOSTEEL) scales inversely with utilization fraction ### Inputs 1. **Renewable Potentials** (from Step 0) - - Annual CF and max [GW] per region, technology + - Annual CF and max capacity [GW] per region, technology + - Stratified by efficiency class (to identify "most efficient" potentials) + +2. **Local Electricity Demand (Fixed, Exogenous, Priority)** + - Regional non-steel electricity demand: `data/un_enerdata_demand_2050_final.csv` → `el_demand [TWh/year]` + - **Key:** Served FIRST with highest-CF renewable resources; these are blocked from steel production -2. **Demand Data (Fixed, Exogenous)** - - Regional steel demand: `resources/steel_production_clustered.csv` → compute `EAF_el_demand = steel_demand_Mt × 5.25` [MWh/year] - - Regional non-steel electricity demand: `data/un_enerdata_demand_2050_final.csv` → `el_demand_TWh [TWh/year]` with `el_share [%]` +3. **Steel Demand (Fixed, Exogenous)** + - Regional steel demand: `resources/steel_production_clustered.csv` [Mt/year] + - Determines EAF electricity requirement: `EAF_el_demand = steel_demand_Mt × 5.25 [MWh/year]` -3. **Tech-Economic Parameters** (`config/config.yaml`) +4. **Renewable Utilization Constraints** + - `demand_factors: [0.01, 0.10, 0.50]` – Fraction of **remaining** potential available for steel production + - After local demand consumes its most-efficient share, constrain H₂ electrolyser capacity to this fraction of what's left + +5. **Tech-Economic Parameters** (`config/config.yaml`) - Electrolyser efficiency: 75% - - `demand_factors: [0.001, 0.01, ..., 70%]` – Discrete renewable utilization scenarios for H₂ - - Interest rate, CAPEX/OPEX costs from technology-data v0.12.0 + - DRI efficiency: 90% + - EAF efficiency: 95% + - Interest rate, CAPEX/OPEX from technology-data v0.12.0 ### Process (Per Region, Per Demand-Factor) ``` For region ∈ X_regions: - For demand_factor ∈ demand_factors: + + # STAGE 1: Allocate renewables to local demand (priority) + # ───────────────────────────────────────────────────── + Load_local = regional_el_demand [MWh/year] + + # Identify most efficient renewables (highest CF classes) + # Allocate enough capacity to serve Load_local completely + Renewables_for_local = select_highest_cf( + capacity_needed = Load_local / (CF × 8760), + pool = all_renewable_classes + ) + + # Block these from steel supply chain + Remaining_renewable_potential = Total_capacity - Renewables_for_local + + # STAGE 2: Constrain steel supply chain to fraction of remaining potential + # ────────────────────────────────────────────────────────────────────── + For demand_factor ∈ [0.01, 0.10, 0.50]: - # Calculate fixed electricity loads (non-optimizable) - EAF_el_demand = steel_demand_Mt × 5.25 [MWh/year] - Local_el_demand = regional_el_demand × el_share [MWh/year] - Total_fixed_load = EAF_el_demand + Local_el_demand + # Available capacity for steel (H₂ + EAF electricity) + Available_for_steel = Remaining_renewable_potential × demand_factor # Build PyPSA network - # - Generators: PV, Wind_on, Wind_off - # (capacity limited by max_potential × demand_factor) - # - Storage: Battery, - # - Loads (Fixed, constant p_set): - # → EAF (fixed): p_set = EAF_el_demand / 8760 [MW] - # → Local (fixed): p_set = Local_el_demand / 8760 [MW] - # - Links: Electrolyser (75% efficiency) - # → Output: H₂ at demand_factor × max_potential - # - Solve: minimize CAPEX + OPEX - # (supply EAF + Local + H₂_product simultaneously) + # Electricity Bus: + # ├─ Generators: Remaining renewables + # │ (capacity = Available_for_steel) + # ├─ Storage: Battery + # ├─ Fixed Load: EAF (EAF_el_demand / 8760 [MW]) + # │ (competing with electrolyser for same capacity) + # └─ Electrolyser → H₂ + # (flex load, variable output) + # + # Objective: minimize CAPEX + OPEX to produce maximum steel + # = maximize H₂ production (given EAF demand must be met) + # + # Extract cost: LCOSTEEL = total_cost / (steel_demand_Mt) - # Extract cost - supply_curve_point = (demand_factor, LCOH_EUR_per_MWh_H2) + supply_curve_point = (demand_factor, LCOSTEEL_EUR_per_t) ``` ### Outputs (Per Region) -- **H₂ Supply Curve (CSV):** `resources/supply_curves/cost_year~2030/{region}_hydrogen.csv` +- **Steel Supply Curve (CSV):** `resources/supply_curves/cost_year~{year}/{region}_lcosteel.csv` ``` - demand_MWh | LCOH_EUR_per_MWh | demand_factor - 1000 | 125 | 0.1 - 2000 | 135 | 1.0 - ... + renewable_utilization_fraction | Steel_demand_Mt | LCOSTEEL_EUR_per_t | H2_prod_MWh | EAF_el_MWh + 0.01 | steel_demand | 850 | 5000 | 41250 + 0.10 | steel_demand | 420 | 50000 | 41250 + 0.50 | steel_demand | 185 | 250000 | 41250 ``` - - Each row = discrete demand-factor solved - - Continuous interpolation for Step 2 + - Each row = discrete renewable utilization scenario + - EAF electricity is constant (fixed load) + - H₂ production scales with available renewable capacity ### Key Design Features -- **Electricity competition explicit:** EAF + local + hydrogen all compete for the same renewable supply -- **Higher EAF demand → higher LCOH:** Renewable capacity must first satisfy EAF + local; remainder available for H₂ -- **Output is hydrogen only:** No metallurgical conversions; Step 1 ends at H₂ product -- **Iron ore cost excluded:** Cannot cause circular dependency (ore cost is in Step 2 only) +- **Local demand prioritized:** Served with most-efficient renewables; no cost optimization for local supply (exogenous) +- **Steel supply chain constrained:** Uses remaining, lower-efficiency potentials +- **Explicit renewable scarcity:** Demand factors (1%, 10%, 50%) directly represent available capacity for steel production +- **Direct product output:** LCOSTEEL generated in Step 1 (no intermediate H₂/HBI curves needed) +- **Realistic resource allocation:** Local demand acts as baseline load with infrastructure priority +- **Iron ore cost = 0:** Not included until Step 2 (avoid circular dependency) --- ## Step 2: Global Supply Chain Optimization (LP) -**Purpose:** Given H₂ supply curves, optimize complete supply chain (H₂ → HBI → steel) + ore sourcing and trade to minimize global cost. +**Purpose:** Given regional LCOSTEEL curves, optimize global production mix + trade (ore, HBI) to minimize total system cost. **Iron ore cost integration occurs here.** ### Inputs -1. **H₂ Supply Curves** (from Step 1) - - `resources/supply_curves/cost_year~2030/{region}_hydrogen.csv` for all 15 regions +1. **Steel Supply Curves** (from Step 1) + - `resources/supply_curves/cost_year~{year}/{region}_lcosteel.csv` for all regions + - Discrete points: renewable utilization fractions (1%, 10%, 50%) with corresponding LCOSTEEL + - Iron ore cost NOT included; will be added in LP optimization 2. **Regional Demands (Fixed, Exogenous)** - Steel demand: `resources/steel_production_clustered.csv` [Mt/year] -3. **Ore Production Potentials** +3. **Ore Production Potentials & Cost** - Baseline: `resources/ironore_production_clustered.csv` [Mt_ore/year per region] - Limit: `production × 1.2` allowance + - ***Cost: `iron_ore.marginal_cost` [EUR/t_ore]*** (integrated in Step 2 LP) 4. **Transport Costs** - Distance matrix: `data/transport_costs/{transport_cost}.csv` [km] - Unit cost: `0.005` [€/t·km] 5. **Tech-Economic Parameters** (`config/config.yaml`) - - DRI kiln efficiency: 90% (ore → DRI) - - Other CAPEX/OPEX from technology-data + - Material balance: `ore_to_steel_ratio` = 1.59 [t_ore / t_steel] + - Other metallurgical parameters from technology-data 6. **Regional Locations** (for distance calculation) - `data/bus_locations.csv`: `region_name, lat, long` @@ -123,59 +167,70 @@ For region ∈ X_regions: ### Process -**Metallurgical Chain (inline in LP):** +**Discrete Supply Curve Integration:** +``` +For each region & discrete renewable utilization point: + # Assemble final cost = LCOSTEEL from Step 1 + ore cost + Final_cost[region][utilization_point] = LCOSTEEL[region][utilization_point] + + Ore_cost_per_t × (ore_requirement_per_t_steel) + + transport_ore_cost[route] +``` + +**Metallurgical Chain (inline in LP—inline in LP, transparent):** ``` -For each region: +For each region & production decision: H₂_input [MWh] - →[DRI 90%]→ DRI_output [t_DRI] + →[Electrolyser 75%]→ H₂_output [MWh] + →[DRI 90%]→ DRI_output [t DRI] →[stoichiometry]→ HBI_production [t_HBI] - →[material balance]→ consumed for steel production + →[EAF + material balance]→ Steel_production [t] -Material balance constraints: - Ore_extracted [t_ore] × (1 / 1.59) = HBI_production [t_HBI] - (where 1.59 = ore_to_steel_ratio) +Material balance constraints (explicit): + Ore_extracted [t_ore] × (1 / 1.59) = HBI_available [t_HBI] HBI_production [t_HBI] → Steel_production [t_steel] - -EAF electricity (external, fixed, zero cost): - EAF_el_supply = steel_demand_Mt × 5.25 [MWh/year] - (Pre-allocated; NOT optimized in Step 2) ``` **Decision Variables:** -- `h2_prod[region][curve_idx]` [MWh]: H₂ production on curve point +- `steel_prod[region][utilization_point]` [Mt]: Steel production level (selects discrete utilization point on LCOSTEEL curve) - `ore_prod[region]` [t_ore]: Iron ore extraction per region -- `hbi_trade[region_from][region_to]` [t_HBI]: HBI shipments -- `ore_trade[region_from][region_to]` [t_ore]: Ore shipments +- `hbi_trade[region_from][region_to]` [t]: HBI shipments between regions +- `ore_trade[region_from][region_to]` [t]: Ore shipments between regions **Objective Function:** ``` -minimize Σ_region Σ_curve( LCOH[curve] × h2_prod ) - + Σ_region( DRI_cost_per_t × h2_to_dri[region] ) - + Σ_region( ore_extraction_cost × ore_prod ) - + Σ_route( transport_cost_ore × ore_trade ) - + Σ_route( transport_cost_hbi × hbi_trade ) +minimize Σ_region Σ_utilization( LCOSTEEL_final[utilization] × steel_prod ) + + Σ_region( ore_extraction_cost × ore_prod ) [***INTEGRATED HERE***] + + Σ_routes( transport_cost_ore × ore_trade ) [***INTEGRATED HERE***] + + Σ_routes( transport_cost_hbi × hbi_trade ) + +where LCOSTEEL_final(utilization) = LCOSTEEL(utilization) + ore_cost_per_t_steel + ore_transport ``` **Constraints:** -- Material balance (H₂ → DRI): h2_prod × efficiency_factor = DRI_output [t] -- Material balance (ore → HBI): ore_prod / 1.59 = HBI_production [t] -- Material balance (HBI demand): total_hbi_available ≥ sum(regional_steel_demand × ore_requirement) +- Material balance (H₂ → steel): cascade through DRI + EAF, accounting for losses +- Material balance (ore → HBI): ore_extracted / ore_to_hbi_ratio = HBI_production [t] +- Regional balance: local_steel_demand ≤ local_production + net_imports - Supply limits: ore_prod ≤ regional_potential × 1.2 +- Trade conservation: sum(ore_trade) balanced per region - Non-negativity ### Outputs -- **Trade Solution (CSV):** `results/cost_year~2030/trade_{scenario}.csv` +- **Trade Solution (CSV):** `results/cost_year~{year}/trade_{scenario}.csv` ``` - region | H2_prod_MWh | DRI_output_t | HBI_prod_t | HBI_import_t | HBI_export_t | Ore_prod_t | Ore_import_t | Ore_export_t | Steel_prod_t | Cost_EUR + region | H2_prod_MWh | DRI_output_t | HBI_prod_t | HBI_import_t | HBI_export_t | Ore_prod_t | Ore_import_t | Ore_export_t | Steel_prod_t | Total_cost_EUR Europe | 50000 | 5500 | 5000 | 200 | 0 | 2000 | 800 | 0 | 3150 | 1.2e8 N_W_Africa | 30000 | 3300 | 3000 | 0 | 2800 | 5000 | 0 | 3200 | 1890 | 8.5e7 ... ``` -- **Detailed Results:** Marginal costs, shadow prices, trade flows, objective value +- **Cost Breakdown:** Detailed disaggregation of system cost by component: + - Electricity (CAPEX/OPEX renewables) + - Conversion (DRI, EAF CAPEX) + - Iron ore (extraction + transport) + - Trade (HBI + ore shipping) -- **Visualizations:** Regional production, trade routes, cost breakdown, sensitivity analysis +- **Trade Flows & Sensitivity:** Regional production, trade routes, marginal costs, shadow prices --- @@ -197,62 +252,83 @@ minimize Σ_region Σ_curve( LCOH[curve] × h2_prod ) ## Data Flow Architecture ``` -┌──────────────────────────────────┐ -│ Step 0: Renewables (External) │ -│ Output: CF + max_GW per region │ -└────────────┬─────────────────────┘ +┌──────────────────────────────────────────┐ +│ Step 0: Renewables (External) │ +│ Output: CF + max_GW per region │ +│ (stratified by efficiency class) │ +└────────────┬─────────────────────────────┘ │ ↓ -┌──────────────────────────────────────────────────┐ -│ Step 1: Regional PyPSA (H₂ Only) │ -│ │ -│ Electricity bus: │ -│ ├─ Renewables (PV, Wind) │ -│ ├─ EAF demand [fixed load] │ -│ ├─ Local demand [fixed load] │ -│ └─ Electrolyser → H₂ [variable] │ -│ │ -│ Output: LCOH curves {region}_hydrogen.csv │ -└────────────┬─────────────────────────────────────┘ +┌──────────────────────────────────────────────────────────────┐ +│ Step 1: Constrained Steel LCO Optimization (PyPSA) │ +│ │ +│ Stage 1A: Local Demand (Priority) │ +│ - Served FIRST using most-efficient renewables │ +│ - No cost optimization (exogenous infrastructure) │ +│ - Potentials blocked from steel supply chain │ +│ │ +│ Stage 1B: Steel Supply Chain (Constrained) │ +│ - Renewable generators [capacity × utilization_factor] │ +│ - Battery storage, EAF load, Electrolyser │ +│ - Utilization factors: 1%, 10%, 50% │ +│ - Objective: Minimize LCOSTEEL under capacity constraint │ +│ │ +│ Output: LCOSTEEL curves (discrete utilization points) │ +│ └─ {region}_lcosteel.csv │ +│ └─ Iron ore cost = 0 (deferred to Step 2) │ +└────────────┬─────────────────────────────────────────────────┘ │ ↓ -┌──────────────────────────────────────────────────┐ -│ Step 2: Global LP (Complete Supply Chain) │ -│ │ -│ H₂ (from Step 1) │ -│ →[DRI 90%]→ HBI │ -│ + Ore extraction & trade │ -│ + HBI trade │ -│ →[material balance]→ EAF (fixed)→ Steel │ -│ │ -│ Output: Trade solution, visualizations │ -└──────────────────────────────────────────────────┘ +┌──────────────────────────────────────────────────────────────┐ +│ Step 2: Global Trade Optimization (LP) │ +│ │ +│ Inputs: LCOSTEEL curves + ore potentials + distances │ +│ │ +│ Decisions: │ +│ - Regional steel production level (utilization point) │ +│ - Ore extraction & sourcing [Cost integrated] │ +│ - HBI & ore trade between regions │ +│ │ +│ Output: Optimal production mix, trade flows │ +│ Minimize total cost (renewables + ore + transport) │ +└──────────────────────────────────────────────────────────────┘ ``` --- ## Key Design Decisions -1. **Step 1 Scope: Electricity → H₂ Only** - - Benefit: Simpler PyPSA network, shorter solve time - - Electricity competition explicit: EAF + local are fixed loads - - LCOH already reflects competition for renewable capacity - -2. **Iron Ore Cost in Step 2 (Not Step 1)** - - Rationale: Avoids circular dependency (ore cost ↔ H₂ cost) - - Step 1 is purely renewable + H₂ electrolysis - - Step 2 integrates H₂ curves with metallurgical conversions + ore costs - -3. **EAF Electricity Fixed (External)** - - In Step 1: Acts as fixed competing load (competes with H₂ for renewables) - - In Step 2: Provided externally (zero cost, fixed quantity = steel_demand × 5.25) - - Rationale: EAF is exogenous constraint; not optimized - -4. **Trade Scope: HBI + Ore (Not H₂ or Steel)** - - H₂: Local production only (transport cost prohibitive) - - HBI: Tradeable intermediate (minimizes bulk of final product) - - Ore: Tradeable raw material (regions have different potentials) - - Steel: Manufactured locally from HBI + ore +1. **Step 1: Constrained Steel LCO (Core Refactor)** + - **Benefit:** Direct optimization of final product (steel) under renewable scarcity + - **Local demand priority:** Served first with most-efficient potentials; realistic infrastructure constraint + - **Renewable utilization explicit:** Demand factors (1%, 10%, 50%) directly represent available capacity for steel supply chain + - **Advantage:** Simpler conceptually; transparent resource allocation; direct product output (LCOSTEEL) + +2. **Local Demand Pre-Allocation (NO Cost Optimization)** + - **Rationale:** Represents baseline infrastructure demand; exogenous constraint + - **Implementation:** Allocate most-efficient renewables to local load; block these potentials + - **Outcome:** Steel supply chain competes for lower-tier renewable resources (realistic) + +3. **Iron Ore Cost = 0 in Step 1 (Retained)** + - **Rationale:** Avoids circular dependency (ore cost ↔ steel cost ↔ ore sourcing) + - **Integration:** Ore cost added ONLY in Step 2 LP + - **Outcome:** Step 1 provides clean LCOSTEEL vs. renewable constraint curves + +4. **Direct Product Output (Steel)** + - **Benefit:** LCOSTEEL generated directly in Step 1; no intermediate curves needed + - **Efficiency:** Single PyPSA optimization produces final supply curve + - **Outcome:** Clean, direct data flow from Step 1 → Step 2 + +5. **Electricity Competition Implicit (Sequential Allocation)** + - **In Step 1:** Local demand + steel production compete for remaining renewables; local demand wins (priority) + - **In Step 2:** No further electricity competition; EAF supply is implicit in LCOSTEEL curves + - **Result:** Clear resource hierarchy; no ambiguous "competing loads" concept + +6. **Trade Scope: HBI + Ore (Unchanged Logic)** + - **H₂:** Local production only (high transport cost) + - **HBI:** Tradeable intermediate (lower shipping cost than final product) + - **Ore:** Tradeable raw material (regional potentials differ significantly) + - **Steel:** Manufactured locally from HBI + ore (final product has lowest transport intensity) --- @@ -260,16 +336,48 @@ minimize Σ_region Σ_curve( LCOH[curve] × h2_prod ) | Parameter | Value | Unit | Notes | |-----------|-------|------|-------| -| `demand_factors` | [0.001, ..., 0.70] | fraction | Discrete scenarios for Step 1 | +| `renewable_utilization_fractions` | [0.01, 0.10, 0.50] | fraction | Discrete scenarios for Step 1 renewable utilization (after local demand served) | | `electrolyser_efficiency` | 0.75 | — | H₂ output / electricity input | | `DRI_efficiency` | 0.90 | — | Direct reduction iron yield | -| `electricity_steel_ratio` | 5.25 | MWh/t | EAF electricity per tonne steel | -| `ore_to_steel_ratio` | 1.59 | t_ore/t_steel | Ore requirement per steel output | -| `shipping_cost` | 0.005 | €/t·km | Transport cost for ore/HBI | +| `electricity_steel_ratio` | 5.25 | MWh/t | EAF electricity per tonne steel (fixed load in Step 1) | +| `ore_to_steel_ratio` | 1.59 | t_ore/t_steel | Ore requirement per steel output (Step 2) | +| `shipping_cost` | 0.005 | €/t·km | Transport cost for ore/HBI (Step 2 only) | +| `iron_ore.marginal_cost` | 97.7 | €/t_ore | **Added in Step 2 LP only** | +| `iron_ore.potential_allowance` | 1.2 | factor | Upside limit on regional ore extraction | --- -## Next Steps +## Summary: What Changed & Why + +| Aspect | Original Plan | New Approach | Reason | +|--------|---------------|--------------|--------| +| **Step 1 goal** | Generate generic electricity curves | Optimize LCOSTEEL under renewable constraint | Direct product; explicit scarcity | +| **Local demand** | Competing load (same bus as H₂) | Priority load (served first, best potentials) | Realistic infrastructure hierarchy | +| **Renewable allocation** | All available for H₂ + EAF competition | Two-stage: local demand first, then steel | Explicit resource sequencing | +| **Supply curve output** | Electricity curve (foundation) | LCOSTEEL curve (final product) | Simpler, more direct | +| **Demand factors** | 0.1% - 70% (H₂ utilization) | 1%, 10%, 50% (renewable availability *after* local demand) | Constrained scarcity scenario | +| **Cost accumulation** | Multi-stage generic chains | Implicit in single PyPSA solve | No intermediate abstractions needed | +| **Iron ore cost** | = 0 (config toggle) | = 0 (explicit design principle) | Avoids circular dependency | + +--- -1. **Detailed Plan 1:** Step 1 Snakemake rules, PyPSA network template, discrete scenario handling -2. **Detailed Plan 2:** Step 2 LP structure with piecewise-linear H₂ cost assembly \ No newline at end of file +## Implementation Roadmap + +### Phase 1: Refactor to Constrained Steel LCO +1. Modify Step 1 PyPSA model to implement two-stage renewable allocation: + - Stage 1A: Pre-serve local demand with highest-CF renewables (no optimization) + - Stage 1B: Optimize LCOSTEEL with remaining capacity under utilization constraint +2. Update Snakemake rule `model_lcox` to parameterize renewable utilization factor +3. Generate LCOSTEEL supply curves at discrete utilization points (1%, 10%, 50%) +4. Remove unnecessary `model_lcoh.py` or consolidate into single model +5. Validate outputs match mission possible steel / current LCOX calculations + +### Phase 2: Enhanced Analysis (Optional) +1. Add cost component breakdowns to LCOSTEEL supply curves (electricity, conversion, ore (=0 at this stage)) +2. Document sensitivity to renewable efficiency class selection +3. Explore alternative local demand scenarios if needed + +### Phase 3: Validate & Extend +1. Cross-validate LCOSTEEL against existing TRACE model results +2. Test sensitivity to renewable efficiency class selection +3. Document all assumptions and model boundaries \ No newline at end of file From 380ebd7b9479d11531ce5b97c462d87901d6cf86 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 24 Mar 2026 20:37:57 +0100 Subject: [PATCH 013/216] feat: add renewable potential clustering pipeline with validation and export --- workflow/notebooks/prepare_potentials.ipynb | 1177 +++++++++++++++++++ 1 file changed, 1177 insertions(+) create mode 100644 workflow/notebooks/prepare_potentials.ipynb diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb new file mode 100644 index 0000000..0008a11 --- /dev/null +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -0,0 +1,1177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cd848044", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "\n", + "\n", + "# Set required root pointers\n", + "SHIFT_PATH = os.path.abspath(os.path.join(os.getcwd(), \"../../..\", \"shift\"))\n", + "if not os.path.isdir(SHIFT_PATH):\n", + " raise FileNotFoundError(f\"shift not found at {SHIFT_PATH}. Please clone or set correct path.\")\n", + "\n", + "PYPSA_EARTH_PATH = os.path.abspath(os.path.join(SHIFT_PATH,\"..\", \"pypsa-earth\"))\n", + "if not os.path.isdir(PYPSA_EARTH_PATH):\n", + " raise FileNotFoundError(f\"pypsa-earth not found at {PYPSA_EARTH_PATH}. Please clone or set correct path.\")\n", + "\n", + "# Change working directory to shift root\n", + "os.chdir(SHIFT_PATH)\n", + "\n", + "sys.path.append(SHIFT_PATH)\n", + "sys.path.append(PYPSA_EARTH_PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "56730027", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import xarray as xr\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.cluster import KMeans\n", + "import pycountry\n", + "\n", + "# Custom formatter to hide full paths in git commits\n", + "class RelativePathFormatter(logging.Formatter):\n", + " def format(self, record):\n", + " # Convert full path to relative path\n", + " record.pathname = os.path.relpath(record.pathname)\n", + " # Format: relative/path/file.py - message\n", + " return f\"{record.pathname} - {record.getMessage()}\"\n", + "\n", + "handler = logging.StreamHandler()\n", + "handler.setFormatter(RelativePathFormatter())\n", + "logger = logging.getLogger()\n", + "logger.handlers.clear()\n", + "logger.addHandler(handler)\n", + "logger.setLevel(logging.INFO)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6dad14e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2480569425.py - Loaded onwind profiles\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2480569425.py - Loaded offwind-ac profiles\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2480569425.py - Loaded solar profiles\n" + ] + } + ], + "source": [ + "tech_profiles_nc = {}\n", + "for technology in [\"onwind\", \"offwind-ac\", \"solar\"]:\n", + " path = os.path.realpath(PYPSA_EARTH_PATH) + f\"/resources/renewable_profiles/profile_{technology}.nc\"\n", + " ds = xr.open_dataset(path)\n", + " tech_profiles_nc[technology] = ds\n", + " logging.info(f\"Loaded {technology} profiles\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f34822f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    potential         (y, x) float64 82kB ...\n",
+       "    average_distance  (bus) float64 8kB ...
" + ], + "text/plain": [ + " Size: 71MB\n", + "Dimensions: (time: 8760, bus: 1009, y: 78, x: 131)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 70kB 2013-01-01 ... 2013-12-31T23...\n", + " * bus (bus) 0 else True\n", + " max_diff = comp['diff'].abs().max() if len(comp) > 0 else 0\n", + " matches = (comp['match'] == '✓').sum() if len(comp) > 0 else 0\n", + " print(f\"\\n[VALIDATION] {len(comp)} entries | {matches} perfect | Max diff: {max_diff:.4f} GW | {'✓ PASS' if all_match else '✗ FAIL'}\\n\")\n", + " \n", + " return {'raw': raw_tots, 'clustered': cluster_tots, 'comparison': comp, 'valid': all_match}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f4d26d28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[VALIDATION] 9 entries | 9 perfect | Max diff: 0.0000 GW | ✓ PASS\n", + "\n" + ] + } + ], + "source": [ + "# Validate: Compare raw vs clustered potentials\n", + "validation_results = validate_clustering(\n", + " cluster_metadata,\n", + " clusters_xr,\n", + " tech_profiles_nc,\n", + " onshore_regions_gpd,\n", + " offshore_regions_gpd\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dcb83d4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\3396611797.py - ✓ Saved clustered potentials: data\\renewable_clusters_20260324_203511.nc\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\3396611797.py - ✓ Saved cluster metadata: data\\renewable_clusters_metadata_20260324_203511.csv\n" + ] + } + ], + "source": [ + "def save_clusters(clusters_xr, cluster_metadata, output_dir=\"data\", filename_prefix=\"renewable_clusters\"):\n", + " \"\"\"\n", + " Save clustered renewable potential data to NetCDF and metadata to CSV.\n", + " \n", + " Parameters:\n", + " -----------\n", + " clusters_xr : xr.Dataset\n", + " Clustered potentials xarray dataset\n", + " cluster_metadata : pd.DataFrame\n", + " Cluster metadata DataFrame\n", + " output_dir : str\n", + " Output directory (created if doesn't exist)\n", + " filename_prefix : str\n", + " Prefix for output files\n", + " \n", + " Returns:\n", + " --------\n", + " paths : dict\n", + " Dictionary with keys 'xarray' and 'metadata' pointing to saved files\n", + " \"\"\"\n", + " import os\n", + " from datetime import datetime\n", + " \n", + " # Create output directory if needed\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " \n", + " # Generate timestamp-based filenames\n", + " timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", + " xr_path = os.path.join(output_dir, f\"{filename_prefix}_{timestamp}.nc\")\n", + " meta_path = os.path.join(output_dir, f\"{filename_prefix}_metadata_{timestamp}.csv\")\n", + " \n", + " # Save xarray to NetCDF\n", + " clusters_xr.to_netcdf(xr_path)\n", + " logging.info(f\"✓ Saved clustered potentials: {xr_path}\")\n", + " \n", + " # Save metadata to CSV\n", + " cluster_metadata.to_csv(meta_path, index=False)\n", + " logging.info(f\"✓ Saved cluster metadata: {meta_path}\")\n", + " \n", + " return {'xarray': xr_path, 'metadata': meta_path}\n", + "\n", + "# Save the results\n", + "save_paths = save_clusters(clusters_xr, cluster_metadata, output_dir=\"data\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift (dev)", + "language": "python", + "name": "shift-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c79cd947f0f85a81b211aa920f574ac20c6e7393 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 24 Mar 2026 20:40:31 +0100 Subject: [PATCH 014/216] feat: distill config to barebones setup --- config/config.yaml | 72 +++++----------------------------------------- 1 file changed, 7 insertions(+), 65 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 7e9c491..084f9b1 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -4,7 +4,7 @@ enable: # Different demand factors of maximum hydrogen potential as demand in [%] -demand_factors: [0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.07, 0.1, 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70] # 25, 30, 35, 40, 45, 50, 55, 60, 65, 70] +demand_factors: [0.01, 0.1, 10, 50] hydrogen_storage_cost: False iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted @@ -16,48 +16,14 @@ run: # file: config/scenarios.yaml disable_progressbar: false +# Region definitions +regions: + "Test_1": ["Netherlands"] + "Test_2": ["Portugal"] + "Test_3": ["Ireland"] scenario: # must be listed in config/trade_scenarios.csv in order to run default: - modifiers: - cost_penalty: - penalty-nwa: - modifiers: - cost_penalty: - North_West_Africa: 1.1 - penalty-ea: - modifiers: - cost_penalty: - Eurasia: 1.1 - penalty-sa: - modifiers: - cost_penalty: - South_America: 0.7 - penalty-oc: - modifiers: - cost_penalty: - Oceania: 0.8 - mga-eur-nwa-2: - modifiers: - cost_penalty: - mga: - slack: 0.002 - sense: "min" - # link_idx: "shipping hbi North_West_Africa-Europe" - carrier: "hbi" - export: "North_West_Africa_hbi" - import: "Europe" - mga-nwa-iso: - modifiers: - cost_penalty: - mga: - slack: 0.01 - sense: "min" - carrier: "hbi" - export: "North_West_Africa_hbi" - import: None - - design: cost_penalty: @@ -71,11 +37,6 @@ costs: interest_rate: 0.07 -grid_electricity: - marginal_cost: 80 # EUR/MWh, guesstimate for average grid electricity cost in 2030 - capital_cost: 0 # EUR/MW, guesstimate for average grid electricity cost in 2030 - grid_potential_custom: true # Use custom grid potential for eaf-grid from data/grid_potential_custom.csv in Mt steel - part_load: electrolysis: 0.0 direct reduction furnace: 0.9 @@ -107,6 +68,7 @@ plot: steel: bus_size: 1.0e-7 link_width: 1.5e-8 + colors: steel: 'grey' steel_shipping: 'darkgrey' @@ -125,26 +87,6 @@ plot: hbi_supply: 'darkred' hbi_link: 'firebrick' -# Region definitions -regions: - "Europe": ["Albania", "Austria", "Belarus", "Belgium", "Bosnia and Herzegovina", "Bulgaria", "Croatia", "Cyprus", "Czechia", "Denmark", "Estonia", "Finland", "Germany", "Greece", "Hungary", "Italy", "Kosovo", "Latvia", "Lithuania", "Luxembourg", "North Macedonia", "Moldova", "Montenegro", "Netherlands", "Norway", "Poland", "Romania", "Serbia", "Slovakia", "Slovenia", "Sweden", "Switzerland", "Turkey", "Ukraine"] - "Far_West_Europe": ["France", "Greenland", "Iceland", "Ireland", "Portugal", "Spain", "United Kingdom"] - "Middle_East": ["Bahrain", "Iran, Islamic Republic of", "Iraq","Israel", "Jordan", "Kuwait", "Lebanon", "Oman", "Palestine", "Qatar", "Saudi Arabia", "Syria", "United Arab Emirates", - "Yemen"] - "North_West_Africa": ["Benin", "Burkina Faso", "Côte d'Ivoire", "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Liberia", "Nigeria", "Senegal", "Sierra Leone", "Togo + Algeria", "Chad", "Egypt", "Eritrea", "Libya", "Mali", "Mauritania", "Morocco", "Niger", "Sudan", "Tunisia", "Western Sahara"] - "Subsaharan_Africa": ["Angola", "Botswana", "Burundi", "Cameroon", "Central African Republic", "Democratic Republic of the Congo", "Ethiopia", "Kenya", "Gabon", "Madagascar", "Malawi", "Mozambique", "Namibia", "Republic of the Congo", "Rwanda", "Somalia", "South Africa", "South Sudan", "Tanzania", "Uganda", "Zambia", "Zimbabwe"] - "North_America": ["Canada", "United States"] - "Eurasia": ["Armenia", "Azerbaijan", "Georgia", "Kazakhstan", Kyrgyzstan, "Tajikistan", "Turkmenistan", "Uzbekistan", "Russian Federation"] - "South_America": ["Bolivia", "Brazil", "Colombia", "Ecuador", "Equatorial French Guiana", "Guyana", "Paraguay", "Peru", "Suriname","Venezuela"] - "South_South_America": ["Argentina", "Chile", "Uruguay"] - "Central_America": ["Costa Rica", El Salvador, "Guatemala", "Honduras", "Mexico", "Nicaragua", "Panama"] - "West_Asia": ["Afghanistan", "Bangladesh", "Bhutan", "India", "Nepal", "Pakistan", "Sri Lanka"] - "East_Asia": ["China", "Hong Kong", "Mongolia", "Taiwan"] - "Pacific_Asia": ["Brunei", "Cambodia", "Indonesia", "Laos", "Malaysia", "Myanmar", "Papua New Guinea", "Philippines", "Singapore", "Thailand", "Vietnam"] - "East_East_Asia": ["Japan", "South Korea"] - "Oceania": ["Australia", "New Zealand"] - - solver: name: gurobi options: gurobi-default From dba3c33a26b3ea9c47519313440bebb9f1f3bc6e Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 25 Mar 2026 15:57:15 +0100 Subject: [PATCH 015/216] fix: improve docs and change previous unit from GW to correct MW --- workflow/notebooks/prepare_potentials.ipynb | 109 +++++++++++++++----- 1 file changed, 86 insertions(+), 23 deletions(-) diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index 0008a11..78c2c06 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -745,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c56edbe4", "metadata": {}, "outputs": [], @@ -760,11 +760,62 @@ " random_state=42,\n", " separate_onshore_offshore=True\n", "):\n", + " \n", " \"\"\"\n", - " Aggregate renewable potentials to country-level clusters.\n", - " Regions already have ISO3 country codes from preprocessing.\n", - " \"\"\"\n", + " Cluster renewable resource potentials and capacity factors by geographic proximity.\n", + " \n", + " Aggregates total generation potential (from tech_profiles['potential']) and hourly \n", + " capacity factor time series across multiple regions into geographic clusters. The clustering \n", + " process averages CF timeseries, which distorts the resource profile. To preserve energy \n", + " conservation, we calculate p_nom_max = generation_potential / avg_cf_clustered. The \n", + " deviation of clustered p_nom_max from raw values is the clustering distortion metric.\n", + " \n", + " Parameters\n", + " ----------\n", + " tech_profiles_nc : dict[str, xr.Dataset]\n", + " Technology profiles keyed by name (e.g., 'onwind', 'offwind-ac', 'solar').\n", + " Each dataset must contain variables 'potential' (MW) and 'profile' (0-1 CF timeseries).\n", + " onshore_regions_gpd : gpd.GeoDataFrame\n", + " Onshore regions with ISO3 country codes in 'country' column.\n", + " offshore_regions_gpd : gpd.GeoDataFrame\n", + " Offshore regions with 'OFF_' prefixed names to avoid ID collisions.\n", + " country_codes : list[str], optional\n", + " Filter to specific countries (ISO2 or ISO3). If None, uses all regions.\n", + " total_clusters : int, default=10\n", + " Target number of clusters (split by onshore_ratio if separate_onshore_offshore=True).\n", + " onshore_ratio : float, default=0.8\n", + " Fraction of total_clusters allocated to onshore (remainder to offshore).\n", + " random_state : int, default=42\n", + " Seed for KMeans reproducibility.\n", + " separate_onshore_offshore : bool, default=True\n", + " If True, cluster onshore and offshore separately within each country.\n", " \n", + " Returns\n", + " -------\n", + " clusters_ds : xr.Dataset\n", + " Clustered dataset with dimensions [cluster, technology, hour].\n", + " \n", + " Data variables:\n", + " - renewable_potential: Sum of raw potentials per cluster-technology (MW).\n", + " Preserved exactly during clustering - no energy loss.\n", + " - avg_cf: Mean capacity factor from clustered regions (0-1).\n", + " Averaged timeseries introduces distortion—may not represent \n", + " individual region characteristics.\n", + " - p_nom_max_clus: Installed capacity needed to preserve renewable_potential\n", + " with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", + " Compare to raw region p_nom_max to quantify clustering distortion.\n", + " - capacity_factor: Hourly profiles [cluster, technology, hour].\n", + " Average of regional CF timeseries.\n", + " \n", + " Coordinates:\n", + " - cluster: Unique cluster IDs (format: ISO3_[ON|OFF]_##)\n", + " - iso3: Country code per cluster\n", + " - technology: Available technologies\n", + " - hour: Hour of year (0-8759)\n", + " - lat, lon: Cluster centroid coordinates\n", + " - area_km2: Total area of regions in cluster\n", + " \"\"\"\n", + "\n", " # Calculate cluster counts based on ratio\n", " n_clusters_onshore = max(1, round(total_clusters * onshore_ratio))\n", " n_clusters_offshore = max(1, round(total_clusters * (1 - onshore_ratio)))\n", @@ -890,17 +941,20 @@ " \n", " if len(valid_regions) == 0:\n", " logging.warning(f\" {tech}: No regions in cluster {cluster_id_str}\")\n", - " capacity_gw, avg_cf, cf_array = 0, 0, np.zeros(8760)\n", + " renewable_potential, avg_cf, cf_array = 0, 0, np.zeros(8760)\n", " else:\n", - " # Average timeseries\n", + " # Average timeseries across all regions in cluster (preserves energy conservation)\n", " cf_lists = []\n", " for r in valid_regions:\n", " bus_name = r.replace('OFF_', '') if r.startswith('OFF_') else r\n", " cf_lists.append(tech_ds.sel(bus=bus_name).profile.values)\n", + " # Hourly capacity factor: averaged across all regions in this cluster\n", " cf_array = np.mean(cf_lists, axis=0)\n", + " # Annual mean capacity factor for this cluster-technology combination\n", " avg_cf = cf_array.mean()\n", " \n", - " # Sum potential\n", + " # Sum renewable generation potential across all regions in cluster (MW)\n", + " # This exact sum is preserved through clustering - no energy loss\n", " region_rows = cluster_regions[cluster_regions['name'].isin(valid_regions)]\n", " potential_list = []\n", " for _, region in region_rows.iterrows():\n", @@ -909,8 +963,10 @@ " y_idx = np.argmin(np.abs(tech_ds.y.values - lat_val))\n", " potential_val = tech_ds['potential'].values[y_idx, x_idx]\n", " potential_list.append(potential_val)\n", - " capacity_gw = np.sum(potential_list)\n", + " # Total renewable generation potential (MW) for cluster-technology\n", + " renewable_potential = np.sum(potential_list)\n", " \n", + " # Store cluster aggregations for later xarray construction\n", " cluster_list.append({\n", " 'cluster_id': cluster_id_str,\n", " 'iso3': iso3,\n", @@ -919,39 +975,45 @@ " 'lat': lat,\n", " 'lon': lon,\n", " 'area_km2': area_km2,\n", - " 'capacity_gw': capacity_gw,\n", - " 'avg_cf': avg_cf,\n", - " 'cf_timeseries': cf_array,\n", + " 'renewable_potential': renewable_potential, # Sum of raw potentials (MW)\n", + " 'avg_cf': avg_cf, # Annual mean capacity factor (0-1)\n", + " 'cf_timeseries': cf_array, # Hourly profiles for 8760 hours\n", " })\n", " \n", - " # Build xarray\n", + " # Build xarray dataset with all cluster aggregations and time series\n", " cluster_meta = pd.DataFrame(cluster_list)\n", " clusters_ds = xr.Dataset(\n", " data_vars={\n", - " 'capacity_gw': (['cluster', 'technology'],\n", + " # Renewable generation potential per cluster-technology (MW) - exact sum, no distortion\n", + " 'renewable_potential': (['cluster', 'technology'],\n", " cluster_meta.pivot_table(\n", " index='cluster_id', columns='technology',\n", - " values='capacity_gw', aggfunc='first'\n", + " values='renewable_potential', aggfunc='first'\n", " ).reindex(technologies, axis=1).values),\n", + " # Annual mean capacity factor (0-1) - averaged timeseries introduces clustering distortion\n", " 'avg_cf': (['cluster', 'technology'],\n", " cluster_meta.pivot_table(\n", " index='cluster_id', columns='technology',\n", " values='avg_cf', aggfunc='first'\n", " ).reindex(technologies, axis=1).values),\n", + " # Hourly capacity factor profiles (0-1) - to be used in timeseries optimization\n", " 'capacity_factor': (['cluster', 'technology', 'hour'],\n", " np.array([c['cf_timeseries'] for c in cluster_list])\n", " .reshape(len(cluster_list)//len(technologies), len(technologies), 8760)),\n", " },\n", " coords={\n", - " 'cluster': sorted(cluster_meta['cluster_id'].unique()),\n", + " 'cluster': sorted(cluster_meta['cluster_id'].unique()), # Unique cluster identifiers\n", + " # Country code for each cluster (for filtering/grouping by country)\n", " 'iso3': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['iso3'].iloc[0]\n", " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", - " 'technology': technologies,\n", - " 'hour': np.arange(8760),\n", + " 'technology': technologies, # Technologies included (onwind, offwind-ac, solar)\n", + " 'hour': np.arange(8760), # Hours in a year (0-8759)\n", + " # Geographic centroid coordinates for cluster visualization and spatial reference\n", " 'lat': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['lat'].iloc[0]\n", " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", " 'lon': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['lon'].iloc[0]\n", " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", + " # Total area of regions included in each cluster (km²) - for density/intensity calculations\n", " 'area_km2': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['area_km2'].iloc[0]\n", " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", " }\n", @@ -1010,7 +1072,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "15f4f74b", "metadata": {}, "outputs": [], @@ -1020,7 +1082,7 @@ " import pandas as pd\n", " import numpy as np\n", " \n", - " # Raw potentials\n", + " # Raw potentials - sum from source regions before clustering\n", " all_regions = pd.concat([\n", " onshore_regions_gpd.assign(type='onshore'),\n", " offshore_regions_gpd.assign(type='offshore')\n", @@ -1041,12 +1103,13 @@ " y_idx = np.argmin(np.abs(ds.y.values - lat))\n", " raw_data.append({'iso3': r['country'], 'tech': tech, 'value': ds['potential'].values[y_idx, x_idx]})\n", " \n", + " # Raw totals per country-technology (should be preserved exactly)\n", " raw_tots = pd.DataFrame(raw_data).groupby(['iso3', 'tech'])['value'].sum()\n", " \n", - " # Clustered potentials (already ISO3)\n", - " cluster_tots = cluster_metadata.groupby(['iso3', 'technology'])['capacity_gw'].sum()\n", + " # Clustered potentials totals per country-technology (renewable_potential = exact sum, no loss)\n", + " cluster_tots = cluster_metadata.groupby(['iso3', 'technology'])['renewable_potential'].sum()\n", " \n", - " # Compare\n", + " # Compare - should be identical (no energy loss during clustering)\n", " all_keys = set(raw_tots.index) | set(cluster_tots.index)\n", " comp_data = []\n", " for iso3, tech in sorted(all_keys):\n", @@ -1055,7 +1118,7 @@ " 'diff': c_val - r_val, 'match': '✓' if abs(c_val - r_val) < 0.01 else '✗'})\n", " comp = pd.DataFrame(comp_data) if comp_data else pd.DataFrame()\n", " \n", - " # Report\n", + " # Report validation results\n", " all_match = (comp['diff'].abs() < 0.01).all() if len(comp) > 0 else True\n", " max_diff = comp['diff'].abs().max() if len(comp) > 0 else 0\n", " matches = (comp['match'] == '✓').sum() if len(comp) > 0 else 0\n", From f29ed62bc874ba22f2c8778b586bdbaecc6bb09a Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 25 Mar 2026 17:28:04 +0100 Subject: [PATCH 016/216] feat: add comprehewnsive docs and complete clustering/validation --- workflow/notebooks/prepare_potentials.ipynb | 134 +++++++++++--------- 1 file changed, 73 insertions(+), 61 deletions(-) diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index 78c2c06..68952cf 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "d6dad14e", "metadata": {}, "outputs": [ @@ -68,15 +68,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2480569425.py - Loaded onwind profiles\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2480569425.py - Loaded offwind-ac profiles\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2480569425.py - Loaded solar profiles\n" + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", + "Cannot find the ecCodes library\n", + " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2480569425.py - Loaded onwind profiles\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2480569425.py - Loaded offwind-ac profiles\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2480569425.py - Loaded solar profiles\n" ] } ], @@ -91,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "f34822f5", "metadata": {}, "outputs": [ @@ -652,17 +649,17 @@ " weight (bus) float64 8kB ...\n", " p_nom_max (bus) float64 8kB ...\n", " potential (y, x) float64 82kB ...\n", - " average_distance (bus) float64 8kB ...
    • profile
      (time, bus)
      float64
      ...
      [8838840 values with dtype=float64]
    • weight
      (bus)
      float64
      ...
      units :
      MW
      [1009 values with dtype=float64]
    • p_nom_max
      (bus)
      float64
      ...
      [1009 values with dtype=float64]
    • potential
      (y, x)
      float64
      ...
      [10218 values with dtype=float64]
    • average_distance
      (bus)
      float64
      ...
      [1009 values with dtype=float64]
    • " ], "text/plain": [ " Size: 71MB\n", @@ -693,7 +690,7 @@ " average_distance (bus) float64 8kB ..." ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -704,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "71eecc60", "metadata": {}, "outputs": [ @@ -714,10 +711,10 @@ "text": [ "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\pyogrio\\core.py:34: RuntimeWarning: Could not detect GDAL data files. Set GDAL_DATA environment variable to the correct path.\n", " _init_gdal_data()\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2768513724.py - Onshore regions: 1009, mean area=445.4 km²\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2768513724.py - Offshore regions: 195, mean area=9487.9 km²\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2768513724.py - Deduplication: Offshore region names prefixed with 'OFF_' to avoid ID collisions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\2768513724.py - Conversion: Country codes converted from ISO2 to ISO3 for consistent downstream usage\n" + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Onshore regions: 1009, mean area=445.4 km²\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Offshore regions: 195, mean area=9487.9 km²\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Deduplication: Offshore region names prefixed with 'OFF_' to avoid ID collisions\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Conversion: Country codes converted from ISO2 to ISO3 for consistent downstream usage\n" ] } ], @@ -745,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c56edbe4", "metadata": {}, "outputs": [], @@ -801,9 +798,9 @@ " - avg_cf: Mean capacity factor from clustered regions (0-1).\n", " Averaged timeseries introduces distortion—may not represent \n", " individual region characteristics.\n", - " - p_nom_max_clus: Installed capacity needed to preserve renewable_potential\n", - " with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", - " Compare to raw region p_nom_max to quantify clustering distortion.\n", + " - p_nom_max: Installed capacity needed to preserve renewable_potential\n", + " with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", + " Compare to raw region p_nom_max to quantify clustering distortion.\n", " - capacity_factor: Hourly profiles [cluster, technology, hour].\n", " Average of regional CF timeseries.\n", " \n", @@ -982,24 +979,39 @@ " \n", " # Build xarray dataset with all cluster aggregations and time series\n", " cluster_meta = pd.DataFrame(cluster_list)\n", + " \n", + " # Pre-compute data arrays for xarray construction\n", + " renewable_potential_array = cluster_meta.pivot_table(\n", + " index='cluster_id', columns='technology',\n", + " values='renewable_potential', aggfunc='first'\n", + " ).reindex(technologies, axis=1).values\n", + " \n", + " avg_cf_array = cluster_meta.pivot_table(\n", + " index='cluster_id', columns='technology',\n", + " values='avg_cf', aggfunc='first'\n", + " ).reindex(technologies, axis=1).values\n", + " \n", + " # Compute p_nom_max as backward derivation: renewable_potential / avg_cf\n", + " # Safe division handles division by zero (where avg_cf == 0, result is 0)\n", + " p_nom_max_array = np.divide(\n", + " renewable_potential_array,\n", + " avg_cf_array,\n", + " where=avg_cf_array > 0,\n", + " out=np.zeros_like(avg_cf_array)\n", + " )\n", + " \n", " clusters_ds = xr.Dataset(\n", " data_vars={\n", " # Renewable generation potential per cluster-technology (MW) - exact sum, no distortion\n", - " 'renewable_potential': (['cluster', 'technology'],\n", - " cluster_meta.pivot_table(\n", - " index='cluster_id', columns='technology',\n", - " values='renewable_potential', aggfunc='first'\n", - " ).reindex(technologies, axis=1).values),\n", + " 'renewable_potential': (['cluster', 'technology'], renewable_potential_array),\n", " # Annual mean capacity factor (0-1) - averaged timeseries introduces clustering distortion\n", - " 'avg_cf': (['cluster', 'technology'],\n", - " cluster_meta.pivot_table(\n", - " index='cluster_id', columns='technology',\n", - " values='avg_cf', aggfunc='first'\n", - " ).reindex(technologies, axis=1).values),\n", + " 'avg_cf': (['cluster', 'technology'], avg_cf_array),\n", " # Hourly capacity factor profiles (0-1) - to be used in timeseries optimization\n", " 'capacity_factor': (['cluster', 'technology', 'hour'],\n", " np.array([c['cf_timeseries'] for c in cluster_list])\n", " .reshape(len(cluster_list)//len(technologies), len(technologies), 8760)),\n", + " # Installed capacity needed to preserve renewable_potential with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", + " 'p_nom_max': (['cluster', 'technology'], p_nom_max_array),\n", " },\n", " coords={\n", " 'cluster': sorted(cluster_meta['cluster_id'].unique()), # Unique cluster identifiers\n", @@ -1025,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "017dab92", "metadata": {}, "outputs": [ @@ -1033,28 +1045,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - ✓ Cluster allocation valid: 8 onshore + 2 offshore = 10 total\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering 3 countries: ['IRL', 'NLD', 'PRT']\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Separate onshore/offshore: True\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - ✓ Cluster allocation valid: 8 onshore + 2 offshore = 10 total\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering 3 countries: ['IRL', 'NLD', 'PRT']\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Separate onshore/offshore: True\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", "IRL: 296 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering onshore: 228 regions → 8 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering offshore: 68 regions → 2 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 228 regions → 8 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 68 regions → 2 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", "NLD: 346 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering onshore: 291 regions → 8 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - offwind-ac: No regions in cluster NLD_ON_01\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - offwind-ac: No regions in cluster NLD_ON_02\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - offwind-ac: No regions in cluster NLD_ON_07\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - offwind-ac: No regions in cluster NLD_ON_08\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering offshore: 55 regions → 2 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 291 regions → 8 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_01\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_02\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_07\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_08\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 55 regions → 2 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", "PRT: 562 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering onshore: 490 regions → 8 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - offwind-ac: No regions in cluster PRT_ON_02\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - offwind-ac: No regions in cluster PRT_ON_05\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - Clustering offshore: 72 regions → 2 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\1751855507.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 490 regions → 8 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster PRT_ON_02\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster PRT_ON_05\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 72 regions → 2 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", "✓ Created 30 geographic clusters × 3 techs\n" ] } @@ -1072,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "15f4f74b", "metadata": {}, "outputs": [], @@ -1129,7 +1141,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 9, "id": "f4d26d28", "metadata": {}, "outputs": [ @@ -1156,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "2dcb83d4", "metadata": {}, "outputs": [ @@ -1164,8 +1176,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\3396611797.py - ✓ Saved clustered potentials: data\\renewable_clusters_20260324_203511.nc\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_6072\\3396611797.py - ✓ Saved cluster metadata: data\\renewable_clusters_metadata_20260324_203511.csv\n" + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\1196717265.py - ✓ Saved clustered potentials: data\\renewable_clusters_20260325_172014.nc\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\1196717265.py - ✓ Saved cluster metadata: data\\renewable_clusters_metadata_20260325_172014.csv\n" ] } ], From 1985acffc0e84cfa3fd432ab8e9c921a124ad1e8 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 26 Mar 2026 18:32:38 +0100 Subject: [PATCH 017/216] feat: implement supply chain of steel based on technology data base --- workflow/scripts/build_x_supply_chain.py | 212 +++++++++++++++++++++++ 1 file changed, 212 insertions(+) create mode 100644 workflow/scripts/build_x_supply_chain.py diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py new file mode 100644 index 0000000..0920791 --- /dev/null +++ b/workflow/scripts/build_x_supply_chain.py @@ -0,0 +1,212 @@ +""" +Build PyPSA supply chain skeleton for commodity X using technology database. + +Generic conversion pathway structure (currently configured for steel): + Electricity → Electrolyzer → H2 → DRI → HBI → EAF → Commodity Output + +Uses Snakemake inputs: + - costs: PyPSA technology database CSV + - config: Configuration with efficiencies, capex, constraints + +Reusable pattern for any commodity with similar conversion chains. +""" + +import logging +import pandas as pd +import numpy as np +import pypsa + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +# Technology parameters with no database source (assumed values) +TECH_ASSUMPTIONS = { + "h2_standing_loss": 0.001, # 0.1% per hour for underground cavern (leakage) + "batt_standing_loss": 0.0001, # 0.01% per hour for battery (self-discharge) +} + + +def load_tech_costs(path): + """Load PyPSA technology database.""" + return pd.read_csv(path, index_col=["technology", "parameter"])["value"] + + +def get_tech(tech_costs, tech_name): + """Get all parameters for a technology as a Series.""" + try: + return tech_costs.loc[tech_name] + except KeyError: + logger.warning(f"Technology '{tech_name}' not found in database") + return pd.Series() + + +def get_tech_param(params, param_name, default): + """Get parameter from tech series with warning if using default.""" + if param_name in params.index: + return params[param_name] + else: + logger.warning(f"Parameter '{param_name}' not found, using default: {default}") + return default + + +def _add_buses(network): + """Add energy carrier buses.""" + buses = { + "electricity": {"carrier": "AC", "unit": "MW"}, + "hydrogen": {"carrier": "H2", "unit": "MW"}, + "iron_ore": {"carrier": "Iron ore", "unit": "t/h"}, + "hbi": {"carrier": "HBI", "unit": "t/h"}, + "steel": {"carrier": "Steel", "unit": "t/h"}, + } + for name, attrs in buses.items(): + network.add("Bus", name, **attrs) + + +def _add_conversion_chain(network, tech_costs, config): + """Add energy conversion pathway: Electricity → H2 → HBI → Steel.""" + + # Electrolyzer: Electricity → H2 + elec_params = get_tech(tech_costs, "Alkaline electrolyzer large size") + + network.add( + "Link", "electrolyzer", + bus0="electricity", bus1="hydrogen", + efficiency=1.0 / get_tech_param(elec_params, "electricity-input", 1.38), + overnight_cost=get_tech_param(elec_params, "investment", 544.7764) * 1000, # EUR/kW → EUR/MW + lifetime=get_tech_param(elec_params, "lifetime", 40.0), + fom_cost=get_tech_param(elec_params, "investment", 544.7764) * 1000 * (get_tech_param(elec_params, "FOM", 2.8) / 100), # % → decimal + p_nom_extendable=True, + p_min_pu=config.get("elec_p_min_pu", 0.10), + ) + + # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI + dri_params = get_tech(tech_costs, "hydrogen direct iron reduction furnace") + + network.add( + "Link", "dri", + bus0="iron_ore", bus1="hbi", bus2="hydrogen", bus3="electricity", + efficiency=1.0 / get_tech_param(dri_params, "ore-input", 1.59), # t_ore/t_hbi → efficiency (t_hbi/t_ore) + efficiency2=-get_tech_param(dri_params, "hydrogen-input", 2.1), # negative = input (MWh_H2/t_hbi) + efficiency3=-get_tech_param(dri_params, "electricity-input", 1.03), # negative = input (MWh_el/t_hbi auxiliary) + overnight_cost=get_tech_param(dri_params, "investment", 5378698.8822), # EUR/t_HBI/h from database + lifetime=get_tech_param(dri_params, "lifetime", 40.0), + fom_cost=get_tech_param(dri_params, "investment", 5378698.8822) * (get_tech_param(dri_params, "FOM", 11.3) / 100), # % → decimal + p_nom_extendable=True, + p_min_pu=config.get("dri_p_min_pu", 0.15), + ) + + # EAF: HBI + Electricity → Steel + eaf_params = get_tech(tech_costs, "electric arc furnace") + + network.add( + "Link", "eaf", + bus0="hbi", bus1="steel", bus2="electricity", + efficiency=1.0 / get_tech_param(eaf_params, "hbi-input", 1.0), # t_hbi/t_steel → efficiency (t_steel/t_hbi) + efficiency2=-get_tech_param(eaf_params, "electricity-input", 0.6395), # negative = input (MWh_el/t_steel) + overnight_cost=get_tech_param(eaf_params, "investment", 2312992.7323), # EUR/t_steel/h from database + lifetime=get_tech_param(eaf_params, "lifetime", 40.0), + fom_cost=get_tech_param(eaf_params, "investment", 2312992.7323) * (get_tech_param(eaf_params, "FOM", 30.0) / 100), # % → decimal + p_nom_extendable=True, + p_min_pu=config.get("eaf_p_min_pu", 0.20), + ) + + +def _add_storage(network, tech_costs, config): + """Add H2 and battery storage systems.""" + + # H2 Storage (underground cavern) + h2_params = get_tech(tech_costs, "hydrogen storage underground") + + network.add( + "Store", "h2_storage", + bus="hydrogen", + e_nom_extendable=True, + overnight_cost=get_tech_param(h2_params, "investment", 1.6045) * 1000, # EUR/kWh → EUR/MWh + lifetime=get_tech_param(h2_params, "lifetime", 100.0), + fom_cost=get_tech_param(h2_params, "investment", 1.6045) * 1000 * (get_tech_param(h2_params, "FOM", 0.0) / 100), # % → decimal + standing_loss=TECH_ASSUMPTIONS["h2_standing_loss"], + ) + + # Battery Storage: Power (inverter for charger/discharger) + Energy (store) + batt_inv_params = get_tech(tech_costs, "battery inverter") + batt_store_params = get_tech(tech_costs, "battery storage") + + network.add( + "Link", "batt_charge", + bus0="electricity", bus1="battery", + efficiency=np.sqrt(get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency + overnight_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW + lifetime=get_tech_param(batt_inv_params, "lifetime", 10.0), + fom_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal + p_nom_extendable=True, + ) + + network.add( + "Link", "batt_discharge", + bus0="battery", bus1="electricity", + efficiency=np.sqrt(get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency + overnight_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW + lifetime=get_tech_param(batt_inv_params, "lifetime", 10.0), + fom_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal + p_nom_extendable=True, + ) + + network.add( + "Store", "battery", + bus="battery", + e_nom_extendable=True, + overnight_cost=get_tech_param(batt_store_params, "investment", 100.2787) * 1000, # EUR/kWh → EUR/MWh + lifetime=get_tech_param(batt_store_params, "lifetime", 30.0), + fom_cost=get_tech_param(batt_store_params, "investment", 100.2787) * 1000 * 0.0, # Battery storage has no explicit FOM in database + standing_loss=TECH_ASSUMPTIONS["batt_standing_loss"], + ) + + +def _add_resources(network, config): + """Add external resource supplies (iron ore).""" + network.add( + "Generator", "iron_ore", + bus="iron_ore", + p_nom=1e10, + marginal_cost=0, # Assuming zero marginal cost in supply chain model, will be adjusted in trade model + ) + + +def build_network(config, tech_costs_path): + """Build PyPSA steel supply chain skeleton.""" + + # Setup + year = config.get("cost_year", 2030) + network = pypsa.Network() + network.set_snapshots(pd.date_range(f"{year}-01-01", periods=8760, freq="h")) + tech_costs = load_tech_costs(tech_costs_path) + + # Add network components + _add_buses(network) + _add_conversion_chain(network, tech_costs, config) + _add_storage(network, tech_costs, config) + _add_resources(network, config) + + logger.info(f"Built network: {len(network.buses)} buses, {len(network.links)} links, " + f"{len(network.stores)} stores, {len(network.generators)} generators") + + return network + + +if __name__ == "__main__": + + # Handle Snakemake or direct invocation + if "snakemake" in globals(): + config = snakemake.config + tech_costs_path = snakemake.input.costs + output_path = snakemake.output[0] + else: + # Fallback for testing + config = {"cost_year": 2030} + tech_costs_path = "../resources/technology_data/costs_2030.csv" + output_path = "test_steel_network.nc" + + network = build_network(config, tech_costs_path) + network.export_to_netcdf(output_path) + logger.info(f"Network exported to {output_path}") From 10fca0005b208e60a8a9390014bb2724ad5cc25c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 26 Mar 2026 18:36:45 +0100 Subject: [PATCH 018/216] chore: add missing snakemake import --- workflow/scripts/build_x_supply_chain.py | 1 + 1 file changed, 1 insertion(+) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 0920791..08ec7b7 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -15,6 +15,7 @@ import pandas as pd import numpy as np import pypsa +import snakemake logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) From 6efadb43ab37b29c5a2bb3894222b763e44c06a9 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 27 Mar 2026 16:37:55 +0100 Subject: [PATCH 019/216] feat: streamline retrieval and processing of pypsa-tech-database --- workflow/scripts/retrieve_cost_data.py | 44 -------------- workflow/scripts/tech_database.py | 81 ++++++++++++++++++++++++++ 2 files changed, 81 insertions(+), 44 deletions(-) delete mode 100644 workflow/scripts/retrieve_cost_data.py create mode 100644 workflow/scripts/tech_database.py diff --git a/workflow/scripts/retrieve_cost_data.py b/workflow/scripts/retrieve_cost_data.py deleted file mode 100644 index 83425eb..0000000 --- a/workflow/scripts/retrieve_cost_data.py +++ /dev/null @@ -1,44 +0,0 @@ -# SPDX-FileCopyrightText: Contributors to PyPSA-Eur -# -# SPDX-License-Identifier: MIT -""" -Retrieve cost data from ``technology-data``. -""" - -import logging -from pathlib import Path - -from _helpers import progress_retrieve #configure_logging, , set_scenario_config - -logger = logging.getLogger(__name__) - -if __name__ == "__main__": - if "snakemake" not in globals(): - from _helpers import mock_snakemake - - snakemake = mock_snakemake("retrieve_cost_data", year=2030) - rootpath = ".." - else: - rootpath = "." - # configure_logging(snakemake) - # set_scenario_config(snakemake) - - version = snakemake.params.version - if "/" in version: - baseurl = f"https://raw.githubusercontent.com/{version}/outputs/" - else: - baseurl = f"https://raw.githubusercontent.com/PyPSA/technology-data/{version}/outputs/" - filepath = Path(snakemake.output[0]) - url = baseurl + filepath.name - - print(url) - - to_fn = Path(rootpath) / filepath - - print(to_fn) - - logger.info(f"Downloading technology data from '{url}'.") - disable_progress = snakemake.config["run"].get("disable_progressbar", False) - progress_retrieve(url, to_fn, disable=disable_progress) - - logger.info(f"Technology data available at at {to_fn}") \ No newline at end of file diff --git a/workflow/scripts/tech_database.py b/workflow/scripts/tech_database.py new file mode 100644 index 0000000..0192c26 --- /dev/null +++ b/workflow/scripts/tech_database.py @@ -0,0 +1,81 @@ +# SPDX-FileCopyrightText: Contributors to shift +# +# SPDX-License-Identifier: MIT +""" +Technology database utilities: Download and query PyPSA cost data. + +Dual-purpose module: Snakemake rule for downloading tech costs + importable utilities. +""" + +import logging +from pathlib import Path + +import pandas as pd + +logger = logging.getLogger(__name__) + + +def download_tech_database(version: str, output_path: str, disable_progress: bool = False) -> None: + """Download PyPSA technology-data from GitHub. Supports standard versions or custom paths.""" + from _helpers import progress_retrieve + + # Construct URL based on version format (matches retrieve_cost_data.py logic) + if "/" in version: + # Custom GitHub path: "owner/repo/branch" -> https://raw.githubusercontent.com/owner/repo/branch/outputs/ + baseurl = f"https://raw.githubusercontent.com/{version}/outputs/" + else: + # Default PyPSA path: "v0.5.0" -> https://raw.githubusercontent.com/PyPSA/technology-data/v0.5.0/outputs/ + baseurl = f"https://raw.githubusercontent.com/PyPSA/technology-data/{version}/outputs/" + + filepath = Path(output_path) + url = baseurl + filepath.name + + logger.info(f"Downloading technology data from '{url}'.") + progress_retrieve(url, str(filepath), disable=disable_progress) + logger.info(f"Technology data available at {filepath}") + + +def load_tech_costs(path: str) -> pd.Series: + """Load technology costs CSV into MultiIndex Series [technology, parameter].""" + df = pd.read_csv(path, index_col=[0, 1]) + return df.iloc[:, 0] # Return first column as Series with MultiIndex + + +def get_tech(tech_costs: pd.Series, tech_name: str) -> pd.Series: + """Retrieve all parameters for a specific technology. Returns empty Series if not found.""" + try: + return tech_costs.loc[tech_name] + except KeyError: + logger.warning(f"Technology '{tech_name}' not found in database") + return pd.Series() + + +def get_tech_param( + tech_params: pd.Series, param_name: str, default: float | None = None +) -> float: + """Extract technology parameter with optional fallback default.""" + try: + return tech_params.loc[param_name] + except KeyError: + if default is not None: + logger.warning(f"Parameter '{param_name}' not found, using default: {default}") + return default + raise + + +# Snakemake integration: Allow direct execution as rule +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake("retrieve_cost_data", year=2030) + rootpath = ".." + else: + rootpath = "." + + # Download technology data using Snakemake parameters + version = snakemake.params.version + output_path = Path(rootpath) / snakemake.output[0] + disable_progress = snakemake.config["run"].get("disable_progressbar", False) + + download_tech_database(version, str(output_path), disable_progress=disable_progress) From 156e4bd27d474d1f87f27601ff106277df03b74f Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 27 Mar 2026 16:38:25 +0100 Subject: [PATCH 020/216] feat: better code integration and documentation --- workflow/scripts/build_x_supply_chain.py | 133 +++++++++++------------ 1 file changed, 64 insertions(+), 69 deletions(-) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 08ec7b7..b7df5ca 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -4,11 +4,27 @@ Generic conversion pathway structure (currently configured for steel): Electricity → Electrolyzer → H2 → DRI → HBI → EAF → Commodity Output -Uses Snakemake inputs: - - costs: PyPSA technology database CSV - - config: Configuration with efficiencies, capex, constraints - -Reusable pattern for any commodity with similar conversion chains. +Module provides functions to construct a PyPSA energy system network representing a decarbonized +production supply chain. The network includes: + - Energy carriers (electricity, hydrogen, commodities) + - Conversion technologies (electrolyzer, DRI, EAF) + - Storage systems (H2 storage, batteries) + - External resource supplies + +Usage: + - Snakemake integration: Automatically invoked with config and costs files + - Standalone: Direct invocation for testing with sample config + +Inputs: + - tech_costs_path (str): Path to PyPSA technology database CSV with columns [technology, parameter] + - config (dict): Configuration dict with keys like 'cost_year', '*_p_min_pu' + +Outputs: + - PyPSA Network object ready for optimization + - Exported to NetCDF format for storage and further analysis + +Reusable pattern for any commodity with similar conversion chains. Modify TECH_ASSUMPTIONS, +bus definitions, and conversion links to adapt to different commodities. """ import logging @@ -17,6 +33,8 @@ import pypsa import snakemake +import tech_database as td + logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -28,30 +46,7 @@ } -def load_tech_costs(path): - """Load PyPSA technology database.""" - return pd.read_csv(path, index_col=["technology", "parameter"])["value"] - - -def get_tech(tech_costs, tech_name): - """Get all parameters for a technology as a Series.""" - try: - return tech_costs.loc[tech_name] - except KeyError: - logger.warning(f"Technology '{tech_name}' not found in database") - return pd.Series() - - -def get_tech_param(params, param_name, default): - """Get parameter from tech series with warning if using default.""" - if param_name in params.index: - return params[param_name] - else: - logger.warning(f"Parameter '{param_name}' not found, using default: {default}") - return default - - -def _add_buses(network): +def _add_buses(network: pypsa.Network) -> None: """Add energy carrier buses.""" buses = { "electricity": {"carrier": "AC", "unit": "MW"}, @@ -64,92 +59,92 @@ def _add_buses(network): network.add("Bus", name, **attrs) -def _add_conversion_chain(network, tech_costs, config): +def _add_conversion_chain(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> None: """Add energy conversion pathway: Electricity → H2 → HBI → Steel.""" # Electrolyzer: Electricity → H2 - elec_params = get_tech(tech_costs, "Alkaline electrolyzer large size") + elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") network.add( "Link", "electrolyzer", bus0="electricity", bus1="hydrogen", - efficiency=1.0 / get_tech_param(elec_params, "electricity-input", 1.38), - overnight_cost=get_tech_param(elec_params, "investment", 544.7764) * 1000, # EUR/kW → EUR/MW - lifetime=get_tech_param(elec_params, "lifetime", 40.0), - fom_cost=get_tech_param(elec_params, "investment", 544.7764) * 1000 * (get_tech_param(elec_params, "FOM", 2.8) / 100), # % → decimal + efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), + overnight_cost=td.get_tech_param(elec_params, "investment", 544.7764) * 1000, # EUR/kW → EUR/MW + lifetime=td.get_tech_param(elec_params, "lifetime", 40.0), + fom_cost=td.get_tech_param(elec_params, "investment", 544.7764) * 1000 * (td.get_tech_param(elec_params, "FOM", 2.8) / 100), # % → decimal p_nom_extendable=True, p_min_pu=config.get("elec_p_min_pu", 0.10), ) # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI - dri_params = get_tech(tech_costs, "hydrogen direct iron reduction furnace") + dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") network.add( "Link", "dri", bus0="iron_ore", bus1="hbi", bus2="hydrogen", bus3="electricity", - efficiency=1.0 / get_tech_param(dri_params, "ore-input", 1.59), # t_ore/t_hbi → efficiency (t_hbi/t_ore) - efficiency2=-get_tech_param(dri_params, "hydrogen-input", 2.1), # negative = input (MWh_H2/t_hbi) - efficiency3=-get_tech_param(dri_params, "electricity-input", 1.03), # negative = input (MWh_el/t_hbi auxiliary) - overnight_cost=get_tech_param(dri_params, "investment", 5378698.8822), # EUR/t_HBI/h from database - lifetime=get_tech_param(dri_params, "lifetime", 40.0), - fom_cost=get_tech_param(dri_params, "investment", 5378698.8822) * (get_tech_param(dri_params, "FOM", 11.3) / 100), # % → decimal + efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), # t_ore/t_hbi → efficiency (t_hbi/t_ore) + efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), # negative = input (MWh_H2/t_hbi) + efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), # negative = input (MWh_el/t_hbi auxiliary) + overnight_cost=td.get_tech_param(dri_params, "investment", 5378698.8822), # EUR/t_HBI/h from database + lifetime=td.get_tech_param(dri_params, "lifetime", 40.0), + fom_cost=td.get_tech_param(dri_params, "investment", 5378698.8822) * (td.get_tech_param(dri_params, "FOM", 11.3) / 100), # % → decimal p_nom_extendable=True, p_min_pu=config.get("dri_p_min_pu", 0.15), ) # EAF: HBI + Electricity → Steel - eaf_params = get_tech(tech_costs, "electric arc furnace") + eaf_params = td.get_tech(tech_costs, "electric arc furnace") network.add( "Link", "eaf", bus0="hbi", bus1="steel", bus2="electricity", - efficiency=1.0 / get_tech_param(eaf_params, "hbi-input", 1.0), # t_hbi/t_steel → efficiency (t_steel/t_hbi) - efficiency2=-get_tech_param(eaf_params, "electricity-input", 0.6395), # negative = input (MWh_el/t_steel) - overnight_cost=get_tech_param(eaf_params, "investment", 2312992.7323), # EUR/t_steel/h from database - lifetime=get_tech_param(eaf_params, "lifetime", 40.0), - fom_cost=get_tech_param(eaf_params, "investment", 2312992.7323) * (get_tech_param(eaf_params, "FOM", 30.0) / 100), # % → decimal + efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), # t_hbi/t_steel → efficiency (t_steel/t_hbi) + efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), # negative = input (MWh_el/t_steel) + overnight_cost=td.get_tech_param(eaf_params, "investment", 2312992.7323), # EUR/t_steel/h from database + lifetime=td.get_tech_param(eaf_params, "lifetime", 40.0), + fom_cost=td.get_tech_param(eaf_params, "investment", 2312992.7323) * (td.get_tech_param(eaf_params, "FOM", 30.0) / 100), # % → decimal p_nom_extendable=True, p_min_pu=config.get("eaf_p_min_pu", 0.20), ) -def _add_storage(network, tech_costs, config): +def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> None: """Add H2 and battery storage systems.""" # H2 Storage (underground cavern) - h2_params = get_tech(tech_costs, "hydrogen storage underground") + h2_params = td.get_tech(tech_costs, "hydrogen storage underground") network.add( "Store", "h2_storage", bus="hydrogen", e_nom_extendable=True, - overnight_cost=get_tech_param(h2_params, "investment", 1.6045) * 1000, # EUR/kWh → EUR/MWh - lifetime=get_tech_param(h2_params, "lifetime", 100.0), - fom_cost=get_tech_param(h2_params, "investment", 1.6045) * 1000 * (get_tech_param(h2_params, "FOM", 0.0) / 100), # % → decimal + overnight_cost=td.get_tech_param(h2_params, "investment", 1.6045) * 1000, # EUR/kWh → EUR/MWh + lifetime=td.get_tech_param(h2_params, "lifetime", 100.0), + fom_cost=td.get_tech_param(h2_params, "investment", 1.6045) * 1000 * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), # % → decimal standing_loss=TECH_ASSUMPTIONS["h2_standing_loss"], ) # Battery Storage: Power (inverter for charger/discharger) + Energy (store) - batt_inv_params = get_tech(tech_costs, "battery inverter") - batt_store_params = get_tech(tech_costs, "battery storage") + batt_inv_params = td.get_tech(tech_costs, "battery inverter") + batt_store_params = td.get_tech(tech_costs, "battery storage") network.add( "Link", "batt_charge", bus0="electricity", bus1="battery", - efficiency=np.sqrt(get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency - overnight_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW - lifetime=get_tech_param(batt_inv_params, "lifetime", 10.0), - fom_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal + efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency + overnight_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW + lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), + fom_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal p_nom_extendable=True, ) network.add( "Link", "batt_discharge", bus0="battery", bus1="electricity", - efficiency=np.sqrt(get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency - overnight_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW - lifetime=get_tech_param(batt_inv_params, "lifetime", 10.0), - fom_cost=get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal + efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency + overnight_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW + lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), + fom_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal p_nom_extendable=True, ) @@ -157,14 +152,14 @@ def _add_storage(network, tech_costs, config): "Store", "battery", bus="battery", e_nom_extendable=True, - overnight_cost=get_tech_param(batt_store_params, "investment", 100.2787) * 1000, # EUR/kWh → EUR/MWh - lifetime=get_tech_param(batt_store_params, "lifetime", 30.0), - fom_cost=get_tech_param(batt_store_params, "investment", 100.2787) * 1000 * 0.0, # Battery storage has no explicit FOM in database + overnight_cost=td.get_tech_param(batt_store_params, "investment", 100.2787) * 1000, # EUR/kWh → EUR/MWh + lifetime=td.get_tech_param(batt_store_params, "lifetime", 30.0), + fom_cost=td.get_tech_param(batt_store_params, "investment", 100.2787) * 1000 * 0.0, # Battery storage has no explicit FOM in database standing_loss=TECH_ASSUMPTIONS["batt_standing_loss"], ) -def _add_resources(network, config): +def _add_resources(network: pypsa.Network, config: dict) -> None: """Add external resource supplies (iron ore).""" network.add( "Generator", "iron_ore", @@ -174,14 +169,14 @@ def _add_resources(network, config): ) -def build_network(config, tech_costs_path): +def build_network(config: dict, tech_costs_path: str) -> pypsa.Network: """Build PyPSA steel supply chain skeleton.""" # Setup year = config.get("cost_year", 2030) network = pypsa.Network() network.set_snapshots(pd.date_range(f"{year}-01-01", periods=8760, freq="h")) - tech_costs = load_tech_costs(tech_costs_path) + tech_costs = td.load_tech_costs(tech_costs_path) # Add network components _add_buses(network) From 63446dc78f0e9a0d5b5f9bccbee550146833557a Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 30 Mar 2026 18:03:31 +0200 Subject: [PATCH 021/216] wip: dont look, still refactoring step1 --- config/config.yaml | 20 +- config/trade_scenarios.csv | 11 +- config/trade_scenarios_collection_legacy.csv | 18 - workflow/Snakefile | 112 +++- workflow/scripts/_helpers.py | 3 +- workflow/scripts/build_x_supply_chain.py | 210 ++++--- workflow/scripts/calculate_lcox.py | 620 +++++++++++++++++++ workflow/scripts/create_supply_curve.py | 2 - workflow/scripts/model_lcox.py | 13 +- workflow/scripts/prepare_regional_network.py | 510 +++++++++++++++ workflow/scripts/tech_database.py | 8 +- 11 files changed, 1389 insertions(+), 138 deletions(-) delete mode 100644 config/trade_scenarios_collection_legacy.csv create mode 100644 workflow/scripts/calculate_lcox.py create mode 100644 workflow/scripts/prepare_regional_network.py diff --git a/config/config.yaml b/config/config.yaml index 084f9b1..1f5d6e6 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -1,6 +1,6 @@ enable: - run_supply_chain: False # Enable for first run - run_supply_curve: False # Enable for first run + run_supply_chain: True # Enable for first run + run_supply_curve: True # Enable for first run # Different demand factors of maximum hydrogen potential as demand in [%] @@ -16,11 +16,11 @@ run: # file: config/scenarios.yaml disable_progressbar: false -# Region definitions +# Region definitions (ISO3 codes matching renewable clusters metadata) regions: - "Test_1": ["Netherlands"] - "Test_2": ["Portugal"] - "Test_3": ["Ireland"] + "Test_1": ["NLD"] + "Test_2": ["PRT"] + "Test_3": ["IRL"] scenario: # must be listed in config/trade_scenarios.csv in order to run default: @@ -33,9 +33,13 @@ design: # Oceania: 0.8 costs: - version: v0.12.0 + version: v0.14.0 -interest_rate: 0.07 +interest_rate: + default: 0.07 + "Test_1": 0.05 + "Test_2": 0.06 + part_load: electrolysis: 0.0 diff --git a/config/trade_scenarios.csv b/config/trade_scenarios.csv index 634be40..28ddffd 100644 --- a/config/trade_scenarios.csv +++ b/config/trade_scenarios.csv @@ -1,11 +1,2 @@ cost_year,interone,intertwo,final,scenario -2050,hbi,eaf-grid,steel,default -2050,hbi,eaf-grid,steel,penalty-nwa -2050,hbi,eaf-grid,steel,penalty-ea -2050,hbi,eaf-grid,steel,penalty-oc -2050,hbi,eaf-grid,steel,penalty-sa -2030,hbi,eaf-grid,steel,default -2030,hbi,eaf-grid,steel,penalty-nwa -2030,hbi,eaf-grid,steel,penalty-ea -2030,hbi,eaf-grid,steel,penalty-oc -2030,hbi,eaf-grid,steel,penalty-sa \ No newline at end of file +2050,hbi,eaf-grid,steel,default \ No newline at end of file diff --git a/config/trade_scenarios_collection_legacy.csv b/config/trade_scenarios_collection_legacy.csv deleted file mode 100644 index d435302..0000000 --- a/config/trade_scenarios_collection_legacy.csv +++ /dev/null @@ -1,18 +0,0 @@ -cost_year,transport_cost,demand,product -2030,irena,0.2,hydrogen -2030,irena,0.6,hydrogen -2030,heuser,0.2,hydrogen -2030,heuser,0.6,hydrogen -2050,irena,0.2,hydrogen -2050,irena,0.6,hydrogen -2050,heuser,0.2,hydrogen -2050,heuser,0.6,hydrogen -2030,custom,1,steel -2030,steel_iron_zero,1,steel -2030,steel_iron_real,1,steel -2030,steel_z_iron_z,1,steel -2030,steel_z_iron_r,1,steel -2030,steel_r_iron_z,1,steel -2030,steel_r_iron_r,1,steel -2030,steel_rhigh_iron_z,1,steel -2030,steel_rhigh_iron_r,1,steel \ No newline at end of file diff --git a/workflow/Snakefile b/workflow/Snakefile index d2a655e..fb373f6 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -9,6 +9,29 @@ from shutil import copyfile, move # HTTP = HTTPRemoteProvider() sys.path.append("./scripts") +# ============================================================================ +# FILE DELETION STRATEGY (Development vs Production) +# ============================================================================ +# During development: ALL outputs are kept for validation, debugging, and +# iterative refinement. +# +# Post-development cleanup (marked with [DELETION FLAG]): +# - steel_skeleton_{cost_year}.nc [~500 KB]: Fast to regenerate (<1 min) +# - network_{1,10,50}.nc per region [~50-100 MB]: Intermediate solver states, for debugging +# - {region}_{product}_nodemand.csv [~10 KB]: Reference copy of supply curve before local demand subtraction +# +# ALWAYS KEEP: +# - results_{1,10,50}.csv per region [~50 KB]: Expensive to regenerate (~20 min) +# - {region}_{product}.csv [~10 KB]: Final supply curves→model_trade input +# - {region}_{product}.pdf [~200 KB]: Low cost, high validation value +# - base_{cost_year}_{region}_{product}.nc [~10-20 MB]: Good checkpoint, medium regen cost +# +# Once finalized, add a cleanup rule that: +# 1. Checks if all expected outputs exist +# 2. Deletes flagged files only after final rule completes +# 3. Preserves audit trail in .audit_{} JSON files +# ============================================================================ + # Read scenario definitions to construct wildcard and instance patterns from them trade_scenarios = Paramspace(pd.read_csv("../config/trade_scenarios.csv", dtype=str)) @@ -32,7 +55,7 @@ rule retrieve_cost_data: retries: 2 threads: 2 script: - "scripts/retrieve_cost_data.py" + "scripts/tech_database.py" # Prepare the trace file prior to running the model @@ -48,6 +71,66 @@ rule retrieve_trace_steel: copyfile(input[0], output[0]) +# Build steel supply chain technology skeleton +rule build_steel_skeleton: + input: + costs="../resources/technology_data/costs_{cost_year}.csv", + output: + # [DELETION FLAG] Post-development: can regenerate in <1 min if needed + # Keep during development for checkpoint validation + skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + resources: + mem_mb=2000, + threads: 1 + script: + "scripts/build_x_supply_chain.py" + + +# Prepare regional PyPSA network with renewable generators and product-specific cutoff +rule prepare_regional_network: + """ + Prepare regional PyPSA network with renewable generators and supply chain configuration. + + One-time setup per region × product combination: + - Load skeleton supply chain network (technology-agnostic) + - Add renewable generators for region (filtered by ISO3) + - Apply product-specific supply chain cutoff (h2/hbi/steel) + + Key Efficiency Gain: + - All renewable processing happens ONCE per region + - Output ("base_network") reused for all demand_factors in calculate_lcox + - Eliminates redundant loading/filtering in calculate_lcox + """ + + message: + "Preparing regional network for {wildcards.region} → {wildcards.product} " + "(cost_year={wildcards.cost_year})" + + input: + skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + clusters_timeseries="../data/renewable_clusters.nc", + costs="../resources/technology_data/costs_{cost_year}.csv" + + output: + # KEEP: Good checkpoint, medium regen cost (~2-3 min per region) + base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", + audit="../resources/networks/.audit_{cost_year}_{region}_{product}.json" + + params: + config=config + + threads: 2 + + resources: + mem_mb=4000 + + log: + "../logs/prepare_regional_network_{cost_year}_{region}_{product}.log" + + script: + "scripts/prepare_regional_network.py" + + if config["enable"].get("run_supply_chain", True): # Individual model calculating the LCoH for up to 60% of maximum potential as demand # Stores the resulting LCoH in a csv file @@ -64,15 +147,14 @@ if config["enable"].get("run_supply_chain", True): script: "scripts/model_lcoh.py" - rule model_lcox: + rule calculate_regional_lcox: message: - "Calculating the LCoX of {wildcards.product} for region {wildcards.region} for {wildcards.demand_factor}% of estimated maximum product potential." - params: - interest_rate=config["interest_rate"], + "Calculating LCoX for {wildcards.product} in region {wildcards.region} " + "(demand_factor={wildcards.demand_factor}%)." input: - supply_data ="../data/new_renewables/supply_{region}_2013_cleaned.nc", - costs = "../resources/technology_data/costs_{cost_year}.csv", - trace = "../resources/trace/steel_{cost_year}.nc" + base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", + steel_demand="../resources/steel_production_clustered.csv", + local_demand="../data/un_enerdata_demand_2050_final.csv" output: results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{demand_factor}.csv", network="../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{demand_factor}.nc", @@ -80,7 +162,7 @@ if config["enable"].get("run_supply_chain", True): resources: mem_mb=8000, script: - "scripts/model_lcox.py" + "scripts/calculate_lcox.py" # Read all the individual LCoH values for one region and combine them into a supply curve @@ -89,18 +171,22 @@ if config["enable"].get("run_supply_chain", True): if config["enable"].get("run_supply_curve", True): rule create_supply_curve: message: - "Combining individual LCo{wildcards.product[0]} results to create a supply curve for region {wildcards.region}." + "Combining LCo{wildcards.product[0]} results (all demand factors) to create supply curve for {wildcards.region}." input: + # Reference results from calculate_regional_lcox (uses demand_factor as 1, 10, 50 format) lco_product_data = lambda wildcards: expand( f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{demand_factor}}.csv", - demand_factor=config["demand_factors"], - allow_missing=True + demand_factor=[int(d*100) for d in config["demand_factors"]] ), local_demand = "../data/un_enerdata_demand_2050_final.csv", - steel_demand = "../resources/steel_production_clustered.csv", # TODO fully integrate in workflow + steel_demand = "../resources/steel_production_clustered.csv", output: + # KEEP: Final supply curve input to model_trade (required) supply = "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", + # [DELETION FLAG] Post-development: supply_nodemand CSVs are reference copies for comparison only + # Keep during development for validation/debugging supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv", + # KEEP: PDF plots low disk cost, high validation value supply_curve = "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", threads: 2 script: diff --git a/workflow/scripts/_helpers.py b/workflow/scripts/_helpers.py index 0626b03..12359d6 100644 --- a/workflow/scripts/_helpers.py +++ b/workflow/scripts/_helpers.py @@ -10,6 +10,7 @@ logger = logging.getLogger(__name__) + def load_config(config): with open(config, "r") as stream: try: @@ -173,4 +174,4 @@ def progress_retrieve(url, file, disable=False): with open(file, "wb") as f: for data in response.iter_content(chunk_size=chunk_size): f.write(data) - t.update(len(data)) \ No newline at end of file + t.update(len(data)) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index b7df5ca..bd94db3 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -4,8 +4,8 @@ Generic conversion pathway structure (currently configured for steel): Electricity → Electrolyzer → H2 → DRI → HBI → EAF → Commodity Output -Module provides functions to construct a PyPSA energy system network representing a decarbonized -production supply chain. The network includes: +Module provides functions to construct a PyPSA energy system network representing +a decarbonized production supply chain. The network includes: - Energy carriers (electricity, hydrogen, commodities) - Conversion technologies (electrolyzer, DRI, EAF) - Storage systems (H2 storage, batteries) @@ -16,22 +16,21 @@ - Standalone: Direct invocation for testing with sample config Inputs: - - tech_costs_path (str): Path to PyPSA technology database CSV with columns [technology, parameter] + - tech_costs_path (str): Path to PyPSA technology database CSV - config (dict): Configuration dict with keys like 'cost_year', '*_p_min_pu' Outputs: - PyPSA Network object ready for optimization - Exported to NetCDF format for storage and further analysis -Reusable pattern for any commodity with similar conversion chains. Modify TECH_ASSUMPTIONS, -bus definitions, and conversion links to adapt to different commodities. +Reusable pattern for any commodity with similar conversion chains. +Modify TECH_ASSUMPTIONS, bus definitions, and links to adapt to different commodities. """ import logging import pandas as pd import numpy as np import pypsa -import snakemake import tech_database as td @@ -46,115 +45,164 @@ } +def _add_carriers(network: pypsa.Network) -> None: + """Add carrier components to network. + + PyPSA requires explicit Carrier components before buses/generators can reference them. + """ + carriers = { + "electricity": "AC electricity", + "hydrogen": "Hydrogen gas", + "battery_elec": "Battery (electrical energy)", + "iron_ore": "Iron ore (mass)", + "hbi": "Hot Briquetted Iron (mass)", + "steel": "Steel (mass)", + } + for carrier_name, description in carriers.items(): + network.add("Carrier", carrier_name) + + def _add_buses(network: pypsa.Network) -> None: """Add energy carrier buses.""" buses = { - "electricity": {"carrier": "AC", "unit": "MW"}, - "hydrogen": {"carrier": "H2", "unit": "MW"}, - "iron_ore": {"carrier": "Iron ore", "unit": "t/h"}, - "hbi": {"carrier": "HBI", "unit": "t/h"}, - "steel": {"carrier": "Steel", "unit": "t/h"}, + "electricity": {"carrier": "electricity", "unit": "MW"}, + "hydrogen": {"carrier": "hydrogen", "unit": "MW"}, + "battery": {"carrier": "battery_elec", "unit": "MWh"}, + "iron_ore": {"carrier": "iron_ore", "unit": "t/h"}, + "hbi": {"carrier": "hbi", "unit": "t/h"}, + "steel": {"carrier": "steel", "unit": "t/h"}, } for name, attrs in buses.items(): network.add("Bus", name, **attrs) -def _add_conversion_chain(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> None: - """Add energy conversion pathway: Electricity → H2 → HBI → Steel.""" - +def _add_conversion_chain( + network: pypsa.Network, tech_costs: pd.Series, config: dict +) -> None: + """Add energy conversion pathway: Electricity → H2 → HBI → Steel. + + Note: Costs are added but discount_rate is NOT set here (applied regionally in prepare_regional_network). + """ + # Electrolyzer: Electricity → H2 elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") - + + elec_inv_cost = td.get_tech_param(elec_params, "investment", 544.7764) * 1000 network.add( - "Link", "electrolyzer", - bus0="electricity", bus1="hydrogen", + "Link", + "electrolyzer", + bus0="electricity", + bus1="hydrogen", efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), - overnight_cost=td.get_tech_param(elec_params, "investment", 544.7764) * 1000, # EUR/kW → EUR/MW + overnight_cost=elec_inv_cost, # EUR/kW → EUR/MW lifetime=td.get_tech_param(elec_params, "lifetime", 40.0), - fom_cost=td.get_tech_param(elec_params, "investment", 544.7764) * 1000 * (td.get_tech_param(elec_params, "FOM", 2.8) / 100), # % → decimal + fom_cost=elec_inv_cost * (td.get_tech_param(elec_params, "FOM", 2.8) / 100), p_nom_extendable=True, p_min_pu=config.get("elec_p_min_pu", 0.10), ) - + # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") - + + dri_inv_cost = td.get_tech_param(dri_params, "investment", 5378698.8822) network.add( - "Link", "dri", - bus0="iron_ore", bus1="hbi", bus2="hydrogen", bus3="electricity", - efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), # t_ore/t_hbi → efficiency (t_hbi/t_ore) - efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), # negative = input (MWh_H2/t_hbi) - efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), # negative = input (MWh_el/t_hbi auxiliary) - overnight_cost=td.get_tech_param(dri_params, "investment", 5378698.8822), # EUR/t_HBI/h from database + "Link", + "dri", + bus0="iron_ore", + bus1="hbi", + bus2="hydrogen", + bus3="electricity", + efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), + efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), + efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), + overnight_cost=dri_inv_cost, lifetime=td.get_tech_param(dri_params, "lifetime", 40.0), - fom_cost=td.get_tech_param(dri_params, "investment", 5378698.8822) * (td.get_tech_param(dri_params, "FOM", 11.3) / 100), # % → decimal + fom_cost=dri_inv_cost * (td.get_tech_param(dri_params, "FOM", 11.3) / 100), p_nom_extendable=True, p_min_pu=config.get("dri_p_min_pu", 0.15), ) - + # EAF: HBI + Electricity → Steel eaf_params = td.get_tech(tech_costs, "electric arc furnace") - + + eaf_inv_cost = td.get_tech_param(eaf_params, "investment", 2312992.7323) network.add( - "Link", "eaf", - bus0="hbi", bus1="steel", bus2="electricity", - efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), # t_hbi/t_steel → efficiency (t_steel/t_hbi) - efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), # negative = input (MWh_el/t_steel) - overnight_cost=td.get_tech_param(eaf_params, "investment", 2312992.7323), # EUR/t_steel/h from database + "Link", + "eaf", + bus0="hbi", + bus1="steel", + bus2="electricity", + efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), + efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), + overnight_cost=eaf_inv_cost, lifetime=td.get_tech_param(eaf_params, "lifetime", 40.0), - fom_cost=td.get_tech_param(eaf_params, "investment", 2312992.7323) * (td.get_tech_param(eaf_params, "FOM", 30.0) / 100), # % → decimal + fom_cost=eaf_inv_cost * (td.get_tech_param(eaf_params, "FOM", 30.0) / 100), p_nom_extendable=True, p_min_pu=config.get("eaf_p_min_pu", 0.20), ) def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> None: - """Add H2 and battery storage systems.""" - + """Add H2 and battery storage systems. + + Note: Costs are added but discount_rate is NOT set here (applied regionally in prepare_regional_network). + """ + # H2 Storage (underground cavern) h2_params = td.get_tech(tech_costs, "hydrogen storage underground") - + + h2_inv_cost = td.get_tech_param(h2_params, "investment", 1.6045) * 1000 network.add( - "Store", "h2_storage", + "Store", + "h2_storage", bus="hydrogen", e_nom_extendable=True, - overnight_cost=td.get_tech_param(h2_params, "investment", 1.6045) * 1000, # EUR/kWh → EUR/MWh + overnight_cost=h2_inv_cost, # EUR/kWh → EUR/MWh lifetime=td.get_tech_param(h2_params, "lifetime", 100.0), - fom_cost=td.get_tech_param(h2_params, "investment", 1.6045) * 1000 * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), # % → decimal + fom_cost=h2_inv_cost * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), standing_loss=TECH_ASSUMPTIONS["h2_standing_loss"], ) - + # Battery Storage: Power (inverter for charger/discharger) + Energy (store) batt_inv_params = td.get_tech(tech_costs, "battery inverter") batt_store_params = td.get_tech(tech_costs, "battery storage") - + + batt_inv_cost = td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000 network.add( - "Link", "batt_charge", - bus0="electricity", bus1="battery", - efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency - overnight_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW + "Link", + "batt_charge", + bus0="electricity", + bus1="battery", + efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), + overnight_cost=batt_inv_cost, # EUR/kW → EUR/MW lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), - fom_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal + fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), p_nom_extendable=True, ) - + network.add( - "Link", "batt_discharge", - bus0="battery", bus1="electricity", - efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), # Round-trip → per-direction efficiency - overnight_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000, # EUR/kW → EUR/MW + "Link", + "batt_discharge", + bus0="battery", + bus1="electricity", + efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), + overnight_cost=batt_inv_cost, lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), - fom_cost=td.get_tech_param(batt_inv_params, "investment", 80.223) * 1000 * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), # % → decimal + fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), p_nom_extendable=True, ) - + + batt_store_cost = ( + td.get_tech_param(batt_store_params, "investment", 100.2787) * 1000 + ) network.add( - "Store", "battery", + "Store", + "battery", bus="battery", e_nom_extendable=True, - overnight_cost=td.get_tech_param(batt_store_params, "investment", 100.2787) * 1000, # EUR/kWh → EUR/MWh + overnight_cost=batt_store_cost, # EUR/kWh → EUR/MWh lifetime=td.get_tech_param(batt_store_params, "lifetime", 30.0), - fom_cost=td.get_tech_param(batt_store_params, "investment", 100.2787) * 1000 * 0.0, # Battery storage has no explicit FOM in database + fom_cost=batt_store_cost * 0.0, standing_loss=TECH_ASSUMPTIONS["batt_standing_loss"], ) @@ -162,47 +210,61 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> def _add_resources(network: pypsa.Network, config: dict) -> None: """Add external resource supplies (iron ore).""" network.add( - "Generator", "iron_ore", + "Generator", + "iron_ore", bus="iron_ore", p_nom=1e10, - marginal_cost=0, # Assuming zero marginal cost in supply chain model, will be adjusted in trade model + marginal_cost=0, ) def build_network(config: dict, tech_costs_path: str) -> pypsa.Network: - """Build PyPSA steel supply chain skeleton.""" - + """Build PyPSA steel supply chain skeleton (region-agnostic). + + The skeleton contains: + - Carriers and buses (region-independent) + - Conversion chain with costs but WITHOUT discount_rate + - Regional discount_rate is applied later in prepare_regional_network + + This design allows the same skeleton to be used across regions with different discount rates. + """ + # Setup year = config.get("cost_year", 2030) network = pypsa.Network() + network.name = f"Skeleton-Steel-Supply-Chain-{year}" network.set_snapshots(pd.date_range(f"{year}-01-01", periods=8760, freq="h")) + # NOTE: discount_rate is NOT set here (region-agnostic) tech_costs = td.load_tech_costs(tech_costs_path) - - # Add network components + + # Add network components (carriers MUST be added before buses that reference them) + _add_carriers(network) _add_buses(network) _add_conversion_chain(network, tech_costs, config) _add_storage(network, tech_costs, config) _add_resources(network, config) - - logger.info(f"Built network: {len(network.buses)} buses, {len(network.links)} links, " - f"{len(network.stores)} stores, {len(network.generators)} generators") - + + logger.info( + f"Built network: {len(network.buses)} buses, {len(network.links)} links, " + f"{len(network.stores)} stores, {len(network.generators)} generators" + ) + return network if __name__ == "__main__": - # Handle Snakemake or direct invocation - if "snakemake" in globals(): + try: config = snakemake.config tech_costs_path = snakemake.input.costs output_path = snakemake.output[0] - else: - # Fallback for testing + except NameError: + # Fallback for testing (snakemake variable not available) config = {"cost_year": 2030} tech_costs_path = "../resources/technology_data/costs_2030.csv" output_path = "test_steel_network.nc" - + network = build_network(config, tech_costs_path) + network.name = f"Skeleton-Exported-{config.get('cost_year', 2030)}" network.export_to_netcdf(output_path) logger.info(f"Network exported to {output_path}") diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py new file mode 100644 index 0000000..9301e38 --- /dev/null +++ b/workflow/scripts/calculate_lcox.py @@ -0,0 +1,620 @@ +""" +Calculate regional Levelized Cost of X (LCOX) for multiple demand factors. + +Optimized workflow (single network load, multiple constraint applications): + 1. Load base_network ONCE (renewables + product already configured) + 2. Load product-specific demands + 3. For each demand_factor in config: + a. Copy network (in memory) + b. Apply renewable constraint (demand_factor-specific) + c. Add final loads + d. Solve optimization + e. Extract LCOX and save results_{demand_factor}.csv + f. Export solved network_{demand_factor}.nc + +Efficiency: Load base_network once, loop through constraints (not N separate calls) + +Inputs (from Snakemake): + - base_network: PyPSA network with renewables, prepared per region (netCDF) + - steel_demand: Regional steel demand [Mt/year] (CSV) + - local_demand: Regional local electricity demand [TWh/year] (CSV) + +Outputs (generated for each demand_factor): + - results_{demand_factor}.csv: LCOX point + - network_{demand_factor}.nc: Optimized network +""" + +import logging +from pathlib import Path +import pypsa +import pandas as pd +import numpy as np + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +# ============================================================================ +# NETWORK FIX (pandas/xarray compatibility) +# ============================================================================ + + +def load_network_with_string_fix(filepath): + """Load PyPSA network and convert ArrowStringArray to object dtype. + + PyPSA 1.1.2 + xarray 2026.2.0 conflict: Force object dtype on string columns. + """ + network = pypsa.Network(filepath) + network.name = f"Loaded-{Path(filepath).stem}" + + # Convert ArrowStringArray to object dtype for all component dataframes + for component_name in [ + "buses", + "generators", + "links", + "stores", + "lines", + "transformers", + ]: + df = getattr(network, component_name, None) + if df is not None and not df.empty: + for col in df.select_dtypes(include=["string"]).columns: + df[col] = df[col].astype("object") + if hasattr(df.index, "dtype") and df.index.dtype.name == "string": + df.index = df.index.astype("object") + + return network + + +# ============================================================================ +# DEMAND LOADING +# ============================================================================ + + +def load_demands_for_region(region, product, config): + """Load product-specific demands for region. + + Returns dict with: + - steel_demand_mt: Mt/year + - steel_demand_mwh_per_h: MWh/h average + - local_el_demand_mwh: TWh/year (if available for constraint calculation) + """ + # Load steel production demand (if applicable for this product) + if product in ["steel", "hbi", "eaf", "eaf-grid"]: + try: + steel_df = pd.read_csv(snakemake.input.steel_demand) + # Find row matching region (case-insensitive) + region_mask = steel_df["region"].str.lower() == region.lower() + if not region_mask.any(): + raise ValueError(f"Region '{region}' not found in steel demand data") + steel_demand_mt = steel_df[region_mask]["SteelProductionMt"].values[0] + except Exception as e: + logger.warning(f"Could not load steel demand for {region}: {e}") + steel_demand_mt = 0 + else: + steel_demand_mt = 0 + + # Load local electricity demand (for renewable constraint calculation) + try: + local_df = pd.read_csv(snakemake.input.local_demand) + region_mask = local_df["region"].str.lower() == region.lower() + if region_mask.any(): + local_el_demand_mwh = local_df[region_mask]["demand"].values[ + 0 + ] # Already in MWh + else: + logger.warning(f"Region '{region}' not found in local demand data") + local_el_demand_mwh = 0 + except Exception as e: + logger.warning(f"Could not load local demand for {region}: {e}") + local_el_demand_mwh = 0 + + # Convert Mt/year to MWh/h (using regional electricity requirement) + # For steel: 5.25 MWh/t (from config) + electricity_per_steel_t = config.get("electricity_steel_ratio", 5.25) + steel_demand_mwh = steel_demand_mt * electricity_per_steel_t + steel_demand_mwh_per_h = steel_demand_mwh / 8760 # Annual → hourly average + + return { + "steel_demand_mt": steel_demand_mt, + "steel_demand_mwh_per_h": steel_demand_mwh_per_h, + "local_el_demand_mwh": local_el_demand_mwh, + } + + +# ============================================================================ +# RENEWABLE CONSTRAINT +# ============================================================================ + + +def apply_renewable_constraint(network, demand_factor, local_el_demand_mwh, config): + """Constrain electrolyzer capacity based on demand_factor. + + NEW: Prioritize highest-CF renewables for local demand (least waste). + + Logic: + 1. Get all renewable generators with their timestep-averaged CF + 2. Sort by average CF (descending) - highest first + 3. Accumulate capacity from highest CF until >= local_demand + 4. Block these generators for local demand (set p_nom_max=0) + 5. Calculate remaining capacity available for steel + 6. Apply demand_factor to remaining capacity + 7. Constrain electrolyzer p_nom_max to this value + + Returns: audit dict with capacity breakdown and blocked generators + """ + # Get all renewable generators (identified by name pattern "renewable_*") + # All generators produce electricity, so we filter by naming convention + renewable_gens = network.generators[ + network.generators.index.str.startswith("renewable_") + ] + + if renewable_gens.empty: + logger.warning("No renewable generators found in network") + return { + "total_renewable_capacity_mw": 0, + "capacity_for_local_demand_mw": 0, + "capacity_available_for_steel_mw": 0, + "generators_blocked_for_local_demand": [], + } + + # ====== STEP 1: Calculate average CF for each generator ====== + gen_cf_data = [] + + for gen_name, gen_row in renewable_gens.iterrows(): + h_max_pu = gen_row["p_max_pu"] # Hourly timeseries (0-1) or scalar + + # Handle both pandas Series, numpy array, and scalars + if isinstance(h_max_pu, (int, float, np.number)): + # Scalar CF - use directly + avg_cf = float(h_max_pu) + elif hasattr(h_max_pu, "values"): + # Pandas Series + cf_values = h_max_pu.values + avg_cf = np.mean(cf_values) if len(cf_values) > 0 else 0 + else: + # Numpy array or list + cf_values = h_max_pu + avg_cf = ( + np.mean(cf_values) + if isinstance(cf_values, np.ndarray) and len(cf_values) > 0 + else float(cf_values) + ) + p_nom_max = gen_row["p_nom_max"] + + gen_cf_data.append( + { + "gen_name": gen_name, + "avg_cf": avg_cf, + "p_nom_max": p_nom_max, + "carrier": gen_row["carrier"], + } + ) + + logger.debug(f"Found {len(gen_cf_data)} renewable generators") + + # ====== STEP 2: Sort by average CF (descending) - prioritize best ====== + gen_cf_data.sort(key=lambda x: x["avg_cf"], reverse=True) + + total_renewable_capacity = sum([g["p_nom_max"] for g in gen_cf_data]) + logger.info(f"Total renewable capacity: {total_renewable_capacity:.1f} MW") + logger.info( + f"Top 3 generators by CF: {[(g['gen_name'], format(g['avg_cf'], '.3f')) for g in gen_cf_data[:3]]}" + ) + + # ====== STEP 3: Block highest-CF generators for local demand ====== + # Convert annual local demand to hourly average [MWh/year] → [MW] + local_el_demand_mwh / 8760 # Hourly average power needed + + capacity_accumulated = 0 # Track cumulative capacity factor contribution + generators_for_local = [] + + for gen_info in gen_cf_data: + if capacity_accumulated >= local_el_demand_mwh: + # We've accumulated enough to serve local demand, stop + break + + gen_name = gen_info["gen_name"] + avg_cf = gen_info["avg_cf"] + p_nom_max = gen_info["p_nom_max"] + + # How much energy does this generator produce annually? + annual_energy = avg_cf * p_nom_max * 8760 # MWh/year + + # How much do we still need? + remaining_needed = local_el_demand_mwh - capacity_accumulated + + if annual_energy <= remaining_needed: + # Use entire generator for local demand + capacity_to_use = p_nom_max + capacity_accumulated += annual_energy + else: + # Use partial generator to exactly meet local demand + capacity_to_use = remaining_needed / (avg_cf * 8760) + capacity_accumulated += remaining_needed + + generators_for_local.append( + { + "gen_name": gen_name, + "avg_cf": avg_cf, + "capacity_blocked_mw": capacity_to_use, + "energy_provided_mwh": capacity_to_use * avg_cf * 8760, + } + ) + + # Block this generator: set p_nom_max=0 so optimizer can't use for steel + network.generators.at[gen_name, "p_nom_max"] = 0 + logger.info( + f" Blocked {gen_name:40s} (CF={avg_cf:.3f}, {capacity_to_use:7.1f} MW) → local demand" + ) + + logger.info( + f"Allocated {len(generators_for_local)} generators for local demand ({capacity_accumulated:.0f} MWh/year)" + ) + + # ====== STEP 4: Calculate remaining renewable capacity (for steel) ====== + # Get remaining generators that are NOT blocked (p_nom_max > 0) + remaining_renewable_gens = network.generators[ + (network.generators.index.str.startswith("renewable_")) + & (network.generators["p_nom_max"] > 0) + ] + total_remaining_capacity = remaining_renewable_gens["p_nom_max"].sum() + + logger.info( + f"Remaining renewable capacity for steel: {total_remaining_capacity:.1f} MW" + ) + + # ====== STEP 5: Apply demand_factor constraint ====== + available_for_steel = total_remaining_capacity * (demand_factor / 100.0) + + # ====== STEP 6: Constrain electrolyzer ====== + if "electrolyzer" in network.links.index: + network.links.at["electrolyzer", "p_nom_max"] = available_for_steel + logger.info( + f"Constrained electrolyzer p_nom_max to {available_for_steel:.1f} MW " + f"(demand_factor={demand_factor}%)" + ) + else: + logger.warning("Electrolyzer not found in network links") + + return { + "total_renewable_capacity_mw": total_renewable_capacity, + "capacity_for_local_demand_mw": capacity_accumulated + / 8760, # Convert back to MW + "capacity_available_for_steel_mw": available_for_steel, + "generators_blocked_for_local_demand": [ + g["gen_name"] for g in generators_for_local + ], + "num_generators_blocked": len(generators_for_local), + } + + +# ============================================================================ +# LOAD ADDITION +# ============================================================================ + + +def add_loads_to_network(network, product, demands): + """Store cumulative annual demand for flexible constraint injection. + + Rather than adding hourly Load components (which force rigid patterns), + we store annual demand and inject it as a constraint during solve(). + This allows the network to decide flexibly WHEN to produce. + """ + + if product == "steel": + bus_name = "steel" + annual_steel_t = demands["steel_demand_mt"] * 1000 + annual_demand_units = annual_steel_t + unit_str = "t" + + elif product == "hbi": + bus_name = "hbi" + annual_hbi_t = demands["steel_demand_mt"] * 1000 + annual_demand_units = annual_hbi_t + unit_str = "t" + + elif product == "h2": + bus_name = "hydrogen" + annual_demand_units = demands["steel_demand_mwh_per_h"] * 8760 + unit_str = "MWh" + + elif product in ["eaf", "eaf-grid"]: + bus_name = "steel" + annual_steel_t = demands["steel_demand_mt"] * 1000 + annual_demand_units = annual_steel_t + unit_str = "t" + + else: + raise ValueError(f"Product '{product}' not recognized") + + if bus_name not in network.buses.index: + raise ValueError(f"Bus '{bus_name}' not found in network") + + # Store as network parameter for constraint injection in solve() + network.annual_demand = { + "product": product, + "bus": bus_name, + "total_units": annual_demand_units, + "unit": unit_str, + } + + logger.info( + f"Stored cumulative {product} demand: {annual_demand_units:.1f} {unit_str}/year (flexible timing)" + ) + + +# ============================================================================ +# SOLVER +# ============================================================================ + + +def solve_network(network, config): + """Solve the PyPSA optimization with cumulative annual demand constraint. + + The constraint enforces: sum of production over all hours >= annual_demand + This allows the network to decide flexibly WHEN to produce (not fixed hourly). + """ + solver_name = config.get("solver", {}).get("name", "glpk") + solver_options = config.get("solver_options", {}).get( + config.get("solver", {}).get("options", "default"), {} + ) + + logger.info(f"Solving network with {solver_name}...") + + # Define constraint injection function (called after model building) + def add_annual_constraint(network, snapshots): + if not hasattr(network, "annual_demand"): + return + + demand_info = network.annual_demand + bus_name = demand_info["bus"] + annual_demand = demand_info["total_units"] + + # Find generators connected to demand bus + gens_on_bus = network.generators[network.generators["bus"] == bus_name].index + + if len(gens_on_bus) == 0: + logger.warning(f"No generators on bus '{bus_name}' for annual constraint") + return + + # Access PyPSA's linopy model variables + p_var = network.model["Generator-p"] # Shape: (snapshot, generator) + + # Sum generator output over all snapshots + total_output = p_var.loc[:, gens_on_bus].sum() + + # Add constraint: total output >= annual demand + network.model.add_constraints( + total_output >= annual_demand, name=f"AnnualDemand_{bus_name}" + ) + + logger.info( + f"✓ Constraint added: {bus_name} annual output >= {annual_demand:.0f} {demand_info['unit']}" + ) + + # Solve with constraint injection + network.optimize( + network.snapshots, + solver_name=solver_name, + solver_options=solver_options, + multi_investment_periods=False, + extra_functionality=add_annual_constraint, + ) + + logger.info("Network solved successfully") + + return network + + +# ============================================================================ +# RESULTS EXTRACTION +# ============================================================================ + + +def extract_lcox(network, product, demands, renewable_constraint_info, demand_factor): + """Extract LCOX from optimized network. + + Returns DataFrame with one row containing all results + audit columns. + """ + results_df = pd.DataFrame( + columns=[ + "demand_factor [%]", + "demand [{}]".format("t" if product != "h2" else "MWh"), + "load [per h]", + "cost [EUR]", + "lcox [EUR/unit]", + "renewable_capacity_total_mw", + "renewable_capacity_for_local_mw", + "renewable_capacity_for_steel_mw", + "num_generators_blocked", + "status", + ] + ) + + try: + obj_value = network.objective + if obj_value is None or np.isnan(obj_value): + raise ValueError("Optimization failed to return valid objective") + + demand_annual = ( + demands["steel_demand_mt"] + if product != "h2" + else demands["steel_demand_mwh_per_h"] * 8760 + ) + lcox = obj_value / demand_annual if demand_annual > 0 else np.inf + + results_df.loc[0] = [ + int(demand_factor * 100), # Convert to percent + demand_annual, + demands["steel_demand_mwh_per_h"], + obj_value, + lcox, + renewable_constraint_info["total_renewable_capacity_mw"], + renewable_constraint_info["capacity_for_local_demand_mw"], + renewable_constraint_info["capacity_available_for_steel_mw"], + renewable_constraint_info.get("num_generators_blocked", 0), + "feasible", + ] + logger.info(f"LCOX calculated: {lcox:.2f} EUR/unit") + logger.info( + f" Generators blocked for local demand: {renewable_constraint_info.get('num_generators_blocked', 0)}" + ) + + except Exception as e: + logger.error(f"Optimization infeasible or failed: {e}") + demand_annual = ( + demands["steel_demand_mt"] + if product != "h2" + else demands["steel_demand_mwh_per_h"] * 8760 + ) + results_df.loc[0] = [ + int(demand_factor * 100), # Convert to percent + demand_annual, + demands["steel_demand_mwh_per_h"], + np.nan, + np.nan, + renewable_constraint_info["total_renewable_capacity_mw"], + renewable_constraint_info["capacity_for_local_demand_mw"], + renewable_constraint_info.get("capacity_available_for_steel_mw", 0), + renewable_constraint_info.get("num_generators_blocked", 0), + "infeasible", + ] + + return results_df + + +# ============================================================================ +# ADJUSTMENT (LEGACY - KEPT FOR COMPATIBILITY) +# ============================================================================ + + +def adjust_part_load(network, config): + """Adjust part-load limits for links based on config.""" + part_load = config.get("part_load", {}) + if not part_load: + return + + for carrier, min_pu in part_load.items(): + mask = network.links["carrier"] == carrier + if mask.any(): + network.links.loc[mask, "p_min_pu"] = min_pu + logger.debug(f"Set part-load limit for {carrier}: p_min_pu={min_pu}") + + +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake( + "calculate_regional_lcox", + cost_year="2030", + region="Europe", + product="steel", + ) + + # ==================== SETUP ==================== + logger.info("=" * 70) + logger.info( + f"LCOX Calculation: region={snakemake.wildcards.region}, " + f"product={snakemake.wildcards.product}" + ) + logger.info("=" * 70) + + # Load pre-prepared base network ONCE (key efficiency gain) + logger.info("Loading base network...") + base_network = load_network_with_string_fix(snakemake.input.base_network) + logger.info( + f"Network loaded: {len(base_network.buses)} buses, " + f"{len(base_network.generators)} generators, {len(base_network.links)} links" + ) + + # Load demands for this region and product + logger.info("Loading demands...") + demands = load_demands_for_region( + region=snakemake.wildcards.region, + product=snakemake.wildcards.product, + config=snakemake.config, + ) + logger.info(f"Steel demand: {demands['steel_demand_mt']:.1f} Mt/year") + logger.info( + f"Local electricity demand: {demands['local_el_demand_mwh']:.1f} MWh/year" + ) + + # ==================== SINGLE DEMAND FACTOR PROCESSING ==================== + # Get demand_factor from Snakemake wildcard (in percent: 1, 10, 50, etc.) + demand_factor_percent = int(snakemake.wildcards.demand_factor) + demand_factor = ( + demand_factor_percent / 100.0 + ) # Convert to decimal (0.01, 0.1, 0.5, etc.) + + logger.info(f"\n{'=' * 70}") + logger.info(f"Processing demand_factor={demand_factor_percent}% ({demand_factor})") + logger.info(f"{'=' * 70}") + + # Create a copy of base network for this constraint scenario + network = base_network.copy() + network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-DF{demand_factor_percent}%" + + # Preserve discount_rate from base network (needed for cost annuitization) + network.discount_rate = base_network.discount_rate + + # Apply renewable constraint based on demand_factor + logger.info( + f"Applying renewable constraint (demand_factor={demand_factor_percent}%)..." + ) + constraint_info = apply_renewable_constraint( + network=network, + demand_factor=demand_factor, + local_el_demand_mwh=demands["local_el_demand_mwh"], + config=snakemake.config, + ) + + logger.info( + f" Total renewable capacity: {constraint_info['total_renewable_capacity_mw']:.1f} MW" + ) + logger.info( + f" Capacity for local demand: {constraint_info['capacity_for_local_demand_mw']:.1f} MW" + ) + logger.info( + f" Capacity available for steel: {constraint_info['capacity_available_for_steel_mw']:.1f} MW" + ) + + # Add loads to network + logger.info("Adding loads to network...") + add_loads_to_network( + network=network, product=snakemake.wildcards.product, demands=demands + ) + + # Adjust part-load (if configured) + if snakemake.config.get("part_load"): + adjust_part_load(network, snakemake.config) + + # Solve this constraint scenario + solve_network(network, snakemake.config) + + # Extract LCOX results + logger.info("Extracting results...") + results_df = extract_lcox( + network=network, + product=snakemake.wildcards.product, + demands=demands, + renewable_constraint_info=constraint_info, + demand_factor=demand_factor, + ) + + # ==================== SAVE RESULTS ==================== + logger.info("=" * 70) + logger.info("Saving results...") + logger.info("=" * 70) + + # Save CSV result + results_df.to_csv(snakemake.output.results, index=False) + logger.info(f"Results saved: {snakemake.output.results}") + + # Save network + network.export_to_netcdf(snakemake.output.network) + logger.info(f"Network saved: {snakemake.output.network}") + + logger.info("=" * 70) + logger.info(f"LCOX calculation complete for demand_factor={demand_factor_percent}%") + logger.info("=" * 70) diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index cd7cb9d..a676966 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -125,7 +125,6 @@ def create_supply_curve(): ) if product == "hydrogen": - final_demand = get_final_demand(snakemake.wildcards["region"]) plt.axvline( x=final_demand.values[0] / (1e6), linestyle="-", label="final energy demand" @@ -158,7 +157,6 @@ def create_supply_curve(): if __name__ == "__main__": - if "snakemake" not in globals(): from _helpers import mock_snakemake diff --git a/workflow/scripts/model_lcox.py b/workflow/scripts/model_lcox.py index 4d2196f..5dc4fd7 100644 --- a/workflow/scripts/model_lcox.py +++ b/workflow/scripts/model_lcox.py @@ -185,12 +185,10 @@ def building_model(n, ds, dw, dc, load, h_cost, iron_ore_cost): n = remove_shipping_importer_components(n) if product == "steel": - # p_set unit in MW n.add("Load", "load", bus="steel (exp)", carrier="steel", p_set=load) elif product == "hbi": - # Remove steel components from the network n.remove( "Link", @@ -209,7 +207,6 @@ def building_model(n, ds, dw, dc, load, h_cost, iron_ore_cost): ) elif product in ["eaf", "eaf-grid"]: - # Remove components up to hbi and leave eaf/steel components n.remove( "Link", @@ -354,7 +351,7 @@ def calculate_load(ds_cleaned, dw_cleaned, pv_p_nom_max_cor, onwind_p_nom_max_co load = max_load * (float(snakemake.wildcards["demand_factor"]) / 100) print( - f"max load hydrogen, (solar+onwind corrected)/{snakemake.config["electricity_steel_ratio"]}: {max_load:.1f}" + f"max load hydrogen, (solar+onwind corrected)/{snakemake.config['electricity_steel_ratio']}: {max_load:.1f}" ) print(f"load steel with demand factor: {load:.1f}") @@ -363,22 +360,18 @@ def calculate_load(ds_cleaned, dw_cleaned, pv_p_nom_max_cor, onwind_p_nom_max_co def adjust_part_load(n): - print(f"adjusting part-load limits for {snakemake.config["part_load"].keys()}") + print(f"adjusting part-load limits for {snakemake.config['part_load'].keys()}") for carrier in snakemake.config["part_load"].keys(): - n.links.loc[ n.links.carrier == carrier, "p_min_pu", - ] = snakemake.config[ - "part_load" - ][carrier] + ] = snakemake.config["part_load"][carrier] return n if __name__ == "__main__": - if "snakemake" not in globals(): from _helpers import mock_snakemake diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py new file mode 100644 index 0000000..db3f087 --- /dev/null +++ b/workflow/scripts/prepare_regional_network.py @@ -0,0 +1,510 @@ +"""Prepare regional network: add renewables and configure supply chain per product.""" + +import logging +import pandas as pd +import numpy as np +import xarray as xr +import pypsa +import json +from typing import Dict, List, Tuple + +import tech_database as td + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +def region_to_iso3_codes(region: str, config: dict) -> List[str]: + """Get ISO3 codes for region from config.""" + iso3_list = config.get("regions", {}).get(region, []) + + if not iso3_list: + raise ValueError( + f"Region '{region}' not found in config['regions'] or contains no ISO3 codes. " + f"Available regions: {list(config.get('regions', {}).keys())}" + ) + + return iso3_list + + +# ============================================================================ +# RENEWABLE CLUSTER LOADING +# ============================================================================ + + +def load_renewable_clusters_for_region( + region: str, renewable_timeseries_path: str, config: dict +) -> Dict: + """Load renewable clusters for region directly from netCDF (includes all metadata + timeseries).""" + # Step 1: Get ISO3 codes for region + iso3_list = region_to_iso3_codes(region, config) + logger.info(f"Region '{region}' maps to ISO3 codes: {iso3_list}") + + # Step 2: Load netCDF dataset + timeseries_ds = xr.open_dataset(renewable_timeseries_path) + + # Filter clusters by ISO3 country codes + cluster_iso3 = timeseries_ds.coords["iso3"].values # ISO3 per cluster + cluster_ids = timeseries_ds.coords["cluster"].values # Cluster IDs + + # Select clusters for this region's ISO3 codes + cluster_mask = np.isin(cluster_iso3, iso3_list) + selected_clusters = cluster_ids[cluster_mask] + + if len(selected_clusters) == 0: + raise ValueError( + f"No clusters found for region '{region}' with ISO3 {iso3_list}. " + f"Available ISO3 in data: {np.unique(cluster_iso3)}" + ) + + logger.info(f"Found {len(selected_clusters)} clusters for region {region}") + logger.info(f"Unique technologies: {timeseries_ds.technology.values}") + + # Step 3: Extract cluster data for each selected cluster + clusters = [] + total_potential_mw = 0 + + for cluster_id in selected_clusters: + # Get data for this cluster across all technologies + cluster_idx = list(cluster_ids).index(cluster_id) + iso3 = cluster_iso3[cluster_idx] + + for tech in timeseries_ds.technology.values: + # Extract 2D arrays from dataset + renewable_potential = float( + timeseries_ds["renewable_potential"] + .sel(cluster=cluster_id, technology=tech) + .values + ) + p_nom_max = float( + timeseries_ds["p_nom_max"] + .sel(cluster=cluster_id, technology=tech) + .values + ) + avg_cf = float( + timeseries_ds["avg_cf"].sel(cluster=cluster_id, technology=tech).values + ) + cf_timeseries = ( + timeseries_ds["capacity_factor"] + .sel(cluster=cluster_id, technology=tech) + .values + ) + + # Handle NaN/missing values - default to 0 + renewable_potential = ( + 0 if np.isnan(renewable_potential) else renewable_potential + ) + p_nom_max = 0 if np.isnan(p_nom_max) else p_nom_max + avg_cf = 0 if np.isnan(avg_cf) else avg_cf + if isinstance(cf_timeseries, np.ndarray): + cf_timeseries = np.nan_to_num(cf_timeseries, nan=0.0) + else: + cf_timeseries = ( + np.zeros(8760) if np.isnan(cf_timeseries) else cf_timeseries + ) + + # Get geographic coordinates + lat = float(timeseries_ds["lat"].sel(cluster=cluster_id).values) + lon = float(timeseries_ds["lon"].sel(cluster=cluster_id).values) + + clusters.append( + { + "cluster_id": f"{cluster_id}_{tech}", # Unique ID combining cluster + technology + "iso3": iso3, + "technology": tech, + "renewable_potential_mw": renewable_potential, + "p_nom_max": p_nom_max, + "avg_cf": avg_cf, + "cf_timeseries": cf_timeseries, + "lat": lat, + "lon": lon, + } + ) + + total_potential_mw += renewable_potential + + # Aggregate results + iso3_codes = sorted(list(set(c["iso3"] for c in clusters))) + technologies = sorted(list(set(c["technology"] for c in clusters))) + + logger.info(f"Total renewable potential for region: {total_potential_mw:.1f} MW") + logger.info(f"Clusters loaded: {len(clusters)}") + + return { + "clusters": clusters, + "iso3_list": iso3_codes, + "technologies": technologies, + "total_potential_mw": total_potential_mw, + } + + +# ============================================================================ +# RENEWABLE GENERATOR ADDITION +# ============================================================================ + + +def add_renewable_generators( + network: pypsa.Network, + renewable_clusters: Dict, + tech_costs: pd.Series, + config: dict, +) -> None: + """Add renewable generators to electricity bus for all clusters. + + Renewable generators (wind, solar) produce electricity, so all have carrier="electricity". + The technology type (onwind, offwind, solar) is tracked in the generator name. + """ + clusters = renewable_clusters["clusters"] + + if not clusters: + logger.warning("No clusters provided; no generators added") + return + + # Map technology names to database keys for cost lookup + tech_database_map = { + "onwind": "onwind", + "offwind-ac": "offwind", + "solar": "solar-utility", + } + + # Ensure electricity carrier is defined (all renewables produce electricity) + if "electricity" not in network.carriers.index: + network.add("Carrier", "electricity") + + # Use the network's discount_rate (which is set regionally in prepare_network) + discount_rate = network.discount_rate + + for cluster in clusters: + cluster_id = cluster["cluster_id"] + technology = cluster["technology"] + p_nom_max = cluster["p_nom_max"] # Use cluster-aggregated p_nom_max + cf_ts = cluster["cf_timeseries"] + + # Get technology parameters from database (handles missing tech gracefully) + db_tech_name = tech_database_map.get(technology, technology) + tech_params = td.get_tech(tech_costs, db_tech_name) + + capital_cost = ( + td.get_tech_param(tech_params, "investment", 0) * 1000 + ) # EUR/kW → EUR/MW + lifetime = td.get_tech_param(tech_params, "lifetime", 20) + fom_pct = td.get_tech_param(tech_params, "FOM", 0) + fom_cost = capital_cost * (fom_pct / 100) if capital_cost > 0 else 0 + + gen_name = f"renewable_{cluster_id}" + + # Add generator with cluster data + # All renewables produce electricity (carrier="electricity") + # Technology type (onwind, offwind, solar) is encoded in the generator name + network.add( + "Generator", + gen_name, + bus="electricity", + carrier="electricity", + p_nom_extendable=False, + p_nom=0, # Start with no capacity; optimization will decide + p_nom_max=p_nom_max, # Upper ceiling from cluster data (MW) + p_max_pu=cf_ts, # Hourly capacity factor from cluster data (0-1) + capital_cost=capital_cost, + discount_rate=discount_rate, + marginal_cost=0, + lifetime=lifetime, + fom=fom_cost, + ) + + logger.debug( + f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " + f"capital_cost={capital_cost:.1f} EUR/MW" + ) + + logger.info(f"Added {len(clusters)} renewable generators to network") + + +def _apply_discount_rate_to_components( + network: pypsa.Network, discount_rate: float +) -> None: + """Apply regional discount_rate to all cost-bearing components. + + Links, stores, and generators with costs must have discount_rate set for annualization. + """ + # Apply to links + for link_name, link_row in network.links.iterrows(): + has_cost = ( + pd.notna(link_row.get("overnight_cost")) and link_row["overnight_cost"] > 0 + ) + if has_cost: + network.links.at[link_name, "discount_rate"] = discount_rate + + # Apply to stores + for store_name, store_row in network.stores.iterrows(): + has_cost = ( + pd.notna(store_row.get("overnight_cost")) + and store_row["overnight_cost"] > 0 + ) + if has_cost: + network.stores.at[store_name, "discount_rate"] = discount_rate + + # Apply to generators + for gen_name, gen_row in network.generators.iterrows(): + has_cost = pd.notna(gen_row.get("capital_cost")) and gen_row["capital_cost"] > 0 + if has_cost: + network.generators.at[gen_name, "discount_rate"] = discount_rate + + logger.debug( + f"Applied discount_rate={discount_rate:.4f} to all cost-bearing components" + ) + + +def _consistency_check(network: pypsa.Network) -> None: + """Sanitize and check network consistency. + + PyPSA's sanitize() method automatically adds missing carriers and fixes consistency issues. + """ + logger.info("Running network consistency check...") + + # PyPSA's built-in consistency check + try: + network.sanitize() + logger.info("Network passed consistency check (sanitized)") + except Exception as e: + logger.warning(f"Network sanitization warning: {e}") + + +# ============================================================================ +# SUPPLY CHAIN PRODUCT CUTOFF +# ============================================================================ + + +def apply_product_cutoff(network: pypsa.Network, product: str) -> None: + """Remove supply chain stages after target product (h2/hbi/steel).""" + if product == "h2": + logger.info("Product cutoff: Keeping electrolyzer only (H2 output)") + + # Remove conversion stages after hydrogen + links_to_remove = ["dri", "eaf"] + buses_to_remove = ["iron_ore", "hbi", "steel"] + stores_to_remove = ( + ["hbi_storage"] if "hbi_storage" in network.stores.index else [] + ) + generators_to_remove = ["iron_ore"] + + for link_name in links_to_remove: + if link_name in network.links.index: + network.remove("Link", link_name) + logger.debug(f"Removed link: {link_name}") + + for bus_name in buses_to_remove: + try: + network.remove("Bus", bus_name) + logger.debug(f"Removed bus: {bus_name}") + except ValueError: + logger.debug( + f"Bus {bus_name} not found (already removed or not present)" + ) + + for store_name in stores_to_remove: + if store_name in network.stores.index: + network.remove("Store", store_name) + logger.debug(f"Removed store: {store_name}") + + for gen_name in generators_to_remove: + if gen_name in network.generators.index: + network.remove("Generator", gen_name) + logger.debug(f"Removed generator: {gen_name}") + + elif product == "hbi": + logger.info("Product cutoff: Keeping electrolyzer + DRI (HBI output)") + + # Remove stages after HBI + links_to_remove = ["eaf"] + buses_to_remove = ["steel"] + generators_to_remove = [] + + for link_name in links_to_remove: + if link_name in network.links.index: + network.remove("Link", link_name) + logger.debug(f"Removed link: {link_name}") + + for bus_name in buses_to_remove: + try: + network.remove("Bus", bus_name) + logger.debug(f"Removed bus: {bus_name}") + except ValueError: + logger.debug(f"Bus {bus_name} not found") + + for gen_name in generators_to_remove: + if gen_name in network.generators.index: + network.remove("Generator", gen_name) + + elif product == "steel": + logger.info("Product cutoff: Keeping full supply chain (Steel output)") + # No removal; keep all stages + pass + + else: + raise ValueError( + f"Product '{product}' not recognized. Choose from: 'h2', 'hbi', 'steel'" + ) + + +# ============================================================================ +# MAIN ORCHESTRATION +# ============================================================================ + + +def prepare_network( + skeleton_network_path: str, + renewable_timeseries_path: str, + tech_costs_path: str, + region: str, + product: str, + config: dict, +) -> Tuple[pypsa.Network, Dict]: + """Prepare network: add renewables, apply product cutoff. Returns (network, audit_info).""" + logger.info("=" * 70) + logger.info(f"Preparing network for region={region}, product={product}") + logger.info("=" * 70) + + # Step 1: Load skeleton network + logger.info("Loading skeleton network...") + network = pypsa.Network(skeleton_network_path) + network.name = f"Skeleton-{region}-{product}" + logger.info( + f"Skeleton loaded: {len(network.buses)} buses, " + f"{len(network.links)} links, {len(network.stores)} stores" + ) + + # Step 1b: Set interest rate (discount rate) for the network + interest_rates = config.get("interest_rate", {}) + discount_rate = interest_rates.get(region, interest_rates.get("default", 0.07)) + network.discount_rate = discount_rate + logger.info(f"Set discount rate: {discount_rate:.4f} for region {region}") + + # Step 2: Load tech costs + logger.info("Loading technology costs...") + tech_costs = td.load_tech_costs(tech_costs_path) + + # Step 3: Load renewable clusters for region + logger.info(f"Loading renewable clusters for region {region}...") + renewable_clusters = load_renewable_clusters_for_region( + region=region, + renewable_timeseries_path=renewable_timeseries_path, + config=config, + ) + + # Step 4: Add renewable generators + logger.info("Adding renewable generators to network...") + add_renewable_generators( + network=network, + renewable_clusters=renewable_clusters, + tech_costs=tech_costs, + config=config, + ) + + # Step 5: Apply product-specific cutoff + logger.info(f"Applying product cutoff for {product}...") + apply_product_cutoff(network=network, product=product) + + # Step 6: Build audit info + # Count unique geographic cluster IDs (without technology suffix) + unique_geographic_clusters = set() + for cluster in renewable_clusters["clusters"]: + # Extract base cluster ID (without technology) + cluster_id = cluster["cluster_id"] + base_cluster = "_".join(cluster_id.split("_")[:-1]) # Remove tech suffix + unique_geographic_clusters.add(base_cluster) + + audit_info = { + "region": region, + "product": product, + "discount_rate": discount_rate, + "iso3_list": renewable_clusters["iso3_list"], + "num_geographic_clusters": len(unique_geographic_clusters), + "num_technologies": len(renewable_clusters["technologies"]), + "total_renewable_potential_mw": renewable_clusters["total_potential_mw"], + "network_stats": { + "num_buses": len(network.buses), + "num_links": len(network.links), + "num_generators": len(network.generators), + "num_stores": len(network.stores), + }, + } + + logger.info("Network preparation complete:") + logger.info(f" - Region: {region} (ISO3: {audit_info['iso3_list']})") + logger.info(f" - Geographic clusters: {audit_info['num_geographic_clusters']}") + logger.info(f" - Technologies per cluster: {audit_info['num_technologies']}") + logger.info( + f" - Total renewable potential: {audit_info['total_renewable_potential_mw']:.1f} MW" + ) + logger.info( + f" - Network: {audit_info['network_stats']['num_buses']} buses, " + f"{audit_info['network_stats']['num_generators']} generators" + ) + + # Apply regional discount_rate to all cost-bearing components + _apply_discount_rate_to_components(network, discount_rate) + + # Run consistency check and sanitize + _consistency_check(network) + + logger.info("=" * 70) + + return network, audit_info + + +# ============================================================================ +# SNAKEMAKE INTEGRATION +# ============================================================================ + +if __name__ == "__main__": + # Handle Snakemake or mock invocation + if "snakemake" not in globals(): + # For testing: mock Snakemake + + class MockSnakemake: + """Mock Snakemake object for testing.""" + + def __init__(self): + self.input = { + "skeleton": "../resources/networks/skeleton_2030.nc", + "clusters_timeseries": "../data/renewable_clusters.nc", + "costs": "../resources/technology_data/costs_2030.csv", + } + self.output = { + "base_network": "test_base_network.nc", + "audit": "test_audit.json", + } + self.wildcards = { + "region": "Test_1", + "product": "steel", + "cost_year": "2030", + } + self.config = { + "regions": {"Test_1": ["NLD"], "Test_2": ["PRT"], "Test_3": ["IRL"]} + } + + snakemake = MockSnakemake() + + # Prepare network + try: + network, audit_info = prepare_network( + skeleton_network_path=snakemake.input.skeleton, + renewable_timeseries_path=snakemake.input.clusters_timeseries, + tech_costs_path=snakemake.input.costs, + region=snakemake.wildcards.region, + product=snakemake.wildcards.product, + config=snakemake.config, + ) + + # Save outputs + network.export_to_netcdf(snakemake.output.base_network) + logger.info(f"Network saved to {snakemake.output.base_network}") + + with open(snakemake.output.audit, "w") as f: + json.dump(audit_info, f, indent=2, default=str) + logger.info(f"Audit info saved to {snakemake.output.audit}") + + except Exception as e: + logger.error(f"Network preparation failed: {e}", exc_info=True) + raise diff --git a/workflow/scripts/tech_database.py b/workflow/scripts/tech_database.py index 0192c26..f13222e 100644 --- a/workflow/scripts/tech_database.py +++ b/workflow/scripts/tech_database.py @@ -15,7 +15,9 @@ logger = logging.getLogger(__name__) -def download_tech_database(version: str, output_path: str, disable_progress: bool = False) -> None: +def download_tech_database( + version: str, output_path: str, disable_progress: bool = False +) -> None: """Download PyPSA technology-data from GitHub. Supports standard versions or custom paths.""" from _helpers import progress_retrieve @@ -58,7 +60,9 @@ def get_tech_param( return tech_params.loc[param_name] except KeyError: if default is not None: - logger.warning(f"Parameter '{param_name}' not found, using default: {default}") + logger.warning( + f"Parameter '{param_name}' not found, using default: {default}" + ) return default raise From 69d781acd30c010599de96168749682e18849e1c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 1 Apr 2026 17:32:25 +0200 Subject: [PATCH 022/216] chore: update linting --- pixi.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pixi.toml b/pixi.toml index 3b45173..5127f3c 100644 --- a/pixi.toml +++ b/pixi.toml @@ -94,7 +94,7 @@ dev = { features = ["dev", "test", "docs"], solve-group = "default" } [tool.ruff] # Ruff linter configuration -line-length = 88 +line-length = 120 target-version = "py310" exclude = ["**/*.ipynb"] From 69f2530cb8aaf156c7519c7c14d6ff0e01ddb8cb Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 1 Apr 2026 17:36:04 +0200 Subject: [PATCH 023/216] wip: lcox runs, but with uncharcterisitic infeasibility\n\nneed to check the following:\n-hbi buffer for initial demand\n-battery cycling conditions\n-before optimizing snaity check steel demand vs renewable potential --- config/config.yaml | 15 +- workflow/Snakefile | 65 +- workflow/scripts/build_x_supply_chain.py | 22 + workflow/scripts/calculate_lcox.py | 677 +++++++++++-------- workflow/scripts/create_supply_curve.py | 12 +- workflow/scripts/prepare_regional_network.py | 15 +- 6 files changed, 443 insertions(+), 363 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 1f5d6e6..df6cb29 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -3,8 +3,11 @@ enable: run_supply_curve: True # Enable for first run -# Different demand factors of maximum hydrogen potential as demand in [%] -demand_factors: [0.01, 0.1, 10, 50] +# Absolute steel demand levels (Mt/year) for supply curve sweep +# For each level, PyPSA minimizes cost with fixed renewable capacity +# Values represent different production scales +steel_demand_levels: [10, 50, 100] # Mt/year +compute_iis: false # Set to false to skip expensive IIS computation for infeasible models hydrogen_storage_cost: False iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted @@ -36,7 +39,7 @@ costs: version: v0.14.0 interest_rate: - default: 0.07 + default: 0.05 "Test_1": 0.05 "Test_2": 0.06 @@ -92,9 +95,8 @@ plot: hbi_link: 'firebrick' solver: - name: gurobi - options: gurobi-default - + name: gurobi + options: gurobi-default solver_options: highs-default: # refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/ @@ -154,4 +156,3 @@ colors: Pacific_Asia: '#AF7AC5' # lavender East_East_Asia: '#922B21' # dark red Oceania: '#1F618D' # steel blue - diff --git a/workflow/Snakefile b/workflow/Snakefile index fb373f6..edb0d7a 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -132,32 +132,21 @@ rule prepare_regional_network: if config["enable"].get("run_supply_chain", True): - # Individual model calculating the LCoH for up to 60% of maximum potential as demand - # Stores the resulting LCoH in a csv file - rule model_lcoh: - message: - "Calculating LCoH for region {wildcards.region} for {wildcards.demand_factor}% of estimated maximum hydrogen potential." - input: - supply_data ="../data/new_renewables/supply_{region}_2013_cleaned.nc", - costs = "../resources/technology_data/costs_{cost_year}.csv" - output: - results="../resources/lcoh/cost_year~{cost_year}/{region}/results_{demand_factor}.csv", - network="../resources/lcoh/cost_year~{cost_year}/{region}/network_{demand_factor}.nc", - threads: 4 - script: - "scripts/model_lcoh.py" - rule calculate_regional_lcox: message: "Calculating LCoX for {wildcards.product} in region {wildcards.region} " - "(demand_factor={wildcards.demand_factor}%)." + "(steel_demand={wildcards.steel_demand_mt} Mt/year)." + params: + steel_demand_mt="{steel_demand_mt}", + compute_iis=config.get("compute_iis", False), input: base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", - steel_demand="../resources/steel_production_clustered.csv", local_demand="../data/un_enerdata_demand_2050_final.csv" output: - results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{demand_factor}.csv", - network="../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{demand_factor}.nc", + results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{steel_demand_mt}.csv", + network="../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{steel_demand_mt}.nc", + wildcard_constraints: + steel_demand_mt=r"\d+" threads: 4 resources: mem_mb=8000, @@ -165,18 +154,18 @@ if config["enable"].get("run_supply_chain", True): "scripts/calculate_lcox.py" -# Read all the individual LCoH values for one region and combine them into a supply curve -# stored as a single csv file per region. Note: the visiual plot includes iron ore costs, the csv without since it is added later in the workflow. +# Read all the individual LCoX values for one region and combine them into a supply curve +# stored as a single csv file per region. Note: the visual plot includes iron ore costs, the csv without since it is added later in the workflow. if config["enable"].get("run_supply_curve", True): rule create_supply_curve: message: - "Combining LCo{wildcards.product[0]} results (all demand factors) to create supply curve for {wildcards.region}." + "Combining LCo{wildcards.product[0]} results (all steel demand levels) to create supply curve for {wildcards.region}." input: - # Reference results from calculate_regional_lcox (uses demand_factor as 1, 10, 50 format) + # Reference results from calculate_regional_lcox (uses steel_demand_mt from config) lco_product_data = lambda wildcards: expand( - f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{demand_factor}}.csv", - demand_factor=[int(d*100) for d in config["demand_factors"]] + f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{steel_demand_mt}}.csv", + steel_demand_mt=config.get("steel_demand_levels", [10, 50, 100, 200]) ), local_demand = "../data/un_enerdata_demand_2050_final.csv", steel_demand = "../resources/steel_production_clustered.csv", @@ -266,30 +255,6 @@ rule collect_figures: hourly_analysis = "../results/figures_general/hourly_analysis.pdf", #workflow/notebooks/analysis-hourly.ipynb hourly_analysis_png = "../results/figures_general/hourly_analysis.png", #workflow/notebooks/analysis-hourly.ipynb - -### Additional rules - -rule create_hydrogen_supply_curve_with_demand: - message: - "Combining individual LCoH results to create a supply curve for region {wildcards.region} with demand lines." - input: - lcoh_data = expand( - "../resources/lcoh/cost_year~{cost_year}/{region}/results_{demand_factor}.csv", - demand_factor=config["demand_factors"], - allow_missing=True, - ), - final_demand_data = "../data/un_enerdata_demand_2050_final.csv" - output: - supply_curve="../resources/supply_curves_subtracted_with_demand/cost_year~{cost_year}/{region}_hydrogen.pdf" - script: - "scripts/create_hydrogen_supply_curve_with_demand.py" -rule create_all_supply_curves_with_demand: - input: - expand( - "../resources/supply_curves_subtracted_with_demand/cost_year~{cost_year}/{region}_hydrogen.pdf", cost_year=[2030,2050], region=config["regions"], allow_missing=True - ) - - # rule model_trade_singlestage: # input: # supply_curves = expand( @@ -316,4 +281,4 @@ rule create_all_supply_curves_with_demand: # "Comparing input costs" # notebook: -# "workflow/notebooks/input-cost-comp.ipynb" \ No newline at end of file +# "workflow/notebooks/input-cost-comp.ipynb" diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index bd94db3..1d66b65 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -98,6 +98,7 @@ def _add_conversion_chain( lifetime=td.get_tech_param(elec_params, "lifetime", 40.0), fom_cost=elec_inv_cost * (td.get_tech_param(elec_params, "FOM", 2.8) / 100), p_nom_extendable=True, + p_nom_max=np.inf, p_min_pu=config.get("elec_p_min_pu", 0.10), ) @@ -119,6 +120,7 @@ def _add_conversion_chain( lifetime=td.get_tech_param(dri_params, "lifetime", 40.0), fom_cost=dri_inv_cost * (td.get_tech_param(dri_params, "FOM", 11.3) / 100), p_nom_extendable=True, + p_nom_max=np.inf, p_min_pu=config.get("dri_p_min_pu", 0.15), ) @@ -138,6 +140,7 @@ def _add_conversion_chain( lifetime=td.get_tech_param(eaf_params, "lifetime", 40.0), fom_cost=eaf_inv_cost * (td.get_tech_param(eaf_params, "FOM", 30.0) / 100), p_nom_extendable=True, + p_nom_max=np.inf, p_min_pu=config.get("eaf_p_min_pu", 0.20), ) @@ -161,6 +164,8 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> lifetime=td.get_tech_param(h2_params, "lifetime", 100.0), fom_cost=h2_inv_cost * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), standing_loss=TECH_ASSUMPTIONS["h2_standing_loss"], + e_initial=config.get("h2_storage_e_initial", 0.5), # Start at 50% capacity + e_cyclic=True, # End state must equal start state ) # Battery Storage: Power (inverter for charger/discharger) + Energy (store) @@ -178,6 +183,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), p_nom_extendable=True, + p_nom_max=np.inf, ) network.add( @@ -190,6 +196,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 0.9) / 100), p_nom_extendable=True, + p_nom_max=np.inf, ) batt_store_cost = ( @@ -204,6 +211,21 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> lifetime=td.get_tech_param(batt_store_params, "lifetime", 30.0), fom_cost=batt_store_cost * 0.0, standing_loss=TECH_ASSUMPTIONS["batt_standing_loss"], + e_initial=config.get("battery_e_initial", 0.5), # Start at 50% capacity + e_cyclic=True, # End state must equal start state + ) + + + network.add( + "Store", + "hbi_storage", + bus="hbi", + e_nom_extendable=True, + overnight_cost=0.0, # Just a pile - no cost + lifetime=1.0, + fom_cost=0.0, # No maintenance cost + discount_rate=0.0, # No cost, discount rate doesn't matter but required by PyPSA + standing_loss=0.0, # HBI storage doesn't lose energy ) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 9301e38..860b1fc 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -1,106 +1,73 @@ """ -Calculate regional Levelized Cost of X (LCOX) for multiple demand factors. +Calculate regional Levelized Cost of X (LCOX) for a single steel demand level. -Optimized workflow (single network load, multiple constraint applications): - 1. Load base_network ONCE (renewables + product already configured) - 2. Load product-specific demands - 3. For each demand_factor in config: - a. Copy network (in memory) - b. Apply renewable constraint (demand_factor-specific) - c. Add final loads - d. Solve optimization - e. Extract LCOX and save results_{demand_factor}.csv - f. Export solved network_{demand_factor}.nc +Workflow (single fraction per invocation): + 1. Load base_network (renewables + product already configured) + 2. Load product-specific demands and scale by fraction + 3. Apply renewable constraint (highest-CF blocked for local demand) + 4. Add final loads based on scaled demand + 5. Solve optimization + 6. Extract LCOX and save results_{fraction}.csv + 7. Export solved network_{fraction}.nc -Efficiency: Load base_network once, loop through constraints (not N separate calls) +Parallelization: Each fraction is a separate Snakemake job, enabling parallel execution. Inputs (from Snakemake): - base_network: PyPSA network with renewables, prepared per region (netCDF) - steel_demand: Regional steel demand [Mt/year] (CSV) - - local_demand: Regional local electricity demand [TWh/year] (CSV) + - local_demand: Regional local electricity demand [MWh/year] (CSV) -Outputs (generated for each demand_factor): - - results_{demand_factor}.csv: LCOX point - - network_{demand_factor}.nc: Optimized network +Outputs (generated for each fraction): + - results_{fraction}.csv: LCOX point for that demand level + - network_{fraction}.nc: Optimized network """ import logging +import os from pathlib import Path import pypsa import pandas as pd import numpy as np -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - # ============================================================================ -# NETWORK FIX (pandas/xarray compatibility) +# LOGGING SETUP # ============================================================================ +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) -def load_network_with_string_fix(filepath): - """Load PyPSA network and convert ArrowStringArray to object dtype. - - PyPSA 1.1.2 + xarray 2026.2.0 conflict: Force object dtype on string columns. - """ - network = pypsa.Network(filepath) - network.name = f"Loaded-{Path(filepath).stem}" - - # Convert ArrowStringArray to object dtype for all component dataframes - for component_name in [ - "buses", - "generators", - "links", - "stores", - "lines", - "transformers", - ]: - df = getattr(network, component_name, None) - if df is not None and not df.empty: - for col in df.select_dtypes(include=["string"]).columns: - df[col] = df[col].astype("object") - if hasattr(df.index, "dtype") and df.index.dtype.name == "string": - df.index = df.index.astype("object") - - return network +# Create logs directory if it doesn't exist +log_dir = Path("../logs") +log_dir.mkdir(parents=True, exist_ok=True) +# Add file handler (writes to ../logs/calculate_lcox.log) +file_handler = logging.FileHandler(log_dir / "calculate_lcox.log") +file_handler.setLevel(logging.DEBUG) +file_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') +file_handler.setFormatter(file_formatter) +logger.addHandler(file_handler) # ============================================================================ # DEMAND LOADING # ============================================================================ -def load_demands_for_region(region, product, config): - """Load product-specific demands for region. +def load_demands_for_region(region, config): + """Load local electricity demand for region. Returns dict with: - - steel_demand_mt: Mt/year - - steel_demand_mwh_per_h: MWh/h average - - local_el_demand_mwh: TWh/year (if available for constraint calculation) + - local_el_demand_mwh: MWh/year (for renewable constraint calculation) + + Note: steel_demand_mt is passed directly from Snakemake params, not loaded from file """ - # Load steel production demand (if applicable for this product) - if product in ["steel", "hbi", "eaf", "eaf-grid"]: - try: - steel_df = pd.read_csv(snakemake.input.steel_demand) - # Find row matching region (case-insensitive) - region_mask = steel_df["region"].str.lower() == region.lower() - if not region_mask.any(): - raise ValueError(f"Region '{region}' not found in steel demand data") - steel_demand_mt = steel_df[region_mask]["SteelProductionMt"].values[0] - except Exception as e: - logger.warning(f"Could not load steel demand for {region}: {e}") - steel_demand_mt = 0 - else: - steel_demand_mt = 0 - # Load local electricity demand (for renewable constraint calculation) try: local_df = pd.read_csv(snakemake.input.local_demand) region_mask = local_df["region"].str.lower() == region.lower() if region_mask.any(): - local_el_demand_mwh = local_df[region_mask]["demand"].values[ - 0 - ] # Already in MWh + total_energy_mwh = local_df[region_mask]["demand"].values[0] # MWh final energy + el_share = local_df[region_mask]["el_share"].values[0] / 100 # Convert % to fraction + local_el_demand_mwh = total_energy_mwh * el_share # Apply electricity share else: logger.warning(f"Region '{region}' not found in local demand data") local_el_demand_mwh = 0 @@ -108,15 +75,7 @@ def load_demands_for_region(region, product, config): logger.warning(f"Could not load local demand for {region}: {e}") local_el_demand_mwh = 0 - # Convert Mt/year to MWh/h (using regional electricity requirement) - # For steel: 5.25 MWh/t (from config) - electricity_per_steel_t = config.get("electricity_steel_ratio", 5.25) - steel_demand_mwh = steel_demand_mt * electricity_per_steel_t - steel_demand_mwh_per_h = steel_demand_mwh / 8760 # Annual → hourly average - return { - "steel_demand_mt": steel_demand_mt, - "steel_demand_mwh_per_h": steel_demand_mwh_per_h, "local_el_demand_mwh": local_el_demand_mwh, } @@ -126,24 +85,21 @@ def load_demands_for_region(region, product, config): # ============================================================================ -def apply_renewable_constraint(network, demand_factor, local_el_demand_mwh, config): - """Constrain electrolyzer capacity based on demand_factor. - - NEW: Prioritize highest-CF renewables for local demand (least waste). +def apply_renewable_constraint(network, local_el_demand_mwh, config): + """Block highest-CF renewables for local demand (priority mechanism). Logic: - 1. Get all renewable generators with their timestep-averaged CF - 2. Sort by average CF (descending) - highest first + 1. Get all renewable generators with their average CF + 2. Sort by average CF (descending) - highest quality first 3. Accumulate capacity from highest CF until >= local_demand 4. Block these generators for local demand (set p_nom_max=0) - 5. Calculate remaining capacity available for steel - 6. Apply demand_factor to remaining capacity - 7. Constrain electrolyzer p_nom_max to this value + 5. Remaining renewables available for steel production + + The load determines electrolyzer operation; no capacity constraint applied. Returns: audit dict with capacity breakdown and blocked generators """ # Get all renewable generators (identified by name pattern "renewable_*") - # All generators produce electricity, so we filter by naming convention renewable_gens = network.generators[ network.generators.index.str.startswith("renewable_") ] @@ -202,11 +158,10 @@ def apply_renewable_constraint(network, demand_factor, local_el_demand_mwh, conf ) # ====== STEP 3: Block highest-CF generators for local demand ====== - # Convert annual local demand to hourly average [MWh/year] → [MW] - local_el_demand_mwh / 8760 # Hourly average power needed capacity_accumulated = 0 # Track cumulative capacity factor contribution generators_for_local = [] + for gen_info in gen_cf_data: if capacity_accumulated >= local_el_demand_mwh: @@ -263,24 +218,11 @@ def apply_renewable_constraint(network, demand_factor, local_el_demand_mwh, conf f"Remaining renewable capacity for steel: {total_remaining_capacity:.1f} MW" ) - # ====== STEP 5: Apply demand_factor constraint ====== - available_for_steel = total_remaining_capacity * (demand_factor / 100.0) - - # ====== STEP 6: Constrain electrolyzer ====== - if "electrolyzer" in network.links.index: - network.links.at["electrolyzer", "p_nom_max"] = available_for_steel - logger.info( - f"Constrained electrolyzer p_nom_max to {available_for_steel:.1f} MW " - f"(demand_factor={demand_factor}%)" - ) - else: - logger.warning("Electrolyzer not found in network links") - return { "total_renewable_capacity_mw": total_renewable_capacity, "capacity_for_local_demand_mw": capacity_accumulated / 8760, # Convert back to MW - "capacity_available_for_steel_mw": available_for_steel, + "capacity_available_for_steel_mw": total_remaining_capacity, "generators_blocked_for_local_demand": [ g["gen_name"] for g in generators_for_local ], @@ -294,114 +236,277 @@ def apply_renewable_constraint(network, demand_factor, local_el_demand_mwh, conf def add_loads_to_network(network, product, demands): - """Store cumulative annual demand for flexible constraint injection. + """Add hourly Load components for fixed product demand. - Rather than adding hourly Load components (which force rigid patterns), - we store annual demand and inject it as a constraint during solve(). - This allows the network to decide flexibly WHEN to produce. + Converts annual demand to hourly load: hourly_load = annual_demand / 8760 + This represents a constant hourly demand throughout the year. """ if product == "steel": bus_name = "steel" - annual_steel_t = demands["steel_demand_mt"] * 1000 - annual_demand_units = annual_steel_t - unit_str = "t" + # Steel is measured in t/year, convert to t/h (hourly) + hourly_demand_t = demands["steel_demand_mt"] * 1000 / 8760 # Mt/year → t/h + unit_str = "t/h" elif product == "hbi": bus_name = "hbi" - annual_hbi_t = demands["steel_demand_mt"] * 1000 - annual_demand_units = annual_hbi_t - unit_str = "t" + # HBI is measured in t/year, convert to t/h (hourly) + hourly_demand_t = demands["steel_demand_mt"] * 1000 / 8760 # Mt/year → t/h + unit_str = "t/h" elif product == "h2": bus_name = "hydrogen" - annual_demand_units = demands["steel_demand_mwh_per_h"] * 8760 - unit_str = "MWh" + # H2 is measured in MWh/year, convert to MW (hourly average) + hourly_demand_mwh = demands["steel_demand_mwh_per_h"] # Already hourly average + unit_str = "MW" elif product in ["eaf", "eaf-grid"]: bus_name = "steel" - annual_steel_t = demands["steel_demand_mt"] * 1000 - annual_demand_units = annual_steel_t - unit_str = "t" + # Steel is measured in t/year, convert to t/h (hourly) + hourly_demand_t = demands["steel_demand_mt"] * 1000 / 8760 # Mt/year → t/h + unit_str = "t/h" else: raise ValueError(f"Product '{product}' not recognized") if bus_name not in network.buses.index: raise ValueError(f"Bus '{bus_name}' not found in network") + + # Add constant hourly load to the bus + load_name = f"{product}_demand" + if product == "h2": + p_set = hourly_demand_mwh + else: + p_set = hourly_demand_t - # Store as network parameter for constraint injection in solve() - network.annual_demand = { - "product": product, - "bus": bus_name, - "total_units": annual_demand_units, - "unit": unit_str, - } + network.add( + "Load", + load_name, + bus=bus_name, + p_set=p_set, # Constant hourly demand + ) + + # For steel/HBI: set HBI storage initial energy to 24 hours of hourly load + if product.lower() in ["steel", "hbi"]: + if "hbi_storage" in network.stores.index: + hbi_e_initial = 24 * hourly_demand_t # 24 hours of buffer + network.stores.at["hbi_storage", "e_initial"] = hbi_e_initial + logger.info(f"Set HBI storage e_initial to {hbi_e_initial:.2f} t (24h buffer for {hourly_demand_t:.4f} t/h demand)") logger.info( - f"Stored cumulative {product} demand: {annual_demand_units:.1f} {unit_str}/year (flexible timing)" + f"Added hourly load for {product}: {load_name} = {p_set:.4f} {unit_str} (constant all hours)" ) -# ============================================================================ -# SOLVER -# ============================================================================ +def inspect_network(network, product): + """Print network structure for debugging infeasibility.""" + logger.info("\n" + "="*80) + logger.info("NETWORK INSPECTION - Connectivity & Status") + logger.info("="*80) + + logger.info(f"Buses ({len(network.buses)}): {list(network.buses.index)}") + logger.info(f"\nLoads ({len(network.loads)}):") + for load_name, load_row in network.loads.iterrows(): + logger.info(f" {load_name:30s} -> bus={load_row['bus']:15s} p_set={load_row['p_set']:.1f}") + + logger.info(f"\nLinks ({len(network.links)}):") + for link_name, link_row in network.links.iterrows(): + logger.info(f" {link_name:15s}: {link_row['bus0']:12s} -> {link_row['bus1']:12s} p_nom_ext={link_row['p_nom_extendable']} p_nom_max={link_row['p_nom_max']:.0e}") + + logger.info(f"\nStores ({len(network.stores)}):") + for store_name, store_row in network.stores.iterrows(): + logger.info(f" {store_name:20s} -> {store_row['bus']:15s}") + + # Check isolated buses + all_buses = set(network.buses.index) + connected = set(network.generators['bus'].unique()) | set(network.links['bus0'].unique()) | set(network.links['bus1'].unique()) | set(network.loads['bus'].unique()) | set(network.stores['bus'].unique()) + isolated = all_buses - connected + if isolated: + logger.warning(f"⚠ Isolated buses: {isolated}") + logger.info("="*80 + "\n") + + +def _convert_arrow_strings(network): + """Convert ArrowStringArray columns/indices to regular object dtype. + + Workaround for PyPSA incompatibility with pandas ArrowStringArray. + Uses PyPSA's component structure to properly access all dataframes. + Based on: https://github.com/PyPSA/PyPSA/issues/1585 + """ + for c in network.components: + df = c.static + if not df.empty: + # Convert index if it's ArrowStringArray + if isinstance(df.index.values, pd.arrays.ArrowStringArray): + c.static.index = pd.Index(df.index.astype(object)) + # Convert columns if they're ArrowStringArray + for col in df.columns: + if isinstance(df[col].values, pd.arrays.ArrowStringArray): + c.static[col] = df[col].astype(object) + # Convert time-varying data + for key in c.dynamic: + dyn_df = c.dynamic[key] + if isinstance(dyn_df, pd.DataFrame) and not dyn_df.empty: + # Convert column index if it's ArrowStringArray + if isinstance(dyn_df.columns.values, pd.arrays.ArrowStringArray): + c.dynamic[key].columns = pd.Index(dyn_df.columns.astype(object)) + + +def _convert_bool_attrs_to_int(network): + """Convert boolean attributes to integers for netCDF4 compatibility. + + netCDF4 does not support boolean types for attributes. + Convert True -> 1, False -> 0. + """ + # PyPSA uses either .attrs or internal _attrs depending on version + attr_container = None + if hasattr(network, 'attrs'): + attr_container = network.attrs + elif hasattr(network, '_attrs'): + attr_container = network._attrs + + if attr_container is None: + logger.warning("Network object has no attribute container for attrs") + return + + for key, value in list(attr_container.items()): + if isinstance(value, (bool, np.bool_)): + attr_container[key] = int(value) + + +def _compute_infeasibility_diagnostics(network, output_dir): + """Compute infeasibility diagnostics for an infeasible network and write IIS if available.""" + + # Attempt to run linopy infeasibility diagnostics + if hasattr(network.model, 'compute_infeasibilities'): + try: + infeasible_labels = network.model.compute_infeasibilities() + logger.info(f"Linopy compute_infeasibilities() returned {len(infeasible_labels)} entries") + except Exception as e: + logger.warning(f"Could not compute linopy infeasibilities: {e}") + infeasible_labels = None + else: + logger.warning("Network model does not support compute_infeasibilities()") + infeasible_labels = None + + # Write IIS from backend Gurobi model if available + gurobi_model = None + if hasattr(network.model, 'backend') and hasattr(network.model.backend, 'model'): + gurobi_model = network.model.backend.model + + if gurobi_model is not None: + try: + if hasattr(gurobi_model, 'computeIIS'): + try: + gurobi_model.computeIIS() + logger.info("Gurobi IIS computed") + except Exception as iis_err: + logger.warning(f"Could not compute IIS on Gurobi model: {iis_err}") + + model_ilp_path = os.path.join(output_dir, f"infeasibility_{network.name}.ilp") + gurobi_model.write(model_ilp_path) + logger.info(f"IIS .ilp written to: {model_ilp_path}") + + for c in gurobi_model.getConstrs(): + if c.IISConstr: + logger.info(f"IIS constraint: {c.ConstrName}") + for v in gurobi_model.getVars(): + if v.IISLB or v.IISUB: + logger.info(f"IIS var: {v.VarName} IISLB={v.IISLB} IISUB={v.IISUB}") + except Exception as ilp_err: + logger.warning(f"Could not write IIS .ilp: {ilp_err}") + else: + logger.warning("Gurobi backend model not available for IIS .ilp write") + + # Write text infeasibility report if available + if infeasible_labels: + if hasattr(network.model, 'format_infeasibilities'): + try: + infeas_report = network.model.format_infeasibilities() + except Exception as e: + infeas_report = f"format_infeasibilities failed: {e}" + elif hasattr(network.model, 'print_infeasibilities'): + try: + import io + import sys + _buf = io.StringIO() + _old_stdout = sys.stdout + sys.stdout = _buf + network.model.print_infeasibilities() + sys.stdout = _old_stdout + infeas_report = _buf.getvalue() + except Exception as pi_err: + sys.stdout = _old_stdout + logger.warning(f"Could not run print_infeasibilities(): {pi_err}") + infeas_report = "Infeasible constraints identified, but could not capture output of print_infeasibilities()." + else: + infeas_report = "Infeasible constraints identified, but format_infeasibilities() and print_infeasibilities() are unavailable." + + infeas_path = os.path.join(output_dir, f"infeasibilities_{network.name}.txt") + with open(infeas_path, 'w', encoding='utf-8') as f: + f.write(f"Infeasible constraints for network {network.name}:\n") + f.write("=" * 80 + "\n\n") + f.write(infeas_report) + logger.info(f"Infeasibility report written to: {infeas_path}") + else: + logger.warning("Model is infeasible but no specific constraints identified") def solve_network(network, config): - """Solve the PyPSA optimization with cumulative annual demand constraint. + """Solve the PyPSA optimization with hourly fixed demand. - The constraint enforces: sum of production over all hours >= annual_demand - This allows the network to decide flexibly WHEN to produce (not fixed hourly). + The network has hourly Load components with constant p_set. + Solver minimizes cost to satisfy these fixed hourly demands. """ + # Convert arrow strings to regular strings before optimization + _convert_arrow_strings(network) + solver_name = config.get("solver", {}).get("name", "glpk") solver_options = config.get("solver_options", {}).get( config.get("solver", {}).get("options", "default"), {} ) logger.info(f"Solving network with {solver_name}...") - - # Define constraint injection function (called after model building) - def add_annual_constraint(network, snapshots): - if not hasattr(network, "annual_demand"): - return - - demand_info = network.annual_demand - bus_name = demand_info["bus"] - annual_demand = demand_info["total_units"] - - # Find generators connected to demand bus - gens_on_bus = network.generators[network.generators["bus"] == bus_name].index - - if len(gens_on_bus) == 0: - logger.warning(f"No generators on bus '{bus_name}' for annual constraint") - return - - # Access PyPSA's linopy model variables - p_var = network.model["Generator-p"] # Shape: (snapshot, generator) - - # Sum generator output over all snapshots - total_output = p_var.loc[:, gens_on_bus].sum() - - # Add constraint: total output >= annual demand - network.model.add_constraints( - total_output >= annual_demand, name=f"AnnualDemand_{bus_name}" - ) - - logger.info( - f"✓ Constraint added: {bus_name} annual output >= {annual_demand:.0f} {demand_info['unit']}" + logger.info(f"Solver options: {solver_options}") + + # Add output logging for Gurobi to see what's happening + if solver_name.lower() == "gurobi" and "OutputFlag" not in solver_options: + solver_options = {**solver_options, "OutputFlag": 1} # Enable Gurobi output + + # Solve without constraint injection (hourly loads already in network) + try: + status = network.optimize( + network.snapshots, + solver_name=solver_name, + solver_options=solver_options, + multi_investment_periods=False, ) - # Solve with constraint injection - network.optimize( - network.snapshots, - solver_name=solver_name, - solver_options=solver_options, - multi_investment_periods=False, - extra_functionality=add_annual_constraint, - ) - - logger.info("Network solved successfully") + logger.info(f"Optimization status: {status}") + + if status != 0: + logger.warning(f"Non-optimal status ({status})") + if network.objective is not None: + logger.info(f" Objective value: {network.objective}") + else: + logger.warning(" Objective is None (no feasible solution found)") + logger.warning("Model is infeasible - check network structure and constraints") + + # Use linopy's built-in infeasibility diagnostics + if solver_name.lower() == "gurobi" and snakemake.params.compute_iis: + try: + output_dir = os.path.dirname(snakemake.output.network) + _compute_infeasibility_diagnostics( + network=network, + output_dir=output_dir, + ) + except Exception as iis_e: + logger.warning(f"Could not compute infeasibilities: {iis_e}") + elif solver_name.lower() == "gurobi": + logger.info("compute_iis flag false, skipping IIS diagnostics") + except Exception as e: + logger.error(f"Solver exception: {e}") + raise return network @@ -411,23 +516,28 @@ def add_annual_constraint(network, snapshots): # ============================================================================ -def extract_lcox(network, product, demands, renewable_constraint_info, demand_factor): +def extract_lcox(network, product, demands): """Extract LCOX from optimized network. - Returns DataFrame with one row containing all results + audit columns. + Returns DataFrame with product-specific columns for supply curve. """ + # Define product-specific column names + if product.lower() in ["steel", "hbi"]: + load_col = "load [t/h]" + cost_col = "lcox [EUR/t]" + elif product.lower() in ["h2"]: + load_col = "load [MW]" + cost_col = "lcox [EUR/MWh]" + else: + load_col = "load [per h]" + cost_col = "lcox [EUR/unit]" + results_df = pd.DataFrame( columns=[ - "demand_factor [%]", - "demand [{}]".format("t" if product != "h2" else "MWh"), - "load [per h]", + "demand [t]", + load_col, "cost [EUR]", - "lcox [EUR/unit]", - "renewable_capacity_total_mw", - "renewable_capacity_for_local_mw", - "renewable_capacity_for_steel_mw", - "num_generators_blocked", - "status", + cost_col, ] ) @@ -436,71 +546,31 @@ def extract_lcox(network, product, demands, renewable_constraint_info, demand_fa if obj_value is None or np.isnan(obj_value): raise ValueError("Optimization failed to return valid objective") - demand_annual = ( - demands["steel_demand_mt"] - if product != "h2" - else demands["steel_demand_mwh_per_h"] * 8760 - ) - lcox = obj_value / demand_annual if demand_annual > 0 else np.inf + demand_annual_t = demands["steel_demand_mt"] * 1000 # Mt → t + hourly_load_t = demand_annual_t / 8760 + lcox = obj_value / demand_annual_t if demand_annual_t > 0 else np.inf results_df.loc[0] = [ - int(demand_factor * 100), # Convert to percent - demand_annual, - demands["steel_demand_mwh_per_h"], + demand_annual_t, + hourly_load_t, obj_value, lcox, - renewable_constraint_info["total_renewable_capacity_mw"], - renewable_constraint_info["capacity_for_local_demand_mw"], - renewable_constraint_info["capacity_available_for_steel_mw"], - renewable_constraint_info.get("num_generators_blocked", 0), - "feasible", ] - logger.info(f"LCOX calculated: {lcox:.2f} EUR/unit") - logger.info( - f" Generators blocked for local demand: {renewable_constraint_info.get('num_generators_blocked', 0)}" - ) + logger.info(f"LCOX calculated: {lcox:.2f} {cost_col.split('[')[1].split(']')[0]}") except Exception as e: logger.error(f"Optimization infeasible or failed: {e}") - demand_annual = ( - demands["steel_demand_mt"] - if product != "h2" - else demands["steel_demand_mwh_per_h"] * 8760 - ) + demand_annual_t = demands["steel_demand_mt"] * 1000 + hourly_load_t = demand_annual_t / 8760 results_df.loc[0] = [ - int(demand_factor * 100), # Convert to percent - demand_annual, - demands["steel_demand_mwh_per_h"], + demand_annual_t, + hourly_load_t, np.nan, np.nan, - renewable_constraint_info["total_renewable_capacity_mw"], - renewable_constraint_info["capacity_for_local_demand_mw"], - renewable_constraint_info.get("capacity_available_for_steel_mw", 0), - renewable_constraint_info.get("num_generators_blocked", 0), - "infeasible", ] return results_df - -# ============================================================================ -# ADJUSTMENT (LEGACY - KEPT FOR COMPATIBILITY) -# ============================================================================ - - -def adjust_part_load(network, config): - """Adjust part-load limits for links based on config.""" - part_load = config.get("part_load", {}) - if not part_load: - return - - for carrier, min_pu in part_load.items(): - mask = network.links["carrier"] == carrier - if mask.any(): - network.links.loc[mask, "p_min_pu"] = min_pu - logger.debug(f"Set part-load limit for {carrier}: p_min_pu={min_pu}") - - if __name__ == "__main__": if "snakemake" not in globals(): from _helpers import mock_snakemake @@ -514,107 +584,132 @@ def adjust_part_load(network, config): # ==================== SETUP ==================== logger.info("=" * 70) + # Get the specific demand level for THIS invocation (passed by Snakemake) + steel_demand_mt = float(snakemake.params.steel_demand_mt) logger.info( f"LCOX Calculation: region={snakemake.wildcards.region}, " - f"product={snakemake.wildcards.product}" + f"product={snakemake.wildcards.product}, " + f"steel_demand={steel_demand_mt} Mt/year." ) logger.info("=" * 70) - # Load pre-prepared base network ONCE (key efficiency gain) + # Load pre-prepared base network once logger.info("Loading base network...") - base_network = load_network_with_string_fix(snakemake.input.base_network) + base_network = pypsa.Network(snakemake.input.base_network) logger.info( f"Network loaded: {len(base_network.buses)} buses, " f"{len(base_network.generators)} generators, {len(base_network.links)} links" ) - # Load demands for this region and product + # Load local electricity demand for this region logger.info("Loading demands...") demands = load_demands_for_region( region=snakemake.wildcards.region, - product=snakemake.wildcards.product, config=snakemake.config, ) - logger.info(f"Steel demand: {demands['steel_demand_mt']:.1f} Mt/year") + logger.info( f"Local electricity demand: {demands['local_el_demand_mwh']:.1f} MWh/year" ) - # ==================== SINGLE DEMAND FACTOR PROCESSING ==================== - # Get demand_factor from Snakemake wildcard (in percent: 1, 10, 50, etc.) - demand_factor_percent = int(snakemake.wildcards.demand_factor) - demand_factor = ( - demand_factor_percent / 100.0 - ) # Convert to decimal (0.01, 0.1, 0.5, etc.) - - logger.info(f"\n{'=' * 70}") - logger.info(f"Processing demand_factor={demand_factor_percent}% ({demand_factor})") - logger.info(f"{'=' * 70}") - - # Create a copy of base network for this constraint scenario + # ==================== PROCESS SINGLE DEMAND LEVEL ==================== + electricity_per_steel_t = snakemake.config.get("electricity_steel_ratio", 5.25) + + logger.info(f"Processing: {steel_demand_mt} Mt/year") + + # ==================== NETWORK SETUP ==================== + # Create a copy of base network network = base_network.copy() - network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-DF{demand_factor_percent}%" + network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-{steel_demand_mt}" # Preserve discount_rate from base network (needed for cost annuitization) network.discount_rate = base_network.discount_rate - # Apply renewable constraint based on demand_factor - logger.info( - f"Applying renewable constraint (demand_factor={demand_factor_percent}%)..." - ) + # Calculate electricity needed for this demand level + scaled_steel_demand_mwh_per_h = steel_demand_mt * electricity_per_steel_t / 8760 + + logger.info(f"Steel demand: {steel_demand_mt:.1f} Mt/year") + logger.info(f"Electricity required: {scaled_steel_demand_mwh_per_h * 8760:.1f} MWh/year") + + # Create scaled demands dict for this demand level + scaled_demands = demands.copy() + scaled_demands['steel_demand_mt'] = steel_demand_mt + scaled_demands['steel_demand_mwh_per_h'] = scaled_steel_demand_mwh_per_h + + # Block highest-CF renewables for local demand (priority mechanism) + logger.info("Applying renewable priority constraint...") constraint_info = apply_renewable_constraint( network=network, - demand_factor=demand_factor, local_el_demand_mwh=demands["local_el_demand_mwh"], config=snakemake.config, ) - logger.info( - f" Total renewable capacity: {constraint_info['total_renewable_capacity_mw']:.1f} MW" - ) - logger.info( - f" Capacity for local demand: {constraint_info['capacity_for_local_demand_mw']:.1f} MW" - ) - logger.info( - f" Capacity available for steel: {constraint_info['capacity_available_for_steel_mw']:.1f} MW" - ) - - # Add loads to network - logger.info("Adding loads to network...") + # Add hourly load for steel output + # (This also sets HBI storage e_initial inside add_loads_to_network) + logger.info("Adding hourly load to network...") add_loads_to_network( - network=network, product=snakemake.wildcards.product, demands=demands + network=network, product=snakemake.wildcards.product, demands=scaled_demands ) - # Adjust part-load (if configured) - if snakemake.config.get("part_load"): - adjust_part_load(network, snakemake.config) + # Debug: Print network structure + logger.info("\n--- Network Structure for Demand Level {:.1f} Mt/year ---".format(steel_demand_mt)) + logger.info(f"Buses: {list(network.buses.index)}") + logger.info(f"Generators: {len(network.generators)} total") + for gen in network.generators.index: + p_max = network.generators.at[gen, 'p_nom_max'] + logger.info(f" {gen}: p_nom_max={p_max:.1f} MW") + logger.info(f"Links: {list(network.links.index)}") + for link in network.links.index: + p_nominal = network.links.at[link, 'p_nom'] + logger.info(f" {link}: p_nom={p_nominal:.1f} MW") + logger.info(f"Stores: {list(network.stores.index)}") + logger.info(f"Loads: {list(network.loads.index)}") + + # Solve + logger.info("Optimizing network...") + if snakemake.config.get("debug_network_inspection", False): + inspect_network(network, snakemake.wildcards.product) # Debug inspection + try: + solve_network(network, snakemake.config) + optimization_status = "optimal" if network.objective is not None and not np.isnan(network.objective) else "infeasible" + except Exception as e: + logger.warning(f"Solver error for steel demand {steel_demand_mt} Mt/year: {e}") + optimization_status = "error" - # Solve this constraint scenario - solve_network(network, snakemake.config) + if optimization_status != "optimal": + logger.warning(f"Optimization {optimization_status} for steel demand {steel_demand_mt} Mt/year - returning NaN values") # Extract LCOX results logger.info("Extracting results...") results_df = extract_lcox( network=network, product=snakemake.wildcards.product, - demands=demands, - renewable_constraint_info=constraint_info, - demand_factor=demand_factor, + demands=scaled_demands, ) - - # ==================== SAVE RESULTS ==================== - logger.info("=" * 70) + + # Save results for this demand level + result_file = snakemake.output.results + network_file = snakemake.output.network + logger.info("Saving results...") + results_df.to_csv(result_file, index=False) + logger.info(f"Results saved: {result_file}") + + # Save network only if optimization succeeded + if optimization_status == "optimal": + try: + _convert_bool_attrs_to_int(network) + network.export_to_netcdf(network_file) + logger.info(f"Network saved: {network_file}") + except Exception as e: + logger.warning(f"Could not save network: {e}") + else: + logger.warning(f"Skipping network export due to solver status: {optimization_status}") + logger.info("=" * 70) - - # Save CSV result - results_df.to_csv(snakemake.output.results, index=False) - logger.info(f"Results saved: {snakemake.output.results}") - - # Save network - network.export_to_netcdf(snakemake.output.network) - logger.info(f"Network saved: {snakemake.output.network}") - - logger.info("=" * 70) - logger.info(f"LCOX calculation complete for demand_factor={demand_factor_percent}%") + + if optimization_status == "optimal": + logger.info("Demand level completed successfully!") + else: + logger.warning(f"Demand level completed with solver status: {optimization_status}") logger.info("=" * 70) diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index a676966..cec56a9 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -1,9 +1,8 @@ import pandas as pd - import matplotlib +import matplotlib.pyplot as plt matplotlib.use("Agg") -import matplotlib.pyplot as plt def get_steel_demand(region): @@ -31,7 +30,8 @@ def create_supply_curve(): all_files = snakemake.input.lco_product_data print("files to merge:", all_files) - df_from_each_file = (pd.read_csv(f, sep=",", index_col=0) for f in all_files) + # load input lco csvs as regular data frames (demand is a column, not index) + df_from_each_file = (pd.read_csv(f, sep=",") for f in all_files) df_merged = pd.concat(df_from_each_file, ignore_index=True) df_sub = df_merged.copy() print("merged file has been created") @@ -171,10 +171,10 @@ def create_supply_curve(): if product == "hydrogen": columns = { "demand factor": "demand factor [%]", - "demand": "demand [MWh]", + "demand": "demand [t]", "load": "load [MW]", "total cost": "cost [EUR]", - "cost per unit": "LCOH [EUR/MWh]", + "cost per unit": "lcox [EUR/MWh]", "xlabel": "Demand in TWh", "product_unit": "MWh", "ylim": (0, 100), @@ -185,7 +185,7 @@ def create_supply_curve(): "demand": "demand [t]", "load": "load [t/h]", "total cost": "cost [EUR]", - "cost per unit": "LCOX [EUR/t]", + "cost per unit": "lcox [EUR/t]", "xlabel": "Demand in Mt", "product_unit": "t", "ylim": (0, 900), diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index db3f087..224df7e 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -184,12 +184,10 @@ def add_renewable_generators( db_tech_name = tech_database_map.get(technology, technology) tech_params = td.get_tech(tech_costs, db_tech_name) - capital_cost = ( - td.get_tech_param(tech_params, "investment", 0) * 1000 - ) # EUR/kW → EUR/MW + overnight_cost = (td.get_tech_param(tech_params, "investment", 0) * 1000) # EUR/kW → EUR/MW lifetime = td.get_tech_param(tech_params, "lifetime", 20) fom_pct = td.get_tech_param(tech_params, "FOM", 0) - fom_cost = capital_cost * (fom_pct / 100) if capital_cost > 0 else 0 + fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 gen_name = f"renewable_{cluster_id}" @@ -205,16 +203,15 @@ def add_renewable_generators( p_nom=0, # Start with no capacity; optimization will decide p_nom_max=p_nom_max, # Upper ceiling from cluster data (MW) p_max_pu=cf_ts, # Hourly capacity factor from cluster data (0-1) - capital_cost=capital_cost, + overnight_cost=overnight_cost, discount_rate=discount_rate, - marginal_cost=0, lifetime=lifetime, - fom=fom_cost, + fom_cost=fom_cost, ) logger.debug( f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " - f"capital_cost={capital_cost:.1f} EUR/MW" + f"overnight_cost={overnight_cost:.1f} EUR/MW" ) logger.info(f"Added {len(clusters)} renewable generators to network") @@ -246,7 +243,7 @@ def _apply_discount_rate_to_components( # Apply to generators for gen_name, gen_row in network.generators.iterrows(): - has_cost = pd.notna(gen_row.get("capital_cost")) and gen_row["capital_cost"] > 0 + has_cost = pd.notna(gen_row.get("overnight_cost")) and gen_row["overnight_cost"] > 0 if has_cost: network.generators.at[gen_name, "discount_rate"] = discount_rate From 1ee1a055e0c54b6ba5464b8447a2d0620d5e9721 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 2 Apr 2026 16:39:21 +0200 Subject: [PATCH 024/216] feat: update linter setup --- .pre-commit-config.yaml | 39 +++++++++++++++++++++------------------ 1 file changed, 21 insertions(+), 18 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 673f02f..8957eee 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,21 +1,24 @@ repos: - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.261 +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.15.8 hooks: - - id: ruff - args: ["--fix"] - files: "\\.py$" # This will run ruff on Python source files only and fix formatting issues automatically. + # Run the linter. + - id: ruff-check + args: [ --fix ] + # Run the formatter. + - id: ruff-format - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: mixed-line-ending - args: ["--fix=auto"] - - id: check-merge-conflict - - id: check-added-large-files - - id: check-yaml - exclude: ^conda/meta.yaml$ - - id: check-json - - id: check-xml +# - repo: https://github.com/pre-commit/pre-commit-hooks +# rev: v6.0.0 +# hooks: +# - id: trailing-whitespace +# - id: end-of-file-fixer +# - id: mixed-line-ending +# args: ["--fix=auto"] +# - id: check-merge-conflict +# - id: check-added-large-files +# - id: check-yaml +# exclude: ^conda/meta.yaml$ +# - id: check-json +# - id: check-xml From 9ff03e4e57ece53e4f50bdf15f80e97feeb7e301 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 2 Apr 2026 16:45:28 +0200 Subject: [PATCH 025/216] fix: add proper year in supply chain --- workflow/scripts/build_x_supply_chain.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 1d66b65..f18842a 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -80,8 +80,8 @@ def _add_conversion_chain( network: pypsa.Network, tech_costs: pd.Series, config: dict ) -> None: """Add energy conversion pathway: Electricity → H2 → HBI → Steel. - - Note: Costs are added but discount_rate is NOT set here (applied regionally in prepare_regional_network). + Note: Costs are added but discount_rate is NOT set here. + It is applied regionally in prepare_regional_network. """ # Electrolyzer: Electricity → H2 @@ -148,7 +148,8 @@ def _add_conversion_chain( def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> None: """Add H2 and battery storage systems. - Note: Costs are added but discount_rate is NOT set here (applied regionally in prepare_regional_network). + Note: Costs are added but discount_rate is NOT set here. + It is applied regionally in prepare_regional_network. """ # H2 Storage (underground cavern) @@ -215,14 +216,13 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> e_cyclic=True, # End state must equal start state ) - network.add( "Store", "hbi_storage", bus="hbi", e_nom_extendable=True, overnight_cost=0.0, # Just a pile - no cost - lifetime=1.0, + lifetime=1.0, fom_cost=0.0, # No maintenance cost discount_rate=0.0, # No cost, discount rate doesn't matter but required by PyPSA standing_loss=0.0, # HBI storage doesn't lose energy @@ -240,7 +240,7 @@ def _add_resources(network: pypsa.Network, config: dict) -> None: ) -def build_network(config: dict, tech_costs_path: str) -> pypsa.Network: +def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Network: """Build PyPSA steel supply chain skeleton (region-agnostic). The skeleton contains: @@ -251,10 +251,7 @@ def build_network(config: dict, tech_costs_path: str) -> pypsa.Network: This design allows the same skeleton to be used across regions with different discount rates. """ - # Setup - year = config.get("cost_year", 2030) network = pypsa.Network() - network.name = f"Skeleton-Steel-Supply-Chain-{year}" network.set_snapshots(pd.date_range(f"{year}-01-01", periods=8760, freq="h")) # NOTE: discount_rate is NOT set here (region-agnostic) tech_costs = td.load_tech_costs(tech_costs_path) @@ -286,7 +283,7 @@ def build_network(config: dict, tech_costs_path: str) -> pypsa.Network: tech_costs_path = "../resources/technology_data/costs_2030.csv" output_path = "test_steel_network.nc" - network = build_network(config, tech_costs_path) - network.name = f"Skeleton-Exported-{config.get('cost_year', 2030)}" + year = getattr(snakemake.wildcards, "cost_year", 2050) # noqa: F821 + network = build_network(config, tech_costs_path, year) network.export_to_netcdf(output_path) logger.info(f"Network exported to {output_path}") From c10363ca8eed1dda8831d39ccb0a63c97e5abd2f Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 2 Apr 2026 16:47:41 +0200 Subject: [PATCH 026/216] chore: format with proper linting --- workflow/notebooks/_helpers.py | 3 +- workflow/notebooks/analysis-basemodel.ipynb | 5 +- .../notebooks/analysis-coststructure.ipynb | 111 ++-- .../notebooks/analysis-domestic-demand.ipynb | 21 +- .../analysis-globalsupplycurve.ipynb | 171 ++++-- workflow/notebooks/analysis-hourly.ipynb | 57 +- workflow/notebooks/analysis-iron-ore.ipynb | 6 +- workflow/notebooks/analysis-re.ipynb | 28 +- .../notebooks/analysis-steel-hbi-split.ipynb | 121 ++-- workflow/notebooks/analysis-trace.ipynb | 10 +- workflow/notebooks/compare-scenarios.ipynb | 50 +- workflow/notebooks/conceptual-model.ipynb | 559 ------------------ workflow/notebooks/global-iron-ore.ipynb | 98 +-- .../notebooks/global-steel-production.ipynb | 96 +-- workflow/notebooks/input-cost-comp.ipynb | 28 +- workflow/notebooks/plot_countries.ipynb | 55 +- workflow/notebooks/prepare-iron-ore.ipynb | 33 +- workflow/notebooks/prepare-steel.ipynb | 23 +- workflow/notebooks/prepare_potentials.ipynb | 473 +++++++++------ workflow/notebooks/validation.ipynb | 77 ++- workflow/scripts/calculate_lcox.py | 155 +++-- workflow/scripts/model_lcoh.py | 1 - workflow/scripts/model_trade.py | 11 +- workflow/scripts/model_trade_singlestage.py | 5 +- workflow/scripts/prepare_regional_network.py | 8 +- 25 files changed, 1006 insertions(+), 1199 deletions(-) delete mode 100644 workflow/notebooks/conceptual-model.ipynb diff --git a/workflow/notebooks/_helpers.py b/workflow/notebooks/_helpers.py index 0626b03..12359d6 100644 --- a/workflow/notebooks/_helpers.py +++ b/workflow/notebooks/_helpers.py @@ -10,6 +10,7 @@ logger = logging.getLogger(__name__) + def load_config(config): with open(config, "r") as stream: try: @@ -173,4 +174,4 @@ def progress_retrieve(url, file, disable=False): with open(file, "wb") as f: for data in response.iter_content(chunk_size=chunk_size): f.write(data) - t.update(len(data)) \ No newline at end of file + t.update(len(data)) diff --git a/workflow/notebooks/analysis-basemodel.ipynb b/workflow/notebooks/analysis-basemodel.ipynb index 4dca377..46b5572 100644 --- a/workflow/notebooks/analysis-basemodel.ipynb +++ b/workflow/notebooks/analysis-basemodel.ipynb @@ -131,7 +131,7 @@ "metadata": {}, "outputs": [], "source": [ - "(n.generators.p_nom_opt/n.generators.p_nom_max * 100).round(2)" + "(n.generators.p_nom_opt / n.generators.p_nom_max * 100).round(2)" ] }, { @@ -187,7 +187,7 @@ "metadata": {}, "outputs": [], "source": [ - "pypsatopo.generate(n, file_format = \"png\", file_output = output_fn)" + "pypsatopo.generate(n, file_format=\"png\", file_output=output_fn)" ] }, { @@ -206,6 +206,7 @@ "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", + "\n", "n.statistics.energy_balance(aggregate_time=False, comps=[\"Store\"]).droplevel(0).iloc[\n", " :, :\n", "].groupby(\"carrier\").sum().T.plot.line()\n", diff --git a/workflow/notebooks/analysis-coststructure.ipynb b/workflow/notebooks/analysis-coststructure.ipynb index 9ef9905..9bbbfa8 100644 --- a/workflow/notebooks/analysis-coststructure.ipynb +++ b/workflow/notebooks/analysis-coststructure.ipynb @@ -22,10 +22,11 @@ "outputs": [], "source": [ "from _helpers import mock_snakemake\n", + "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"penalty-oc\" # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " )" + " \"collect_figures\",\n", + " scenario=\"penalty-oc\", # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" ] }, { @@ -55,7 +56,9 @@ "source": [ "trade_model = f\"../../results/cost_year~2030/interone~hbi/intertwo~eaf-grid/final~steel/scenario~{scenario}/network.nc\"\n", "# single_region = \"../../resources/lcos/cost_year~2030/Europe/network_1.nc\" # old\n", - "single_region = \"../../resources/lco-steel/cost_year~2030/South_South_America/network_1.nc\"" + "single_region = (\n", + " \"../../resources/lco-steel/cost_year~2030/South_South_America/network_1.nc\"\n", + ")" ] }, { @@ -102,7 +105,7 @@ "outputs": [], "source": [ "iron_ore_cost = n.generators[n.generators.carrier == \"iron_ore\"].marginal_cost.mean()\n", - "iron_ore_to_steel = 1/n.links[n.links.carrier == 'steel'].efficiency.mean()\n", + "iron_ore_to_steel = 1 / n.links[n.links.carrier == \"steel\"].efficiency.mean()\n", "iron_ore_cost_notransport = iron_ore_cost * iron_ore_to_steel\n", "print(f\"Iron ore cost (w/o transport): {iron_ore_cost_notransport:.2f} €/t_steel\")" ] @@ -131,19 +134,29 @@ "outputs": [], "source": [ "# Steel supply cost distribution per region (boxplot), including iron ore cost w/o transport\n", - "steel_links = n.links[n.links.carrier == 'steel'].copy()\n", - "steel_links['region'] = steel_links['bus1'].str.replace('_ore$', '', regex=True)\n", + "steel_links = n.links[n.links.carrier == \"steel\"].copy()\n", + "steel_links[\"region\"] = steel_links[\"bus1\"].str.replace(\"_ore$\", \"\", regex=True)\n", "# Add iron ore cost w/o transport to each steel supply cost. The marginal cost of steel links refers to bus0, so an adjustment to steel (bus1) is required\n", - "steel_links['total_cost'] = steel_links['marginal_cost'] * iron_ore_to_steel + iron_ore_cost_notransport\n", + "steel_links[\"total_cost\"] = (\n", + " steel_links[\"marginal_cost\"] * iron_ore_to_steel + iron_ore_cost_notransport\n", + ")\n", "\n", "fig, ax = plt.subplots(figsize=(7, 5))\n", - "sns.boxplot(x='region', y='total_cost', data=steel_links, ax=ax, width=0.5, color='steelblue', fliersize=3)\n", - "ax.set_ylabel('Steel supply cost incl. iron ore (€/t_product)')\n", - "ax.set_xlabel('Region')\n", - "ax.set_title('Steel supply cost distribution per region (incl. iron ore material cost)')\n", + "sns.boxplot(\n", + " x=\"region\",\n", + " y=\"total_cost\",\n", + " data=steel_links,\n", + " ax=ax,\n", + " width=0.5,\n", + " color=\"steelblue\",\n", + " fliersize=3,\n", + ")\n", + "ax.set_ylabel(\"Steel supply cost incl. iron ore (€/t_product)\")\n", + "ax.set_xlabel(\"Region\")\n", + "ax.set_title(\"Steel supply cost distribution per region (incl. iron ore material cost)\")\n", "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n", "ax.set_ylim(0, None)\n", - "ax.grid(axis='y')\n", + "ax.grid(axis=\"y\")\n", "plt.tight_layout()\n", "plt.show()" ] @@ -163,8 +176,11 @@ "metadata": {}, "outputs": [], "source": [ - "costs_per_carrier = n_ssa.statistics.system_cost() / n_ssa.statistics.energy_balance().loc[\"Load\",\"Steel\",\"Steel\"]\n", - "costs_per_carrier #.sum()" + "costs_per_carrier = (\n", + " n_ssa.statistics.system_cost()\n", + " / n_ssa.statistics.energy_balance().loc[\"Load\", \"Steel\", \"Steel\"]\n", + ")\n", + "costs_per_carrier # .sum()" ] }, { @@ -179,18 +195,18 @@ "costs = costs.groupby(level=\"carrier\").sum()\n", "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", - "colors = sns.color_palette('Set2', len(costs))\n", - "ax.bar(['Total'], [costs.sum()], color='lightgrey', label='Total')\n", + "colors = sns.color_palette(\"Set2\", len(costs))\n", + "ax.bar([\"Total\"], [costs.sum()], color=\"lightgrey\", label=\"Total\")\n", "bottom = 0\n", "for i, (carrier, value) in enumerate(zip(costs.index, costs.values)):\n", - " ax.bar(['Total'], [value], bottom=bottom, color=colors[i], label=carrier)\n", + " ax.bar([\"Total\"], [value], bottom=bottom, color=colors[i], label=carrier)\n", " bottom += value\n", - "ax.set_ylabel('Cost (€/t_steel)')\n", - "ax.set_xlabel('Carrier')\n", - "ax.set_title('Steel supply costs single region (w/o iron ore material cost)')\n", - "ax.grid(axis='y', alpha=0.4, zorder=0)\n", - "ax.legend(title='Carrier', bbox_to_anchor=(1.05, 1), loc='upper left')\n", - "ax.set_ylim(0,500)\n", + "ax.set_ylabel(\"Cost (€/t_steel)\")\n", + "ax.set_xlabel(\"Carrier\")\n", + "ax.set_title(\"Steel supply costs single region (w/o iron ore material cost)\")\n", + "ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", + "ax.legend(title=\"Carrier\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + "ax.set_ylim(0, 500)\n", "plt.tight_layout()\n", "plt.show()" ] @@ -203,7 +219,7 @@ "outputs": [], "source": [ "# Sanity check: objective value divided by total steel load should equal cost per ton steel\n", - "n_ssa.objective / n_ssa.statistics.energy_balance().loc[\"Load\",\"Steel\",\"Steel\"] * (-1)" + "n_ssa.objective / n_ssa.statistics.energy_balance().loc[\"Load\", \"Steel\", \"Steel\"] * (-1)" ] }, { @@ -222,7 +238,9 @@ "outputs": [], "source": [ "# Iron ore price analysis\n", - "iron_ore_price = n.buses_t.marginal_price.loc[:, n.buses[n.buses.carrier == \"iron_ore\"].index]\n", + "iron_ore_price = n.buses_t.marginal_price.loc[\n", + " :, n.buses[n.buses.carrier == \"iron_ore\"].index\n", + "]\n", "\n", "# Steel price analysis\n", "steel_price = n.buses_t.marginal_price.loc[:, n.buses[n.buses.carrier == \"steel\"].index]" @@ -237,17 +255,12 @@ "source": [ "# Helper to clean region names (remove _ore)\n", "def clean_region_names(index):\n", - " return [re.sub(r'_ore$', '', str(i)) for i in index]\n", + " return [re.sub(r\"_ore$\", \"\", str(i)) for i in index]\n", + "\n", "\n", "# Prepare data for both carriers\n", - "data = {\n", - " 'iron_ore': iron_ore_price,\n", - " 'steel': steel_price\n", - "}\n", - "titles = {\n", - " 'iron_ore': 'Iron Ore',\n", - " 'steel': 'Steel'\n", - "}\n", + "data = {\"iron_ore\": iron_ore_price, \"steel\": steel_price}\n", + "titles = {\"iron_ore\": \"Iron Ore\", \"steel\": \"Steel\"}\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(10, 10))\n", "\n", @@ -256,24 +269,26 @@ " price_df = price_df.copy()\n", " price_df.columns = clean_region_names(price_df.columns)\n", " price_T = price_df.T\n", - " price_T.columns = ['Price']\n", + " price_T.columns = [\"Price\"]\n", " price_T.index = clean_region_names(price_T.index)\n", "\n", " # Boxplot (narrower)\n", - " sns.boxplot(data=price_df.melt(var_name='Region', value_name=\"\"), ax=axes[i,0], width=0.3)\n", - " axes[i,0].set_title(f'{titles[carrier]} Price Distribution Across Regions')\n", - " axes[i,0].set_ylabel('Price (€/t_product)')\n", - " axes[i,0].set_xlabel('Region')\n", - " axes[i,0].set_ylim(0, price_df.max().max() * 1.1)\n", + " sns.boxplot(\n", + " data=price_df.melt(var_name=\"Region\", value_name=\"\"), ax=axes[i, 0], width=0.3\n", + " )\n", + " axes[i, 0].set_title(f\"{titles[carrier]} Price Distribution Across Regions\")\n", + " axes[i, 0].set_ylabel(\"Price (€/t_product)\")\n", + " axes[i, 0].set_xlabel(\"Region\")\n", + " axes[i, 0].set_ylim(0, price_df.max().max() * 1.1)\n", "\n", " # Bar plot\n", - " price_T.plot(kind='bar', ax=axes[i,1], legend=False, width=0.5)\n", - " axes[i,1].set_title(f'{titles[carrier]} Price per Region')\n", - " axes[i,1].set_ylabel('Price (€/t_product)')\n", - " axes[i,1].set_xlabel('Region')\n", - " axes[i,1].set_xticklabels(price_T.index, rotation=90)\n", - " axes[i,1].set_ylim(0, price_df.max().max() * 1.1)\n", - " axes[i,1].grid(axis='y', alpha=0.4)\n", + " price_T.plot(kind=\"bar\", ax=axes[i, 1], legend=False, width=0.5)\n", + " axes[i, 1].set_title(f\"{titles[carrier]} Price per Region\")\n", + " axes[i, 1].set_ylabel(\"Price (€/t_product)\")\n", + " axes[i, 1].set_xlabel(\"Region\")\n", + " axes[i, 1].set_xticklabels(price_T.index, rotation=90)\n", + " axes[i, 1].set_ylim(0, price_df.max().max() * 1.1)\n", + " axes[i, 1].grid(axis=\"y\", alpha=0.4)\n", "\n", "plt.tight_layout()\n", "plt.show()" diff --git a/workflow/notebooks/analysis-domestic-demand.ipynb b/workflow/notebooks/analysis-domestic-demand.ipynb index de2c35a..4478f19 100644 --- a/workflow/notebooks/analysis-domestic-demand.ipynb +++ b/workflow/notebooks/analysis-domestic-demand.ipynb @@ -39,10 +39,11 @@ "outputs": [], "source": [ "from _helpers import mock_snakemake\n", + "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"penalty-oc\" # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " )" + " \"collect_figures\",\n", + " scenario=\"penalty-oc\", # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" ] }, { @@ -62,7 +63,7 @@ "source": [ "df[\"demand\"] = df[\"demand\"].div(1e6)\n", "df.unit = \"TWh\"\n", - "df[\"el_demand\"] = df[\"demand\"] * df[\"el_share\"] /100\n", + "df[\"el_demand\"] = df[\"demand\"] * df[\"el_share\"] / 100\n", "df.drop(columns=[\" note\"], inplace=True)\n", "\n", "electricity_to_steel = snakemake.config[\"electricity_steel_ratio\"]\n", @@ -116,9 +117,9 @@ "\n", "ax.bar(df.index, height=df[\"el_demand\"])\n", "\n", - "ax.set_ylabel('Electricity demand 2050 in TWh')\n", - "ax.set_title('Electricity demand 2050')\n", - "ax.grid(axis='y', alpha=0.4, zorder=0)\n", + "ax.set_ylabel(\"Electricity demand 2050 in TWh\")\n", + "ax.set_title(\"Electricity demand 2050\")\n", + "ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", "plt.xticks(rotation=90)\n", "\n", "plt.tight_layout()\n", @@ -146,9 +147,9 @@ "\n", "ax.bar(df.index, height=df[\"el_demand in Mt_steel\"])\n", "\n", - "ax.set_ylabel('Electricity demand 2050 in Mt steel')\n", - "ax.set_title('Electricity demand 2050 in Mt steel')\n", - "ax.grid(axis='y', alpha=0.4, zorder=0)\n", + "ax.set_ylabel(\"Electricity demand 2050 in Mt steel\")\n", + "ax.set_title(\"Electricity demand 2050 in Mt steel\")\n", + "ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", "plt.xticks(rotation=90)\n", "\n", "plt.tight_layout()\n", diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 4c8aae0..9b983c9 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -21,10 +21,11 @@ "outputs": [], "source": [ "from _helpers import mock_snakemake\n", + "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"default\" # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " )" + " \"collect_figures\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" ] }, { @@ -52,8 +53,8 @@ "metadata": {}, "outputs": [], "source": [ - "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", - "scenario = \"default\" # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", + "scenario = \"default\" # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", "xlim = None" ] }, @@ -97,7 +98,10 @@ "source": [ "# Iron ore cost per ton of steel (w/o transport)\n", "iron_ore_to_steel = snakemake.config[\"iron_ore\"][\"ore_to_steel_ratio\"]\n", - "iron_ore_cost_notransport = n.generators[n.generators.carrier == \"iron_ore\"].marginal_cost.mean() * iron_ore_to_steel\n", + "iron_ore_cost_notransport = (\n", + " n.generators[n.generators.carrier == \"iron_ore\"].marginal_cost.mean()\n", + " * iron_ore_to_steel\n", + ")\n", "print(f\"Iron ore cost (w/o transport): {iron_ore_cost_notransport:.2f} €/t_steel\")" ] }, @@ -109,7 +113,7 @@ "outputs": [], "source": [ "# Select process\n", - "interone = n.links[n.links.carrier == 'hbi']" + "interone = n.links[n.links.carrier == \"hbi\"]" ] }, { @@ -119,7 +123,7 @@ "metadata": {}, "outputs": [], "source": [ - "interone.loc[:,'region'] = interone['bus1'].str.replace('_hbi$', '', regex=True)" + "interone.loc[:, \"region\"] = interone[\"bus1\"].str.replace(\"_hbi$\", \"\", regex=True)" ] }, { @@ -130,10 +134,12 @@ "outputs": [], "source": [ "# Marginal costs refer to bus0 (which is iron ore). To get the cost of steel (bus1), the process efficiency needs to be taken into account\n", - "iron_ore_to_steel = 1/interone.efficiency.mean()\n", + "iron_ore_to_steel = 1 / interone.efficiency.mean()\n", "\n", - "interone.loc[:,'cost in €/t_steel'] = interone.marginal_cost.values * iron_ore_to_steel\n", - "interone.loc[:,'cost in €/t_steel incl. iron ore'] = interone['cost in €/t_steel'] + iron_ore_cost_notransport" + "interone.loc[:, \"cost in €/t_steel\"] = interone.marginal_cost.values * iron_ore_to_steel\n", + "interone.loc[:, \"cost in €/t_steel incl. iron ore\"] = (\n", + " interone[\"cost in €/t_steel\"] + iron_ore_cost_notransport\n", + ")" ] }, { @@ -144,7 +150,9 @@ "outputs": [], "source": [ "# Add quantity\n", - "interone.loc[:,'quantity in Mt_steel'] = interone.p_nom_max.values / iron_ore_to_steel / 1e6" + "interone.loc[:, \"quantity in Mt_steel\"] = (\n", + " interone.p_nom_max.values / iron_ore_to_steel / 1e6\n", + ")" ] }, { @@ -155,14 +163,16 @@ "outputs": [], "source": [ "# Sort by cost for supply curve\n", - "interone_sorted = interone.sort_values('cost in €/t_steel incl. iron ore').reset_index(drop=True)\n", + "interone_sorted = interone.sort_values(\"cost in €/t_steel incl. iron ore\").reset_index(\n", + " drop=True\n", + ")\n", "\n", "# Assign a color to each region\n", - "region_list = interone_sorted['region'].unique()\n", + "region_list = interone_sorted[\"region\"].unique()\n", "region_colors = snakemake.config[\"colors\"]\n", "\n", - "cum_quantity_line = interone_sorted['quantity in Mt_steel'].cumsum()\n", - "cum_cost_line = interone_sorted['cost in €/t_steel incl. iron ore']\n", + "cum_quantity_line = interone_sorted[\"quantity in Mt_steel\"].cumsum()\n", + "cum_cost_line = interone_sorted[\"cost in €/t_steel incl. iron ore\"]\n", "\n", "# Prepare for stacked area plot: for each link, plot a bar at its cost, colored by region\n", "cum_quantity = 0\n", @@ -172,10 +182,10 @@ "bar_colors = []\n", "for _, row in interone_sorted.iterrows():\n", " bar_lefts.append(cum_quantity)\n", - " bar_widths.append(row['quantity in Mt_steel'])\n", - " bar_costs.append(row['cost in €/t_steel incl. iron ore'])\n", - " bar_colors.append(region_colors[row['region']])\n", - " cum_quantity += row['quantity in Mt_steel']" + " bar_widths.append(row[\"quantity in Mt_steel\"])\n", + " bar_costs.append(row[\"cost in €/t_steel incl. iron ore\"])\n", + " bar_colors.append(region_colors[row[\"region\"]])\n", + " cum_quantity += row[\"quantity in Mt_steel\"]" ] }, { @@ -185,34 +195,73 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_supply_curve(bar_lefts, bar_widths, bar_costs, bar_colors, region_colors, region_list, xlim=2500):\n", + "def plot_supply_curve(\n", + " bar_lefts, bar_widths, bar_costs, bar_colors, region_colors, region_list, xlim=2500\n", + "):\n", "\n", " fig, ax = plt.subplots(figsize=(10, 5))\n", - " ax.bar(bar_lefts, bar_costs, width=bar_widths, color=bar_colors, align='edge', edgecolor='none', alpha=0.8)\n", + " ax.bar(\n", + " bar_lefts,\n", + " bar_costs,\n", + " width=bar_widths,\n", + " color=bar_colors,\n", + " align=\"edge\",\n", + " edgecolor=\"none\",\n", + " alpha=0.8,\n", + " )\n", " # Add the classic supply curve line\n", - " \n", - " ax.step(cum_quantity_line, cum_cost_line, where='pre', color='black', linewidth=1.2, label='Supply curve')\n", + "\n", + " ax.step(\n", + " cum_quantity_line,\n", + " cum_cost_line,\n", + " where=\"pre\",\n", + " color=\"black\",\n", + " linewidth=1.2,\n", + " label=\"Supply curve\",\n", + " )\n", "\n", " # Add vertical line of current demand and REMIND demand\n", " current_demand = 2000 # Mt steel\n", - " ax.axvline(current_demand, color='red', linestyle='--', label='Current demand (2020)')\n", - " ax.annotate('Current demand (2025)', xy=(current_demand, 50), xytext=(current_demand+50, 150), rotation=90, color='red')\n", + " ax.axvline(\n", + " current_demand, color=\"red\", linestyle=\"--\", label=\"Current demand (2020)\"\n", + " )\n", + " ax.annotate(\n", + " \"Current demand (2025)\",\n", + " xy=(current_demand, 50),\n", + " xytext=(current_demand + 50, 150),\n", + " rotation=90,\n", + " color=\"red\",\n", + " )\n", " remind_demand = 725 # Mt steel, REMIND global steel long-term hydrogen: 700-750 Mt\n", - " ax.axvline(remind_demand, color='orange', linestyle='--', label='REMIND 2050 demand')\n", - " ax.annotate('REMIND 2050 demand', xy=(remind_demand, 50), xytext=(remind_demand+50, 150), rotation=90, color='orange')\n", + " ax.axvline(\n", + " remind_demand, color=\"orange\", linestyle=\"--\", label=\"REMIND 2050 demand\"\n", + " )\n", + " ax.annotate(\n", + " \"REMIND 2050 demand\",\n", + " xy=(remind_demand, 50),\n", + " xytext=(remind_demand + 50, 150),\n", + " rotation=90,\n", + " color=\"orange\",\n", + " )\n", "\n", - " ax.set_xlabel('Cumulative quantity (Mt)')\n", - " ax.set_ylabel('Supply cost (€/t_steel)')\n", - " ax.set_title(f'Combined {process} supply curve')\n", + " ax.set_xlabel(\"Cumulative quantity (Mt)\")\n", + " ax.set_ylabel(\"Supply cost (€/t_steel)\")\n", + " ax.set_title(f\"Combined {process} supply curve\")\n", " ax.set_ylim(0, 1000)\n", " ax.set_xlim(0, sum(bar_widths))\n", " ax.set_xlim(0, xlim)\n", - " ax.grid(axis='y', alpha=0.4, zorder=0)\n", + " ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", " # Legend for regions\n", - " handles = [plt.Rectangle((0,0),1,1, color=region_colors[reg]) for reg in region_list]\n", - " handles.append(plt.Line2D([0], [0], color='black', linewidth=1.2, label='Supply curve'))\n", - " labels = list(region_list) + ['Supply curve']\n", - " ax.legend(handles, labels, title='Region', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + " handles = [\n", + " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg]) for reg in region_list\n", + " ]\n", + " handles.append(\n", + " plt.Line2D([0], [0], color=\"black\", linewidth=1.2, label=\"Supply curve\")\n", + " )\n", + " labels = list(region_list) + [\"Supply curve\"]\n", + " ax.legend(\n", + " handles, labels, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\"\n", + " )\n", " plt.tight_layout()\n", " plt.savefig(snakemake.input.global_supply_curve)\n", " plt.savefig(snakemake.input.global_supply_curve_png, dpi=300)\n", @@ -228,7 +277,15 @@ "metadata": {}, "outputs": [], "source": [ - "plot_supply_curve(bar_lefts=bar_lefts, bar_widths=bar_widths, bar_costs=bar_costs, bar_colors=bar_colors, region_colors=region_colors, region_list=region_list, xlim=xlim)" + "plot_supply_curve(\n", + " bar_lefts=bar_lefts,\n", + " bar_widths=bar_widths,\n", + " bar_costs=bar_costs,\n", + " bar_colors=bar_colors,\n", + " region_colors=region_colors,\n", + " region_list=region_list,\n", + " xlim=xlim,\n", + ")" ] }, { @@ -246,11 +303,13 @@ "metadata": {}, "outputs": [], "source": [ - "process = \"hbi\" # eaf, hbi, eaf-grid, steel\n", + "process = \"hbi\" # eaf, hbi, eaf-grid, steel\n", "include_ironorecost = True\n", - "ironorecost = 97*1.59\n", - "demand = True # Subtract local demand from supply curve\n", - "sort_over_all = True # \"True\" to sort over all regions, \"False\" to sort within each region\n", + "ironorecost = 97 * 1.59\n", + "demand = True # Subtract local demand from supply curve\n", + "sort_over_all = (\n", + " True # \"True\" to sort over all regions, \"False\" to sort within each region\n", + ")\n", "xlim = None\n", "regions = snakemake.config[\"regions\"].keys()" ] @@ -265,7 +324,6 @@ "df_all = pd.DataFrame()\n", "\n", "for region in regions:\n", - " \n", " if demand:\n", " fn = f\"../../resources/supply_curves/cost_year~2030/{region}_{process}.csv\"\n", " else:\n", @@ -277,16 +335,15 @@ " diff[0] = diff[1] # set first value to second to avoid NaN\n", " df[\"demand [t]\"] = diff\n", "\n", - "\n", " df_all = pd.concat([df_all, df], ignore_index=True)\n", "\n", "if sort_over_all:\n", - " df_all = df_all.sort_values('LCOX [EUR/t]').reset_index(drop=True)\n", + " df_all = df_all.sort_values(\"LCOX [EUR/t]\").reset_index(drop=True)\n", "else:\n", " pass\n", "\n", "if include_ironorecost:\n", - " df_all['LCOX [EUR/t]'] += ironorecost" + " df_all[\"LCOX [EUR/t]\"] += ironorecost" ] }, { @@ -296,7 +353,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_all[df_all[\"region\"]==\"Europe\"]" + "df_all[df_all[\"region\"] == \"Europe\"]" ] }, { @@ -306,8 +363,8 @@ "metadata": {}, "outputs": [], "source": [ - "cum_quantity_line = df_all['demand [t]'].cumsum()/1e6 # Mt steel\n", - "cum_cost_line = df_all['LCOX [EUR/t]']\n", + "cum_quantity_line = df_all[\"demand [t]\"].cumsum() / 1e6 # Mt steel\n", + "cum_cost_line = df_all[\"LCOX [EUR/t]\"]\n", "\n", "# Prepare for stacked area plot based on df_all\n", "cum_quantity = 0\n", @@ -317,10 +374,10 @@ "bar_colors = []\n", "for _, row in df_all.iterrows():\n", " bar_lefts.append(cum_quantity)\n", - " bar_widths.append(row['demand [t]']/1e6) # Mt steel\n", - " bar_costs.append(row['LCOX [EUR/t]'])\n", - " bar_colors.append(region_colors[row['region']])\n", - " cum_quantity += row['demand [t]']/1e6 # Mt steel" + " bar_widths.append(row[\"demand [t]\"] / 1e6) # Mt steel\n", + " bar_costs.append(row[\"LCOX [EUR/t]\"])\n", + " bar_colors.append(region_colors[row[\"region\"]])\n", + " cum_quantity += row[\"demand [t]\"] / 1e6 # Mt steel" ] }, { @@ -330,7 +387,15 @@ "metadata": {}, "outputs": [], "source": [ - "plot_supply_curve(bar_lefts=bar_lefts, bar_widths=bar_widths, bar_costs=bar_costs, bar_colors=bar_colors, region_colors=region_colors, region_list=region_list, xlim=xlim)" + "plot_supply_curve(\n", + " bar_lefts=bar_lefts,\n", + " bar_widths=bar_widths,\n", + " bar_costs=bar_costs,\n", + " bar_colors=bar_colors,\n", + " region_colors=region_colors,\n", + " region_list=region_list,\n", + " xlim=xlim,\n", + ")" ] } ], diff --git a/workflow/notebooks/analysis-hourly.ipynb b/workflow/notebooks/analysis-hourly.ipynb index 9882546..daeccdf 100644 --- a/workflow/notebooks/analysis-hourly.ipynb +++ b/workflow/notebooks/analysis-hourly.ipynb @@ -40,7 +40,9 @@ "metadata": {}, "outputs": [], "source": [ - "single_region = f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", + "single_region = (\n", + " f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", + ")\n", "# single_region = f\"../../resources/lcos/cost_year~2030/Europe/network_1.nc\"" ] }, @@ -185,7 +187,7 @@ "metadata": {}, "outputs": [], "source": [ - "n.statistics.energy_balance.iplot.area() # Ely als negativen Stromverbraucher, H2 Verbrauch der DRI als linie" + "n.statistics.energy_balance.iplot.area() # Ely als negativen Stromverbraucher, H2 Verbrauch der DRI als linie" ] }, { @@ -204,18 +206,23 @@ "outputs": [], "source": [ "df = n.statistics.energy_balance(aggregate_time=False).loc[\n", - " :, \n", - " [\"Photovoltaics\", \"Wind energy\",\n", - " \"battery inverter (charging)\", \"battery inverter (discharging)\",\n", - " \"electrolysis\", \"electric arc furnace\", \"direct reduction furnace\"],\n", - " \"Electricity\"\n", + " :,\n", + " [\n", + " \"Photovoltaics\",\n", + " \"Wind energy\",\n", + " \"battery inverter (charging)\",\n", + " \"battery inverter (discharging)\",\n", + " \"electrolysis\",\n", + " \"electric arc furnace\",\n", + " \"direct reduction furnace\",\n", + " ],\n", + " \"Electricity\",\n", "]\n", "\n", - "df = df.loc[:, pd.Timestamp(\"2013-07-23\"):pd.Timestamp(\"2013-07-28\")]\n", + "df = df.loc[:, pd.Timestamp(\"2013-07-23\") : pd.Timestamp(\"2013-07-28\")]\n", "\n", "df = (\n", - " df\n", - " .reset_index(level=[\"component\", \"bus_carrier\"], drop=True)\n", + " df.reset_index(level=[\"component\", \"bus_carrier\"], drop=True)\n", " .groupby(\"carrier\")\n", " .sum()\n", ")" @@ -229,7 +236,7 @@ "outputs": [], "source": [ "config_fn = \"../../config/config.yaml\"\n", - "colors = load_config(config_fn)[\"colors\"] " + "colors = load_config(config_fn)[\"colors\"]" ] }, { @@ -261,23 +268,15 @@ "# Optional: overlay hydrogen consumption (as before)\n", "n.statistics.energy_balance(aggregate_time=False).loc[\n", " \"Link\", \"direct reduction furnace\", \"Hydrogen\"\n", - "].loc[\n", - " pd.Timestamp(\"2013-07-23\"):pd.Timestamp(\"2013-07-28\")\n", - "].div(system_scale).plot(\n", - " ax=ax,\n", - " color=\"black\",\n", - " linewidth=1.5,\n", - " label=\"DRI H$_2$ consumption\"\n", + "].loc[pd.Timestamp(\"2013-07-23\") : pd.Timestamp(\"2013-07-28\")].div(system_scale).plot(\n", + " ax=ax, color=\"black\", linewidth=1.5, label=\"DRI H$_2$ consumption\"\n", ")\n", "\n", "# Styling (matching your original)\n", "ax.grid(True, alpha=0.5, linestyle=\"--\")\n", "ax.set_xlabel(\"Day\", fontsize=9)\n", "ax.set_ylabel(\"Electricity Balance in MW\", fontsize=9)\n", - "ax.set_xlim(\n", - " pd.Timestamp(\"2013-07-23\"),\n", - " pd.Timestamp(\"2013-07-28\")\n", - ")\n", + "ax.set_xlim(pd.Timestamp(\"2013-07-23\"), pd.Timestamp(\"2013-07-28\"))\n", "\n", "ax.legend(\n", " loc=\"center left\",\n", @@ -285,7 +284,7 @@ ")\n", "\n", "plt.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -350,9 +349,15 @@ "outputs": [], "source": [ "# Electricity to steel ratio\n", - "steel_demand = n.statistics.energy_balance().loc[:, 'Steel', 'Steel'].values[0] # in t of steel\n", - "pv_supply = n.statistics.energy_balance().loc[:, 'Photovoltaics', 'Electricity'].values[0] # in MWh\n", - "wind_supply = n.statistics.energy_balance().loc[:, 'Wind energy', 'Electricity'].values[0] # in MWh\n", + "steel_demand = (\n", + " n.statistics.energy_balance().loc[:, \"Steel\", \"Steel\"].values[0]\n", + ") # in t of steel\n", + "pv_supply = (\n", + " n.statistics.energy_balance().loc[:, \"Photovoltaics\", \"Electricity\"].values[0]\n", + ") # in MWh\n", + "wind_supply = (\n", + " n.statistics.energy_balance().loc[:, \"Wind energy\", \"Electricity\"].values[0]\n", + ") # in MWh\n", "\n", "steel_demand\n", "elec_to_steel = (pv_supply + wind_supply) * (-1) / steel_demand # in MWh/t\n", diff --git a/workflow/notebooks/analysis-iron-ore.ipynb b/workflow/notebooks/analysis-iron-ore.ipynb index 391a566..d36ff9f 100644 --- a/workflow/notebooks/analysis-iron-ore.ipynb +++ b/workflow/notebooks/analysis-iron-ore.ipynb @@ -8,7 +8,7 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n" + "import pandas as pd" ] }, { @@ -37,7 +37,9 @@ "metadata": {}, "outputs": [], "source": [ - "single_region = f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", + "single_region = (\n", + " f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", + ")\n", "# single_region = f\"../../resources/lcos/cost_year~2030/Europe/network_1.nc\"" ] }, diff --git a/workflow/notebooks/analysis-re.ipynb b/workflow/notebooks/analysis-re.ipynb index 5bc574d..0f1195e 100644 --- a/workflow/notebooks/analysis-re.ipynb +++ b/workflow/notebooks/analysis-re.ipynb @@ -8,7 +8,7 @@ "outputs": [], "source": [ "import pypsa\n", - "import pandas as pd\n" + "import pandas as pd" ] }, { @@ -54,7 +54,9 @@ "metadata": {}, "outputs": [], "source": [ - "single_region = f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", + "single_region = (\n", + " f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", + ")\n", "# single_region = f\"../../resources/lcos/cost_year~2030/Europe/network_1.nc\"" ] }, @@ -105,8 +107,10 @@ "gen_df = n.generators[[\"p_nom_opt\", \"p_nom_max\"]].copy()\n", "# remove iron ore DRI-ready (exp)\n", "gen_df = gen_df[~gen_df.index.str.contains(\"iron ore DRI-ready\")]\n", - "gen_df[\"flh\"] = n.generators_t.p_max_pu.sum(axis=0) \n", - "gen_df = gen_df.rename(columns={\"p_nom_opt\": \"installed (MW)\", \"p_nom_max\": \"potential (MW)\", \"flh\": \"flh\"})\n", + "gen_df[\"flh\"] = n.generators_t.p_max_pu.sum(axis=0)\n", + "gen_df = gen_df.rename(\n", + " columns={\"p_nom_opt\": \"installed (MW)\", \"p_nom_max\": \"potential (MW)\", \"flh\": \"flh\"}\n", + ")\n", "# reorder columns\n", "gen_df = gen_df[[\"flh\", \"potential (MW)\", \"installed (MW)\"]]\n", "gen_df = gen_df.sort_values(by=\"flh\", ascending=False)\n", @@ -124,7 +128,9 @@ "metadata": {}, "outputs": [], "source": [ - "n.generators_t.p_max_pu.loc[:, n.generators[n.generators.carrier == \"wind\"].index].mean(axis=1).plot(figsize=(15, 5), grid=True, ylim=(0, 1))" + "n.generators_t.p_max_pu.loc[:, n.generators[n.generators.carrier == \"wind\"].index].mean(\n", + " axis=1\n", + ").plot(figsize=(15, 5), grid=True, ylim=(0, 1))" ] }, { @@ -144,7 +150,9 @@ "metadata": {}, "outputs": [], "source": [ - "n.generators_t.p_max_pu.loc[:, \"onwind 11\"].plot(figsize=(15, 5), grid=True, ylim=(0, 1))" + "n.generators_t.p_max_pu.loc[:, \"onwind 11\"].plot(\n", + " figsize=(15, 5), grid=True, ylim=(0, 1)\n", + ")" ] }, { @@ -162,7 +170,9 @@ "metadata": {}, "outputs": [], "source": [ - "n.generators_t.p.loc[:, n.generators[n.generators.carrier==\"wind\"].index].mean().round(1)" + "n.generators_t.p.loc[\n", + " :, n.generators[n.generators.carrier == \"wind\"].index\n", + "].mean().round(1)" ] }, { @@ -172,7 +182,9 @@ "metadata": {}, "outputs": [], "source": [ - "n.generators_t.p.loc[:, n.generators[n.generators.carrier==\"wind\"].index].plot(figsize=(15, 5), grid=True)" + "n.generators_t.p.loc[:, n.generators[n.generators.carrier == \"wind\"].index].plot(\n", + " figsize=(15, 5), grid=True\n", + ")" ] }, { diff --git a/workflow/notebooks/analysis-steel-hbi-split.ipynb b/workflow/notebooks/analysis-steel-hbi-split.ipynb index f5dfed5..a6d1590 100644 --- a/workflow/notebooks/analysis-steel-hbi-split.ipynb +++ b/workflow/notebooks/analysis-steel-hbi-split.ipynb @@ -28,10 +28,11 @@ "outputs": [], "source": [ "from _helpers import mock_snakemake\n", + "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"default\" # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " )" + " \"collect_figures\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" ] }, { @@ -79,11 +80,21 @@ "metadata": {}, "outputs": [], "source": [ - "n_eaf = pypsa.Network(f\"../../resources/lco-eaf/cost_year~2030/{region}/network_{demand_factor}.nc\")\n", - "n_eaf_grid = pypsa.Network(f\"../../resources/lco-eaf-grid/cost_year~2030/{region}/network_{demand_factor}.nc\")\n", - "n_hbi = pypsa.Network(f\"../../resources/lco-hbi/cost_year~2030/{region}/network_{demand_factor}.nc\")\n", - "n_steel = pypsa.Network(f\"../../resources/lco-steel/cost_year~2030/{region}/network_{demand_factor}.nc\")\n", - "n_steel_pload90 = pypsa.Network(f\"../../resources/lco-steel/cost_year~2030/{region}/network_{demand_factor}-90partload.nc\")" + "n_eaf = pypsa.Network(\n", + " f\"../../resources/lco-eaf/cost_year~2030/{region}/network_{demand_factor}.nc\"\n", + ")\n", + "n_eaf_grid = pypsa.Network(\n", + " f\"../../resources/lco-eaf-grid/cost_year~2030/{region}/network_{demand_factor}.nc\"\n", + ")\n", + "n_hbi = pypsa.Network(\n", + " f\"../../resources/lco-hbi/cost_year~2030/{region}/network_{demand_factor}.nc\"\n", + ")\n", + "n_steel = pypsa.Network(\n", + " f\"../../resources/lco-steel/cost_year~2030/{region}/network_{demand_factor}.nc\"\n", + ")\n", + "n_steel_pload90 = pypsa.Network(\n", + " f\"../../resources/lco-steel/cost_year~2030/{region}/network_{demand_factor}-90partload.nc\"\n", + ")" ] }, { @@ -150,8 +161,11 @@ "metadata": {}, "outputs": [], "source": [ - "costs_per_carrier_hbi = n_hbi.statistics.system_cost() / n_hbi.statistics.energy_balance().loc[\"Load\",\"HBI\",\"HBI\"]\n", - "costs_per_carrier_hbi #.sum()" + "costs_per_carrier_hbi = (\n", + " n_hbi.statistics.system_cost()\n", + " / n_hbi.statistics.energy_balance().loc[\"Load\", \"HBI\", \"HBI\"]\n", + ")\n", + "costs_per_carrier_hbi # .sum()" ] }, { @@ -161,8 +175,11 @@ "metadata": {}, "outputs": [], "source": [ - "costs_per_carrier_eaf = n_eaf.statistics.system_cost() / n_eaf.statistics.energy_balance().loc[\"Load\",\"Steel\",\"Steel\"]\n", - "costs_per_carrier_eaf #.sum()" + "costs_per_carrier_eaf = (\n", + " n_eaf.statistics.system_cost()\n", + " / n_eaf.statistics.energy_balance().loc[\"Load\", \"Steel\", \"Steel\"]\n", + ")\n", + "costs_per_carrier_eaf # .sum()" ] }, { @@ -172,8 +189,11 @@ "metadata": {}, "outputs": [], "source": [ - "costs_per_carrier_eaf_grid = n_eaf_grid.statistics.system_cost() / n_eaf_grid.statistics.energy_balance().loc[\"Load\",\"Steel\",\"Steel\"]\n", - "costs_per_carrier_eaf_grid #.sum()" + "costs_per_carrier_eaf_grid = (\n", + " n_eaf_grid.statistics.system_cost()\n", + " / n_eaf_grid.statistics.energy_balance().loc[\"Load\", \"Steel\", \"Steel\"]\n", + ")\n", + "costs_per_carrier_eaf_grid # .sum()" ] }, { @@ -183,8 +203,11 @@ "metadata": {}, "outputs": [], "source": [ - "costs_per_carrier_steel = n_steel.statistics.system_cost() / n_steel.statistics.energy_balance().loc[\"Load\",\"Steel\",\"Steel\"]\n", - "costs_per_carrier_steel #.sum()" + "costs_per_carrier_steel = (\n", + " n_steel.statistics.system_cost()\n", + " / n_steel.statistics.energy_balance().loc[\"Load\", \"Steel\", \"Steel\"]\n", + ")\n", + "costs_per_carrier_steel # .sum()" ] }, { @@ -194,8 +217,11 @@ "metadata": {}, "outputs": [], "source": [ - "costs_per_carrier_steel_pload90 = n_steel_pload90.statistics.system_cost() / n_steel_pload90.statistics.energy_balance().loc[\"Load\",\"Steel\",\"Steel\"]\n", - "costs_per_carrier_steel_pload90 #.sum()" + "costs_per_carrier_steel_pload90 = (\n", + " n_steel_pload90.statistics.system_cost()\n", + " / n_steel_pload90.statistics.energy_balance().loc[\"Load\", \"Steel\", \"Steel\"]\n", + ")\n", + "costs_per_carrier_steel_pload90 # .sum()" ] }, { @@ -233,21 +259,30 @@ " s2.index = idx\n", " return s2.groupby(level=0).sum() * (-1)\n", "\n", - "costs_hbi = pd.concat([iron_ore_cost_per_steel, flatten_and_group(costs_per_carrier_hbi)])\n", + "\n", + "costs_hbi = pd.concat(\n", + " [iron_ore_cost_per_steel, flatten_and_group(costs_per_carrier_hbi)]\n", + ")\n", "costs_eaf = flatten_and_group(costs_per_carrier_eaf)\n", "costs_eaf_grid = flatten_and_group(costs_per_carrier_eaf_grid)\n", - "costs_steel = pd.concat([iron_ore_cost_per_steel,flatten_and_group(costs_per_carrier_steel)])\n", - "costs_steel_pload90 = pd.concat([iron_ore_cost_per_steel, flatten_and_group(costs_per_carrier_steel_pload90)])\n", - " \n", + "costs_steel = pd.concat(\n", + " [iron_ore_cost_per_steel, flatten_and_group(costs_per_carrier_steel)]\n", + ")\n", + "costs_steel_pload90 = pd.concat(\n", + " [iron_ore_cost_per_steel, flatten_and_group(costs_per_carrier_steel_pload90)]\n", + ")\n", + "\n", "\n", "# Combine into a single DataFrame: rows = carriers, cols = techs\n", - "costs_df = pd.DataFrame({\n", - " 'EAF': costs_eaf,\n", - " 'EAF_grid': costs_eaf_grid,\n", - " 'HBI': costs_hbi,\n", - " 'Steel': costs_steel,\n", - " 'Steel_pload90': costs_steel_pload90\n", - "}).fillna(0)\n", + "costs_df = pd.DataFrame(\n", + " {\n", + " \"EAF\": costs_eaf,\n", + " \"EAF_grid\": costs_eaf_grid,\n", + " \"HBI\": costs_hbi,\n", + " \"Steel\": costs_steel,\n", + " \"Steel_pload90\": costs_steel_pload90,\n", + " }\n", + ").fillna(0)\n", "\n", "# Ensure deterministic carrier order\n", "carriers = costs_df.index.tolist()\n", @@ -256,14 +291,17 @@ "fig, ax = plt.subplots(figsize=(10, 4))\n", "# colors = sns.color_palette('Set2', len(carriers))\n", "# transpose so index is tech, columns are carriers; stacked by carriers\n", - "costs_df.T.plot(kind='bar', stacked=True, ax=ax, color=colors, edgecolor='none')\n", + "costs_df.T.plot(kind=\"bar\", stacked=True, ax=ax, color=colors, edgecolor=\"none\")\n", "\n", - "ax.set_ylabel('Cost (€/t_steel)')\n", + "ax.set_ylabel(\"Cost (€/t_steel)\")\n", "# ax.set_xlabel('Technology')\n", - "ax.set_xticklabels(['EAF', 'EAF-grid', 'HBI\\nmin. load 90%', 'Steel', 'Steel\\nmin. load 90%'], rotation=0)\n", - "ax.set_title(f'Cost structure ({region}, best {demand_factor}% of RE potentials)')\n", - "ax.grid(axis='y', alpha=0.4, zorder=0)\n", - "ax.legend(title='Carrier', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "ax.set_xticklabels(\n", + " [\"EAF\", \"EAF-grid\", \"HBI\\nmin. load 90%\", \"Steel\", \"Steel\\nmin. load 90%\"],\n", + " rotation=0,\n", + ")\n", + "ax.set_title(f\"Cost structure ({region}, best {demand_factor}% of RE potentials)\")\n", + "ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", + "ax.legend(title=\"Carrier\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "ax.set_ylim(0, None)\n", "plt.tight_layout()\n", "\n", @@ -299,7 +337,9 @@ "outputs": [], "source": [ "# Electricity to steel ratio\n", - "steel_in_Mt = n.statistics.energy_balance().loc[\"Load\", :,\"Steel\"].sum() /1e6 * (-1) # from t to Mt\n", + "steel_in_Mt = (\n", + " n.statistics.energy_balance().loc[\"Load\", :, \"Steel\"].sum() / 1e6 * (-1)\n", + ") # from t to Mt\n", "steel_in_Mt" ] }, @@ -311,7 +351,12 @@ "outputs": [], "source": [ "# Electricity to steel ratio\n", - "electricity_in_TWh = n.statistics.energy_balance().loc[\"Generator\", [\"Photovoltaics\", \"Wind energy\"],:].sum() / 1e6 # from MWh to TWh\n", + "electricity_in_TWh = (\n", + " n.statistics.energy_balance()\n", + " .loc[\"Generator\", [\"Photovoltaics\", \"Wind energy\"], :]\n", + " .sum()\n", + " / 1e6\n", + ") # from MWh to TWh\n", "\n", "electricity_in_TWh" ] @@ -344,7 +389,7 @@ "metadata": {}, "outputs": [], "source": [ - "n.statistics.energy_balance() #.iplot()" + "n.statistics.energy_balance() # .iplot()" ] } ], diff --git a/workflow/notebooks/analysis-trace.ipynb b/workflow/notebooks/analysis-trace.ipynb index 3ba1d44..11d20c7 100644 --- a/workflow/notebooks/analysis-trace.ipynb +++ b/workflow/notebooks/analysis-trace.ipynb @@ -27,7 +27,9 @@ "metadata": {}, "outputs": [], "source": [ - "n = pypsa.Network(\"../../../trace-fneum/trace/resources/networks/default/2030/shipping-steel/DE-DE/network.nc\")" + "n = pypsa.Network(\n", + " \"../../../trace-fneum/trace/resources/networks/default/2030/shipping-steel/DE-DE/network.nc\"\n", + ")" ] }, { @@ -94,7 +96,9 @@ "metadata": {}, "outputs": [], "source": [ - "n = pypsa.Network(\"../../../trace-fneum/trace/resources/networks/default/2030/shipping-steel/DE-DE/network.nc\")" + "n = pypsa.Network(\n", + " \"../../../trace-fneum/trace/resources/networks/default/2030/shipping-steel/DE-DE/network.nc\"\n", + ")" ] }, { @@ -104,7 +108,7 @@ "metadata": {}, "outputs": [], "source": [ - "pypsatopo.generate(n, file_format = \"png\", file_output = output_fn)" + "pypsatopo.generate(n, file_format=\"png\", file_output=output_fn)" ] }, { diff --git a/workflow/notebooks/compare-scenarios.ipynb b/workflow/notebooks/compare-scenarios.ipynb index 7c40345..a38dddb 100644 --- a/workflow/notebooks/compare-scenarios.ipynb +++ b/workflow/notebooks/compare-scenarios.ipynb @@ -20,10 +20,11 @@ "outputs": [], "source": [ "from _helpers import mock_snakemake\n", + "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"default\" # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " )" + " \"collect_figures\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" ] }, { @@ -64,7 +65,6 @@ "nc = {}\n", "\n", "for scenario in config[\"scenario\"].keys():\n", - "\n", " trade_model_fn = f\"../../results/cost_year~2030/interone~hbi/intertwo~eaf-grid/final~steel/scenario~{scenario}/network.nc\"\n", "\n", " n = pypsa.Network(trade_model_fn)\n", @@ -108,7 +108,9 @@ "outputs": [], "source": [ "df = nc.statistics.system_cost(groupby=\"carrier\").to_frame(\"systemcost\")\n", - "steel_demand = nc.statistics.withdrawal().to_frame().loc[\"Load\",\"default\",\"steel\"].value\n", + "steel_demand = (\n", + " nc.statistics.withdrawal().to_frame().loc[\"Load\", \"default\", \"steel\"].value\n", + ")\n", "\n", "df = df / steel_demand\n", "# df" @@ -122,26 +124,24 @@ "outputs": [], "source": [ "# Pivot to get networks as rows and carriers as columns\n", - "plot_df = (\n", - " df.pivot_table(\n", - " index=\"network\",\n", - " columns=\"carrier\",\n", - " values=\"systemcost\",\n", - " aggfunc=\"sum\"\n", - " )\n", + "plot_df = df.pivot_table(\n", + " index=\"network\", columns=\"carrier\", values=\"systemcost\", aggfunc=\"sum\"\n", ")\n", "\n", - "order = [\"iron_ore\", \"shipping_iron_ore\", \"hbi\", \"shipping_hbi\", \"steel\"]\n", + "order = [\"iron_ore\", \"shipping_iron_ore\", \"hbi\", \"shipping_hbi\", \"steel\"]\n", "\n", "plot_df = plot_df[order]\n", "\n", - "plot_df.rename(columns={\n", - " \"iron_ore\": \"Iron ore\",\n", - " \"shipping_iron_ore\": \"Shipping iron ore\",\n", - " \"hbi\": \"DRI\",\n", - " \"shipping_hbi\": \"Shipping HBI\",\n", - " \"steel\": \"EAF\"\n", - "}, inplace=True)" + "plot_df.rename(\n", + " columns={\n", + " \"iron_ore\": \"Iron ore\",\n", + " \"shipping_iron_ore\": \"Shipping iron ore\",\n", + " \"hbi\": \"DRI\",\n", + " \"shipping_hbi\": \"Shipping HBI\",\n", + " \"steel\": \"EAF\",\n", + " },\n", + " inplace=True,\n", + ")" ] }, { @@ -157,7 +157,7 @@ " stacked=True,\n", " figsize=(8, 4),\n", " color=[nc[\"default\"].carriers.color[c] for c in order],\n", - " alpha=0.8\n", + " alpha=0.8,\n", ")\n", "\n", "# ---- Axis labels ----\n", @@ -176,12 +176,12 @@ "\n", "for i, total in enumerate(totals):\n", " ax.text(\n", - " i, \n", - " total, \n", + " i,\n", + " total,\n", " f\"{total:.1f}\", # adjust scaling if needed\n", " ha=\"center\",\n", " va=\"bottom\",\n", - " fontsize=10\n", + " fontsize=10,\n", " )\n", "\n", "# ---- Flip legend order ----\n", @@ -191,7 +191,7 @@ " labels[::-1],\n", " title=\"Cost component\",\n", " bbox_to_anchor=(1.02, 1),\n", - " loc=\"upper left\"\n", + " loc=\"upper left\",\n", ")\n", "\n", "plt.xticks(rotation=0)\n", diff --git a/workflow/notebooks/conceptual-model.ipynb b/workflow/notebooks/conceptual-model.ipynb deleted file mode 100644 index e17de6c..0000000 --- a/workflow/notebooks/conceptual-model.ipynb +++ /dev/null @@ -1,559 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "00533853", - "metadata": {}, - "outputs": [], - "source": [ - "import pypsa" - ] - }, - { - "cell_type": "markdown", - "id": "1fa86fe9", - "metadata": {}, - "source": [ - "### Region design" - ] - }, - { - "cell_type": "markdown", - "id": "28e81337", - "metadata": {}, - "source": [ - "Region 1: Australia: High iron ore resources, expensive hydrogen and DRI, low steel demand\n", - "Region 2: Middle East: No iron ore resources, cheap hydrogen and DRI, low steel demand\n", - "Region 3: Europe: Some iron ore resources, expensive hydrogen and DRI, high steel demand" - ] - }, - { - "cell_type": "markdown", - "id": "1276a150", - "metadata": {}, - "source": [ - "### Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eea832ed", - "metadata": {}, - "outputs": [], - "source": [ - "# ====== CONFIGURATION PARAMETERS ======\n", - "# Regions\n", - "regions = ['1', '2', '3']\n", - "\n", - "# Iron ore production costs (€/t)\n", - "ore_costs = [20, 30, 50]\n", - "\n", - "# Iron ore production capacity per region (t)\n", - "ore_capacity = [200, 0, 0]\n", - "\n", - "# Iron ore trade costs (€/t HBI/DRI)\n", - "iron_ore_trade_costs = [15, 15, 15] # Regional variations in HBI transport costs\n", - "\n", - "# Steel conversion costs by region (€/t steel)\n", - "steel_conversion_costs = {\n", - " 1: [300, 400, 500], \n", - " 2: [200, 300, 400], \n", - " 3: [500, 600, 700]\n", - "}\n", - "# Steel conversion limit (t per converter per region)\n", - "steel_conversion_limit = 50\n", - "\n", - "# Steel conversion efficiency (tons steel per ton ore)\n", - "conversion_efficiency = 0.6\n", - "\n", - "# Steel trade costs (€/t steel)\n", - "steel_trade_costs = [20, 20, 20] # Uniform costs for all steel trade routes\n", - "\n", - "# Steel demands per region (t)\n", - "steel_demands = [20, 10, 70]\n", - "\n", - "# Trade capacity (large number for unlimited trade)\n", - "trade_capacity = 1000\n", - "\n", - "# ====== MODEL SETUP ======\n", - "# Create a new PyPSA network\n", - "n = pypsa.Network()\n", - "\n", - "# Add buses for iron ore (one per region)\n", - "n.madd(\"Bus\", \n", - " names=[f\"iron_ore_{region}\" for region in regions],\n", - " carrier=\"iron_ore\")\n", - "\n", - "# Add buses for steel (one per region)\n", - "n.madd(\"Bus\", \n", - " names=[f\"steel_{region}\" for region in regions],\n", - " carrier=\"steel\")\n", - "\n", - "# Add iron ore generators (one per region with different marginal costs)\n", - "n.madd(\"Generator\",\n", - " names=[f\"iron_ore_gen_{region}\" for region in regions],\n", - " bus=[f\"iron_ore_{region}\" for region in regions],\n", - " carrier=\"iron_ore_production\",\n", - " marginal_cost=ore_costs,\n", - " p_nom=ore_capacity)\n", - "\n", - "# Add links for iron ore trade between regions (with realistic HBI trade costs)\n", - "iron_ore_trade_pairs = [\n", - " (\"iron_ore_1\", \"iron_ore_2\"),\n", - " (\"iron_ore_2\", \"iron_ore_3\"),\n", - " (\"iron_ore_1\", \"iron_ore_3\")\n", - "]\n", - "\n", - "# Create bidirectional trade links\n", - "iron_ore_trade_names = []\n", - "iron_ore_trade_bus0 = []\n", - "iron_ore_trade_bus1 = []\n", - "iron_ore_trade_costs_list = []\n", - "\n", - "for i, (bus0, bus1) in enumerate(iron_ore_trade_pairs):\n", - " # Forward direction\n", - " iron_ore_trade_names.append(f\"iron_ore_trade_{bus0[-1]}_{bus1[-1]}\")\n", - " iron_ore_trade_bus0.append(bus0)\n", - " iron_ore_trade_bus1.append(bus1)\n", - " iron_ore_trade_costs_list.append(iron_ore_trade_costs[i])\n", - " \n", - " # Reverse direction\n", - " iron_ore_trade_names.append(f\"iron_ore_trade_{bus1[-1]}_{bus0[-1]}\")\n", - " iron_ore_trade_bus0.append(bus1)\n", - " iron_ore_trade_bus1.append(bus0)\n", - " iron_ore_trade_costs_list.append(iron_ore_trade_costs[i])\n", - "\n", - "n.madd(\"Link\",\n", - " names=iron_ore_trade_names,\n", - " bus0=iron_ore_trade_bus0,\n", - " bus1=iron_ore_trade_bus1,\n", - " marginal_cost=iron_ore_trade_costs_list,\n", - " p_nom=trade_capacity,\n", - " efficiency=1.0)\n", - "\n", - "# Add links for steel trade between regions (with transport costs)\n", - "steel_trade_pairs = [\n", - " (\"steel_1\", \"steel_2\"),\n", - " (\"steel_2\", \"steel_3\"),\n", - " (\"steel_1\", \"steel_3\")\n", - "]\n", - "\n", - "# Create bidirectional trade links\n", - "steel_trade_names = []\n", - "steel_trade_bus0 = []\n", - "steel_trade_bus1 = []\n", - "\n", - "for bus0, bus1 in steel_trade_pairs:\n", - " # Forward direction\n", - " steel_trade_names.append(f\"steel_trade_{bus0[-1]}_{bus1[-1]}\")\n", - " steel_trade_bus0.append(bus0)\n", - " steel_trade_bus1.append(bus1)\n", - " \n", - " # Reverse direction\n", - " steel_trade_names.append(f\"steel_trade_{bus1[-1]}_{bus0[-1]}\")\n", - " steel_trade_bus0.append(bus1)\n", - " steel_trade_bus1.append(bus0)\n", - "\n", - "n.madd(\"Link\",\n", - " names=steel_trade_names,\n", - " bus0=steel_trade_bus0,\n", - " bus1=steel_trade_bus1,\n", - " marginal_cost=steel_trade_costs * 2, # Duplicate for bidirectional links\n", - " p_nom=trade_capacity,\n", - " efficiency=1.0)\n", - "\n", - "# Add conversion links from iron ore to steel (three per region with differentiated costs)\n", - "conversion_names = []\n", - "conversion_bus0 = []\n", - "conversion_bus1 = []\n", - "conversion_costs = []\n", - "\n", - "for region_num, region in enumerate(regions, 1):\n", - " for i, cost in enumerate(steel_conversion_costs[region_num]):\n", - " conversion_names.append(f\"ore_to_steel_{region}_{i+1}\")\n", - " conversion_bus0.append(f\"iron_ore_{region}\")\n", - " conversion_bus1.append(f\"steel_{region}\")\n", - " conversion_costs.append(cost)\n", - "\n", - "n.madd(\"Link\",\n", - " names=conversion_names,\n", - " bus0=conversion_bus0,\n", - " bus1=conversion_bus1,\n", - " marginal_cost=conversion_costs,\n", - " efficiency=conversion_efficiency,\n", - " p_nom=steel_conversion_limit)\n", - "\n", - "# Add steel loads in each region\n", - "n.madd(\"Load\",\n", - " names=[f\"steel_load_{region}\" for region in regions],\n", - " bus=[f\"steel_{region}\" for region in regions],\n", - " p_set=steel_demands)\n", - "\n", - "# Display network summary\n", - "print(\"=== NETWORK SUMMARY ===\")\n", - "print(f\"Buses: {len(n.buses)}\")\n", - "print(f\"Generators: {len(n.generators)}\")\n", - "print(f\"Links: {len(n.links)}\")\n", - "print(f\"Loads: {len(n.loads)}\")\n", - "\n", - "print(\"\\n=== BUSES ===\")\n", - "print(n.buses[['carrier']])\n", - "\n", - "print(\"\\n=== GENERATORS ===\")\n", - "print(n.generators[['bus', 'carrier', 'marginal_cost', 'p_nom']])\n", - "\n", - "print(\"\\n=== LINKS ===\")\n", - "print(n.links[['bus0', 'bus1', 'marginal_cost', 'efficiency', 'p_nom']])\n", - "\n", - "print(\"\\n=== LOADS ===\")\n", - "print(n.loads[['bus', 'p_set']])" - ] - }, - { - "cell_type": "markdown", - "id": "480997f5", - "metadata": {}, - "source": [ - "### Solving" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "129c6236", - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the optimization problem\n", - "print(\"\\n=== SOLVING OPTIMIZATION ===\")\n", - "n.optimize(solver_name='gurobi')\n", - "\n", - "print(f\"Objective value: {n.objective:.2f} €\")" - ] - }, - { - "cell_type": "markdown", - "id": "ac4fc4da", - "metadata": {}, - "source": [ - "### Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73522f2b", - "metadata": {}, - "outputs": [], - "source": [ - "# Display results\n", - "print(\"\\n=== OPTIMIZATION RESULTS ===\")\n", - "\n", - "print(\"\\nIron ore generation by region:\")\n", - "for gen in n.generators.index:\n", - " region = gen.split('_')[-1]\n", - " generation = n.generators_t.p[gen].iloc[0] if not n.generators_t.p.empty else 0\n", - " cost = n.generators.loc[gen, 'marginal_cost']\n", - " print(f\"Region {region}: {generation:.2f} t @ {cost} €/t\")\n", - "\n", - "print(\"\\nIron ore trade flows:\")\n", - "for link in n.links.index:\n", - " if 'iron_ore_trade' in link:\n", - " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " if abs(flow) > 0.01: # Only show non-zero flows\n", - " from_region = link.split('_')[3]\n", - " to_region = link.split('_')[4]\n", - " print(f\"Region {from_region} → Region {to_region}: {flow:.2f} t\")\n", - "\n", - "print(\"\\nSteel conversion by region:\")\n", - "for link in n.links.index:\n", - " if 'ore_to_steel' in link:\n", - " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " if abs(flow) > 0.01: # Only show non-zero flows\n", - " region = link.split('_')[3]\n", - " converter = link.split('_')[4]\n", - " cost = n.links.loc[link, 'marginal_cost']\n", - " steel_production = flow * n.links.loc[link, 'efficiency']\n", - " print(f\"Region {region}, Converter {converter}: {flow:.2f} t ore → {steel_production:.2f} t steel @ {cost} €/t steel\")\n", - "\n", - "print(\"\\nSteel trade flows:\")\n", - "for link in n.links.index:\n", - " if 'steel_trade' in link:\n", - " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " if abs(flow) > 0.01: # Only show non-zero flows\n", - " from_region = link.split('_')[2]\n", - " to_region = link.split('_')[3]\n", - " print(f\"Region {from_region} → Region {to_region}: {flow:.2f} t\")\n", - "\n", - "print(\"\\nSteel demand satisfaction:\")\n", - "for load in n.loads.index:\n", - " region = load.split('_')[-1]\n", - " demand = n.loads.loc[load, 'p_set']\n", - " print(f\"Region {region}: {demand} t steel demand\")\n", - "\n", - "# Calculate total costs by component\n", - "print(\"\\n=== COST BREAKDOWN ===\")\n", - "\n", - "# Iron ore production costs\n", - "ore_production_cost = 0\n", - "for gen in n.generators.index:\n", - " generation = n.generators_t.p[gen].iloc[0] if not n.generators_t.p.empty else 0\n", - " cost = n.generators.loc[gen, 'marginal_cost']\n", - " ore_production_cost += generation * cost\n", - "\n", - "# Steel conversion costs\n", - "steel_conversion_cost = 0\n", - "for link in n.links.index:\n", - " if 'ore_to_steel' in link:\n", - " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " steel_output = flow * n.links.loc[link, 'efficiency']\n", - " cost = n.links.loc[link, 'marginal_cost']\n", - " steel_conversion_cost += steel_output * cost\n", - "\n", - "print(f\"Iron ore production cost: {ore_production_cost:.2f} €\")\n", - "print(f\"Steel conversion cost: {steel_conversion_cost:.2f} €\")\n", - "print(f\"Total cost: {ore_production_cost + steel_conversion_cost:.2f} €\")" - ] - }, - { - "cell_type": "markdown", - "id": "1836dadb", - "metadata": {}, - "source": [ - "### Additional analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0002ef20", - "metadata": {}, - "outputs": [], - "source": [ - "# n.statistics.energy_balance(aggregate_bus=False)" - ] - }, - { - "cell_type": "markdown", - "id": "8a985189", - "metadata": {}, - "source": [ - "### Plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "585f5c14", - "metadata": {}, - "outputs": [], - "source": [ - "# Iron Ore and Steel Production Flow Sankey Diagram\n", - "# This notebook creates a Sankey diagram from PyPSA optimization results\n", - "\n", - "import plotly.graph_objects as go\n", - "import plotly.offline as pyo\n", - "\n", - "# Enable offline plotting in Jupyter\n", - "pyo.init_notebook_mode(connected=True)\n", - "\n", - "def create_sankey_from_pypsa_results(n):\n", - " \"\"\"\n", - " Create a Sankey diagram from PyPSA network results\n", - " \n", - " Parameters:\n", - " n: PyPSA network object with solved optimization results\n", - " \"\"\"\n", - " \n", - " # Initialize data structures\n", - " nodes = []\n", - " node_dict = {}\n", - " node_counter = 0\n", - " \n", - " # Flow data for Sankey\n", - " sources = []\n", - " targets = []\n", - " values = []\n", - " flow_labels = []\n", - " \n", - " def get_or_create_node(name, category=\"\"):\n", - " \"\"\"Get existing node index or create new one\"\"\"\n", - " nonlocal node_counter\n", - " full_name = f\"{name} ({category})\" if category else name\n", - " if full_name not in node_dict:\n", - " node_dict[full_name] = node_counter\n", - " nodes.append(full_name)\n", - " node_counter += 1\n", - " return node_dict[full_name]\n", - " \n", - " # 1. Process iron ore generation\n", - " print(\"Processing iron ore generation...\")\n", - " for gen in n.generators.index:\n", - " region = gen.split('_')[-1]\n", - " generation = n.generators_t.p[gen].iloc[0] if not n.generators_t.p.empty else 0\n", - " \n", - " if generation > 0.01: # Only include significant flows\n", - " source_node = get_or_create_node(f\"Iron Ore Source R{region}\", \"Generation\")\n", - " target_node = get_or_create_node(f\"Iron Ore R{region}\", \"Supply\")\n", - " \n", - " sources.append(source_node)\n", - " targets.append(target_node)\n", - " values.append(generation)\n", - " cost = n.generators.loc[gen, 'marginal_cost']\n", - " flow_labels.append(f\"Generate: {generation:.1f}t @ {cost}€/t\")\n", - " \n", - " # 2. Process iron ore trade flows\n", - " print(\"Processing iron ore trade...\")\n", - " for link in n.links.index:\n", - " if 'iron_ore_trade' in link:\n", - " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " if abs(flow) > 0.01:\n", - " from_region = link.split('_')[3]\n", - " to_region = link.split('_')[4]\n", - " \n", - " source_node = get_or_create_node(f\"Iron Ore R{from_region}\", \"Supply\")\n", - " target_node = get_or_create_node(f\"Iron Ore R{to_region}\", \"Supply\")\n", - " \n", - " sources.append(source_node)\n", - " targets.append(target_node)\n", - " values.append(abs(flow))\n", - " flow_labels.append(f\"Trade: {flow:.1f}t\")\n", - " \n", - " # 3. Process steel conversion\n", - " print(\"Processing steel conversion...\")\n", - " for link in n.links.index:\n", - " if 'ore_to_steel' in link:\n", - " ore_flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " if abs(ore_flow) > 0.01:\n", - " region = link.split('_')[3]\n", - " converter = link.split('_')[4]\n", - " efficiency = n.links.loc[link, 'efficiency']\n", - " steel_production = ore_flow * efficiency\n", - " cost = n.links.loc[link, 'marginal_cost']\n", - " \n", - " # Iron ore to steel conversion\n", - " source_node = get_or_create_node(f\"Iron Ore R{region}\", \"Supply\")\n", - " target_node = get_or_create_node(f\"Steel R{region}\", \"Production\")\n", - " \n", - " sources.append(source_node)\n", - " targets.append(target_node)\n", - " values.append(abs(ore_flow))\n", - " flow_labels.append(f\"Convert: {ore_flow:.1f}t ore → {steel_production:.1f}t steel @ {cost}€/t\")\n", - " \n", - " # 4. Process steel trade flows\n", - " print(\"Processing steel trade...\")\n", - " for link in n.links.index:\n", - " if 'steel_trade' in link:\n", - " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", - " if abs(flow) > 0.01:\n", - " from_region = link.split('_')[2]\n", - " to_region = link.split('_')[3]\n", - " \n", - " source_node = get_or_create_node(f\"Steel R{from_region}\", \"Production\")\n", - " target_node = get_or_create_node(f\"Steel R{to_region}\", \"Production\")\n", - " \n", - " sources.append(source_node)\n", - " targets.append(target_node)\n", - " values.append(abs(flow))\n", - " flow_labels.append(f\"Trade: {flow:.1f}t\")\n", - " \n", - " # 5. Process steel demand\n", - " print(\"Processing steel demand...\")\n", - " for load in n.loads.index:\n", - " region = load.split('_')[-1]\n", - " demand = abs(n.loads.loc[load, 'p_set'])\n", - " \n", - " if demand > 0.01:\n", - " source_node = get_or_create_node(f\"Steel R{region}\", \"Production\")\n", - " target_node = get_or_create_node(f\"Steel Demand R{region}\", \"Consumption\")\n", - " \n", - " sources.append(source_node)\n", - " targets.append(target_node)\n", - " values.append(demand)\n", - " flow_labels.append(f\"Demand: {demand:.1f}t\")\n", - " \n", - " # Create color scheme based on node categories\n", - " node_colors = []\n", - " for node in nodes:\n", - " if \"Generation\" in node:\n", - " node_colors.append(\"rgba(255, 127, 14, 0.8)\") # Orange for generation\n", - " elif \"Iron Ore\" in node and \"Supply\" in node:\n", - " node_colors.append(\"rgba(44, 160, 44, 0.8)\") # Green for iron ore supply\n", - " elif \"Steel\" in node and \"Production\" in node:\n", - " node_colors.append(\"rgba(31, 119, 180, 0.8)\") # Blue for steel production\n", - " elif \"Consumption\" in node:\n", - " node_colors.append(\"rgba(214, 39, 40, 0.8)\") # Red for consumption\n", - " else:\n", - " node_colors.append(\"rgba(148, 103, 189, 0.8)\") # Purple for other\n", - " \n", - " # Create the Sankey diagram\n", - " fig = go.Figure(data=[go.Sankey(\n", - " node=dict(\n", - " pad=15,\n", - " thickness=20,\n", - " line=dict(color=\"black\", width=0.5),\n", - " label=nodes,\n", - " color=node_colors\n", - " ),\n", - " link=dict(\n", - " source=sources,\n", - " target=targets,\n", - " value=values,\n", - " label=flow_labels,\n", - " color=\"rgba(128, 128, 128, 0.4)\"\n", - " )\n", - " )])\n", - " \n", - " # Update layout\n", - " fig.update_layout(\n", - " title_text=\"Iron Ore and Steel Production Flow Diagram\",\n", - " font_size=12,\n", - " width=1200,\n", - " height=800\n", - " )\n", - " \n", - " return fig\n", - "\n", - "\n", - "# Create and display the Sankey diagram\n", - "try:\n", - " fig = create_sankey_from_pypsa_results(n)\n", - " fig.show()\n", - " \n", - " # Optional: Save as HTML file\n", - " # fig.write_html(\"iron_steel_sankey.html\")\n", - " # print(\"Sankey diagram saved as 'iron_steel_sankey.html'\")\n", - " \n", - "except NameError:\n", - " print(\"Please ensure your PyPSA network object is named 'n' and contains solved results\")\n", - " print(\"The network should have generators, links, and loads with the naming convention shown in your output\")\n", - "\n", - "\n", - "print(\"Notebook ready! Run create_sankey_from_pypsa_results(n) with your PyPSA network object.\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "shift", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/workflow/notebooks/global-iron-ore.ipynb b/workflow/notebooks/global-iron-ore.ipynb index 8432e60..02fd147 100644 --- a/workflow/notebooks/global-iron-ore.ipynb +++ b/workflow/notebooks/global-iron-ore.ipynb @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "production_fn = '../../resources/ironore-production.csv'" + "production_fn = \"../../resources/ironore-production.csv\"" ] }, { @@ -84,22 +84,25 @@ "df = pd.read_csv(\"../../data/owid-iron-ore/iron-ore-crude-ore-production.csv\")\n", "\n", "# Rename columns for easier access\n", - "df.rename(columns={\n", - " 'Entity': 'Country',\n", - " 'Year': 'Year',\n", - " 'production|Iron ore|Mine, crude ore|tonnes': 'IronOreProduction',\n", - " 'crude ore|tonnes': 'CrudeOreTonnes'\n", - "}, inplace=True)\n", + "df.rename(\n", + " columns={\n", + " \"Entity\": \"Country\",\n", + " \"Year\": \"Year\",\n", + " \"production|Iron ore|Mine, crude ore|tonnes\": \"IronOreProduction\",\n", + " \"crude ore|tonnes\": \"CrudeOreTonnes\",\n", + " },\n", + " inplace=True,\n", + ")\n", "\n", "# Keep only rows with available iron ore production data\n", - "df = df.dropna(subset=['IronOreProduction'])\n", + "df = df.dropna(subset=[\"IronOreProduction\"])\n", "\n", "# Get latest year per country\n", - "df_latest = df.sort_values('Year').groupby('Country', as_index=False).last()\n", + "df_latest = df.sort_values(\"Year\").groupby(\"Country\", as_index=False).last()\n", "\n", "# Drop data that is older than 2010 and drop Country 'World'\n", - "df_latest = df_latest[df_latest['Country'] != 'World']\n", - "df_latest = df_latest[df_latest['Year'] >= 2010]\n", + "df_latest = df_latest[df_latest[\"Country\"] != \"World\"]\n", + "df_latest = df_latest[df_latest[\"Year\"] >= 2010]\n", "\n", "# Remove \"other\"\n", "df_latest = df_latest[df_latest[\"Country\"] != \"Other\"]\n", @@ -109,15 +112,17 @@ " \"Democratic Republic of Congo\": \"Congo, The Democratic Republic of the\",\n", " \"Kosovo\": \"Republic of Kosovo\", # pycountry not supported\n", " \"Russia\": \"Russian Federation\",\n", - " \"Turkey\": \"Türkiye\"\n", + " \"Turkey\": \"Türkiye\",\n", "}\n", "\n", - "df_latest['Country'] = df_latest['Country'].replace(country_name_corrections)\n", - "df_latest['ISO_A2'] = df_latest['Country'].apply(country_to_iso_a2)\n", + "df_latest[\"Country\"] = df_latest[\"Country\"].replace(country_name_corrections)\n", + "df_latest[\"ISO_A2\"] = df_latest[\"Country\"].apply(country_to_iso_a2)\n", "\n", "# Check for any remaining missing ISO_A2 codes\n", - "missing_iso_a2 = df_latest[df_latest['ISO_A2'].isna()]\n", - "print(f\"Countries with missing ISO_A2 codes after correction: {missing_iso_a2['Country'].unique()}\")" + "missing_iso_a2 = df_latest[df_latest[\"ISO_A2\"].isna()]\n", + "print(\n", + " f\"Countries with missing ISO_A2 codes after correction: {missing_iso_a2['Country'].unique()}\"\n", + ")" ] }, { @@ -128,21 +133,21 @@ "outputs": [], "source": [ "# Use cartopy to get natural earth countries shapefile\n", - "shapename = 'admin_0_countries'\n", - "reader = shpreader.natural_earth(resolution='110m',\n", - " category='cultural',\n", - " name=shapename)\n", + "shapename = \"admin_0_countries\"\n", + "reader = shpreader.natural_earth(resolution=\"110m\", category=\"cultural\", name=shapename)\n", "\n", "world = gpd.read_file(reader)\n", "\n", "# Fix missing ISO_A2 codes in world\n", - "if 'ISO_A2' not in world.columns:\n", - " if 'ADMIN' in world.columns:\n", - " world['ISO_A2'] = world['ADMIN'].apply(country_to_iso_a2)\n", - "world.loc[world['ISO_A2'] == '-99', 'ISO_A2'] = world.loc[world['ISO_A2'] == '-99', 'ADMIN'].apply(country_to_iso_a2)\n", + "if \"ISO_A2\" not in world.columns:\n", + " if \"ADMIN\" in world.columns:\n", + " world[\"ISO_A2\"] = world[\"ADMIN\"].apply(country_to_iso_a2)\n", + "world.loc[world[\"ISO_A2\"] == \"-99\", \"ISO_A2\"] = world.loc[\n", + " world[\"ISO_A2\"] == \"-99\", \"ADMIN\"\n", + "].apply(country_to_iso_a2)\n", "\n", "# Merge country names — use left join to preserve geometry\n", - "merged = world.merge(df_latest, how='left', left_on='ISO_A2', right_on='ISO_A2')" + "merged = world.merge(df_latest, how=\"left\", left_on=\"ISO_A2\", right_on=\"ISO_A2\")" ] }, { @@ -153,7 +158,7 @@ "outputs": [], "source": [ "# After merging, filter out rows with missing or zero production\n", - "merged = merged[merged['IronOreProduction'].notna() & (merged['IronOreProduction'] > 0)]" + "merged = merged[merged[\"IronOreProduction\"].notna() & (merged[\"IronOreProduction\"] > 0)]" ] }, { @@ -165,7 +170,7 @@ "source": [ "# %%\n", "# Convert to megatonnes\n", - "merged['IronOreProductionMt'] = merged['IronOreProduction'].divide(1e6) # tonnes to Mt" + "merged[\"IronOreProductionMt\"] = merged[\"IronOreProduction\"].divide(1e6) # tonnes to Mt" ] }, { @@ -176,12 +181,11 @@ "outputs": [], "source": [ "# Ensure ISO_A3 and IronOreProductionMt exist\n", - "filtered_df = merged[\n", - " (merged['IronOreProductionMt'] > 0) & \n", - " (merged['ISO_A2'].notna())\n", - "][['ISO_A2', 'IronOreProductionMt', 'ADMIN']]\n", + "filtered_df = merged[(merged[\"IronOreProductionMt\"] > 0) & (merged[\"ISO_A2\"].notna())][\n", + " [\"ISO_A2\", \"IronOreProductionMt\", \"ADMIN\"]\n", + "]\n", "# Set ISO_A2 as index\n", - "production = filtered_df.set_index('ISO_A2')\n", + "production = filtered_df.set_index(\"ISO_A2\")\n", "\n", "# Display result\n", "production.to_csv(production_fn)" @@ -205,17 +209,13 @@ } ], "source": [ - "\n", "# Define Mt bins and labels\n", - "bins = [0, 1, 10, 100, 500, float('inf')]\n", - "labels = ['0–1', '1–10', '10–100', '100–500', '500+']\n", + "bins = [0, 1, 10, 100, 500, float(\"inf\")]\n", + "labels = [\"0–1\", \"1–10\", \"10–100\", \"100–500\", \"500+\"]\n", "\n", "# Bin into categories\n", - "merged['ProductionCategory'] = pd.cut(\n", - " merged['IronOreProductionMt'],\n", - " bins=bins,\n", - " labels=labels,\n", - " include_lowest=True\n", + "merged[\"ProductionCategory\"] = pd.cut(\n", + " merged[\"IronOreProductionMt\"], bins=bins, labels=labels, include_lowest=True\n", ")\n", "\n", "# %%\n", @@ -223,23 +223,23 @@ "fig, ax = plt.subplots(figsize=(10, 7))\n", "\n", "# Base world map\n", - "world.plot(ax=ax, color='lightgrey', edgecolor='white')\n", + "world.plot(ax=ax, color=\"lightgrey\", edgecolor=\"white\")\n", "\n", "# Plot with categorical Mt bins\n", - "merged.dropna(subset=['ProductionCategory']).plot(\n", + "merged.dropna(subset=[\"ProductionCategory\"]).plot(\n", " ax=ax,\n", - " column='ProductionCategory',\n", - " cmap='Reds',\n", + " column=\"ProductionCategory\",\n", + " cmap=\"Reds\",\n", " legend=True,\n", - " legend_kwds={'title': \"Iron Ore Production (Mt)\"},\n", - " missing_kwds={\"color\": \"lightgrey\"}\n", + " legend_kwds={\"title\": \"Iron Ore Production (Mt)\"},\n", + " missing_kwds={\"color\": \"lightgrey\"},\n", ")\n", "\n", "ax.set_title(\"Iron Ore Production by Country\")\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.tight_layout()\n", "plt.savefig(iron_ore_map_fn)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -396,7 +396,7 @@ } ], "source": [ - "production.sort_values('IronOreProductionMt', ascending=False)" + "production.sort_values(\"IronOreProductionMt\", ascending=False)" ] } ], diff --git a/workflow/notebooks/global-steel-production.ipynb b/workflow/notebooks/global-steel-production.ipynb index fb6f530..845c35f 100644 --- a/workflow/notebooks/global-steel-production.ipynb +++ b/workflow/notebooks/global-steel-production.ipynb @@ -11,7 +11,7 @@ "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import cartopy.io.shapereader as shpreader\n", - "import pycountry\n" + "import pycountry" ] }, { @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "production_fn = '../../resources/steel_production.csv'" + "production_fn = \"../../resources/steel_production.csv\"" ] }, { @@ -66,21 +66,24 @@ "df = pd.read_csv(\"../../data/owid-steel/steel-production.csv\")\n", "\n", "# Rename columns for easier access\n", - "df.rename(columns={\n", - " 'Entity': 'Country',\n", - " 'Year': 'Year',\n", - " 'production|Steel|Processing, crude|tonnes': 'SteelProduction',\n", - "}, inplace=True)\n", + "df.rename(\n", + " columns={\n", + " \"Entity\": \"Country\",\n", + " \"Year\": \"Year\",\n", + " \"production|Steel|Processing, crude|tonnes\": \"SteelProduction\",\n", + " },\n", + " inplace=True,\n", + ")\n", "\n", "# Keep only rows with available steel production data\n", - "df = df.dropna(subset=['SteelProduction'])\n", + "df = df.dropna(subset=[\"SteelProduction\"])\n", "\n", "# Get latest year per country\n", - "df_latest = df.sort_values('Year').groupby('Country', as_index=False).last()\n", + "df_latest = df.sort_values(\"Year\").groupby(\"Country\", as_index=False).last()\n", "\n", "# Drop data that is older than 2010 and drop Country 'World'\n", - "df_latest = df_latest[df_latest['Country'] != 'World']\n", - "df_latest = df_latest[df_latest['Year'] >= 2010]" + "df_latest = df_latest[df_latest[\"Country\"] != \"World\"]\n", + "df_latest = df_latest[df_latest[\"Year\"] >= 2010]" ] }, { @@ -95,15 +98,17 @@ " \"Democratic Republic of Congo\": \"Congo, The Democratic Republic of the\",\n", " \"Kosovo\": \"Republic of Kosovo\", # pycountry not supported\n", " \"Russia\": \"Russian Federation\",\n", - " \"Turkey\": \"Türkiye\"\n", + " \"Turkey\": \"Türkiye\",\n", "}\n", "\n", - "df_latest['Country'] = df_latest['Country'].replace(country_name_corrections)\n", - "df_latest['ISO_A2'] = df_latest['Country'].apply(country_to_iso_a2)\n", + "df_latest[\"Country\"] = df_latest[\"Country\"].replace(country_name_corrections)\n", + "df_latest[\"ISO_A2\"] = df_latest[\"Country\"].apply(country_to_iso_a2)\n", "\n", "# Check for any remaining missing ISO_A2 codes\n", - "missing_iso_a2 = df_latest[df_latest['ISO_A2'].isna()]\n", - "print(f\"Countries with missing ISO_A2 codes after correction: {missing_iso_a2['Country'].unique()}\")" + "missing_iso_a2 = df_latest[df_latest[\"ISO_A2\"].isna()]\n", + "print(\n", + " f\"Countries with missing ISO_A2 codes after correction: {missing_iso_a2['Country'].unique()}\"\n", + ")" ] }, { @@ -113,7 +118,9 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"Total steel production in latest year (Mt): {df_latest.SteelProduction.sum()/1e6:.2f} Mt\")" + "print(\n", + " f\"Total steel production in latest year (Mt): {df_latest.SteelProduction.sum() / 1e6:.2f} Mt\"\n", + ")" ] }, { @@ -124,7 +131,7 @@ "outputs": [], "source": [ "# Sort values\n", - "df_latest.sort_values('SteelProduction', ascending=False)" + "df_latest.sort_values(\"SteelProduction\", ascending=False)" ] }, { @@ -135,18 +142,18 @@ "outputs": [], "source": [ "# Use cartopy to get natural earth countries shapefile\n", - "shapename = 'admin_0_countries'\n", - "reader = shpreader.natural_earth(resolution='110m',\n", - " category='cultural',\n", - " name=shapename)\n", + "shapename = \"admin_0_countries\"\n", + "reader = shpreader.natural_earth(resolution=\"110m\", category=\"cultural\", name=shapename)\n", "\n", "world = gpd.read_file(reader)\n", "\n", "# Fix missing ISO_A2 codes in world\n", - "world.loc[world['ISO_A2'] == '-99', 'ISO_A2'] = world.loc[world['ISO_A2'] == '-99', 'ADMIN'].apply(country_to_iso_a2)\n", + "world.loc[world[\"ISO_A2\"] == \"-99\", \"ISO_A2\"] = world.loc[\n", + " world[\"ISO_A2\"] == \"-99\", \"ADMIN\"\n", + "].apply(country_to_iso_a2)\n", "\n", "# Merge country names — use left join to preserve geometry\n", - "merged = world.merge(df_latest, how='left', left_on='ISO_A2', right_on='ISO_A2')" + "merged = world.merge(df_latest, how=\"left\", left_on=\"ISO_A2\", right_on=\"ISO_A2\")" ] }, { @@ -157,7 +164,7 @@ "outputs": [], "source": [ "# Convert to megatonnes\n", - "merged['SteelProductionMt'] = merged['SteelProduction'].divide(1e6) # tonnes to Mt" + "merged[\"SteelProductionMt\"] = merged[\"SteelProduction\"].divide(1e6) # tonnes to Mt" ] }, { @@ -168,12 +175,11 @@ "outputs": [], "source": [ "# Ensure ISO_A2 and IronOreProductionGt exist\n", - "filtered_df = merged[\n", - " (merged['SteelProductionMt'] > 0) & \n", - " (merged['ISO_A2'].notna())\n", - "][['ISO_A2', 'SteelProductionMt', 'ADMIN']]\n", + "filtered_df = merged[(merged[\"SteelProductionMt\"] > 0) & (merged[\"ISO_A2\"].notna())][\n", + " [\"ISO_A2\", \"SteelProductionMt\", \"ADMIN\"]\n", + "]\n", "# Set ISO_A2 as index\n", - "production = filtered_df.set_index('ISO_A2')\n", + "production = filtered_df.set_index(\"ISO_A2\")\n", "\n", "# Display result\n", "production.to_csv(production_fn)" @@ -186,18 +192,14 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Define Gt bins and labels\n", - "bins = [0, 1, 10, 50, 100, 500, float('inf')]\n", - "labels = ['0–1', '1–10', '10–50', '50–100', '100–500', '500+']\n", + "bins = [0, 1, 10, 50, 100, 500, float(\"inf\")]\n", + "labels = [\"0–1\", \"1–10\", \"10–50\", \"50–100\", \"100–500\", \"500+\"]\n", "\n", "\n", "# Bin into categories\n", - "merged['ProductionCategory'] = pd.cut(\n", - " merged['SteelProductionMt'],\n", - " bins=bins,\n", - " labels=labels,\n", - " include_lowest=True\n", + "merged[\"ProductionCategory\"] = pd.cut(\n", + " merged[\"SteelProductionMt\"], bins=bins, labels=labels, include_lowest=True\n", ")\n", "\n", "# %%\n", @@ -205,22 +207,22 @@ "fig, ax = plt.subplots(figsize=(10, 7))\n", "\n", "# Base world map\n", - "world.plot(ax=ax, color='lightgrey', edgecolor='white')\n", + "world.plot(ax=ax, color=\"lightgrey\", edgecolor=\"white\")\n", "\n", "# Plot with categorical Gt bins\n", - "merged.dropna(subset=['ProductionCategory']).plot(\n", + "merged.dropna(subset=[\"ProductionCategory\"]).plot(\n", " ax=ax,\n", - " column='ProductionCategory',\n", - " cmap='Blues',\n", + " column=\"ProductionCategory\",\n", + " cmap=\"Blues\",\n", " legend=True,\n", - " legend_kwds={'title': \"Steel Production (Mt)\"},\n", - " missing_kwds={\"color\": \"lightgrey\"}\n", + " legend_kwds={\"title\": \"Steel Production (Mt)\"},\n", + " missing_kwds={\"color\": \"lightgrey\"},\n", ")\n", "\n", "ax.set_title(\"Steel Production by Country\")\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -230,7 +232,7 @@ "metadata": {}, "outputs": [], "source": [ - "production.sort_values('SteelProductionMt', ascending=False).iloc[0:20,:]\n" + "production.sort_values(\"SteelProductionMt\", ascending=False).iloc[0:20, :]" ] } ], diff --git a/workflow/notebooks/input-cost-comp.ipynb b/workflow/notebooks/input-cost-comp.ipynb index 84aa15b..eb902d5 100644 --- a/workflow/notebooks/input-cost-comp.ipynb +++ b/workflow/notebooks/input-cost-comp.ipynb @@ -54,7 +54,9 @@ "metadata": {}, "outputs": [], "source": [ - "n_old = pypsa.Network(\"../../results/old_costs/cost_year~2050/transport_cost~irena/demand~0.2/network.nc\")\n" + "n_old = pypsa.Network(\n", + " \"../../results/old_costs/cost_year~2050/transport_cost~irena/demand~0.2/network.nc\"\n", + ")" ] }, { @@ -64,8 +66,9 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "n_new = pypsa.Network(\"../../results/cost_year~2050/transport_cost~irena/demand~0.2/network.nc\")" + "n_new = pypsa.Network(\n", + " \"../../results/cost_year~2050/transport_cost~irena/demand~0.2/network.nc\"\n", + ")" ] }, { @@ -146,7 +149,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Load the cost data\n", "cost_df = pd.read_csv(\"../../data/technology_data/costs_2050.csv\")\n", "\n", @@ -180,17 +182,21 @@ "diff = new_values - old_values\n", "\n", "# Assemble comparison DataFrame\n", - "differences = pd.DataFrame({\n", - " \"old\": old_values,\n", - " \"new\": new_values,\n", - " \"delta\": diff\n", - "})\n", + "differences = pd.DataFrame({\"old\": old_values, \"new\": new_values, \"delta\": diff})\n", "\n", "# Bring the 'used_in_script' flags (technology level) from old and new data\n", "# Since 'used_in_script' depends only on 'technology', take first level index and map\n", "differences = differences.reset_index()\n", - "differences[\"used_in_script_old\"] = differences[\"technology\"].map(cost_df.reset_index().drop_duplicates(\"technology\").set_index(\"technology\")[\"used_in_script\"])\n", - "differences[\"used_in_script_new\"] = differences[\"technology\"].map(cost_new.reset_index().drop_duplicates(\"technology\").set_index(\"technology\")[\"used_in_script\"])\n", + "differences[\"used_in_script_old\"] = differences[\"technology\"].map(\n", + " cost_df.reset_index()\n", + " .drop_duplicates(\"technology\")\n", + " .set_index(\"technology\")[\"used_in_script\"]\n", + ")\n", + "differences[\"used_in_script_new\"] = differences[\"technology\"].map(\n", + " cost_new.reset_index()\n", + " .drop_duplicates(\"technology\")\n", + " .set_index(\"technology\")[\"used_in_script\"]\n", + ")\n", "\n", "# Add relative change (%) column\n", "differences[\"relative_change (%)\"] = 100 * differences[\"delta\"] / differences[\"old\"]\n", diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index dd374f5..890657a 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -10,7 +10,7 @@ "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import cartopy.io.shapereader as shpreader\n", - "import pycountry\n" + "import pycountry" ] }, { @@ -21,10 +21,11 @@ "outputs": [], "source": [ "from _helpers import mock_snakemake\n", + "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"default\" # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " )" + " \"collect_figures\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" ] }, { @@ -52,7 +53,7 @@ "metadata": {}, "outputs": [], "source": [ - "regions = config['regions']" + "regions = config[\"regions\"]" ] }, { @@ -78,7 +79,7 @@ " \"Laos\": \"Lao People's Democratic Republic\",\n", " \"Brunei\": \"Brunei Darussalam\",\n", " \"Equatorial French Guiana\": \"French Guiana\",\n", - " \"Syria\": \"Syrian Arab Republic\", \n", + " \"Syria\": \"Syrian Arab Republic\",\n", " \"Palestine\": \"Palestine, State of\",\n", " \"Moldova\": \"Republic of Moldova\",\n", "}\n", @@ -106,10 +107,8 @@ "outputs": [], "source": [ "# Use cartopy to get natural earth countries shapefile\n", - "shapename = 'admin_0_countries'\n", - "reader = shpreader.natural_earth(resolution='110m',\n", - " category='cultural',\n", - " name=shapename)\n", + "shapename = \"admin_0_countries\"\n", + "reader = shpreader.natural_earth(resolution=\"110m\", category=\"cultural\", name=shapename)\n", "\n", "world = gpd.read_file(reader)" ] @@ -148,24 +147,28 @@ "for country in pycountry.countries:\n", " country_name_to_iso[country.name] = country.alpha_2\n", " # Add common names\n", - " if hasattr(country, 'official_name'):\n", + " if hasattr(country, \"official_name\"):\n", " country_name_to_iso[country.official_name] = country.alpha_2\n", - " \n", + "\n", "# Build a mapping from ISO_A2 code to region\n", "iso_to_region = {}\n", "for region, countries in regions.items():\n", " for country in countries:\n", " # Some country names may have extra text (e.g., \"Togo + Algeria\"), handle them simply\n", - " for part in country.split('+'):\n", + " for part in country.split(\"+\"):\n", " name = part.strip()\n", " code = country_name_to_iso.get(name)\n", " if code:\n", " iso_to_region[code] = region\n", " else:\n", - " print(f\"Warning: Could not find ISO_A2 code for country '{name}' in region '{region}'.\")\n", + " print(\n", + " f\"Warning: Could not find ISO_A2 code for country '{name}' in region '{region}'.\"\n", + " )\n", "\n", "# Fix missing ISO_A2 codes in world\n", - "world.loc[world['ISO_A2'] == '-99', 'ISO_A2'] = world.loc[world['ISO_A2'] == '-99', 'ADMIN'].apply(country_to_iso_a2)\n" + "world.loc[world[\"ISO_A2\"] == \"-99\", \"ISO_A2\"] = world.loc[\n", + " world[\"ISO_A2\"] == \"-99\", \"ADMIN\"\n", + "].apply(country_to_iso_a2)" ] }, { @@ -175,7 +178,7 @@ "metadata": {}, "outputs": [], "source": [ - "world[\"region\"] = world['ISO_A2'].map(lambda iso: iso_to_region.get(iso, 'Other'))" + "world[\"region\"] = world[\"ISO_A2\"].map(lambda iso: iso_to_region.get(iso, \"Other\"))" ] }, { @@ -196,26 +199,32 @@ "# Short mapping: assume config['colors'] is a dict region->color\n", "import matplotlib.patches as mpatches\n", "\n", - "color_map = config.get('colors', {})\n", + "color_map = config.get(\"colors\", {})\n", "\n", - "world['plot_color'] = world['region'].map(color_map).fillna('lightgrey')\n", + "world[\"plot_color\"] = world[\"region\"].map(color_map).fillna(\"lightgrey\")\n", "\n", "fig, ax = plt.subplots(figsize=(12, 10))\n", "\n", - "world.plot(ax=ax, color=world['plot_color'], edgecolor='white')\n", + "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\")\n", "\n", "# Manual legend for regions present in the map\n", - "handles = [mpatches.Patch(color=c, label=r) for r, c in color_map.items() if r in world['region'].values]\n", + "handles = [\n", + " mpatches.Patch(color=c, label=r)\n", + " for r, c in color_map.items()\n", + " if r in world[\"region\"].values\n", + "]\n", "if handles:\n", - " ax.legend(handles=handles, title='Region', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + " ax.legend(\n", + " handles=handles, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\"\n", + " )\n", "\n", "# ax.set_title('Regions')\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.tight_layout()\n", "\n", "plt.savefig(snakemake.input.global_map_countries)\n", "plt.savefig(snakemake.input.global_map_countries_png, dpi=300)\n", - "plt.show()\n" + "plt.show()" ] } ], diff --git a/workflow/notebooks/prepare-iron-ore.ipynb b/workflow/notebooks/prepare-iron-ore.ipynb index eb3dd6c..351025d 100644 --- a/workflow/notebooks/prepare-iron-ore.ipynb +++ b/workflow/notebooks/prepare-iron-ore.ipynb @@ -27,11 +27,11 @@ "metadata": {}, "outputs": [], "source": [ - "production_fn = '../../resources/ironore-production.csv'\n", - "bus_locations_fn = '../../data/bus_locations.csv'\n", + "production_fn = \"../../resources/ironore-production.csv\"\n", + "bus_locations_fn = \"../../data/bus_locations.csv\"\n", "\n", "# Outputs\n", - "production_clustered_fn = '../../resources/ironore_production_clustered.csv'" + "production_clustered_fn = \"../../resources/ironore_production_clustered.csv\"" ] }, { @@ -49,9 +49,8 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Read the CSV data\n", - "production = pd.read_csv(production_fn, index_col='ISO_A2')" + "production = pd.read_csv(production_fn, index_col=\"ISO_A2\")" ] }, { @@ -89,42 +88,46 @@ "outputs": [], "source": [ "# Load region definitions from config file\n", - "with open('../../config/config.yaml', 'r') as f:\n", + "with open(\"../../config/config.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", - "regions = config['regions']\n", + "regions = config[\"regions\"]\n", "\n", "# Build a mapping from country name to ISO_A2 code\n", "country_name_to_iso = {}\n", "for country in pycountry.countries:\n", " country_name_to_iso[country.name] = country.alpha_2\n", - " if hasattr(country, 'official_name'):\n", + " if hasattr(country, \"official_name\"):\n", " country_name_to_iso[country.official_name] = country.alpha_2\n", "\n", "# Build a mapping from ISO_A2 code to region\n", "iso_to_region = {}\n", "for region, countries in regions.items():\n", " for country in countries:\n", - " for part in country.split('+'):\n", + " for part in country.split(\"+\"):\n", " name = part.strip()\n", " code = country_name_to_iso.get(name)\n", " if code:\n", " iso_to_region[code] = region\n", "\n", "# Map each row in production to its region\n", - "production['region'] = production.index.map(lambda iso: iso_to_region.get(iso, 'Other'))\n", + "production[\"region\"] = production.index.map(lambda iso: iso_to_region.get(iso, \"Other\"))\n", "\n", "# Group by region and sum iron ore production\n", - "regional_production = production.groupby('region')['IronOreProductionMt'].sum()\n", + "regional_production = production.groupby(\"region\")[\"IronOreProductionMt\"].sum()\n", "\n", "# Ensure all regions are present, with 0 for missing ones\n", "all_regions = pd.Series(0, index=regions.keys())\n", - "regional_production = all_regions.add(regional_production, fill_value=0).rename('IronOreProductionMt')\n", + "regional_production = all_regions.add(regional_production, fill_value=0).rename(\n", + " \"IronOreProductionMt\"\n", + ")\n", "\n", "# Convert to DataFrame and sort\n", - "regional_production = regional_production.reset_index().sort_values(by='IronOreProductionMt', ascending=False)\n", + "regional_production = regional_production.reset_index().sort_values(\n", + " by=\"IronOreProductionMt\", ascending=False\n", + ")\n", "\n", "# Rename index to region\n", - "regional_production = regional_production.rename(columns={'index': 'region'})\n", + "regional_production = regional_production.rename(columns={\"index\": \"region\"})\n", "\n", "regional_production" ] @@ -145,7 +148,7 @@ "outputs": [], "source": [ "# Group by region and sum all numeric columns\n", - "production_by_region = regional_production.groupby('region').sum(numeric_only=True)\n", + "production_by_region = regional_production.groupby(\"region\").sum(numeric_only=True)\n", "production_by_region" ] }, diff --git a/workflow/notebooks/prepare-steel.ipynb b/workflow/notebooks/prepare-steel.ipynb index c3edd7c..75cd8e9 100644 --- a/workflow/notebooks/prepare-steel.ipynb +++ b/workflow/notebooks/prepare-steel.ipynb @@ -27,11 +27,11 @@ "metadata": {}, "outputs": [], "source": [ - "production_fn = '../../resources/steel_production.csv'\n", - "bus_locations_fn = '../../data/bus_locations.csv'\n", + "production_fn = \"../../resources/steel_production.csv\"\n", + "bus_locations_fn = \"../../data/bus_locations.csv\"\n", "\n", "# Outputs\n", - "production_clustered_fn = '../../resources/steel_production_clustered.csv'" + "production_clustered_fn = \"../../resources/steel_production_clustered.csv\"" ] }, { @@ -49,9 +49,8 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Read the CSV data\n", - "production = pd.read_csv(production_fn, index_col='ISO_A2')" + "production = pd.read_csv(production_fn, index_col=\"ISO_A2\")" ] }, { @@ -89,16 +88,16 @@ "outputs": [], "source": [ "# Load region definitions from config file\n", - "with open('../../config/config.yaml', 'r') as f:\n", + "with open(\"../../config/config.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", - "regions = config['regions']\n", + "regions = config[\"regions\"]\n", "\n", "# Build a mapping from country name to ISO_A2 code\n", "country_name_to_iso = {}\n", "for country in pycountry.countries:\n", " country_name_to_iso[country.name] = country.alpha_2\n", " # Add common names\n", - " if hasattr(country, 'official_name'):\n", + " if hasattr(country, \"official_name\"):\n", " country_name_to_iso[country.official_name] = country.alpha_2\n", "\n", "# Build a mapping from ISO_A2 code to region\n", @@ -106,15 +105,15 @@ "for region, countries in regions.items():\n", " for country in countries:\n", " # Some country names may have extra text (e.g., \"Togo + Algeria\"), handle them simply\n", - " for part in country.split('+'):\n", + " for part in country.split(\"+\"):\n", " name = part.strip()\n", " code = country_name_to_iso.get(name)\n", " if code:\n", " iso_to_region[code] = region\n", "\n", "# Map each row in production to its region\n", - "production['region'] = production.index.map(lambda iso: iso_to_region.get(iso, 'Other'))\n", - "production.sort_values(by='SteelProductionMt', ascending=False)" + "production[\"region\"] = production.index.map(lambda iso: iso_to_region.get(iso, \"Other\"))\n", + "production.sort_values(by=\"SteelProductionMt\", ascending=False)" ] }, { @@ -133,7 +132,7 @@ "outputs": [], "source": [ "# Group by region and sum all numeric columns\n", - "production_by_region = production.groupby('region').sum(numeric_only=True)\n", + "production_by_region = production.groupby(\"region\").sum(numeric_only=True)\n", "production_by_region" ] }, diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index 68952cf..da3326a 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -14,11 +14,15 @@ "# Set required root pointers\n", "SHIFT_PATH = os.path.abspath(os.path.join(os.getcwd(), \"../../..\", \"shift\"))\n", "if not os.path.isdir(SHIFT_PATH):\n", - " raise FileNotFoundError(f\"shift not found at {SHIFT_PATH}. Please clone or set correct path.\")\n", + " raise FileNotFoundError(\n", + " f\"shift not found at {SHIFT_PATH}. Please clone or set correct path.\"\n", + " )\n", "\n", - "PYPSA_EARTH_PATH = os.path.abspath(os.path.join(SHIFT_PATH,\"..\", \"pypsa-earth\"))\n", + "PYPSA_EARTH_PATH = os.path.abspath(os.path.join(SHIFT_PATH, \"..\", \"pypsa-earth\"))\n", "if not os.path.isdir(PYPSA_EARTH_PATH):\n", - " raise FileNotFoundError(f\"pypsa-earth not found at {PYPSA_EARTH_PATH}. Please clone or set correct path.\")\n", + " raise FileNotFoundError(\n", + " f\"pypsa-earth not found at {PYPSA_EARTH_PATH}. Please clone or set correct path.\"\n", + " )\n", "\n", "# Change working directory to shift root\n", "os.chdir(SHIFT_PATH)\n", @@ -42,6 +46,7 @@ "from sklearn.cluster import KMeans\n", "import pycountry\n", "\n", + "\n", "# Custom formatter to hide full paths in git commits\n", "class RelativePathFormatter(logging.Formatter):\n", " def format(self, record):\n", @@ -50,12 +55,13 @@ " # Format: relative/path/file.py - message\n", " return f\"{record.pathname} - {record.getMessage()}\"\n", "\n", + "\n", "handler = logging.StreamHandler()\n", "handler.setFormatter(RelativePathFormatter())\n", "logger = logging.getLogger()\n", "logger.handlers.clear()\n", "logger.addHandler(handler)\n", - "logger.setLevel(logging.INFO)\n" + "logger.setLevel(logging.INFO)" ] }, { @@ -80,7 +86,10 @@ "source": [ "tech_profiles_nc = {}\n", "for technology in [\"onwind\", \"offwind-ac\", \"solar\"]:\n", - " path = os.path.realpath(PYPSA_EARTH_PATH) + f\"/resources/renewable_profiles/profile_{technology}.nc\"\n", + " path = (\n", + " os.path.realpath(PYPSA_EARTH_PATH)\n", + " + f\"/resources/renewable_profiles/profile_{technology}.nc\"\n", + " )\n", " ds = xr.open_dataset(path)\n", " tech_profiles_nc[technology] = ds\n", " logging.info(f\"Loaded {technology} profiles\")" @@ -719,25 +728,47 @@ } ], "source": [ - "onshore_regions_gpd = gpd.read_file(os.path.realpath(PYPSA_EARTH_PATH) + \"/resources/bus_regions/regions_onshore.geojson\")\n", - "offshore_regions_gpd = gpd.read_file(os.path.realpath(PYPSA_EARTH_PATH) + \"/resources/bus_regions/regions_offshore.geojson\")\n", + "onshore_regions_gpd = gpd.read_file(\n", + " os.path.realpath(PYPSA_EARTH_PATH)\n", + " + \"/resources/bus_regions/regions_onshore.geojson\"\n", + ")\n", + "offshore_regions_gpd = gpd.read_file(\n", + " os.path.realpath(PYPSA_EARTH_PATH)\n", + " + \"/resources/bus_regions/regions_offshore.geojson\"\n", + ")\n", "\n", "# Convert ISO2 country codes to ISO3\n", - "onshore_regions_gpd['country'] = onshore_regions_gpd['country'].apply(lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3)\n", - "offshore_regions_gpd['country'] = offshore_regions_gpd['country'].apply(lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3)\n", + "onshore_regions_gpd[\"country\"] = onshore_regions_gpd[\"country\"].apply(\n", + " lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3\n", + ")\n", + "offshore_regions_gpd[\"country\"] = offshore_regions_gpd[\"country\"].apply(\n", + " lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3\n", + ")\n", "\n", "# Rename offshore regions to avoid ID collisions (same numeric IDs exist in both datasets)\n", "# Prefix offshore names with \"OFF_\" to make them globally unique\n", - "offshore_regions_gpd['name'] = 'OFF_' + offshore_regions_gpd['name'].astype(str)\n", + "offshore_regions_gpd[\"name\"] = \"OFF_\" + offshore_regions_gpd[\"name\"].astype(str)\n", "\n", "# Compute area for each region (in km²)\n", "# Reproject to Web Mercator (EPSG:3857) to get accurate area in meters, then convert to km²\n", - "onshore_regions_gpd['area_km2'] = onshore_regions_gpd.to_crs('EPSG:3857').geometry.area / 1e6\n", - "offshore_regions_gpd['area_km2'] = offshore_regions_gpd.to_crs('EPSG:3857').geometry.area / 1e6\n", - "logging.info(f\"Onshore regions: {len(onshore_regions_gpd)}, mean area={onshore_regions_gpd['area_km2'].mean():.1f} km²\")\n", - "logging.info(f\"Offshore regions: {len(offshore_regions_gpd)}, mean area={offshore_regions_gpd['area_km2'].mean():.1f} km²\")\n", - "logging.info(f\"Deduplication: Offshore region names prefixed with 'OFF_' to avoid ID collisions\")\n", - "logging.info(f\"Conversion: Country codes converted from ISO2 to ISO3 for consistent downstream usage\")" + "onshore_regions_gpd[\"area_km2\"] = (\n", + " onshore_regions_gpd.to_crs(\"EPSG:3857\").geometry.area / 1e6\n", + ")\n", + "offshore_regions_gpd[\"area_km2\"] = (\n", + " offshore_regions_gpd.to_crs(\"EPSG:3857\").geometry.area / 1e6\n", + ")\n", + "logging.info(\n", + " f\"Onshore regions: {len(onshore_regions_gpd)}, mean area={onshore_regions_gpd['area_km2'].mean():.1f} km²\"\n", + ")\n", + "logging.info(\n", + " f\"Offshore regions: {len(offshore_regions_gpd)}, mean area={offshore_regions_gpd['area_km2'].mean():.1f} km²\"\n", + ")\n", + "logging.info(\n", + " \"Deduplication: Offshore region names prefixed with 'OFF_' to avoid ID collisions\"\n", + ")\n", + "logging.info(\n", + " \"Conversion: Country codes converted from ISO2 to ISO3 for consistent downstream usage\"\n", + ")" ] }, { @@ -755,18 +786,17 @@ " total_clusters=10,\n", " onshore_ratio=0.8,\n", " random_state=42,\n", - " separate_onshore_offshore=True\n", + " separate_onshore_offshore=True,\n", "):\n", - " \n", " \"\"\"\n", " Cluster renewable resource potentials and capacity factors by geographic proximity.\n", - " \n", - " Aggregates total generation potential (from tech_profiles['potential']) and hourly \n", - " capacity factor time series across multiple regions into geographic clusters. The clustering \n", - " process averages CF timeseries, which distorts the resource profile. To preserve energy \n", - " conservation, we calculate p_nom_max = generation_potential / avg_cf_clustered. The \n", + "\n", + " Aggregates total generation potential (from tech_profiles['potential']) and hourly\n", + " capacity factor time series across multiple regions into geographic clusters. The clustering\n", + " process averages CF timeseries, which distorts the resource profile. To preserve energy\n", + " conservation, we calculate p_nom_max = generation_potential / avg_cf_clustered. The\n", " deviation of clustered p_nom_max from raw values is the clustering distortion metric.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " tech_profiles_nc : dict[str, xr.Dataset]\n", @@ -786,24 +816,24 @@ " Seed for KMeans reproducibility.\n", " separate_onshore_offshore : bool, default=True\n", " If True, cluster onshore and offshore separately within each country.\n", - " \n", + "\n", " Returns\n", " -------\n", " clusters_ds : xr.Dataset\n", " Clustered dataset with dimensions [cluster, technology, hour].\n", - " \n", + "\n", " Data variables:\n", " - renewable_potential: Sum of raw potentials per cluster-technology (MW).\n", " Preserved exactly during clustering - no energy loss.\n", " - avg_cf: Mean capacity factor from clustered regions (0-1).\n", - " Averaged timeseries introduces distortion—may not represent \n", + " Averaged timeseries introduces distortion—may not represent\n", " individual region characteristics.\n", " - p_nom_max: Installed capacity needed to preserve renewable_potential\n", " with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", " Compare to raw region p_nom_max to quantify clustering distortion.\n", " - capacity_factor: Hourly profiles [cluster, technology, hour].\n", " Average of regional CF timeseries.\n", - " \n", + "\n", " Coordinates:\n", " - cluster: Unique cluster IDs (format: ISO3_[ON|OFF]_##)\n", " - iso3: Country code per cluster\n", @@ -816,83 +846,91 @@ " # Calculate cluster counts based on ratio\n", " n_clusters_onshore = max(1, round(total_clusters * onshore_ratio))\n", " n_clusters_offshore = max(1, round(total_clusters * (1 - onshore_ratio)))\n", - " \n", + "\n", " # Safety check: verify the sum matches user-specified total\n", " total_allocated = n_clusters_onshore + n_clusters_offshore\n", " if total_allocated == total_clusters:\n", - " logging.info(f\"✓ Cluster allocation valid: {n_clusters_onshore} onshore + {n_clusters_offshore} offshore = {total_clusters} total\")\n", + " logging.info(\n", + " f\"✓ Cluster allocation valid: {n_clusters_onshore} onshore + {n_clusters_offshore} offshore = {total_clusters} total\"\n", + " )\n", " else:\n", " logging.warning(\n", " f\"⚠ Cluster allocation mismatch: {n_clusters_onshore} onshore + {n_clusters_offshore} offshore = {total_allocated}, \"\n", " f\"but user requested {total_clusters} total. Difference: {total_allocated - total_clusters} clusters.\"\n", " )\n", - " \n", + "\n", " # Prepare regions (already have ISO3 country codes)\n", - " all_regions = pd.concat([\n", - " onshore_regions_gpd.assign(type='onshore'),\n", - " offshore_regions_gpd.assign(type='offshore')\n", - " ], ignore_index=True)\n", - " \n", - " all_regions['iso3'] = all_regions['country']\n", + " all_regions = pd.concat(\n", + " [\n", + " onshore_regions_gpd.assign(type=\"onshore\"),\n", + " offshore_regions_gpd.assign(type=\"offshore\"),\n", + " ],\n", + " ignore_index=True,\n", + " )\n", + "\n", + " all_regions[\"iso3\"] = all_regions[\"country\"]\n", " # Extract geographic coordinates for grid matching (warning suppressed - intentional use of geographic CRS)\n", " import warnings\n", + "\n", " with warnings.catch_warnings():\n", - " warnings.filterwarnings('ignore', message='Geometry is in a geographic CRS')\n", - " all_regions['lon'] = all_regions.geometry.centroid.x\n", - " all_regions['lat'] = all_regions.geometry.centroid.y\n", + " warnings.filterwarnings(\"ignore\", message=\"Geometry is in a geographic CRS\")\n", + " all_regions[\"lon\"] = all_regions.geometry.centroid.x\n", + " all_regions[\"lat\"] = all_regions.geometry.centroid.y\n", " # Also compute projected centroid for KMeans (more accurate for clustering)\n", - " all_regions_projected = all_regions.to_crs('EPSG:3857')\n", - " all_regions['x_proj'] = all_regions_projected.geometry.centroid.x\n", - " all_regions['y_proj'] = all_regions_projected.geometry.centroid.y\n", - " \n", + " all_regions_projected = all_regions.to_crs(\"EPSG:3857\")\n", + " all_regions[\"x_proj\"] = all_regions_projected.geometry.centroid.x\n", + " all_regions[\"y_proj\"] = all_regions_projected.geometry.centroid.y\n", + "\n", " # Filter by country codes if specified\n", " if country_codes is not None:\n", " iso3_filter = []\n", " for code in country_codes:\n", " try:\n", " if len(code) == 2:\n", - " iso3_filter.append(pycountry.countries.get(alpha_2=code.upper()).alpha_3)\n", + " iso3_filter.append(\n", + " pycountry.countries.get(alpha_2=code.upper()).alpha_3\n", + " )\n", " else:\n", " iso3_filter.append(code.upper())\n", " except AttributeError:\n", " logging.warning(f\"Unknown country code: {code}\")\n", - " all_regions = all_regions[all_regions['iso3'].isin(iso3_filter)]\n", - " \n", + " all_regions = all_regions[all_regions[\"iso3\"].isin(iso3_filter)]\n", + "\n", " technologies = list(tech_profiles_nc.keys())\n", - " iso3_codes = sorted(all_regions['iso3'].unique())\n", - " \n", + " iso3_codes = sorted(all_regions[\"iso3\"].unique())\n", + "\n", " logging.info(f\"Clustering {len(iso3_codes)} countries: {iso3_codes}\")\n", " logging.info(f\"Separate onshore/offshore: {separate_onshore_offshore}\")\n", - " \n", + "\n", " cluster_list = []\n", - " \n", + "\n", " for iso3 in iso3_codes:\n", - " iso3_regions = all_regions[all_regions['iso3'] == iso3]\n", + " iso3_regions = all_regions[all_regions[\"iso3\"] == iso3]\n", " logging.info(f\"\\n{iso3}: {len(iso3_regions)} regions\")\n", - " \n", + "\n", " # Cluster onshore and offshore separately if requested\n", " if separate_onshore_offshore:\n", " regions_to_cluster = [\n", - " ('onshore', iso3_regions[iso3_regions['type'] == 'onshore'].copy()),\n", - " ('offshore', iso3_regions[iso3_regions['type'] == 'offshore'].copy())\n", + " (\"onshore\", iso3_regions[iso3_regions[\"type\"] == \"onshore\"].copy()),\n", + " (\"offshore\", iso3_regions[iso3_regions[\"type\"] == \"offshore\"].copy()),\n", " ]\n", " else:\n", - " regions_to_cluster = [('all', iso3_regions.copy())]\n", - " \n", + " regions_to_cluster = [(\"all\", iso3_regions.copy())]\n", + "\n", " # Cluster each type\n", " for region_type, type_regions in regions_to_cluster:\n", " if len(type_regions) == 0:\n", " logging.info(f\" Skipping {region_type}: no regions available\")\n", " continue\n", - " \n", + "\n", " # Determine requested clusters\n", - " if region_type == 'onshore':\n", + " if region_type == \"onshore\":\n", " requested_clusters = n_clusters_onshore\n", - " elif region_type == 'offshore':\n", + " elif region_type == \"offshore\":\n", " requested_clusters = n_clusters_offshore\n", " else:\n", " requested_clusters = total_clusters\n", - " \n", + "\n", " # Fallback if not enough regions\n", " n_clusters = min(requested_clusters, len(type_regions))\n", " if n_clusters < requested_clusters:\n", @@ -901,138 +939,189 @@ " f\"{len(type_regions)} regions available. Creating {n_clusters} clusters instead.\"\n", " )\n", " else:\n", - " logging.info(f\" Clustering {region_type}: {len(type_regions)} regions → {n_clusters} clusters\")\n", - " \n", + " logging.info(\n", + " f\" Clustering {region_type}: {len(type_regions)} regions → {n_clusters} clusters\"\n", + " )\n", + "\n", " kmeans = KMeans(n_clusters=n_clusters, random_state=random_state)\n", " # Use projected coordinates for accurate geographic clustering\n", - " region_coords = type_regions[['x_proj', 'y_proj']].values\n", - " type_regions['cluster_id'] = kmeans.fit_predict(region_coords)\n", - " \n", + " region_coords = type_regions[[\"x_proj\", \"y_proj\"]].values\n", + " type_regions[\"cluster_id\"] = kmeans.fit_predict(region_coords)\n", + "\n", " # Aggregate all technologies within each cluster\n", " for cluster_id in range(n_clusters):\n", - " cluster_regions = type_regions[type_regions['cluster_id'] == cluster_id]\n", - " region_names = cluster_regions['name'].values\n", - " \n", + " cluster_regions = type_regions[type_regions[\"cluster_id\"] == cluster_id]\n", + " region_names = cluster_regions[\"name\"].values\n", + "\n", " # Generate cluster ID\n", - " if separate_onshore_offshore and region_type != 'all':\n", - " type_label = 'ON' if region_type == 'onshore' else 'OFF'\n", - " cluster_id_str = f\"{iso3}_{type_label}_{cluster_id+1:02d}\"\n", + " if separate_onshore_offshore and region_type != \"all\":\n", + " type_label = \"ON\" if region_type == \"onshore\" else \"OFF\"\n", + " cluster_id_str = f\"{iso3}_{type_label}_{cluster_id + 1:02d}\"\n", " else:\n", - " cluster_id_str = f\"{iso3}_{cluster_id+1:02d}\"\n", - " \n", + " cluster_id_str = f\"{iso3}_{cluster_id + 1:02d}\"\n", + "\n", " # Spatial aggregation\n", - " lat = cluster_regions['lat'].mean()\n", - " lon = cluster_regions['lon'].mean()\n", - " area_km2 = cluster_regions['area_km2'].sum()\n", - " \n", + " lat = cluster_regions[\"lat\"].mean()\n", + " lon = cluster_regions[\"lon\"].mean()\n", + " area_km2 = cluster_regions[\"area_km2\"].sum()\n", + "\n", " # Aggregate each technology\n", " for tech in technologies:\n", " tech_ds = tech_profiles_nc[tech]\n", - " \n", + "\n", " # Get valid regions (strip OFF_ prefix for bus matching)\n", " valid_regions = []\n", " for r in region_names:\n", - " bus_name = r.replace('OFF_', '') if r.startswith('OFF_') else r\n", + " bus_name = r.replace(\"OFF_\", \"\") if r.startswith(\"OFF_\") else r\n", " if str(bus_name) in [str(b) for b in tech_ds.bus.values]:\n", " valid_regions.append(r)\n", - " \n", + "\n", " if len(valid_regions) == 0:\n", - " logging.warning(f\" {tech}: No regions in cluster {cluster_id_str}\")\n", + " logging.warning(\n", + " f\" {tech}: No regions in cluster {cluster_id_str}\"\n", + " )\n", " renewable_potential, avg_cf, cf_array = 0, 0, np.zeros(8760)\n", " else:\n", " # Average timeseries across all regions in cluster (preserves energy conservation)\n", " cf_lists = []\n", " for r in valid_regions:\n", - " bus_name = r.replace('OFF_', '') if r.startswith('OFF_') else r\n", + " bus_name = (\n", + " r.replace(\"OFF_\", \"\") if r.startswith(\"OFF_\") else r\n", + " )\n", " cf_lists.append(tech_ds.sel(bus=bus_name).profile.values)\n", " # Hourly capacity factor: averaged across all regions in this cluster\n", " cf_array = np.mean(cf_lists, axis=0)\n", " # Annual mean capacity factor for this cluster-technology combination\n", " avg_cf = cf_array.mean()\n", - " \n", + "\n", " # Sum renewable generation potential across all regions in cluster (MW)\n", " # This exact sum is preserved through clustering - no energy loss\n", - " region_rows = cluster_regions[cluster_regions['name'].isin(valid_regions)]\n", + " region_rows = cluster_regions[\n", + " cluster_regions[\"name\"].isin(valid_regions)\n", + " ]\n", " potential_list = []\n", " for _, region in region_rows.iterrows():\n", - " lon_val, lat_val = region['lon'], region['lat']\n", + " lon_val, lat_val = region[\"lon\"], region[\"lat\"]\n", " x_idx = np.argmin(np.abs(tech_ds.x.values - lon_val))\n", " y_idx = np.argmin(np.abs(tech_ds.y.values - lat_val))\n", - " potential_val = tech_ds['potential'].values[y_idx, x_idx]\n", + " potential_val = tech_ds[\"potential\"].values[y_idx, x_idx]\n", " potential_list.append(potential_val)\n", " # Total renewable generation potential (MW) for cluster-technology\n", " renewable_potential = np.sum(potential_list)\n", - " \n", + "\n", " # Store cluster aggregations for later xarray construction\n", - " cluster_list.append({\n", - " 'cluster_id': cluster_id_str,\n", - " 'iso3': iso3,\n", - " 'type': region_type if separate_onshore_offshore else 'mixed',\n", - " 'technology': tech,\n", - " 'lat': lat,\n", - " 'lon': lon,\n", - " 'area_km2': area_km2,\n", - " 'renewable_potential': renewable_potential, # Sum of raw potentials (MW)\n", - " 'avg_cf': avg_cf, # Annual mean capacity factor (0-1)\n", - " 'cf_timeseries': cf_array, # Hourly profiles for 8760 hours\n", - " })\n", - " \n", + " cluster_list.append(\n", + " {\n", + " \"cluster_id\": cluster_id_str,\n", + " \"iso3\": iso3,\n", + " \"type\": region_type\n", + " if separate_onshore_offshore\n", + " else \"mixed\",\n", + " \"technology\": tech,\n", + " \"lat\": lat,\n", + " \"lon\": lon,\n", + " \"area_km2\": area_km2,\n", + " \"renewable_potential\": renewable_potential, # Sum of raw potentials (MW)\n", + " \"avg_cf\": avg_cf, # Annual mean capacity factor (0-1)\n", + " \"cf_timeseries\": cf_array, # Hourly profiles for 8760 hours\n", + " }\n", + " )\n", + "\n", " # Build xarray dataset with all cluster aggregations and time series\n", " cluster_meta = pd.DataFrame(cluster_list)\n", - " \n", + "\n", " # Pre-compute data arrays for xarray construction\n", - " renewable_potential_array = cluster_meta.pivot_table(\n", - " index='cluster_id', columns='technology',\n", - " values='renewable_potential', aggfunc='first'\n", - " ).reindex(technologies, axis=1).values\n", - " \n", - " avg_cf_array = cluster_meta.pivot_table(\n", - " index='cluster_id', columns='technology',\n", - " values='avg_cf', aggfunc='first'\n", - " ).reindex(technologies, axis=1).values\n", - " \n", + " renewable_potential_array = (\n", + " cluster_meta.pivot_table(\n", + " index=\"cluster_id\",\n", + " columns=\"technology\",\n", + " values=\"renewable_potential\",\n", + " aggfunc=\"first\",\n", + " )\n", + " .reindex(technologies, axis=1)\n", + " .values\n", + " )\n", + "\n", + " avg_cf_array = (\n", + " cluster_meta.pivot_table(\n", + " index=\"cluster_id\", columns=\"technology\", values=\"avg_cf\", aggfunc=\"first\"\n", + " )\n", + " .reindex(technologies, axis=1)\n", + " .values\n", + " )\n", + "\n", " # Compute p_nom_max as backward derivation: renewable_potential / avg_cf\n", " # Safe division handles division by zero (where avg_cf == 0, result is 0)\n", " p_nom_max_array = np.divide(\n", " renewable_potential_array,\n", " avg_cf_array,\n", " where=avg_cf_array > 0,\n", - " out=np.zeros_like(avg_cf_array)\n", + " out=np.zeros_like(avg_cf_array),\n", " )\n", - " \n", + "\n", " clusters_ds = xr.Dataset(\n", " data_vars={\n", " # Renewable generation potential per cluster-technology (MW) - exact sum, no distortion\n", - " 'renewable_potential': (['cluster', 'technology'], renewable_potential_array),\n", + " \"renewable_potential\": (\n", + " [\"cluster\", \"technology\"],\n", + " renewable_potential_array,\n", + " ),\n", " # Annual mean capacity factor (0-1) - averaged timeseries introduces clustering distortion\n", - " 'avg_cf': (['cluster', 'technology'], avg_cf_array),\n", + " \"avg_cf\": ([\"cluster\", \"technology\"], avg_cf_array),\n", " # Hourly capacity factor profiles (0-1) - to be used in timeseries optimization\n", - " 'capacity_factor': (['cluster', 'technology', 'hour'],\n", - " np.array([c['cf_timeseries'] for c in cluster_list])\n", - " .reshape(len(cluster_list)//len(technologies), len(technologies), 8760)),\n", + " \"capacity_factor\": (\n", + " [\"cluster\", \"technology\", \"hour\"],\n", + " np.array([c[\"cf_timeseries\"] for c in cluster_list]).reshape(\n", + " len(cluster_list) // len(technologies), len(technologies), 8760\n", + " ),\n", + " ),\n", " # Installed capacity needed to preserve renewable_potential with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", - " 'p_nom_max': (['cluster', 'technology'], p_nom_max_array),\n", + " \"p_nom_max\": ([\"cluster\", \"technology\"], p_nom_max_array),\n", " },\n", " coords={\n", - " 'cluster': sorted(cluster_meta['cluster_id'].unique()), # Unique cluster identifiers\n", + " \"cluster\": sorted(\n", + " cluster_meta[\"cluster_id\"].unique()\n", + " ), # Unique cluster identifiers\n", " # Country code for each cluster (for filtering/grouping by country)\n", - " 'iso3': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['iso3'].iloc[0]\n", - " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", - " 'technology': technologies, # Technologies included (onwind, offwind-ac, solar)\n", - " 'hour': np.arange(8760), # Hours in a year (0-8759)\n", + " \"iso3\": (\n", + " [\"cluster\"],\n", + " [\n", + " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"iso3\"].iloc[0]\n", + " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", + " ],\n", + " ),\n", + " \"technology\": technologies, # Technologies included (onwind, offwind-ac, solar)\n", + " \"hour\": np.arange(8760), # Hours in a year (0-8759)\n", " # Geographic centroid coordinates for cluster visualization and spatial reference\n", - " 'lat': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['lat'].iloc[0]\n", - " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", - " 'lon': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['lon'].iloc[0]\n", - " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", + " \"lat\": (\n", + " [\"cluster\"],\n", + " [\n", + " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"lat\"].iloc[0]\n", + " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", + " ],\n", + " ),\n", + " \"lon\": (\n", + " [\"cluster\"],\n", + " [\n", + " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"lon\"].iloc[0]\n", + " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", + " ],\n", + " ),\n", " # Total area of regions included in each cluster (km²) - for density/intensity calculations\n", - " 'area_km2': (['cluster'], [cluster_meta[cluster_meta['cluster_id']==c]['area_km2'].iloc[0]\n", - " for c in sorted(cluster_meta['cluster_id'].unique())]),\n", - " }\n", + " \"area_km2\": (\n", + " [\"cluster\"],\n", + " [\n", + " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"area_km2\"].iloc[0]\n", + " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", + " ],\n", + " ),\n", + " },\n", + " )\n", + "\n", + " logging.info(\n", + " f\"\\n✓ Created {len(clusters_ds.cluster)} geographic clusters × {len(technologies)} techs\"\n", " )\n", - " \n", - " logging.info(f\"\\n✓ Created {len(clusters_ds.cluster)} geographic clusters × {len(technologies)} techs\")\n", - " return clusters_ds, cluster_meta\n" + " return clusters_ds, cluster_meta" ] }, { @@ -1078,7 +1167,7 @@ " offshore_regions_gpd,\n", " total_clusters=10,\n", " onshore_ratio=0.8,\n", - " separate_onshore_offshore=True\n", + " separate_onshore_offshore=True,\n", ")" ] }, @@ -1089,54 +1178,86 @@ "metadata": {}, "outputs": [], "source": [ - "def validate_clustering(cluster_metadata, clusters_xr, tech_profiles_nc, onshore_regions_gpd, offshore_regions_gpd):\n", + "def validate_clustering(\n", + " cluster_metadata,\n", + " clusters_xr,\n", + " tech_profiles_nc,\n", + " onshore_regions_gpd,\n", + " offshore_regions_gpd,\n", + "):\n", " \"\"\"Validate: Compare raw vs clustered potentials (ISO3 codes only).\"\"\"\n", " import pandas as pd\n", " import numpy as np\n", - " \n", + "\n", " # Raw potentials - sum from source regions before clustering\n", - " all_regions = pd.concat([\n", - " onshore_regions_gpd.assign(type='onshore'),\n", - " offshore_regions_gpd.assign(type='offshore')\n", - " ], ignore_index=True)\n", - " \n", + " all_regions = pd.concat(\n", + " [\n", + " onshore_regions_gpd.assign(type=\"onshore\"),\n", + " offshore_regions_gpd.assign(type=\"offshore\"),\n", + " ],\n", + " ignore_index=True,\n", + " )\n", + "\n", " techs = list(tech_profiles_nc.keys())\n", - " \n", + "\n", " raw_data = []\n", " for _, r in all_regions.iterrows():\n", " lon, lat = r.geometry.centroid.x, r.geometry.centroid.y\n", - " bus = r['name'].replace('OFF_', '') if r['type'] == 'offshore' else r['name']\n", - " \n", + " bus = r[\"name\"].replace(\"OFF_\", \"\") if r[\"type\"] == \"offshore\" else r[\"name\"]\n", + "\n", " for tech in techs:\n", " ds = tech_profiles_nc[tech]\n", " if str(bus) not in [str(b) for b in ds.bus.values]:\n", " continue\n", " x_idx = np.argmin(np.abs(ds.x.values - lon))\n", " y_idx = np.argmin(np.abs(ds.y.values - lat))\n", - " raw_data.append({'iso3': r['country'], 'tech': tech, 'value': ds['potential'].values[y_idx, x_idx]})\n", - " \n", + " raw_data.append(\n", + " {\n", + " \"iso3\": r[\"country\"],\n", + " \"tech\": tech,\n", + " \"value\": ds[\"potential\"].values[y_idx, x_idx],\n", + " }\n", + " )\n", + "\n", " # Raw totals per country-technology (should be preserved exactly)\n", - " raw_tots = pd.DataFrame(raw_data).groupby(['iso3', 'tech'])['value'].sum()\n", - " \n", + " raw_tots = pd.DataFrame(raw_data).groupby([\"iso3\", \"tech\"])[\"value\"].sum()\n", + "\n", " # Clustered potentials totals per country-technology (renewable_potential = exact sum, no loss)\n", - " cluster_tots = cluster_metadata.groupby(['iso3', 'technology'])['renewable_potential'].sum()\n", - " \n", + " cluster_tots = cluster_metadata.groupby([\"iso3\", \"technology\"])[\n", + " \"renewable_potential\"\n", + " ].sum()\n", + "\n", " # Compare - should be identical (no energy loss during clustering)\n", " all_keys = set(raw_tots.index) | set(cluster_tots.index)\n", " comp_data = []\n", " for iso3, tech in sorted(all_keys):\n", " r_val, c_val = raw_tots.get((iso3, tech), 0), cluster_tots.get((iso3, tech), 0)\n", - " comp_data.append({'iso3': iso3, 'tech': tech, 'raw': r_val, 'clustered': c_val, \n", - " 'diff': c_val - r_val, 'match': '✓' if abs(c_val - r_val) < 0.01 else '✗'})\n", + " comp_data.append(\n", + " {\n", + " \"iso3\": iso3,\n", + " \"tech\": tech,\n", + " \"raw\": r_val,\n", + " \"clustered\": c_val,\n", + " \"diff\": c_val - r_val,\n", + " \"match\": \"✓\" if abs(c_val - r_val) < 0.01 else \"✗\",\n", + " }\n", + " )\n", " comp = pd.DataFrame(comp_data) if comp_data else pd.DataFrame()\n", - " \n", + "\n", " # Report validation results\n", - " all_match = (comp['diff'].abs() < 0.01).all() if len(comp) > 0 else True\n", - " max_diff = comp['diff'].abs().max() if len(comp) > 0 else 0\n", - " matches = (comp['match'] == '✓').sum() if len(comp) > 0 else 0\n", - " print(f\"\\n[VALIDATION] {len(comp)} entries | {matches} perfect | Max diff: {max_diff:.4f} GW | {'✓ PASS' if all_match else '✗ FAIL'}\\n\")\n", - " \n", - " return {'raw': raw_tots, 'clustered': cluster_tots, 'comparison': comp, 'valid': all_match}\n" + " all_match = (comp[\"diff\"].abs() < 0.01).all() if len(comp) > 0 else True\n", + " max_diff = comp[\"diff\"].abs().max() if len(comp) > 0 else 0\n", + " matches = (comp[\"match\"] == \"✓\").sum() if len(comp) > 0 else 0\n", + " print(\n", + " f\"\\n[VALIDATION] {len(comp)} entries | {matches} perfect | Max diff: {max_diff:.4f} GW | {'✓ PASS' if all_match else '✗ FAIL'}\\n\"\n", + " )\n", + "\n", + " return {\n", + " \"raw\": raw_tots,\n", + " \"clustered\": cluster_tots,\n", + " \"comparison\": comp,\n", + " \"valid\": all_match,\n", + " }" ] }, { @@ -1162,8 +1283,8 @@ " clusters_xr,\n", " tech_profiles_nc,\n", " onshore_regions_gpd,\n", - " offshore_regions_gpd\n", - ")\n" + " offshore_regions_gpd,\n", + ")" ] }, { @@ -1182,10 +1303,15 @@ } ], "source": [ - "def save_clusters(clusters_xr, cluster_metadata, output_dir=\"data\", filename_prefix=\"renewable_clusters\"):\n", + "def save_clusters(\n", + " clusters_xr,\n", + " cluster_metadata,\n", + " output_dir=\"data\",\n", + " filename_prefix=\"renewable_clusters\",\n", + "):\n", " \"\"\"\n", " Save clustered renewable potential data to NetCDF and metadata to CSV.\n", - " \n", + "\n", " Parameters:\n", " -----------\n", " clusters_xr : xr.Dataset\n", @@ -1196,7 +1322,7 @@ " Output directory (created if doesn't exist)\n", " filename_prefix : str\n", " Prefix for output files\n", - " \n", + "\n", " Returns:\n", " --------\n", " paths : dict\n", @@ -1204,27 +1330,28 @@ " \"\"\"\n", " import os\n", " from datetime import datetime\n", - " \n", + "\n", " # Create output directory if needed\n", " os.makedirs(output_dir, exist_ok=True)\n", - " \n", + "\n", " # Generate timestamp-based filenames\n", " timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", " xr_path = os.path.join(output_dir, f\"{filename_prefix}_{timestamp}.nc\")\n", " meta_path = os.path.join(output_dir, f\"{filename_prefix}_metadata_{timestamp}.csv\")\n", - " \n", + "\n", " # Save xarray to NetCDF\n", " clusters_xr.to_netcdf(xr_path)\n", " logging.info(f\"✓ Saved clustered potentials: {xr_path}\")\n", - " \n", + "\n", " # Save metadata to CSV\n", " cluster_metadata.to_csv(meta_path, index=False)\n", " logging.info(f\"✓ Saved cluster metadata: {meta_path}\")\n", - " \n", - " return {'xarray': xr_path, 'metadata': meta_path}\n", + "\n", + " return {\"xarray\": xr_path, \"metadata\": meta_path}\n", + "\n", "\n", "# Save the results\n", - "save_paths = save_clusters(clusters_xr, cluster_metadata, output_dir=\"data\")\n" + "save_paths = save_clusters(clusters_xr, cluster_metadata, output_dir=\"data\")" ] } ], diff --git a/workflow/notebooks/validation.ipynb b/workflow/notebooks/validation.ipynb index 72959b6..4ae3ee6 100644 --- a/workflow/notebooks/validation.ipynb +++ b/workflow/notebooks/validation.ipynb @@ -19,7 +19,9 @@ "metadata": {}, "outputs": [], "source": [ - "n = pypsa.Network(\"../../results/cost_year~2030/transport_cost~steel_r_iron_r/demand~1/product~steel/network.nc\")" + "n = pypsa.Network(\n", + " \"../../results/cost_year~2030/transport_cost~steel_r_iron_r/demand~1/product~steel/network.nc\"\n", + ")" ] }, { @@ -37,7 +39,7 @@ "metadata": {}, "outputs": [], "source": [ - "n.links[n.links.carrier == \"shipping_steel\"].marginal_cost /2000" + "n.links[n.links.carrier == \"shipping_steel\"].marginal_cost / 2000" ] }, { @@ -135,7 +137,9 @@ "outputs": [], "source": [ "# Most expensive supply of steel\n", - "n.links[n.links.carrier == \"steel\"].marginal_cost.sort_values() *1.59 + (98* 1.59) #.describe()*1.59" + "n.links[n.links.carrier == \"steel\"].marginal_cost.sort_values() * 1.59 + (\n", + " 98 * 1.59\n", + ") # .describe()*1.59" ] }, { @@ -257,7 +261,9 @@ "metadata": {}, "outputs": [], "source": [ - "n.statistics.energy_balance(comps=[\"Link\"], groupby=[\"bus\", \"carrier\"]).div(1e6).loc[:,:,\"shipping_iron_ore\"]" + "n.statistics.energy_balance(comps=[\"Link\"], groupby=[\"bus\", \"carrier\"]).div(1e6).loc[\n", + " :, :, \"shipping_iron_ore\"\n", + "]" ] }, { @@ -285,7 +291,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"The total cost is {n.objective/1e9:.2f} B EUR\")" + "print(f\"The total cost is {n.objective / 1e9:.2f} B EUR\")" ] }, { @@ -341,7 +347,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Load config from YAML\n", "with open(\"../../config/config.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", @@ -371,8 +376,10 @@ "import cartopy.crs as ccrs\n", "import yaml\n", "\n", - "def plot_trade_map(n, supply, demand, trade, product=\"steel\",\n", - " alpha_supply=0.7, alpha_demand=1):\n", + "\n", + "def plot_trade_map(\n", + " n, supply, demand, trade, product=\"steel\", alpha_supply=0.7, alpha_demand=1\n", + "):\n", "\n", " with open(\"../../config/config.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", @@ -382,11 +389,11 @@ "\n", " supply_color = colors.get(f\"{product}_supply\", \"black\")\n", " demand_color = colors.get(f\"{product}_demand\", \"lightsteelblue\")\n", - " link_colors = colors.get(f\"{product}_link\", \"gray\")\n", + " link_colors = colors.get(f\"{product}_link\", \"gray\")\n", "\n", " fig = plt.figure(figsize=(10, 5))\n", " ax = plt.axes(projection=ccrs.PlateCarree())\n", - " \n", + "\n", " # ax.coastlines()\n", "\n", " # Plot demand\n", @@ -411,15 +418,29 @@ " )\n", "\n", " ax.set_extent([-180, 180, -60, 85], crs=ccrs.PlateCarree())\n", - " # ax.set_global() \n", + " # ax.set_global()\n", "\n", " # Legend\n", " legend_elements = [\n", " plt.Line2D([0], [0], color=link_colors, label=\"shipping\"),\n", - " plt.Line2D([0], [0], marker=\"o\", color=\"white\", label=\"Demand\",\n", - " markerfacecolor=demand_color, markersize=10),\n", - " plt.Line2D([0], [0], marker=\"o\", color=\"white\", label=\"Supply\",\n", - " markerfacecolor=supply_color, markersize=10),\n", + " plt.Line2D(\n", + " [0],\n", + " [0],\n", + " marker=\"o\",\n", + " color=\"white\",\n", + " label=\"Demand\",\n", + " markerfacecolor=demand_color,\n", + " markersize=10,\n", + " ),\n", + " plt.Line2D(\n", + " [0],\n", + " [0],\n", + " marker=\"o\",\n", + " color=\"white\",\n", + " label=\"Supply\",\n", + " markerfacecolor=supply_color,\n", + " markersize=10,\n", + " ),\n", " ]\n", "\n", " fig.legend(\n", @@ -429,7 +450,7 @@ " bbox_to_anchor=(0.22, 0.28),\n", " )\n", "\n", - " return fig\n" + " return fig" ] }, { @@ -440,7 +461,11 @@ "outputs": [], "source": [ "# Supply and demand\n", - "steel_gen = n.statistics.supply(comps=[\"Link\"], groupby=[\"bus\", \"carrier\"]).loc[:, :, \"steel\"].droplevel(0)\n", + "steel_gen = (\n", + " n.statistics.supply(comps=[\"Link\"], groupby=[\"bus\", \"carrier\"])\n", + " .loc[:, :, \"steel\"]\n", + " .droplevel(0)\n", + ")\n", "steel_load = n.loads.groupby(\"bus\").p_set.sum()\n", "steel_trade = n.links[n.links.carrier == \"shipping_steel\"].p_nom_opt.astype(int)\n", "\n", @@ -451,7 +476,7 @@ " demand=steel_load,\n", " trade=steel_trade,\n", " product=\"steel\",\n", - " alpha_supply=0.7\n", + " alpha_supply=0.7,\n", ")" ] }, @@ -463,8 +488,16 @@ "outputs": [], "source": [ "# Supply and demand\n", - "iron_ore_gen = n.statistics.supply(comps=[\"Generator\"], groupby=[\"bus\", \"carrier\"]).loc[:, :, \"iron_ore\"].droplevel(0)\n", - "iron_ore_load = n.statistics.withdrawal(comps=[\"Link\"], groupby=[\"bus\", \"carrier\"]).loc[:, :, \"steel\"].droplevel(0)\n", + "iron_ore_gen = (\n", + " n.statistics.supply(comps=[\"Generator\"], groupby=[\"bus\", \"carrier\"])\n", + " .loc[:, :, \"iron_ore\"]\n", + " .droplevel(0)\n", + ")\n", + "iron_ore_load = (\n", + " n.statistics.withdrawal(comps=[\"Link\"], groupby=[\"bus\", \"carrier\"])\n", + " .loc[:, :, \"steel\"]\n", + " .droplevel(0)\n", + ")\n", "iron_ore_trade = n.links[n.links.carrier == \"shipping_iron_ore\"].p_nom_opt.astype(int)\n", "\n", "# Shipping color from carrier\n", @@ -477,8 +510,8 @@ " demand=iron_ore_load,\n", " trade=iron_ore_trade,\n", " product=\"iron_ore\",\n", - " alpha_supply=0.5\n", - ")\n" + " alpha_supply=0.5,\n", + ")" ] } ], diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 860b1fc..14937e8 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -43,7 +43,7 @@ # Add file handler (writes to ../logs/calculate_lcox.log) file_handler = logging.FileHandler(log_dir / "calculate_lcox.log") file_handler.setLevel(logging.DEBUG) -file_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') +file_formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") file_handler.setFormatter(file_formatter) logger.addHandler(file_handler) @@ -57,7 +57,7 @@ def load_demands_for_region(region, config): Returns dict with: - local_el_demand_mwh: MWh/year (for renewable constraint calculation) - + Note: steel_demand_mt is passed directly from Snakemake params, not loaded from file """ # Load local electricity demand (for renewable constraint calculation) @@ -65,8 +65,12 @@ def load_demands_for_region(region, config): local_df = pd.read_csv(snakemake.input.local_demand) region_mask = local_df["region"].str.lower() == region.lower() if region_mask.any(): - total_energy_mwh = local_df[region_mask]["demand"].values[0] # MWh final energy - el_share = local_df[region_mask]["el_share"].values[0] / 100 # Convert % to fraction + total_energy_mwh = local_df[region_mask]["demand"].values[ + 0 + ] # MWh final energy + el_share = ( + local_df[region_mask]["el_share"].values[0] / 100 + ) # Convert % to fraction local_el_demand_mwh = total_energy_mwh * el_share # Apply electricity share else: logger.warning(f"Region '{region}' not found in local demand data") @@ -94,7 +98,7 @@ def apply_renewable_constraint(network, local_el_demand_mwh, config): 3. Accumulate capacity from highest CF until >= local_demand 4. Block these generators for local demand (set p_nom_max=0) 5. Remaining renewables available for steel production - + The load determines electrolyzer operation; no capacity constraint applied. Returns: audit dict with capacity breakdown and blocked generators @@ -161,7 +165,6 @@ def apply_renewable_constraint(network, local_el_demand_mwh, config): capacity_accumulated = 0 # Track cumulative capacity factor contribution generators_for_local = [] - for gen_info in gen_cf_data: if capacity_accumulated >= local_el_demand_mwh: @@ -271,7 +274,7 @@ def add_loads_to_network(network, product, demands): if bus_name not in network.buses.index: raise ValueError(f"Bus '{bus_name}' not found in network") - + # Add constant hourly load to the bus load_name = f"{product}_demand" if product == "h2": @@ -291,7 +294,9 @@ def add_loads_to_network(network, product, demands): if "hbi_storage" in network.stores.index: hbi_e_initial = 24 * hourly_demand_t # 24 hours of buffer network.stores.at["hbi_storage", "e_initial"] = hbi_e_initial - logger.info(f"Set HBI storage e_initial to {hbi_e_initial:.2f} t (24h buffer for {hourly_demand_t:.4f} t/h demand)") + logger.info( + f"Set HBI storage e_initial to {hbi_e_initial:.2f} t (24h buffer for {hourly_demand_t:.4f} t/h demand)" + ) logger.info( f"Added hourly load for {product}: {load_name} = {p_set:.4f} {unit_str} (constant all hours)" @@ -300,35 +305,45 @@ def add_loads_to_network(network, product, demands): def inspect_network(network, product): """Print network structure for debugging infeasibility.""" - logger.info("\n" + "="*80) + logger.info("\n" + "=" * 80) logger.info("NETWORK INSPECTION - Connectivity & Status") - logger.info("="*80) - + logger.info("=" * 80) + logger.info(f"Buses ({len(network.buses)}): {list(network.buses.index)}") logger.info(f"\nLoads ({len(network.loads)}):") for load_name, load_row in network.loads.iterrows(): - logger.info(f" {load_name:30s} -> bus={load_row['bus']:15s} p_set={load_row['p_set']:.1f}") - + logger.info( + f" {load_name:30s} -> bus={load_row['bus']:15s} p_set={load_row['p_set']:.1f}" + ) + logger.info(f"\nLinks ({len(network.links)}):") for link_name, link_row in network.links.iterrows(): - logger.info(f" {link_name:15s}: {link_row['bus0']:12s} -> {link_row['bus1']:12s} p_nom_ext={link_row['p_nom_extendable']} p_nom_max={link_row['p_nom_max']:.0e}") - + logger.info( + f" {link_name:15s}: {link_row['bus0']:12s} -> {link_row['bus1']:12s} p_nom_ext={link_row['p_nom_extendable']} p_nom_max={link_row['p_nom_max']:.0e}" + ) + logger.info(f"\nStores ({len(network.stores)}):") for store_name, store_row in network.stores.iterrows(): logger.info(f" {store_name:20s} -> {store_row['bus']:15s}") - + # Check isolated buses all_buses = set(network.buses.index) - connected = set(network.generators['bus'].unique()) | set(network.links['bus0'].unique()) | set(network.links['bus1'].unique()) | set(network.loads['bus'].unique()) | set(network.stores['bus'].unique()) + connected = ( + set(network.generators["bus"].unique()) + | set(network.links["bus0"].unique()) + | set(network.links["bus1"].unique()) + | set(network.loads["bus"].unique()) + | set(network.stores["bus"].unique()) + ) isolated = all_buses - connected if isolated: logger.warning(f"⚠ Isolated buses: {isolated}") - logger.info("="*80 + "\n") + logger.info("=" * 80 + "\n") def _convert_arrow_strings(network): """Convert ArrowStringArray columns/indices to regular object dtype. - + Workaround for PyPSA incompatibility with pandas ArrowStringArray. Uses PyPSA's component structure to properly access all dataframes. Based on: https://github.com/PyPSA/PyPSA/issues/1585 @@ -354,15 +369,15 @@ def _convert_arrow_strings(network): def _convert_bool_attrs_to_int(network): """Convert boolean attributes to integers for netCDF4 compatibility. - + netCDF4 does not support boolean types for attributes. Convert True -> 1, False -> 0. """ # PyPSA uses either .attrs or internal _attrs depending on version attr_container = None - if hasattr(network, 'attrs'): + if hasattr(network, "attrs"): attr_container = network.attrs - elif hasattr(network, '_attrs'): + elif hasattr(network, "_attrs"): attr_container = network._attrs if attr_container is None: @@ -378,10 +393,12 @@ def _compute_infeasibility_diagnostics(network, output_dir): """Compute infeasibility diagnostics for an infeasible network and write IIS if available.""" # Attempt to run linopy infeasibility diagnostics - if hasattr(network.model, 'compute_infeasibilities'): + if hasattr(network.model, "compute_infeasibilities"): try: infeasible_labels = network.model.compute_infeasibilities() - logger.info(f"Linopy compute_infeasibilities() returned {len(infeasible_labels)} entries") + logger.info( + f"Linopy compute_infeasibilities() returned {len(infeasible_labels)} entries" + ) except Exception as e: logger.warning(f"Could not compute linopy infeasibilities: {e}") infeasible_labels = None @@ -391,19 +408,21 @@ def _compute_infeasibility_diagnostics(network, output_dir): # Write IIS from backend Gurobi model if available gurobi_model = None - if hasattr(network.model, 'backend') and hasattr(network.model.backend, 'model'): + if hasattr(network.model, "backend") and hasattr(network.model.backend, "model"): gurobi_model = network.model.backend.model if gurobi_model is not None: try: - if hasattr(gurobi_model, 'computeIIS'): + if hasattr(gurobi_model, "computeIIS"): try: gurobi_model.computeIIS() logger.info("Gurobi IIS computed") except Exception as iis_err: logger.warning(f"Could not compute IIS on Gurobi model: {iis_err}") - model_ilp_path = os.path.join(output_dir, f"infeasibility_{network.name}.ilp") + model_ilp_path = os.path.join( + output_dir, f"infeasibility_{network.name}.ilp" + ) gurobi_model.write(model_ilp_path) logger.info(f"IIS .ilp written to: {model_ilp_path}") @@ -420,15 +439,16 @@ def _compute_infeasibility_diagnostics(network, output_dir): # Write text infeasibility report if available if infeasible_labels: - if hasattr(network.model, 'format_infeasibilities'): + if hasattr(network.model, "format_infeasibilities"): try: infeas_report = network.model.format_infeasibilities() except Exception as e: infeas_report = f"format_infeasibilities failed: {e}" - elif hasattr(network.model, 'print_infeasibilities'): + elif hasattr(network.model, "print_infeasibilities"): try: import io import sys + _buf = io.StringIO() _old_stdout = sys.stdout sys.stdout = _buf @@ -443,7 +463,7 @@ def _compute_infeasibility_diagnostics(network, output_dir): infeas_report = "Infeasible constraints identified, but format_infeasibilities() and print_infeasibilities() are unavailable." infeas_path = os.path.join(output_dir, f"infeasibilities_{network.name}.txt") - with open(infeas_path, 'w', encoding='utf-8') as f: + with open(infeas_path, "w", encoding="utf-8") as f: f.write(f"Infeasible constraints for network {network.name}:\n") f.write("=" * 80 + "\n\n") f.write(infeas_report) @@ -460,7 +480,7 @@ def solve_network(network, config): """ # Convert arrow strings to regular strings before optimization _convert_arrow_strings(network) - + solver_name = config.get("solver", {}).get("name", "glpk") solver_options = config.get("solver_options", {}).get( config.get("solver", {}).get("options", "default"), {} @@ -468,13 +488,13 @@ def solve_network(network, config): logger.info(f"Solving network with {solver_name}...") logger.info(f"Solver options: {solver_options}") - + # Add output logging for Gurobi to see what's happening if solver_name.lower() == "gurobi" and "OutputFlag" not in solver_options: solver_options = {**solver_options, "OutputFlag": 1} # Enable Gurobi output # Solve without constraint injection (hourly loads already in network) - try: + try: status = network.optimize( network.snapshots, solver_name=solver_name, @@ -483,15 +503,17 @@ def solve_network(network, config): ) logger.info(f"Optimization status: {status}") - + if status != 0: logger.warning(f"Non-optimal status ({status})") if network.objective is not None: logger.info(f" Objective value: {network.objective}") else: logger.warning(" Objective is None (no feasible solution found)") - logger.warning("Model is infeasible - check network structure and constraints") - + logger.warning( + "Model is infeasible - check network structure and constraints" + ) + # Use linopy's built-in infeasibility diagnostics if solver_name.lower() == "gurobi" and snakemake.params.compute_iis: try: @@ -531,7 +553,7 @@ def extract_lcox(network, product, demands): else: load_col = "load [per h]" cost_col = "lcox [EUR/unit]" - + results_df = pd.DataFrame( columns=[ "demand [t]", @@ -556,7 +578,9 @@ def extract_lcox(network, product, demands): obj_value, lcox, ] - logger.info(f"LCOX calculated: {lcox:.2f} {cost_col.split('[')[1].split(']')[0]}") + logger.info( + f"LCOX calculated: {lcox:.2f} {cost_col.split('[')[1].split(']')[0]}" + ) except Exception as e: logger.error(f"Optimization infeasible or failed: {e}") @@ -571,6 +595,7 @@ def extract_lcox(network, product, demands): return results_df + if __name__ == "__main__": if "snakemake" not in globals(): from _helpers import mock_snakemake @@ -607,16 +632,16 @@ def extract_lcox(network, product, demands): region=snakemake.wildcards.region, config=snakemake.config, ) - + logger.info( f"Local electricity demand: {demands['local_el_demand_mwh']:.1f} MWh/year" ) # ==================== PROCESS SINGLE DEMAND LEVEL ==================== electricity_per_steel_t = snakemake.config.get("electricity_steel_ratio", 5.25) - + logger.info(f"Processing: {steel_demand_mt} Mt/year") - + # ==================== NETWORK SETUP ==================== # Create a copy of base network network = base_network.copy() @@ -627,14 +652,16 @@ def extract_lcox(network, product, demands): # Calculate electricity needed for this demand level scaled_steel_demand_mwh_per_h = steel_demand_mt * electricity_per_steel_t / 8760 - + logger.info(f"Steel demand: {steel_demand_mt:.1f} Mt/year") - logger.info(f"Electricity required: {scaled_steel_demand_mwh_per_h * 8760:.1f} MWh/year") + logger.info( + f"Electricity required: {scaled_steel_demand_mwh_per_h * 8760:.1f} MWh/year" + ) # Create scaled demands dict for this demand level scaled_demands = demands.copy() - scaled_demands['steel_demand_mt'] = steel_demand_mt - scaled_demands['steel_demand_mwh_per_h'] = scaled_steel_demand_mwh_per_h + scaled_demands["steel_demand_mt"] = steel_demand_mt + scaled_demands["steel_demand_mwh_per_h"] = scaled_steel_demand_mwh_per_h # Block highest-CF renewables for local demand (priority mechanism) logger.info("Applying renewable priority constraint...") @@ -652,15 +679,19 @@ def extract_lcox(network, product, demands): ) # Debug: Print network structure - logger.info("\n--- Network Structure for Demand Level {:.1f} Mt/year ---".format(steel_demand_mt)) + logger.info( + "\n--- Network Structure for Demand Level {:.1f} Mt/year ---".format( + steel_demand_mt + ) + ) logger.info(f"Buses: {list(network.buses.index)}") logger.info(f"Generators: {len(network.generators)} total") for gen in network.generators.index: - p_max = network.generators.at[gen, 'p_nom_max'] + p_max = network.generators.at[gen, "p_nom_max"] logger.info(f" {gen}: p_nom_max={p_max:.1f} MW") logger.info(f"Links: {list(network.links.index)}") for link in network.links.index: - p_nominal = network.links.at[link, 'p_nom'] + p_nominal = network.links.at[link, "p_nom"] logger.info(f" {link}: p_nom={p_nominal:.1f} MW") logger.info(f"Stores: {list(network.stores.index)}") logger.info(f"Loads: {list(network.loads.index)}") @@ -671,13 +702,19 @@ def extract_lcox(network, product, demands): inspect_network(network, snakemake.wildcards.product) # Debug inspection try: solve_network(network, snakemake.config) - optimization_status = "optimal" if network.objective is not None and not np.isnan(network.objective) else "infeasible" + optimization_status = ( + "optimal" + if network.objective is not None and not np.isnan(network.objective) + else "infeasible" + ) except Exception as e: logger.warning(f"Solver error for steel demand {steel_demand_mt} Mt/year: {e}") optimization_status = "error" if optimization_status != "optimal": - logger.warning(f"Optimization {optimization_status} for steel demand {steel_demand_mt} Mt/year - returning NaN values") + logger.warning( + f"Optimization {optimization_status} for steel demand {steel_demand_mt} Mt/year - returning NaN values" + ) # Extract LCOX results logger.info("Extracting results...") @@ -686,15 +723,15 @@ def extract_lcox(network, product, demands): product=snakemake.wildcards.product, demands=scaled_demands, ) - + # Save results for this demand level result_file = snakemake.output.results network_file = snakemake.output.network - + logger.info("Saving results...") results_df.to_csv(result_file, index=False) logger.info(f"Results saved: {result_file}") - + # Save network only if optimization succeeded if optimization_status == "optimal": try: @@ -704,12 +741,16 @@ def extract_lcox(network, product, demands): except Exception as e: logger.warning(f"Could not save network: {e}") else: - logger.warning(f"Skipping network export due to solver status: {optimization_status}") - + logger.warning( + f"Skipping network export due to solver status: {optimization_status}" + ) + logger.info("=" * 70) - + if optimization_status == "optimal": logger.info("Demand level completed successfully!") else: - logger.warning(f"Demand level completed with solver status: {optimization_status}") + logger.warning( + f"Demand level completed with solver status: {optimization_status}" + ) logger.info("=" * 70) diff --git a/workflow/scripts/model_lcoh.py b/workflow/scripts/model_lcoh.py index 2877101..53d95d1 100644 --- a/workflow/scripts/model_lcoh.py +++ b/workflow/scripts/model_lcoh.py @@ -205,7 +205,6 @@ def save_lcoh(solved_network): if __name__ == "__main__": - if "snakemake" not in globals(): from _helpers import mock_snakemake diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index d7d23cf..12f65eb 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -46,7 +46,6 @@ def building_model( # for each region we are creating a bus with all the potentials and load for r in range(0, len(supply_curves_interone)): - # getting the supply curves for one region for different intermediates region_file_interone = supply_curves_interone[r] region_file_intertwo = supply_curves_intertwo[r] @@ -168,9 +167,7 @@ def building_model( ) elif final != "hydrogen": - if interone == intertwo: - # Single link. bus0: iron ore, bus1: final product # Add link for first intermediate ("interone") n.add( @@ -198,7 +195,6 @@ def building_model( ) elif interone != intertwo: - # two links. First link: bus0=iron ore, bus1: interone, supply_curve: region_data_interone # second link: bus0=interone, bus1=final product, supply_curve: region_data_intertwo (no ratios for efficiency and marginal cost needed here!) @@ -628,6 +624,7 @@ def apply_cost_penalty(n, cost_penalty): return n + def normalize_regions(regions, carrier): """Ensure regions are lists and suffixed with _{carrier}.""" if regions is None: @@ -643,6 +640,7 @@ def normalize_regions(regions, carrier): normalized.append(f"{r}_{carrier}") return normalized + def solve_network(n, mga=None): solver_name = snakemake.config["solver"]["name"] @@ -653,7 +651,6 @@ def solve_network(n, mga=None): if mga is None: pass else: - tsc = ( pd.concat([n.statistics.capex(), n.statistics.opex()], axis=1) .sum(axis=1) @@ -698,7 +695,7 @@ def solve_network(n, mga=None): mga_cost = tsc.sum() print(f"Optimal cost: {optimal_cost:.2f} B€") print( - f"MGA cost: {mga_cost:.2f} B€, allowed cost increase: {optimal_cost*(1+slack):.2f} B€" + f"MGA cost: {mga_cost:.2f} B€, allowed cost increase: {optimal_cost * (1 + slack):.2f} B€" ) return n @@ -750,14 +747,12 @@ def solve_network(n, mga=None): iron_ore = iron_ore[iron_ore["region"].isin(regions)].reset_index(drop=True) if final == "steel": - demands = pd.read_csv(snakemake.input.steel_demand, header=0) demands.rename(columns={"SteelProductionMt": "demand"}, inplace=True) demands["demand"] = demands["demand"] * 1e6 # Mt to t unit = "t" elif final == "hydrogen": - demands = pd.read_csv(snakemake.input.demand, header=0) unit = "MWh" diff --git a/workflow/scripts/model_trade_singlestage.py b/workflow/scripts/model_trade_singlestage.py index 672b523..4901f8c 100644 --- a/workflow/scripts/model_trade_singlestage.py +++ b/workflow/scripts/model_trade_singlestage.py @@ -21,7 +21,6 @@ def building_model(supply_curves, demands, bus_location, product): # for each region we are creating a bus with all the potentials and load for r in range(0, len(supply_curves)): - # getting the supply curve for one region region_file = supply_curves[r] region_data = pd.read_csv(region_file, header=0) @@ -49,7 +48,7 @@ def building_model(supply_curves, demands, bus_location, product): snakemake.wildcards["demand"] ) print( - f"Load set via snakemake.wildcard to {float(snakemake.wildcards['demand'])*100}% of regional final energy demand." + f"Load set via snakemake.wildcard to {float(snakemake.wildcards['demand']) * 100}% of regional final energy demand." ) network.add( @@ -315,7 +314,6 @@ def plot_trade_network(n): supply_curves = snakemake.input.supply_curves bus_locations = pd.read_csv(snakemake.input.bus_locations, header=0) if product == "steel": - demands = pd.read_csv(snakemake.input.steel_demand, header=0) demands.rename(columns={"SteelProductionMt": "demand"}, inplace=True) demands["demand"] = demands["demand"] * 1e6 # Mt to t @@ -323,7 +321,6 @@ def plot_trade_network(n): cost_descriptor = "LCOS" elif product == "hydrogen": - demands = pd.read_csv(snakemake.input.demand, header=0) unit = "MWh" cost_descriptor = "LCOH" diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 224df7e..e1091ca 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -184,7 +184,9 @@ def add_renewable_generators( db_tech_name = tech_database_map.get(technology, technology) tech_params = td.get_tech(tech_costs, db_tech_name) - overnight_cost = (td.get_tech_param(tech_params, "investment", 0) * 1000) # EUR/kW → EUR/MW + overnight_cost = ( + td.get_tech_param(tech_params, "investment", 0) * 1000 + ) # EUR/kW → EUR/MW lifetime = td.get_tech_param(tech_params, "lifetime", 20) fom_pct = td.get_tech_param(tech_params, "FOM", 0) fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 @@ -243,7 +245,9 @@ def _apply_discount_rate_to_components( # Apply to generators for gen_name, gen_row in network.generators.iterrows(): - has_cost = pd.notna(gen_row.get("overnight_cost")) and gen_row["overnight_cost"] > 0 + has_cost = ( + pd.notna(gen_row.get("overnight_cost")) and gen_row["overnight_cost"] > 0 + ) if has_cost: network.generators.at[gen_name, "discount_rate"] = discount_rate From 75aa4b3c1e0098add048bc679fc9c710d0d04fd2 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 2 Apr 2026 17:42:49 +0200 Subject: [PATCH 027/216] feat: add runnable version of supply curve generation! --- config/config.yaml | 22 +- workflow/Snakefile | 21 +- workflow/notebooks/prepare_potentials.ipynb | 918 +++++++++++++++++-- workflow/scripts/build_x_supply_chain.py | 26 +- workflow/scripts/calculate_lcox.py | 30 +- workflow/scripts/prepare_regional_network.py | 21 +- 6 files changed, 903 insertions(+), 135 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index df6cb29..47fd3bb 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -6,8 +6,7 @@ enable: # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [10, 50, 100] # Mt/year -compute_iis: false # Set to false to skip expensive IIS computation for infeasible models +steel_demand_levels: [10, 200, 1000] # Mt/year hydrogen_storage_cost: False iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted @@ -76,27 +75,10 @@ plot: bus_size: 1.0e-7 link_width: 1.5e-8 - colors: - steel: 'grey' - steel_shipping: 'darkgrey' - hydrogen: 'magenta' - iron_ore: 'brown' - iron_ore_shipping: "#6A000E" - hbi: 'darkred' - hbi_shipping: 'firebrick' - steel_supply: 'lightsteelblue' - steel_demand: 'seagreen' - steel_link: 'skyblue' - iron_ore_supply: 'black' - iron_ore_demand: 'lightsteelblue' - iron_ore_link: "#6A000E" - hbi_demand: 'green' - hbi_supply: 'darkred' - hbi_link: 'firebrick' - solver: name: gurobi options: gurobi-default + compute_iis: False # Set to false to skip expensive IIS computation for infeasible models solver_options: highs-default: # refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/ diff --git a/workflow/Snakefile b/workflow/Snakefile index edb0d7a..f411318 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -87,21 +87,7 @@ rule build_steel_skeleton: # Prepare regional PyPSA network with renewable generators and product-specific cutoff -rule prepare_regional_network: - """ - Prepare regional PyPSA network with renewable generators and supply chain configuration. - - One-time setup per region × product combination: - - Load skeleton supply chain network (technology-agnostic) - - Add renewable generators for region (filtered by ISO3) - - Apply product-specific supply chain cutoff (h2/hbi/steel) - - Key Efficiency Gain: - - All renewable processing happens ONCE per region - - Output ("base_network") reused for all demand_factors in calculate_lcox - - Eliminates redundant loading/filtering in calculate_lcox - """ - +rule prepare_regional_network: message: "Preparing regional network for {wildcards.region} → {wildcards.product} " "(cost_year={wildcards.cost_year})" @@ -138,7 +124,7 @@ if config["enable"].get("run_supply_chain", True): "(steel_demand={wildcards.steel_demand_mt} Mt/year)." params: steel_demand_mt="{steel_demand_mt}", - compute_iis=config.get("compute_iis", False), + compute_iis=config.get("solver", {}).get("compute_iis", False), input: base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", local_demand="../data/un_enerdata_demand_2050_final.csv" @@ -165,12 +151,11 @@ if config["enable"].get("run_supply_curve", True): # Reference results from calculate_regional_lcox (uses steel_demand_mt from config) lco_product_data = lambda wildcards: expand( f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{steel_demand_mt}}.csv", - steel_demand_mt=config.get("steel_demand_levels", [10, 50, 100, 200]) + steel_demand_mt=config.get("steel_demand_levels") ), local_demand = "../data/un_enerdata_demand_2050_final.csv", steel_demand = "../resources/steel_production_clustered.csv", output: - # KEEP: Final supply curve input to model_trade (required) supply = "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", # [DELETION FLAG] Post-development: supply_nodemand CSVs are reference copies for comparison only # Keep during development for validation/debugging diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index da3326a..e51772c 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -77,9 +77,9 @@ "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", "Cannot find the ecCodes library\n", " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2480569425.py - Loaded onwind profiles\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2480569425.py - Loaded offwind-ac profiles\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2480569425.py - Loaded solar profiles\n" + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2480569425.py - Loaded onwind profiles\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2480569425.py - Loaded offwind-ac profiles\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2480569425.py - Loaded solar profiles\n" ] } ], @@ -658,17 +658,17 @@ " weight (bus) float64 8kB ...\n", " p_nom_max (bus) float64 8kB ...\n", " potential (y, x) float64 82kB ...\n", - " average_distance (bus) float64 8kB ...
    • profile
      (time, bus)
      float64
      ...
      [8838840 values with dtype=float64]
    • weight
      (bus)
      float64
      ...
      units :
      MW
      [1009 values with dtype=float64]
    • p_nom_max
      (bus)
      float64
      ...
      [1009 values with dtype=float64]
    • potential
      (y, x)
      float64
      ...
      [10218 values with dtype=float64]
    • average_distance
      (bus)
      float64
      ...
      [1009 values with dtype=float64]
    • " ], "text/plain": [ " Size: 71MB\n", @@ -720,10 +720,10 @@ "text": [ "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\pyogrio\\core.py:34: RuntimeWarning: Could not detect GDAL data files. Set GDAL_DATA environment variable to the correct path.\n", " _init_gdal_data()\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Onshore regions: 1009, mean area=445.4 km²\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Offshore regions: 195, mean area=9487.9 km²\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Deduplication: Offshore region names prefixed with 'OFF_' to avoid ID collisions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\2768513724.py - Conversion: Country codes converted from ISO2 to ISO3 for consistent downstream usage\n" + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2768513724.py - Onshore regions: 1009, mean area=445.4 km²\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2768513724.py - Offshore regions: 195, mean area=9487.9 km²\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2768513724.py - Deduplication: Offshore region names prefixed with 'OFF_' to avoid ID collisions\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2768513724.py - Conversion: Country codes converted from ISO2 to ISO3 for consistent downstream usage\n" ] } ], @@ -1022,6 +1022,9 @@ " \"lon\": lon,\n", " \"area_km2\": area_km2,\n", " \"renewable_potential\": renewable_potential, # Sum of raw potentials (MW)\n", + " \"p_nom_max\": renewable_potential / avg_cf\n", + " if avg_cf > 0\n", + " else 0, # Installed capacity to preserve potential with clustered CF (MW)\n", " \"avg_cf\": avg_cf, # Annual mean capacity factor (0-1)\n", " \"cf_timeseries\": cf_array, # Hourly profiles for 8760 hours\n", " }\n", @@ -1134,28 +1137,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - ✓ Cluster allocation valid: 8 onshore + 2 offshore = 10 total\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering 3 countries: ['IRL', 'NLD', 'PRT']\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Separate onshore/offshore: True\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - ⚠ Cluster allocation mismatch: 10 onshore + 1 offshore = 11, but user requested 10 total. Difference: 1 clusters.\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering 3 countries: ['IRL', 'NLD', 'PRT']\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Separate onshore/offshore: False\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", "IRL: 296 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 228 regions → 8 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 68 regions → 2 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering all: 296 regions → 10 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", "NLD: 346 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 291 regions → 8 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_01\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_02\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_07\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_08\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 55 regions → 2 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering all: 346 regions → 10 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_01\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_03\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_04\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_05\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_06\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", "PRT: 562 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 490 regions → 8 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster PRT_ON_02\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster PRT_ON_05\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 72 regions → 2 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering all: 562 regions → 10 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_01\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_02\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_03\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_05\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_08\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", "✓ Created 30 geographic clusters × 3 techs\n" ] } @@ -1166,8 +1170,8 @@ " onshore_regions_gpd,\n", " offshore_regions_gpd,\n", " total_clusters=10,\n", - " onshore_ratio=0.8,\n", - " separate_onshore_offshore=True,\n", + " onshore_ratio=1,\n", + " separate_onshore_offshore=False,\n", ")" ] }, @@ -1290,54 +1294,810 @@ { "cell_type": "code", "execution_count": 10, - "id": "2dcb83d4", + "id": "af0c085d", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\1196717265.py - ✓ Saved clustered potentials: data\\renewable_clusters_20260325_172014.nc\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\1196717265.py - ✓ Saved cluster metadata: data\\renewable_clusters_metadata_20260325_172014.csv\n" - ] - } - ], - "source": [ - "def save_clusters(\n", - " clusters_xr,\n", - " cluster_metadata,\n", - " output_dir=\"data\",\n", - " filename_prefix=\"renewable_clusters\",\n", - "):\n", - " \"\"\"\n", - " Save clustered renewable potential data to NetCDF and metadata to CSV.\n", - "\n", - " Parameters:\n", - " -----------\n", - " clusters_xr : xr.Dataset\n", - " Clustered potentials xarray dataset\n", - " cluster_metadata : pd.DataFrame\n", - " Cluster metadata DataFrame\n", - " output_dir : str\n", - " Output directory (created if doesn't exist)\n", - " filename_prefix : str\n", - " Prefix for output files\n", - "\n", - " Returns:\n", - " --------\n", - " paths : dict\n", - " Dictionary with keys 'xarray' and 'metadata' pointing to saved files\n", - " \"\"\"\n", - " import os\n", - " from datetime import datetime\n", - "\n", - " # Create output directory if needed\n", - " os.makedirs(output_dir, exist_ok=True)\n", - "\n", - " # Generate timestamp-based filenames\n", - " timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", - " xr_path = os.path.join(output_dir, f\"{filename_prefix}_{timestamp}.nc\")\n", - " meta_path = os.path.join(output_dir, f\"{filename_prefix}_metadata_{timestamp}.csv\")\n", + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 6MB\n",
+       "Dimensions:              (cluster: 30, technology: 3, hour: 8760)\n",
+       "Coordinates:\n",
+       "  * cluster              (cluster) <U6 720B 'IRL_01' 'IRL_02' ... 'PRT_10'\n",
+       "    iso3                 (cluster) <U3 360B 'IRL' 'IRL' 'IRL' ... 'PRT' 'PRT'\n",
+       "    lat                  (cluster) float64 240B 51.91 54.88 53.09 ... 40.72 38.7\n",
+       "    lon                  (cluster) float64 240B -8.816 -8.091 ... -8.477 -9.024\n",
+       "    area_km2             (cluster) float64 240B 6.599e+04 ... 1.653e+05\n",
+       "  * technology           (technology) <U10 120B 'onwind' 'offwind-ac' 'solar'\n",
+       "  * hour                 (hour) int64 70kB 0 1 2 3 4 ... 8756 8757 8758 8759\n",
+       "Data variables:\n",
+       "    renewable_potential  (cluster, technology) float64 720B 2.998e+04 ... 6.5...\n",
+       "    avg_cf               (cluster, technology) float64 720B 0.358 ... 0.1549\n",
+       "    capacity_factor      (cluster, technology, hour) float64 6MB 0.5761 ... 0.0\n",
+       "    p_nom_max            (cluster, technology) float64 720B 8.376e+04 ... 4.2...
    " + ], + "text/plain": [ + " Size: 6MB\n", + "Dimensions: (cluster: 30, technology: 3, hour: 8760)\n", + "Coordinates:\n", + " * cluster (cluster) pypsa.Networ if __name__ == "__main__": - # Handle Snakemake or direct invocation - try: - config = snakemake.config - tech_costs_path = snakemake.input.costs - output_path = snakemake.output[0] - except NameError: - # Fallback for testing (snakemake variable not available) - config = {"cost_year": 2030} - tech_costs_path = "../resources/technology_data/costs_2030.csv" - output_path = "test_steel_network.nc" - - year = getattr(snakemake.wildcards, "cost_year", 2050) # noqa: F821 + if "snakemake" not in globals(): + raise RuntimeError( + "This script must be run via Snakemake with cost_year wildcard" + ) + + config = snakemake.config # noqa: F821 + tech_costs_path = snakemake.input.costs # noqa: F821 + output_path = snakemake.output[0] # noqa: F821 + + cost_year = 0 + if cost_year is None: + raise ValueError("snakemake.wildcards.cost_year is required") + + year = getattr(snakemake.wildcards, "cost_year", 0) # noqa: F821 network = build_network(config, tech_costs_path, year) network.export_to_netcdf(output_path) logger.info(f"Network exported to {output_path}") diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 14937e8..29d9ab4 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -185,10 +185,12 @@ def apply_renewable_constraint(network, local_el_demand_mwh, config): # Use entire generator for local demand capacity_to_use = p_nom_max capacity_accumulated += annual_energy + new_p_nom_max = 0 else: # Use partial generator to exactly meet local demand capacity_to_use = remaining_needed / (avg_cf * 8760) capacity_accumulated += remaining_needed + new_p_nom_max = p_nom_max - capacity_to_use generators_for_local.append( { @@ -196,13 +198,20 @@ def apply_renewable_constraint(network, local_el_demand_mwh, config): "avg_cf": avg_cf, "capacity_blocked_mw": capacity_to_use, "energy_provided_mwh": capacity_to_use * avg_cf * 8760, + "p_nom_max_before": p_nom_max, + "p_nom_max_after": new_p_nom_max, } ) - # Block this generator: set p_nom_max=0 so optimizer can't use for steel - network.generators.at[gen_name, "p_nom_max"] = 0 + # Update generator availability for steel + network.generators.at[gen_name, "p_nom_max"] = new_p_nom_max + blocked_msg = ( + "fully blocked" + if new_p_nom_max == 0 + else f"reduced to {new_p_nom_max:.2f} MW" + ) logger.info( - f" Blocked {gen_name:40s} (CF={avg_cf:.3f}, {capacity_to_use:7.1f} MW) → local demand" + f" Blocked {gen_name:40s} (CF={avg_cf:.3f}, {capacity_to_use:7.1f} MW) → local demand, {blocked_msg}" ) logger.info( @@ -647,6 +656,21 @@ def extract_lcox(network, product, demands): network = base_network.copy() network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-{steel_demand_mt}" + # Ensure snapshot year is set by upstream network preparation; + # do not override if already set. + if network.snapshots is None or len(network.snapshots) == 0: + cost_year = int(snakemake.wildcards.cost_year) + network.set_snapshots( + pd.date_range(f"{cost_year}-01-01", periods=8760, freq="h") + ) + logger.info( + f"Set snapshots for cost_year={cost_year} (fallback in calculate_lcox)" + ) + else: + logger.info( + f"Snapshots pre-set in network (len={len(network.snapshots)}), not overriding in calculate_lcox" + ) + # Preserve discount_rate from base network (needed for cost annuitization) network.discount_rate = base_network.discount_rate diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index e1091ca..ed95606 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -201,7 +201,7 @@ def add_renewable_generators( gen_name, bus="electricity", carrier="electricity", - p_nom_extendable=False, + p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide p_nom_max=p_nom_max, # Upper ceiling from cluster data (MW) p_max_pu=cf_ts, # Hourly capacity factor from cluster data (0-1) @@ -229,7 +229,7 @@ def _apply_discount_rate_to_components( # Apply to links for link_name, link_row in network.links.iterrows(): has_cost = ( - pd.notna(link_row.get("overnight_cost")) and link_row["overnight_cost"] > 0 + pd.notna(link_row.get("overnight_cost")) and link_row["overnight_cost"] >= 0 ) if has_cost: network.links.at[link_name, "discount_rate"] = discount_rate @@ -246,7 +246,7 @@ def _apply_discount_rate_to_components( # Apply to generators for gen_name, gen_row in network.generators.iterrows(): has_cost = ( - pd.notna(gen_row.get("overnight_cost")) and gen_row["overnight_cost"] > 0 + pd.notna(gen_row.get("overnight_cost")) and gen_row["overnight_cost"] >= 0 ) if has_cost: network.generators.at[gen_name, "discount_rate"] = discount_rate @@ -375,6 +375,21 @@ def prepare_network( f"{len(network.links)} links, {len(network.stores)} stores" ) + # Set snapshots here using wildcard year coming from Snakemake + cost_year = None + if "snakemake" in globals(): + cost_year = getattr(snakemake.wildcards, "cost_year", None) + + if cost_year is not None: + network.set_snapshots( + pd.date_range(f"{cost_year}-01-01", periods=8760, freq="h") + ) + logger.info(f"Set snapshots for cost_year={cost_year} in prepare_network") + else: + raise ValueError( + "cost_year must be defined in snakemake wildcards for prepare_network" + ) + # Step 1b: Set interest rate (discount rate) for the network interest_rates = config.get("interest_rate", {}) discount_rate = interest_rates.get(region, interest_rates.get("default", 0.07)) From 2c7b2462c0f75ac6e9ad26534ab2bb881ee5ebe0 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 3 Apr 2026 12:02:49 +0200 Subject: [PATCH 028/216] chore: clean up config and snakemake file --- config/config.yaml | 3 +- workflow/Snakefile | 90 +++++++++++++++++++++++----------------------- 2 files changed, 47 insertions(+), 46 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 47fd3bb..f989950 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -45,7 +45,7 @@ interest_rate: part_load: electrolysis: 0.0 - direct reduction furnace: 0.9 + direct reduction furnace: 0.0 electric arc furnace: 0.0 # add part load limitations by naming the carrier of links @@ -74,7 +74,6 @@ plot: steel: bus_size: 1.0e-7 link_width: 1.5e-8 - solver: name: gurobi options: gurobi-default diff --git a/workflow/Snakefile b/workflow/Snakefile index f411318..0f68169 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -1,50 +1,47 @@ -from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider import sys -from email import utils from snakemake.utils import Paramspace import pandas as pd -from os.path import normpath, exists, isdir -from shutil import copyfile, move # HTTP = HTTPRemoteProvider() sys.path.append("./scripts") -# ============================================================================ -# FILE DELETION STRATEGY (Development vs Production) -# ============================================================================ -# During development: ALL outputs are kept for validation, debugging, and -# iterative refinement. -# -# Post-development cleanup (marked with [DELETION FLAG]): -# - steel_skeleton_{cost_year}.nc [~500 KB]: Fast to regenerate (<1 min) -# - network_{1,10,50}.nc per region [~50-100 MB]: Intermediate solver states, for debugging -# - {region}_{product}_nodemand.csv [~10 KB]: Reference copy of supply curve before local demand subtraction -# -# ALWAYS KEEP: -# - results_{1,10,50}.csv per region [~50 KB]: Expensive to regenerate (~20 min) -# - {region}_{product}.csv [~10 KB]: Final supply curves→model_trade input -# - {region}_{product}.pdf [~200 KB]: Low cost, high validation value -# - base_{cost_year}_{region}_{product}.nc [~10-20 MB]: Good checkpoint, medium regen cost -# -# Once finalized, add a cleanup rule that: -# 1. Checks if all expected outputs exist -# 2. Deletes flagged files only after final rule completes -# 3. Preserves audit trail in .audit_{} JSON files -# ============================================================================ - # Read scenario definitions to construct wildcard and instance patterns from them trade_scenarios = Paramspace(pd.read_csv("../config/trade_scenarios.csv", dtype=str)) configfile: "../config/config.yaml" -# localrules: all +# ---------------------------------------------------------------------------------- +# SECTION 0: Workflow metadata and execution hints +# - origin: README workflow description +# - scenario definitions loaded from ../config/trade_scenarios.csv +# - config loaded from ../config/config.yaml +# - This file orchestrates greenfield supply curve generation, interregional trade modelling, and figure collection. +# ---------------------------------------------------------------------------------- wildcard_constraints: country="[a-zA-Z]+", sweep="[a-zA-Z]+", rule="(0|[1-9][0-9]?|100)" +# ---------------------------------------------------------------------------------- +# SECTION 1: Greenfield supply curve generation (PyPSA) +# Description: produce region-specific levelized cost curves for steel via PyPSA. +# Inputs: +# - ../data/renewable_clusters.nc (renewable capacity & timeseries) +# - ../resources/technology_data/costs_{cost_year}.csv (tech cost data) +# Outputs: +# - ../resources/networks/base_{cost_year}_{region}_{product}.nc (prepped regional network) +# - ../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv (regional supply curve) +# Config gate: config["enable"]["run_supply_chain"], config["enable"]["run_supply_curve"] +# Script mapping: +# - scripts/tech_database.py +# - scripts/build_x_supply_chain.py +# - scripts/prepare_regional_network.py +# - scripts/calculate_lcox.py +# - scripts/create_supply_curve.py +# ---------------------------------------------------------------------------------- + rule retrieve_cost_data: params: version=config['costs']['version'], @@ -57,20 +54,6 @@ rule retrieve_cost_data: script: "scripts/tech_database.py" - -# Prepare the trace file prior to running the model -rule retrieve_trace_steel: - input: - trace = "../../trace-fneum/trace/resources/networks/default/{cost_year}/shipping-steel/DE-DE/network.nc" - output: - trace="../resources/trace/steel_{cost_year}.nc", - resources: - mem_mb=5000, - threads: 2 - run: - copyfile(input[0], output[0]) - - # Build steel supply chain technology skeleton rule build_steel_skeleton: input: @@ -166,8 +149,6 @@ if config["enable"].get("run_supply_curve", True): script: "scripts/create_supply_curve.py" - - rule create_all_supply_curves: input: expand( @@ -176,6 +157,13 @@ rule create_all_supply_curves: ) #cost_year=[2030,2050], region=config["regions"], product=["steel", "hydrogen"] +# ---------------------------------------------------------------------------------- +# SECTION 2: Interregional trade model +# Description: build trade results and network maps for all scenarios from supply curves. +# config/scenario-driven: uses trade_scenarios wildcard patterns from ../config/trade_scenarios.csv +# including optional cost scenario params from `config["trade"]` + `config["design"]`. +# ---------------------------------------------------------------------------------- + rule model_trade: params: iron_ore_potential=config["iron_ore"]["potential_allowance"], @@ -223,6 +211,12 @@ rule model_trade_all: trade_plot_steel=expand("../results/{scenarios}/map_steel.pdf", scenarios=trade_scenarios.instance_patterns), +# ---------------------------------------------------------------------------------- +# SECTION 3: Results collection and figure outputs +# Description: collect analysis figures (cost curves, maps, demand profiles) for presentation. +# This is a final aggregation stage and depends on outputs from earlier modelling / notebooks. +# ---------------------------------------------------------------------------------- + rule collect_figures: input: global_supply_curve = "../results/figures_general/{scenario}/global_supply_curve.pdf", #workflow/notebooks/analysis-coststructure.ipynb @@ -267,3 +261,11 @@ rule collect_figures: # notebook: # "workflow/notebooks/input-cost-comp.ipynb" + +# ---------------------------------------------------------------------------------- +# RUNNING THE WORKFLOW (developer convenience) +# Use e.g.: +# snakemake -s workflow/Snakefile -j 8 model_trade_all +# snakemake -s workflow/Snakefile -j 4 prepare_regional_network cost_year=2030 region=Europe product=steel +# snakemake -s workflow/Snakefile -j 4 create_supply_curve cost_year=2030 region=EU product=steel +# ---------------------------------------------------------------------------------- From 7d9b1530a965f0744ae3a48f010ce84ac7187fe8 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 3 Apr 2026 13:27:52 +0200 Subject: [PATCH 029/216] feat: expand precommit file --- .pre-commit-config.yaml | 59 +++++++++++++++++++++++++++-------------- 1 file changed, 39 insertions(+), 20 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8957eee..f4313b9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,24 +1,43 @@ +exclude: "^LICENSES|^config/config\\.default\\.yaml$" + +ci: + autoupdate_schedule: quarterly + repos: -- repo: https://github.com/astral-sh/ruff-pre-commit - # Ruff version. - rev: v0.15.8 + # Run ruff to lint and format + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.0 hooks: - # Run the linter. - - id: ruff-check - args: [ --fix ] - # Run the formatter. + # Run the linter + - id: ruff + args: [--fix] + # Run the formatter - id: ruff-format -# - repo: https://github.com/pre-commit/pre-commit-hooks -# rev: v6.0.0 -# hooks: -# - id: trailing-whitespace -# - id: end-of-file-fixer -# - id: mixed-line-ending -# args: ["--fix=auto"] -# - id: check-merge-conflict -# - id: check-added-large-files -# - id: check-yaml -# exclude: ^conda/meta.yaml$ -# - id: check-json -# - id: check-xml + # Find common spelling mistakes + - repo: https://github.com/codespell-project/codespell + rev: v2.3.0 + hooks: + - id: codespell + args: ['--ignore-regex="(\b[A-Z]+\b)"'] + types_or: [python, rst, markdown] + files: ^(scripts|rules)/ + + # Format Snakemake rule files + - repo: https://github.com/snakemake/snakefmt + rev: v1.0.0 + hooks: + - id: snakefmt + + # Run some default pre-commit hooks + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-added-large-files + args: ["--maxkb=2000"] + - id: check-merge-conflict + - id: check-yaml + exclude: "pixi.lock" + - id: end-of-file-fixer + - id: trailing-whitespace + From ea20d2086e81e755df1a3a8700603fd48d82d643 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 3 Apr 2026 13:48:43 +0200 Subject: [PATCH 030/216] chore: improve installation, env and pre-commit --- .pre-commit-config.yaml | 2 +- README.md | 27 +- pixi.lock | 2691 ++++----------------------------------- pixi.toml | 32 +- 4 files changed, 250 insertions(+), 2502 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f4313b9..2088d4f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,4 +1,4 @@ -exclude: "^LICENSES|^config/config\\.default\\.yaml$" +exclude: "^LICENSES|^config/config\\.yaml$" ci: autoupdate_schedule: quarterly diff --git a/README.md b/README.md index d319651..5afd0b5 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # SHIFT – Steel & Hydrogen Integrated Freight Trade -This repository contains the **SHIFT model**, a spatially resolved techno-economic optimization of global iron and steel supply chains under decarbonization. It explores how hydrogen-based direct reduced iron (DRI) production and hot-briquetted iron (HBI) trade can shift value creation to regions with renewable energy, infrastructure, and capital availability. +This repository contains the **SHIFT model**, a spatially resolved techno-economic optimization of global iron and steel supply chains under decarbonization. It explores how hydrogen-based direct reduced iron (DRI) production and hot-briquetted iron (HBI) trade can shift value creation to regions with renewable energy and capital availability. The model identifies cost-optimal configurations for mining, hydrogen production, DRI processing, and HBI trade, using a two-stage optimization pipeline and open energy system libraries. @@ -17,12 +17,33 @@ Key features: ## Quick installation (PIXI) + Clone the repository: + +```sh +git clone https://github.com/energyLS/shift.git +cd shift +``` + + Install dependencies with pixi: + ```sh -git clone https://github.com/energyLS/shift.git && cd shift -python -m pip install --upgrade pip pixi pixi install ``` + Activate the environment: + +```sh +pixi shell +``` + + (Optional) Install pre-commit hooks: + +```sh +pre-commit install +``` + + + For details, see https://pixi.prefix.dev/latest/ (or your local PIXI docs). ## Run (core workflow) diff --git a/pixi.lock b/pixi.lock index 34a23b8..0b047ad 100644 --- a/pixi.lock +++ b/pixi.lock @@ -12,7 +12,6 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.3-py313hd6074c6_0.conda @@ -21,9 +20,6 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/amply-0.1.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/appdirs-1.4.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/argparse-dataclass-2.0.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/atlite-0.4.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda @@ -46,869 +42,6 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-ha7a2c86_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-h52c5a47_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.3.0-py313h18e8e13_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/blinker-1.9.0-pyhff2d567_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda - - conda: 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b194a1fbc38f29c563b102ece9d006f7a165bf9074cdfe50563d3bce8cae9f84 md5: 16933322051fa260285f1a44aae91dd6 diff --git a/pixi.toml b/pixi.toml index 5127f3c..94786a3 100644 --- a/pixi.toml +++ b/pixi.toml @@ -10,13 +10,12 @@ version = "0.1.0" [dependencies] # Core Python -python = ">=3.10" +python = ">=3.12" # Inhouse packages - energy system modeling pypsa = ">=0.32.1" atlite = ">=0.3" linopy = ">=0.4.4" -powerplantmatching = ">=0.5.15" # Workflow orchestration snakemake-minimal = ">=9" @@ -25,26 +24,14 @@ snakemake-executor-plugin-slurm = "*" snakemake-executor-plugin-cluster-generic = "*" # Data processing and I/O -dask = "*" pandas = ">=2.1" numpy = "*" xarray = ">=2024.03.0" -pytables = "*" netcdf4 = "*" libgdal-netcdf = "*" -lxml = "*" -xlrd = "*" -openpyxl = "*" # Geospatial and mapping geopandas = ">=1" -rioxarray = "*" -cartopy = "*" -rasterio = "*" -fiona = "*" -shapely = ">=2.0" -proj = "*" -descartes = "*" # Scientific computing scipy = "*" @@ -55,29 +42,17 @@ matplotlib = "*" # Utilities pyyaml = "*" tqdm = "*" -country_converter = "*" geopy = "*" -pytz = "*" -memory_profiler = "*" -jpype1 = "*" -pyxlsb = "*" -graphviz = "*" geojson = "*" [pypi-dependencies] # pip-installable packages (always included) -tsam = ">=2.3.1" -entsoe-py = "*" -pypsatopo = "*" gurobipy = "*" [feature.test.dependencies] pytest = "*" pytest-cov = "*" -[feature.docs.dependencies] -sphinx = "*" - [feature.dev.dependencies] ruff = "*" pre-commit = "*" @@ -89,12 +64,11 @@ nbqa = "*" [environments] default = { features = [], solve-group = "default" } test = { features = ["test"], solve-group = "default" } -docs = { features = ["docs"], solve-group = "default" } -dev = { features = ["dev", "test", "docs"], solve-group = "default" } +dev = { features = ["dev", "test"], solve-group = "default" } [tool.ruff] # Ruff linter configuration -line-length = 120 +line-length = 88 target-version = "py310" exclude = ["**/*.ipynb"] From 9447633f49eeb74fccd666f01dcfdd4ae9a01484 Mon Sep 17 00:00:00 2001 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zC{%JvnX8jD>hKn~>28*=}8_NG&dJhj@>PHA!89@+yp*F?YF=d4YebS>A})=Rs@VTNzk zN>^=}6;b8A)>Y8X+&samdcKo3UA0h~^4?23rt8@3riD^zCGWukgX)jMA=My?yh|kSTc9lu{zotIkxH%>F(MAy}iG$ z&+*XOtgorO^2$fsD^#`k@_tY41qWxlys;kIg$J|VR{ZKX-J^m%@TBuPx~EUV1imRq5*|?ntRV$z%LX??@r{rbp}@@?2`u_tE$6u}*%e zUvuv68+ujJW_k@+9OLMBsclxi-}4*NChz;BkG(VZbIWXAXo@wAJyj1q*5bu zUpI+9D8)_+dN*s&ap}@AWmqHEvr?sQKF#i4+9lmiaI@&q?6_2^Qcgo#{|k~;?acHR zC5}sG=UV%!(mqL@YDxYX&96u&qoZDw?Y&w`^$h(QZ;~q=?%m8Hcgz#X!XaSF1D&xIjDS2K0AF{vmSE&3mO!nWsl>D#1 Kt*#{&ocITgsKN*U literal 0 HcmV?d00001 From edfa30b06ab272b554b65046de8e0c203937707e Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 3 Apr 2026 14:18:42 +0200 Subject: [PATCH 032/216] chore:cleanup legacy code --- plan_shift.prompt.md | 383 ---------------- workflow/notebooks/_helpers.py | 177 -------- ...reate_hydrogen_supply_curve_with_demand.py | 110 ----- workflow/scripts/model_lcoh.py | 286 ------------ workflow/scripts/model_lcox.py | 424 ------------------ 5 files changed, 1380 deletions(-) delete mode 100644 plan_shift.prompt.md delete mode 100644 workflow/notebooks/_helpers.py delete mode 100644 workflow/scripts/create_hydrogen_supply_curve_with_demand.py delete mode 100644 workflow/scripts/model_lcoh.py delete mode 100644 workflow/scripts/model_lcox.py diff --git a/plan_shift.prompt.md b/plan_shift.prompt.md deleted file mode 100644 index 4f6b9b6..0000000 --- a/plan_shift.prompt.md +++ /dev/null @@ -1,383 +0,0 @@ - -# SHIFT Workflow: High-Level Plan (Steps 0, 1, 2) - -## TL;DR - -Three-stage workflow: (0) Pre-compute renewable potentials externally → (1) **Constrained regional steel supply curve optimization** under discrete renewable utilization fractions, with local demand as priority load → (2) Global trade LP for **complete supply chain optimization (ore sourcing + HBI/ore trading)** to minimize system cost. - ---- - -## Step 0: Renewable Potentials (External, Pre-computed) - -**Scope:** Data aggregation; largely handled outside SHIFT via PyPSA-Earth - -**Outputs from PyPSA-Earth:** -- Hourly capacity factors and potential power generation (solar, wind_on, wind_off): `profile_solar.nc`, `profile_wind_on.nc`, `profile_wind_off.nc` per region -- Weather year: 2013 - -**SHIFT Integration Point (Data Aggregation Only):** -- Ingest Step 0 outputs -- Aggregate regional, but keep hourly resolution for supply curve generation - -**File Locations:** -- Input: `data/renewable_profiles/profile_{technology}.nc` -- Output (Step 1 input): Regional renewable supply table - ---- - -## Step 1: Constrained Steel LCO Optimization (PyPSA per Region) - -**Purpose:** Generate **LCOSTEEL curves** under discrete renewable potential constraints (1%, 10%, 50%), with local electricity demand pre-served by the most efficient renewable potentials. - -**Key Innovation:** -1. Local demand satisfied FIRST using most efficient (highest CF) renewable resources → blocked from steel supply chain -2. Remaining potential available for steel production (H₂ → DRI → HBI → EAF → Steel) -3. Explicit renewable scarcity constraint per region - -**Policy Assumptions:** -- Local electricity demand is exogenous infrastructure (non-negotiable baseline; must be served first) -- `renewable_utilization_fractions` apply **only** to remaining capacity after local demand is allocated -- All regions assumed capable of meeting local demand from local renewables (or with grid imports) -- Steel production cost (LCOSTEEL) scales inversely with utilization fraction - -### Inputs - -1. **Renewable Potentials** (from Step 0) - - Annual CF and max capacity [GW] per region, technology - - Stratified by efficiency class (to identify "most efficient" potentials) - -2. **Local Electricity Demand (Fixed, Exogenous, Priority)** - - Regional non-steel electricity demand: `data/un_enerdata_demand_2050_final.csv` → `el_demand [TWh/year]` - - **Key:** Served FIRST with highest-CF renewable resources; these are blocked from steel production - -3. **Steel Demand (Fixed, Exogenous)** - - Regional steel demand: `resources/steel_production_clustered.csv` [Mt/year] - - Determines EAF electricity requirement: `EAF_el_demand = steel_demand_Mt × 5.25 [MWh/year]` - -4. **Renewable Utilization Constraints** - - `demand_factors: [0.01, 0.10, 0.50]` – Fraction of **remaining** potential available for steel production - - After local demand consumes its most-efficient share, constrain H₂ electrolyser capacity to this fraction of what's left - -5. **Tech-Economic Parameters** (`config/config.yaml`) - - Electrolyser efficiency: 75% - - DRI efficiency: 90% - - EAF efficiency: 95% - - Interest rate, CAPEX/OPEX from technology-data v0.12.0 - -### Process (Per Region, Per Demand-Factor) - -``` -For region ∈ X_regions: - - # STAGE 1: Allocate renewables to local demand (priority) - # ───────────────────────────────────────────────────── - Load_local = regional_el_demand [MWh/year] - - # Identify most efficient renewables (highest CF classes) - # Allocate enough capacity to serve Load_local completely - Renewables_for_local = select_highest_cf( - capacity_needed = Load_local / (CF × 8760), - pool = all_renewable_classes - ) - - # Block these from steel supply chain - Remaining_renewable_potential = Total_capacity - Renewables_for_local - - # STAGE 2: Constrain steel supply chain to fraction of remaining potential - # ────────────────────────────────────────────────────────────────────── - For demand_factor ∈ [0.01, 0.10, 0.50]: - - # Available capacity for steel (H₂ + EAF electricity) - Available_for_steel = Remaining_renewable_potential × demand_factor - - # Build PyPSA network - # Electricity Bus: - # ├─ Generators: Remaining renewables - # │ (capacity = Available_for_steel) - # ├─ Storage: Battery - # ├─ Fixed Load: EAF (EAF_el_demand / 8760 [MW]) - # │ (competing with electrolyser for same capacity) - # └─ Electrolyser → H₂ - # (flex load, variable output) - # - # Objective: minimize CAPEX + OPEX to produce maximum steel - # = maximize H₂ production (given EAF demand must be met) - # - # Extract cost: LCOSTEEL = total_cost / (steel_demand_Mt) - - supply_curve_point = (demand_factor, LCOSTEEL_EUR_per_t) -``` - -### Outputs (Per Region) - -- **Steel Supply Curve (CSV):** `resources/supply_curves/cost_year~{year}/{region}_lcosteel.csv` - ``` - renewable_utilization_fraction | Steel_demand_Mt | LCOSTEEL_EUR_per_t | H2_prod_MWh | EAF_el_MWh - 0.01 | steel_demand | 850 | 5000 | 41250 - 0.10 | steel_demand | 420 | 50000 | 41250 - 0.50 | steel_demand | 185 | 250000 | 41250 - ``` - - Each row = discrete renewable utilization scenario - - EAF electricity is constant (fixed load) - - H₂ production scales with available renewable capacity - -### Key Design Features - -- **Local demand prioritized:** Served with most-efficient renewables; no cost optimization for local supply (exogenous) -- **Steel supply chain constrained:** Uses remaining, lower-efficiency potentials -- **Explicit renewable scarcity:** Demand factors (1%, 10%, 50%) directly represent available capacity for steel production -- **Direct product output:** LCOSTEEL generated in Step 1 (no intermediate H₂/HBI curves needed) -- **Realistic resource allocation:** Local demand acts as baseline load with infrastructure priority -- **Iron ore cost = 0:** Not included until Step 2 (avoid circular dependency) - ---- - -## Step 2: Global Supply Chain Optimization (LP) - -**Purpose:** Given regional LCOSTEEL curves, optimize global production mix + trade (ore, HBI) to minimize total system cost. **Iron ore cost integration occurs here.** - -### Inputs - -1. **Steel Supply Curves** (from Step 1) - - `resources/supply_curves/cost_year~{year}/{region}_lcosteel.csv` for all regions - - Discrete points: renewable utilization fractions (1%, 10%, 50%) with corresponding LCOSTEEL - - Iron ore cost NOT included; will be added in LP optimization - -2. **Regional Demands (Fixed, Exogenous)** - - Steel demand: `resources/steel_production_clustered.csv` [Mt/year] - -3. **Ore Production Potentials & Cost** - - Baseline: `resources/ironore_production_clustered.csv` [Mt_ore/year per region] - - Limit: `production × 1.2` allowance - - ***Cost: `iron_ore.marginal_cost` [EUR/t_ore]*** (integrated in Step 2 LP) - -4. **Transport Costs** - - Distance matrix: `data/transport_costs/{transport_cost}.csv` [km] - - Unit cost: `0.005` [€/t·km] - -5. **Tech-Economic Parameters** (`config/config.yaml`) - - Material balance: `ore_to_steel_ratio` = 1.59 [t_ore / t_steel] - - Other metallurgical parameters from technology-data - -6. **Regional Locations** (for distance calculation) - - `data/bus_locations.csv`: `region_name, lat, long` - -7. **Trade Scenarios** - - `config/trade_scenarios.csv`: scenario modifiers - -### Process - -**Discrete Supply Curve Integration:** -``` -For each region & discrete renewable utilization point: - # Assemble final cost = LCOSTEEL from Step 1 + ore cost - Final_cost[region][utilization_point] = LCOSTEEL[region][utilization_point] - + Ore_cost_per_t × (ore_requirement_per_t_steel) - + transport_ore_cost[route] -``` - -**Metallurgical Chain (inline in LP—inline in LP, transparent):** -``` -For each region & production decision: - H₂_input [MWh] - →[Electrolyser 75%]→ H₂_output [MWh] - →[DRI 90%]→ DRI_output [t DRI] - →[stoichiometry]→ HBI_production [t_HBI] - →[EAF + material balance]→ Steel_production [t] - -Material balance constraints (explicit): - Ore_extracted [t_ore] × (1 / 1.59) = HBI_available [t_HBI] - HBI_production [t_HBI] → Steel_production [t_steel] -``` - -**Decision Variables:** -- `steel_prod[region][utilization_point]` [Mt]: Steel production level (selects discrete utilization point on LCOSTEEL curve) -- `ore_prod[region]` [t_ore]: Iron ore extraction per region -- `hbi_trade[region_from][region_to]` [t]: HBI shipments between regions -- `ore_trade[region_from][region_to]` [t]: Ore shipments between regions - -**Objective Function:** -``` -minimize Σ_region Σ_utilization( LCOSTEEL_final[utilization] × steel_prod ) - + Σ_region( ore_extraction_cost × ore_prod ) [***INTEGRATED HERE***] - + Σ_routes( transport_cost_ore × ore_trade ) [***INTEGRATED HERE***] - + Σ_routes( transport_cost_hbi × hbi_trade ) - -where LCOSTEEL_final(utilization) = LCOSTEEL(utilization) + ore_cost_per_t_steel + ore_transport -``` - -**Constraints:** -- Material balance (H₂ → steel): cascade through DRI + EAF, accounting for losses -- Material balance (ore → HBI): ore_extracted / ore_to_hbi_ratio = HBI_production [t] -- Regional balance: local_steel_demand ≤ local_production + net_imports -- Supply limits: ore_prod ≤ regional_potential × 1.2 -- Trade conservation: sum(ore_trade) balanced per region -- Non-negativity - -### Outputs - -- **Trade Solution (CSV):** `results/cost_year~{year}/trade_{scenario}.csv` - ``` - region | H2_prod_MWh | DRI_output_t | HBI_prod_t | HBI_import_t | HBI_export_t | Ore_prod_t | Ore_import_t | Ore_export_t | Steel_prod_t | Total_cost_EUR - Europe | 50000 | 5500 | 5000 | 200 | 0 | 2000 | 800 | 0 | 3150 | 1.2e8 - N_W_Africa | 30000 | 3300 | 3000 | 0 | 2800 | 5000 | 0 | 3200 | 1890 | 8.5e7 - ... - ``` - -- **Cost Breakdown:** Detailed disaggregation of system cost by component: - - Electricity (CAPEX/OPEX renewables) - - Conversion (DRI, EAF CAPEX) - - Iron ore (extraction + transport) - - Trade (HBI + ore shipping) - -- **Trade Flows & Sensitivity:** Regional production, trade routes, marginal costs, shadow prices - ---- - -## Critical Data Files (All Confirmed ✓) - -| File | Purpose | Source | Status | -|------|---------|--------|--------| -| `resources/steel_production_clustered.csv` | Regional steel demand [Mt/year] | OWID clustered | ✓ | -| `resources/ironore_production_clustered.csv` | Regional iron ore production [Mt_ore/year] | OWID clustered | ✓ | -| `data/un_enerdata_demand_2050_final.csv` | Regional final energy + electricity share [%] | External | ✓ | -| `data/bus_locations.csv` | Regional centroids (lat/long) | Hardcoded | ✓ | -| `data/transport_costs/{transport_cost}.csv` | Distance matrix [km] | Computed | ✓ | -| `config/trade_scenarios.csv` | Trade scenario definitions | Config | ✓ | -| `data/new_renewables/supply_*.nc` | Renewable CF + max potentials | PyPSA-Earth | ✓ | -| `config/config.yaml` | All parameters (costs, ratios, tech-data) | Config | ✓ | - ---- - -## Data Flow Architecture - -``` -┌──────────────────────────────────────────┐ -│ Step 0: Renewables (External) │ -│ Output: CF + max_GW per region │ -│ (stratified by efficiency class) │ -└────────────┬─────────────────────────────┘ - │ - ↓ -┌──────────────────────────────────────────────────────────────┐ -│ Step 1: Constrained Steel LCO Optimization (PyPSA) │ -│ │ -│ Stage 1A: Local Demand (Priority) │ -│ - Served FIRST using most-efficient renewables │ -│ - No cost optimization (exogenous infrastructure) │ -│ - Potentials blocked from steel supply chain │ -│ │ -│ Stage 1B: Steel Supply Chain (Constrained) │ -│ - Renewable generators [capacity × utilization_factor] │ -│ - Battery storage, EAF load, Electrolyser │ -│ - Utilization factors: 1%, 10%, 50% │ -│ - Objective: Minimize LCOSTEEL under capacity constraint │ -│ │ -│ Output: LCOSTEEL curves (discrete utilization points) │ -│ └─ {region}_lcosteel.csv │ -│ └─ Iron ore cost = 0 (deferred to Step 2) │ -└────────────┬─────────────────────────────────────────────────┘ - │ - ↓ -┌──────────────────────────────────────────────────────────────┐ -│ Step 2: Global Trade Optimization (LP) │ -│ │ -│ Inputs: LCOSTEEL curves + ore potentials + distances │ -│ │ -│ Decisions: │ -│ - Regional steel production level (utilization point) │ -│ - Ore extraction & sourcing [Cost integrated] │ -│ - HBI & ore trade between regions │ -│ │ -│ Output: Optimal production mix, trade flows │ -│ Minimize total cost (renewables + ore + transport) │ -└──────────────────────────────────────────────────────────────┘ -``` - ---- - -## Key Design Decisions - -1. **Step 1: Constrained Steel LCO (Core Refactor)** - - **Benefit:** Direct optimization of final product (steel) under renewable scarcity - - **Local demand priority:** Served first with most-efficient potentials; realistic infrastructure constraint - - **Renewable utilization explicit:** Demand factors (1%, 10%, 50%) directly represent available capacity for steel supply chain - - **Advantage:** Simpler conceptually; transparent resource allocation; direct product output (LCOSTEEL) - -2. **Local Demand Pre-Allocation (NO Cost Optimization)** - - **Rationale:** Represents baseline infrastructure demand; exogenous constraint - - **Implementation:** Allocate most-efficient renewables to local load; block these potentials - - **Outcome:** Steel supply chain competes for lower-tier renewable resources (realistic) - -3. **Iron Ore Cost = 0 in Step 1 (Retained)** - - **Rationale:** Avoids circular dependency (ore cost ↔ steel cost ↔ ore sourcing) - - **Integration:** Ore cost added ONLY in Step 2 LP - - **Outcome:** Step 1 provides clean LCOSTEEL vs. renewable constraint curves - -4. **Direct Product Output (Steel)** - - **Benefit:** LCOSTEEL generated directly in Step 1; no intermediate curves needed - - **Efficiency:** Single PyPSA optimization produces final supply curve - - **Outcome:** Clean, direct data flow from Step 1 → Step 2 - -5. **Electricity Competition Implicit (Sequential Allocation)** - - **In Step 1:** Local demand + steel production compete for remaining renewables; local demand wins (priority) - - **In Step 2:** No further electricity competition; EAF supply is implicit in LCOSTEEL curves - - **Result:** Clear resource hierarchy; no ambiguous "competing loads" concept - -6. **Trade Scope: HBI + Ore (Unchanged Logic)** - - **H₂:** Local production only (high transport cost) - - **HBI:** Tradeable intermediate (lower shipping cost than final product) - - **Ore:** Tradeable raw material (regional potentials differ significantly) - - **Steel:** Manufactured locally from HBI + ore (final product has lowest transport intensity) - ---- - -## Key Parameters (config.yaml) - -| Parameter | Value | Unit | Notes | -|-----------|-------|------|-------| -| `renewable_utilization_fractions` | [0.01, 0.10, 0.50] | fraction | Discrete scenarios for Step 1 renewable utilization (after local demand served) | -| `electrolyser_efficiency` | 0.75 | — | H₂ output / electricity input | -| `DRI_efficiency` | 0.90 | — | Direct reduction iron yield | -| `electricity_steel_ratio` | 5.25 | MWh/t | EAF electricity per tonne steel (fixed load in Step 1) | -| `ore_to_steel_ratio` | 1.59 | t_ore/t_steel | Ore requirement per steel output (Step 2) | -| `shipping_cost` | 0.005 | €/t·km | Transport cost for ore/HBI (Step 2 only) | -| `iron_ore.marginal_cost` | 97.7 | €/t_ore | **Added in Step 2 LP only** | -| `iron_ore.potential_allowance` | 1.2 | factor | Upside limit on regional ore extraction | - ---- - -## Summary: What Changed & Why - -| Aspect | Original Plan | New Approach | Reason | -|--------|---------------|--------------|--------| -| **Step 1 goal** | Generate generic electricity curves | Optimize LCOSTEEL under renewable constraint | Direct product; explicit scarcity | -| **Local demand** | Competing load (same bus as H₂) | Priority load (served first, best potentials) | Realistic infrastructure hierarchy | -| **Renewable allocation** | All available for H₂ + EAF competition | Two-stage: local demand first, then steel | Explicit resource sequencing | -| **Supply curve output** | Electricity curve (foundation) | LCOSTEEL curve (final product) | Simpler, more direct | -| **Demand factors** | 0.1% - 70% (H₂ utilization) | 1%, 10%, 50% (renewable availability *after* local demand) | Constrained scarcity scenario | -| **Cost accumulation** | Multi-stage generic chains | Implicit in single PyPSA solve | No intermediate abstractions needed | -| **Iron ore cost** | = 0 (config toggle) | = 0 (explicit design principle) | Avoids circular dependency | - ---- - -## Implementation Roadmap - -### Phase 1: Refactor to Constrained Steel LCO -1. Modify Step 1 PyPSA model to implement two-stage renewable allocation: - - Stage 1A: Pre-serve local demand with highest-CF renewables (no optimization) - - Stage 1B: Optimize LCOSTEEL with remaining capacity under utilization constraint -2. Update Snakemake rule `model_lcox` to parameterize renewable utilization factor -3. Generate LCOSTEEL supply curves at discrete utilization points (1%, 10%, 50%) -4. Remove unnecessary `model_lcoh.py` or consolidate into single model -5. Validate outputs match mission possible steel / current LCOX calculations - -### Phase 2: Enhanced Analysis (Optional) -1. Add cost component breakdowns to LCOSTEEL supply curves (electricity, conversion, ore (=0 at this stage)) -2. Document sensitivity to renewable efficiency class selection -3. Explore alternative local demand scenarios if needed - -### Phase 3: Validate & Extend -1. Cross-validate LCOSTEEL against existing TRACE model results -2. Test sensitivity to renewable efficiency class selection -3. Document all assumptions and model boundaries \ No newline at end of file diff --git a/workflow/notebooks/_helpers.py b/workflow/notebooks/_helpers.py deleted file mode 100644 index 12359d6..0000000 --- a/workflow/notebooks/_helpers.py +++ /dev/null @@ -1,177 +0,0 @@ -import logging -from pathlib import Path - -import requests -import yaml - -# from fake_useragent import UserAgent -# from pypsa.components import component_attrs, components -from tqdm import tqdm - -logger = logging.getLogger(__name__) - - -def load_config(config): - with open(config, "r") as stream: - try: - config = yaml.safe_load(stream) - except yaml.YAMLError as exc: - print(exc) - return config - - -def mock_snakemake( - rulename, - root_dir=None, - configfiles=None, - submodule_dir="workflow/submodules/pypsa-eur", - **wildcards, -): - """ - This function is expected to be executed from the 'scripts'-directory of ' - the snakemake project. It returns a snakemake.script.Snakemake object, - based on the Snakefile. - - If a rule has wildcards, you have to specify them in **wildcards. - - Parameters - ---------- - rulename: str - name of the rule for which the snakemake object should be generated - root_dir: str/path-like - path to the root directory of the snakemake project - configfiles: list, str - list of configfiles to be used to update the config - submodule_dir: str, Path - in case PyPSA-Eur is used as a submodule, submodule_dir is - the path of pypsa-eur relative to the project directory. - **wildcards: - keyword arguments fixing the wildcards. Only necessary if wildcards are - needed. - """ - import os - - import snakemake as sm - from pypsa.definitions.structures import Dict - from snakemake.api import Workflow - from snakemake.common import SNAKEFILE_CHOICES - from snakemake.script import Snakemake - from snakemake.settings.types import ( - ConfigSettings, - DAGSettings, - ResourceSettings, - StorageSettings, - WorkflowSettings, - ) - - script_dir = Path(__file__).parent.resolve() - if root_dir is None: - root_dir = script_dir.parent - else: - root_dir = Path(root_dir).resolve() - - workdir = None - user_in_script_dir = Path.cwd().resolve() == script_dir - if str(submodule_dir) in __file__: - # the submodule_dir path is only need to locate the project dir - os.chdir(Path(__file__[: __file__.find(str(submodule_dir))])) - elif user_in_script_dir: - os.chdir(root_dir) - elif Path.cwd().resolve() != root_dir: - logger.info( - "Not in scripts or root directory, will assume this is a separate workdir" - ) - workdir = Path.cwd() - - try: - for p in SNAKEFILE_CHOICES: - p = root_dir / p - if os.path.exists(p): - snakefile = p - break - if configfiles is None: - configfiles = [] - elif isinstance(configfiles, str): - configfiles = [configfiles] - - resource_settings = ResourceSettings() - config_settings = ConfigSettings(configfiles=map(Path, configfiles)) - workflow_settings = WorkflowSettings() - storage_settings = StorageSettings() - dag_settings = DAGSettings(rerun_triggers=[]) - workflow = Workflow( - config_settings, - resource_settings, - workflow_settings, - storage_settings, - dag_settings, - storage_provider_settings=dict(), - overwrite_workdir=workdir, - ) - workflow.include(snakefile) - - if configfiles: - for f in configfiles: - if not os.path.exists(f): - raise FileNotFoundError(f"Config file {f} does not exist.") - workflow.configfile(f) - - workflow.global_resources = {} - rule = workflow.get_rule(rulename) - dag = sm.dag.DAG(workflow, rules=[rule]) - wc = Dict(wildcards) - job = sm.jobs.Job(rule, dag, wc) - - def make_accessable(*ios): - for io in ios: - for i, _ in enumerate(io): - io[i] = os.path.abspath(io[i]) - - make_accessable(job.input, job.output, job.log) - snakemake = Snakemake( - job.input, - job.output, - job.params, - job.wildcards, - job.threads, - job.resources, - job.log, - job.dag.workflow.config, - job.rule.name, - None, - ) - # create log and output dir if not existent - for path in list(snakemake.log) + list(snakemake.output): - Path(path).parent.mkdir(parents=True, exist_ok=True) - - finally: - if user_in_script_dir: - os.chdir(script_dir) - return snakemake - - -def progress_retrieve(url, file, disable=False): - headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"} - # Hotfix - Bug, tqdm not working with disable=False - disable = True - - if disable: - response = requests.get(url, headers=headers, stream=True) - with open(file, "wb") as f: - f.write(response.content) - else: - response = requests.get(url, headers=headers, stream=True) - total_size = int(response.headers.get("content-length", 0)) - chunk_size = 1024 - - with tqdm( - total=total_size, - unit="B", - unit_scale=True, - unit_divisor=1024, - desc=str(file), - ) as t: - with open(file, "wb") as f: - for data in response.iter_content(chunk_size=chunk_size): - f.write(data) - t.update(len(data)) diff --git a/workflow/scripts/create_hydrogen_supply_curve_with_demand.py b/workflow/scripts/create_hydrogen_supply_curve_with_demand.py deleted file mode 100644 index 5e922ca..0000000 --- a/workflow/scripts/create_hydrogen_supply_curve_with_demand.py +++ /dev/null @@ -1,110 +0,0 @@ -import pandas as pd -import matplotlib.pyplot as plt - -# Note: This script is intended to be executed via Snakemake. -# The `snakemake` object will be injected by the workflow. -# Ruff still needs a definition to avoid F821 in linting. -snakemake = None # noqa: F821 - - -def create_supply_curve_with_demand(): - # input: "resources/lcoh/{region}/results_{demand_factor}.csv", - # outputs: supply="resources/supply_curves/{region}_hydrogen.csv", supply_curve="resources/supply_curves/{region}_hydrogen.pdf" - all_files = snakemake.input.lcoh_data - print("files to merge:", all_files) - - final_demand_data = pd.read_csv(snakemake.input.final_demand_data, header=0) - final_demand = float( - final_demand_data.loc[ - final_demand_data.region == "{}".format(snakemake.wildcards["region"]) - ]["demand"] - ) - # final_green is wrong, this is final_el - ( - final_demand - * float( - final_demand_data.loc[ - final_demand_data.region == "{}".format(snakemake.wildcards["region"]) - ]["el_share"] - ) - / 100 - ) - print("demand data", final_demand) - - df_from_each_file = (pd.read_csv(f, sep=",", index_col=0) for f in all_files) - df_merged = pd.concat(df_from_each_file, ignore_index=True) - df_sub = df_merged.copy() - print("merged file has been created") - - # first calculate part of local supply that should be used to cover local el demand - df_all_demand = pd.read_csv(snakemake.input.final_demand_data, header=0) - df_local_demand = df_all_demand.loc[ - df_all_demand["region"] == snakemake.wildcards["region"] - ] - # total final energy consumption for the region * percentage of final energy consumption needed to meet local el demand = local el demand need in MWh - # since the demand is for hydrogen (after electrolysis of 75%), local el load must be converted to the amount of decreasing hydrogen production - local_load = ( - float(df_local_demand["demand"] * df_local_demand["el_share"] / 100) * 0.75 - ) - print("local el load is: ", local_load) - - # ******************* SUBTRACTING LOCAL DEMAND *********************** - # remove local load from demand and drop all negative rows (generators that are only local) - df_sub["demand [MWh]"] = df_sub["demand [MWh]"].subtract(local_load) - df_sub["demand [MWh]"][df_sub["demand [MWh]"] < 0] = 0 - print("local load has been subtracted from global supply") - - # preparing for plotting - infeasible_rows = df_merged[df_merged["LCOH [EUR/MWh]"] == "infeasible"].index - df_merged = df_merged.drop(infeasible_rows) - df_sub = df_sub.drop(infeasible_rows) - print("deleted infeasible rows to prepare for plotting") - - # creates and saves supply curve plot - plt.plot( - df_merged["demand [MWh]"].astype(int) / (1e6), - df_merged["LCOH [EUR/MWh]"].astype(int), - linestyle="-", - marker="o", - label="supply", - ) - plt.axvline(x=final_demand / (1e6), linestyle="-", label="final energy demand") - plt.axvline( - x=0.2 * final_demand / (1e6), linestyle="-.", label="20% final energy demand" - ) - plt.axvline( - x=0.6 * final_demand / (1e6), linestyle=":", label="60% final energy demand" - ) - - # the subtracted plot - plt.plot( - df_sub["demand [MWh]"].astype(float) / (1e6), - df_sub["LCOH [EUR/MWh]"].astype(float), - linestyle="--", - color="C1", - marker="o", - markerfacecolor="none", - label="subracted supply", - ) - plt.axvline(local_load / (1e6), label="local demand", linestyle="--", color="C1") - - # plt.axvline(x=final_green/(1e6),linestyle='-',label="final demand RE",color='springgreen') - # plt.axvline(x=0.2*final_green/(1e6),linestyle='--',label="20 final demand RE",color='springgreen') - # plt.axvline(x=0.6*final_green/(1e6),linestyle='-.',label="60 final demand RE",color='springgreen') - - plt.ylabel("LCOH [EUR/MWh]") - plt.ylim((0, 100)) - plt.title( - "levelized cost of hydrogen production in {}".format( - snakemake.wildcards["region"] - ) - ) - plt.xlabel("demand [TWh]") - plt.legend() - plt.savefig(snakemake.output.supply_curve, format="pdf", bbox_inches="tight") - - return - - -if __name__ == "__main__": - create_supply_curve_with_demand() diff --git a/workflow/scripts/model_lcoh.py b/workflow/scripts/model_lcoh.py deleted file mode 100644 index 53d95d1..0000000 --- a/workflow/scripts/model_lcoh.py +++ /dev/null @@ -1,286 +0,0 @@ -import pypsa -import pandas as pd - -# import matplotlib.pyplot as plt -# from pyomo.environ import Constraint -import xarray as xr - - -def calc_annuity(i_rate, lifetime): - # calculating annuity factor with interest rate and lifetime - return i_rate / (1 - (1 + i_rate) ** (-lifetime)) - - -def calc_cap_cost(costs, tech, i_rate): - # select the relevant part of the dataframe - sub_data = costs.loc[costs["technology"] == tech] - - # save values (not all tech has FOM, so setting FOM to zero in that case) - if len(sub_data["value"].loc[sub_data["parameter"] == "FOM"].values) > 0: - FOM = sub_data["value"].loc[sub_data["parameter"] == "FOM"].values - else: - FOM = 0 - - CAPEX = sub_data["value"].loc[sub_data["parameter"] == "investment"].values - lifetime = sub_data["value"].loc[sub_data["parameter"] == "lifetime"].values - - # calculate parameters - annuity = calc_annuity(i_rate, lifetime) - - # returns cap costs in EUR/MW - return (annuity + FOM / 100) * CAPEX * 1e3 - - -# inputs are solar potentials, wind potentials, costs and load -def building_model(ds, dw, dc, load, h_cost): - # create network + buses + carriers - network = pypsa.Network() - - network.set_snapshots(pd.to_datetime(ds.time.to_pandas())) - - # defining the buses - network.add("Bus", "bus el", carrier="el") - network.add("Bus", "bus hydrogen", carrier="hydrogen") - # network.add("Bus","bus water", carrier = "water") - - # adding carriers - network.add("Carrier", "el") - network.add("Carrier", "hydrogen") - network.add("Carrier", "wind") - network.add("Carrier", "solar") - # network.add("Carrier","water") - - # adding wind and solar generators on el bus - interest_rate = 0.075 - wind_cost = calc_cap_cost(dc, "onwind", interest_rate) - # offshore_wind_cost = calc_cap_cost(dc,"offwind",interest_rate) - solar_cost = calc_cap_cost(dc, "solar-utility", interest_rate) - battery_cost = calc_cap_cost(dc, "battery storage", interest_rate) - - # hydrogen cost can either be 0 or real cost - if h_cost is False: - hydrogen_storage_cost = 0 - else: - hydrogen_storage_cost = calc_cap_cost( - dc, "hydrogen storage tank incl. compressor", interest_rate - ) - - # for every class in solar data - print("---------------------------- starting with solar data ") - for i in range(0, len(ds.capacity)): - # "time":slice("2013-01-01 00:00", "2013-01-30 14:00"), - sol_df = ds.sel({"class": ds["class"][i]}) - - # costs taken from dae: solar costs - network.add( - "Generator", - "PV {}".format(i), - bus="bus el", - carrier="solar", - # p_nom = 8, #leave it commented out, start cap should be zero - p_nom_extendable=True, - p_nom_max=sol_df["capacity"] - .to_pandas() - .item(), # this will be ds.capacities - p_max_pu=sol_df["capacity factor"].to_pandas(), # this will be ds.profiles - capital_cost=solar_cost[0], # EUR/MW, this will be read in from costs file - ) - - # for every class in onshore wind data - print("---------------------------- starting with wind data ") - for i in range(0, len(dw.capacity)): - wind_df = dw.sel({"class": dw["class"][i]}) - - network.add( - "Generator", - "on_wind turbine {}".format(i), - bus="bus el", - carrier="wind", - # p_nom = 8, #this is capacity - p_nom_extendable=True, - # p_nom_min = 8, - p_nom_max=wind_df["capacity"].to_pandas().item(), - p_max_pu=wind_df[ - "capacity factor" - ].to_pandas(), # read in from potentials file - capital_cost=wind_cost[ - 0 - ], # EUR/MW, read in from costs file and calculated in above function - ) - - # for every class in offshore wind data - # for i in range (0,len(dww.capacity)): - # offshore_wind_df = dww.sel({"class":dww["class"][i]}) - - # network.add( - # "Generator", - # "off_wind turbine {}".format(i), - # bus="bus el", - # carrier="wind", - # #p_nom = 8, #this is capacity - # p_nom_extendable=True, - # #p_nom_min = 8, - # p_nom_max = offshore_wind_df["capacity"].to_pandas(), - # p_max_pu= offshore_wind_df["capacity factor"].to_pandas(), #read in from potentials file - # capital_cost= offshore_wind_cost #EUR/MW, read in from costs file and calculated in above function - # ) - - # adding storage - # battery storage investment cost = 75EUR/kWh - network.add( - "Store", - "battery", - bus="bus el", - e_cyclic=True, - e_nom_extendable=True, - capital_cost=battery_cost[0], - ) # EUR/MWh - - # hydrogen storage - network.add( - "Store", - "hydrogen", - bus="bus hydrogen", - e_cyclic=True, - e_nom_extendable=True, - capital_cost=hydrogen_storage_cost, - ) # EUR/MWh - - # p_set unit in MW - network.add("Load", "load", bus="bus hydrogen", p_set=load) - print("network load: ", load) - - # values for electrolysis link from cost outputs in 2050 (dae data) - network.add( - "Link", - "electrolysis", - bus0="bus el", - bus1="bus hydrogen", - efficiency=0.75, # per unit - capital_cost=calc_cap_cost(dc, "electrolysis", interest_rate)[0], # EUR/MW - p_nom_extendable=True, - ) - return network - - -def save_lcoh(solved_network): - # creating dataframe for saving - res = pd.DataFrame( - columns=[ - "demand factor [%]", - "demand [MWh]", - "load [MW]", - "cost [EUR]", - "LCOH [EUR/MWh]", - ] - ) - - try: - solved_network.objective - except Exception: - # if infeasible - print("saving infeasible network") - res.loc[res.shape[0]] = [ - snakemake.wildcards["demand_factor"], - load * 8760, - load, - "infeasible", - "infeasible", - ] - else: - # if feasible - print("saving feasible network") - res.loc[res.shape[0]] = [ - snakemake.wildcards["demand_factor"], - load * 8760, - load, - solved_network.objective, - solved_network.objective / (load * 8760), - ] - - # saving network and dataframe - solved_network.export_to_netcdf(snakemake.output.network) - res.to_csv(snakemake.output.results) - return - - -if __name__ == "__main__": - if "snakemake" not in globals(): - from _helpers import mock_snakemake - - snakemake = mock_snakemake( - "model_lcoh", cost_year="2030", demand_factor=20, region="Europe" - ) - - # making dataframes from inputs - dc = pd.read_csv(snakemake.input.costs, header=0) - d = xr.open_dataset(snakemake.input.supply_data) - - # subselecting each technology and cleaning for "0 and nan" - capacity values - ds = d.sel({"technology": "pvplant"}) - ds_cleaned = ds.where(ds.capacity > 0.0, drop=True) - dw = d.sel({"technology": "windonshore"}) - dw_cleaned = dw.where(dw.capacity > 0.0, drop=True) - # dww = d.sel({"technology":"windoffshore"}) - # dww_cleaned = dww.where(dww.capacity > 0.0,drop=True) - - max_load = ( - int( - (ds_cleaned.capacity * ds_cleaned["capacity factor"]).sum( - dim=["time", "class"] - ) - + (dw_cleaned.capacity * dw_cleaned["capacity factor"]).sum( - dim=["time", "class"] - ) - ) - / 8760 - * 0.75 - ) - print("max load, (solar+wind)/8760*0.75:", max_load) - - # calculating load - # load = int(ds_cleaned.capacity.max()+dw_cleaned.capacity.max())*(int(snakemake.wildcards['demand_factor'])/100) - load = float( - ( - (ds_cleaned.capacity * ds_cleaned["capacity factor"]).sum( - dim=["time", "class"] - ) - + (dw_cleaned.capacity * dw_cleaned["capacity factor"]).sum( - dim=["time", "class"] - ) - # + - # ( - # dww_cleaned.capacity * dww_cleaned["capacity factor"] - # ).sum(dim=["time","class"]) - ) - / 8760 - * (int(snakemake.wildcards["demand_factor"]) / 100) - ) - print("load:", load) - print("diff:", max_load - load) - print("data loaded successfully") - - # building model - print("building model") - network = building_model( - ds_cleaned, dw_cleaned, dc, load, snakemake.config["hydrogen_storage_cost"] - ) - - # solving model - print("solving model") - network.optimize( - network.snapshots, - solver_name="gurobi", - solver_options={ - "crossover": 0, - "method": 2, - "BarConvTol": 1.0e-5, - "OptimalityTol": 1.0e-5, - }, - ) - # , "barHomogeneous":1, "FeasibilityTol": 1.e-5, - print("network was solved succesfully") - - # saving results and calculating LCOH - print("saving results and calculating lcoh") - save_lcoh(network) diff --git a/workflow/scripts/model_lcox.py b/workflow/scripts/model_lcox.py deleted file mode 100644 index 5dc4fd7..0000000 --- a/workflow/scripts/model_lcox.py +++ /dev/null @@ -1,424 +0,0 @@ -import pypsa -import pandas as pd -import numpy as np - -# import matplotlib.pyplot as plt -# from pyomo.environ import Constraint -import xarray as xr - - -def calc_annuity(i_rate, lifetime): - # calculating annuity factor with interest rate and lifetime - return i_rate / (1 - (1 + i_rate) ** (-lifetime)) - - -def calc_cap_cost(costs, tech, i_rate): - # select the relevant part of the dataframe - sub_data = costs.loc[costs["technology"] == tech] - - # save values (not all tech has FOM, so setting FOM to zero in that case) - if len(sub_data["value"].loc[sub_data["parameter"] == "FOM"].values) > 0: - FOM = sub_data["value"].loc[sub_data["parameter"] == "FOM"].values - else: - FOM = 0 - - CAPEX = sub_data["value"].loc[sub_data["parameter"] == "investment"].values - lifetime = sub_data["value"].loc[sub_data["parameter"] == "lifetime"].values - - # calculate parameters - annuity = calc_annuity(i_rate, lifetime) - - # returns cap costs in EUR/MW - return (annuity + FOM / 100) * CAPEX * 1e3 - - -def rename_trace_carriers(n): - - # Index name and new carrier - carrier_rename_dict = { - "electrolysis (exp)": "electrolysis", - "battery inverter (charging, exp)": "battery inverter (charging)", - "battery inverter (discharging, exp)": "battery inverter (discharging)", - "hydrogen direct iron reduction furnace": "direct reduction furnace", - "electric arc furnace": "electric arc furnace", - } - - nice_names = { - "electrolysis": "electrolysis", - "battery inverter (charging)": "battery inverter (charging)", - "battery inverter (discharging)": "battery inverter (discharging)", - "direct reduction furnace": "direct reduction furnace", - "electric arc furnace": "electric arc furnace", - } - colors = snakemake.config["colors"] - - n.add( - "Carrier", - carrier_rename_dict.values(), - nice_name=[nice_names[carrier] for carrier in carrier_rename_dict.values()], - color=[colors[carrier] for carrier in nice_names.values()], - ) - - for idx, new_carrier in carrier_rename_dict.items(): - n.links.loc[idx, "carrier"] = new_carrier - - # Adjust colors of all carriers, overwriting the TRACE colors - for carrier in n.carriers.index: - n.carriers.loc[carrier, "color"] = colors[carrier] - - # Add carrier to DRI generator - n.generators.loc["iron ore DRI-ready (exp)", "carrier"] = "iron ore" - - return n - - -def remove_shipping_importer_components(n): - # Remove trace shipping components - n.remove( - "Link", - ["ship loading (exp)", "ship unloading (imp)"], - ) - n.remove("Bus", ["berth (exp)", "berth (imp)", "steel (imp)"]) - n.remove("Store", ["steel storage (exp)", "steel storage (imp)"]) - - return n - - -# inputs are solar potentials, wind potentials, costs and load -def building_model(n, ds, dw, dc, load, h_cost, iron_ore_cost): - - if product != "eaf-grid": - # adding wind and solar generators on el bus - interest_rate = snakemake.params.interest_rate - wind_cost = calc_cap_cost(dc, "onwind", interest_rate) - # offshore_wind_cost = calc_cap_cost(dc,"offwind",interest_rate) - solar_cost = calc_cap_cost(dc, "solar-utility", interest_rate) - - # for every class in solar data - print("---------------------------- starting with solar data ") - for i in range(0, len(ds.capacity)): - # "time":slice("2013-01-01 00:00", "2013-01-30 14:00"), - sol_df = ds.sel({"class": ds["class"][i]}) - - # costs taken from dae: solar costs - n.add( - "Generator", - "pv {}".format(i), - bus="electricity (exp)", - carrier="pv", - p_nom_extendable=True, - p_nom_max=sol_df["capacity"].to_pandas().item() - * pv_p_nom_max_cor, # this will be ds.capacities - p_max_pu=sol_df["capacity factor"] - .to_pandas() - .clip(lower=0), # this will be ds.profiles - capital_cost=solar_cost[ - 0 - ], # EUR/MW, this will be read in from costs file - ) - - # for every class in onshore wind data - print("---------------------------- starting with wind data ") - for i in range(0, len(dw.capacity)): - wind_df = dw.sel({"class": dw["class"][i]}) - - n.add( - "Generator", - "onwind {}".format(i), - bus="electricity (exp)", - carrier="wind", - p_nom_extendable=True, - p_nom_max=wind_df["capacity"].to_pandas().item() * onwind_p_nom_max_cor, - p_max_pu=wind_df["capacity factor"] - .to_pandas() - .clip(lower=0), # read in from potentials file - capital_cost=wind_cost[ - 0 - ], # EUR/MW, read in from costs file and calculated in above function - ) - - # for every class in offshore wind data - # for i in range (0,len(dww.capacity)): - # offshore_wind_df = dww.sel({"class":dww["class"][i]}) - - # network.add( - # "Generator", - # "off_wind turbine {}".format(i), - # bus="bus el", - # carrier="wind", - # #p_nom = 8, #this is capacity - # p_nom_extendable=True, - # #p_nom_min = 8, - # p_nom_max = offshore_wind_df["capacity"].to_pandas(), - # p_max_pu= offshore_wind_df["capacity factor"].to_pandas(), #read in from potentials file - # capital_cost= offshore_wind_cost #EUR/MW, read in from costs file and calculated in above function - # ) - - elif product == "eaf-grid": - # adding only electricity grid on el bus for eaf-grid case - n.add( - "Generator", - "grid-electricity", - bus="electricity (exp)", - carrier="electricity", - p_nom_extendable=True, - p_nom_max=np.inf, - capital_cost=snakemake.config["grid_electricity"]["capital_cost"], # EUR/MW - marginal_cost=snakemake.config["grid_electricity"][ - "marginal_cost" - ], # EUR/MW - ) - - else: - raise ValueError("product not recognized, choose steel, hbi, eaf, eaf-grid") - - # hydrogen cost can either be 0 or real cost. Real cost is the default of the imported network - if not h_cost: - n.stores.at[ - "hydrogen storage tank type 1 including compressor (exp)", "capital_cost" - ] = 0 - - if not iron_ore_cost: - n.generators.at["iron ore DRI-ready (exp)", "marginal_cost"] = 0 - - # Remove trace shipping components - n = remove_shipping_importer_components(n) - - if product == "steel": - # p_set unit in MW - n.add("Load", "load", bus="steel (exp)", carrier="steel", p_set=load) - - elif product == "hbi": - # Remove steel components from the network - n.remove( - "Link", - ["electric arc furnace"], - ) - n.remove("Bus", ["steel (exp)"]) - n.remove("Carrier", ["steel", "electric arc furnace"]) - - # p_set unit in MW - n.add( - "Load", - "load", - bus="hot briquetted iron (exp)", - carrier="hot briquetted iron", - p_set=load, - ) - - elif product in ["eaf", "eaf-grid"]: - # Remove components up to hbi and leave eaf/steel components - n.remove( - "Link", - ["electrolysis (exp)", "hydrogen direct iron reduction furnace"], - ) - n.remove( - "Bus", - ["hydrogen (g) (exp)", "hydrogen (g) storage (exp)", "iron ore (exp)"], - ) - n.remove( - "Carrier", - [ - "hydrogen", - "iron ore", - "electrolysis", - "direct reduction furnace", - ], - ) - n.remove( - "Store", - [ - "hydrogen storage tank type 1 including compressor (exp)", - "HBI storage (exp)", - ], - ) - - n.remove("Generator", ["iron ore DRI-ready (exp)"]) - - # Add Generator as HBI input (at no cost) - n.add( - "Generator", - "hbi input", - bus="hot briquetted iron (exp)", - carrier="hot briquetted iron", - p_nom_extendable=True, - capital_cost=0.1, - marginal_cost=0.1, - ) - - # p_set unit in MW - n.add("Load", "load", bus="steel (exp)", carrier="steel", p_set=load) - - else: - raise ValueError("product not recognized, choose steel, hbi, eaf, eaf-grid") - - print("network load: ", load) - - return n - - -def save_lcox(solved_network): - # creating dataframe for saving - res = pd.DataFrame( - columns=[ - "demand factor [%]", - "demand [t]", - "load [t/h]", - "cost [EUR]", - "LCOX [EUR/t]", - ] - ) - - try: - obj = solved_network.objective - if obj is None: - raise AttributeError - except AttributeError: - # if infeasible - print("saving infeasible network") - res.loc[res.shape[0]] = [ - snakemake.wildcards["demand_factor"], - load * 8760, - load, - "infeasible", - "infeasible", - ] - else: - # if feasible - print("saving feasible network") - res.loc[res.shape[0]] = [ - snakemake.wildcards["demand_factor"], - load * 8760, - load, - solved_network.objective, - solved_network.objective / (load * 8760), - ] - - # saving network and dataframe - solved_network.export_to_netcdf(snakemake.output.network) - res.to_csv(snakemake.output.results) - return - - -def solve_network(n): - - solver_name = snakemake.config["solver"]["name"] - options = snakemake.config["solver_options"][snakemake.config["solver"]["options"]] - - print("solving model") - n.optimize(n.snapshots, solver_name=solver_name, solver_options=options) - # , "barHomogeneous":1, "FeasibilityTol": 1.e-5, - print("network was solved succesfully") - - return n - - -def prepare_re(d): - - # subselecting each technology and cleaning for "0 and nan" - capacity values - ds = d.sel({"technology": "pvplant"}) - ds_cleaned = ds.where(ds.capacity > 0.0, drop=True) - dw = d.sel({"technology": "windonshore"}) - dw_cleaned = dw.where(dw.capacity > 0.0, drop=True) - # dww = d.sel({"technology":"windoffshore"}) - # dww_cleaned = dww.where(dww.capacity > 0.0,drop=True) - - return ds_cleaned, dw_cleaned - - -def calculate_load(ds_cleaned, dw_cleaned, pv_p_nom_max_cor, onwind_p_nom_max_cor): - - # calculating (max) load - max_load = ( - int( - ( - ds_cleaned.capacity * pv_p_nom_max_cor * ds_cleaned["capacity factor"] - ).sum(dim=["time", "class"]) - + ( - dw_cleaned.capacity - * onwind_p_nom_max_cor - * dw_cleaned["capacity factor"] - ).sum(dim=["time", "class"]) - # + - # ( - # dww_cleaned.capacity * dww_cleaned["capacity factor"] - # ).sum(dim=["time","class"]) - ) - / 8760 - / snakemake.config["electricity_steel_ratio"] - ) - - load = max_load * (float(snakemake.wildcards["demand_factor"]) / 100) - - print( - f"max load hydrogen, (solar+onwind corrected)/{snakemake.config['electricity_steel_ratio']}: {max_load:.1f}" - ) - print(f"load steel with demand factor: {load:.1f}") - - return load - - -def adjust_part_load(n): - - print(f"adjusting part-load limits for {snakemake.config['part_load'].keys()}") - - for carrier in snakemake.config["part_load"].keys(): - n.links.loc[ - n.links.carrier == carrier, - "p_min_pu", - ] = snakemake.config["part_load"][carrier] - - return n - - -if __name__ == "__main__": - if "snakemake" not in globals(): - from _helpers import mock_snakemake - - snakemake = mock_snakemake( - "model_lcox", - cost_year="2030", - region="Europe", - product="hbi", - ) - - # making dataframes from inputs - dc = pd.read_csv(snakemake.input.costs, header=0) - d = xr.open_dataset(snakemake.input.supply_data) - - # load TRACE steel model - n = pypsa.Network(snakemake.input.trace) - n = rename_trace_carriers(n) - n = adjust_part_load(n) - - # Get correction factors and product - pv_p_nom_max_cor = snakemake.config["pv_p_nom_max_cor"] - onwind_p_nom_max_cor = snakemake.config["onwind_p_nom_max_cor"] - product = snakemake.wildcards.product - - # preparing RE data - ds_cleaned, dw_cleaned = prepare_re(d) - - # calculating load - load = calculate_load( - ds_cleaned, dw_cleaned, pv_p_nom_max_cor, onwind_p_nom_max_cor - ) - - # building model - print("adding RE to network") - n = building_model( - n, - ds_cleaned, - dw_cleaned, - dc, - load, - snakemake.config["hydrogen_storage_cost"], - snakemake.config["iron_ore_cost_in_supply_chain"], - ) - - # solving model - n = solve_network(n) - - # saving results and calculating LCOX - print("saving results and calculating lcoX") - save_lcox(n) From 396d4fa3266482dec14f06a464caac128a42018b Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 7 Apr 2026 11:34:06 +0200 Subject: [PATCH 033/216] chore: implement better markdown style - each sentence in a new line --- README.md | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 5afd0b5..d6784c2 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,14 @@ # SHIFT – Steel & Hydrogen Integrated Freight Trade -This repository contains the **SHIFT model**, a spatially resolved techno-economic optimization of global iron and steel supply chains under decarbonization. It explores how hydrogen-based direct reduced iron (DRI) production and hot-briquetted iron (HBI) trade can shift value creation to regions with renewable energy and capital availability. +This repository contains the **SHIFT model**, a spatially resolved techno-economic optimization of global iron and steel supply chains under decarbonization. +It explores how hydrogen-based direct reduced iron (DRI) production and hot-briquetted iron (HBI) trade can shift value creation to regions with renewable energy and capital availability. The model identifies cost-optimal configurations for mining, hydrogen production, DRI processing, and HBI trade, using a two-stage optimization pipeline and open energy system libraries. ## Introduction SHIFT -SHIFT evaluates global supply and trade of low-carbon iron and steel at high spatial resolution. The model quantifies where to produce, where to ship, and how to meet demand cost-effectively under decarbonization constraints. +SHIFT evaluates global supply and trade of low-carbon iron and steel at high spatial resolution. +The model quantifies where to produce, where to ship, and how to meet demand cost-effectively under decarbonization constraints. Key features: - 🚀 two-stage process: greenfield supply curves + cross-region LP trade @@ -43,7 +45,6 @@ pre-commit install ``` - For details, see https://pixi.prefix.dev/latest/ (or your local PIXI docs). ## Run (core workflow) @@ -66,21 +67,29 @@ pixi run snakemake -call collect_figures ### Step 0: Renewable potentials (Atlite + GIS) -In this stage we generate the supply-side resource backbone. PyPSA-Earth assembles spatial inputs (country polygons, exclusion masks, weather datasets) and computes hourly capacity-factor series and maximum deployable potentials for wind and solar in each region. The workflow uses the [`build_renewable_profiles`](https://pypsa-earth.readthedocs.io/en/latest/user-guide/rules-reference/populate/build-renewable-profiles/) Snakefile rule, and it can produce .nc outputs for per-region, per-technology capacity factor distributions and installable potentials. +In this stage we generate the supply-side resource backbone. +PyPSA-Earth assembles spatial inputs (country polygons, exclusion masks, weather datasets) and computes hourly capacity-factor series and maximum deployable potentials for wind and solar in each region. +The workflow uses the [`build_renewable_profiles`](https://pypsa-earth.readthedocs.io/en/latest/user-guide/rules-reference/populate/build-renewable-profiles/) Snakefile rule, and it can produce .nc outputs for per-region, per-technology capacity factor distributions and installable potentials. > **Note:** SHIFT may consume precomputed Step 0 datasets to avoid the long runtime of full GIS processing; this is the recommended default for day-to-day scenario work. > ### Step 1: Greenfield supply curve generation (PyPSA) -With renewable profiles and [techno-economic assumptions](https://github.com/PyPSA/technology-data) in place, SHIFT builds regional PyPSA optimization models to size generation, storage, and process assets. It evaluates each candidate plant (H2 electrolyser, DRI furnace, HBI plant, steel mills) across resource quality and cost parameters to produce levelized cost curves (LCOX) as a function of capacity. The result is a fleet of supply curve elements (capacity buckets with marginal costs and metadata) for H2, DRI, HBI, and steel by region. +With renewable profiles and [techno-economic assumptions](https://github.com/PyPSA/technology-data) in place, SHIFT builds regional PyPSA optimization models to size generation, storage, and process assets. +It evaluates each candidate plant (H2 electrolyser, DRI furnace, HBI plant, steel mills) across resource quality and cost parameters to produce levelized cost curves (LCOX) as a function of capacity. +The result is a fleet of supply curve elements (capacity buckets with marginal costs and metadata) for H2, DRI, HBI, and steel by region. -Each greenfield run schemes the spot around: location selection, renewable share, process stack, cost adders, and available build option integration. The output is a harmonized set of offer curves used as input for the trade stage. +Each greenfield run schemes the spot around: location selection, renewable share, process stack, cost adders, and available build option integration. +The output is a harmonized set of offer curves used as input for the trade stage. ### Step 2: Global trade optimization (LP) -This stage takes regional supply curves and demand obligations, then runs a linear program over the regional network. It includes transport cost matrices, ore production constraints, and market compatibility. The solver decides how much each region should produce versus import/export, by product and route. +This stage takes regional supply curves and demand obligations, then runs a linear program over the regional network. +It includes transport cost matrices, ore production constraints, and market compatibility. +The solver decides how much each region should produce versus import/export, by product and route. -The trade solution yields detailed outputs: regional production volume and shipped quantities. It can also be reconciled with scenarios for demand, policy constraints, and infrastructure availability. +The trade solution yields detailed outputs: regional production volume and shipped quantities. +It can also be reconciled with scenarios for demand, policy constraints, and infrastructure availability. ## Acknowledgements From 31d401cbe934dba902922058e35235f2d0f6abfb Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 8 Apr 2026 16:47:21 +0200 Subject: [PATCH 034/216] feat: add comprehensive renewable potential processing --- pixi.lock | 34 + pixi.toml | 2 + workflow/notebooks/prepare_potentials.ipynb | 2975 +++++++------------ 3 files changed, 1139 insertions(+), 1872 deletions(-) diff --git a/pixi.lock b/pixi.lock index 0b047ad..06abe15 100644 --- a/pixi.lock +++ b/pixi.lock @@ -109,6 +109,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.7.0-py313h6b9daa2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.2.0-pyhd8ed1ab_0.conda - 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conda: https://conda.anaconda.org/conda-forge/noarch/pydeck-0.9.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda @@ -2265,6 +2275,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/frozenlist-1.7.0-py313h0c48a3b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.2.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geographiclib-2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/geohash2-1.1-py_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/geojson-3.2.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.1.3-pyha770c72_0.conda @@ -2425,6 +2436,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-23.0.1-py313hc76bb52_0_cpu.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pyasn1-0.6.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pyasn1-modules-0.4.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycountry-24.6.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pydeck-0.9.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda @@ -5087,6 +5099,16 @@ packages: - pkg:pypi/geographiclib?source=hash-mapping size: 40836 timestamp: 1755865181359 +- conda: https://conda.anaconda.org/conda-forge/noarch/geohash2-1.1-py_0.tar.bz2 + sha256: 9652ffe18e48fd9a217e4ed1cc162f8f5d568c57646e480216da6316eee3497a + md5: d8eeeda3a0fd791973bc8be2ac8179fe + depends: + - python + license: GPL-3.0 + license_family: GPL + purls: + - pkg:pypi/geohash2?source=hash-mapping + size: 5709 - conda: https://conda.anaconda.org/conda-forge/noarch/geojson-3.2.0-pyhd8ed1ab_0.conda sha256: bfa3b5159e6696872586b2154ff9956e7e81d86d85a6a5601d25b26ca2bb916d md5: 9f9840fb1c2e009fb0009a2f9461e64a @@ -10596,6 +10618,18 @@ packages: - pkg:pypi/pycodestyle?source=hash-mapping size: 35182 timestamp: 1750616054854 +- conda: https://conda.anaconda.org/conda-forge/noarch/pycountry-24.6.1-pyhd8ed1ab_0.conda + sha256: de60a268ee916eab46016e8b76b6bbd858710dcedeb7188d5e100b863c24cd1c + md5: 62ed8c560f1b5b8d74ed11e68e9ae223 + depends: + - python >=3.6,<4.0 + - setuptools + license: LGPL-2.1-or-later + license_family: LGPL + purls: + - pkg:pypi/pycountry?source=hash-mapping + size: 3105570 + timestamp: 1718094617616 - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda sha256: 79db7928d13fab2d892592223d7570f5061c192f27b9febd1a418427b719acc6 md5: 12c566707c80111f9799308d9e265aef diff --git a/pixi.toml b/pixi.toml index 94786a3..d39dcfa 100644 --- a/pixi.toml +++ b/pixi.toml @@ -32,6 +32,8 @@ libgdal-netcdf = "*" # Geospatial and mapping geopandas = ">=1" +pycountry = "*" +geohash2 = "*" # Scientific computing scipy = "*" diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index e51772c..b9116c0 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -1,17 +1,48 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "083dc0a6", + "metadata": {}, + "source": [ + "# Renewable Energy Profiles Dataset Construction\n", + "\n", + "This notebook builds per-bus renewable energy profiles (capacity factors, potentials, geometry) from PyPSA-Earth data sources without clustering. The output is a single NetCDF file containing hourly time series for all geographic regions and renewable technologies.\n", + "\n", + "## Workflow\n", + "1. **Setup & Configuration**: Initialize paths and logging\n", + "2. **Load Data**: Import renewable profiles and geojson region boundaries\n", + "3. **Inspect Data**: Validate data structure and bus-region matching\n", + "4. **Build Profiles**: Extract and aggregate per-bus profiles with energy conservation validation\n", + "5. **Analyze Results**: Inspect output and validate energy conservation\n" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "cd848044", "metadata": {}, "outputs": [], "source": [ + "\"\"\"\n", + "SHIFT Renewable Profiles Builder\n", + "=================================\n", + "Module for constructing per-bus renewable energy profiles from PyPSA-Earth data.\n", + "\"\"\"\n", + "\n", "import os\n", "import sys\n", - "\n", - "\n", - "# Set required root pointers\n", + "import logging\n", + "import xarray as xr # Multidimensional array I/O (NetCDF)\n", + "import geopandas as gpd # Geospatial vector data\n", + "import pandas as pd # Tabular data manipulation\n", + "import numpy as np # Numerical computing\n", + "import pycountry # Country code conversion\n", + "import geohash2 # Spatial hashing for bus IDs\n", + "from datetime import datetime # Timestamping output files\n", + "from tqdm import tqdm # Progress bars\n", + "\n", + "# ===== SETUP: Environment Paths =====\n", "SHIFT_PATH = os.path.abspath(os.path.join(os.getcwd(), \"../../..\", \"shift\"))\n", "if not os.path.isdir(SHIFT_PATH):\n", " raise FileNotFoundError(\n", @@ -24,49 +55,36 @@ " f\"pypsa-earth not found at {PYPSA_EARTH_PATH}. Please clone or set correct path.\"\n", " )\n", "\n", - "# Change working directory to shift root\n", "os.chdir(SHIFT_PATH)\n", - "\n", "sys.path.append(SHIFT_PATH)\n", - "sys.path.append(PYPSA_EARTH_PATH)" + "sys.path.append(PYPSA_EARTH_PATH)\n", + "\n", + "# ===== SETUP: Logging Configuration =====\n", + "logging.basicConfig(\n", + " level=logging.INFO, format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n", + ")\n", + "logger = logging.getLogger(__name__)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "56730027", "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.cluster import KMeans\n", - "import pycountry\n", - "\n", - "\n", - "# Custom formatter to hide full paths in git commits\n", - "class RelativePathFormatter(logging.Formatter):\n", - " def format(self, record):\n", - " # Convert full path to relative path\n", - " record.pathname = os.path.relpath(record.pathname)\n", - " # Format: relative/path/file.py - message\n", - " return f\"{record.pathname} - {record.getMessage()}\"\n", - "\n", - "\n", - "handler = logging.StreamHandler()\n", - "handler.setFormatter(RelativePathFormatter())\n", - "logger = logging.getLogger()\n", - "logger.handlers.clear()\n", - "logger.addHandler(handler)\n", - "logger.setLevel(logging.INFO)" - ] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-08 15:57:19,988 - numexpr.utils - INFO - NumExpr defaulting to 16 threads.\n" + ] + } + ], + "source": [] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "d6dad14e", "metadata": {}, "outputs": [ @@ -74,660 +92,135 @@ "name": "stderr", "output_type": "stream", "text": [ + "2026-04-08 16:00:44,203 - __main__ - INFO - Loading 3 renewable technology profiles...\n", "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", "Cannot find the ecCodes library\n", " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2480569425.py - Loaded onwind profiles\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2480569425.py - Loaded offwind-ac profiles\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\2480569425.py - Loaded solar profiles\n" + "2026-04-08 16:00:44,583 - __main__ - INFO - ✓ Loaded all 3 profiles\n" ] } ], "source": [ + "# ===== SECTION 2: Load Renewable Technology Profiles =====\n", + "\"\"\"\n", + "Load capacity factor and potential data for onshore wind, offshore wind, and solar.\n", + "\n", + "Note: Source data contains 8784 hours (leap year or extra 24h).\n", + "Trimming to 8760 hours (calendar year 2013) occurs during extraction.\n", + "\"\"\"\n", + "\n", "tech_profiles_nc = {}\n", - "for technology in [\"onwind\", \"offwind-ac\", \"solar\"]:\n", + "technologies = [\"onwind\", \"offwind-ac\", \"solar\"]\n", + "\n", + "logger.info(f\"Loading {len(technologies)} renewable technology profiles...\")\n", + "for technology in technologies:\n", " path = (\n", " os.path.realpath(PYPSA_EARTH_PATH)\n", " + f\"/resources/renewable_profiles/profile_{technology}.nc\"\n", " )\n", " ds = xr.open_dataset(path)\n", " tech_profiles_nc[technology] = ds\n", - " logging.info(f\"Loaded {technology} profiles\")" + " n_hours = len(ds.coords.get(\"time\", ds.coords.get(\"hour\", [])))\n", + " logger.debug(\n", + " f\" {technology}: {n_hours} hours, {len(ds.bus)} buses, grid {len(ds.x)}×{len(ds.y)}\"\n", + " )\n", + "\n", + "logger.info(f\"✓ Loaded all {len(technologies)} profiles\")" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "f34822f5", + "execution_count": 5, + "id": "057b6873", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "

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    <xarray.Dataset> Size: 71MB\n",
-       "Dimensions:           (time: 8760, bus: 1009, y: 78, x: 131)\n",
-       "Coordinates:\n",
-       "  * time              (time) datetime64[ns] 70kB 2013-01-01 ... 2013-12-31T23...\n",
-       "  * bus               (bus) <U4 16kB '176' '180' '182' ... '1258' '1259' '1260'\n",
-       "  * y                 (y) float64 624B 32.4 32.7 33.0 33.3 ... 54.9 55.2 55.5\n",
-       "  * x                 (x) float64 1kB -31.5 -31.2 -30.9 -30.6 ... 6.9 7.2 7.5\n",
-       "Data variables:\n",
-       "    profile           (time, bus) float64 71MB ...\n",
-       "    weight            (bus) float64 8kB ...\n",
-       "    p_nom_max         (bus) float64 8kB ...\n",
-       "    potential         (y, x) float64 82kB ...\n",
-       "    average_distance  (bus) float64 8kB ...
    " - ], - "text/plain": [ - " Size: 71MB\n", - "Dimensions: (time: 8760, bus: 1009, y: 78, x: 131)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 70kB 2013-01-01 ... 2013-12-31T23...\n", - " * bus (bus) threshold.\n", + "\n", + " output_dir : str, default=\"data\"\n", + " Directory for output file (created if doesn't exist).\n", + "\n", + " filename_prefix : str\n", + " Prefix for output NetCDF file.\n", "\n", " Returns\n", " -------\n", - " clusters_ds : xr.Dataset\n", - " Clustered dataset with dimensions [cluster, technology, hour].\n", - "\n", - " Data variables:\n", - " - renewable_potential: Sum of raw potentials per cluster-technology (MW).\n", - " Preserved exactly during clustering - no energy loss.\n", - " - avg_cf: Mean capacity factor from clustered regions (0-1).\n", - " Averaged timeseries introduces distortion—may not represent\n", - " individual region characteristics.\n", - " - p_nom_max: Installed capacity needed to preserve renewable_potential\n", - " with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", - " Compare to raw region p_nom_max to quantify clustering distortion.\n", - " - capacity_factor: Hourly profiles [cluster, technology, hour].\n", - " Average of regional CF timeseries.\n", - "\n", - " Coordinates:\n", - " - cluster: Unique cluster IDs (format: ISO3_[ON|OFF]_##)\n", - " - iso3: Country code per cluster\n", - " - technology: Available technologies\n", - " - hour: Hour of year (0-8759)\n", - " - lat, lon: Cluster centroid coordinates\n", - " - area_km2: Total area of regions in cluster\n", - " \"\"\"\n", + " dataset : xr.Dataset\n", + " Renewable profiles dataset with dimensions [bus, technology, hour].\n", "\n", - " # Calculate cluster counts based on ratio\n", - " n_clusters_onshore = max(1, round(total_clusters * onshore_ratio))\n", - " n_clusters_offshore = max(1, round(total_clusters * (1 - onshore_ratio)))\n", + " validation_report : dict\n", + " Report with 'total_buses', 'flagged_buses', 'flagged_entries', 'summary'.\n", + " \"\"\"\n", "\n", - " # Safety check: verify the sum matches user-specified total\n", - " total_allocated = n_clusters_onshore + n_clusters_offshore\n", - " if total_allocated == total_clusters:\n", - " logging.info(\n", - " f\"✓ Cluster allocation valid: {n_clusters_onshore} onshore + {n_clusters_offshore} offshore = {total_clusters} total\"\n", - " )\n", - " else:\n", - " logging.warning(\n", - " f\"⚠ Cluster allocation mismatch: {n_clusters_onshore} onshore + {n_clusters_offshore} offshore = {total_allocated}, \"\n", - " f\"but user requested {total_clusters} total. Difference: {total_allocated - total_clusters} clusters.\"\n", - " )\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " technologies = list(tech_profiles_nc.keys())\n", "\n", - " # Prepare regions (already have ISO3 country codes)\n", + " # Merge regions: onshore + offshore with type label\n", " all_regions = pd.concat(\n", " [\n", - " onshore_regions_gpd.assign(type=\"onshore\"),\n", - " offshore_regions_gpd.assign(type=\"offshore\"),\n", + " onshore_regions_gpd.assign(onshore_offshore=\"onshore\"),\n", + " offshore_regions_gpd.assign(onshore_offshore=\"offshore\"),\n", " ],\n", " ignore_index=True,\n", " )\n", "\n", " all_regions[\"iso3\"] = all_regions[\"country\"]\n", - " # Extract geographic coordinates for grid matching (warning suppressed - intentional use of geographic CRS)\n", + " logger.info(\n", + " f\"Processing {len(all_regions)} regions ({len(onshore_regions_gpd)} onshore, {len(offshore_regions_gpd)} offshore)\"\n", + " )\n", + "\n", + " # ===== STAGE 1: Extract coordinates and generate bus IDs =====\n", + " # Extract centroids for geohashing\n", " import warnings\n", "\n", " with warnings.catch_warnings():\n", " warnings.filterwarnings(\"ignore\", message=\"Geometry is in a geographic CRS\")\n", " all_regions[\"lon\"] = all_regions.geometry.centroid.x\n", " all_regions[\"lat\"] = all_regions.geometry.centroid.y\n", - " # Also compute projected centroid for KMeans (more accurate for clustering)\n", - " all_regions_projected = all_regions.to_crs(\"EPSG:3857\")\n", - " all_regions[\"x_proj\"] = all_regions_projected.geometry.centroid.x\n", - " all_regions[\"y_proj\"] = all_regions_projected.geometry.centroid.y\n", - "\n", - " # Filter by country codes if specified\n", - " if country_codes is not None:\n", - " iso3_filter = []\n", - " for code in country_codes:\n", - " try:\n", - " if len(code) == 2:\n", - " iso3_filter.append(\n", - " pycountry.countries.get(alpha_2=code.upper()).alpha_3\n", - " )\n", - " else:\n", - " iso3_filter.append(code.upper())\n", - " except AttributeError:\n", - " logging.warning(f\"Unknown country code: {code}\")\n", - " all_regions = all_regions[all_regions[\"iso3\"].isin(iso3_filter)]\n", "\n", - " technologies = list(tech_profiles_nc.keys())\n", - " iso3_codes = sorted(all_regions[\"iso3\"].unique())\n", - "\n", - " logging.info(f\"Clustering {len(iso3_codes)} countries: {iso3_codes}\")\n", - " logging.info(f\"Separate onshore/offshore: {separate_onshore_offshore}\")\n", + " # Generate geohash-based bus IDs with collision handling\n", + " bus_id_list = []\n", + " geohash_tracker = {} # Track duplicates\n", + "\n", + " for idx, row in tqdm(\n", + " all_regions.iterrows(),\n", + " total=len(all_regions),\n", + " desc=\"STAGE 1: Generating geohash IDs\",\n", + " unit=\"region\",\n", + " ):\n", + " iso3 = row[\"iso3\"]\n", + " on_off = \"ON\" if row[\"onshore_offshore\"] == \"onshore\" else \"OFF\"\n", + " lat, lon = row[\"lat\"], row[\"lon\"]\n", + "\n", + " # Generate geohash\n", + " gh = geohash2.encode(lat, lon, precision=geohash_precision)\n", + "\n", + " # Create base bus ID\n", + " base_bus_id = f\"{iso3}_{on_off}_{gh}\"\n", + "\n", + " # Handle collision: append index if not unique\n", + " if base_bus_id in geohash_tracker:\n", + " geohash_tracker[base_bus_id] += 1\n", + " bus_id = f\"{base_bus_id}_{geohash_tracker[base_bus_id]:03d}\"\n", + " else:\n", + " geohash_tracker[base_bus_id] = 0\n", + " bus_id = base_bus_id\n", "\n", - " cluster_list = []\n", + " bus_id_list.append(bus_id)\n", "\n", - " for iso3 in iso3_codes:\n", - " iso3_regions = all_regions[all_regions[\"iso3\"] == iso3]\n", - " logging.info(f\"\\n{iso3}: {len(iso3_regions)} regions\")\n", + " all_regions[\"bus_id\"] = bus_id_list\n", + " n_buses = len(all_regions)\n", + " collisions = sum(1 for cnt in geohash_tracker.values() if cnt > 0)\n", + " logger.info(\n", + " f\"✓ STAGE 1: Generated {n_buses} unique bus IDs ({collisions} geohash collisions handled)\"\n", + " )\n", "\n", - " # Cluster onshore and offshore separately if requested\n", - " if separate_onshore_offshore:\n", - " regions_to_cluster = [\n", - " (\"onshore\", iso3_regions[iso3_regions[\"type\"] == \"onshore\"].copy()),\n", - " (\"offshore\", iso3_regions[iso3_regions[\"type\"] == \"offshore\"].copy()),\n", - " ]\n", - " else:\n", - " regions_to_cluster = [(\"all\", iso3_regions.copy())]\n", + " # ===== STAGE 2: Extract geometry as WKT =====\n", + " geometry_wkt_list = []\n", + " for idx, row in tqdm(\n", + " all_regions.iterrows(),\n", + " total=len(all_regions),\n", + " desc=\"STAGE 2: Converting geometry to WKT\",\n", + " unit=\"region\",\n", + " ):\n", + " geometry_wkt_list.append(row.geometry.wkt)\n", + " all_regions[\"geometry_wkt\"] = geometry_wkt_list\n", + " logger.info(f\"✓ STAGE 2: Converted {len(all_regions)} geometries to WKT\")\n", + "\n", + " # ===== STAGE 3: Extract capacity factors and potentials =====\n", + " # OPTIMIZATION: Load all data per technology once, then trim, then index\n", + " logger.info(\"STAGE 3: Loading and extracting technology profiles...\")\n", + " tech_data_cache = {} # tech -> {profile_array, potential_array, bus_to_idx, x, y}\n", + "\n", + " for tech in tqdm(technologies, desc=\" Loading profiles\", unit=\"tech\"):\n", + " tech_ds = tech_profiles_nc[tech]\n", + "\n", + " # Load full profile array and trim to 8760 hours\n", + " profile_full = tech_ds[\"profile\"].load().values # Shape: [time, bus]\n", + " n_hours_original = profile_full.shape[0]\n", + " profile_trimmed = profile_full[:8760, :] # Trim to exactly 8760 hours\n", + " logger.debug(\n", + " f\" {tech}: loaded {profile_full.shape[0]} hours, trimmed to 8760\"\n", + " )\n", "\n", - " # Cluster each type\n", - " for region_type, type_regions in regions_to_cluster:\n", - " if len(type_regions) == 0:\n", - " logging.info(f\" Skipping {region_type}: no regions available\")\n", + " # Load potential array (gridded [y, x])\n", + " potential_array = tech_ds[\"potential\"].load().values\n", + "\n", + " # Get x, y coordinates for grid lookup\n", + " x_coords = tech_ds.x.values\n", + " y_coords = tech_ds.y.values\n", + "\n", + " # Build bus name → index mapping for fast lookup\n", + " bus_names = [str(b) for b in tech_ds.bus.values]\n", + " bus_to_idx = {name: idx for idx, name in enumerate(bus_names)}\n", + "\n", + " tech_data_cache[tech] = {\n", + " \"profile\": profile_trimmed,\n", + " \"potential\": potential_array,\n", + " \"bus_to_idx\": bus_to_idx,\n", + " \"x\": x_coords,\n", + " \"y\": y_coords,\n", + " \"original_hours\": n_hours_original,\n", + " }\n", + "\n", + " # Pre-allocate storage for efficiency\n", + " cf_data = {}\n", + " potential_data = {}\n", + " avg_cf_data = {}\n", + " validation_flags = {}\n", + "\n", + " logger.debug(f\"Extracting profiles for {n_buses} regions...\")\n", + " for bus_idx, (idx, region) in enumerate(\n", + " tqdm(\n", + " all_regions.iterrows(),\n", + " total=n_buses,\n", + " desc=\"STAGE 3: Extracting profiles\",\n", + " unit=\"bus\",\n", + " )\n", + " ):\n", + " bus_id = region[\"bus_id\"]\n", + " lon, lat = region[\"lon\"], region[\"lat\"]\n", + " bus_name = region[\"name\"]\n", + "\n", + " cf_data[bus_id] = {}\n", + " potential_data[bus_id] = {}\n", + " avg_cf_data[bus_id] = {}\n", + "\n", + " for tech in technologies:\n", + " tech_cache = tech_data_cache[tech]\n", + "\n", + " # Check if region exists in tech dataset\n", + " bus_name_clean = (\n", + " bus_name.replace(\"OFF_\", \"\")\n", + " if isinstance(bus_name, str) and bus_name.startswith(\"OFF_\")\n", + " else bus_name\n", + " )\n", + " bus_name_clean = str(bus_name_clean)\n", + "\n", + " if bus_name_clean not in tech_cache[\"bus_to_idx\"]:\n", + " # Region not in this tech dataset → fill with NaN\n", + " cf_data[bus_id][tech] = np.full(8760, np.nan)\n", + " potential_data[bus_id][tech] = np.nan\n", + " avg_cf_data[bus_id][tech] = np.nan\n", + " validation_flags[(bus_id, tech)] = True\n", " continue\n", "\n", - " # Determine requested clusters\n", - " if region_type == \"onshore\":\n", - " requested_clusters = n_clusters_onshore\n", - " elif region_type == \"offshore\":\n", - " requested_clusters = n_clusters_offshore\n", - " else:\n", - " requested_clusters = total_clusters\n", - "\n", - " # Fallback if not enough regions\n", - " n_clusters = min(requested_clusters, len(type_regions))\n", - " if n_clusters < requested_clusters:\n", - " logging.warning(\n", - " f\" {region_type}: Requested {requested_clusters} clusters but only \"\n", - " f\"{len(type_regions)} regions available. Creating {n_clusters} clusters instead.\"\n", - " )\n", + " # Fast index-based lookup (already in memory)\n", + " bus_idx_in_tech = tech_cache[\"bus_to_idx\"][bus_name_clean]\n", + " cf_array = tech_cache[\"profile\"][:, bus_idx_in_tech] # Shape: [8760]\n", + "\n", + " # Extract potential by nearest grid point\n", + " x_idx = np.argmin(np.abs(tech_cache[\"x\"] - lon))\n", + " y_idx = np.argmin(np.abs(tech_cache[\"y\"] - lat))\n", + " potential_val = tech_cache[\"potential\"][y_idx, x_idx]\n", + "\n", + " # Compute average CF\n", + " avg_cf = np.nanmean(cf_array) if not np.all(np.isnan(cf_array)) else np.nan\n", + "\n", + " # Store data\n", + " cf_data[bus_id][tech] = cf_array\n", + " potential_data[bus_id][tech] = potential_val\n", + " avg_cf_data[bus_id][tech] = avg_cf\n", + "\n", + " # Validate energy conservation: potential ≈ p_nom_max × avg_cf\n", + " # ONLY flag actual energy conservation errors, NOT missing data (NaN/zero)\n", + " if not np.isnan(avg_cf) and avg_cf > 0 and potential_val > 0:\n", + " # Valid data exists - check energy conservation\n", + " p_nom_max_implied = potential_val / avg_cf\n", + " reconstructed_potential = p_nom_max_implied * avg_cf\n", + " energy_diff = abs(potential_val - reconstructed_potential)\n", + " validation_flags[(bus_id, tech)] = energy_diff < energy_threshold_mw\n", " else:\n", - " logging.info(\n", - " f\" Clustering {region_type}: {len(type_regions)} regions → {n_clusters} clusters\"\n", - " )\n", + " # Missing data (NaN avg_cf, zero avg_cf, or zero potential) - NOT an error\n", + " validation_flags[(bus_id, tech)] = True\n", "\n", - " kmeans = KMeans(n_clusters=n_clusters, random_state=random_state)\n", - " # Use projected coordinates for accurate geographic clustering\n", - " region_coords = type_regions[[\"x_proj\", \"y_proj\"]].values\n", - " type_regions[\"cluster_id\"] = kmeans.fit_predict(region_coords)\n", - "\n", - " # Aggregate all technologies within each cluster\n", - " for cluster_id in range(n_clusters):\n", - " cluster_regions = type_regions[type_regions[\"cluster_id\"] == cluster_id]\n", - " region_names = cluster_regions[\"name\"].values\n", - "\n", - " # Generate cluster ID\n", - " if separate_onshore_offshore and region_type != \"all\":\n", - " type_label = \"ON\" if region_type == \"onshore\" else \"OFF\"\n", - " cluster_id_str = f\"{iso3}_{type_label}_{cluster_id + 1:02d}\"\n", - " else:\n", - " cluster_id_str = f\"{iso3}_{cluster_id + 1:02d}\"\n", - "\n", - " # Spatial aggregation\n", - " lat = cluster_regions[\"lat\"].mean()\n", - " lon = cluster_regions[\"lon\"].mean()\n", - " area_km2 = cluster_regions[\"area_km2\"].sum()\n", - "\n", - " # Aggregate each technology\n", - " for tech in technologies:\n", - " tech_ds = tech_profiles_nc[tech]\n", - "\n", - " # Get valid regions (strip OFF_ prefix for bus matching)\n", - " valid_regions = []\n", - " for r in region_names:\n", - " bus_name = r.replace(\"OFF_\", \"\") if r.startswith(\"OFF_\") else r\n", - " if str(bus_name) in [str(b) for b in tech_ds.bus.values]:\n", - " valid_regions.append(r)\n", - "\n", - " if len(valid_regions) == 0:\n", - " logging.warning(\n", - " f\" {tech}: No regions in cluster {cluster_id_str}\"\n", - " )\n", - " renewable_potential, avg_cf, cf_array = 0, 0, np.zeros(8760)\n", - " else:\n", - " # Average timeseries across all regions in cluster (preserves energy conservation)\n", - " cf_lists = []\n", - " for r in valid_regions:\n", - " bus_name = (\n", - " r.replace(\"OFF_\", \"\") if r.startswith(\"OFF_\") else r\n", - " )\n", - " cf_lists.append(tech_ds.sel(bus=bus_name).profile.values)\n", - " # Hourly capacity factor: averaged across all regions in this cluster\n", - " cf_array = np.mean(cf_lists, axis=0)\n", - " # Annual mean capacity factor for this cluster-technology combination\n", - " avg_cf = cf_array.mean()\n", - "\n", - " # Sum renewable generation potential across all regions in cluster (MW)\n", - " # This exact sum is preserved through clustering - no energy loss\n", - " region_rows = cluster_regions[\n", - " cluster_regions[\"name\"].isin(valid_regions)\n", - " ]\n", - " potential_list = []\n", - " for _, region in region_rows.iterrows():\n", - " lon_val, lat_val = region[\"lon\"], region[\"lat\"]\n", - " x_idx = np.argmin(np.abs(tech_ds.x.values - lon_val))\n", - " y_idx = np.argmin(np.abs(tech_ds.y.values - lat_val))\n", - " potential_val = tech_ds[\"potential\"].values[y_idx, x_idx]\n", - " potential_list.append(potential_val)\n", - " # Total renewable generation potential (MW) for cluster-technology\n", - " renewable_potential = np.sum(potential_list)\n", - "\n", - " # Store cluster aggregations for later xarray construction\n", - " cluster_list.append(\n", - " {\n", - " \"cluster_id\": cluster_id_str,\n", - " \"iso3\": iso3,\n", - " \"type\": region_type\n", - " if separate_onshore_offshore\n", - " else \"mixed\",\n", - " \"technology\": tech,\n", - " \"lat\": lat,\n", - " \"lon\": lon,\n", - " \"area_km2\": area_km2,\n", - " \"renewable_potential\": renewable_potential, # Sum of raw potentials (MW)\n", - " \"p_nom_max\": renewable_potential / avg_cf\n", - " if avg_cf > 0\n", - " else 0, # Installed capacity to preserve potential with clustered CF (MW)\n", - " \"avg_cf\": avg_cf, # Annual mean capacity factor (0-1)\n", - " \"cf_timeseries\": cf_array, # Hourly profiles for 8760 hours\n", - " }\n", - " )\n", - "\n", - " # Build xarray dataset with all cluster aggregations and time series\n", - " cluster_meta = pd.DataFrame(cluster_list)\n", - "\n", - " # Pre-compute data arrays for xarray construction\n", - " renewable_potential_array = (\n", - " cluster_meta.pivot_table(\n", - " index=\"cluster_id\",\n", - " columns=\"technology\",\n", - " values=\"renewable_potential\",\n", - " aggfunc=\"first\",\n", - " )\n", - " .reindex(technologies, axis=1)\n", - " .values\n", + " logger.info(\n", + " f\"✓ STAGE 3: Extracted capacity factors and potentials for {len(cf_data)} buses\"\n", " )\n", "\n", - " avg_cf_array = (\n", - " cluster_meta.pivot_table(\n", - " index=\"cluster_id\", columns=\"technology\", values=\"avg_cf\", aggfunc=\"first\"\n", - " )\n", - " .reindex(technologies, axis=1)\n", - " .values\n", + " # ===== STAGE 4: Construct xarray Dataset =====\n", + " sorted_bus_ids = sorted(cf_data.keys())\n", + " n_buses_final = len(sorted_bus_ids)\n", + "\n", + " # Prepare coordinate data\n", + " coord_dict = {\n", + " \"bus_id\": sorted_bus_ids,\n", + " \"country\": [\n", + " all_regions[all_regions[\"bus_id\"] == bid][\"iso3\"].iloc[0]\n", + " for bid in sorted_bus_ids\n", + " ],\n", + " \"onshore_offshore\": [\n", + " all_regions[all_regions[\"bus_id\"] == bid][\"onshore_offshore\"].iloc[0]\n", + " for bid in sorted_bus_ids\n", + " ],\n", + " \"x\": [\n", + " all_regions[all_regions[\"bus_id\"] == bid][\"lon\"].iloc[0]\n", + " for bid in sorted_bus_ids\n", + " ],\n", + " \"y\": [\n", + " all_regions[all_regions[\"bus_id\"] == bid][\"lat\"].iloc[0]\n", + " for bid in sorted_bus_ids\n", + " ],\n", + " \"area_km2\": [\n", + " all_regions[all_regions[\"bus_id\"] == bid]\n", + " .to_crs(\"EPSG:6933\")\n", + " .geometry.area.iloc[0]\n", + " / 1e6\n", + " for bid in sorted_bus_ids\n", + " ],\n", + " \"geometry_wkt\": [\n", + " all_regions[all_regions[\"bus_id\"] == bid][\"geometry_wkt\"].iloc[0]\n", + " for bid in sorted_bus_ids\n", + " ],\n", + " }\n", + "\n", + " # Build data variable arrays: [bus, technology, hour] for profiles\n", + " cf_array = np.full(\n", + " (n_buses_final, len(technologies), 8760), np.nan, dtype=np.float32\n", + " )\n", + " potential_array = np.full(\n", + " (n_buses_final, len(technologies)), np.nan, dtype=np.float32\n", " )\n", + " p_nom_max_array = np.full(\n", + " (n_buses_final, len(technologies)), np.nan, dtype=np.float32\n", + " )\n", + " avg_cf_array = np.full((n_buses_final, len(technologies)), np.nan, dtype=np.float32)\n", + " validation_flag_array = np.zeros((n_buses_final, len(technologies)), dtype=bool)\n", + "\n", + " for bus_idx, bus_id in enumerate(sorted_bus_ids):\n", + " for tech_idx, tech in enumerate(technologies):\n", + " cf_array[bus_idx, tech_idx, :] = cf_data[bus_id][tech]\n", + " potential_array[bus_idx, tech_idx] = potential_data[bus_id][tech]\n", + " avg_cf_array[bus_idx, tech_idx] = avg_cf_data[bus_id][tech]\n", + "\n", + " # Compute p_nom_max = potential / avg_cf (safe division)\n", + " if (\n", + " not np.isnan(avg_cf_array[bus_idx, tech_idx])\n", + " and avg_cf_array[bus_idx, tech_idx] > 0\n", + " ):\n", + " p_nom_max_array[bus_idx, tech_idx] = (\n", + " potential_array[bus_idx, tech_idx] / avg_cf_array[bus_idx, tech_idx]\n", + " )\n", + " else:\n", + " p_nom_max_array[bus_idx, tech_idx] = np.nan\n", + "\n", + " validation_flag_array[bus_idx, tech_idx] = validation_flags[(bus_id, tech)]\n", "\n", - " # Compute p_nom_max as backward derivation: renewable_potential / avg_cf\n", - " # Safe division handles division by zero (where avg_cf == 0, result is 0)\n", - " p_nom_max_array = np.divide(\n", - " renewable_potential_array,\n", - " avg_cf_array,\n", - " where=avg_cf_array > 0,\n", - " out=np.zeros_like(avg_cf_array),\n", + " # Build weight (potential-based normalization: sum of potentials across all technologies)\n", + " weight_array = np.nansum(potential_array, axis=1).astype(\n", + " np.float32\n", + " ) # Sum potentials across techs\n", + " weight_array = weight_array / weight_array.sum() # Normalize to sum to 1\n", + "\n", + " logger.debug(\n", + " f\"✓ STAGE 4: Built data arrays: {n_buses_final} buses × {len(technologies)} techs × 8760 hours\"\n", " )\n", "\n", - " clusters_ds = xr.Dataset(\n", + " # ===== STAGE 5: Create xarray Dataset =====\n", + " dataset = xr.Dataset(\n", " data_vars={\n", - " # Renewable generation potential per cluster-technology (MW) - exact sum, no distortion\n", - " \"renewable_potential\": (\n", - " [\"cluster\", \"technology\"],\n", - " renewable_potential_array,\n", - " ),\n", - " # Annual mean capacity factor (0-1) - averaged timeseries introduces clustering distortion\n", - " \"avg_cf\": ([\"cluster\", \"technology\"], avg_cf_array),\n", - " # Hourly capacity factor profiles (0-1) - to be used in timeseries optimization\n", " \"capacity_factor\": (\n", - " [\"cluster\", \"technology\", \"hour\"],\n", - " np.array([c[\"cf_timeseries\"] for c in cluster_list]).reshape(\n", - " len(cluster_list) // len(technologies), len(technologies), 8760\n", - " ),\n", + " [\"bus\", \"technology\", \"hour\"],\n", + " cf_array,\n", + " {\n", + " \"description\": \"Hourly capacity factor (0-1) for each bus-technology\",\n", + " \"units\": \"fraction\",\n", + " },\n", " ),\n", - " # Installed capacity needed to preserve renewable_potential with clustered CF timeseries: = renewable_potential / avg_cf (MW).\n", - " \"p_nom_max\": ([\"cluster\", \"technology\"], p_nom_max_array),\n", - " },\n", - " coords={\n", - " \"cluster\": sorted(\n", - " cluster_meta[\"cluster_id\"].unique()\n", - " ), # Unique cluster identifiers\n", - " # Country code for each cluster (for filtering/grouping by country)\n", - " \"iso3\": (\n", - " [\"cluster\"],\n", - " [\n", - " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"iso3\"].iloc[0]\n", - " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", - " ],\n", + " \"potential\": (\n", + " [\"bus\", \"technology\"],\n", + " potential_array,\n", + " {\n", + " \"description\": \"Generation potential (MW) extracted at bus location\",\n", + " \"units\": \"MW\",\n", + " },\n", " ),\n", - " \"technology\": technologies, # Technologies included (onwind, offwind-ac, solar)\n", - " \"hour\": np.arange(8760), # Hours in a year (0-8759)\n", - " # Geographic centroid coordinates for cluster visualization and spatial reference\n", - " \"lat\": (\n", - " [\"cluster\"],\n", - " [\n", - " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"lat\"].iloc[0]\n", - " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", - " ],\n", + " \"p_nom_max\": (\n", + " [\"bus\", \"technology\"],\n", + " p_nom_max_array,\n", + " {\n", + " \"description\": \"Maximum installable capacity (potential / avg_cf)\",\n", + " \"units\": \"MW\",\n", + " },\n", " ),\n", - " \"lon\": (\n", - " [\"cluster\"],\n", - " [\n", - " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"lon\"].iloc[0]\n", - " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", - " ],\n", + " \"avg_cf\": (\n", + " [\"bus\", \"technology\"],\n", + " avg_cf_array,\n", + " {\n", + " \"description\": \"Annual mean capacity factor (0-1)\",\n", + " \"units\": \"fraction\",\n", + " },\n", " ),\n", - " # Total area of regions included in each cluster (km²) - for density/intensity calculations\n", - " \"area_km2\": (\n", - " [\"cluster\"],\n", - " [\n", - " cluster_meta[cluster_meta[\"cluster_id\"] == c][\"area_km2\"].iloc[0]\n", - " for c in sorted(cluster_meta[\"cluster_id\"].unique())\n", - " ],\n", + " \"weight\": (\n", + " [\"bus\"],\n", + " weight_array,\n", + " {\n", + " \"description\": \"Bus weight (potential-normalized, sum of renewable potentials)\",\n", + " \"units\": \"fraction\",\n", + " },\n", " ),\n", + " \"energy_conservation_flag\": (\n", + " [\"bus\", \"technology\"],\n", + " validation_flag_array,\n", + " {\n", + " \"description\": \"True if energy conservation check passed\",\n", + " \"units\": \"bool\",\n", + " },\n", + " ),\n", + " },\n", + " coords={\n", + " \"bus\": (\"bus\", coord_dict[\"bus_id\"]),\n", + " \"country\": (\"bus\", coord_dict[\"country\"]),\n", + " \"onshore_offshore\": (\"bus\", coord_dict[\"onshore_offshore\"]),\n", + " \"x\": (\"bus\", np.array(coord_dict[\"x\"], dtype=np.float32)),\n", + " \"y\": (\"bus\", np.array(coord_dict[\"y\"], dtype=np.float32)),\n", + " \"area_km2\": (\"bus\", np.array(coord_dict[\"area_km2\"], dtype=np.float32)),\n", + " \"geometry_wkt\": (\"bus\", coord_dict[\"geometry_wkt\"]),\n", + " \"technology\": (\"technology\", technologies),\n", + " \"hour\": (\"hour\", np.arange(8760)),\n", " },\n", " )\n", "\n", - " logging.info(\n", - " f\"\\n✓ Created {len(clusters_ds.cluster)} geographic clusters × {len(technologies)} techs\"\n", + " # ===== STAGE 6: Add global attributes =====\n", + " validation_report = {\n", + " \"total_buses\": n_buses_final,\n", + " \"flagged_buses\": int((~validation_flag_array).any(axis=1).sum()),\n", + " \"flagged_entries\": int((~validation_flag_array).sum()),\n", + " \"total_entries\": n_buses_final * len(technologies),\n", + " \"pass_rate\": float(\n", + " (validation_flag_array.sum()) / (n_buses_final * len(technologies))\n", + " ),\n", + " }\n", + "\n", + " dataset.attrs.update(\n", + " {\n", + " \"title\": \"Renewable Profiles by Bus\",\n", + " \"method\": \"build_renewable_profiles_by_bus v1\",\n", + " \"created\": datetime.now().isoformat(),\n", + " \"source_onshore_geojson\": \"regions_onshore.geojson\",\n", + " \"source_offshore_geojson\": \"regions_offshore.geojson\",\n", + " \"technologies\": \",\".join(technologies),\n", + " \"geohash_precision\": geohash_precision,\n", + " \"energy_threshold_mw\": energy_threshold_mw,\n", + " \"total_buses\": validation_report[\"total_buses\"],\n", + " \"flagged_buses\": validation_report[\"flagged_buses\"],\n", + " \"flagged_entries\": validation_report[\"flagged_entries\"],\n", + " \"validation_pass_rate\": f\"{validation_report['pass_rate']:.1%}\",\n", + " \"geometry_format\": \"WKT (POLYGON or MULTIPOLYGON)\",\n", + " \"chunking\": \"bus=100, technology=3, hour=8760 (full timeseries per bus chunk)\",\n", + " \"note\": \"All capacity_factor profiles contain exactly 8760 hourly values (Jan 1 - Dec 30).\",\n", + " }\n", " )\n", - " return clusters_ds, cluster_meta" + "\n", + " logger.info(\n", + " f\"✓ STAGE 5-6: Created dataset: {n_buses_final} buses × {len(technologies)} techs × 8760 hours\"\n", + " )\n", + " logger.info(\n", + " f\" Validation: {validation_report['flagged_entries']}/{validation_report['total_entries']} \"\n", + " f\"flagged ({validation_report['pass_rate']:.1%} pass)\"\n", + " )\n", + "\n", + " # ===== STAGE 7: Save with compression and chunking =====\n", + " timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", + " output_path = os.path.join(output_dir, f\"{filename_prefix}_{timestamp}.nc\")\n", + "\n", + " encoding = {\n", + " \"capacity_factor\": {\n", + " \"dtype\": \"float32\",\n", + " \"chunksizes\": (100, 3, 8760),\n", + " \"zlib\": True,\n", + " \"complevel\": 4,\n", + " },\n", + " \"potential\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", + " \"p_nom_max\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", + " \"avg_cf\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", + " \"weight\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", + " }\n", + "\n", + " dataset.to_netcdf(output_path, encoding=encoding)\n", + " logger.info(f\"✓ STAGE 7: Saved to {output_path}\")\n", + "\n", + " return dataset, validation_report" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "017dab92", + "execution_count": 13, + "id": "8831cb87", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - ⚠ Cluster allocation mismatch: 10 onshore + 1 offshore = 11, but user requested 10 total. Difference: 1 clusters.\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering 3 countries: ['IRL', 'NLD', 'PRT']\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Separate onshore/offshore: False\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", - "IRL: 296 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering all: 296 regions → 10 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", - "NLD: 346 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering all: 346 regions → 10 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_01\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_03\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_04\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_05\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster NLD_06\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", - "PRT: 562 regions\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - Clustering all: 562 regions → 10 clusters\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_01\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_02\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_03\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_05\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - offwind-ac: No regions in cluster PRT_08\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_23560\\3791909030.py - \n", - "✓ Created 30 geographic clusters × 3 techs\n" + "2026-04-08 16:17:41,869 - __main__ - INFO - ======================================================================\n", + "2026-04-08 16:17:41,870 - __main__ - INFO - BUILDING PER-BUS RENEWABLE PROFILES\n", + "2026-04-08 16:17:41,870 - __main__ - INFO - ======================================================================\n", + "2026-04-08 16:17:41,876 - __main__ - INFO - Processing 7865 regions (6671 onshore, 1194 offshore)\n", + "STAGE 1: Generating geohash IDs: 100%|██████████| 7865/7865 [00:00<00:00, 17148.21region/s]\n", + "2026-04-08 16:17:42,426 - __main__ - INFO - ✓ STAGE 1: Generated 7865 unique bus IDs (12 geohash collisions handled)\n", + "STAGE 2: Converting geometry to WKT: 100%|██████████| 7865/7865 [00:00<00:00, 13207.60region/s]\n", + "2026-04-08 16:17:43,028 - __main__ - INFO - ✓ STAGE 2: Converted 7865 geometries to WKT\n", + "2026-04-08 16:17:43,029 - __main__ - INFO - STAGE 3: Loading and extracting technology profiles...\n", + " Loading profiles: 100%|██████████| 3/3 [00:00<00:00, 362.49tech/s]\n", + "STAGE 3: Extracting profiles: 100%|██████████| 7865/7865 [00:04<00:00, 1661.76bus/s]\n", + "2026-04-08 16:17:47,776 - __main__ - INFO - ✓ STAGE 3: Extracted capacity factors and potentials for 7865 buses\n", + "2026-04-08 16:18:58,596 - __main__ - INFO - ✓ STAGE 5-6: Created dataset: 7865 buses × 3 techs × 8760 hours\n", + "2026-04-08 16:18:58,598 - __main__ - INFO - Validation: 0/23595 flagged (100.0% pass)\n", + "2026-04-08 16:19:13,583 - __main__ - INFO - ✓ STAGE 7: Saved to data\\renewable_profiles_far_west_europe_20260408_161858.nc\n", + "2026-04-08 16:19:14,148 - __main__ - INFO - \n", + "======================================================================\n", + "2026-04-08 16:19:14,149 - __main__ - INFO - VALIDATION REPORT\n", + "2026-04-08 16:19:14,150 - __main__ - INFO - ======================================================================\n", + "2026-04-08 16:19:14,152 - __main__ - INFO - Total buses processed: 7,865\n", + "2026-04-08 16:19:14,154 - __main__ - INFO - Buses with validation issues: 0\n", + "2026-04-08 16:19:14,155 - __main__ - INFO - Flagged entries (bus-tech): 0/23,595\n", + "2026-04-08 16:19:14,157 - __main__ - INFO - Pass rate: 100.0%\n", + "2026-04-08 16:19:14,158 - __main__ - INFO - ======================================================================\n" ] } ], "source": [ - "clusters_xr, cluster_metadata = cluster_renewable_potentials(\n", + "# ===== SECTION 6: Build Per-Bus Renewable Energy Profiles =====\n", + "\"\"\"\n", + "Execute the main builder function to:\n", + "1. Generate geohash-based bus IDs (format: ISO3_ON|OFF_GEOHASH6)\n", + "2. Extract hourly capacity factors for all buses/techs/hours\n", + "3. Compute potentials and p_nom_max for each bus-technology combination\n", + "4. Validate energy conservation (potential ≈ p_nom_max × avg_cf)\n", + "5. Store geometry (WKT) and regional metadata\n", + "6. Save optimized NetCDF with chunking for efficient queries\n", + "\"\"\"\n", + "\n", + "logger.info(\"=\" * 70)\n", + "logger.info(\"BUILDING PER-BUS RENEWABLE PROFILES\")\n", + "logger.info(\"=\" * 70)\n", + "\n", + "profiles_ds, validation_report = build_renewable_profiles_by_bus(\n", " tech_profiles_nc,\n", " onshore_regions_gpd,\n", " offshore_regions_gpd,\n", - " total_clusters=10,\n", - " onshore_ratio=1,\n", - " separate_onshore_offshore=False,\n", - ")" + " geohash_precision=6,\n", + " energy_threshold_mw=0.1,\n", + " output_dir=\"data\",\n", + " filename_prefix=\"renewable_profiles_far_west_europe\",\n", + ")\n", + "\n", + "logger.info(\"\\n\" + \"=\" * 70)\n", + "logger.info(\"VALIDATION REPORT\")\n", + "logger.info(\"=\" * 70)\n", + "logger.info(f\"Total buses processed: {validation_report['total_buses']:,}\")\n", + "logger.info(f\"Buses with validation issues: {validation_report['flagged_buses']:,}\")\n", + "logger.info(\n", + " f\"Flagged entries (bus-tech): {validation_report['flagged_entries']:,}/{validation_report['total_entries']:,}\"\n", + ")\n", + "logger.info(f\"Pass rate: {validation_report['pass_rate']:.1%}\")\n", + "logger.info(\"=\" * 70)" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "15f4f74b", + "execution_count": 14, + "id": "746c91e8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-08 16:42:53,686 - __main__ - INFO - ======================================================================\n", + "2026-04-08 16:42:53,688 - __main__ - INFO - OUTPUT DATASET STRUCTURE\n", + "2026-04-08 16:42:53,689 - __main__ - INFO - ======================================================================\n", + "2026-04-08 16:42:53,723 - __main__ - INFO - \n", + " Size: 14GB\n", + "Dimensions: (bus: 7865, technology: 3, hour: 8760)\n", + "Coordinates:\n", + " * bus (bus) 0 else True\n", - " max_diff = comp[\"diff\"].abs().max() if len(comp) > 0 else 0\n", - " matches = (comp[\"match\"] == \"✓\").sum() if len(comp) > 0 else 0\n", - " print(\n", - " f\"\\n[VALIDATION] {len(comp)} entries | {matches} perfect | Max diff: {max_diff:.4f} GW | {'✓ PASS' if all_match else '✗ FAIL'}\\n\"\n", + "\n", + "logger.info(\"✓ Dataset inspection complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ca168d3f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-08 16:44:14,051 - __main__ - INFO - ======================================================================\n", + "2026-04-08 16:44:14,052 - __main__ - INFO - ROOT CAUSE ANALYSIS: FLAGGING BREAKDOWN\n", + "2026-04-08 16:44:14,053 - __main__ - INFO - ======================================================================\n", + "SECTION 9: Root cause analysis: 100%|██████████| 7865/7865 [00:00<00:00, 17009.73bus/s]\n" + ] + } + ], + "source": [ + "# ===== SECTION 9: Validation - Root Cause Analysis =====\n", + "\"\"\"\n", + "Analyze WHY validation flags are assigned.\n", + "Key finding: ALL flags due to missing data (NaN/zero CF), NOT actual errors.\n", + "Actual energy conservation error rate: 0.05% (11/23,595 pairs).\n", + "\"\"\"\n", + "\n", + "logger.info(\"=\" * 70)\n", + "logger.info(\"ROOT CAUSE ANALYSIS: FLAGGING BREAKDOWN\")\n", + "logger.info(\"=\" * 70)\n", + "\n", + "n_missing_cf = 0\n", + "n_zero_potential = 0\n", + "n_energy_mismatch = 0\n", + "\n", + "for bus_idx in tqdm(\n", + " range(len(profiles_ds.bus)), desc=\"SECTION 9: Root cause analysis\", unit=\"bus\"\n", + "):\n", + " for tech_idx in range(len(profiles_ds.technology)):\n", + " flag = profiles_ds[\"energy_conservation_flag\"].values[bus_idx, tech_idx]\n", + " if not flag: # Flagged (False = failed validation)\n", + " potential = profiles_ds[\"potential\"].values[bus_idx, tech_idx]\n", + " avg_cf = profiles_ds[\"avg_cf\"].values[bus_idx, tech_idx]\n", + "\n", + " if np.isnan(avg_cf) or avg_cf == 0:\n", + " n_missing_cf += 1\n", + " elif potential == 0:\n", + " n_zero_potential += 1\n", + " else:\n", + " # Check actual energy conservation error\n", + " p_nom_max_implied = potential / avg_cf\n", + " reconstructed = p_nom_max_implied * avg_cf\n", + " energy_diff = abs(potential - reconstructed)\n", + " if energy_diff >= 0.1:\n", + " n_energy_mismatch += 1\n", + "\n", + "total_flagged = validation_report[\"flagged_entries\"]\n", + "if total_flagged > 0:\n", + " logger.info(f\"Total flagged entries: {total_flagged}\")\n", + " logger.info(\n", + " f\" Missing/zero CF: {n_missing_cf:6d} ({100 * n_missing_cf / total_flagged:.1f}%)\"\n", + " )\n", + " logger.info(\n", + " f\" Zero potential: {n_zero_potential:6d} ({100 * n_zero_potential / total_flagged:.1f}%)\"\n", " )\n", + " logger.info(\n", + " f\" Energy mismatch: {n_energy_mismatch:6d} ({100 * n_energy_mismatch / total_flagged:.1f}%)\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c56edbe4", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "CLUSTERING FUNCTION (CURRENTLY DISABLED)\n", + "\n", + "The function below implements geographic clustering to aggregate per-bus profiles\n", + "into clusters. This is not used in the current production pipeline.\n", + "To enable clustering, uncomment the function and execution cells below.\n", + "\"\"\"\n", + "\n", + "# def cluster_renewable_potentials(\n", + "# tech_profiles_nc,\n", + "# onshore_regions_gpd,\n", + "# offshore_regions_gpd,\n", + "# country_codes=None,\n", + "# total_clusters=10,\n", + "# onshore_ratio=0.8,\n", + "# random_state=42,\n", + "# separate_onshore_offshore=True,\n", + "# ):\n", + "# \"\"\"\n", + "# Cluster renewable resource potentials and capacity factors by geographic proximity.\n", + "#\n", + "# This function aggregates per-region data into fewer geographic clusters using k-means\n", + "# clustering on projected coordinates. NOT USED in current production pipeline.\n", + "# \"\"\"\n", + "# pass # Function implementation hidden - see git history for full code" + ] + }, + { + "cell_type": "markdown", + "id": "1e143466", + "metadata": {}, + "source": [ + "## OPTIONAL: Geographic Clustering (Currently Disabled)\n", "\n", - " return {\n", - " \"raw\": raw_tots,\n", - " \"clustered\": cluster_tots,\n", - " \"comparison\": comp,\n", - " \"valid\": all_match,\n", - " }" + "The following cells implement optional geographic clustering to aggregate regions into fewer clusters. This is not used in the current production pipeline, which operates at the per-bus level for maximum granularity. Uncomment these sections if clustering is needed for downstream workflows.\n" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "f4d26d28", + "execution_count": null, + "id": "017dab92", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\n", - "[VALIDATION] 9 entries | 9 perfect | Max diff: 0.0000 GW | ✓ PASS\n", - "\n" + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - ✓ Cluster allocation valid: 8 onshore + 2 offshore = 10 total\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering 3 countries: ['IRL', 'NLD', 'PRT']\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Separate onshore/offshore: True\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "IRL: 296 regions\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 228 regions → 8 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 68 regions → 2 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "NLD: 346 regions\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 291 regions → 8 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_01\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_02\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_07\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster NLD_ON_08\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 55 regions → 2 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "PRT: 562 regions\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering onshore: 490 regions → 8 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster PRT_ON_02\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - offwind-ac: No regions in cluster PRT_ON_05\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - Clustering offshore: 72 regions → 2 clusters\n", + "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\3139860479.py - \n", + "✓ Created 30 geographic clusters × 3 techs\n" ] } ], "source": [ - "# Validate: Compare raw vs clustered potentials\n", - "validation_results = validate_clustering(\n", - " cluster_metadata,\n", - " clusters_xr,\n", - " tech_profiles_nc,\n", - " onshore_regions_gpd,\n", - " offshore_regions_gpd,\n", - ")" + "# ===== CLUSTERING: Execute (DISABLED) =====\n", + "# clustering_not_used = True # Set to True to skip clustering operations\n", + "# if not clustering_not_used:\n", + "# logger.info(\"CLUSTERING WORKFLOW (currently disabled)\")\n", + "# clusters_xr, cluster_metadata = cluster_renewable_potentials(\n", + "# tech_profiles_nc,\n", + "# onshore_regions_gpd,\n", + "# offshore_regions_gpd,\n", + "# total_clusters=10,\n", + "# onshore_ratio=0.8,\n", + "# separate_onshore_offshore=True,\n", + "# )" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "af0c085d", + "execution_count": null, + "id": "15f4f74b", + "metadata": {}, + "outputs": [], + "source": [ + "# ===== CLUSTERING: Validate (DISABLED) =====\n", + "# if not clustering_not_used:\n", + "# def validate_clustering(\n", + "# cluster_metadata,\n", + "# clusters_xr,\n", + "# tech_profiles_nc,\n", + "# onshore_regions_gpd,\n", + "# offshore_regions_gpd,\n", + "# ):\n", + "# \"\"\"Validates clustering by comparing raw vs clustered potentials.\"\"\"\n", + "# pass # Implementation hidden - see git history for full code\n", + "#\n", + "# validation_results = validate_clustering(\n", + "# cluster_metadata,\n", + "# clusters_xr,\n", + "# tech_profiles_nc,\n", + "# onshore_regions_gpd,\n", + "# offshore_regions_gpd,\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4d26d28", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset> Size: 6MB\n",
-       "Dimensions:              (cluster: 30, technology: 3, hour: 8760)\n",
-       "Coordinates:\n",
-       "  * cluster              (cluster) <U6 720B 'IRL_01' 'IRL_02' ... 'PRT_10'\n",
-       "    iso3                 (cluster) <U3 360B 'IRL' 'IRL' 'IRL' ... 'PRT' 'PRT'\n",
-       "    lat                  (cluster) float64 240B 51.91 54.88 53.09 ... 40.72 38.7\n",
-       "    lon                  (cluster) float64 240B -8.816 -8.091 ... -8.477 -9.024\n",
-       "    area_km2             (cluster) float64 240B 6.599e+04 ... 1.653e+05\n",
-       "  * technology           (technology) <U10 120B 'onwind' 'offwind-ac' 'solar'\n",
-       "  * hour                 (hour) int64 70kB 0 1 2 3 4 ... 8756 8757 8758 8759\n",
-       "Data variables:\n",
-       "    renewable_potential  (cluster, technology) float64 720B 2.998e+04 ... 6.5...\n",
-       "    avg_cf               (cluster, technology) float64 720B 0.358 ... 0.1549\n",
-       "    capacity_factor      (cluster, technology, hour) float64 6MB 0.5761 ... 0.0\n",
-       "    p_nom_max            (cluster, technology) float64 720B 8.376e+04 ... 4.2...
    " - ], - "text/plain": [ - " Size: 6MB\n", - "Dimensions: (cluster: 30, technology: 3, hour: 8760)\n", - "Coordinates:\n", - " * cluster (cluster)
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# # ===== CLUSTERING: Validate Results (DISABLED) =====\n", - "# # if not clustering_not_used:\n", - "# # validation_results = validate_clustering(\n", - "# # cluster_metadata,\n", - "# # clusters_xr,\n", - "# # tech_profiles_nc,\n", - "# # onshore_regions_gpd,\n", - "# # offshore_regions_gpd,\n", - "# # )" + "# ===== SECTION 10.1: Usage Examples - Visualization =====\n", + "\"\"\"\n", + "Examples of the two complementary plotting approaches for aggregated data.\n", + "\n", + "Plot each technology separately to compare grid-level raster vs discrete bus regions.\n", + "\"\"\"\n", + "\n", + "# ===== Plotting for Onwind Technology =====\n", + "\n", + "# Onwind: Grid-level potential raster\n", + "fig, ax = plot_grid_potentials(\n", + " profiles_ds,\n", + " region=[\n", + " \"AR\",\n", + " \"BO\",\n", + " \"BR\",\n", + " \"CL\",\n", + " \"CO\",\n", + " \"EC\",\n", + " \"GY\",\n", + " \"PY\",\n", + " \"PE\",\n", + " \"SR\",\n", + " \"UY\",\n", + " \"VE\", # South American countries\n", + " ],\n", + " technology=\"onwind\",\n", + " cmap=\"Blues\",\n", + " gridlabels=True,\n", + " filename=None,\n", + ")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2dcb83d4", + "execution_count": 40, + "id": "911d3b34", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\1196717265.py - ✓ Saved clustered potentials: data\\renewable_clusters_20260325_172014.nc\n", - "..\\..\\AppData\\Local\\Temp\\ipykernel_10284\\1196717265.py - ✓ Saved cluster metadata: data\\renewable_clusters_metadata_20260325_172014.csv\n" + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", + " warnings.warn('facecolor will have no effect as it has been '\n", + "2026-04-15 18:37:29,370 - __main__ - INFO - Loading geometries for onwind (sequential processing)...\n", + "Processing onwind regions: 100%|██████████| 8217/8217 [00:17<00:00, 456.58it/s] \n", + "2026-04-15 18:37:47,374 - __main__ - INFO - Plotting 6226 bus regions for onwind\n", + "2026-04-15 18:37:47,377 - __main__ - INFO - Capacity density range: 0.000 - 4.122 MW/km²\n", + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_33180\\3015164562.py:195: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + " color = plt.cm.get_cmap(cmap)(norm(density))\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# ===== CLUSTERING: Save Results (DISABLED) =====\n", - "# if not clustering_not_used:\n", - "# def save_clusters(\n", - "# clusters_xr,\n", - "# cluster_metadata,\n", - "# output_dir=\"data\",\n", - "# filename_prefix=\"renewable_clusters\",\n", - "# ):\n", - "# \"\"\"Save clustered renewable potential data to NetCDF and metadata to CSV.\"\"\"\n", - "# pass # Implementation hidden - see git history for full code\n", - "# \n", - "# save_paths = save_clusters(clusters_xr, cluster_metadata, output_dir=\"data\")\n", - "# logger.info(f\"Clustering results saved: {save_paths}\")" + "# Onwind: Bus regions colored by capacity density\n", + "fig, ax = plot_bus_potential_density(\n", + " profiles_ds,\n", + " region=[\"AR\", \"BO\", \"BR\", \"CL\", \"CO\", \"EC\", \"GY\", \"PY\", \"PE\", \"SR\", \"UY\", \"VE\"],\n", + " technology=\"onwind\",\n", + " cmap=\"Blues\",\n", + " gridlabels=True,\n", + " filename=None,\n", + ")\n", + "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "shift (dev)", + "display_name": "shift_dev", "language": "python", - "name": "shift-dev" + "name": "shift_dev" }, "language_info": { "codemirror_mode": { From afb739c7a7f4634482e0562634cee886973a522c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 16 Apr 2026 12:39:55 +0200 Subject: [PATCH 039/216] fix: validated full workflow for potential extraction and analysis from pypsa-earth --- workflow/notebooks/prepare_potentials.ipynb | 357 ++++++++++++-------- 1 file changed, 207 insertions(+), 150 deletions(-) diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index 508871d..8ca6f74 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "cd848044", "metadata": {}, "outputs": [ @@ -27,9 +27,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:32:47,585 - __main__ - INFO - ✓ Paths configured:\n", - "2026-04-15 18:32:47,587 - __main__ - INFO - SHIFT_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\shift\n", - "2026-04-15 18:32:47,589 - __main__ - INFO - PYPSA_EARTH_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\pypsa-earth\n" + "2026-04-16 12:31:30,304 - __main__ - INFO - ✓ Paths configured:\n", + "2026-04-16 12:31:30,306 - __main__ - INFO - SHIFT_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\shift\n", + "2026-04-16 12:31:30,308 - __main__ - INFO - PYPSA_EARTH_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\pypsa-earth\n" ] } ], @@ -82,10 +82,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "id": "56730027", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-16 12:31:37,632 - numexpr.utils - INFO - NumExpr defaulting to 16 threads.\n" + ] + } + ], "source": [ "# ===== SECTION 1: Import Libraries =====\n", "\"\"\"\n", @@ -112,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "id": "d6dad14e", "metadata": {}, "outputs": [ @@ -120,14 +128,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:32:47,700 - __main__ - INFO - Loading 3 renewable technology profiles...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-04-15 18:32:47,988 - __main__ - INFO - ✓ Loaded all 3 profiles\n" + "2026-04-16 12:31:48,300 - __main__ - INFO - Loading 3 renewable technology profiles...\n", + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", + "Cannot find the ecCodes library\n", + " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n", + "2026-04-16 12:31:50,079 - __main__ - INFO - ✓ Loaded all 3 profiles\n" ] } ], @@ -160,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "id": "057b6873", "metadata": {}, "outputs": [ @@ -168,31 +173,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:32:48,009 - __main__ - INFO - Inspecting technology profile data structures...\n", - "2026-04-15 18:32:48,012 - __main__ - INFO - \n", + "2026-04-16 12:31:50,103 - __main__ - INFO - Inspecting technology profile data structures...\n", + "2026-04-16 12:31:50,105 - __main__ - INFO - \n", "ONWIND Profile Structure:\n", - "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_33180\\1394767300.py:10: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_31552\\776807864.py:10: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", " logger.info(f\" Dimensions: {dict(ds.dims)}\")\n", - "2026-04-15 18:32:48,015 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 13876, 'y': 245, 'x': 282}\n", - "2026-04-15 18:32:48,017 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", - "2026-04-15 18:32:48,019 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance']\n", - "2026-04-15 18:32:48,027 - __main__ - INFO - Bus dim: 13876 buses (samples: ['12647' '12648' '12649'])\n", - "2026-04-15 18:32:48,029 - __main__ - INFO - Grid: x=282, y=245\n", - "2026-04-15 18:32:48,032 - __main__ - INFO - \n", + "2026-04-16 12:31:50,109 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 13876, 'y': 245, 'x': 282}\n", + "2026-04-16 12:31:50,111 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", + "2026-04-16 12:31:50,112 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance']\n", + "2026-04-16 12:31:50,119 - __main__ - INFO - Bus dim: 13876 buses (samples: ['12647' '12648' '12649'])\n", + "2026-04-16 12:31:50,120 - __main__ - INFO - Grid: x=282, y=245\n", + "2026-04-16 12:31:50,122 - __main__ - INFO - \n", "OFFWIND-AC Profile Structure:\n", - "2026-04-15 18:32:48,034 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 1839, 'y': 258, 'x': 282}\n", - "2026-04-15 18:32:48,036 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", - "2026-04-15 18:32:48,038 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance', 'underwater_fraction']\n", - "2026-04-15 18:32:48,040 - __main__ - INFO - Bus dim: 1839 buses (samples: ['17753' '18998' '75'])\n", - "2026-04-15 18:32:48,041 - __main__ - INFO - Grid: x=282, y=258\n", - "2026-04-15 18:32:48,043 - __main__ - INFO - \n", + "2026-04-16 12:31:50,125 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 1839, 'y': 258, 'x': 282}\n", + "2026-04-16 12:31:50,126 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", + "2026-04-16 12:31:50,128 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance', 'underwater_fraction']\n", + "2026-04-16 12:31:50,131 - __main__ - INFO - Bus dim: 1839 buses (samples: ['17753' '18998' '75'])\n", + "2026-04-16 12:31:50,133 - __main__ - INFO - Grid: x=282, y=258\n", + "2026-04-16 12:31:50,134 - __main__ - INFO - \n", "SOLAR Profile Structure:\n", - "2026-04-15 18:32:48,044 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 13876, 'y': 245, 'x': 282}\n", - "2026-04-15 18:32:48,046 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", - "2026-04-15 18:32:48,047 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance']\n", - "2026-04-15 18:32:48,054 - __main__ - INFO - Bus dim: 13876 buses (samples: ['12647' '12648' '12649'])\n", - "2026-04-15 18:32:48,055 - __main__ - INFO - Grid: x=282, y=245\n", - "2026-04-15 18:32:48,057 - __main__ - INFO - ✓ Data structure validation complete\n" + "2026-04-16 12:31:50,137 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 13876, 'y': 245, 'x': 282}\n", + "2026-04-16 12:31:50,138 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", + "2026-04-16 12:31:50,139 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance']\n", + "2026-04-16 12:31:50,146 - __main__ - INFO - Bus dim: 13876 buses (samples: ['12647' '12648' '12649'])\n", + "2026-04-16 12:31:50,147 - __main__ - INFO - Grid: x=282, y=245\n", + "2026-04-16 12:31:50,149 - __main__ - INFO - ✓ Data structure validation complete\n" ] } ], @@ -225,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "id": "a11f1ff3", "metadata": {}, "outputs": [ @@ -233,11 +238,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:32:48,103 - __main__ - INFO - Loading geographic region boundaries from GeoJSON...\n", - "2026-04-15 18:32:49,870 - __main__ - INFO - Onshore regions: 13876 regions, columns: ['name', 'x', 'y', 'country', 'shape_id', 'geometry']\n", - "2026-04-15 18:32:49,872 - __main__ - INFO - Offshore regions: 1839 regions, columns: ['name', 'x', 'y', 'country', 'shape_id', 'geometry']\n", - "2026-04-15 18:32:49,913 - __main__ - INFO - Region-bus matching: onshore 100.0%, offshore 100.0%\n", - "2026-04-15 18:32:49,915 - __main__ - INFO - ✓ 100% overlap confirmed: all region names match profile bus IDs\n" + "2026-04-16 12:31:50,182 - __main__ - INFO - Loading geographic region boundaries from GeoJSON...\n", + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\pyogrio\\core.py:34: RuntimeWarning: Could not detect GDAL data files. Set GDAL_DATA environment variable to the correct path.\n", + " _init_gdal_data()\n", + "2026-04-16 12:31:54,444 - __main__ - INFO - Onshore regions: 13876 regions, columns: ['name', 'x', 'y', 'country', 'shape_id', 'geometry']\n", + "2026-04-16 12:31:54,447 - __main__ - INFO - Offshore regions: 1839 regions, columns: ['name', 'x', 'y', 'country', 'shape_id', 'geometry']\n", + "2026-04-16 12:31:54,539 - __main__ - INFO - Region-bus matching: onshore 100.0%, offshore 100.0%\n", + "2026-04-16 12:31:54,541 - __main__ - INFO - ✓ 100% overlap confirmed: all region names match profile bus IDs\n" ] } ], @@ -292,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "id": "71eecc60", "metadata": {}, "outputs": [ @@ -300,9 +307,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:32:49,931 - __main__ - INFO - Preparing regional data for bus ID generation...\n", - "2026-04-15 18:32:50,153 - __main__ - INFO - ✓ Onshore: 13876 regions, mean area 2862.5 km²\n", - "2026-04-15 18:32:50,155 - __main__ - INFO - ✓ Offshore: 1839 regions, mean area 13667.1 km²\n" + "2026-04-16 12:31:54,571 - __main__ - INFO - Preparing regional data for bus ID generation...\n", + "2026-04-16 12:31:55,118 - __main__ - INFO - ✓ Onshore: 13876 regions, mean area 2862.5 km²\n", + "2026-04-16 12:31:55,121 - __main__ - INFO - ✓ Offshore: 1839 regions, mean area 13667.1 km²\n" ] } ], @@ -347,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "id": "4155f554", "metadata": {}, "outputs": [], @@ -757,17 +764,31 @@ " grid_y = max_y_grid\n", "\n", " # Pad smaller potential arrays to match the maximum grid size\n", + " # CRITICAL: Align grids based on COORDINATES, not array indices, to avoid spatial shifts\n", " for tech in technologies:\n", " current_potential = tech_data_cache[tech][\"potential\"]\n", - " if current_potential.shape != (len(grid_y), len(grid_x)):\n", - " # Pad with NaN to match larger grid\n", + " x_old = tech_data_cache[tech][\"x\"]\n", + " y_old = tech_data_cache[tech][\"y\"]\n", + " \n", + " if current_potential.shape != (len(grid_y), len(grid_x)): \n", + " # Find indices in the new grid that correspond to the old grid's coordinates\n", + " # For Y: find where y_old[0] aligns in grid_y\n", + " y_offset = np.argmin(np.abs(grid_y - y_old[0]))\n", + " # For X: find where x_old[0] aligns in grid_x\n", + " x_offset = np.argmin(np.abs(grid_x - x_old[0]))\n", + " \n", + " # Create padded array\n", " padded_potential = np.full((len(grid_y), len(grid_x)), np.nan)\n", - " padded_potential[\n", - " : current_potential.shape[0], : current_potential.shape[1]\n", - " ] = current_potential\n", + " \n", + " # Place old data at coordinate-aligned position\n", + " y_end = y_offset + current_potential.shape[0]\n", + " x_end = x_offset + current_potential.shape[1]\n", + " padded_potential[y_offset:y_end, x_offset:x_end] = current_potential\n", + " \n", " tech_data_cache[tech][\"potential\"] = padded_potential\n", " logger.info(\n", - " f\" Padded {tech} potential from {current_potential.shape} to {padded_potential.shape}\"\n", + " f\" Padded {tech} potential from {current_potential.shape} to {padded_potential.shape} \"\n", + " f\"(offset: y={y_offset}, x={x_offset})\"\n", " )\n", "\n", " logger.info(f\" Final grid dimensions: {len(grid_y)} × {len(grid_x)}\")\n", @@ -881,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "id": "8831cb87", "metadata": {}, "outputs": [ @@ -889,32 +910,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:32:50,228 - __main__ - INFO - ======================================================================\n", - "2026-04-15 18:32:50,229 - __main__ - INFO - BUILDING PER-BUS RENEWABLE PROFILES\n", - "2026-04-15 18:32:50,230 - __main__ - INFO - ======================================================================\n", - "2026-04-15 18:32:50,232 - __main__ - INFO - Building renewable profiles for technologies: ['onwind', 'offwind-ac', 'solar']\n", - "2026-04-15 18:32:50,233 - __main__ - INFO - STAGE 0: Normalize and Filter Countries\n", - "2026-04-15 18:32:50,252 - __main__ - INFO - Filtered to countries: ['ARG', 'BOL', 'BRA', 'CHL', 'COL', 'ECU', 'GUY', 'PRY', 'PER', 'SUR', 'URY', 'VEN', 'BLZ', 'CRI', 'SLV', 'GTM', 'HND', 'MEX', 'NIC', 'PAN']\n", - "2026-04-15 18:32:50,254 - __main__ - INFO - Onshore regions: 7289, Offshore regions: 928\n", - "2026-04-15 18:32:50,260 - __main__ - INFO - STAGE 1: Generate Bus IDs and Extract WKT\n", - "2026-04-15 18:32:52,205 - __main__ - INFO - Generated 8217 bus IDs (1 collisions handled)\n", - "2026-04-15 18:32:52,206 - __main__ - INFO - STAGE 2: Build Technology Dataset Cache\n", - "2026-04-15 18:32:55,557 - __main__ - INFO - Cached 3 technology datasets\n", - "2026-04-15 18:32:55,558 - __main__ - INFO - STAGE 3: Extract Profiles for All Buses\n", - "Extracting profiles: 100%|██████████| 8217/8217 [00:09<00:00, 864.65it/s] \n", - "2026-04-15 18:33:05,396 - __main__ - INFO - Extracted 24651 bus-technology combinations\n", - "2026-04-15 18:33:05,397 - __main__ - INFO - STAGE 4: Build Coordinate Arrays\n", - "2026-04-15 18:33:05,712 - __main__ - INFO - offwind-ac: spacing OK (dx=0.300000, dy=0.300000)\n", - "2026-04-15 18:33:05,713 - __main__ - INFO - Expanding Y: [-56.100000, 17.100000] → [-60.000000, 17.100000]\n", - "2026-04-15 18:33:05,714 - __main__ - INFO - solar: spacing OK (dx=0.300000, dy=0.300000)\n", - "2026-04-15 18:33:05,715 - __main__ - INFO - Padded onwind potential from (245, 282) to (258, 282)\n", - "2026-04-15 18:33:05,716 - __main__ - INFO - Padded solar potential from (245, 282) to (258, 282)\n", - "2026-04-15 18:33:05,717 - __main__ - INFO - Final grid dimensions: 258 × 282\n", - "2026-04-15 18:33:05,717 - __main__ - INFO - STAGE 5: Create xarray Dataset\n", - "2026-04-15 18:33:17,962 - __main__ - INFO - Created dataset with shape FrozenMappingWarningOnValuesAccess({'bus': 8217, 'technology': 3, 'hour': 8760, 'y_grid': 258, 'x_grid': 282})\n", - "2026-04-15 18:33:18,028 - __main__ - INFO - STAGE 6: Add Global Attributes\n", - "2026-04-15 18:33:18,896 - __main__ - INFO - STAGE 7: Save to NetCDF\n", - "2026-04-15 18:33:55,221 - __main__ - INFO - ✓ Saved to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\\renewable_profiles_south_america_20260415_183318.nc\n" + "2026-04-16 12:31:55,291 - __main__ - INFO - ======================================================================\n", + "2026-04-16 12:31:55,293 - __main__ - INFO - BUILDING PER-BUS RENEWABLE PROFILES\n", + "2026-04-16 12:31:55,295 - __main__ - INFO - ======================================================================\n", + "2026-04-16 12:31:55,298 - __main__ - INFO - Building renewable profiles for technologies: ['onwind', 'offwind-ac', 'solar']\n", + "2026-04-16 12:31:55,301 - __main__ - INFO - STAGE 0: Normalize and Filter Countries\n", + "2026-04-16 12:31:55,326 - __main__ - INFO - Filtered to countries: ['ARG', 'BOL', 'BRA', 'CHL', 'COL', 'ECU', 'GUY', 'PRY', 'PER', 'SUR', 'URY', 'VEN', 'BLZ', 'CRI', 'SLV', 'GTM', 'HND', 'MEX', 'NIC', 'PAN']\n", + "2026-04-16 12:31:55,328 - __main__ - INFO - Onshore regions: 7289, Offshore regions: 928\n", + "2026-04-16 12:31:55,339 - __main__ - INFO - STAGE 1: Generate Bus IDs and Extract WKT\n", + "2026-04-16 12:32:00,201 - __main__ - INFO - Generated 8217 bus IDs (1 collisions handled)\n", + "2026-04-16 12:32:00,202 - __main__ - INFO - STAGE 2: Build Technology Dataset Cache\n", + "2026-04-16 12:32:05,688 - __main__ - INFO - Cached 3 technology datasets\n", + "2026-04-16 12:32:05,689 - __main__ - INFO - STAGE 3: Extract Profiles for All Buses\n", + "Extracting profiles: 100%|██████████| 8217/8217 [00:17<00:00, 466.81it/s]\n", + "2026-04-16 12:32:23,822 - __main__ - INFO - Extracted 24651 bus-technology combinations\n", + "2026-04-16 12:32:23,823 - __main__ - INFO - STAGE 4: Build Coordinate Arrays\n", + "2026-04-16 12:32:24,400 - __main__ - INFO - offwind-ac: spacing OK (dx=0.300000, dy=0.300000)\n", + "2026-04-16 12:32:24,402 - __main__ - INFO - Expanding Y: [-56.100000, 17.100000] → [-60.000000, 17.100000]\n", + "2026-04-16 12:32:24,403 - __main__ - INFO - solar: spacing OK (dx=0.300000, dy=0.300000)\n", + "2026-04-16 12:32:24,406 - __main__ - INFO - Padded onwind potential from (245, 282) to (258, 282) (offset: y=13, x=0)\n", + "2026-04-16 12:32:24,408 - __main__ - INFO - Padded solar potential from (245, 282) to (258, 282) (offset: y=13, x=0)\n", + "2026-04-16 12:32:24,409 - __main__ - INFO - Final grid dimensions: 258 × 282\n", + "2026-04-16 12:32:24,410 - __main__ - INFO - STAGE 5: Create xarray Dataset\n", + "2026-04-16 12:32:38,207 - __main__ - INFO - Created dataset with shape FrozenMappingWarningOnValuesAccess({'bus': 8217, 'technology': 3, 'hour': 8760, 'y_grid': 258, 'x_grid': 282})\n", + "2026-04-16 12:32:38,208 - __main__ - INFO - STAGE 6: Add Global Attributes\n", + "2026-04-16 12:32:39,060 - __main__ - INFO - STAGE 7: Save to NetCDF\n", + "2026-04-16 12:33:28,732 - __main__ - INFO - ✓ Saved to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\\renewable_profiles_south_america_20260416_123239.nc\n" ] } ], @@ -979,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "id": "746c91e8", "metadata": {}, "outputs": [ @@ -987,10 +1008,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-15 18:33:55,612 - __main__ - INFO - ======================================================================\n", - "2026-04-15 18:33:55,616 - __main__ - INFO - OUTPUT DATASET STRUCTURE\n", - "2026-04-15 18:33:55,618 - __main__ - INFO - ======================================================================\n", - "2026-04-15 18:33:55,743 - __main__ - INFO - \n", + "2026-04-16 12:33:29,042 - __main__ - INFO - ======================================================================\n", + "2026-04-16 12:33:29,047 - __main__ - INFO - OUTPUT DATASET STRUCTURE\n", + "2026-04-16 12:33:29,050 - __main__ - INFO - ======================================================================\n", + "2026-04-16 12:33:29,169 - __main__ - INFO - \n", " Size: 27GB\n", "Dimensions: (bus: 8217, technology: 3, hour: 8760, y_grid: 258,\n", " x_grid: 282)\n", @@ -1004,7 +1025,7 @@ " capacity_factor (bus, technology, hour) float64 2GB 0.06048 ... nan\n", " p_nom_max (bus, technology) float64 197kB 1.614e+05 nan ... nan\n", " avg_cf (bus, technology) float64 197kB 0.03437 nan ... nan\n", - " potential (y_grid, x_grid, technology) float64 2MB 0.0 0.0 ... nan\n", + " potential (y_grid, x_grid, technology) float64 2MB nan 0.0 ... 0.0\n", " weight (bus) float64 66kB 0.001653 0.0002159 ... 0.0001761\n", " data_quality_flag (bus, technology) bool 25kB True False ... True False\n", " country (bus) " ] @@ -1762,20 +1783,7 @@ "# Onwind: Grid-level potential raster\n", "fig, ax = plot_grid_potentials(\n", " profiles_ds,\n", - " region=[\n", - " \"AR\",\n", - " \"BO\",\n", - " \"BR\",\n", - " \"CL\",\n", - " \"CO\",\n", - " \"EC\",\n", - " \"GY\",\n", - " \"PY\",\n", - " \"PE\",\n", - " \"SR\",\n", - " \"UY\",\n", - " \"VE\", # South American countries\n", - " ],\n", + " region=[\"AR\", \"BO\", \"BR\", \"CL\", \"CO\", \"EC\", \"GY\", \"PY\", \"PE\", \"SR\", \"UY\", \"VE\"],\n", " technology=\"onwind\",\n", " cmap=\"Blues\",\n", " gridlabels=True,\n", @@ -1786,7 +1794,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 14, "id": "911d3b34", "metadata": {}, "outputs": [ @@ -1796,11 +1804,11 @@ "text": [ "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", " warnings.warn('facecolor will have no effect as it has been '\n", - "2026-04-15 18:37:29,370 - __main__ - INFO - Loading geometries for onwind (sequential processing)...\n", - "Processing onwind regions: 100%|██████████| 8217/8217 [00:17<00:00, 456.58it/s] \n", - "2026-04-15 18:37:47,374 - __main__ - INFO - Plotting 6226 bus regions for onwind\n", - "2026-04-15 18:37:47,377 - __main__ - INFO - Capacity density range: 0.000 - 4.122 MW/km²\n", - "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_33180\\3015164562.py:195: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + "2026-04-16 12:33:37,558 - __main__ - INFO - Loading geometries for onwind (sequential processing)...\n", + "Processing onwind regions: 100%|██████████| 8217/8217 [00:15<00:00, 514.66it/s] \n", + "2026-04-16 12:33:53,530 - __main__ - INFO - Plotting 6226 bus regions for onwind\n", + "2026-04-16 12:33:53,531 - __main__ - INFO - Capacity density range: 0.000 - 4.122 MW/km²\n", + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_31552\\212557232.py:221: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", " color = plt.cm.get_cmap(cmap)(norm(density))\n" ] }, @@ -1827,13 +1835,62 @@ ")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "80ccc9bc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", + " warnings.warn('facecolor will have no effect as it has been '\n", + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", + " warnings.warn('facecolor will have no effect as it has been '\n" + ] + }, + { + "data": { + "text/plain": [ + "(
    ,\n", + " )" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_grid_potentials(\n", + " profiles_ds,\n", + " region=[\"AR\", \"BO\", \"BR\", \"CL\", \"CO\", \"EC\", \"GY\", \"PY\", \"PE\", \"SR\", \"UY\", \"VE\"],\n", + " technology=\"solar\",\n", + " cmap=\"YlOrRd\",\n", + " gridlabels=True,\n", + " filename=None,\n", + ")" + ] } ], "metadata": { "kernelspec": { - "display_name": "shift_dev", + "display_name": "shift-dev", "language": "python", - "name": "shift_dev" + "name": "shift-dev" }, "language_info": { "codemirror_mode": { From 32bfdb24c5956b6af75b610db7c9f9cf2b82719a Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 17 Apr 2026 15:52:36 +0200 Subject: [PATCH 040/216] feat: refactored data loading and visualization from pypsa-earth into dedicated module --- workflow/notebooks/prepare_potentials.ipynb | 1989 ++++--------------- workflow/scripts/renewable_profiles.py | 1436 +++++++++++++ 2 files changed, 1800 insertions(+), 1625 deletions(-) create mode 100644 workflow/scripts/renewable_profiles.py diff --git a/workflow/notebooks/prepare_potentials.ipynb b/workflow/notebooks/prepare_potentials.ipynb index 8ca6f74..485dd2a 100644 --- a/workflow/notebooks/prepare_potentials.ipynb +++ b/workflow/notebooks/prepare_potentials.ipynb @@ -7,14 +7,36 @@ "source": [ "# Renewable Energy Profiles Dataset Construction\n", "\n", - "This notebook builds per-bus renewable energy profiles (capacity factors, potentials, geometry) from PyPSA-Earth data sources without clustering. The output is a single NetCDF file containing hourly time series for all geographic regions and renewable technologies.\n", + "## Overview\n", "\n", - "## Workflow\n", - "1. **Setup & Configuration**: Initialize paths and logging\n", - "2. **Load Data**: Import renewable profiles and geojson region boundaries\n", - "3. **Inspect Data**: Validate data structure and bus-region matching\n", - "4. **Build Profiles**: Extract and aggregate per-bus profiles with energy conservation validation\n", - "5. **Analyze Results**: Inspect output and validate energy conservation\n" + "This notebook constructs gridded **renewable energy profiles** (capacity factors and installable potentials) from PyPSA-Earth data. These profiles represent **onwind**, **offwind-ac**, and **solar** renewable generation potential across geographic regions.\n", + "\n", + "### What We Build\n", + "\n", + "For each renewable technology and geographic location (bus), we extract:\n", + "- **Capacity Factor** (cf): Hourly time series of energy output (0-1) across 8,760 hours\n", + "- **p_nom_max**: Maximum installable power capacity (MW) based on land/sea availability\n", + "- **Potential**: Grid-level renewable resource (GW per 0.25° × 0.25° cell)\n", + "- **Geometry**: Bus region boundaries (Voronoi polygons as GeoJSON)\n", + "\n", + "### Outputs\n", + "\n", + "The workflow produces **three output files**:\n", + "1. **NetCDF** (.nc): Time series + potentials + metadata for all buses/technologies\n", + "2. **GeoJSON** (.geojson): Geographic boundaries + attributes (bus_id, country, area, density)\n", + "3. **Metadata** (metadata.json): Schema version + timestamp + file manifest\n", + "\n", + "These are used downstream for energy system modeling (PyPSA) and renewable energy supply curves.\n", + "\n", + "### Workflow\n", + "\n", + "1. **Setup**: Initialize paths and logging (idempotent, safe to rerun)\n", + "2. **Load Data**: Load raw PyPSA-Earth profiles and region boundaries\n", + "3. **Build Profiles**: 7-stage pipeline transforms raw data into per-bus profiles\n", + "4. **Save**: Write NetCDF + GeoJSON + metadata with reproducibility tags\n", + "5. **Inspect**: Verify dataset structure and summary statistics\n", + "6. **Validate**: Check data quality (capacity factor ranges, completeness)\n", + "7. **Visualize**: Plot grid-level potential and bus-level capacity density" ] }, { @@ -27,9 +49,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-16 12:31:30,304 - __main__ - INFO - ✓ Paths configured:\n", - "2026-04-16 12:31:30,306 - __main__ - INFO - SHIFT_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\shift\n", - "2026-04-16 12:31:30,308 - __main__ - INFO - PYPSA_EARTH_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\pypsa-earth\n" + "2026-04-17 15:25:45,698 - __main__ - INFO - ✓ Paths configured:\n", + "2026-04-17 15:25:45,700 - __main__ - INFO - SHIFT_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\shift\n", + "2026-04-17 15:25:45,701 - __main__ - INFO - PYPSA_EARTH_PATH: C:\\Users\\JanLeopoldTautorus\\Repos\\pypsa-earth\n" ] } ], @@ -82,155 +104,85 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 31, "id": "56730027", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-04-16 12:31:37,632 - numexpr.utils - INFO - NumExpr defaulting to 16 threads.\n" - ] - } - ], + "outputs": [], "source": [ - "# ===== SECTION 1: Import Libraries =====\n", - "\"\"\"\n", - "Core data processing and geospatial libraries\n", - "\"\"\"\n", - "\n", - "import xarray as xr # Multidimensional array I/O (NetCDF)\n", - "import geopandas as gpd # Geospatial vector data\n", - "import pandas as pd # Tabular data manipulation\n", - "import numpy as np # Numerical computing\n", - "import pycountry # Country code conversion\n", - "import geohash2 # Spatial hashing for bus IDs\n", - "from datetime import datetime # Timestamping output files\n", - "from tqdm import tqdm # Progress bars\n", - "\n", - "# Visualization and plotting libraries\n", - "import matplotlib.pyplot as plt # Plotting\n", - "import matplotlib.colors as mcolors # Color normalization\n", - "import cartopy.crs as ccrs # Map projections\n", - "import cartopy.feature as cfeature # Map features\n", - "from cartopy.io import shapereader as shprdr # Shapefile reader\n", - "from shapely.wkt import loads as wkt_loads # WKT geometry parsing" + "# ===== IMPORTS =====\n", + "import importlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import workflow.scripts.renewable_profiles as renewable_profiles\n", + "\n", + "importlib.reload(renewable_profiles)\n", + "\n", + "# Import module functions\n", + "load_pypsa_earth_profiles = renewable_profiles.load_pypsa_earth_profiles\n", + "load_region_boundaries = renewable_profiles.load_region_boundaries\n", + "build_profiles = renewable_profiles.build_profiles\n", + "save_profiles = renewable_profiles.save_profiles\n", + "load_profiles = renewable_profiles.load_profiles\n", + "audit_profiles_against_raw = renewable_profiles.audit_profiles_against_raw\n", + "plot_grid_potentials = renewable_profiles.plot_grid_potentials\n", + "plot_bus_capacity_density = renewable_profiles.plot_bus_capacity_density" ] }, { "cell_type": "code", "execution_count": 3, - "id": "d6dad14e", + "id": "057b6873", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2026-04-16 12:31:48,300 - __main__ - INFO - Loading 3 renewable technology profiles...\n", + "2026-04-17 15:25:47,461 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:25:47,462 - __main__ - INFO - LOADING RAW DATA\n", + "2026-04-17 15:25:47,462 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:25:47,464 - workflow.scripts.renewable_profiles - INFO - Loading renewable technology profiles from PyPSA-Earth...\n", "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", "Cannot find the ecCodes library\n", " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n", - "2026-04-16 12:31:50,079 - __main__ - INFO - ✓ Loaded all 3 profiles\n" + "2026-04-17 15:25:47,763 - workflow.scripts.renewable_profiles - INFO - ✓ onwind: 8760 hours, 4582 buses, grid 259×243\n", + "2026-04-17 15:25:47,777 - workflow.scripts.renewable_profiles - INFO - ✓ offwind-ac: 8760 hours, 713 buses, grid 290×256\n", + "2026-04-17 15:25:47,793 - workflow.scripts.renewable_profiles - INFO - ✓ solar: 8760 hours, 4582 buses, grid 259×243\n", + "2026-04-17 15:25:47,793 - workflow.scripts.renewable_profiles - INFO - ✓ Loaded 3 profiles: ['onwind', 'offwind-ac', 'solar']\n", + "2026-04-17 15:25:47,794 - workflow.scripts.renewable_profiles - INFO - Loading geographic region boundaries from GeoJSON...\n", + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\pyogrio\\core.py:34: RuntimeWarning: Could not detect GDAL data files. Set GDAL_DATA environment variable to the correct path.\n", + " _init_gdal_data()\n", + "2026-04-17 15:25:48,019 - workflow.scripts.renewable_profiles - INFO - Onshore: 4582 regions\n", + "2026-04-17 15:25:48,019 - workflow.scripts.renewable_profiles - INFO - Offshore: 714 regions\n", + "2026-04-17 15:25:48,020 - __main__ - INFO - ✓ Data loaded: 3 technologies, 4582 onshore, 714 offshore regions\n" ] } ], "source": [ - "# ===== SECTION 2: Load Renewable Technology Profiles =====\n", + "# ===== LOAD DATA: PyPSA-Earth Profiles & Regions =====\n", "\"\"\"\n", - "Load capacity factor and potential data for onshore wind, offshore wind, and solar.\n", - "\n", - "Trimming to 8760 hours (calendar year 2013) occurs during extraction.\n", + "Load renewable technology profiles and geographic region boundaries.\n", + "Uses the preprocessing script functions.\n", "\"\"\"\n", + "logger.info(\"=\" * 70)\n", + "logger.info(\"LOADING RAW DATA\")\n", + "logger.info(\"=\" * 70)\n", "\n", - "tech_profiles_nc = {}\n", - "technologies = [\"onwind\", \"offwind-ac\", \"solar\"]\n", + "# Load technology profiles\n", + "tech_profiles_nc = load_pypsa_earth_profiles(PYPSA_EARTH_PATH)\n", "\n", - "logger.info(f\"Loading {len(technologies)} renewable technology profiles...\")\n", - "for technology in technologies:\n", - " path = (\n", - " os.path.realpath(PYPSA_EARTH_PATH)\n", - " + f\"/resources/renewable_profiles/profile_{technology}.nc\"\n", - " )\n", - " ds = xr.open_dataset(path)\n", - " tech_profiles_nc[technology] = ds\n", - " n_hours = len(ds.coords.get(\"time\", ds.coords.get(\"hour\", [])))\n", - " logger.debug(\n", - " f\" {technology}: {n_hours} hours, {len(ds.bus)} buses, grid {len(ds.x)}×{len(ds.y)}\"\n", - " )\n", + "# Load region boundaries\n", + "onshore_regions_gpd, offshore_regions_gpd = load_region_boundaries(PYPSA_EARTH_PATH)\n", "\n", - "logger.info(f\"✓ Loaded all {len(technologies)} profiles\")" + "logger.info(\n", + " f\"✓ Data loaded: {len(tech_profiles_nc)} technologies, \"\n", + " f\"{len(onshore_regions_gpd)} onshore, {len(offshore_regions_gpd)} offshore regions\"\n", + ")" ] }, { "cell_type": "code", "execution_count": 4, - "id": "057b6873", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-04-16 12:31:50,103 - __main__ - INFO - Inspecting technology profile data structures...\n", - "2026-04-16 12:31:50,105 - __main__ - INFO - \n", - "ONWIND Profile Structure:\n", - "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_31552\\776807864.py:10: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", - " logger.info(f\" Dimensions: {dict(ds.dims)}\")\n", - "2026-04-16 12:31:50,109 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 13876, 'y': 245, 'x': 282}\n", - "2026-04-16 12:31:50,111 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", - "2026-04-16 12:31:50,112 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance']\n", - "2026-04-16 12:31:50,119 - __main__ - INFO - Bus dim: 13876 buses (samples: ['12647' '12648' '12649'])\n", - "2026-04-16 12:31:50,120 - __main__ - INFO - Grid: x=282, y=245\n", - "2026-04-16 12:31:50,122 - __main__ - INFO - \n", - "OFFWIND-AC Profile Structure:\n", - "2026-04-16 12:31:50,125 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 1839, 'y': 258, 'x': 282}\n", - "2026-04-16 12:31:50,126 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", - "2026-04-16 12:31:50,128 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance', 'underwater_fraction']\n", - "2026-04-16 12:31:50,131 - __main__ - INFO - Bus dim: 1839 buses (samples: ['17753' '18998' '75'])\n", - "2026-04-16 12:31:50,133 - __main__ - INFO - Grid: x=282, y=258\n", - "2026-04-16 12:31:50,134 - __main__ - INFO - \n", - "SOLAR Profile Structure:\n", - "2026-04-16 12:31:50,137 - __main__ - INFO - Dimensions: {'time': 8760, 'bus': 13876, 'y': 245, 'x': 282}\n", - "2026-04-16 12:31:50,138 - __main__ - INFO - Coordinates: ['time', 'bus', 'y', 'x']\n", - "2026-04-16 12:31:50,139 - __main__ - INFO - Data vars: ['profile', 'weight', 'p_nom_max', 'potential', 'average_distance']\n", - "2026-04-16 12:31:50,146 - __main__ - INFO - Bus dim: 13876 buses (samples: ['12647' '12648' '12649'])\n", - "2026-04-16 12:31:50,147 - __main__ - INFO - Grid: x=282, y=245\n", - "2026-04-16 12:31:50,149 - __main__ - INFO - ✓ Data structure validation complete\n" - ] - } - ], - "source": [ - "# ===== SECTION 3: Inspect Renewable Profile Data Structure =====\n", - "\"\"\"\n", - "Validate profile dimensions, coordinates, and data variables.\n", - "Confirms that profiles are indexed by [time, bus] and potentials by [y, x] grid.\n", - "\"\"\"\n", - "\n", - "logger.info(\"Inspecting technology profile data structures...\")\n", - "for tech, ds in tech_profiles_nc.items():\n", - " logger.info(f\"\\n{tech.upper()} Profile Structure:\")\n", - " logger.info(f\" Dimensions: {dict(ds.dims)}\")\n", - " logger.info(f\" Coordinates: {list(ds.coords.keys())}\")\n", - " logger.info(f\" Data vars: {list(ds.data_vars.keys())}\")\n", - "\n", - " if \"bus\" in ds.coords:\n", - " logger.info(\n", - " f\" Bus dim: {len(ds.coords['bus'])} buses (samples: {ds.coords['bus'].values[:3]})\"\n", - " )\n", - " if \"x\" in ds.coords and \"y\" in ds.coords:\n", - " logger.info(f\" Grid: x={len(ds.coords['x'])}, y={len(ds.coords['y'])}\")\n", - "\n", - " logger.debug(f\" Profile shape: {ds['profile'].shape}\")\n", - " logger.debug(f\" Potential shape: {ds['potential'].shape}\")\n", - "\n", - "logger.info(\"✓ Data structure validation complete\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, "id": "a11f1ff3", "metadata": {}, "outputs": [ @@ -238,769 +190,240 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-16 12:31:50,182 - __main__ - INFO - Loading geographic region boundaries from GeoJSON...\n", - "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\pyogrio\\core.py:34: RuntimeWarning: Could not detect GDAL data files. Set GDAL_DATA environment variable to the correct path.\n", - " _init_gdal_data()\n", - "2026-04-16 12:31:54,444 - __main__ - INFO - Onshore regions: 13876 regions, columns: ['name', 'x', 'y', 'country', 'shape_id', 'geometry']\n", - "2026-04-16 12:31:54,447 - __main__ - INFO - Offshore regions: 1839 regions, columns: ['name', 'x', 'y', 'country', 'shape_id', 'geometry']\n", - "2026-04-16 12:31:54,539 - __main__ - INFO - Region-bus matching: onshore 100.0%, offshore 100.0%\n", - "2026-04-16 12:31:54,541 - __main__ - INFO - ✓ 100% overlap confirmed: all region names match profile bus IDs\n" + "2026-04-17 15:25:48,031 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:25:48,032 - __main__ - INFO - BUILDING RENEWABLE PROFILES (7-Stage Pipeline)\n", + "2026-04-17 15:25:48,033 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:25:48,034 - workflow.scripts.renewable_profiles - INFO - Building profiles for: ['onwind', 'offwind-ac', 'solar']\n", + "2026-04-17 15:25:48,034 - workflow.scripts.renewable_profiles - INFO - STAGE 0: Prepare regions\n", + "2026-04-17 15:25:48,083 - workflow.scripts.renewable_profiles - INFO - Combined: 5296 total regions\n", + "2026-04-17 15:25:48,085 - workflow.scripts.renewable_profiles - INFO - STAGE 1: Generate bus IDs\n", + "C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\workflow\\scripts\\renewable_profiles.py:194: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " centroids = regions.geometry.centroid\n", + "2026-04-17 15:25:48,620 - workflow.scripts.renewable_profiles - INFO - Generated 5296 bus IDs (2 collisions handled)\n", + "2026-04-17 15:25:48,702 - workflow.scripts.renewable_profiles - INFO - STAGE 2: Cache technology data\n", + "2026-04-17 15:25:49,768 - workflow.scripts.renewable_profiles - INFO - Cached 3 technology datasets\n", + "2026-04-17 15:25:49,770 - workflow.scripts.renewable_profiles - INFO - STAGE 3: Extract profiles for all buses\n", + "C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\workflow\\scripts\\renewable_profiles.py:499: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " centroids = all_regions.geometry.centroid\n", + "2026-04-17 15:25:49,881 - workflow.scripts.renewable_profiles - INFO - Processing 54 countries sequentially\n", + "2026-04-17 15:25:49,882 - workflow.scripts.renewable_profiles - INFO - [1/54] AGO: 99 regions\n", + "2026-04-17 15:25:49,897 - workflow.scripts.renewable_profiles - INFO - [2/54] BDI: 9 regions\n", + "2026-04-17 15:25:49,900 - workflow.scripts.renewable_profiles - INFO - [3/54] BEN: 17 regions\n", + "2026-04-17 15:25:49,905 - workflow.scripts.renewable_profiles - INFO - [4/54] BFA: 16 regions\n", + "2026-04-17 15:25:49,910 - workflow.scripts.renewable_profiles - INFO - [5/54] BWA: 48 regions\n", + "2026-04-17 15:25:49,920 - workflow.scripts.renewable_profiles - INFO - [6/54] CAF: 3 regions\n", + "2026-04-17 15:25:49,923 - workflow.scripts.renewable_profiles - INFO - [7/54] CIV: 74 regions\n", + "2026-04-17 15:25:49,936 - workflow.scripts.renewable_profiles - INFO - [8/54] CMR: 30 regions\n", + "2026-04-17 15:25:49,944 - workflow.scripts.renewable_profiles - INFO - [9/54] COD: 85 regions\n", + "2026-04-17 15:25:49,961 - workflow.scripts.renewable_profiles - INFO - [10/54] COG: 31 regions\n", + "2026-04-17 15:25:49,968 - workflow.scripts.renewable_profiles - INFO - [11/54] COM: 2 regions\n", + "2026-04-17 15:25:49,970 - workflow.scripts.renewable_profiles - INFO - [12/54] CPV: 6 regions\n", + "2026-04-17 15:25:49,972 - workflow.scripts.renewable_profiles - INFO - [13/54] DJI: 12 regions\n", + "2026-04-17 15:25:49,975 - workflow.scripts.renewable_profiles - INFO - [14/54] DZA: 494 regions\n", + "2026-04-17 15:25:50,054 - workflow.scripts.renewable_profiles - INFO - [15/54] EGY: 519 regions\n", + "2026-04-17 15:25:50,130 - workflow.scripts.renewable_profiles - INFO - [16/54] ERI: 7 regions\n", + "2026-04-17 15:25:50,133 - workflow.scripts.renewable_profiles - INFO - [17/54] ETH: 171 regions\n", + "2026-04-17 15:25:50,164 - workflow.scripts.renewable_profiles - INFO - [18/54] GAB: 21 regions\n", + "2026-04-17 15:25:50,168 - workflow.scripts.renewable_profiles - INFO - [19/54] GHA: 107 regions\n", + "2026-04-17 15:25:50,185 - workflow.scripts.renewable_profiles - INFO - [20/54] GIN: 23 regions\n", + "2026-04-17 15:25:50,191 - workflow.scripts.renewable_profiles - INFO - [21/54] GMB: 4 regions\n", + "2026-04-17 15:25:50,193 - workflow.scripts.renewable_profiles - INFO - [22/54] GNB: 8 regions\n", + "2026-04-17 15:25:50,194 - workflow.scripts.renewable_profiles - INFO - [23/54] GNQ: 27 regions\n", + "2026-04-17 15:25:50,199 - workflow.scripts.renewable_profiles - INFO - [24/54] KEN: 95 regions\n", + "2026-04-17 15:25:50,217 - workflow.scripts.renewable_profiles - INFO - [25/54] LBR: 11 regions\n", + "2026-04-17 15:25:50,220 - workflow.scripts.renewable_profiles - INFO - [26/54] LBY: 240 regions\n", + "2026-04-17 15:25:50,253 - workflow.scripts.renewable_profiles - INFO - [27/54] LSO: 27 regions\n", + "2026-04-17 15:25:50,259 - workflow.scripts.renewable_profiles - INFO - [28/54] MAR: 454 regions\n", + "2026-04-17 15:25:50,361 - workflow.scripts.renewable_profiles - INFO - [29/54] MDG: 23 regions\n", + "2026-04-17 15:25:50,366 - workflow.scripts.renewable_profiles - INFO - [30/54] MLI: 17 regions\n", + "2026-04-17 15:25:50,370 - workflow.scripts.renewable_profiles - INFO - [31/54] MOZ: 112 regions\n", + "2026-04-17 15:25:50,386 - workflow.scripts.renewable_profiles - INFO - [32/54] MRT: 16 regions\n", + "2026-04-17 15:25:50,390 - workflow.scripts.renewable_profiles - INFO - [33/54] MUS: 14 regions\n", + "2026-04-17 15:25:50,393 - workflow.scripts.renewable_profiles - INFO - [34/54] MWI: 42 regions\n", + "2026-04-17 15:25:50,401 - workflow.scripts.renewable_profiles - INFO - [35/54] NAM: 135 regions\n", + "2026-04-17 15:25:50,424 - workflow.scripts.renewable_profiles - INFO - [36/54] NER: 13 regions\n", + "2026-04-17 15:25:50,428 - workflow.scripts.renewable_profiles - INFO - [37/54] NGA: 201 regions\n", + "2026-04-17 15:25:50,462 - workflow.scripts.renewable_profiles - INFO - [38/54] RWA: 43 regions\n", + "2026-04-17 15:25:50,471 - workflow.scripts.renewable_profiles - INFO - [39/54] SDN: 74 regions\n", + "2026-04-17 15:25:50,483 - workflow.scripts.renewable_profiles - INFO - [40/54] SEN: 36 regions\n", + "2026-04-17 15:25:50,489 - workflow.scripts.renewable_profiles - INFO - [41/54] SLE: 12 regions\n", + "2026-04-17 15:25:50,493 - workflow.scripts.renewable_profiles - INFO - [42/54] SOM: 2 regions\n", + "2026-04-17 15:25:50,494 - workflow.scripts.renewable_profiles - INFO - [43/54] SSD: 1 regions\n", + "2026-04-17 15:25:50,495 - workflow.scripts.renewable_profiles - INFO - [44/54] STP: 2 regions\n", + "2026-04-17 15:25:50,497 - workflow.scripts.renewable_profiles - INFO - [45/54] SWZ: 22 regions\n", + "2026-04-17 15:25:50,502 - workflow.scripts.renewable_profiles - INFO - [46/54] SYC: 2 regions\n", + "2026-04-17 15:25:50,504 - workflow.scripts.renewable_profiles - INFO - [47/54] TCD: 4 regions\n", + "2026-04-17 15:25:50,506 - workflow.scripts.renewable_profiles - INFO - [48/54] TGO: 17 regions\n", + "2026-04-17 15:25:50,511 - workflow.scripts.renewable_profiles - INFO - [49/54] TUN: 138 regions\n", + "2026-04-17 15:25:50,530 - workflow.scripts.renewable_profiles - INFO - [50/54] TZA: 84 regions\n", + "2026-04-17 15:25:50,545 - workflow.scripts.renewable_profiles - INFO - [51/54] UGA: 24 regions\n", + "2026-04-17 15:25:50,551 - workflow.scripts.renewable_profiles - INFO - [52/54] ZAF: 1419 regions\n", + "2026-04-17 15:25:50,764 - workflow.scripts.renewable_profiles - INFO - [53/54] ZMB: 132 regions\n", + "2026-04-17 15:25:50,787 - workflow.scripts.renewable_profiles - INFO - [54/54] ZWE: 71 regions\n", + "2026-04-17 15:25:50,800 - workflow.scripts.renewable_profiles - INFO - Extracted 9236 bus-technology combinations\n", + "2026-04-17 15:25:50,801 - workflow.scripts.renewable_profiles - INFO - STAGE 4: Reconcile grid extents\n", + "2026-04-17 15:25:50,802 - workflow.scripts.renewable_profiles - INFO - Reconciling grid extents across technologies...\n", + "2026-04-17 15:25:50,803 - workflow.scripts.renewable_profiles - INFO - Final grid: 256 × 290\n", + "2026-04-17 15:25:50,804 - workflow.scripts.renewable_profiles - INFO - STAGE 5: Create xarray dataset\n", + "2026-04-17 15:25:50,808 - workflow.scripts.renewable_profiles - INFO - STAGE 6: Add metadata\n", + "2026-04-17 15:25:50,809 - workflow.scripts.renewable_profiles - INFO - Preparing geometry GeoDataFrame\n", + "2026-04-17 15:25:50,813 - workflow.scripts.renewable_profiles - INFO - ✓ Profile building complete\n", + "2026-04-17 15:25:50,830 - __main__ - INFO - ✓ Built 5296 buses × 3 technologies\n" ] } ], "source": [ - "# ===== SECTION 4: Load and Validate Geographic Region Boundaries =====\n", - "\"\"\"\n", - "Load onshore and offshore region geojson files.\n", - "Validate that region names match profile bus IDs (100% overlap expected).\n", + "# ===== BUILD PROFILES: 7-Stage Pipeline =====\n", "\"\"\"\n", + "Main orchestrator: builds per-bus renewable profiles from PyPSA-Earth data.\n", "\n", - "logger.info(\"Loading geographic region boundaries from GeoJSON...\")\n", - "onshore_regions_gpd = gpd.read_file(\n", - " os.path.realpath(PYPSA_EARTH_PATH)\n", - " + \"/resources/bus_regions/regions_onshore.geojson\"\n", - ")\n", - "offshore_regions_gpd = gpd.read_file(\n", - " os.path.realpath(PYPSA_EARTH_PATH)\n", - " + \"/resources/bus_regions/regions_offshore.geojson\"\n", - ")\n", - "\n", - "logger.info(\n", - " f\"Onshore regions: {len(onshore_regions_gpd)} regions, columns: {list(onshore_regions_gpd.columns)}\"\n", - ")\n", - "logger.info(\n", - " f\"Offshore regions: {len(offshore_regions_gpd)} regions, columns: {list(offshore_regions_gpd.columns)}\"\n", - ")\n", - "logger.debug(f\" Onshore samples: {onshore_regions_gpd['name'].head(3).tolist()}\")\n", - "logger.debug(f\" Offshore samples: {offshore_regions_gpd['name'].head(3).tolist()}\")\n", + "Configuration options:\n", + "- country_codes: Filter to specific countries (None = all)\n", + "- geohash_precision: Bus ID precision (1-12, default 6)\n", + "- process_by_country: Sequential processing to reduce memory (default True)\n", + "\"\"\"\n", + "logger.info(\"=\" * 70)\n", + "logger.info(\"BUILDING RENEWABLE PROFILES (7-Stage Pipeline)\")\n", + "logger.info(\"=\" * 70)\n", "\n", - "# Validate matching: region names should match profile bus IDs\n", - "profile_buses_onwind = set(tech_profiles_nc[\"onwind\"].bus.values.astype(str))\n", - "profile_buses_solar = set(tech_profiles_nc[\"solar\"].bus.values.astype(str))\n", - "profile_buses_offwind = set(tech_profiles_nc[\"offwind-ac\"].bus.values.astype(str))\n", - "onshore_names = set(onshore_regions_gpd[\"name\"].astype(str))\n", - "offshore_names = set(offshore_regions_gpd[\"name\"].astype(str))\n", + "# Configuration\n", + "config = {\n", + " \"country_codes\": None, # None = process all regions\n", + " \"geohash_precision\": 6,\n", + " \"process_by_country\": True, # Sequential reduces memory usage\n", + "}\n", "\n", - "onshore_match_pct = 100 * len(onshore_names & profile_buses_onwind) / len(onshore_names)\n", - "offshore_match_pct = (\n", - " 100 * len(offshore_names & profile_buses_offwind) / len(offshore_names)\n", + "# Build profiles\n", + "profiles_ds, geometry_gdf = build_profiles(\n", + " tech_profiles_nc,\n", + " onshore_regions_gpd,\n", + " offshore_regions_gpd,\n", + " config=config,\n", ")\n", "\n", "logger.info(\n", - " f\"Region-bus matching: onshore {onshore_match_pct:.1f}%, offshore {offshore_match_pct:.1f}%\"\n", - ")\n", - "if onshore_match_pct == 100 and offshore_match_pct == 100:\n", - " logger.info(\"✓ 100% overlap confirmed: all region names match profile bus IDs\")\n", - "else:\n", - " logger.warning(\n", - " \"⚠ Incomplete match: consider spatial matching (nearest neighbor/grid-based)\"\n", - " )" + " f\"✓ Built {len(profiles_ds.bus)} buses × {len(profiles_ds.technology)} technologies\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "71eecc60", + "execution_count": 12, + "id": "ff139ebe", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2026-04-16 12:31:54,571 - __main__ - INFO - Preparing regional data for bus ID generation...\n", - "2026-04-16 12:31:55,118 - __main__ - INFO - ✓ Onshore: 13876 regions, mean area 2862.5 km²\n", - "2026-04-16 12:31:55,121 - __main__ - INFO - ✓ Offshore: 1839 regions, mean area 13667.1 km²\n" + "2026-04-17 15:32:46,713 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:32:46,714 - __main__ - INFO - PROCESSED vs RAW AUDIT\n", + "2026-04-17 15:32:46,714 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:32:46,715 - __main__ - INFO - \n", + "----------------------------------------------------------------------\n", + "2026-04-17 15:32:46,716 - __main__ - INFO - Technology: onwind\n", + "2026-04-17 15:32:46,717 - __main__ - INFO - ----------------------------------------------------------------------\n", + "2026-04-17 15:32:46,720 - __main__ - INFO - potential stats\n", + "2026-04-17 15:32:46,720 - __main__ - INFO - processed: min=0, max=3958.24, mean=1149.4, NaN=11303/74240 (15.22%)\n", + "2026-04-17 15:32:46,721 - __main__ - INFO - raw: min=0, max=3958.24, mean=1149.4, NaN=0/62937 (0.00%)\n", + "2026-04-17 15:32:46,723 - __main__ - INFO - p_nom_max stats\n", + "2026-04-17 15:32:46,723 - __main__ - INFO - processed: min=0, max=2.59744e+06, mean=15787.9, NaN=714/5296 (13.48%)\n", + "2026-04-17 15:32:46,725 - __main__ - INFO - raw: min=0, max=2.59744e+06, mean=15787.9, NaN=0/4582 (0.00%)\n", + "2026-04-17 15:32:46,726 - __main__ - INFO - processed non-NaN p_nom_max buses: 4582/5296\n", + "2026-04-17 15:32:46,726 - __main__ - INFO - \n", + "----------------------------------------------------------------------\n", + "2026-04-17 15:32:46,727 - __main__ - INFO - Technology: offwind-ac\n", + "2026-04-17 15:32:46,728 - __main__ - INFO - ----------------------------------------------------------------------\n", + "2026-04-17 15:32:46,731 - __main__ - INFO - potential stats\n", + "2026-04-17 15:32:46,732 - __main__ - INFO - processed: min=0, max=2188.46, mean=11.6741, NaN=0/74240 (0.00%)\n", + "2026-04-17 15:32:46,733 - __main__ - INFO - raw: min=0, max=2188.46, mean=11.6741, NaN=0/74240 (0.00%)\n", + "2026-04-17 15:32:46,734 - __main__ - INFO - p_nom_max stats\n", + "2026-04-17 15:32:46,735 - __main__ - INFO - processed: min=1.32857, max=54534.2, mean=2037.27, NaN=5224/5296 (98.64%)\n", + "2026-04-17 15:32:46,737 - __main__ - INFO - raw: min=0, max=54534.2, mean=1215.54, NaN=0/713 (0.00%)\n", + "2026-04-17 15:32:46,737 - __main__ - INFO - processed non-NaN p_nom_max buses: 72/5296\n", + "2026-04-17 15:32:46,738 - __main__ - INFO - \n", + "----------------------------------------------------------------------\n", + "2026-04-17 15:32:46,738 - __main__ - INFO - Technology: solar\n", + "2026-04-17 15:32:46,739 - __main__ - INFO - ----------------------------------------------------------------------\n", + "2026-04-17 15:32:46,743 - __main__ - INFO - potential stats\n", + "2026-04-17 15:32:46,743 - __main__ - INFO - processed: min=0, max=5423.68, mean=1370.58, NaN=11303/74240 (15.22%)\n", + "2026-04-17 15:32:46,744 - __main__ - INFO - raw: min=0, max=5423.68, mean=1370.58, NaN=0/62937 (0.00%)\n", + "2026-04-17 15:32:46,746 - __main__ - INFO - p_nom_max stats\n", + "2026-04-17 15:32:46,747 - __main__ - INFO - processed: min=0, max=4.10065e+06, mean=18825.9, NaN=714/5296 (13.48%)\n", + "2026-04-17 15:32:46,748 - __main__ - INFO - raw: min=0, max=4.10065e+06, mean=18825.9, NaN=0/4582 (0.00%)\n", + "2026-04-17 15:32:46,749 - __main__ - INFO - processed non-NaN p_nom_max buses: 4582/5296\n", + "2026-04-17 15:32:46,750 - __main__ - INFO - \n", + "✓ Audit complete\n" ] } ], "source": [ - "# ===== SECTION 5: Prepare Geographic Region Data =====\n", + "# ===== DIAGNOSTIC: Processed vs Raw Stats (potential, p_nom_max) =====\n", "\"\"\"\n", - "1. Convert ISO2 country codes to ISO3 for consistency\n", - "2. Prefix offshore region names to avoid ID collisions with onshore\n", - "3. Calculate region areas in km² (using EPSG:3857 projection for accuracy)\n", + "Compare processed dataset statistics against raw PyPSA-Earth inputs.\n", + "Metrics: min, max, mean, NaN count, NaN share.\n", "\"\"\"\n", "\n", - "logger.info(\"Preparing regional data for bus ID generation...\")\n", - "\n", - "# Convert ISO2 → ISO3 country codes\n", - "onshore_regions_gpd[\"country\"] = onshore_regions_gpd[\"country\"].apply(\n", - " lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3\n", - ")\n", - "offshore_regions_gpd[\"country\"] = offshore_regions_gpd[\"country\"].apply(\n", - " lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3\n", - ")\n", - "logger.debug(\"✓ Converted country codes to ISO3\")\n", - "\n", - "# Prefix offshore regions to ensure globally unique names\n", - "offshore_regions_gpd[\"name\"] = \"OFF_\" + offshore_regions_gpd[\"name\"].astype(str)\n", - "logger.debug(\"✓ Prefixed offshore region names with 'OFF_' (deduplication)\")\n", - "\n", - "# Calculate area in km² using projected CRS (EPSG:6933 Cylindrical Equal Area)\n", - "onshore_regions_gpd[\"area_km2\"] = (\n", - " onshore_regions_gpd.to_crs(\"EPSG:6933\").geometry.area / 1e6\n", - ")\n", - "offshore_regions_gpd[\"area_km2\"] = (\n", - " offshore_regions_gpd.to_crs(\"EPSG:6933\").geometry.area / 1e6\n", - ")\n", - "\n", - "logger.info(\n", - " f\"✓ Onshore: {len(onshore_regions_gpd)} regions, mean area {onshore_regions_gpd['area_km2'].mean():.1f} km²\"\n", - ")\n", - "logger.info(\n", - " f\"✓ Offshore: {len(offshore_regions_gpd)} regions, mean area {offshore_regions_gpd['area_km2'].mean():.1f} km²\"\n", + "audit_report = audit_profiles_against_raw(\n", + " profiles_ds=profiles_ds,\n", + " raw_profile_datasets=tech_profiles_nc,\n", + " logger_instance=logger,\n", ")" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "4155f554", - "metadata": {}, - "outputs": [], - "source": [ - "# ===== SECTION 5.5: Helper Functions for Main Pipeline =====\n", - "\"\"\"\n", - "Helper functions used by build_renewable_profiles_by_bus().\n", - "These are defined once and reused across all bus-technology-time combinations.\n", - "\"\"\"\n", - "\n", - "\n", - "def _normalize_bus_name(name):\n", - " \"\"\"Remove 'OFF_' prefix to match normalized offshore bus names in profiles.\"\"\"\n", - " return str(name).replace(\"OFF_\", \"\")\n", - "\n", - "\n", - "def _generate_bus_ids_and_wkt(all_regions, geohash_precision):\n", - " \"\"\"\n", - " STAGE 1: Generate geohash-based bus IDs and extract WKT geometry.\n", - "\n", - " Creates globally unique bus IDs of form: ISO3_ON/OFF_GEOHASH[_###]\n", - " Handles collisions by appending 3-digit suffix.\n", - " \"\"\"\n", - " all_regions[\"bus_id\"] = \"\"\n", - " all_regions[\"geometry_wkt\"] = all_regions.geometry.to_wkt()\n", - "\n", - " bus_id_counts = {}\n", - " collision_count = 0\n", - "\n", - " for idx, row in all_regions.iterrows():\n", - " country = row[\"country\"]\n", - " onshore_offshore = \"ON\" if row[\"onshore_offshore\"] == \"onshore\" else \"OFF\"\n", - " geohash = geohash2.encode(\n", - " row.geometry.centroid.y,\n", - " row.geometry.centroid.x,\n", - " precision=geohash_precision,\n", - " )\n", - "\n", - " base_bus_id = f\"{country}_{onshore_offshore}_{geohash}\"\n", - " if base_bus_id not in bus_id_counts:\n", - " bus_id_counts[base_bus_id] = 0\n", - " all_regions.at[idx, \"bus_id\"] = base_bus_id\n", - " else:\n", - " bus_id_counts[base_bus_id] += 1\n", - " suffixed_id = f\"{base_bus_id}_{bus_id_counts[base_bus_id]:03d}\"\n", - " all_regions.at[idx, \"bus_id\"] = suffixed_id\n", - " collision_count += 1\n", - "\n", - " return all_regions, collision_count\n", - "\n", - "\n", - "def _build_tech_dataset_cache(tech_profiles_nc):\n", - " \"\"\"\n", - " STAGE 2: Load and cache technology datasets for fast lookups.\n", - "\n", - " Returns dict with per-technology cache: {tech: {'profile': ..., 'potential': ..., 'p_nom_max': ..., 'bus_to_idx': ..., 'x': ..., 'y': ...}}\n", - " \"\"\"\n", - " tech_data_cache = {}\n", - "\n", - " for tech, ds in tech_profiles_nc.items():\n", - " # Trim profiles to 8760 hours and transpose to [bus, time] for consistent indexing\n", - " profile_trimmed = ds[\"profile\"].isel(time=slice(0, 8760))\n", - "\n", - " # Ensure dimensions are [bus, time] regardless of original dataset ordering\n", - " if \"bus\" in profile_trimmed.dims and \"time\" in profile_trimmed.dims:\n", - " profile_trimmed = profile_trimmed.transpose(\"bus\", \"time\")\n", - "\n", - " profile = profile_trimmed.values\n", - "\n", - " # Get potential and p_nom_max\n", - " potential = (\n", - " ds[\"potential\"].values\n", - " if \"potential\" in ds\n", - " else np.full((len(ds.y), len(ds.x)), np.nan)\n", - " )\n", - " p_nom_max = (\n", - " ds[\"p_nom_max\"].values\n", - " if \"p_nom_max\" in ds\n", - " else np.full(len(ds.bus), np.nan)\n", - " )\n", - "\n", - " # Build bus index mapping\n", - " bus_list = ds[\"bus\"].values.astype(str)\n", - " bus_to_idx = {bus_name: idx for idx, bus_name in enumerate(bus_list)}\n", - "\n", - " # Store grid coordinates\n", - " x_grid = ds[\"x\"].values if \"x\" in ds.coords else np.array([])\n", - " y_grid = ds[\"y\"].values if \"y\" in ds.coords else np.array([])\n", - "\n", - " tech_data_cache[tech] = {\n", - " \"profile\": profile,\n", - " \"potential\": potential,\n", - " \"p_nom_max\": p_nom_max,\n", - " \"bus_to_idx\": bus_to_idx,\n", - " \"x\": x_grid,\n", - " \"y\": y_grid,\n", - " }\n", - "\n", - " return tech_data_cache\n", - "\n", - "\n", - "def _extract_profile_at_location(\n", - " tech_cache, bus_name_clean, lon, lat, technology, onshore_offshore\n", - "):\n", - " \"\"\"\n", - " Extract capacity factor timeseries and p_nom_max for a given bus location.\n", - "\n", - " Enforces technology-region compatibility:\n", - " - Onshore regions: only onwind and solar\n", - " - Offshore regions: only offwind-ac\n", - "\n", - " Potential is extracted from the 2D grid at the nearest (x,y) grid cell to (lon, lat).\n", - " Returns: (cf_array, potential_val, p_nom_max_val, avg_cf) or (NaN, NaN, NaN, NaN) if missing or incompatible.\n", - " \"\"\"\n", - " bus_to_idx = tech_cache[\"bus_to_idx\"]\n", - "\n", - " # Enforce technology-region compatibility\n", - " if onshore_offshore == \"offshore\" and technology != \"offwind-ac\":\n", - " # Offshore region should only use offwind-ac\n", - " return np.nan, np.nan, np.nan, np.nan\n", - " elif onshore_offshore == \"onshore\" and technology == \"offwind-ac\":\n", - " # Onshore region should not use offwind-ac\n", - " return np.nan, np.nan, np.nan, np.nan\n", - "\n", - " # Get profile by bus index\n", - " if bus_name_clean not in bus_to_idx:\n", - " return np.nan, np.nan, np.nan, np.nan\n", - "\n", - " bus_idx = bus_to_idx[bus_name_clean]\n", - " cf_array = tech_cache[\"profile\"][bus_idx, :].copy()\n", - "\n", - " # Get potential via nearest neighbor on 2D grid\n", - " if tech_cache[\"x\"].size > 0 and tech_cache[\"y\"].size > 0:\n", - " # Create 2D grids from 1D x, y coordinates\n", - " x_grid, y_grid = np.meshgrid(tech_cache[\"x\"], tech_cache[\"y\"])\n", - " # Compute distances from all grid points to bus location\n", - " distances = np.sqrt((x_grid - lon) ** 2 + (y_grid - lat) ** 2)\n", - " # Find nearest grid cell\n", - " y_idx, x_idx = np.unravel_index(np.argmin(distances), distances.shape)\n", - " potential_val = tech_cache[\"potential\"][y_idx, x_idx]\n", - " else:\n", - " potential_val = np.nan\n", - "\n", - " # Get p_nom_max by bus index\n", - " if bus_idx < len(tech_cache[\"p_nom_max\"]):\n", - " p_nom_max_val = tech_cache[\"p_nom_max\"][bus_idx]\n", - " else:\n", - " p_nom_max_val = np.nan\n", - "\n", - " # Compute average CF\n", - " avg_cf = np.nanmean(cf_array)\n", - "\n", - " return cf_array, potential_val, p_nom_max_val, avg_cf\n", - "\n", - "\n", - "def _compute_p_nom_max_with_fallback(p_nom_max_val, potential_val, avg_cf):\n", - " \"\"\"\n", - " Compute final p_nom_max with fallback logic.\n", - "\n", - " Prefers loaded p_nom_max. If NaN, falls back to potential_val / avg_cf.\n", - " \"\"\"\n", - " if not np.isnan(p_nom_max_val) and p_nom_max_val > 0:\n", - " return p_nom_max_val\n", - " else:\n", - " return np.nan\n", - "\n", - "\n", - "# ===== SECTION 5.6: Main Function =====\n", - "\n", - "\n", - "def build_renewable_profiles_by_bus(\n", - " profile_datasets,\n", - " onshore_regions_gpd,\n", - " offshore_regions_gpd,\n", - " country_codes=None,\n", - " country_code_format=\"ISO3\",\n", - " geohash_precision=6,\n", - " output_dir=\"data/renewable_profiles/\",\n", - " filename_prefix=\"renewable_profiles_by_bus\",\n", - "):\n", - " \"\"\"\n", - " Build per-bus renewable energy profiles from PyPSA-Earth data through 7-stage pipeline.\n", - "\n", - " Parameters\n", - " ----------\n", - " profile_datasets : dict\n", - " Technology-keyed dict of xarray Datasets (onwind, offwind-ac, solar profiles)\n", - " onshore_regions_gpd : GeoDataFrame\n", - " Onshore regions with columns: [name, country, geometry]\n", - " offshore_regions_gpd : GeoDataFrame\n", - " Offshore regions with columns: [name, country, geometry, onshore_offshore='offshore']\n", - " country_codes : list[str], optional\n", - " List of countries to filter to (ISO2 or ISO3 format). If None, processes all. Default: None\n", - " country_code_format : str\n", - " \"ISO2\" or \"ISO3\" code format for country_codes parameter. Default: \"ISO3\"\n", - " geohash_precision : int\n", - " Precision for geohash bus ID generation (1-12). Default: 6\n", - " output_dir : str\n", - " Directory for output NetCDF file. Default: \"data/renewable_profiles/\"\n", - " filename_prefix : str\n", - " Prefix for output filename. Full: {filename_prefix}_{timestamp}.nc. Default: \"renewable_profiles_by_bus\"\n", - "\n", - " Returns\n", - " -------\n", - " (dataset, validation_report) : (xr.Dataset, dict)\n", - " dataset : xarray Dataset with per-bus profiles and grid potentials\n", - " validation_report : dict with data quality metrics\n", - " \"\"\"\n", - "\n", - " technologies = list(profile_datasets.keys())\n", - " logger.info(f\"Building renewable profiles for technologies: {technologies}\")\n", - "\n", - " # ===== STAGE 0: Normalize and Filter Countries =====\n", - " logger.info(\"STAGE 0: Normalize and Filter Countries\")\n", - "\n", - " if country_codes is not None:\n", - " # Convert ISO2 to ISO3 if needed\n", - " if country_code_format == \"ISO2\":\n", - " country_codes = [\n", - " pycountry.countries.get(alpha_2=cc).alpha_3 for cc in country_codes\n", - " ]\n", - "\n", - " onshore_regions_gpd = onshore_regions_gpd[\n", - " onshore_regions_gpd[\"country\"].isin(country_codes)\n", - " ].copy()\n", - " offshore_regions_gpd = offshore_regions_gpd[\n", - " offshore_regions_gpd[\"country\"].isin(country_codes)\n", - " ].copy()\n", - " logger.info(f\" Filtered to countries: {country_codes}\")\n", - " else:\n", - " logger.info(\" Processing all countries\")\n", - "\n", - " logger.info(\n", - " f\" Onshore regions: {len(onshore_regions_gpd)}, Offshore regions: {len(offshore_regions_gpd)}\"\n", - " )\n", - "\n", - " # Combine regions\n", - " onshore_regions_gpd[\"onshore_offshore\"] = \"onshore\"\n", - " offshore_regions_gpd[\"onshore_offshore\"] = \"offshore\"\n", - " all_regions = pd.concat(\n", - " [onshore_regions_gpd, offshore_regions_gpd], ignore_index=True\n", - " )\n", - "\n", - " # ===== STAGE 1: Generate Bus IDs and Extract WKT =====\n", - " logger.info(\"STAGE 1: Generate Bus IDs and Extract WKT\")\n", - " all_regions, collision_count = _generate_bus_ids_and_wkt(\n", - " all_regions, geohash_precision\n", - " )\n", - " logger.info(\n", - " f\" Generated {len(all_regions)} bus IDs ({collision_count} collisions handled)\"\n", - " )\n", - "\n", - " # ===== STAGE 2: Build Technology Dataset Cache =====\n", - " logger.info(\"STAGE 2: Build Technology Dataset Cache\")\n", - " tech_data_cache = _build_tech_dataset_cache(profile_datasets)\n", - " logger.info(f\" Cached {len(tech_data_cache)} technology datasets\")\n", - "\n", - " # ===== STAGE 3: Extract Profiles for All Buses =====\n", - " logger.info(\"STAGE 3: Extract Profiles for All Buses\")\n", - "\n", - " n_buses = len(all_regions)\n", - " n_techs = len(technologies)\n", - " n_hours = 8760\n", - "\n", - " # Pre-allocate arrays\n", - " cf_array = np.full((n_buses, n_techs, n_hours), np.nan)\n", - " p_nom_max_array = np.full((n_buses, n_techs), np.nan)\n", - " avg_cf_array = np.full((n_buses, n_techs), np.nan)\n", - " quality_flag_array = np.full((n_buses, n_techs), False, dtype=bool)\n", - "\n", - " bus_metadata = {}\n", - "\n", - " for bus_idx, (_, row) in enumerate(\n", - " tqdm(all_regions.iterrows(), total=n_buses, desc=\"Extracting profiles\")\n", - " ):\n", - " bus_id = row[\"bus_id\"]\n", - " bus_name_clean = _normalize_bus_name(row[\"name\"])\n", - " lon, lat = row.geometry.centroid.x, row.geometry.centroid.y\n", - "\n", - " bus_metadata[bus_id] = {\n", - " \"country\": row[\"country\"],\n", - " \"onshore_offshore\": row[\"onshore_offshore\"],\n", - " \"x\": lon,\n", - " \"y\": lat,\n", - " \"area_km2\": row[\"area_km2\"],\n", - " \"geometry_wkt\": row[\"geometry_wkt\"],\n", - " }\n", - "\n", - " for tech_idx, tech in enumerate(technologies):\n", - " cf, potential, p_nom_max, avg_cf = _extract_profile_at_location(\n", - " tech_data_cache[tech],\n", - " bus_name_clean,\n", - " lon,\n", - " lat,\n", - " tech,\n", - " row[\"onshore_offshore\"],\n", - " )\n", - "\n", - " cf_array[bus_idx, tech_idx, :] = cf\n", - " p_nom_max_array[bus_idx, tech_idx] = _compute_p_nom_max_with_fallback(\n", - " p_nom_max, potential, avg_cf\n", - " )\n", - " avg_cf_array[bus_idx, tech_idx] = avg_cf\n", - "\n", - " quality_flag_array[bus_idx, tech_idx] = (avg_cf > 0) and (\n", - " p_nom_max_array[bus_idx, tech_idx] > 0\n", - " )\n", - "\n", - " logger.info(f\" Extracted {n_buses * n_techs} bus-technology combinations\")\n", - "\n", - " # ===== STAGE 4: Build xarray Dataset Coordinate Arrays =====\n", - " logger.info(\"STAGE 4: Build Coordinate Arrays\")\n", - "\n", - " bus_ids = [row[\"bus_id\"] for _, row in all_regions.iterrows()]\n", - " countries = [bus_metadata[bid][\"country\"] for bid in bus_ids]\n", - " onshore_offshore = [bus_metadata[bid][\"onshore_offshore\"] for bid in bus_ids]\n", - " x_coords = [bus_metadata[bid][\"x\"] for bid in bus_ids]\n", - " y_coords = [bus_metadata[bid][\"y\"] for bid in bus_ids]\n", - " area_km2_coords = [bus_metadata[bid][\"area_km2\"] for bid in bus_ids]\n", - " wkt_coords = [bus_metadata[bid][\"geometry_wkt\"] for bid in bus_ids]\n", - "\n", - " # Normalize area weights\n", - " area_sum = sum(area_km2_coords)\n", - " weights = [a / area_sum for a in area_km2_coords]\n", - "\n", - " # Extract grid coordinates from first technology\n", - " grid_x = tech_data_cache[technologies[0]][\"x\"]\n", - " grid_y = tech_data_cache[technologies[0]][\"y\"]\n", - "\n", - " # Validate and reconcile grid consistency across all technologies\n", - " # Grids should all be on same 0.25x0.25 degree resolution, but may have different extents\n", - " # Find the maximum grid dimensions that encompasses all technologies\n", - " max_y_grid = grid_y\n", - " max_x_grid = grid_x\n", - " max_y_len = len(grid_y)\n", - " max_x_len = len(grid_x)\n", - "\n", - " reference_dx = np.diff(grid_x).mean() # Should be ~0.25\n", - " reference_dy = np.diff(grid_y).mean() # Should be ~0.25\n", - "\n", - " for tech in technologies[1:]:\n", - " grid_x_tech = tech_data_cache[tech][\"x\"]\n", - " grid_y_tech = tech_data_cache[tech][\"y\"]\n", - "\n", - " # Check if spacing matches (allows for floating point tolerance)\n", - " dx_tech = np.diff(grid_x_tech).mean()\n", - " dy_tech = np.diff(grid_y_tech).mean()\n", - "\n", - " if not (\n", - " np.isclose(dx_tech, reference_dx, rtol=1e-5)\n", - " and np.isclose(dy_tech, reference_dy, rtol=1e-5)\n", - " ):\n", - " raise ValueError(\n", - " f\"Grid spacing mismatch for {tech}: \"\n", - " f\"dx={dx_tech:.6f} vs {reference_dx:.6f}, \"\n", - " f\"dy={dy_tech:.6f} vs {reference_dy:.6f}. \"\n", - " f\"Cannot reconcile grids with different resolutions.\"\n", - " )\n", - "\n", - " logger.info(f\" {tech}: spacing OK (dx={dx_tech:.6f}, dy={dy_tech:.6f})\")\n", - "\n", - " # For each tech, expand grid extent to encompass all technologies\n", - " # Since all grids have same spacing, we can merge them\n", - " x_min_tech = grid_x_tech.min()\n", - " x_max_tech = grid_x_tech.max()\n", - " y_min_tech = grid_y_tech.min()\n", - " y_max_tech = grid_y_tech.max()\n", - "\n", - " x_min_current = max_x_grid.min()\n", - " x_max_current = max_x_grid.max()\n", - " y_min_current = max_y_grid.min()\n", - " y_max_current = max_y_grid.max()\n", - "\n", - " # Find union bounds\n", - " x_min_union = min(x_min_current, x_min_tech)\n", - " x_max_union = max(x_max_current, x_max_tech)\n", - " y_min_union = min(y_min_current, y_min_tech)\n", - " y_max_union = max(y_max_current, y_max_tech)\n", - "\n", - " # Rebuild grid arrays if needed (to ensure regular spacing)\n", - " if not (\n", - " np.isclose(x_min_union, x_min_current)\n", - " and np.isclose(x_max_union, x_max_current)\n", - " ):\n", - " logger.info(\n", - " f\" Expanding X: [{x_min_current:.6f}, {x_max_current:.6f}] → [{x_min_union:.6f}, {x_max_union:.6f}]\"\n", - " )\n", - " # Reconstruct x grid with spacing from reference\n", - " max_x_grid = np.arange(\n", - " x_min_union, x_max_union + reference_dx / 2, reference_dx\n", - " )\n", - "\n", - " if not (\n", - " np.isclose(y_min_union, y_min_current)\n", - " and np.isclose(y_max_union, y_max_current)\n", - " ):\n", - " logger.info(\n", - " f\" Expanding Y: [{y_min_current:.6f}, {y_max_current:.6f}] → [{y_min_union:.6f}, {y_max_union:.6f}]\"\n", - " )\n", - " # Reconstruct y grid with spacing from reference\n", - " max_y_grid = np.arange(\n", - " y_min_union, y_max_union + reference_dy / 2, reference_dy\n", - " )\n", - "\n", - " # Use the maximum grid dimensions\n", - " grid_x = max_x_grid\n", - " grid_y = max_y_grid\n", - "\n", - " # Pad smaller potential arrays to match the maximum grid size\n", - " # CRITICAL: Align grids based on COORDINATES, not array indices, to avoid spatial shifts\n", - " for tech in technologies:\n", - " current_potential = tech_data_cache[tech][\"potential\"]\n", - " x_old = tech_data_cache[tech][\"x\"]\n", - " y_old = tech_data_cache[tech][\"y\"]\n", - " \n", - " if current_potential.shape != (len(grid_y), len(grid_x)): \n", - " # Find indices in the new grid that correspond to the old grid's coordinates\n", - " # For Y: find where y_old[0] aligns in grid_y\n", - " y_offset = np.argmin(np.abs(grid_y - y_old[0]))\n", - " # For X: find where x_old[0] aligns in grid_x\n", - " x_offset = np.argmin(np.abs(grid_x - x_old[0]))\n", - " \n", - " # Create padded array\n", - " padded_potential = np.full((len(grid_y), len(grid_x)), np.nan)\n", - " \n", - " # Place old data at coordinate-aligned position\n", - " y_end = y_offset + current_potential.shape[0]\n", - " x_end = x_offset + current_potential.shape[1]\n", - " padded_potential[y_offset:y_end, x_offset:x_end] = current_potential\n", - " \n", - " tech_data_cache[tech][\"potential\"] = padded_potential\n", - " logger.info(\n", - " f\" Padded {tech} potential from {current_potential.shape} to {padded_potential.shape} \"\n", - " f\"(offset: y={y_offset}, x={x_offset})\"\n", - " )\n", - "\n", - " logger.info(f\" Final grid dimensions: {len(grid_y)} × {len(grid_x)}\")\n", - "\n", - " # ===== STAGE 5: Create xarray Dataset =====\n", - " logger.info(\"STAGE 5: Create xarray Dataset\")\n", - "\n", - " # Build 3D grid potential array [y_grid, x_grid, technology]\n", - " n_y_grid = len(grid_y)\n", - " n_x_grid = len(grid_x)\n", - " grid_potential_3d = np.full((n_y_grid, n_x_grid, n_techs), np.nan)\n", - "\n", - " for tech_idx, tech in enumerate(technologies):\n", - " grid_potential_3d[:, :, tech_idx] = tech_data_cache[tech][\"potential\"]\n", - "\n", - " # Create dataset\n", - " dataset = xr.Dataset(\n", - " {\n", - " \"capacity_factor\": ([\"bus\", \"technology\", \"hour\"], cf_array),\n", - " \"p_nom_max\": ([\"bus\", \"technology\"], p_nom_max_array),\n", - " \"avg_cf\": ([\"bus\", \"technology\"], avg_cf_array),\n", - " \"potential\": ([\"y_grid\", \"x_grid\", \"technology\"], grid_potential_3d),\n", - " \"weight\": ([\"bus\"], weights),\n", - " \"data_quality_flag\": ([\"bus\", \"technology\"], quality_flag_array),\n", - " \"country\": ([\"bus\"], countries),\n", - " \"onshore_offshore\": ([\"bus\"], onshore_offshore),\n", - " \"x_bus_centroid\": ([\"bus\"], x_coords),\n", - " \"y_bus_centroid\": ([\"bus\"], y_coords),\n", - " \"area_km2\": ([\"bus\"], area_km2_coords),\n", - " \"geometry_wkt\": ([\"bus\"], wkt_coords),\n", - " },\n", - " coords={\n", - " \"bus\": bus_ids,\n", - " \"technology\": technologies,\n", - " \"hour\": np.arange(n_hours),\n", - " \"x_grid\": grid_x,\n", - " \"y_grid\": grid_y,\n", - " },\n", - " )\n", - "\n", - " logger.info(f\" Created dataset with shape {dataset.dims}\")\n", - "\n", - " # ===== STAGE 6: Add Global Attributes =====\n", - " logger.info(\"STAGE 6: Add Global Attributes\")\n", - "\n", - " # Compute validation metrics\n", - " valid_entries = np.sum(quality_flag_array)\n", - " total_entries = n_buses * n_techs\n", - " incomplete_buses = len(\n", - " [b for b in bus_ids if not np.all(quality_flag_array[bus_ids.index(b), :])]\n", - " )\n", - "\n", - " validation_report = {\n", - " \"total_buses\": len(bus_ids),\n", - " \"incomplete_buses\": incomplete_buses,\n", - " \"incomplete_entries\": total_entries - valid_entries,\n", - " \"complete_rate\": valid_entries / total_entries,\n", - " }\n", - "\n", - " dataset.attrs.update(\n", - " {\n", - " \"title\": \"Renewable Profiles by Bus\",\n", - " \"method\": \"build_renewable_profiles_by_bus\",\n", - " \"created\": datetime.now().isoformat(),\n", - " \"source_onshore_geojson\": \"regions_onshore.geojson\",\n", - " \"source_offshore_geojson\": \"regions_offshore.geojson\",\n", - " \"technologies\": \",\".join(technologies),\n", - " \"geohash_precision\": geohash_precision,\n", - " \"filename_prefix\": filename_prefix,\n", - " \"country_filter\": \",\".join(sorted(country_codes))\n", - " if country_codes\n", - " else \"all\",\n", - " \"total_buses\": validation_report[\"total_buses\"],\n", - " \"incomplete_buses\": validation_report[\"incomplete_buses\"],\n", - " \"incomplete_entries\": validation_report[\"incomplete_entries\"],\n", - " \"data_complete_rate\": f\"{validation_report['complete_rate']:.1%}\",\n", - " \"geometry_format\": \"WKT (POLYGON or MULTIPOLYGON)\",\n", - " \"chunking\": \"bus=100, technology=3, hour=8760 (full timeseries per bus chunk)\",\n", - " \"grid_structure\": f\"Potential stored as 3D array [y_grid={len(grid_y)}, x_grid={len(grid_x)}, technology={n_techs}]\",\n", - " \"note\": \"All capacity_factor profiles contain exactly 8760 hourly values (Jan 1 - Dec 30). p_nom_max loaded from input data. Grid-level potentials stored in 3D potential array indexed by [y_grid, x_grid, technology].\",\n", - " }\n", - " )\n", - "\n", - " # ===== STAGE 7: Save to NetCDF =====\n", - " logger.info(\"STAGE 7: Save to NetCDF\")\n", - "\n", - " os.makedirs(output_dir, exist_ok=True)\n", - " timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", - " output_filename = os.path.join(output_dir, f\"{filename_prefix}_{timestamp}.nc\")\n", - "\n", - " # Encoding for compression\n", - " encoding = {\n", - " \"capacity_factor\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"p_nom_max\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"avg_cf\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"potential\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"weight\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"area_km2\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"x_bus_centroid\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " \"y_bus_centroid\": {\"dtype\": \"float32\", \"zlib\": True, \"complevel\": 4},\n", - " }\n", - "\n", - " # Chunking for efficient access\n", - " dataset[\"capacity_factor\"].encoding.update({\"chunks\": (100, 3, 8760)})\n", - "\n", - " dataset.to_netcdf(output_filename, encoding=encoding, unlimited_dims=[\"bus\"])\n", - " logger.info(f\"✓ Saved to {output_filename}\")\n", - "\n", - " return dataset, validation_report" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8831cb87", + "execution_count": 14, + "id": "71eecc60", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2026-04-16 12:31:55,291 - __main__ - INFO - ======================================================================\n", - "2026-04-16 12:31:55,293 - __main__ - INFO - BUILDING PER-BUS RENEWABLE PROFILES\n", - "2026-04-16 12:31:55,295 - __main__ - INFO - ======================================================================\n", - "2026-04-16 12:31:55,298 - __main__ - INFO - Building renewable profiles for technologies: ['onwind', 'offwind-ac', 'solar']\n", - "2026-04-16 12:31:55,301 - __main__ - INFO - STAGE 0: Normalize and Filter Countries\n", - "2026-04-16 12:31:55,326 - __main__ - INFO - Filtered to countries: ['ARG', 'BOL', 'BRA', 'CHL', 'COL', 'ECU', 'GUY', 'PRY', 'PER', 'SUR', 'URY', 'VEN', 'BLZ', 'CRI', 'SLV', 'GTM', 'HND', 'MEX', 'NIC', 'PAN']\n", - "2026-04-16 12:31:55,328 - __main__ - INFO - Onshore regions: 7289, Offshore regions: 928\n", - "2026-04-16 12:31:55,339 - __main__ - INFO - STAGE 1: Generate Bus IDs and Extract WKT\n", - "2026-04-16 12:32:00,201 - __main__ - INFO - Generated 8217 bus IDs (1 collisions handled)\n", - "2026-04-16 12:32:00,202 - __main__ - INFO - STAGE 2: Build Technology Dataset Cache\n", - "2026-04-16 12:32:05,688 - __main__ - INFO - Cached 3 technology datasets\n", - "2026-04-16 12:32:05,689 - __main__ - INFO - STAGE 3: Extract Profiles for All Buses\n", - "Extracting profiles: 100%|██████████| 8217/8217 [00:17<00:00, 466.81it/s]\n", - "2026-04-16 12:32:23,822 - __main__ - INFO - Extracted 24651 bus-technology combinations\n", - "2026-04-16 12:32:23,823 - __main__ - INFO - STAGE 4: Build Coordinate Arrays\n", - "2026-04-16 12:32:24,400 - __main__ - INFO - offwind-ac: spacing OK (dx=0.300000, dy=0.300000)\n", - "2026-04-16 12:32:24,402 - __main__ - INFO - Expanding Y: [-56.100000, 17.100000] → [-60.000000, 17.100000]\n", - "2026-04-16 12:32:24,403 - __main__ - INFO - solar: spacing OK (dx=0.300000, dy=0.300000)\n", - "2026-04-16 12:32:24,406 - __main__ - INFO - Padded onwind potential from (245, 282) to (258, 282) (offset: y=13, x=0)\n", - "2026-04-16 12:32:24,408 - __main__ - INFO - Padded solar potential from (245, 282) to (258, 282) (offset: y=13, x=0)\n", - "2026-04-16 12:32:24,409 - __main__ - INFO - Final grid dimensions: 258 × 282\n", - "2026-04-16 12:32:24,410 - __main__ - INFO - STAGE 5: Create xarray Dataset\n", - "2026-04-16 12:32:38,207 - __main__ - INFO - Created dataset with shape FrozenMappingWarningOnValuesAccess({'bus': 8217, 'technology': 3, 'hour': 8760, 'y_grid': 258, 'x_grid': 282})\n", - "2026-04-16 12:32:38,208 - __main__ - INFO - STAGE 6: Add Global Attributes\n", - "2026-04-16 12:32:39,060 - __main__ - INFO - STAGE 7: Save to NetCDF\n", - "2026-04-16 12:33:28,732 - __main__ - INFO - ✓ Saved to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\\renewable_profiles_south_america_20260416_123239.nc\n" + "2026-04-17 15:02:24,289 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:02:24,289 - __main__ - INFO - SAVING PROFILES\n", + "2026-04-17 15:02:24,290 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:02:24,292 - workflow.scripts.renewable_profiles - INFO - Saving NetCDF to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\\renewable_profiles_africa__20260417_150224.nc\n", + "2026-04-17 15:02:31,403 - workflow.scripts.renewable_profiles - INFO - Saving GeoJSON to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\\renewable_profiles_africa__20260417_150224.geojson\n", + "2026-04-17 15:02:31,716 - pyogrio._io - INFO - Created 5,296 records\n", + "2026-04-17 15:02:31,717 - workflow.scripts.renewable_profiles - INFO - Saving metadata to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\\renewable_profiles_africa__20260417_150224_metadata.json\n", + "2026-04-17 15:02:31,719 - workflow.scripts.renewable_profiles - INFO - ✓ Saved: renewable_profiles_africa__20260417_150224.nc, renewable_profiles_africa__20260417_150224.geojson, renewable_profiles_africa__20260417_150224_metadata.json\n", + "2026-04-17 15:02:31,720 - __main__ - INFO - ✓ Saved to C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\data\\renewable_profiles\n", + "2026-04-17 15:02:31,720 - __main__ - INFO - - renewable_profiles_africa__20260417_150224.nc\n", + "2026-04-17 15:02:31,721 - __main__ - INFO - - renewable_profiles_africa__20260417_150224.geojson\n", + "2026-04-17 15:02:31,721 - __main__ - INFO - - renewable_profiles_africa__20260417_150224_metadata.json\n" ] } ], "source": [ - "# ===== SECTION 6: Build Per-Bus Renewable Energy Profiles =====\n", + "# ===== SAVE PROFILES =====\n", "\"\"\"\n", - "Execute the main builder function through 7-stage pipeline:\n", - " STAGE 0: Normalize and filter countries (optional)\n", - " STAGE 1: Generate geohash-based bus IDs and extract WKT geometry\n", - " STAGE 2: Load and cache technology datasets (profiles, potentials, p_nom_max)\n", - " STAGE 3: Extract hourly capacity factors for all buses/techs\n", - " STAGE 4: Build xarray dataset coordinate arrays\n", - " STAGE 5: Create xarray Dataset with all data variables\n", - " STAGE 6: Add global attributes and metadata\n", - " STAGE 7: Save to NetCDF with compression and optimized chunking\n", - "\n", - "Country filtering (optional):\n", - "- Provide country_codes list with country_code_format=\"ISO3\" (default) or \"ISO2\"\n", - "- If country_codes is None (default), processes all regions\n", + "Save output as NetCDF + GeoJSON + metadata.json for reproducibility.\n", "\"\"\"\n", - "\n", "logger.info(\"=\" * 70)\n", - "logger.info(\"BUILDING PER-BUS RENEWABLE PROFILES\")\n", + "logger.info(\"SAVING PROFILES\")\n", "logger.info(\"=\" * 70)\n", "\n", - "# Use absolute path for output (relative to SHIFT_PATH's data directory)\n", - "output_directory = SHIFT_PATH / \"data\" / \"renewable_profiles\"\n", + "output_dir = SHIFT_PATH / \"data\" / \"renewable_profiles\"\n", + "nc_path, geojson_path, metadata_path = save_profiles(\n", + " profiles_ds,\n", + " geometry_gdf,\n", + " output_dir=output_dir,\n", + " filename_prefix=\"renewable_profiles_africa\",\n", + ")\n", "\n", - "# Example 1: Process all countries (default)\n", - "profiles_ds, validation_report = build_renewable_profiles_by_bus(\n", - " tech_profiles_nc,\n", - " onshore_regions_gpd,\n", - " offshore_regions_gpd,\n", - " country_codes=[\n", - " \"AR\",\n", - " \"BO\",\n", - " \"BR\",\n", - " \"CL\",\n", - " \"CO\",\n", - " \"EC\",\n", - " \"GY\",\n", - " \"PY\",\n", - " \"PE\",\n", - " \"SR\",\n", - " \"UY\",\n", - " \"VE\", # South American countries\n", - " \"BZ\",\n", - " \"CR\",\n", - " \"SV\",\n", - " \"GT\",\n", - " \"HN\",\n", - " \"MX\",\n", - " \"NI\",\n", - " \"PA\", # Central American countries\n", - " ],\n", - " country_code_format=\"ISO2\",\n", - " geohash_precision=6,\n", - " output_dir=str(output_directory),\n", - " filename_prefix=\"renewable_profiles_south_america\",\n", - ")" + "logger.info(f\"✓ Saved to {output_dir}\")\n", + "logger.info(f\" - {Path(nc_path).name}\")\n", + "logger.info(f\" - {Path(geojson_path).name}\")\n", + "logger.info(f\" - {Path(metadata_path).name}\")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "746c91e8", "metadata": {}, "outputs": [ @@ -1008,744 +431,74 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-04-16 12:33:29,042 - __main__ - INFO - ======================================================================\n", - "2026-04-16 12:33:29,047 - __main__ - INFO - OUTPUT DATASET STRUCTURE\n", - "2026-04-16 12:33:29,050 - __main__ - INFO - ======================================================================\n", - "2026-04-16 12:33:29,169 - __main__ - INFO - \n", - " Size: 27GB\n", - "Dimensions: (bus: 8217, technology: 3, hour: 8760, y_grid: 258,\n", - " x_grid: 282)\n", + "2026-04-17 15:31:15,815 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:31:15,816 - __main__ - INFO - OUTPUT DATASET SUMMARY\n", + "2026-04-17 15:31:15,817 - __main__ - INFO - ======================================================================\n", + "2026-04-17 15:31:15,823 - __main__ - INFO - \n", + " Size: 558MB\n", + "Dimensions: (bus: 5296, technology: 3, hour: 8760, y_grid: 256,\n", + " x_grid: 290)\n", "Coordinates:\n", - " * bus (bus) 0\n", - " - avg_cf computed correctly from profile (mean over 8760 hours)\n", - "\"\"\"\n", - "\n", - "logger.info(\"=\" * 70)\n", - "logger.info(\"DATA QUALITY VALIDATION\")\n", - "logger.info(\"=\" * 70)\n", - "\n", - "# Check 1: Capacity factor values should be in [0, 1]\n", - "cf_min = np.nanmin(profiles_ds[\"capacity_factor\"].values)\n", - "cf_max = np.nanmax(profiles_ds[\"capacity_factor\"].values)\n", - "cf_out_of_range = (\n", - " (profiles_ds[\"capacity_factor\"].values < 0)\n", - " | (profiles_ds[\"capacity_factor\"].values > 1)\n", - ").sum()\n", - "\n", - "logger.info(\"\\nCapacity Factor Range Check:\")\n", - "logger.info(f\" Min: {cf_min:.6f}, Max: {cf_max:.6f}\")\n", - "logger.info(f\" Out of [0,1]: {cf_out_of_range} entries\")\n", - "if cf_out_of_range == 0:\n", - " logger.info(\" ✓ PASS - all CF values in valid range\")\n", - "else:\n", - " logger.warning(f\" ✗ FAIL - {cf_out_of_range} CF values outside [0,1]\")\n", - "\n", - "# Check 2: p_nom_max values should be positive\n", - "p_nom_max_nonzero = (profiles_ds[\"p_nom_max\"].values > 0).sum()\n", - "p_nom_max_zero_or_nan = (\n", - " (profiles_ds[\"p_nom_max\"].values <= 0) | (np.isnan(profiles_ds[\"p_nom_max\"].values))\n", - ").sum()\n", - "\n", - "logger.info(\"\\np_nom_max Positivity Check:\")\n", - "logger.info(f\" Positive entries: {p_nom_max_nonzero}\")\n", - "logger.info(f\" Zero/NaN entries: {p_nom_max_zero_or_nan}\")\n", - "logger.info(\n", - " f\" Pass rate: {100 * p_nom_max_nonzero / (p_nom_max_nonzero + p_nom_max_zero_or_nan):.1f}%\"\n", - ")\n", - "\n", - "# Check 3: avg_cf should be consistent with profile\n", - "# Verify that avg_cf = mean of profile over all 8760 hours\n", - "cf_consistency_errors = 0\n", - "for bus_idx in range(min(100, len(profiles_ds.bus))): # Sample check first 100 buses\n", - " for tech_idx in range(len(profiles_ds.technology)):\n", - " cf_profile = profiles_ds[\"capacity_factor\"].values[bus_idx, tech_idx, :]\n", - " avg_cf_stored = profiles_ds[\"avg_cf\"].values[bus_idx, tech_idx]\n", - " if not (np.isnan(cf_profile).all() or np.isnan(avg_cf_stored)):\n", - " avg_cf_computed = np.nanmean(cf_profile)\n", - " if (\n", - " abs(avg_cf_computed - avg_cf_stored) > 1e-4\n", - " ): # Allow small numerical differences\n", - " cf_consistency_errors += 1\n", - "\n", - "logger.info(\"\\nCapacity Factor Consistency Check (sample of 100 buses × 3 techs):\")\n", - "logger.info(f\" Errors: {cf_consistency_errors}\")\n", - "if cf_consistency_errors == 0:\n", - " logger.info(\" ✓ PASS - avg_cf correctly computed from hourly profiles\")\n", - "else:\n", - " logger.warning(\n", - " f\" ✗ FAIL - {cf_consistency_errors} entries have inconsistent avg_cf\"\n", - " )\n", - "\n", - "# Summary\n", - "logger.info(\"\\n\" + \"=\" * 70)\n", - "logger.info(\"VALIDATION SUMMARY\")\n", - "logger.info(\"=\" * 70)\n", - "logger.info(\"✓ Dataset structure validated\")\n", - "logger.info(\"✓ All capacity factor values in [0, 1]\")\n", - "logger.info(f\"✓ {p_nom_max_nonzero} entries have positive p_nom_max\")\n", - "logger.info(\"✓ avg_cf field correctly computed from hourly profiles\")\n", - "logger.info(\"\\nDataset is READY FOR USE\")\n", - "logger.info(\"=\" * 70)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "be14c800", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_grid_potentials(\n", - " dataset,\n", - " region,\n", - " technology=\"onwind\",\n", - " figsize=(14, 11),\n", - " projection=ccrs.PlateCarree(),\n", - " cmap=\"Blues\",\n", - " title=None,\n", - " filename=None,\n", - " gridlabels=True,\n", - "):\n", - " \"\"\"\n", - " Plot grid-level potential data as a raster map.\n", - "\n", - " Displays 2D grid potentials from the dataset's potential array,\n", - " showing spatial variation at the grid resolution level.\n", - "\n", - " Parameters\n", - " ----------\n", - " dataset : xr.Dataset\n", - " Output from build_renewable_profiles_by_bus()\n", - " region : str or list\n", - " Region filter (ISO2/ISO3 or country names), e.g., \"KE,TZ,UG\" or [\"KE\", \"TZ\"]\n", - " technology : str\n", - " Technology to visualize: \"onwind\", \"offwind-ac\", \"solar\". Default: \"onwind\"\n", - " figsize : tuple\n", - " Figure size (width, height) in inches. Default: (14, 11)\n", - " projection : cartopy CRS\n", - " Map projection. Default: PlateCarree (lat/lon)\n", - " cmap : str\n", - " Matplotlib colormap name. Default: \"Blues\"\n", - " title : str\n", - " Plot title. If None, auto-generated. Default: None\n", - " filename : str\n", - " If provided, save plot to this path. Include extension (.pdf, .png). Default: None\n", - " gridlabels : bool\n", - " Add latitude/longitude gridlines. Default: True\n", - "\n", - " Returns\n", - " -------\n", - " fig, ax : matplotlib figure and axes objects\n", - " \"\"\"\n", - "\n", - " font_scale = figsize[0] / 10\n", - " plt.rcParams.update({\"font.size\": 10 * font_scale})\n", - "\n", - " fig, ax = plt.subplots(figsize=figsize, subplot_kw={\"projection\": projection})\n", - "\n", - " # Parse region list\n", - " if isinstance(region, str):\n", - " region = [r.strip() for r in region.split(\",\") if r.strip()]\n", - " else:\n", - " region = [item for item in region if item is not None]\n", - " expanded = []\n", - " for item in region:\n", - " if isinstance(item, str) and \",\" in item:\n", - " expanded.extend([r.strip() for r in item.split(\",\") if r.strip()])\n", - " else:\n", - " expanded.append(item)\n", - " region = expanded\n", - "\n", - " # Get natural earth country shapes for extent calculation\n", - " nat_earth_shp = shprdr.natural_earth(\n", - " resolution=\"110m\", category=\"cultural\", name=\"admin_0_countries\"\n", - " )\n", - " countries = list(shprdr.Reader(nat_earth_shp).records())\n", - "\n", - " def normalize_label(label):\n", - " return label.strip() if isinstance(label, str) else \"\"\n", - "\n", - " def country_matches(label, country):\n", - " label_upper = normalize_label(label).upper()\n", - " if not label_upper:\n", - " return False\n", - " return any(\n", - " label_upper == str(country.attributes.get(attr, \"\")).upper()\n", - " for attr in (\"ISO_A2\", \"ISO_A3\", \"NAME_LONG\", \"NAME\", \"ABBREV\")\n", - " )\n", - "\n", - " shapes = (\n", - " gpd.GeoDataFrame(\n", - " geometry=[\n", - " next(\n", - " (\n", - " c.geometry.buffer(0)\n", - " for c in countries\n", - " if country_matches(country, c)\n", - " ),\n", - " None,\n", - " )\n", - " for country in region\n", - " ],\n", - " crs=\"EPSG:4326\",\n", - " )\n", - " .dropna(subset=[\"geometry\"])\n", - " .reset_index(drop=True)\n", - " )\n", - "\n", - " if shapes.empty:\n", - " raise ValueError(\n", - " \"No matching countries found for region list. \"\n", - " \"Use country names or ISO2/ISO3 codes like ['KE', 'TZ', 'UG'].\"\n", - " )\n", - "\n", - " # Set map extent with buffer\n", - " minx, miny, maxx, maxy = shapes.to_crs(ccrs.PlateCarree()).total_bounds\n", - " buffer = 1.0\n", - " minx -= buffer\n", - " miny -= buffer\n", - " maxx += buffer\n", - " maxy += buffer\n", - " ax.set_extent([minx, maxx, miny, maxy], crs=ccrs.PlateCarree())\n", - "\n", - " # Add background map features (subtle)\n", - " ne_scale = \"50m\"\n", - " ax.add_feature(\n", - " cfeature.OCEAN.with_scale(ne_scale), facecolor=\"#e6f2ff\", zorder=0, alpha=0.3\n", - " )\n", - " ax.add_feature(cfeature.LAND.with_scale(ne_scale), facecolor=\"#f5f5f5\", zorder=0)\n", - " ax.add_feature(\n", - " cfeature.COASTLINE.with_scale(ne_scale),\n", - " linewidth=1.0,\n", - " zorder=1,\n", - " alpha=0.7,\n", - " color=\"#1a1a1a\",\n", - " )\n", - "\n", - " # Extract potential data for selected technology\n", - " tech_idx = list(dataset.technology.values).index(technology)\n", - " potential_grid = (\n", - " dataset[\"potential\"].values[:, :, tech_idx] / 1e3\n", - " ) # Convert from MW to GW for better color scaling\n", - "\n", - " # Extract grid coordinates\n", - " x_grid = dataset.coords[\"x_grid\"].values\n", - " y_grid = dataset.coords[\"y_grid\"].values\n", - "\n", - " # Create 2D mesh for pcolormesh\n", - " X, Y = np.meshgrid(x_grid, y_grid)\n", - "\n", - " # Plot potential as raster\n", - " im = ax.pcolormesh(\n", - " X,\n", - " Y,\n", - " potential_grid,\n", - " transform=ccrs.PlateCarree(),\n", - " cmap=cmap,\n", - " shading=\"auto\",\n", - " zorder=2,\n", - " alpha=0.85,\n", - " )\n", - "\n", - " # Add prominent country borders on top\n", - " ax.add_feature(\n", - " cfeature.BORDERS.with_scale(ne_scale),\n", - " linewidth=1.5,\n", - " linestyle=\"-\",\n", - " zorder=10,\n", - " alpha=0.7,\n", - " color=\"#1a1a1a\",\n", - " edgecolor=\"#1a1a1a\",\n", - " )\n", - "\n", - " # Add colorbar with improved labeling\n", - " cbar = plt.colorbar(im, ax=ax, shrink=0.75, pad=0.08, aspect=25)\n", - " cbar.set_label(\"Renewable Potential (GW)\", fontsize=11 * font_scale, fontweight=\"bold\")\n", - " cbar.ax.tick_params(labelsize=9 * font_scale)\n", - "\n", - " # Title with improved formatting\n", - " if title is None:\n", - " title = f\"{technology.upper().replace('-', ' ')} - Grid-Level Potential\"\n", - " ax.set_title(title, fontsize=14 * font_scale, fontweight=\"bold\", pad=20)\n", - "\n", - " # Improve axis labels and formatting\n", - " ax.set_xlabel(\"Longitude (°E)\", fontsize=10 * font_scale, fontweight=\"bold\")\n", - " ax.set_ylabel(\"Latitude (°N)\", fontsize=10 * font_scale, fontweight=\"bold\")\n", - "\n", - " # Gridlines with better visibility\n", - " if gridlabels:\n", - " gl = ax.gridlines(\n", - " crs=ccrs.PlateCarree(),\n", - " draw_labels=True,\n", - " linewidth=0.5,\n", - " color=\"gray\",\n", - " alpha=0.3,\n", - " linestyle=\"--\",\n", - " zorder=2,\n", - " )\n", - " gl.top_labels = False\n", - " gl.right_labels = False\n", - " gl.xlabel_style = {\"size\": 9 * font_scale, \"color\": \"#555555\"}\n", - " gl.ylabel_style = {\"size\": 9 * font_scale, \"color\": \"#555555\"}\n", - "\n", - " # Add subtle border around the plot\n", - " ax.spines[\"geo\"].set_visible(True)\n", - " ax.spines[\"geo\"].set_linewidth(1.5)\n", - " ax.spines[\"geo\"].set_edgecolor(\"#1a1a1a\")\n", - "\n", - " # Save\n", - " if filename is not None:\n", - " plt.savefig(filename, dpi=300, bbox_inches=\"tight\", facecolor=\"white\")\n", - " logger.info(f\"✓ Saved plot to {filename}\")\n", - "\n", - " return fig, ax" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3369eff7", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_bus_potential_density(\n", - " dataset,\n", - " region,\n", - " technology=\"onwind\",\n", - " figsize=(14, 11),\n", - " projection=ccrs.PlateCarree(),\n", - " cmap=\"Blues\",\n", - " title=None,\n", - " filename=None,\n", - " gridlabels=True,\n", - " edgecolor=\"black\",\n", - " linewidth=0.3,\n", - "):\n", - " \"\"\"\n", - " Plot bus region geometries colored by capacity density (p_nom_max per km²).\n", - "\n", - " Each bus region (from WKT geometry) is colored according to its installed capacity\n", - " density for the selected technology. Regions are discrete polygons.\n", - "\n", - " Geometries are loaded sequentially to avoid memory issues with large WKT strings.\n", - "\n", - " Parameters\n", - " ----------\n", - " dataset : xr.Dataset\n", - " Output from build_renewable_profiles_by_bus()\n", - " region : str or list\n", - " Region filter (ISO2/ISO3 or country names), e.g., \"KE,TZ,UG\" or [\"KE\", \"TZ\"]\n", - " technology : str\n", - " Technology to visualize: \"onwind\", \"offwind-ac\", \"solar\". Default: \"onwind\"\n", - " figsize : tuple\n", - " Figure size (width, height) in inches. Default: (14, 11)\n", - " projection : cartopy CRS\n", - " Map projection. Default: PlateCarree (lat/lon)\n", - " cmap : str\n", - " Matplotlib colormap name. Default: \"YlOrRd\"\n", - " title : str\n", - " Plot title. If None, auto-generated. Default: None\n", - " filename : str\n", - " If provided, save plot to this path. Include extension (.pdf, .png). Default: None\n", - " gridlabels : bool\n", - " Add latitude/longitude gridlines. Default: True\n", - " edgecolor : str\n", - " Color of region boundaries. Default: \"black\"\n", - " linewidth : float\n", - " Width of region edges. Default: 0.3\n", - "\n", - " Returns\n", - " -------\n", - " fig, ax : matplotlib figure and axes objects\n", - " \"\"\"\n", - "\n", - " font_scale = figsize[0] / 10\n", - " plt.rcParams.update({\"font.size\": 10 * font_scale})\n", - "\n", - " fig, ax = plt.subplots(figsize=figsize, subplot_kw={\"projection\": projection})\n", - "\n", - " # Parse region list and convert ISO2 to ISO3\n", - " if isinstance(region, str):\n", - " region = [r.strip() for r in region.split(\",\") if r.strip()]\n", - " else:\n", - " region = [item for item in region if item is not None]\n", - " expanded = []\n", - " for item in region:\n", - " if isinstance(item, str) and \",\" in item:\n", - " expanded.extend([r.strip() for r in item.split(\",\") if r.strip()])\n", - " else:\n", - " expanded.append(item)\n", - " region = expanded\n", - "\n", - " # Convert ISO2 to ISO3 for dataset filtering (ISO3 is used in dataset)\n", - " region_iso3 = []\n", - " for r in region:\n", - " r_upper = r.upper()\n", - " if len(r_upper) == 2:\n", - " try:\n", - " iso3 = pycountry.countries.get(alpha_2=r_upper).alpha_3\n", - " region_iso3.append(iso3)\n", - " except:\n", - " region_iso3.append(r_upper)\n", - " else:\n", - " region_iso3.append(r_upper)\n", - " region = region_iso3\n", - "\n", - " # Get natural earth country shapes for extent calculation and overlay\n", - " nat_earth_shp = shprdr.natural_earth(\n", - " resolution=\"110m\", category=\"cultural\", name=\"admin_0_countries\"\n", - " )\n", - " countries = list(shprdr.Reader(nat_earth_shp).records())\n", - "\n", - " def normalize_label(label):\n", - " return label.strip() if isinstance(label, str) else \"\"\n", - "\n", - " def country_matches(label, country):\n", - " label_upper = normalize_label(label).upper()\n", - " if not label_upper:\n", - " return False\n", - " return any(\n", - " label_upper == str(country.attributes.get(attr, \"\")).upper()\n", - " for attr in (\"ISO_A2\", \"ISO_A3\", \"NAME_LONG\", \"NAME\", \"ABBREV\")\n", - " )\n", - "\n", - " shapes = (\n", - " gpd.GeoDataFrame(\n", - " geometry=[\n", - " next(\n", - " (\n", - " c.geometry.buffer(0)\n", - " for c in countries\n", - " if country_matches(country, c)\n", - " ),\n", - " None,\n", - " )\n", - " for country in region\n", - " ],\n", - " crs=\"EPSG:4326\",\n", - " )\n", - " .dropna(subset=[\"geometry\"])\n", - " .reset_index(drop=True)\n", - " )\n", - "\n", - " if shapes.empty:\n", - " raise ValueError(\n", - " \"No matching countries found for region list. \"\n", - " \"Use country names or ISO2/ISO3 codes like ['KE', 'TZ', 'UG'].\"\n", - " )\n", - "\n", - " # Set map extent with buffer\n", - " minx, miny, maxx, maxy = shapes.to_crs(ccrs.PlateCarree()).total_bounds\n", - " buffer = 1.0\n", - " minx -= buffer\n", - " miny -= buffer\n", - " maxx += buffer\n", - " maxy += buffer\n", - " ax.set_extent([minx, maxx, miny, maxy], crs=ccrs.PlateCarree())\n", - "\n", - " # Add background map features (subtle)\n", - " ne_scale = \"50m\"\n", - " ax.add_feature(\n", - " cfeature.OCEAN.with_scale(ne_scale), facecolor=\"#e6f2ff\", zorder=0, alpha=0.3\n", - " )\n", - " ax.add_feature(cfeature.LAND.with_scale(ne_scale), facecolor=\"#f5f5f5\", zorder=0)\n", - " ax.add_feature(\n", - " cfeature.COASTLINE.with_scale(ne_scale),\n", - " linewidth=1.0,\n", - " zorder=1,\n", - " alpha=0.6,\n", - " color=\"#333333\",\n", - " )\n", - "\n", - " # Extract data for selected technology\n", - " tech_idx = list(dataset.technology.values).index(technology)\n", - " dataset_tech = dataset.isel(technology=tech_idx)\n", - "\n", - " # Filter to region countries\n", - " countries_in_dataset = set(dataset[\"country\"].values)\n", - " matching_countries = [c for c in countries_in_dataset if c in region]\n", - "\n", - " # Load and process geometries SEQUENTIALLY to avoid memory issues\n", - " # Instead of extracting all at once, iterate through dataset\n", - " geometries = []\n", - " densities = []\n", - " valid_count = 0\n", - "\n", - " logger.info(f\"Loading geometries for {technology} (sequential processing)...\")\n", - "\n", - " # Get indices where country matches\n", - " country_data = dataset[\"country\"].values\n", - " p_nom_max_data = dataset_tech[\"p_nom_max\"].values\n", - " area_km2_data = dataset[\"area_km2\"].values\n", - "\n", - " for bus_idx in tqdm(\n", - " range(len(dataset.bus)), desc=f\"Processing {technology} regions\"\n", - " ):\n", - " bus_country = country_data[bus_idx]\n", - "\n", - " # Skip if not in matching countries\n", - " if bus_country not in matching_countries:\n", - " continue\n", - "\n", - " p_nom_max = p_nom_max_data[bus_idx]\n", - " area_km2 = area_km2_data[bus_idx]\n", - "\n", - " # Only process if valid values\n", - " if (\n", - " not np.isnan(p_nom_max)\n", - " and not np.isnan(area_km2)\n", - " and p_nom_max > 0\n", - " and area_km2 > 0\n", - " ):\n", - " try:\n", - " # Load WKT geometry ONE AT A TIME (memory efficient)\n", - " wkt_str = dataset[\"geometry_wkt\"].values[bus_idx]\n", - " geom = wkt_loads(wkt_str)\n", - " geometries.append(geom)\n", - " # Calculate capacity density: MW/km²\n", - " density = p_nom_max / area_km2\n", - " densities.append(density)\n", - " valid_count += 1\n", - " except Exception as e:\n", - " logger.debug(\n", - " f\"Warning: Could not parse geometry for bus {bus_idx}: {e}\"\n", - " )\n", - " continue\n", - "\n", - " if not geometries:\n", - " raise ValueError(\n", - " f\"No valid bus geometries found for region {region} and technology {technology}\"\n", - " )\n", - "\n", - " logger.info(f\"Plotting {len(geometries)} bus regions for {technology}\")\n", - " logger.info(\n", - " f\" Capacity density range: {np.min(densities):.3f} - {np.max(densities):.3f} MW/km²\"\n", - " )\n", - "\n", - " # Normalize density for color scaling (use 5-95 percentile for better color spread)\n", - " vmin, vmax = np.nanpercentile(densities, [5, 95])\n", - " norm = mcolors.Normalize(vmin=vmin, vmax=vmax)\n", - "\n", - " # Plot each region colored by capacity density\n", - " for geom, density in zip(geometries, densities):\n", - " color = plt.cm.get_cmap(cmap)(norm(density))\n", - " ax.add_geometries(\n", - " [geom],\n", - " crs=ccrs.PlateCarree(),\n", - " facecolor=color,\n", - " edgecolor=edgecolor,\n", - " linewidth=linewidth,\n", - " alpha=0.85,\n", - " zorder=5,\n", - " )\n", - "\n", - " # Add prominent country borders on top\n", - " ax.add_feature(\n", - " cfeature.BORDERS.with_scale(ne_scale),\n", - " linewidth=1.5,\n", - " linestyle=\"-\",\n", - " zorder=10,\n", - " alpha=0.7,\n", - " color=\"#1a1a1a\",\n", - " edgecolor=\"#1a1a1a\",\n", - " )\n", - "\n", - " # Add colorbar with improved labeling\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " cbar = plt.colorbar(sm, ax=ax, shrink=0.75, pad=0.08, aspect=25)\n", - " cbar.set_label(\n", - " \"Installable Capacity Density (MW/km²)\",\n", - " fontsize=11 * font_scale,\n", - " fontweight=\"bold\",\n", - " )\n", - " cbar.ax.tick_params(labelsize=9 * font_scale)\n", - "\n", - " # Title with improved formatting\n", - " if title is None:\n", - " title = f\"{technology.upper().replace('-', ' ')} - Installable Capacity Density\"\n", - " ax.set_title(title, fontsize=14 * font_scale, fontweight=\"bold\", pad=20)\n", - "\n", - " # Improve axis labels and formatting\n", - " ax.set_xlabel(\"Longitude (°E)\", fontsize=10 * font_scale, fontweight=\"bold\")\n", - " ax.set_ylabel(\"Latitude (°N)\", fontsize=10 * font_scale, fontweight=\"bold\")\n", - "\n", - " # Gridlines with better visibility\n", - " if gridlabels:\n", - " gl = ax.gridlines(\n", - " crs=ccrs.PlateCarree(),\n", - " draw_labels=True,\n", - " linewidth=0.5,\n", - " color=\"gray\",\n", - " alpha=0.3,\n", - " linestyle=\"--\",\n", - " zorder=2,\n", - " )\n", - " gl.top_labels = False\n", - " gl.right_labels = False\n", - " gl.xlabel_style = {\"size\": 9 * font_scale, \"color\": \"#555555\"}\n", - " gl.ylabel_style = {\"size\": 9 * font_scale, \"color\": \"#555555\"}\n", - "\n", - " # Add subtle border around the plot\n", - " ax.spines[\"geo\"].set_visible(True)\n", - " ax.spines[\"geo\"].set_linewidth(1.5)\n", - " ax.spines[\"geo\"].set_edgecolor(\"#1a1a1a\")\n", - "\n", - " # Save\n", - " if filename is not None:\n", - " plt.savefig(filename, dpi=300, bbox_inches=\"tight\", facecolor=\"white\")\n", - " logger.info(f\"✓ Saved plot to {filename}\")\n", - "\n", - " return fig, ax" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "752a1aa6", "metadata": {}, "outputs": [ @@ -1753,15 +506,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", - " warnings.warn('facecolor will have no effect as it has been '\n", + "2026-04-17 15:37:52,698 - workflow.scripts.renewable_profiles - WARNING - No Natural Earth country match for: ['KM', 'MU', 'SC', 'ST']. These labels are ignored for extent calculation.\n", + "2026-04-17 15:37:52,700 - workflow.scripts.renewable_profiles - INFO - Computed extent from 34 matched countries [min_lon, max_lon, min_lat, max_lat]: [-18.02, 52.13, -35.82, 38.35]\n", "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", " warnings.warn('facecolor will have no effect as it has been '\n" ] }, { "data": { - "image/png": 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", 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    " ] @@ -1771,30 +524,61 @@ } ], "source": [ - "# ===== SECTION 10.1: Usage Examples - Visualization =====\n", - "\"\"\"\n", - "Examples of the two complementary plotting approaches for aggregated data.\n", - "\n", - "Plot each technology separately to compare grid-level raster vs discrete bus regions.\n", - "\"\"\"\n", - "\n", - "# ===== Plotting for Onwind Technology =====\n", - "\n", - "# Onwind: Grid-level potential raster\n", - "fig, ax = plot_grid_potentials(\n", + "# Grid-level raster map (GW per 0.25° grid cell)\n", + "fig1, ax1 = plot_grid_potentials(\n", " profiles_ds,\n", - " region=[\"AR\", \"BO\", \"BR\", \"CL\", \"CO\", \"EC\", \"GY\", \"PY\", \"PE\", \"SR\", \"UY\", \"VE\"],\n", " technology=\"onwind\",\n", + " region=[\n", + " \"BI\",\n", + " \"KM\",\n", + " \"DJ\",\n", + " \"ER\",\n", + " \"ET\",\n", + " \"KE\",\n", + " \"MG\",\n", + " \"MW\",\n", + " \"MU\",\n", + " \"MZ\",\n", + " \"RW\",\n", + " \"SC\",\n", + " \"SO\",\n", + " \"SS\",\n", + " \"TZ\",\n", + " \"UG\",\n", + " \"ZM\",\n", + " \"ZW\", # Africa — Eastern Africa\n", + " \"AO\",\n", + " \"CM\",\n", + " \"CF\",\n", + " \"TD\",\n", + " \"CG\",\n", + " \"CD\",\n", + " \"GQ\",\n", + " \"GA\",\n", + " \"ST\", # Africa — Middle Africa\n", + " \"DZ\",\n", + " \"EG\",\n", + " \"LY\",\n", + " \"MA\",\n", + " \"SD\",\n", + " \"TN\", # Africa — Northern Africa\n", + " \"BW\",\n", + " \"SZ\",\n", + " \"LS\",\n", + " \"NA\",\n", + " \"ZA\", # Africa — Southern Africa\n", + " ],\n", " cmap=\"Blues\",\n", " gridlabels=True,\n", - " filename=None,\n", + " filename=None, # Set to \"onwind_grid.png\" to save\n", ")\n", + "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 32, "id": "911d3b34", "metadata": {}, "outputs": [ @@ -1802,19 +586,19 @@ "name": "stderr", "output_type": "stream", "text": [ + "2026-04-17 15:46:41,109 - workflow.scripts.renewable_profiles - INFO - Region not specified. Using all countries from geometry_gdf: ['AGO', 'BDI', 'BEN', 'BFA', 'BWA', 'CAF', 'CIV', 'CMR', 'COD', 'COG', 'COM', 'CPV', 'DJI', 'DZA', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GMB', 'GNB', 'GNQ', 'KEN', 'LBR', 'LBY', 'LSO', 'MAR', 'MDG', 'MLI', 'MOZ', 'MRT', 'MUS', 'MWI', 'NAM', 'NER', 'NGA', 'RWA', 'SDN', 'SEN', 'SLE', 'SOM', 'SSD', 'STP', 'SWZ', 'SYC', 'TCD', 'TGO', 'TUN', 'TZA', 'UGA', 'ZAF', 'ZMB', 'ZWE']\n", + "2026-04-17 15:46:41,172 - workflow.scripts.renewable_profiles - WARNING - No Natural Earth country match for: ['COM', 'CPV', 'MUS', 'STP', 'SYC']. These labels are ignored for extent calculation.\n", + "2026-04-17 15:46:41,174 - workflow.scripts.renewable_profiles - INFO - Computed extent from 49 matched countries [min_lon, max_lon, min_lat, max_lat]: [-18.63, 52.13, -35.82, 38.35]\n", "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", " warnings.warn('facecolor will have no effect as it has been '\n", - "2026-04-16 12:33:37,558 - __main__ - INFO - Loading geometries for onwind (sequential processing)...\n", - "Processing onwind regions: 100%|██████████| 8217/8217 [00:15<00:00, 514.66it/s] \n", - "2026-04-16 12:33:53,530 - __main__ - INFO - Plotting 6226 bus regions for onwind\n", - "2026-04-16 12:33:53,531 - __main__ - INFO - Capacity density range: 0.000 - 4.122 MW/km²\n", - "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_31552\\212557232.py:221: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", - " color = plt.cm.get_cmap(cmap)(norm(density))\n" + "2026-04-17 15:46:41,190 - workflow.scripts.renewable_profiles - INFO - Loading 5296 bus geometries for onwind...\n", + "Processing geometries: 100%|██████████| 5296/5296 [00:01<00:00, 3311.88it/s]\n", + "2026-04-17 15:46:42,793 - workflow.scripts.renewable_profiles - INFO - Plotting 4556 regions, density range: 0.001 - 3.615 MW/km²\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1824,66 +608,21 @@ } ], "source": [ - "# Onwind: Bus regions colored by capacity density\n", - "fig, ax = plot_bus_potential_density(\n", + "# Bus-level capacity density map (GW/km² per Voronoi region)\n", + "# Pass geometry_gdf for better performance (uses GeoJSON directly)\n", + "fig2, ax2 = plot_bus_capacity_density(\n", " profiles_ds,\n", - " region=[\"AR\", \"BO\", \"BR\", \"CL\", \"CO\", \"EC\", \"GY\", \"PY\", \"PE\", \"SR\", \"UY\", \"VE\"],\n", " technology=\"onwind\",\n", + " geometry_gdf=geometry_gdf, # Optional: pass GeoDataFrame for faster geometry lookup\n", " cmap=\"Blues\",\n", + " vmin=None, # Auto-scale to 5-95 percentile\n", + " vmax=None,\n", " gridlabels=True,\n", - " filename=None,\n", + " filename=None, # Set to \"onwind_density.png\" to save\n", ")\n", + "plt.tight_layout()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "80ccc9bc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", - " warnings.warn('facecolor will have no effect as it has been '\n", - "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\cartopy\\mpl\\feature_artist.py:143: UserWarning: facecolor will have no effect as it has been defined as \"never\".\n", - " warnings.warn('facecolor will have no effect as it has been '\n" - ] - }, - { - "data": { - "text/plain": [ - "(
    ,\n", - " )" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_grid_potentials(\n", - " profiles_ds,\n", - " region=[\"AR\", \"BO\", \"BR\", \"CL\", \"CO\", \"EC\", \"GY\", \"PY\", \"PE\", \"SR\", \"UY\", \"VE\"],\n", - " technology=\"solar\",\n", - " cmap=\"YlOrRd\",\n", - " gridlabels=True,\n", - " filename=None,\n", - ")" - ] } ], "metadata": { diff --git a/workflow/scripts/renewable_profiles.py b/workflow/scripts/renewable_profiles.py new file mode 100644 index 0000000..53cc218 --- /dev/null +++ b/workflow/scripts/renewable_profiles.py @@ -0,0 +1,1436 @@ +""" +Renewable Profiles Preprocessing Script + +Builds per-bus renewable energy profiles from PyPSA-Earth data. +Pure functions — no hardcoded globals, fully parameterized for notebook/Snakemake use. + +Public API: + build_profiles() — Main orchestrator (7-stage pipeline) + load_pypsa_earth_profiles() — Load raw PyPSA-Earth profile data + load_region_boundaries() — Load GeoJSON region boundaries + save_profiles() — Write NetCDF + GeoJSON + metadata.json + load_profiles() — Load saved files with version check + audit_profiles_against_raw() — Compare processed vs raw stats (min/max/mean/NaN) + +Visualization API: + plot_grid_potentials() — Grid-level raster map (GW/cell) + plot_bus_capacity_density() — Bus regions colored by capacity density (MW/km²) +""" + +import json +import logging +from pathlib import Path +from datetime import datetime + +import numpy as np +import pandas as pd +import xarray as xr +import geopandas as gpd +import pycountry +import geohash2 +from tqdm import tqdm + +# Visualization imports (required) +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import cartopy.crs as ccrs +import cartopy.feature as cfeature +from cartopy.io import shapereader as shprdr + +logger = logging.getLogger(__name__) + +SCHEMA_VERSION = "1.0" + +# Region/technology compatibility rules. +REGION_TECH_COMPAT = { + "onshore": {"onwind", "solar"}, + "offshore": {"offwind-ac"}, +} + +# Plotting style constants +MAP_STYLE = { + "ne_scale": "50m", + "buffer": 1.0, + "ocean_color": "#e6f2ff", + "land_color": "#f5f5f5", + "coastline_color": "#1a1a1a", + "coastline_width": 1.0, + "border_color": "#1a1a1a", + "border_width": 1.5, + "grid_color": "gray", +} + +# Cache Natural Earth data (expensive to load) +_NE_COUNTRIES_CACHE = None + + +def load_pypsa_earth_profiles(pypsa_earth_path, technologies=None): + """ + Load renewable technology profiles from PyPSA-Earth. + + Parameters + ---------- + pypsa_earth_path : str or Path + Path to pypsa-earth repository + technologies : list, optional + List of technologies to load. Default: ["onwind", "offwind-ac", "solar"] + + Returns + ------- + dict + Technology-keyed dict of xarray Datasets + """ + if technologies is None: + technologies = ["onwind", "offwind-ac", "solar"] + + pypsa_earth_path = Path(pypsa_earth_path) + tech_profiles = {} + + logger.info("Loading renewable technology profiles from PyPSA-Earth...") + for tech in technologies: + path = ( + pypsa_earth_path / "resources" / "renewable_profiles" / f"profile_{tech}.nc" + ) + try: + ds = xr.open_dataset(path) + tech_profiles[tech] = ds + n_hours = len(ds.coords.get("time", ds.coords.get("hour", []))) + logger.info( + f" ✓ {tech}: {n_hours} hours, {len(ds.bus)} buses, " + f"grid {len(ds.x)}×{len(ds.y)}" + ) + except FileNotFoundError: + logger.warning(f" ✗ {tech}: File not found at {path}") + except Exception as e: + logger.warning( + f" ✗ {tech}: Failed to load ({type(e).__name__}: {str(e)[:50]})" + ) + + if not tech_profiles: + raise RuntimeError("No renewable technology profiles could be loaded!") + + logger.info(f"✓ Loaded {len(tech_profiles)} profiles: {list(tech_profiles.keys())}") + return tech_profiles + + +def load_region_boundaries(pypsa_earth_path): + """ + Load onshore and offshore region boundaries from GeoJSON. + + Parameters + ---------- + pypsa_earth_path : str or Path + Path to pypsa-earth repository + + Returns + ------- + tuple + (onshore_gpd, offshore_gpd) — GeoDataFrames + """ + pypsa_earth_path = Path(pypsa_earth_path) + + logger.info("Loading geographic region boundaries from GeoJSON...") + onshore_gpd = gpd.read_file( + pypsa_earth_path / "resources" / "bus_regions" / "regions_onshore.geojson" + ) + offshore_gpd = gpd.read_file( + pypsa_earth_path / "resources" / "bus_regions" / "regions_offshore.geojson" + ) + + logger.info(f" Onshore: {len(onshore_gpd)} regions") + logger.info(f" Offshore: {len(offshore_gpd)} regions") + + return onshore_gpd, offshore_gpd + + +def _prepare_regions(onshore_gpd, offshore_gpd, country_codes=None): + """ + Prepare regions: normalize country codes and calculate areas. + + Returns + ------- + GeoDataFrame + Combined onshore + offshore regions with standardized columns + """ + onshore_gpd = onshore_gpd.copy() + offshore_gpd = offshore_gpd.copy() + + # Convert ISO2 → ISO3 country codes + onshore_gpd["country"] = onshore_gpd["country"].apply( + lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3 + ) + offshore_gpd["country"] = offshore_gpd["country"].apply( + lambda iso2: pycountry.countries.get(alpha_2=iso2).alpha_3 + ) + + # Filter countries if specified + if country_codes is not None: + onshore_gpd = onshore_gpd[onshore_gpd["country"].isin(country_codes)] + offshore_gpd = offshore_gpd[offshore_gpd["country"].isin(country_codes)] + logger.info(f"Filtered to countries: {country_codes}") + + # Keep original PyPSA region IDs unchanged for robust matching to raw profiles. + onshore_gpd["name"] = onshore_gpd["name"].astype(str) + offshore_gpd["name"] = offshore_gpd["name"].astype(str) + + # Calculate area in km² using EPSG:6933 projection + onshore_gpd["area_km2"] = onshore_gpd.to_crs("EPSG:6933").geometry.area / 1e6 + offshore_gpd["area_km2"] = offshore_gpd.to_crs("EPSG:6933").geometry.area / 1e6 + + # Mark region type + onshore_gpd["onshore_offshore"] = "onshore" + offshore_gpd["onshore_offshore"] = "offshore" + + # Combine + all_regions = pd.concat([onshore_gpd, offshore_gpd], ignore_index=True) + logger.info(f"Combined: {len(all_regions)} total regions") + + return all_regions + + +def _generate_bus_ids(regions, geohash_precision=6): + """Generate bus IDs as ISO3_ON/OFF_geohash with collision handling.""" + centroids = regions.geometry.centroid + lons = centroids.x.values + lats = centroids.y.values + + # Vectorized geohash encoding + geohash_func = np.vectorize( + lambda lat, lon: geohash2.encode(lat, lon, precision=geohash_precision) + ) + geohashes = geohash_func(lats, lons) + country_codes = regions["country"].astype(str).values + region_flags = np.where( + regions["onshore_offshore"].astype(str).values == "offshore", + "OFF", + "ON", + ) + base_ids = [ + f"{country}_{flag}_{gh}" + for country, flag, gh in zip(country_codes, region_flags, geohashes) + ] + + # Handle collisions + bus_ids = [] + collision_count = 0 + seen_hashes = {} + + for base_id in base_ids: + if base_id in seen_hashes: + seen_hashes[base_id] += 1 + bus_id = f"{base_id}_{seen_hashes[base_id]:03d}" + collision_count += 1 + else: + seen_hashes[base_id] = 0 + bus_id = base_id + bus_ids.append(bus_id) + + logger.info( + f"Generated {len(bus_ids)} bus IDs ({collision_count} collisions handled)" + ) + return bus_ids + + +def _cache_tech_data(profile_datasets): + """ + Cache technology datasets: trim to 8760 hours, sort grids, pre-compute fast lookups. + + Returns + ------- + dict + Technology-keyed cache with profiles, potentials, p_nom_max arrays, + coordinates, and normalized bus→index mapping. + """ + tech_data_cache = {} + + for tech, ds in profile_datasets.items(): + profile_8760 = ds["profile"].isel(time=slice(0, 8760)) + if "bus" in profile_8760.dims and "time" in profile_8760.dims: + profile_8760 = profile_8760.transpose("bus", "time") + profile_8760 = profile_8760.values.astype(np.float32) + + potential = ds["potential"].values.astype(np.float32) + x_coords = ds.coords["x"].values.astype(np.float32) + y_coords = ds.coords["y"].values.astype(np.float32) + p_nom_max = ds["p_nom_max"].values.astype(np.float32) + + # Ensure grids are sorted for binary search + if not np.all(np.diff(x_coords) > 0): + x_sort_idx = np.argsort(x_coords) + x_coords = x_coords[x_sort_idx] + potential = potential[:, x_sort_idx] + + if not np.all(np.diff(y_coords) > 0): + y_sort_idx = np.argsort(y_coords) + y_coords = y_coords[y_sort_idx] + potential = potential[y_sort_idx, :] + + # Pre-compute bus→index lookup + bus_to_idx = {str(bus_id): idx for idx, bus_id in enumerate(ds.bus.values)} + + tech_data_cache[tech] = { + "profile": profile_8760, + "potential": potential, + "p_nom_max": p_nom_max, + "x": x_coords, + "y": y_coords, + "bus_to_idx": bus_to_idx, + } + + logger.info(f"Cached {len(tech_data_cache)} technology datasets") + return tech_data_cache + + +def _nearest_index(sorted_values, target): + """Return index of nearest value in an ascending 1D array.""" + idx = np.searchsorted(sorted_values, target) + if idx <= 0: + return 0 + if idx >= len(sorted_values): + return len(sorted_values) - 1 + left = sorted_values[idx - 1] + right = sorted_values[idx] + return idx - 1 if abs(target - left) <= abs(right - target) else idx + + +def _extract_profiles_for_bus( + region_name, region_type, lon, lat, tech, tech_data_cache +): + """ + Extract profile data for a single bus-technology pair. + + Returns + ------- + tuple or None + (cf, p_nom_max, avg_cf, quality_flag) or None if missing/incompatible + """ + allowed_techs = REGION_TECH_COMPAT.get(region_type, set()) + if tech not in allowed_techs: + return None + + cache = tech_data_cache[tech] + bus_to_idx = cache["bus_to_idx"] + region_name_str = str(region_name) + + # Offshore regions are prefixed as OFF_* in prepared regions, while raw + # PyPSA-Earth bus IDs are often unprefixed numeric names. + candidate_names = [region_name_str] + if region_name_str.startswith("OFF_"): + candidate_names.append(region_name_str[4:]) + + bus_idx = None + for candidate in candidate_names: + if candidate in bus_to_idx: + bus_idx = bus_to_idx[candidate] + break + + if bus_idx is None: + return None + + try: + cf = cache["profile"][bus_idx, :] + p_nom_max = float(cache["p_nom_max"][bus_idx]) + + x_idx = _nearest_index(cache["x"], lon) + y_idx = _nearest_index(cache["y"], lat) + potential_val = cache["potential"][y_idx, x_idx] + + avg_cf = float(np.nanmean(cf)) if not np.isnan(cf).all() else np.nan + + if np.isnan(p_nom_max) and not np.isnan(potential_val): + p_nom_max = float(potential_val) + + quality_flag = (avg_cf > 0) and (p_nom_max > 0) + return (cf, p_nom_max, avg_cf, quality_flag) + + except Exception as e: + logger.debug( + f"Error extracting {region_name}/{tech}: {type(e).__name__}: {str(e)[:80]}" + ) + return None + + +def _reconcile_grids(tech_data_cache, technologies): + """ + Reconcile grid extents across all technologies. + + Returns + ------- + tuple + (grid_x, grid_y, reconciled_potentials_dict) + """ + logger.info("Reconciling grid extents across technologies...") + + grid_x = tech_data_cache[technologies[0]]["x"] + grid_y = tech_data_cache[technologies[0]]["y"] + + reference_dx = np.diff(grid_x).mean() + reference_dy = np.diff(grid_y).mean() + + max_x_grid = grid_x.copy() + max_y_grid = grid_y.copy() + + for tech in technologies[1:]: + x_tech = tech_data_cache[tech]["x"] + y_tech = tech_data_cache[tech]["y"] + + dx = np.diff(x_tech).mean() + dy = np.diff(y_tech).mean() + + if not ( + np.isclose(dx, reference_dx, rtol=1e-5) + and np.isclose(dy, reference_dy, rtol=1e-5) + ): + raise ValueError( + f"Grid spacing mismatch for {tech}: " + f"dx={dx:.6f} vs {reference_dx:.6f}, dy={dy:.6f} vs {reference_dy:.6f}" + ) + + # Expand bounds + x_min_union = min(max_x_grid.min(), x_tech.min()) + x_max_union = max(max_x_grid.max(), x_tech.max()) + y_min_union = min(max_y_grid.min(), y_tech.min()) + y_max_union = max(max_y_grid.max(), y_tech.max()) + + if not ( + np.isclose(x_min_union, max_x_grid.min()) + and np.isclose(x_max_union, max_x_grid.max()) + ): + max_x_grid = np.arange( + x_min_union, x_max_union + reference_dx / 2, reference_dx + ) + + if not ( + np.isclose(y_min_union, max_y_grid.min()) + and np.isclose(y_max_union, max_y_grid.max()) + ): + max_y_grid = np.arange( + y_min_union, y_max_union + reference_dy / 2, reference_dy + ) + + # Pad potentials to match unified grid + reconciled_potentials = {} + for tech in technologies: + current_potential = tech_data_cache[tech]["potential"] + x_old = tech_data_cache[tech]["x"] + y_old = tech_data_cache[tech]["y"] + + if current_potential.shape != (len(max_y_grid), len(max_x_grid)): + y_offset = np.argmin(np.abs(max_y_grid - y_old[0])) + x_offset = np.argmin(np.abs(max_x_grid - x_old[0])) + + padded = np.full( + (len(max_y_grid), len(max_x_grid)), np.nan, dtype=np.float32 + ) + y_end = y_offset + current_potential.shape[0] + x_end = x_offset + current_potential.shape[1] + padded[y_offset:y_end, x_offset:x_end] = current_potential + + reconciled_potentials[tech] = padded + else: + reconciled_potentials[tech] = current_potential + + logger.info(f"Final grid: {len(max_y_grid)} × {len(max_x_grid)}") + return max_x_grid, max_y_grid, reconciled_potentials + + +def build_profiles( + profile_datasets, + onshore_regions_gpd, + offshore_regions_gpd, + config=None, +): + """ + Main orchestrator: 7-stage pipeline to build renewable profiles. + + Parameters + ---------- + profile_datasets : dict + Technology-keyed dict of xarray Datasets + onshore_regions_gpd : GeoDataFrame + Onshore regions + offshore_regions_gpd : GeoDataFrame + Offshore regions + config : dict, optional + Configuration with keys: + - country_codes (list): ISO3 country codes to filter to [None = all] + - geohash_precision (int): 1-12 [default: 6] + - process_by_country (bool): Sequential processing to reduce memory [default: True] + + Returns + ------- + tuple + (dataset, geometry_gdf) where: + - dataset: xr.Dataset with capacity_factor, p_nom_max, avg_cf, potential + - geometry_gdf: GeoDataFrame with bus_id, country, onshore_offshore, centroids, area_km2 + """ + if config is None: + config = {} + + country_codes = config.get("country_codes", None) + geohash_precision = config.get("geohash_precision", 6) + process_by_country = config.get("process_by_country", True) + + technologies = list(profile_datasets.keys()) + logger.info(f"Building profiles for: {technologies}") + + # ===== STAGE 0: Prepare Regions ===== + logger.info("STAGE 0: Prepare regions") + all_regions = _prepare_regions( + onshore_regions_gpd, offshore_regions_gpd, country_codes + ) + + # Validate region typing contract used throughout extraction. + if "onshore_offshore" not in all_regions.columns: + raise ValueError( + "Missing required column 'onshore_offshore' after region preparation" + ) + valid_region_types = {"onshore", "offshore"} + found_region_types = set(all_regions["onshore_offshore"].astype(str).unique()) + if not found_region_types.issubset(valid_region_types): + raise ValueError( + f"Invalid values in 'onshore_offshore': {sorted(found_region_types)}. " + f"Expected subset of {sorted(valid_region_types)}" + ) + + # ===== STAGE 1: Generate Bus IDs ===== + logger.info("STAGE 1: Generate bus IDs") + all_regions["pypsa_region_id"] = all_regions["name"].astype(str) + bus_ids = _generate_bus_ids(all_regions, geohash_precision) + all_regions["bus_id"] = bus_ids + all_regions["geometry_wkt"] = all_regions.geometry.apply(lambda geom: geom.wkt) + + # ===== STAGE 2: Cache Tech Data ===== + logger.info("STAGE 2: Cache technology data") + tech_data_cache = _cache_tech_data(profile_datasets) + + # ===== STAGE 3: Extract Profiles ===== + logger.info("STAGE 3: Extract profiles for all buses") + n_buses = len(all_regions) + n_techs = len(technologies) + n_hours = 8760 + + cf_array = np.full((n_buses, n_techs, n_hours), np.nan, dtype=np.float32) + p_nom_max_array = np.full((n_buses, n_techs), np.nan, dtype=np.float32) + avg_cf_array = np.full((n_buses, n_techs), np.nan, dtype=np.float32) + quality_flag_array = np.full((n_buses, n_techs), False, dtype=bool) + + # Pre-extract arrays once to avoid expensive per-row DataFrame access. + region_names = all_regions["name"].astype(str).values + region_types = all_regions["onshore_offshore"].astype(str).values + countries_arr = all_regions["country"].astype(str).values + centroids = all_regions.geometry.centroid + x_coords = centroids.x.values.astype(np.float32) + y_coords = centroids.y.values.astype(np.float32) + + if process_by_country: + countries_list = sorted(np.unique(countries_arr)) + logger.info(f"Processing {len(countries_list)} countries sequentially") + + for country_idx, country in enumerate(countries_list): + bus_indices = np.where(countries_arr == country)[0] + logger.info( + f" [{country_idx + 1}/{len(countries_list)}] {country}: {len(bus_indices)} regions" + ) + + for bus_idx in bus_indices: + for tech_idx, tech in enumerate(technologies): + result = _extract_profiles_for_bus( + region_name=region_names[bus_idx], + region_type=region_types[bus_idx], + lon=float(x_coords[bus_idx]), + lat=float(y_coords[bus_idx]), + tech=tech, + tech_data_cache=tech_data_cache, + ) + + if result is not None: + cf, p_nom_max, avg_cf, flag = result + cf_array[bus_idx, tech_idx, :] = cf + p_nom_max_array[bus_idx, tech_idx] = p_nom_max + avg_cf_array[bus_idx, tech_idx] = avg_cf + quality_flag_array[bus_idx, tech_idx] = flag + else: + for bus_idx in tqdm(range(n_buses), total=n_buses): + for tech_idx, tech in enumerate(technologies): + result = _extract_profiles_for_bus( + region_name=region_names[bus_idx], + region_type=region_types[bus_idx], + lon=float(x_coords[bus_idx]), + lat=float(y_coords[bus_idx]), + tech=tech, + tech_data_cache=tech_data_cache, + ) + + if result is not None: + cf, p_nom_max, avg_cf, flag = result + cf_array[bus_idx, tech_idx, :] = cf + p_nom_max_array[bus_idx, tech_idx] = p_nom_max + avg_cf_array[bus_idx, tech_idx] = avg_cf + quality_flag_array[bus_idx, tech_idx] = flag + + logger.info( + f"Extracted {np.sum(~np.isnan(p_nom_max_array))} bus-technology combinations" + ) + + # ===== STAGE 4: Reconcile Grids ===== + logger.info("STAGE 4: Reconcile grid extents") + grid_x, grid_y, reconciled_potentials = _reconcile_grids( + tech_data_cache, technologies + ) + + # ===== STAGE 5: Create xarray Dataset ===== + logger.info("STAGE 5: Create xarray dataset") + + # Vectorized extraction (replaces 6× iterrows() calls with direct array access) + bus_ids_final = all_regions["bus_id"].values + countries = countries_arr + onshore_offshore = region_types + area_km2_coords = all_regions["area_km2"].values.astype(np.float32) + + area_sum = np.sum(area_km2_coords) + weights = area_km2_coords / area_sum + + # 3D potential array + n_y_grid = len(grid_y) + n_x_grid = len(grid_x) + grid_potential_3d = np.full((n_y_grid, n_x_grid, n_techs), np.nan, dtype=np.float32) + for tech_idx, tech in enumerate(technologies): + grid_potential_3d[:, :, tech_idx] = reconciled_potentials[tech] + + # Create dataset (only energy/profile data, no GIS metadata) + dataset = xr.Dataset( + { + "capacity_factor": (["bus", "technology", "hour"], cf_array), + "p_nom_max": (["bus", "technology"], p_nom_max_array), + "avg_cf": (["bus", "technology"], avg_cf_array), + "potential": (["y_grid", "x_grid", "technology"], grid_potential_3d), + "weight": (["bus"], weights), + "data_quality_flag": (["bus", "technology"], quality_flag_array), + }, + coords={ + "bus": bus_ids_final, + "technology": technologies, + "hour": np.arange(n_hours, dtype=np.int32), + "x_grid": grid_x, + "y_grid": grid_y, + }, + ) + + # ===== STAGE 6: Add Metadata ===== + logger.info("STAGE 6: Add metadata") + + valid_entries = np.sum(quality_flag_array) + total_entries = n_buses * n_techs + + dataset.attrs.update( + { + "schema_version": SCHEMA_VERSION, + "created": datetime.now().isoformat(), + "technologies": ",".join(technologies), + "geohash_precision": geohash_precision, + "total_buses": len(bus_ids_final), + "complete_rate": f"{100 * valid_entries / total_entries:.1f}%", + } + ) + + # ===== Prepare Geometry GeoDataFrame ===== + logger.info("Preparing geometry GeoDataFrame") + + geometry_gdf = gpd.GeoDataFrame( + { + "bus_id": bus_ids_final, + "pypsa_region_id": all_regions["pypsa_region_id"].values.astype(str), + "country": countries, + "onshore_offshore": onshore_offshore, + "x_centroid": x_coords, + "y_centroid": y_coords, + "area_km2": area_km2_coords, + }, + geometry=all_regions.geometry.values, + crs="EPSG:4326", + ) + + logger.info("✓ Profile building complete") + return dataset, geometry_gdf + + +def save_profiles( + dataset, geometry_gdf, output_dir, filename_prefix="renewable_profiles" +): + """ + Save profiles to NetCDF + GeoJSON + metadata.json. + + Parameters + ---------- + dataset : xr.Dataset + From build_profiles() + geometry_gdf : GeoDataFrame + From build_profiles() + output_dir : str or Path + Output directory + filename_prefix : str + Filename prefix (before timestamp) + + Returns + ------- + tuple + (nc_path, geojson_path, metadata_path) + """ + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + nc_filename = f"{filename_prefix}__{timestamp}.nc" + geojson_filename = f"{filename_prefix}__{timestamp}.geojson" + metadata_filename = f"{filename_prefix}__{timestamp}_metadata.json" + + nc_path = output_dir / nc_filename + geojson_path = output_dir / geojson_filename + metadata_path = output_dir / metadata_filename + + # Save NetCDF with compression + logger.info(f"Saving NetCDF to {nc_path}") + + # Build encoding dict only for float variables in energy data + float_vars = ["capacity_factor", "p_nom_max", "avg_cf", "potential", "weight"] + encoding = {} + for var in float_vars: + if var in dataset.data_vars: + encoding[var] = {"dtype": "float32", "zlib": True, "complevel": 4} + + # Save with bus as unlimited dimension (allows future appending) + dataset.to_netcdf(nc_path, encoding=encoding, unlimited_dims=["bus"]) + + # Save GeoJSON + logger.info(f"Saving GeoJSON to {geojson_path}") + geometry_gdf.to_file(geojson_path, driver="GeoJSON") + + # Save metadata with version pinning + logger.info(f"Saving metadata to {metadata_path}") + metadata = { + "schema_version": SCHEMA_VERSION, + "timestamp": datetime.now().isoformat(), + "technologies": dataset.attrs.get("technologies", "").split(","), + "n_buses": int(dataset.attrs.get("total_buses", 0)), + "nc_file": nc_filename, + "geojson_file": geojson_filename, + } + with open(metadata_path, "w") as f: + json.dump(metadata, f, indent=2) + + logger.info(f"✓ Saved: {nc_path.name}, {geojson_path.name}, {metadata_path.name}") + return str(nc_path), str(geojson_path), str(metadata_path) + + +def load_profiles(nc_path, geojson_path, metadata_path=None): + """ + Load saved profiles with schema version check. + + Parameters + ---------- + nc_path : str or Path + Path to NetCDF file + geojson_path : str or Path + Path to GeoJSON file + metadata_path : str or Path, optional + Path to metadata.json for version check + + Returns + ------- + tuple + (dataset, geometry_gdf) + """ + nc_path = Path(nc_path) + geojson_path = Path(geojson_path) + + # Load metadata and check version + if metadata_path: + metadata_path = Path(metadata_path) + with open(metadata_path) as f: + metadata = json.load(f) + + if metadata.get("schema_version") != SCHEMA_VERSION: + logger.warning( + f"Schema version mismatch: file={metadata.get('schema_version')}, " + f"code={SCHEMA_VERSION}. Attempting to load anyway..." + ) + + # Load NetCDF + logger.info(f"Loading NetCDF from {nc_path}") + dataset = xr.open_dataset(nc_path) + + # Load GeoJSON + logger.info(f"Loading GeoJSON from {geojson_path}") + geometry_gdf = gpd.read_file(geojson_path) + + logger.info( + f"✓ Loaded {len(dataset.bus)} buses, {len(dataset.technology)} technologies" + ) + return dataset, geometry_gdf + + +def _array_stats(arr): + """Compute min/max/mean and NaN counts for a numeric array.""" + arr = np.asarray(arr) + total = int(arr.size) + nan_count = int(np.isnan(arr).sum()) + valid = arr[~np.isnan(arr)] + + if valid.size == 0: + return { + "min": np.nan, + "max": np.nan, + "mean": np.nan, + "nan_count": nan_count, + "total": total, + "nan_share_pct": float(100 * nan_count / max(total, 1)), + } + + return { + "min": float(np.min(valid)), + "max": float(np.max(valid)), + "mean": float(np.mean(valid)), + "nan_count": nan_count, + "total": total, + "nan_share_pct": float(100 * nan_count / max(total, 1)), + } + + +def audit_profiles_against_raw(profiles_ds, raw_profile_datasets, logger_instance=None): + """ + Compare processed dataset stats against raw PyPSA-Earth datasets. + + Parameters + ---------- + profiles_ds : xr.Dataset + Processed dataset from build_profiles(). + raw_profile_datasets : dict[str, xr.Dataset] + Raw technology-keyed datasets from load_pypsa_earth_profiles(). + logger_instance : logging.Logger, optional + Logger to use for output. Defaults to module logger. + + Returns + ------- + dict + Nested stats by technology and variable. + """ + log = logger_instance or logger + audit = {} + + log.info("=" * 70) + log.info("PROCESSED vs RAW AUDIT") + log.info("=" * 70) + + for tech in profiles_ds.technology.values: + tech_key = str(tech) + if tech_key not in raw_profile_datasets: + log.warning( + f"Skipping {tech_key}: technology not present in raw_profile_datasets" + ) + continue + + raw_ds = raw_profile_datasets[tech_key] + tech_result = {} + + log.info("\n" + "-" * 70) + log.info(f"Technology: {tech_key}") + log.info("-" * 70) + + # Potential stats + proc_potential = profiles_ds["potential"].sel(technology=tech_key).values + raw_potential = raw_ds["potential"].values + proc_potential_stats = _array_stats(proc_potential) + raw_potential_stats = _array_stats(raw_potential) + + log.info("potential stats") + log.info( + f" processed: min={proc_potential_stats['min']:.6g}, max={proc_potential_stats['max']:.6g}, " + f"mean={proc_potential_stats['mean']:.6g}, NaN={proc_potential_stats['nan_count']}/{proc_potential_stats['total']} " + f"({proc_potential_stats['nan_share_pct']:.2f}%)" + ) + log.info( + f" raw: min={raw_potential_stats['min']:.6g}, max={raw_potential_stats['max']:.6g}, " + f"mean={raw_potential_stats['mean']:.6g}, NaN={raw_potential_stats['nan_count']}/{raw_potential_stats['total']} " + f"({raw_potential_stats['nan_share_pct']:.2f}%)" + ) + + tech_result["potential"] = { + "processed": proc_potential_stats, + "raw": raw_potential_stats, + } + + # p_nom_max stats + proc_p_nom = profiles_ds["p_nom_max"].sel(technology=tech_key).values + proc_p_nom_stats = _array_stats(proc_p_nom) + + log.info("p_nom_max stats") + log.info( + f" processed: min={proc_p_nom_stats['min']:.6g}, max={proc_p_nom_stats['max']:.6g}, " + f"mean={proc_p_nom_stats['mean']:.6g}, NaN={proc_p_nom_stats['nan_count']}/{proc_p_nom_stats['total']} " + f"({proc_p_nom_stats['nan_share_pct']:.2f}%)" + ) + + p_nom_section = {"processed": proc_p_nom_stats} + if "p_nom_max" in raw_ds.data_vars: + raw_p_nom = raw_ds["p_nom_max"].values + raw_p_nom_stats = _array_stats(raw_p_nom) + log.info( + f" raw: min={raw_p_nom_stats['min']:.6g}, max={raw_p_nom_stats['max']:.6g}, " + f"mean={raw_p_nom_stats['mean']:.6g}, NaN={raw_p_nom_stats['nan_count']}/{raw_p_nom_stats['total']} " + f"({raw_p_nom_stats['nan_share_pct']:.2f}%)" + ) + p_nom_section["raw"] = raw_p_nom_stats + else: + log.info(" raw: p_nom_max not present in raw dataset") + + proc_non_nan = int(np.sum(~np.isnan(proc_p_nom))) + log.info( + f" processed non-NaN p_nom_max buses: {proc_non_nan}/{proc_p_nom.size}" + ) + p_nom_section["processed_non_nan_buses"] = { + "non_nan": proc_non_nan, + "total": int(proc_p_nom.size), + } + + tech_result["p_nom_max"] = p_nom_section + audit[tech_key] = tech_result + + log.info("\n✓ Audit complete") + return audit + + +# ============================================================================ +# VISUALIZATION FUNCTIONS (Refactored from notebook) +# ============================================================================ + + +def _get_natural_earth_countries(): + """Load and cache Natural Earth country shapes (expensive operation).""" + global _NE_COUNTRIES_CACHE + if _NE_COUNTRIES_CACHE is None: + nat_earth_shp = shprdr.natural_earth( + resolution="110m", category="cultural", name="admin_0_countries" + ) + _NE_COUNTRIES_CACHE = list(shprdr.Reader(nat_earth_shp).records()) + return _NE_COUNTRIES_CACHE + + +def _parse_region_list(region, dataset=None, geometry_gdf=None): + """ + Parse region input (str or list) into normalized list. + Handles comma-separated strings and nested lists. + If region is None and dataset is provided, returns all unique countries in dataset. + + Parameters + ---------- + region : str, list, or None + Region(s) to parse. If None and dataset is provided, uses all countries in dataset. + dataset : xr.Dataset, optional + Dataset to extract countries from if region is None. + + Returns + ------- + list + Normalized list of region codes. + """ + # If region is None, extract all unique countries from dataset + if region is None: + if geometry_gdf is not None and "country" in geometry_gdf.columns: + unique_countries = sorted( + geometry_gdf["country"].dropna().astype(str).unique().tolist() + ) + logger.info( + "Region not specified. Using all countries from geometry_gdf: " + f"{unique_countries}" + ) + return unique_countries + + if dataset is not None and "country" in dataset: + unique_countries = sorted(list(set(dataset["country"].values))) + logger.info( + f"Region not specified. Using all countries in dataset: {unique_countries}" + ) + return unique_countries + + raise ValueError( + "region is None but no country metadata is available. " + "Pass region explicitly, or provide geometry_gdf with a 'country' column." + ) + + if isinstance(region, str): + return [r.strip() for r in region.split(",") if r.strip()] + + region = [item for item in region if item is not None] + expanded = [] + for item in region: + if isinstance(item, str) and "," in item: + expanded.extend([r.strip() for r in item.split(",") if r.strip()]) + else: + expanded.append(item) + return expanded + + +def _get_country_extent(regions): + """ + Get map extent (bounds) for a list of country codes/names using Natural Earth data. + + Returns + ------- + (min_lon, max_lon, min_lat, max_lat) : tuple + Geographic bounds with buffer applied, ordered for cartopy.Axes.set_extent + """ + countries = _get_natural_earth_countries() + + def normalize_label(label): + return label.strip().upper() if isinstance(label, str) else "" + + def country_matches(label, country_record): + label_upper = normalize_label(label) + if not label_upper: + return False + return any( + label_upper == str(country_record.attributes.get(attr, "")).upper() + for attr in ("ISO_A2", "ISO_A3", "NAME_LONG", "NAME", "ABBREV") + ) + + matched_geometries = [] + unmatched_regions = [] + for r in regions: + geom = next( + (c.geometry.buffer(0) for c in countries if country_matches(r, c)), + None, + ) + if geom is None: + unmatched_regions.append(r) + else: + matched_geometries.append(geom) + + shapes = gpd.GeoDataFrame(geometry=matched_geometries, crs="EPSG:4326") + + if unmatched_regions: + logger.warning( + f"No Natural Earth country match for: {unmatched_regions}. " + "These labels are ignored for extent calculation." + ) + + if shapes.empty: + logger.warning( + f"No Natural Earth matches for regions {regions}. Using global extent." + ) + return -180, -90, 180, 90 + + # Natural Earth geometries are lon/lat; bounds can be taken directly in EPSG:4326. + minx, miny, maxx, maxy = shapes.total_bounds + buffer = MAP_STYLE["buffer"] + extent = (minx - buffer, maxx + buffer, miny - buffer, maxy + buffer) + logger.info( + "Computed extent from %d matched countries [min_lon, max_lon, min_lat, max_lat]: " + "[%.2f, %.2f, %.2f, %.2f]", + len(shapes), + extent[0], + extent[1], + extent[2], + extent[3], + ) + return extent + + +def _setup_map_features(ax, extent): + """Apply standard map styling and background features.""" + ax.set_extent(extent, crs=ccrs.PlateCarree()) + + ne_scale = MAP_STYLE["ne_scale"] + + # Background + ax.add_feature( + cfeature.OCEAN.with_scale(ne_scale), + facecolor=MAP_STYLE["ocean_color"], + zorder=0, + alpha=0.3, + ) + ax.add_feature( + cfeature.LAND.with_scale(ne_scale), + facecolor=MAP_STYLE["land_color"], + zorder=0, + ) + ax.add_feature( + cfeature.COASTLINE.with_scale(ne_scale), + linewidth=MAP_STYLE["coastline_width"], + zorder=1, + alpha=0.7, + color=MAP_STYLE["coastline_color"], + ) + + # Borders on top + ax.add_feature( + cfeature.BORDERS.with_scale(ne_scale), + linewidth=MAP_STYLE["border_width"], + linestyle="-", + zorder=10, + alpha=0.7, + color=MAP_STYLE["border_color"], + edgecolor=MAP_STYLE["border_color"], + ) + + +def plot_grid_potentials( + dataset, + region=None, + technology="onwind", + figsize=(14, 11), + projection=ccrs.PlateCarree(), + cmap="Blues", + title=None, + filename=None, + gridlabels=True, +): + """ + Plot grid-level renewable potential as raster map. + + Parameters + ---------- + dataset : xr.Dataset + Output from build_profiles() or load_profiles() + region : str, list, or None + Region(s) to display: country codes (ISO2/ISO3) or names, e.g., "US,CA" or ["US", "CA"]. + If None, uses all countries present in dataset. Default: None + technology : str + Technology: "onwind", "offwind-ac", or "solar". Default: "onwind" + figsize : tuple + Figure size (width, height) in inches. Default: (14, 11) + projection : cartopy CRS + Map projection. Default: PlateCarree (lat/lon) + cmap : str + Matplotlib colormap name. Default: "Blues" + title : str + Plot title. If None, auto-generated. Default: None + filename : str + Save path if provided (e.g., "plot.png", "plot.pdf"). Default: None + gridlabels : bool + Show latitude/longitude gridlines. Default: True + + Returns + ------- + fig, ax : matplotlib Figure and Axes objects + """ + regions = _parse_region_list(region, dataset=dataset) + extent = _get_country_extent(regions) + + font_scale = figsize[0] / 10 + plt.rcParams.update({"font.size": 10 * font_scale}) + + fig, ax = plt.subplots(figsize=figsize, subplot_kw={"projection": projection}) + + # Setup map + _setup_map_features(ax, extent) + + # Extract and plot grid potential + tech_idx = list(dataset.technology.values).index(technology) + potential_gw = dataset["potential"].values[:, :, tech_idx] / 1e3 # MW → GW + + x_grid = dataset.coords["x_grid"].values + y_grid = dataset.coords["y_grid"].values + X, Y = np.meshgrid(x_grid, y_grid) + + im = ax.pcolormesh( + X, + Y, + potential_gw, + transform=ccrs.PlateCarree(), + cmap=cmap, + shading="auto", + zorder=2, + alpha=0.85, + ) + + # Colorbar + cbar = plt.colorbar(im, ax=ax, shrink=0.75, pad=0.08, aspect=25) + cbar.set_label( + "Renewable Potential (GW)", fontsize=11 * font_scale, fontweight="bold" + ) + cbar.ax.tick_params(labelsize=9 * font_scale) + + # Labels and title + if title is None: + title = f"{technology.upper().replace('-', ' ')} - Grid-Level Potential" + ax.set_title(title, fontsize=14 * font_scale, fontweight="bold", pad=20) + ax.set_xlabel("Longitude (°E)", fontsize=10 * font_scale, fontweight="bold") + ax.set_ylabel("Latitude (°N)", fontsize=10 * font_scale, fontweight="bold") + + # Gridlines + if gridlabels: + gl = ax.gridlines( + crs=ccrs.PlateCarree(), + draw_labels=True, + linewidth=0.5, + color=MAP_STYLE["grid_color"], + alpha=0.3, + linestyle="--", + zorder=2, + ) + gl.top_labels = False + gl.right_labels = False + gl.xlabel_style = {"size": 9 * font_scale, "color": "#555555"} + gl.ylabel_style = {"size": 9 * font_scale, "color": "#555555"} + + # Border + ax.spines["geo"].set_visible(True) + ax.spines["geo"].set_linewidth(1.5) + ax.spines["geo"].set_edgecolor(MAP_STYLE["border_color"]) + + # Save + if filename is not None: + plt.savefig(filename, dpi=300, bbox_inches="tight", facecolor="white") + logger.info(f"✓ Saved plot to {filename}") + + return fig, ax + + +def plot_bus_capacity_density( + dataset, + region=None, + technology="onwind", + geometry_gdf=None, + figsize=(14, 11), + projection=ccrs.PlateCarree(), + cmap="Blues", + vmin=None, + vmax=None, + title=None, + filename=None, + gridlabels=True, + edgecolor="black", + linewidth=0.3, +): + """ + Plot bus region geometries colored by installable capacity density. + + Each bus region (Voronoi polygon) is colored by its p_nom_max per unit area. + Useful for comparing capacity potential across regions. + + Parameters + ---------- + dataset : xr.Dataset + Output from build_profiles() or load_profiles() + region : str, list, or None + Region(s) to display: country codes (ISO2/ISO3) or names. + If None, uses all countries present in dataset. Default: None + technology : str + Technology: "onwind", "offwind-ac", or "solar". Default: "onwind" + geometry_gdf : GeoDataFrame + **REQUIRED**. GeoDataFrame with bus geometries, country, and area_km2 (from build_profiles() or load_profiles()). + GIS metadata is now stored only in GeoJSON, not in the xarray dataset. + figsize : tuple + Figure size (width, height) in inches. Default: (14, 11) + projection : cartopy CRS + Map projection. Default: PlateCarree (lat/lon) + cmap : str + Matplotlib colormap. Default: "Blues" + vmin, vmax : float, optional + Color normalization limits (MW/km²). If None, uses 5-95 percentile for automatic scaling. + title : str + Plot title. If None, auto-generated. Default: None + filename : str + Save path if provided. Default: None + gridlabels : bool + Show latitude/longitude gridlines. Default: True + edgecolor : str + Color of region boundaries. Default: "black" + linewidth : float + Width of region edges. Default: 0.3 + + Returns + ------- + fig, ax : matplotlib Figure and Axes objects + + Examples + -------- + # Load from saved files + dataset, geometry_gdf = load_profiles("data.nc", "data.geojson") + # Plot specific region + fig, ax = plot_bus_capacity_density(dataset, "US", geometry_gdf=geometry_gdf) + + # Plot all countries in dataset + fig, ax = plot_bus_capacity_density(dataset, geometry_gdf=geometry_gdf) + """ + # geometry_gdf is now REQUIRED (GIS data no longer in xarray) + if geometry_gdf is None: + raise ValueError( + "geometry_gdf parameter is required. GIS data (geometries, country, area) " + "are now stored only in GeoJSON, not in the xarray dataset." + ) + + regions = _parse_region_list(region, dataset=dataset, geometry_gdf=geometry_gdf) + + # Convert ISO2 → ISO3 for dataset filtering + region_iso3 = [] + for r in regions: + r_upper = r.upper() + if len(r_upper) == 2: + try: + iso3 = pycountry.countries.get(alpha_2=r_upper).alpha_3 + region_iso3.append(iso3) + except AttributeError: + region_iso3.append(r_upper) + else: + region_iso3.append(r_upper) + + extent = _get_country_extent(regions) + + font_scale = figsize[0] / 10 + plt.rcParams.update({"font.size": 10 * font_scale}) + + fig, ax = plt.subplots(figsize=figsize, subplot_kw={"projection": projection}) + + # Setup map + _setup_map_features(ax, extent) + + # Extract technology index + tech_idx = list(dataset.technology.values).index(technology) + p_nom_max_data = dataset.isel(technology=tech_idx)["p_nom_max"].values + bus_ids = dataset["bus"].values + + # Get country and area data from geometry_gdf + geometry_gdf = geometry_gdf.copy() + if "bus_id" not in geometry_gdf.columns: + geometry_gdf["bus_id"] = geometry_gdf.get("name", geometry_gdf.index) + + countries_in_gdf = set(geometry_gdf.get("country", ["UNK"]).unique()) + matching_countries = [c for c in countries_in_gdf if c in region_iso3] + + if not matching_countries: + raise ValueError( + f"No data for region {region_iso3} and technology {technology}. " + f"Available countries in GeoJSON: {sorted(countries_in_gdf)}" + ) + + # Filter geometry_gdf to matching countries + geometry_gdf_filtered = geometry_gdf[ + geometry_gdf.get("country", "UNK").isin(matching_countries) + ] + + # Extract geometries and calculate densities + geometries = [] + densities = [] + + logger.info( + f"Loading {len(geometry_gdf_filtered)} bus geometries for {technology}..." + ) + + for bus_idx, (_, gdf_row) in enumerate( + tqdm( + geometry_gdf_filtered.iterrows(), + total=len(geometry_gdf_filtered), + desc="Processing geometries", + ) + ): + try: + # Get bus ID to match with dataset + bus_id = gdf_row.get( + "bus_id", + gdf_row.name if isinstance(gdf_row.name, str) else str(bus_idx), + ) + + # Find matching index in dataset + dataset_idx = None + for ds_idx, ds_bus_id in enumerate(bus_ids): + if str(ds_bus_id) == str(bus_id): + dataset_idx = ds_idx + break + + if dataset_idx is None: + continue + + p_nom_max = p_nom_max_data[dataset_idx] + area_km2 = gdf_row.get("area_km2", 1.0) + geom = gdf_row.geometry + + if not ( + np.isnan(p_nom_max) + or np.isnan(area_km2) + or p_nom_max <= 0 + or area_km2 <= 0 + ): + geometries.append(geom) + densities.append(p_nom_max / area_km2) # MW/km² + except Exception as e: + logger.debug(f"Skipped bus {bus_idx}: {e}") + + if not geometries: + raise ValueError(f"No valid geometries for {region} and {technology}") + + logger.info( + f"Plotting {len(geometries)} regions, density range: " + f"{np.min(densities):.3f} - {np.max(densities):.3f} MW/km²" + ) + + # Normalize color scale + if vmin is None or vmax is None: + vmin, vmax = np.nanpercentile(densities, [5, 95]) + norm = mcolors.Normalize(vmin=vmin, vmax=vmax) + + # Plot geometries + for geom, density in zip(geometries, densities): + color = plt.cm.get_cmap(cmap)(norm(density)) + ax.add_geometries( + [geom], + crs=ccrs.PlateCarree(), + facecolor=color, + edgecolor=edgecolor, + linewidth=linewidth, + alpha=0.85, + zorder=5, + ) + + # Colorbar + sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) + sm.set_array([]) + cbar = plt.colorbar(sm, ax=ax, shrink=0.75, pad=0.08, aspect=25) + cbar.set_label( + "Capacity Density (MW/km²)", fontsize=11 * font_scale, fontweight="bold" + ) + cbar.ax.tick_params(labelsize=9 * font_scale) + + # Labels and title + if title is None: + title = f"{technology.upper().replace('-', ' ')} - Installable Capacity Density" + ax.set_title(title, fontsize=14 * font_scale, fontweight="bold", pad=20) + ax.set_xlabel("Longitude (°E)", fontsize=10 * font_scale, fontweight="bold") + ax.set_ylabel("Latitude (°N)", fontsize=10 * font_scale, fontweight="bold") + + # Gridlines + if gridlabels: + gl = ax.gridlines( + crs=ccrs.PlateCarree(), + draw_labels=True, + linewidth=0.5, + color=MAP_STYLE["grid_color"], + alpha=0.3, + linestyle="--", + zorder=2, + ) + gl.top_labels = False + gl.right_labels = False + gl.xlabel_style = {"size": 9 * font_scale, "color": "#555555"} + gl.ylabel_style = {"size": 9 * font_scale, "color": "#555555"} + + # Border + ax.spines["geo"].set_visible(True) + ax.spines["geo"].set_linewidth(1.5) + ax.spines["geo"].set_edgecolor(MAP_STYLE["border_color"]) + + # Save + if filename is not None: + plt.savefig(filename, dpi=300, bbox_inches="tight", facecolor="white") + logger.info(f"✓ Saved plot to {filename}") + + return fig, ax + + +if __name__ == "__main__": + # Placeholder for future CLI/Snakemake integration + raise NotImplementedError( + "CLI interface not yet implemented. Use as a module: " + "from workflow.scripts.renewable_profiles import build_profiles" + ) From 3468ea3ea2a21c82a64d11d0f48fb4a0f866d6cd Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 17 Apr 2026 16:02:41 +0200 Subject: [PATCH 041/216] chore: add clear documentation about renewable potential data format --- workflow/scripts/renewable_profiles.py | 45 ++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/workflow/scripts/renewable_profiles.py b/workflow/scripts/renewable_profiles.py index 53cc218..2b69e24 100644 --- a/workflow/scripts/renewable_profiles.py +++ b/workflow/scripts/renewable_profiles.py @@ -443,6 +443,51 @@ def build_profiles( """ Main orchestrator: 7-stage pipeline to build renewable profiles. + ============================================================================ + OUTPUT DATA FORMAT + ============================================================================ + + This function produces TWO complementary data structures: + + 1. xarray.Dataset (Energy Data) + ───────────────────────────── + Dimensions: [bus, technology, hour, y_grid, x_grid] + + Coordinates: + • bus: Unique renewable region IDs representing a voronoi cell (format: ISO3_ON/OFF_geohash) + • technology: ["onwind", "offwind-ac", "solar"] + • hour: 0–8759 (hourly steps in a year, Jan 1 – Dec 30) + • x_grid, y_grid: 0.25° × 0.25° grid cell corners + + Data Variables (all float32): + • capacity_factor[bus, tech, hour]: Hourly CF timeseries (0–1) + • p_nom_max[bus, tech]: Max installable capacity (MW) + • avg_cf[bus, tech]: Annual average capacity factor + • potential[y_grid, x_grid, tech]: Grid-level potential (GW/cell) + • weight[bus]: Area-normalized weight (sum=1 across all buses) + • data_quality_flag[bus, tech]: Boolean indicating data completeness + + → Saved to NetCDF (.nc) with zlib compression, chunked by bus + + 2. GeoDataFrame (Geometry & Attributes) + ────────────────────────────────── + Columns: + • bus_id: Unique identifier (matches Dataset bus coordinate) + • pypsa_region_id: Original PyPSA-Earth region name + • country: ISO3 country code + • onshore_offshore: "onshore" or "offshore" + • x_centroid, y_centroid: Polygon centroid (lon, lat) + • area_km2: Voronoi cell area in km² + • geometry: WKT polygon (Voronoi cell boundary) + + → Saved to GeoJSON (.geojson) with full spatial reference + + Note: GIS data (geometries, country, area) are stored ONLY in GeoJSON, + not duplicated in NetCDF (reduces file size from ~27GB → ~558MB). + Use geometry_gdf for all spatial operations and attribute lookups. + + ============================================================================ + Parameters ---------- profile_datasets : dict From a7eef4e54b3ba7253e3112131ced38aed1ddf89f Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 20 Apr 2026 17:38:45 +0200 Subject: [PATCH 042/216] fix: improve grid reconiliation to catch edgecases --- workflow/scripts/renewable_profiles.py | 138 +++++++++++++++---------- 1 file changed, 86 insertions(+), 52 deletions(-) diff --git a/workflow/scripts/renewable_profiles.py b/workflow/scripts/renewable_profiles.py index 2b69e24..a7d7da9 100644 --- a/workflow/scripts/renewable_profiles.py +++ b/workflow/scripts/renewable_profiles.py @@ -354,6 +354,9 @@ def _reconcile_grids(tech_data_cache, technologies): """ Reconcile grid extents across all technologies. + Technologies may have identical spacing (e.g., 0.25°) but different start/end points. + This function finds the union of all extents and returns a unified grid that covers all data. + Returns ------- tuple @@ -361,77 +364,90 @@ def _reconcile_grids(tech_data_cache, technologies): """ logger.info("Reconciling grid extents across technologies...") - grid_x = tech_data_cache[technologies[0]]["x"] - grid_y = tech_data_cache[technologies[0]]["y"] + # Extract grids from all technologies + grids = { + tech: (tech_data_cache[tech]["x"], tech_data_cache[tech]["y"]) + for tech in technologies + } - reference_dx = np.diff(grid_x).mean() - reference_dy = np.diff(grid_y).mean() + # Get reference spacing from first technology + ref_x, ref_y = grids[technologies[0]] + reference_dx = np.diff(ref_x).mean() + reference_dy = np.diff(ref_y).mean() - max_x_grid = grid_x.copy() - max_y_grid = grid_y.copy() + logger.info(f" Reference spacing: dx={reference_dx:.6f}, dy={reference_dy:.6f}") + # Verify all technologies have compatible spacing for tech in technologies[1:]: - x_tech = tech_data_cache[tech]["x"] - y_tech = tech_data_cache[tech]["y"] - + x_tech, y_tech = grids[tech] dx = np.diff(x_tech).mean() dy = np.diff(y_tech).mean() if not ( - np.isclose(dx, reference_dx, rtol=1e-5) - and np.isclose(dy, reference_dy, rtol=1e-5) + np.isclose(dx, reference_dx, rtol=1e-3) + and np.isclose(dy, reference_dy, rtol=1e-3) ): raise ValueError( - f"Grid spacing mismatch for {tech}: " - f"dx={dx:.6f} vs {reference_dx:.6f}, dy={dy:.6f} vs {reference_dy:.6f}" + f"Spacing mismatch for {tech}: dx={dx:.6f} vs {reference_dx:.6f}, " + f"dy={dy:.6f} vs {reference_dy:.6f}. All technologies must have compatible spacing." ) - # Expand bounds - x_min_union = min(max_x_grid.min(), x_tech.min()) - x_max_union = max(max_x_grid.max(), x_tech.max()) - y_min_union = min(max_y_grid.min(), y_tech.min()) - y_max_union = max(max_y_grid.max(), y_tech.max()) + # Find union bounds + x_min_union = min(x.min() for x, _ in grids.values()) + x_max_union = max(x.max() for x, _ in grids.values()) + y_min_union = min(y.min() for _, y in grids.values()) + y_max_union = max(y.max() for _, y in grids.values()) - if not ( - np.isclose(x_min_union, max_x_grid.min()) - and np.isclose(x_max_union, max_x_grid.max()) - ): - max_x_grid = np.arange( - x_min_union, x_max_union + reference_dx / 2, reference_dx - ) + # Reconstruct unified grid with proper uniform spacing + n_x = int(np.round((x_max_union - x_min_union) / reference_dx)) + 1 + n_y = int(np.round((y_max_union - y_min_union) / reference_dy)) + 1 - if not ( - np.isclose(y_min_union, max_y_grid.min()) - and np.isclose(y_max_union, max_y_grid.max()) - ): - max_y_grid = np.arange( - y_min_union, y_max_union + reference_dy / 2, reference_dy - ) + grid_x = np.linspace(x_min_union, x_max_union, n_x) + grid_y = np.linspace(y_min_union, y_max_union, n_y) + + logger.info( + f" Union bounds: X=[{x_min_union:.4f}, {x_max_union:.4f}], Y=[{y_min_union:.4f}, {y_max_union:.4f}]" + ) + logger.info( + f" Unified grid: {len(grid_x)} × {len(grid_y)}, spacing dx={np.diff(grid_x).mean():.6f}, dy={np.diff(grid_y).mean():.6f}" + ) - # Pad potentials to match unified grid + # Map each technology's potential to unified grid reconciled_potentials = {} for tech in technologies: - current_potential = tech_data_cache[tech]["potential"] - x_old = tech_data_cache[tech]["x"] - y_old = tech_data_cache[tech]["y"] + x_old, y_old = grids[tech] + potential_old = tech_data_cache[tech]["potential"] - if current_potential.shape != (len(max_y_grid), len(max_x_grid)): - y_offset = np.argmin(np.abs(max_y_grid - y_old[0])) - x_offset = np.argmin(np.abs(max_x_grid - x_old[0])) + if potential_old.shape != (len(grid_y), len(grid_x)): + # Calculate where old grid starts in new grid (in grid indices) + dx_new = np.diff(grid_x).mean() + dy_new = np.diff(grid_y).mean() - padded = np.full( - (len(max_y_grid), len(max_x_grid)), np.nan, dtype=np.float32 - ) - y_end = y_offset + current_potential.shape[0] - x_end = x_offset + current_potential.shape[1] - padded[y_offset:y_end, x_offset:x_end] = current_potential + x_offset = int(np.round((x_old[0] - grid_x[0]) / dx_new)) + y_offset = int(np.round((y_old[0] - grid_y[0]) / dy_new)) + + # Clamp to valid range + x_offset = max(0, min(x_offset, len(grid_x))) + y_offset = max(0, min(y_offset, len(grid_y))) + + # Pad with NaN + padded = np.full((len(grid_y), len(grid_x)), np.nan, dtype=np.float32) + y_end = min(y_offset + potential_old.shape[0], len(grid_y)) + x_end = min(x_offset + potential_old.shape[1], len(grid_x)) + + padded[y_offset:y_end, x_offset:x_end] = potential_old[ + : y_end - y_offset, : x_end - x_offset + ] reconciled_potentials[tech] = padded + logger.info( + f" {tech}: padded {potential_old.shape} → {padded.shape} (offset: y={y_offset}, x={x_offset})" + ) else: - reconciled_potentials[tech] = current_potential + reconciled_potentials[tech] = potential_old - logger.info(f"Final grid: {len(max_y_grid)} × {len(max_x_grid)}") - return max_x_grid, max_y_grid, reconciled_potentials + logger.info("✓ Grid reconciliation complete") + return grid_x, grid_y, reconciled_potentials def build_profiles( @@ -1123,6 +1139,7 @@ def plot_grid_potentials( dataset, region=None, technology="onwind", + geometry_gdf=None, figsize=(14, 11), projection=ccrs.PlateCarree(), cmap="Blues", @@ -1142,6 +1159,9 @@ def plot_grid_potentials( If None, uses all countries present in dataset. Default: None technology : str Technology: "onwind", "offwind-ac", or "solar". Default: "onwind" + geometry_gdf : GeoDataFrame, optional + GeoDataFrame with bus geometries and country data (from build_profiles() or load_profiles()). + Used to look up countries when region is None. Default: None figsize : tuple Figure size (width, height) in inches. Default: (14, 11) projection : cartopy CRS @@ -1159,7 +1179,7 @@ def plot_grid_potentials( ------- fig, ax : matplotlib Figure and Axes objects """ - regions = _parse_region_list(region, dataset=dataset) + regions = _parse_region_list(region, dataset=dataset, geometry_gdf=geometry_gdf) extent = _get_country_extent(regions) font_scale = figsize[0] / 10 @@ -1170,13 +1190,27 @@ def plot_grid_potentials( # Setup map _setup_map_features(ax, extent) - # Extract and plot grid potential + # Extract grid tech_idx = list(dataset.technology.values).index(technology) potential_gw = dataset["potential"].values[:, :, tech_idx] / 1e3 # MW → GW x_grid = dataset.coords["x_grid"].values y_grid = dataset.coords["y_grid"].values - X, Y = np.meshgrid(x_grid, y_grid) + + # Convert cell centers to edges for proper pcolormesh alignment + # If spacing is uniform (from linspace), compute half-cell offsets + dx = (x_grid[-1] - x_grid[0]) / (len(x_grid) - 1) if len(x_grid) > 1 else 0.25 + dy = (y_grid[-1] - y_grid[0]) / (len(y_grid) - 1) if len(y_grid) > 1 else 0.25 + + # Create edge arrays: add half-cell boundaries + x_edges = np.concatenate( + [[x_grid[0] - dx / 2], (x_grid[:-1] + x_grid[1:]) / 2, [x_grid[-1] + dx / 2]] + ) + y_edges = np.concatenate( + [[y_grid[0] - dy / 2], (y_grid[:-1] + y_grid[1:]) / 2, [y_grid[-1] + dy / 2]] + ) + + X, Y = np.meshgrid(x_edges, y_edges) im = ax.pcolormesh( X, @@ -1184,7 +1218,7 @@ def plot_grid_potentials( potential_gw, transform=ccrs.PlateCarree(), cmap=cmap, - shading="auto", + shading="flat", # 'flat' works with edges zorder=2, alpha=0.85, ) From a16c2ee8e086dd04e51f6761496cb0e2069745f6 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 22 Apr 2026 13:19:03 +0200 Subject: [PATCH 043/216] feat: change input format of renewable potential to minimal xarray dataset --- config/config.yaml | 29 + workflow/scripts/prepare_regional_network.py | 567 +++++++++++++------ 2 files changed, 423 insertions(+), 173 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index f989950..4dc8c95 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -24,6 +24,35 @@ regions: "Test_2": ["PRT"] "Test_3": ["IRL"] +# Renewable technologies to include in filtering +# Available: ["solar", "onwind", "offwind-ac"] +renewable_technologies: ["solar", "onwind"] + +# countries: [ +# "BI","KM","DJ","ER","ET","KE","MG","MW","MU","MZ","RW","SC","SO","SS","TZ","UG","ZM","ZW", # Africa — Eastern Africa +# "AO","CM","CF","TD","CG","CD","GQ","GA","ST", # Africa — Middle Africa +# "DZ","EG","LY","MA","SD","TN", # Africa — Northern Africa +# "BW","SZ","LS","NA","ZA", # Africa — Southern Africa +# "BJ","BF","CV","CI","GM","GH","GN","GW","LR","ML","MR","NE","NG","SN","SL","TG", # Africa — Western Africa +# "CA","US", # Americas — Northern America +# "AG","BS","BB","CU","DM","DO","GD","HT","JM","KN","LC","VC","TT", # Americas — Caribbean +# "BZ","CR","SV","GT","HN","MX","NI","PA", # Americas — Central America +# "AR","BO","BR","CL","CO","EC","GY","PY","PE","SR","UY","VE", # Americas — South America +# "KZ","KG","TJ","TM","UZ", # Asia — Central Asia +# "CN","JP","KP","KR","MN","TW", # Asia — Eastern Asia +# "BN","KH","ID","LA","MY","MM","PH","SG","TH","TL","VN", # Asia — South-eastern Asia +# "AF","BD","BT","IN","IR","MV","NP","PK","LK", # Asia — Southern Asia +# "AM","AZ","BH","CY","GE","IQ","IL","JO","KW","LB","OM","QA","SA","PS","SY","TR","AE","YE", # Asia — Western Asia +# "BY","BG","CZ","HU","MD","PL","RO","RU","SK","UA", # Europe — Eastern Europe +# "DK","EE","FI","IS","IE","LV","LT","NO","SE","GB", # Europe — Northern Europe +# "AL","AD","BA","HR","GR","IT","MT","ME","MK","PT","SM","RS","SI","ES","VA", # Europe — Southern Europe +# "AT","BE","FR","DE","LI","LU","MC","NL","CH", # Europe — Western Europe +# "AU","NZ", # Oceania — Australia and New Zealand +# "FJ","PG","SB","VU", # Oceania — Melanesia +# "FM","KI","MH","NR","PW", # Oceania — Micronesia +# "TO","TV","WS" # Oceania — Polynesia +# ] + scenario: # must be listed in config/trade_scenarios.csv in order to run default: diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index ed95606..351923d 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -28,114 +28,252 @@ def region_to_iso3_codes(region: str, config: dict) -> List[str]: # ============================================================================ -# RENEWABLE CLUSTER LOADING +# RENEWABLE PROFILES LOADING (NEW FORMAT) # ============================================================================ -def load_renewable_clusters_for_region( - region: str, renewable_timeseries_path: str, config: dict -) -> Dict: - """Load renewable clusters for region directly from netCDF (includes all metadata + timeseries).""" - # Step 1: Get ISO3 codes for region +def load_renewable_profiles(nc_path: str) -> xr.Dataset: + """Load renewable profiles from new xarray format (single .nc file). + + Parameters + ---------- + nc_path : str + Path to renewable profiles netCDF file + + Returns + ------- + xr.Dataset + Dataset with dimensions [bus, technology, hour] and variables: + - capacity_factor[bus, tech, hour] + - p_nom_max[bus, tech] + - avg_cf[bus, tech] + """ + logger.info(f"Loading renewable profiles from {nc_path}...") + + try: + dataset = xr.open_dataset(nc_path) + except FileNotFoundError as e: + raise FileNotFoundError(f"Renewable profiles file not found: {nc_path}") from e + except Exception as e: + raise RuntimeError(f"Failed to load renewable profiles: {e}") from e + + # Validate dimensions + required_dims = {"bus", "technology", "hour"} + actual_dims = set(dataset.dims.keys()) + if not required_dims.issubset(actual_dims): + raise ValueError( + f"Dataset missing required dimensions. Required: {required_dims}, " + f"Found: {actual_dims}" + ) + + # Validate data variables + required_vars = {"capacity_factor", "p_nom_max", "avg_cf"} + actual_vars = set(dataset.data_vars.keys()) + if not required_vars.issubset(actual_vars): + raise ValueError( + f"Dataset missing required variables. Required: {required_vars}, " + f"Found: {actual_vars}" + ) + + logger.info( + f"✓ Loaded renewable profiles: {len(dataset.bus)} buses, " + f"{len(dataset.technology)} technologies, {len(dataset.hour)} hours" + ) + + return dataset + + +def filter_by_region(dataset: xr.Dataset, region: str, config: dict) -> xr.Dataset: + """Filter renewable profiles by region using ISO3 codes from bus_id. + + Parameters + ---------- + dataset : xr.Dataset + Unfiltered renewable profiles + region : str + Region name (key in config["regions"]) + config : dict + Config dict with regions mapping + + Returns + ------- + xr.Dataset + Filtered dataset (subset of buses matching region's ISO3 codes) + """ + # Get ISO3 codes for this region iso3_list = region_to_iso3_codes(region, config) logger.info(f"Region '{region}' maps to ISO3 codes: {iso3_list}") - # Step 2: Load netCDF dataset - timeseries_ds = xr.open_dataset(renewable_timeseries_path) + # Parse ISO3 from bus_id strings (format: "{ISO3}_{other_identifiers}") + bus_ids = dataset.coords["bus"].values + bus_iso3_codes = [] - # Filter clusters by ISO3 country codes - cluster_iso3 = timeseries_ds.coords["iso3"].values # ISO3 per cluster - cluster_ids = timeseries_ds.coords["cluster"].values # Cluster IDs + for bus_id in bus_ids: + bus_id_str = str(bus_id) + # Extract ISO3 from bus_id (first component before underscore) + iso3 = bus_id_str.split("_")[0] + bus_iso3_codes.append(iso3) - # Select clusters for this region's ISO3 codes - cluster_mask = np.isin(cluster_iso3, iso3_list) - selected_clusters = cluster_ids[cluster_mask] + # Create mask for buses in this region + bus_mask = np.isin(bus_iso3_codes, iso3_list) + selected_buses = bus_ids[bus_mask] - if len(selected_clusters) == 0: + if len(selected_buses) == 0: raise ValueError( - f"No clusters found for region '{region}' with ISO3 {iso3_list}. " - f"Available ISO3 in data: {np.unique(cluster_iso3)}" + f"No buses found for region '{region}' with ISO3 {iso3_list}. " + f"Available ISO3 codes in data: {np.unique(bus_iso3_codes)}" ) - logger.info(f"Found {len(selected_clusters)} clusters for region {region}") - logger.info(f"Unique technologies: {timeseries_ds.technology.values}") + # Filter dataset to selected buses + filtered = dataset.sel(bus=selected_buses) - # Step 3: Extract cluster data for each selected cluster - clusters = [] - total_potential_mw = 0 + logger.info( + f"Filtered dataset to {len(selected_buses)} buses in region {region} " + f"(from {len(bus_ids)} total)" + ) - for cluster_id in selected_clusters: - # Get data for this cluster across all technologies - cluster_idx = list(cluster_ids).index(cluster_id) - iso3 = cluster_iso3[cluster_idx] + return filtered - for tech in timeseries_ds.technology.values: - # Extract 2D arrays from dataset - renewable_potential = float( - timeseries_ds["renewable_potential"] - .sel(cluster=cluster_id, technology=tech) - .values - ) - p_nom_max = float( - timeseries_ds["p_nom_max"] - .sel(cluster=cluster_id, technology=tech) - .values - ) - avg_cf = float( - timeseries_ds["avg_cf"].sel(cluster=cluster_id, technology=tech).values - ) - cf_timeseries = ( - timeseries_ds["capacity_factor"] - .sel(cluster=cluster_id, technology=tech) - .values - ) - # Handle NaN/missing values - default to 0 - renewable_potential = ( - 0 if np.isnan(renewable_potential) else renewable_potential - ) - p_nom_max = 0 if np.isnan(p_nom_max) else p_nom_max - avg_cf = 0 if np.isnan(avg_cf) else avg_cf - if isinstance(cf_timeseries, np.ndarray): - cf_timeseries = np.nan_to_num(cf_timeseries, nan=0.0) - else: - cf_timeseries = ( - np.zeros(8760) if np.isnan(cf_timeseries) else cf_timeseries - ) +def filter_by_technologies(dataset: xr.Dataset, config: dict) -> xr.Dataset: + """Filter renewable technologies based on config. - # Get geographic coordinates - lat = float(timeseries_ds["lat"].sel(cluster=cluster_id).values) - lon = float(timeseries_ds["lon"].sel(cluster=cluster_id).values) - - clusters.append( - { - "cluster_id": f"{cluster_id}_{tech}", # Unique ID combining cluster + technology - "iso3": iso3, - "technology": tech, - "renewable_potential_mw": renewable_potential, - "p_nom_max": p_nom_max, - "avg_cf": avg_cf, - "cf_timeseries": cf_timeseries, - "lat": lat, - "lon": lon, - } - ) + Parameters + ---------- + dataset : xr.Dataset + Renewable profiles with all technologies + config : dict + Config dict with optional `renewable_technologies` list - total_potential_mw += renewable_potential + Returns + ------- + xr.Dataset + Filtered dataset with only specified technologies + """ + # Get allowed technologies from config, default to ["solar", "onwind"] + allowed_techs = config.get("renewable_technologies", ["solar", "onwind"]) + logger.info(f"Allowed renewable technologies: {allowed_techs}") - # Aggregate results - iso3_codes = sorted(list(set(c["iso3"] for c in clusters))) - technologies = sorted(list(set(c["technology"] for c in clusters))) + # Get available technologies in dataset + available_techs = list(dataset.technology.values) + logger.info(f"Available technologies in dataset: {available_techs}") - logger.info(f"Total renewable potential for region: {total_potential_mw:.1f} MW") - logger.info(f"Clusters loaded: {len(clusters)}") + # Find intersection of allowed and available + techs_to_keep = [t for t in available_techs if str(t) in allowed_techs] - return { - "clusters": clusters, - "iso3_list": iso3_codes, - "technologies": technologies, - "total_potential_mw": total_potential_mw, - } + if not techs_to_keep: + raise ValueError( + f"No renewable technologies available after filtering. " + f"Allowed: {allowed_techs}, Available: {available_techs}" + ) + + # Filter dataset + filtered = dataset.sel(technology=techs_to_keep) + + logger.info( + f"Filtered to {len(techs_to_keep)} technologies: {techs_to_keep} " + f"(from {len(available_techs)} available)" + ) + + return filtered + + +# ============================================================================ +# ELECTRICITY BACK-PROPAGATION +# ============================================================================ + + +def back_propagate_electricity_need( + tech_costs: pd.Series, product: str, config: dict +) -> float: + """Calculate electricity requirement (MWh) per tonne of product. + + Back-propagates through supply chain efficiency chain: + - steel (t): Electrolyzer(elec) + DRI(elec + H2) + EAF(elec) + - hbi (t): Electrolyzer(elec) + DRI(elec + H2) + - h2 (t): Electrolyzer(elec) only + + Parameters + ---------- + tech_costs : pd.Series + Technology cost database (MultiIndex by [tech_name, parameter]) + product : str + Product: "steel", "hbi", or "h2" + config : dict + Config dict with optional overrides: "electricity_per_tonne_{product}_mwh" + + Returns + ------- + float + Electricity requirement in MWh per tonne of product + """ + # Check for config override first + override_key = f"electricity_per_tonne_{product}_mwh" + if override_key in config: + elec_need = config[override_key] + logger.info( + f"Using config override for {product}: " + f"{override_key} = {elec_need:.4f} MWh/t" + ) + return elec_need + + # Extract efficiencies from tech database + # Electrolyzer: Electricity → H2 + elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") + elec_mwh_per_mwh_h2 = td.get_tech_param(elec_params, "electricity-input", 1.38) + logger.debug( + f"Electrolyzer electricity input: {elec_mwh_per_mwh_h2:.4f} MWh/MWh H2" + ) + + if product == "h2": + # H2 only: just electrolyzer electricity + # Note: 1 MWh H2 ≈ 1 t H2 for energy accounting (MWh/MWh = MWh/t in energy terms) + elec_need = elec_mwh_per_mwh_h2 + logger.info( + f"Back-propagated electricity for H2: {elec_need:.4f} MWh/t H2 " + f"(electrolyzer only)" + ) + return elec_need + + # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI + dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") + h2_per_t_hbi = td.get_tech_param(dri_params, "hydrogen-input", 2.1) + dri_elec_per_t_hbi = td.get_tech_param(dri_params, "electricity-input", 1.03) + logger.debug(f"DRI hydrogen input: {h2_per_t_hbi:.4f} t H2/t HBI") + logger.debug(f"DRI electricity input: {dri_elec_per_t_hbi:.4f} MWh/t HBI") + + # Electricity for H2 production (via electrolyzer) + h2_elec_per_t_hbi = h2_per_t_hbi * elec_mwh_per_mwh_h2 + + if product == "hbi": + # HBI: H2 production + DRI electricity + elec_need = h2_elec_per_t_hbi + dri_elec_per_t_hbi + logger.info( + f"Back-propagated electricity for HBI: {elec_need:.4f} MWh/t HBI " + f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f})" + ) + return elec_need + + if product == "steel": + # EAF: HBI + Electricity → Steel + eaf_params = td.get_tech(tech_costs, "electric arc furnace") + eaf_elec_per_t_steel = td.get_tech_param( + eaf_params, "electricity-input", 0.6395 + ) + logger.debug(f"EAF electricity input: {eaf_elec_per_t_steel:.4f} MWh/t Steel") + + # Steel: H2 production + DRI electricity + EAF electricity + elec_need = h2_elec_per_t_hbi + dri_elec_per_t_hbi + eaf_elec_per_t_steel + logger.info( + f"Back-propagated electricity for Steel: {elec_need:.4f} MWh/t Steel " + f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f}, " + f"EAF: {eaf_elec_per_t_steel:.4f})" + ) + return elec_need + + raise ValueError( + f"Product '{product}' not recognized. Choose from: 'h2', 'hbi', 'steel'" + ) # ============================================================================ @@ -145,21 +283,28 @@ def load_renewable_clusters_for_region( def add_renewable_generators( network: pypsa.Network, - renewable_clusters: Dict, + dataset: xr.Dataset, tech_costs: pd.Series, config: dict, -) -> None: - """Add renewable generators to electricity bus for all clusters. - - Renewable generators (wind, solar) produce electricity, so all have carrier="electricity". - The technology type (onwind, offwind, solar) is tracked in the generator name. +) -> Dict: + """Add renewable generators from xarray dataset to electricity bus. + + Parameters + ---------- + network : pypsa.Network + PyPSA network to add generators to + dataset : xr.Dataset + Filtered renewable profiles with dimensions [bus, technology, hour] + tech_costs : pd.Series + Technology cost parameters + config : dict + Configuration dict + + Returns + ------- + dict + Audit info with counts and statistics """ - clusters = renewable_clusters["clusters"] - - if not clusters: - logger.warning("No clusters provided; no generators added") - return - # Map technology names to database keys for cost lookup tech_database_map = { "onwind": "onwind", @@ -174,49 +319,103 @@ def add_renewable_generators( # Use the network's discount_rate (which is set regionally in prepare_network) discount_rate = network.discount_rate - for cluster in clusters: - cluster_id = cluster["cluster_id"] - technology = cluster["technology"] - p_nom_max = cluster["p_nom_max"] # Use cluster-aggregated p_nom_max - cf_ts = cluster["cf_timeseries"] - - # Get technology parameters from database (handles missing tech gracefully) - db_tech_name = tech_database_map.get(technology, technology) - tech_params = td.get_tech(tech_costs, db_tech_name) - - overnight_cost = ( - td.get_tech_param(tech_params, "investment", 0) * 1000 - ) # EUR/kW → EUR/MW - lifetime = td.get_tech_param(tech_params, "lifetime", 20) - fom_pct = td.get_tech_param(tech_params, "FOM", 0) - fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 - - gen_name = f"renewable_{cluster_id}" - - # Add generator with cluster data - # All renewables produce electricity (carrier="electricity") - # Technology type (onwind, offwind, solar) is encoded in the generator name - network.add( - "Generator", - gen_name, - bus="electricity", - carrier="electricity", - p_nom_extendable=True, - p_nom=0, # Start with no capacity; optimization will decide - p_nom_max=p_nom_max, # Upper ceiling from cluster data (MW) - p_max_pu=cf_ts, # Hourly capacity factor from cluster data (0-1) - overnight_cost=overnight_cost, - discount_rate=discount_rate, - lifetime=lifetime, - fom_cost=fom_cost, - ) + # Validate data quality + n_total_combos = len(dataset.bus) * len(dataset.technology) + n_valid_combos = 0 + n_added_generators = 0 + total_p_nom_max = 0 + iso3_set = set() + + for bus_id in dataset.bus.values: + # Extract ISO3 from bus_id + iso3 = str(bus_id).split("_")[0] + iso3_set.add(iso3) - logger.debug( - f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " - f"overnight_cost={overnight_cost:.1f} EUR/MW" + for tech in dataset.technology.values: + tech_str = str(tech) + + # Extract data for this bus-tech combination + p_nom_max = float( + dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values + ) + avg_cf = float(dataset["avg_cf"].sel(bus=bus_id, technology=tech).values) + cf_timeseries = ( + dataset["capacity_factor"].sel(bus=bus_id, technology=tech).values + ) + + # Skip invalid combinations (NaN or ≤0) + if np.isnan(p_nom_max) or p_nom_max <= 0 or np.isnan(avg_cf) or avg_cf <= 0: + continue + + n_valid_combos += 1 + + # Handle timeseries NaNs + if isinstance(cf_timeseries, np.ndarray): + cf_timeseries = np.nan_to_num(cf_timeseries, nan=0.0) + else: + cf_timeseries = np.zeros(8760) + + # Get technology parameters from database + db_tech_name = tech_database_map.get(tech_str, tech_str) + tech_params = td.get_tech(tech_costs, db_tech_name) + + overnight_cost = ( + td.get_tech_param(tech_params, "investment", 0) * 1000 + ) # EUR/kW → EUR/MW + lifetime = td.get_tech_param(tech_params, "lifetime", 20) + fom_pct = td.get_tech_param(tech_params, "FOM", 0) + fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 + + gen_name = f"renewable_{bus_id}_{tech_str}" + + # Add generator + network.add( + "Generator", + gen_name, + bus="electricity", + carrier="electricity", + p_nom_extendable=True, + p_nom=0, # Start with no capacity; optimization will decide + p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) + p_max_pu=cf_timeseries, # Hourly capacity factor (0-1) + overnight_cost=overnight_cost, + discount_rate=discount_rate, + lifetime=lifetime, + fom_cost=fom_cost, + ) + + n_added_generators += 1 + total_p_nom_max += p_nom_max + + logger.debug( + f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " + f"avg_cf={avg_cf:.3f}, overnight_cost={overnight_cost:.1f} EUR/MW" + ) + + # Build audit info + coverage_pct = (n_valid_combos / n_total_combos * 100) if n_total_combos > 0 else 0 + + logger.info( + f"Added {n_added_generators} renewable generators to network " + f"({n_valid_combos}/{n_total_combos} valid combos, {coverage_pct:.1f}% coverage)" + ) + logger.info(f"Total p_nom_max capacity: {total_p_nom_max:.1f} MW") + + if coverage_pct < 50: + logger.warning( + f"Low data coverage: {coverage_pct:.1f}% valid combos. " + f"Consider checking data source." ) - logger.info(f"Added {len(clusters)} renewable generators to network") + return { + "n_generators_added": n_added_generators, + "n_valid_bus_tech_combos": n_valid_combos, + "n_total_bus_tech_combos": n_total_combos, + "coverage_pct": coverage_pct, + "total_p_nom_max_mw": total_p_nom_max, + "iso3_codes": sorted(list(iso3_set)), + "technologies": sorted([str(t) for t in dataset.technology.values]), + } def _apply_discount_rate_to_components( @@ -355,13 +554,34 @@ def apply_product_cutoff(network: pypsa.Network, product: str) -> None: def prepare_network( skeleton_network_path: str, - renewable_timeseries_path: str, + renewable_nc_path: str, tech_costs_path: str, region: str, product: str, config: dict, ) -> Tuple[pypsa.Network, Dict]: - """Prepare network: add renewables, apply product cutoff. Returns (network, audit_info).""" + """Prepare network: add renewables, apply product cutoff. Returns (network, audit_info). + + Parameters + ---------- + skeleton_network_path : str + Path to skeleton network + renewable_nc_path : str + Path to renewable profiles .nc file (NEW xarray format) + tech_costs_path : str + Path to technology costs CSV + region : str + Region name + product : str + Product (h2, hbi, or steel) + config : dict + Configuration dict + + Returns + ------- + tuple + (network: pypsa.Network, audit_info: dict) + """ logger.info("=" * 70) logger.info(f"Preparing network for region={region}, product={product}") logger.info("=" * 70) @@ -375,7 +595,7 @@ def prepare_network( f"{len(network.links)} links, {len(network.stores)} stores" ) - # Set snapshots here using wildcard year coming from Snakemake + # Set snapshots cost_year = None if "snakemake" in globals(): cost_year = getattr(snakemake.wildcards, "cost_year", None) @@ -384,13 +604,13 @@ def prepare_network( network.set_snapshots( pd.date_range(f"{cost_year}-01-01", periods=8760, freq="h") ) - logger.info(f"Set snapshots for cost_year={cost_year} in prepare_network") + logger.info(f"Set snapshots for cost_year={cost_year}") else: raise ValueError( "cost_year must be defined in snakemake wildcards for prepare_network" ) - # Step 1b: Set interest rate (discount rate) for the network + # Step 1b: Set interest rate (discount rate) interest_rates = config.get("interest_rate", {}) discount_rate = interest_rates.get(region, interest_rates.get("default", 0.07)) network.discount_rate = discount_rate @@ -400,19 +620,21 @@ def prepare_network( logger.info("Loading technology costs...") tech_costs = td.load_tech_costs(tech_costs_path) - # Step 3: Load renewable clusters for region - logger.info(f"Loading renewable clusters for region {region}...") - renewable_clusters = load_renewable_clusters_for_region( - region=region, - renewable_timeseries_path=renewable_timeseries_path, - config=config, - ) + # Step 3: Load renewable profiles (NEW FORMAT) + logger.info(f"Loading renewable profiles for region {region}...") + renewable_dataset = load_renewable_profiles(renewable_nc_path) + + # Step 3b: Filter by region + renewable_dataset = filter_by_region(renewable_dataset, region, config) + + # Step 3c: Filter by allowed technologies + renewable_dataset = filter_by_technologies(renewable_dataset, config) # Step 4: Add renewable generators logger.info("Adding renewable generators to network...") - add_renewable_generators( + gen_audit = add_renewable_generators( network=network, - renewable_clusters=renewable_clusters, + dataset=renewable_dataset, tech_costs=tech_costs, config=config, ) @@ -422,22 +644,17 @@ def prepare_network( apply_product_cutoff(network=network, product=product) # Step 6: Build audit info - # Count unique geographic cluster IDs (without technology suffix) - unique_geographic_clusters = set() - for cluster in renewable_clusters["clusters"]: - # Extract base cluster ID (without technology) - cluster_id = cluster["cluster_id"] - base_cluster = "_".join(cluster_id.split("_")[:-1]) # Remove tech suffix - unique_geographic_clusters.add(base_cluster) - audit_info = { "region": region, "product": product, "discount_rate": discount_rate, - "iso3_list": renewable_clusters["iso3_list"], - "num_geographic_clusters": len(unique_geographic_clusters), - "num_technologies": len(renewable_clusters["technologies"]), - "total_renewable_potential_mw": renewable_clusters["total_potential_mw"], + "iso3_list": gen_audit["iso3_codes"], + "num_buses_in_region": len(renewable_dataset.bus), + "num_technologies": len(gen_audit["technologies"]), + "num_generators_added": gen_audit["n_generators_added"], + "data_coverage_pct": gen_audit["coverage_pct"], + "total_renewable_p_nom_max_mw": gen_audit["total_p_nom_max_mw"], + "technologies": gen_audit["technologies"], "network_stats": { "num_buses": len(network.buses), "num_links": len(network.links), @@ -448,10 +665,11 @@ def prepare_network( logger.info("Network preparation complete:") logger.info(f" - Region: {region} (ISO3: {audit_info['iso3_list']})") - logger.info(f" - Geographic clusters: {audit_info['num_geographic_clusters']}") - logger.info(f" - Technologies per cluster: {audit_info['num_technologies']}") + logger.info(f" - Buses in region: {audit_info['num_buses_in_region']}") + logger.info(f" - Generators added: {audit_info['num_generators_added']}") + logger.info(f" - Data coverage: {audit_info['data_coverage_pct']:.1f}%") logger.info( - f" - Total renewable potential: {audit_info['total_renewable_potential_mw']:.1f} MW" + f" - Total p_nom_max: {audit_info['total_renewable_p_nom_max_mw']:.1f} MW" ) logger.info( f" - Network: {audit_info['network_stats']['num_buses']} buses, " @@ -461,7 +679,7 @@ def prepare_network( # Apply regional discount_rate to all cost-bearing components _apply_discount_rate_to_components(network, discount_rate) - # Run consistency check and sanitize + # Run consistency check _consistency_check(network) logger.info("=" * 70) @@ -484,7 +702,7 @@ class MockSnakemake: def __init__(self): self.input = { "skeleton": "../resources/networks/skeleton_2030.nc", - "clusters_timeseries": "../data/renewable_clusters.nc", + "renewable_nc": "../data/renewable_profiles/renewable_profiles_EU__20260420_142941.nc", "costs": "../resources/technology_data/costs_2030.csv", } self.output = { @@ -492,12 +710,15 @@ def __init__(self): "audit": "test_audit.json", } self.wildcards = { - "region": "Test_1", + "region": "EU", "product": "steel", "cost_year": "2030", } self.config = { - "regions": {"Test_1": ["NLD"], "Test_2": ["PRT"], "Test_3": ["IRL"]} + "regions": { + "EU": ["DEU", "FRA", "ITA", "NLD"], + "Africa": ["EGY", "ZAF"], + } } snakemake = MockSnakemake() @@ -506,7 +727,7 @@ def __init__(self): try: network, audit_info = prepare_network( skeleton_network_path=snakemake.input.skeleton, - renewable_timeseries_path=snakemake.input.clusters_timeseries, + renewable_nc_path=snakemake.input.renewable_nc, tech_costs_path=snakemake.input.costs, region=snakemake.wildcards.region, product=snakemake.wildcards.product, From 6aa9ea59b3da5339a45abc57b41c7f7452ded139 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 22 Apr 2026 13:57:22 +0200 Subject: [PATCH 044/216] feat: implement two-stage renewable filtering + generalize product demand naming MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two-stage filtering in prepare_regional_network.py: - STAGE 1: Reserve highest-CF renewables for domestic electricity demand - STAGE 2: Select per-technology-weighted set for product at 5× multiplier - Extract nested incremental subsets (10 ⊆ 200 ⊆ 1000 Mt/year) - Build single base network with max demand, reuse across all levels Product naming generalization: - Rename all steel-specific variables to product-agnostic naming: * demands["steel_demand_mt"] → demands["product_demand_mt"] * demands["steel_demand_mwh_per_h"] → demands["product_demand_mwh_per_h"] * max_steel_demand_mt → max_product_demand_mt * electricity_per_steel_t → electricity_per_product_t --- workflow/scripts/calculate_lcox.py | 296 ++++----- workflow/scripts/prepare_regional_network.py | 602 ++++++++++++++++++- 2 files changed, 699 insertions(+), 199 deletions(-) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 29d9ab4..d22939f 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -1,25 +1,27 @@ """ -Calculate regional Levelized Cost of X (LCOX) for a single steel demand level. +Calculate regional Levelized Cost of X (LCOX) for a single product demand level. -Workflow (single fraction per invocation): +Supports: steel, hbi, h2 (flexible product support) + +Workflow (single demand level per invocation): 1. Load base_network (renewables + product already configured) - 2. Load product-specific demands and scale by fraction - 3. Apply renewable constraint (highest-CF blocked for local demand) - 4. Add final loads based on scaled demand + 2. Load product-specific demands + 3. Apply incremental generator selection (delete generators outside demand level set) + 4. Add hourly loads based on demand 5. Solve optimization - 6. Extract LCOX and save results_{fraction}.csv - 7. Export solved network_{fraction}.nc + 6. Extract LCOX and save results_{demand_level}.csv + 7. Export solved network_{demand_level}.nc -Parallelization: Each fraction is a separate Snakemake job, enabling parallel execution. +Parallelization: Each demand level is a separate Snakemake job, enabling parallel execution. Inputs (from Snakemake): - base_network: PyPSA network with renewables, prepared per region (netCDF) - - steel_demand: Regional steel demand [Mt/year] (CSV) + - incremental_sets: Pre-filtered generator sets per demand level (JSON) - local_demand: Regional local electricity demand [MWh/year] (CSV) -Outputs (generated for each fraction): - - results_{fraction}.csv: LCOX point for that demand level - - network_{fraction}.nc: Optimized network +Outputs (generated for each demand level): + - results_{demand_level}.csv: LCOX point for that demand level + - network_{demand_level}.nc: Optimized network """ import logging @@ -57,8 +59,6 @@ def load_demands_for_region(region, config): Returns dict with: - local_el_demand_mwh: MWh/year (for renewable constraint calculation) - - Note: steel_demand_mt is passed directly from Snakemake params, not loaded from file """ # Load local electricity demand (for renewable constraint calculation) try: @@ -89,156 +89,98 @@ def load_demands_for_region(region, config): # ============================================================================ -def apply_renewable_constraint(network, local_el_demand_mwh, config): - """Block highest-CF renewables for local demand (priority mechanism). - - Logic: - 1. Get all renewable generators with their average CF - 2. Sort by average CF (descending) - highest quality first - 3. Accumulate capacity from highest CF until >= local_demand - 4. Block these generators for local demand (set p_nom_max=0) - 5. Remaining renewables available for steel production +# ============================================================================ +# INCREMENTAL RENEWABLE SELECTION (upstream filtering, Phase 3) +# ============================================================================ - The load determines electrolyzer operation; no capacity constraint applied. - Returns: audit dict with capacity breakdown and blocked generators +def apply_incremental_generator_selection(network, product_demand_mt, incremental_sets): + """Delete renewable generators NOT in the incremental set for this demand level. + + NEW WORKFLOW (Phase 3): + - Base network contains ALL generators from max-demand filtering + - Incremental sets pre-computed in prepare_regional_network.py + - For each demand level, delete generators outside that level's set + + OLD WORKFLOW (Phase 2) REMOVED: + - apply_renewable_constraint() - blocked highest-CF for local demand + - Now: filtering done once upstream, no per-demand redundancy + + Parameters + ---------- + network : pypsa.Network + Network with full renewable set (from filtering at max demand) + product_demand_mt : float + Current product demand level in Mt/year + incremental_sets : dict + Mapping: {demand_mt: [selected_generators]} + Each generator dict has: bus_id, technology, p_nom_max, avg_cf + + Returns + ------- + dict + Audit info with generator deletion stats """ - # Get all renewable generators (identified by name pattern "renewable_*") - renewable_gens = network.generators[ - network.generators.index.str.startswith("renewable_") - ] + logger.info("=" * 70) + logger.info("APPLYING INCREMENTAL GENERATOR SELECTION") + logger.info("=" * 70) - if renewable_gens.empty: - logger.warning("No renewable generators found in network") + # Get the selected generators for this demand level + if product_demand_mt not in incremental_sets: + logger.warning( + f"Demand level {product_demand_mt} Mt not in incremental sets: {list(incremental_sets.keys())}" + ) return { - "total_renewable_capacity_mw": 0, - "capacity_for_local_demand_mw": 0, - "capacity_available_for_steel_mw": 0, - "generators_blocked_for_local_demand": [], + "total_generators_before": len(network.generators), + "generators_deleted": 0, + "generators_kept": len(network.generators), + "selected_for_demand": 0, } - # ====== STEP 1: Calculate average CF for each generator ====== - gen_cf_data = [] + selected_generators = incremental_sets[product_demand_mt] - for gen_name, gen_row in renewable_gens.iterrows(): - h_max_pu = gen_row["p_max_pu"] # Hourly timeseries (0-1) or scalar + # Build set of generator names for this demand level + # Generator names are formatted as: renewable_{bus_id}_{technology} + selected_gen_names = set() + for gen_dict in selected_generators: + gen_name = f"renewable_{gen_dict['bus_id']}_{gen_dict['technology']}" + selected_gen_names.add(gen_name) - # Handle both pandas Series, numpy array, and scalars - if isinstance(h_max_pu, (int, float, np.number)): - # Scalar CF - use directly - avg_cf = float(h_max_pu) - elif hasattr(h_max_pu, "values"): - # Pandas Series - cf_values = h_max_pu.values - avg_cf = np.mean(cf_values) if len(cf_values) > 0 else 0 - else: - # Numpy array or list - cf_values = h_max_pu - avg_cf = ( - np.mean(cf_values) - if isinstance(cf_values, np.ndarray) and len(cf_values) > 0 - else float(cf_values) - ) - p_nom_max = gen_row["p_nom_max"] - - gen_cf_data.append( - { - "gen_name": gen_name, - "avg_cf": avg_cf, - "p_nom_max": p_nom_max, - "carrier": gen_row["carrier"], - } - ) - - logger.debug(f"Found {len(gen_cf_data)} renewable generators") - - # ====== STEP 2: Sort by average CF (descending) - prioritize best ====== - gen_cf_data.sort(key=lambda x: x["avg_cf"], reverse=True) - - total_renewable_capacity = sum([g["p_nom_max"] for g in gen_cf_data]) - logger.info(f"Total renewable capacity: {total_renewable_capacity:.1f} MW") logger.info( - f"Top 3 generators by CF: {[(g['gen_name'], format(g['avg_cf'], '.3f')) for g in gen_cf_data[:3]]}" + f"Selected generators for {product_demand_mt} Mt: {len(selected_gen_names)}" ) - # ====== STEP 3: Block highest-CF generators for local demand ====== - - capacity_accumulated = 0 # Track cumulative capacity factor contribution - generators_for_local = [] - - for gen_info in gen_cf_data: - if capacity_accumulated >= local_el_demand_mwh: - # We've accumulated enough to serve local demand, stop - break - - gen_name = gen_info["gen_name"] - avg_cf = gen_info["avg_cf"] - p_nom_max = gen_info["p_nom_max"] - - # How much energy does this generator produce annually? - annual_energy = avg_cf * p_nom_max * 8760 # MWh/year - - # How much do we still need? - remaining_needed = local_el_demand_mwh - capacity_accumulated - - if annual_energy <= remaining_needed: - # Use entire generator for local demand - capacity_to_use = p_nom_max - capacity_accumulated += annual_energy - new_p_nom_max = 0 - else: - # Use partial generator to exactly meet local demand - capacity_to_use = remaining_needed / (avg_cf * 8760) - capacity_accumulated += remaining_needed - new_p_nom_max = p_nom_max - capacity_to_use - - generators_for_local.append( - { - "gen_name": gen_name, - "avg_cf": avg_cf, - "capacity_blocked_mw": capacity_to_use, - "energy_provided_mwh": capacity_to_use * avg_cf * 8760, - "p_nom_max_before": p_nom_max, - "p_nom_max_after": new_p_nom_max, - } - ) + # Get all renewable generators in network + renewable_gens = network.generators[ + network.generators.index.str.startswith("renewable_") + ] - # Update generator availability for steel - network.generators.at[gen_name, "p_nom_max"] = new_p_nom_max - blocked_msg = ( - "fully blocked" - if new_p_nom_max == 0 - else f"reduced to {new_p_nom_max:.2f} MW" - ) - logger.info( - f" Blocked {gen_name:40s} (CF={avg_cf:.3f}, {capacity_to_use:7.1f} MW) → local demand, {blocked_msg}" - ) + # Find generators to delete (those NOT in selected set) + generators_to_delete = [ + gen_name + for gen_name in renewable_gens.index + if gen_name not in selected_gen_names + ] logger.info( - f"Allocated {len(generators_for_local)} generators for local demand ({capacity_accumulated:.0f} MWh/year)" + f"Deleting {len(generators_to_delete)} generators not in incremental set" ) - # ====== STEP 4: Calculate remaining renewable capacity (for steel) ====== - # Get remaining generators that are NOT blocked (p_nom_max > 0) - remaining_renewable_gens = network.generators[ - (network.generators.index.str.startswith("renewable_")) - & (network.generators["p_nom_max"] > 0) - ] - total_remaining_capacity = remaining_renewable_gens["p_nom_max"].sum() + # Delete generators + for gen_name in generators_to_delete: + network.remove("Generator", gen_name) + logger.debug(f"Deleted generator: {gen_name}") logger.info( - f"Remaining renewable capacity for steel: {total_remaining_capacity:.1f} MW" + f"Kept {len(selected_gen_names)} generators for demand level {product_demand_mt} Mt" ) + logger.info("=" * 70) return { - "total_renewable_capacity_mw": total_renewable_capacity, - "capacity_for_local_demand_mw": capacity_accumulated - / 8760, # Convert back to MW - "capacity_available_for_steel_mw": total_remaining_capacity, - "generators_blocked_for_local_demand": [ - g["gen_name"] for g in generators_for_local - ], - "num_generators_blocked": len(generators_for_local), + "total_generators_before": len(renewable_gens), + "generators_deleted": len(generators_to_delete), + "generators_kept": len(selected_gen_names), + "selected_for_demand": len(selected_gen_names), } @@ -257,25 +199,27 @@ def add_loads_to_network(network, product, demands): if product == "steel": bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["steel_demand_mt"] * 1000 / 8760 # Mt/year → t/h + hourly_demand_t = demands["product_demand_mt"] * 1000 / 8760 # Mt/year → t/h unit_str = "t/h" elif product == "hbi": bus_name = "hbi" # HBI is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["steel_demand_mt"] * 1000 / 8760 # Mt/year → t/h + hourly_demand_t = demands["product_demand_mt"] * 1000 / 8760 # Mt/year → t/h unit_str = "t/h" elif product == "h2": bus_name = "hydrogen" # H2 is measured in MWh/year, convert to MW (hourly average) - hourly_demand_mwh = demands["steel_demand_mwh_per_h"] # Already hourly average + hourly_demand_mwh = demands[ + "product_demand_mwh_per_h" + ] # Already hourly average unit_str = "MW" elif product in ["eaf", "eaf-grid"]: bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["steel_demand_mt"] * 1000 / 8760 # Mt/year → t/h + hourly_demand_t = demands["product_demand_mt"] * 1000 / 8760 # Mt/year → t/h unit_str = "t/h" else: @@ -577,7 +521,7 @@ def extract_lcox(network, product, demands): if obj_value is None or np.isnan(obj_value): raise ValueError("Optimization failed to return valid objective") - demand_annual_t = demands["steel_demand_mt"] * 1000 # Mt → t + demand_annual_t = demands["product_demand_mt"] * 1000 # Mt → t hourly_load_t = demand_annual_t / 8760 lcox = obj_value / demand_annual_t if demand_annual_t > 0 else np.inf @@ -593,7 +537,7 @@ def extract_lcox(network, product, demands): except Exception as e: logger.error(f"Optimization infeasible or failed: {e}") - demand_annual_t = demands["steel_demand_mt"] * 1000 + demand_annual_t = demands["product_demand_mt"] * 1000 hourly_load_t = demand_annual_t / 8760 results_df.loc[0] = [ demand_annual_t, @@ -619,11 +563,11 @@ def extract_lcox(network, product, demands): # ==================== SETUP ==================== logger.info("=" * 70) # Get the specific demand level for THIS invocation (passed by Snakemake) - steel_demand_mt = float(snakemake.params.steel_demand_mt) + product_demand_mt = float(snakemake.params.product_demand_mt) logger.info( f"LCOX Calculation: region={snakemake.wildcards.region}, " f"product={snakemake.wildcards.product}, " - f"steel_demand={steel_demand_mt} Mt/year." + f"demand={product_demand_mt} Mt/year." ) logger.info("=" * 70) @@ -647,14 +591,14 @@ def extract_lcox(network, product, demands): ) # ==================== PROCESS SINGLE DEMAND LEVEL ==================== - electricity_per_steel_t = snakemake.config.get("electricity_steel_ratio", 5.25) + electricity_per_product_t = snakemake.config.get("electricity_steel_ratio", 5.25) - logger.info(f"Processing: {steel_demand_mt} Mt/year") + logger.info(f"Processing: {product_demand_mt} Mt/year") # ==================== NETWORK SETUP ==================== # Create a copy of base network network = base_network.copy() - network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-{steel_demand_mt}" + network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-{product_demand_mt}" # Ensure snapshot year is set by upstream network preparation; # do not override if already set. @@ -675,24 +619,42 @@ def extract_lcox(network, product, demands): network.discount_rate = base_network.discount_rate # Calculate electricity needed for this demand level - scaled_steel_demand_mwh_per_h = steel_demand_mt * electricity_per_steel_t / 8760 + scaled_product_demand_mwh_per_h = ( + product_demand_mt * electricity_per_product_t / 8760 + ) - logger.info(f"Steel demand: {steel_demand_mt:.1f} Mt/year") + logger.info(f"Product demand: {product_demand_mt:.1f} Mt/year") logger.info( - f"Electricity required: {scaled_steel_demand_mwh_per_h * 8760:.1f} MWh/year" + f"Electricity required: {scaled_product_demand_mwh_per_h * 8760:.1f} MWh/year" ) # Create scaled demands dict for this demand level scaled_demands = demands.copy() - scaled_demands["steel_demand_mt"] = steel_demand_mt - scaled_demands["steel_demand_mwh_per_h"] = scaled_steel_demand_mwh_per_h + scaled_demands["product_demand_mt"] = product_demand_mt + scaled_demands["product_demand_mwh_per_h"] = scaled_product_demand_mwh_per_h + + # Load incremental generator sets and apply filtering + logger.info("Loading incremental generator sets...") + try: + import json - # Block highest-CF renewables for local demand (priority mechanism) - logger.info("Applying renewable priority constraint...") - constraint_info = apply_renewable_constraint( + with open(snakemake.input.incremental_sets, "r") as f: + incremental_sets_raw = json.load(f) + # Convert keys from strings back to floats + incremental_sets = {float(k): v for k, v in incremental_sets_raw.items()} + logger.info( + f"Loaded incremental sets for demand levels: {list(incremental_sets.keys())}" + ) + except Exception as e: + logger.error(f"Failed to load incremental sets: {e}") + raise + + # Apply incremental generator selection (delete generators outside this demand level) + logger.info("Applying incremental generator selection...") + selection_info = apply_incremental_generator_selection( network=network, - local_el_demand_mwh=demands["local_el_demand_mwh"], - config=snakemake.config, + product_demand_mt=product_demand_mt, + incremental_sets=incremental_sets, ) # Add hourly load for steel output @@ -705,7 +667,7 @@ def extract_lcox(network, product, demands): # Debug: Print network structure logger.info( "\n--- Network Structure for Demand Level {:.1f} Mt/year ---".format( - steel_demand_mt + product_demand_mt ) ) logger.info(f"Buses: {list(network.buses.index)}") @@ -732,12 +694,14 @@ def extract_lcox(network, product, demands): else "infeasible" ) except Exception as e: - logger.warning(f"Solver error for steel demand {steel_demand_mt} Mt/year: {e}") + logger.warning( + f"Solver error for product demand {product_demand_mt} Mt/year: {e}" + ) optimization_status = "error" if optimization_status != "optimal": logger.warning( - f"Optimization {optimization_status} for steel demand {steel_demand_mt} Mt/year - returning NaN values" + f"Optimization {optimization_status} for product demand {product_demand_mt} Mt/year - returning NaN values" ) # Extract LCOX results diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 351923d..8e7fe7b 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -276,6 +276,379 @@ def back_propagate_electricity_need( ) +# ============================================================================ +# TWO-STEP RENEWABLE FILTERING +# ============================================================================ + + +def load_local_demand(local_demand_path: str, region: str, config: dict) -> float: + """Load local electricity demand for region from CSV file. + + Parameters + ---------- + local_demand_path : str + Path to local demand CSV (columns: region, demand, el_share or similar) + region : str + Region name + config : dict + Configuration dict + + Returns + ------- + float + Local electricity demand in MWh/year. Returns 0 if region not found (with warning). + """ + try: + df = pd.read_csv(local_demand_path) + except FileNotFoundError: + logger.warning( + f"Local demand file not found: {local_demand_path}. Using 0 MWh/year." + ) + return 0.0 + except Exception as e: + logger.warning(f"Failed to load local demand: {e}. Using 0 MWh/year.") + return 0.0 + + # Look for region in dataframe (try common column names) + region_col = None + for col in ["region", "Region", "name", "Name"]: + if col in df.columns: + region_col = col + break + + if region_col is None: + logger.warning( + f"Could not find region column in {local_demand_path}. Using 0 MWh/year." + ) + return 0.0 + + # Find demand value for this region + region_data = df[df[region_col] == region] + + if region_data.empty: + logger.warning( + f"Region '{region}' not found in local demand data. " + f"Available regions: {df[region_col].unique()}. Using 0 MWh/year." + ) + return 0.0 + + # Look for demand column (try common names) + demand_col = None + for col in ["demand", "Demand", "demand_mwh", "demand_MWh", "el_demand"]: + if col in df.columns: + demand_col = col + break + + if demand_col is None: + logger.warning( + f"Could not find demand column in {local_demand_path}. Using 0 MWh/year." + ) + return 0.0 + + demand_mwh = float(region_data[demand_col].iloc[0]) + logger.info( + f"Loaded local electricity demand for {region}: {demand_mwh:.1f} MWh/year" + ) + + return demand_mwh + + +def filter_renewable_generators_by_potential( + dataset: xr.Dataset, + region: str, + product: str, + tech_costs: pd.Series, + config: dict, + local_demand_mwh: float, + max_product_demand_mt: float, +) -> Tuple[List[Dict], Dict]: + """Two-step renewable filtering: reserve for domestic + select for product (per-tech). + + Parameters + ---------- + dataset : xr.Dataset + Filtered renewable profiles (already region + tech filtered) + region : str + Region name + product : str + Product (steel, hbi, h2) + tech_costs : pd.Series + Technology cost database + config : dict + Configuration dict + local_demand_mwh : float + Local electricity demand in MWh/year + max_product_demand_mt : float + Maximum product demand in Mt/year (for capacity target) + + Returns + ------- + tuple + (selected_generators, filter_audit) + - selected_generators: List of dicts with bus, tech, p_nom_max, avg_cf + - filter_audit: Dict with filtering statistics + """ + logger.info("=" * 70) + logger.info("TWO-STEP RENEWABLE FILTERING") + logger.info("=" * 70) + + # Compute product electricity need per tonne + product_elec_per_t = back_propagate_electricity_need(tech_costs, product, config) + + # Target capacity (MW) for product production + max_product_elec_mwh = max_product_demand_mt * product_elec_per_t + max_product_elec_mw = max_product_elec_mwh / (365 * 24) + + # Multiplier from config (default 5) + multiplier = config.get("renewable_coverage_multiplier", 5) + target_capacity_mw = multiplier * max_product_elec_mw + + logger.info(f"{product.upper()} demand: {max_product_demand_mt:.1f} Mt") + logger.info( + f"{product.upper()} electricity need: {product_elec_per_t:.4f} MWh/t → {max_product_elec_mw:.1f} MW average" + ) + logger.info( + f"Target renewable capacity ({multiplier}×): {target_capacity_mw:.1f} MW" + ) + logger.info( + f"Local demand: {local_demand_mwh:.1f} MWh/year → {local_demand_mwh / (365 * 24):.1f} MW average" + ) + + # STEP 1: Build bus-tech candidate list with all data + candidates = [] + for bus_id in dataset.bus.values: + for tech in dataset.technology.values: + p_nom_max = float( + dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values + ) + avg_cf = float(dataset["avg_cf"].sel(bus=bus_id, technology=tech).values) + + # Skip invalid combos + if np.isnan(p_nom_max) or p_nom_max <= 0 or np.isnan(avg_cf) or avg_cf <= 0: + continue + + candidates.append( + { + "bus_id": str(bus_id), + "technology": str(tech), + "p_nom_max": p_nom_max, + "avg_cf": avg_cf, + "capacity_factor_ts": dataset["capacity_factor"] + .sel(bus=bus_id, technology=tech) + .values, + "potential": p_nom_max * avg_cf, # Quality metric (MW × CF) + } + ) + + if not candidates: + logger.warning("No valid bus-tech candidates after filtering!") + return [], {} + + logger.info(f"Total candidates: {len(candidates)}") + + # STEP 1: DOMESTIC RESERVATION + logger.info("-" * 70) + logger.info("STEP 1: RESERVE FOR DOMESTIC DEMAND") + logger.info("-" * 70) + + # Sort globally by avg_cf (descending) for domestic reservation + candidates_sorted_cf = sorted(candidates, key=lambda x: x["avg_cf"], reverse=True) + + local_demand_mw = local_demand_mwh / (365 * 24) + reserved_generators = [] + reserved_capacity_mw = 0 + + for candidate in candidates_sorted_cf: + if reserved_capacity_mw >= local_demand_mw: + break + reserved_generators.append(candidate) + reserved_capacity_mw += candidate["p_nom_max"] + + logger.info( + f"Reserved {len(reserved_generators)} bus-tech combos for domestic demand" + ) + logger.info( + f"Reserved capacity: {reserved_capacity_mw:.1f} MW (target: {local_demand_mw:.1f} MW)" + ) + + # Create set of reserved IDs for filtering + reserved_ids = {(g["bus_id"], g["technology"]) for g in reserved_generators} + + # STEP 2: STEEL SELECTION (PER-TECHNOLOGY) + logger.info("-" * 70) + logger.info("STEP 2: SELECT FOR STEEL PRODUCTION (PER-TECHNOLOGY)") + logger.info("-" * 70) + + # Get non-reserved candidates + non_reserved = [ + c for c in candidates if (c["bus_id"], c["technology"]) not in reserved_ids + ] + + # Calculate per-technology potential shares + tech_potentials = {} + total_potential = 0 + for candidate in non_reserved: + tech = candidate["technology"] + potential = candidate["potential"] + tech_potentials[tech] = tech_potentials.get(tech, 0) + potential + total_potential += potential + + if total_potential <= 0: + logger.warning("Total potential <= 0 in non-reserved generators!") + total_potential = 1.0 # Avoid division by zero + + logger.info(f"Total non-reserved potential: {total_potential:.1f} MW·CF") + + # Per-tech targets (proportional to potential) + tech_targets = {} + for tech, pot in tech_potentials.items(): + share = pot / total_potential + target_mw = share * target_capacity_mw + tech_targets[tech] = target_mw + logger.info(f" {tech}: {share * 100:.1f}% share → target {target_mw:.1f} MW") + + # Select from each tech by avg_cf + selected_generators = [] + selected_capacity_by_tech = {} + + for tech in sorted(tech_targets.keys()): + target_mw = tech_targets[tech] + + # Get candidates for this tech, sort by avg_cf + tech_candidates = sorted( + [c for c in non_reserved if c["technology"] == tech], + key=lambda x: x["avg_cf"], + reverse=True, + ) + + tech_selected = [] + tech_capacity = 0 + + for candidate in tech_candidates: + if tech_capacity >= target_mw: + break + selected_generators.append(candidate) + tech_selected.append(candidate) + tech_capacity += candidate["p_nom_max"] + + selected_capacity_by_tech[tech] = tech_capacity + logger.info( + f" {tech}: Selected {len(tech_selected)} generators, " + f"{tech_capacity:.1f} MW (target: {target_mw:.1f} MW)" + ) + + total_selected_capacity = sum(selected_capacity_by_tech.values()) + + logger.info("-" * 70) + logger.info("FILTERING COMPLETE") + logger.info("-" * 70) + logger.info(f"Total generators selected: {len(selected_generators)}") + logger.info(f"Total capacity selected: {total_selected_capacity:.1f} MW") + logger.info( + f"Coverage ratio: {total_selected_capacity / target_capacity_mw:.2f}× target" + ) + + # Build audit info + filter_audit = { + "region": region, + "product": product, + "local_demand_mwh": local_demand_mwh, + "max_product_demand_mt": max_product_demand_mt, + "product_elec_need_mwh_per_t": product_elec_per_t, + "multiplier": multiplier, + "target_capacity_mw": target_capacity_mw, + "n_reserved_generators": len(reserved_generators), + "reserved_capacity_mw": reserved_capacity_mw, + "n_selected_generators": len(selected_generators), + "selected_capacity_mw": total_selected_capacity, + "selected_by_tech": selected_capacity_by_tech, + "coverage_ratio": total_selected_capacity / target_capacity_mw + if target_capacity_mw > 0 + else 0, + } + + logger.info("=" * 70) + + return selected_generators, filter_audit + + +# ============================================================================ +# EXTRACT INCREMENTAL GENERATOR SETS +# ============================================================================ + + +def extract_incremental_generator_sets( + selected_generators: List[Dict], + product_demand_levels: List[float], + product: str, + tech_costs: pd.Series, + config: dict, +) -> Dict[float, List[Dict]]: + """Extract incremental generator subsets for each demand level. + + Given a ranked list of selected generators (from max demand), extracts smaller + subsets for each demand level. Each subset is a proper subset of the next. + + Parameters + ---------- + selected_generators : List[Dict] + Ranked list from filter_renewable_generators_by_potential (at max demand) + product_demand_levels : List[float] + Product demand levels in Mt/year (e.g., [10, 200, 1000]) + product : str + Product (steel, hbi, h2) + tech_costs : pd.Series + Technology cost database + config : dict + Configuration dict + + Returns + ------- + dict + Mapping: {demand_mt: [selected_generators_for_that_level]} + Sets are nested: 10 Mt ⊆ 200 Mt ⊆ 1000 Mt + """ + logger.info("=" * 70) + logger.info("EXTRACTING INCREMENTAL GENERATOR SETS") + logger.info("=" * 70) + + # Compute electricity need for this product + elec_per_t = back_propagate_electricity_need(tech_costs, product, config) + multiplier = config.get("renewable_coverage_multiplier", 5) + + # Calculate target MW for each demand level + demand_targets = {} # demand_mt -> target_mw + for demand_mt in product_demand_levels: + elec_mwh = demand_mt * elec_per_t + elec_mw = elec_mwh / (365 * 24) + target_mw = multiplier * elec_mw + demand_targets[demand_mt] = target_mw + logger.info(f"Demand {demand_mt:.1f} Mt → Target {target_mw:.1f} MW") + + # For each demand level, select generators up to its target + incremental_sets = {} + for demand_mt in sorted(product_demand_levels): + target_mw = demand_targets[demand_mt] + + # Accumulate generators until reaching target + subset = [] + accumulated_mw = 0 + for gen in selected_generators: + if accumulated_mw >= target_mw: + break + subset.append(gen) + accumulated_mw += gen["p_nom_max"] + + incremental_sets[demand_mt] = subset + logger.info( + f" {demand_mt:.1f} Mt: {len(subset)} generators, " + f"{accumulated_mw:.1f} MW (target {target_mw:.1f} MW)" + ) + + logger.info("=" * 70) + return incremental_sets + + # ============================================================================ # RENEWABLE GENERATOR ADDITION # ============================================================================ @@ -286,8 +659,9 @@ def add_renewable_generators( dataset: xr.Dataset, tech_costs: pd.Series, config: dict, + selected_generators: List[Dict] = None, ) -> Dict: - """Add renewable generators from xarray dataset to electricity bus. + """Add renewable generators from xarray dataset or pre-filtered list to electricity bus. Parameters ---------- @@ -299,6 +673,10 @@ def add_renewable_generators( Technology cost parameters config : dict Configuration dict + selected_generators : List[Dict], optional + Pre-filtered list from filter_renewable_generators_by_potential(). + If provided, ONLY these generators are added (faster). + If None, all valid combinations from dataset are added. Returns ------- @@ -326,28 +704,21 @@ def add_renewable_generators( total_p_nom_max = 0 iso3_set = set() - for bus_id in dataset.bus.values: - # Extract ISO3 from bus_id - iso3 = str(bus_id).split("_")[0] - iso3_set.add(iso3) - - for tech in dataset.technology.values: - tech_str = str(tech) - - # Extract data for this bus-tech combination - p_nom_max = float( - dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values - ) - avg_cf = float(dataset["avg_cf"].sel(bus=bus_id, technology=tech).values) - cf_timeseries = ( - dataset["capacity_factor"].sel(bus=bus_id, technology=tech).values - ) - - # Skip invalid combinations (NaN or ≤0) - if np.isnan(p_nom_max) or p_nom_max <= 0 or np.isnan(avg_cf) or avg_cf <= 0: - continue + # If pre-filtered generators provided, use fast path + if selected_generators is not None: + logger.info( + f"Adding {len(selected_generators)} pre-filtered generators (fast path)" + ) + for gen_dict in selected_generators: + bus_id = gen_dict["bus_id"] + tech_str = gen_dict["technology"] + p_nom_max = gen_dict["p_nom_max"] + cf_timeseries = gen_dict["capacity_factor_ts"] + avg_cf = gen_dict["avg_cf"] - n_valid_combos += 1 + # Extract ISO3 from bus_id + iso3 = str(bus_id).split("_")[0] + iso3_set.add(iso3) # Handle timeseries NaNs if isinstance(cf_timeseries, np.ndarray): @@ -392,6 +763,85 @@ def add_renewable_generators( f"avg_cf={avg_cf:.3f}, overnight_cost={overnight_cost:.1f} EUR/MW" ) + n_valid_combos = len(selected_generators) + else: + # Original slow path: iterate through all xarray combos + logger.info( + f"Adding generators from full dataset ({n_total_combos} combos, slow path)" + ) + for bus_id in dataset.bus.values: + # Extract ISO3 from bus_id + iso3 = str(bus_id).split("_")[0] + iso3_set.add(iso3) + + for tech in dataset.technology.values: + tech_str = str(tech) + + # Extract data for this bus-tech combination + p_nom_max = float( + dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values + ) + avg_cf = float( + dataset["avg_cf"].sel(bus=bus_id, technology=tech).values + ) + cf_timeseries = ( + dataset["capacity_factor"].sel(bus=bus_id, technology=tech).values + ) + + # Skip invalid combinations (NaN or ≤0) + if ( + np.isnan(p_nom_max) + or p_nom_max <= 0 + or np.isnan(avg_cf) + or avg_cf <= 0 + ): + continue + + n_valid_combos += 1 + + # Handle timeseries NaNs + if isinstance(cf_timeseries, np.ndarray): + cf_timeseries = np.nan_to_num(cf_timeseries, nan=0.0) + else: + cf_timeseries = np.zeros(8760) + + # Get technology parameters from database + db_tech_name = tech_database_map.get(tech_str, tech_str) + tech_params = td.get_tech(tech_costs, db_tech_name) + + overnight_cost = ( + td.get_tech_param(tech_params, "investment", 0) * 1000 + ) # EUR/kW → EUR/MW + lifetime = td.get_tech_param(tech_params, "lifetime", 20) + fom_pct = td.get_tech_param(tech_params, "FOM", 0) + fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 + + gen_name = f"renewable_{bus_id}_{tech_str}" + + # Add generator + network.add( + "Generator", + gen_name, + bus="electricity", + carrier="electricity", + p_nom_extendable=True, + p_nom=0, # Start with no capacity; optimization will decide + p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) + p_max_pu=cf_timeseries, # Hourly capacity factor (0-1) + overnight_cost=overnight_cost, + discount_rate=discount_rate, + lifetime=lifetime, + fom_cost=fom_cost, + ) + + n_added_generators += 1 + total_p_nom_max += p_nom_max + + logger.debug( + f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " + f"avg_cf={avg_cf:.3f}, overnight_cost={overnight_cost:.1f} EUR/MW" + ) + # Build audit info coverage_pct = (n_valid_combos / n_total_combos * 100) if n_total_combos > 0 else 0 @@ -559,8 +1009,17 @@ def prepare_network( region: str, product: str, config: dict, -) -> Tuple[pypsa.Network, Dict]: - """Prepare network: add renewables, apply product cutoff. Returns (network, audit_info). + local_demand_path: str = None, +) -> Tuple[pypsa.Network, Dict, Dict[float, List[Dict]]]: + """Prepare network with incremental renewable filtering. + + Workflow: + 1. Load skeleton + renewables (filtered by region + tech) + 2. Call filter_renewable_generators_by_potential() with MAX demand + 3. Extract incremental subsets for each demand level (10 ⊆ 200 ⊆ 1000) + 4. Build FULL network with max demand set + 5. Apply product cutoff + 6. Return: (network, audit_info, incremental_sets) Parameters ---------- @@ -575,17 +1034,31 @@ def prepare_network( product : str Product (h2, hbi, or steel) config : dict - Configuration dict + Configuration dict with: + - steel_demand_levels: [10, 200, 1000] Mt/year (applies to any product) + - renewable_technologies: ["solar", "onwind"] + - renewable_coverage_multiplier: 5 + local_demand_path : str, optional + Path to local electricity demand CSV (columns: region, demand_mwh) Returns ------- tuple - (network: pypsa.Network, audit_info: dict) + (network, audit_info, incremental_sets) + - network: PyPSA Network with full max-demand renewable set + - audit_info: Dict with filtering + network stats + - incremental_sets: {demand_mt: [selected_generators]} nested subsets """ logger.info("=" * 70) logger.info(f"Preparing network for region={region}, product={product}") logger.info("=" * 70) + # Step 0: Extract demand levels and max demand (config uses 'steel_demand_levels' for backward compat) + product_demand_levels = config.get("steel_demand_levels", [10, 200, 1000]) + max_product_demand_mt = max(product_demand_levels) + logger.info(f"Demand levels: {product_demand_levels} Mt/year") + logger.info(f"Max demand: {max_product_demand_mt} Mt/year") + # Step 1: Load skeleton network logger.info("Loading skeleton network...") network = pypsa.Network(skeleton_network_path) @@ -630,20 +1103,50 @@ def prepare_network( # Step 3c: Filter by allowed technologies renewable_dataset = filter_by_technologies(renewable_dataset, config) - # Step 4: Add renewable generators - logger.info("Adding renewable generators to network...") + # Step 4: LOAD LOCAL DEMAND for renewable filtering + local_demand_mwh = 0.0 + if local_demand_path: + local_demand_mwh = load_local_demand(local_demand_path, region, config) + else: + logger.warning("local_demand_path not provided, using 0 MWh/year for filtering") + + # Step 5: FILTER RENEWABLES (with MAX demand) + logger.info("Filtering renewable generators (max demand set)...") + selected_generators, filter_audit = filter_renewable_generators_by_potential( + dataset=renewable_dataset, + region=region, + product=product, + tech_costs=tech_costs, + config=config, + local_demand_mwh=local_demand_mwh, + max_product_demand_mt=max_product_demand_mt, + ) + + # Step 6: EXTRACT INCREMENTAL GENERATOR SETS + logger.info("Extracting incremental generator sets...") + incremental_sets = extract_incremental_generator_sets( + selected_generators=selected_generators, + product_demand_levels=product_demand_levels, + product=product, + tech_costs=tech_costs, + config=config, + ) + + # Step 7: Add renewable generators (FULL SET from filtering) + logger.info("Adding renewable generators to network (full max-demand set)...") gen_audit = add_renewable_generators( network=network, dataset=renewable_dataset, tech_costs=tech_costs, config=config, + selected_generators=selected_generators, # Pre-filtered list (fast path) ) - # Step 5: Apply product-specific cutoff + # Step 8: Apply product-specific cutoff logger.info(f"Applying product cutoff for {product}...") apply_product_cutoff(network=network, product=product) - # Step 6: Build audit info + # Step 9: Build audit info audit_info = { "region": region, "product": product, @@ -655,6 +1158,10 @@ def prepare_network( "data_coverage_pct": gen_audit["coverage_pct"], "total_renewable_p_nom_max_mw": gen_audit["total_p_nom_max_mw"], "technologies": gen_audit["technologies"], + "filter_audit": filter_audit, + "incremental_set_counts": { + demand_mt: len(gen_list) for demand_mt, gen_list in incremental_sets.items() + }, "network_stats": { "num_buses": len(network.buses), "num_links": len(network.links), @@ -671,6 +1178,7 @@ def prepare_network( logger.info( f" - Total p_nom_max: {audit_info['total_renewable_p_nom_max_mw']:.1f} MW" ) + logger.info(f" - Incremental sets: {audit_info['incremental_set_counts']}") logger.info( f" - Network: {audit_info['network_stats']['num_buses']} buses, " f"{audit_info['network_stats']['num_generators']} generators" @@ -684,7 +1192,7 @@ def prepare_network( logger.info("=" * 70) - return network, audit_info + return network, audit_info, incremental_sets # ============================================================================ @@ -704,10 +1212,12 @@ def __init__(self): "skeleton": "../resources/networks/skeleton_2030.nc", "renewable_nc": "../data/renewable_profiles/renewable_profiles_EU__20260420_142941.nc", "costs": "../resources/technology_data/costs_2030.csv", + "local_demand": "../data/local_demand.csv", } self.output = { "base_network": "test_base_network.nc", "audit": "test_audit.json", + "incremental_sets": "test_incremental_sets.json", } self.wildcards = { "region": "EU", @@ -718,20 +1228,29 @@ def __init__(self): "regions": { "EU": ["DEU", "FRA", "ITA", "NLD"], "Africa": ["EGY", "ZAF"], - } + }, + "renewable_technologies": ["solar", "onwind"], + "renewable_coverage_multiplier": 5, + "steel_demand_levels": [10, 200, 1000], + "interest_rate": {"default": 0.07}, } snakemake = MockSnakemake() # Prepare network try: - network, audit_info = prepare_network( + local_demand_path = None + if hasattr(snakemake.input, "local_demand") and snakemake.input.local_demand: + local_demand_path = snakemake.input.local_demand + + network, audit_info, incremental_sets = prepare_network( skeleton_network_path=snakemake.input.skeleton, renewable_nc_path=snakemake.input.renewable_nc, tech_costs_path=snakemake.input.costs, region=snakemake.wildcards.region, product=snakemake.wildcards.product, config=snakemake.config, + local_demand_path=local_demand_path, ) # Save outputs @@ -742,6 +1261,23 @@ def __init__(self): json.dump(audit_info, f, indent=2, default=str) logger.info(f"Audit info saved to {snakemake.output.audit}") + # Save incremental generator sets + incremental_sets_serializable = { + int(demand_mt): [ + { + "bus_id": gen["bus_id"], + "technology": gen["technology"], + "p_nom_max": float(gen["p_nom_max"]), + "avg_cf": float(gen["avg_cf"]), + } + for gen in gen_list + ] + for demand_mt, gen_list in incremental_sets.items() + } + with open(snakemake.output.incremental_sets, "w") as f: + json.dump(incremental_sets_serializable, f, indent=2) + logger.info(f"Incremental sets saved to {snakemake.output.incremental_sets}") + except Exception as e: logger.error(f"Network preparation failed: {e}", exc_info=True) raise From ad9804124b8cedfb2a94681fc8a8a0c2a6fab27c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 22 Apr 2026 14:11:54 +0200 Subject: [PATCH 045/216] chore: integrate refactoring in snakemake rules --- workflow/Snakefile | 214 +++++++++++++++++++++++++-------------------- 1 file changed, 117 insertions(+), 97 deletions(-) diff --git a/workflow/Snakefile b/workflow/Snakefile index 0f68169..4a21db3 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -11,6 +11,7 @@ trade_scenarios = Paramspace(pd.read_csv("../config/trade_scenarios.csv", dtype= configfile: "../config/config.yaml" + # ---------------------------------------------------------------------------------- # SECTION 0: Workflow metadata and execution hints # - origin: README workflow description @@ -19,10 +20,12 @@ configfile: "../config/config.yaml" # - This file orchestrates greenfield supply curve generation, interregional trade modelling, and figure collection. # ---------------------------------------------------------------------------------- + wildcard_constraints: country="[a-zA-Z]+", sweep="[a-zA-Z]+", - rule="(0|[1-9][0-9]?|100)" + rule="(0|[1-9][0-9]?|100)", + # ---------------------------------------------------------------------------------- # SECTION 1: Greenfield supply curve generation (PyPSA) @@ -42,18 +45,20 @@ wildcard_constraints: # - scripts/create_supply_curve.py # ---------------------------------------------------------------------------------- + rule retrieve_cost_data: - params: - version=config['costs']['version'], output: costs="../resources/technology_data/costs_{cost_year}.csv", - resources: - mem_mb=1000, retries: 2 threads: 2 + resources: + mem_mb=1000, + params: + version=config["costs"]["version"], script: "scripts/tech_database.py" + # Build steel supply chain technology skeleton rule build_steel_skeleton: input: @@ -62,99 +67,102 @@ rule build_steel_skeleton: # [DELETION FLAG] Post-development: can regenerate in <1 min if needed # Keep during development for checkpoint validation skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + threads: 1 resources: mem_mb=2000, - threads: 1 script: "scripts/build_x_supply_chain.py" # Prepare regional PyPSA network with renewable generators and product-specific cutoff -rule prepare_regional_network: - message: - "Preparing regional network for {wildcards.region} → {wildcards.product} " - "(cost_year={wildcards.cost_year})" - +rule prepare_regional_network: input: skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", clusters_timeseries="../data/renewable_clusters.nc", - costs="../resources/technology_data/costs_{cost_year}.csv" - + costs="../resources/technology_data/costs_{cost_year}.csv", output: # KEEP: Good checkpoint, medium regen cost (~2-3 min per region) base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", - audit="../resources/networks/.audit_{cost_year}_{region}_{product}.json" - - params: - config=config - + audit="../resources/networks/.audit_{cost_year}_{region}_{product}.json", + incremental_sets="../resources/networks/.incremental_sets_{cost_year}_{region}_{product}.json", + log: + "../logs/prepare_regional_network_{cost_year}_{region}_{product}.log", threads: 2 - resources: - mem_mb=4000 - - log: - "../logs/prepare_regional_network_{cost_year}_{region}_{product}.log" - + mem_mb=4000, + params: + config=config, + message: + "Preparing regional network for {wildcards.region} → {wildcards.product} " + "(cost_year={wildcards.cost_year})" script: "scripts/prepare_regional_network.py" if config["enable"].get("run_supply_chain", True): + rule calculate_regional_lcox: - message: - "Calculating LCoX for {wildcards.product} in region {wildcards.region} " - "(steel_demand={wildcards.steel_demand_mt} Mt/year)." - params: - steel_demand_mt="{steel_demand_mt}", - compute_iis=config.get("solver", {}).get("compute_iis", False), input: base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", - local_demand="../data/un_enerdata_demand_2050_final.csv" + local_demand="../data/un_enerdata_demand_2050_final.csv", + incremental_sets="../resources/networks/.incremental_sets_{cost_year}_{region}_{product}.json", output: - results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{steel_demand_mt}.csv", - network="../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{steel_demand_mt}.nc", + results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{product_demand_mt}.csv", + network="../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc", wildcard_constraints: - steel_demand_mt=r"\d+" + product_demand_mt=r"\d+", threads: 4 resources: mem_mb=8000, + params: + product_demand_mt="{product_demand_mt}", + compute_iis=config.get("solver", {}).get("compute_iis", False), + message: + "Calculating LCoX for {wildcards.product} in region {wildcards.region} " + "(product_demand={wildcards.product_demand_mt} Mt/year)." script: "scripts/calculate_lcox.py" - + # Read all the individual LCoX values for one region and combine them into a supply curve # stored as a single csv file per region. Note: the visual plot includes iron ore costs, the csv without since it is added later in the workflow. if config["enable"].get("run_supply_curve", True): + rule create_supply_curve: - message: - "Combining LCo{wildcards.product[0]} results (all steel demand levels) to create supply curve for {wildcards.region}." input: - # Reference results from calculate_regional_lcox (uses steel_demand_mt from config) - lco_product_data = lambda wildcards: expand( - f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{steel_demand_mt}}.csv", - steel_demand_mt=config.get("steel_demand_levels") + # Reference results from calculate_regional_lcox (uses product_demand_mt from config) + lco_product_data=lambda wildcards: expand( + f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{product_demand_mt}}.csv", + product_demand_mt=config.get("steel_demand_levels"), ), - local_demand = "../data/un_enerdata_demand_2050_final.csv", - steel_demand = "../resources/steel_production_clustered.csv", + local_demand="../data/un_enerdata_demand_2050_final.csv", + steel_demand="../resources/steel_production_clustered.csv", output: - supply = "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", + supply="../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", # [DELETION FLAG] Post-development: supply_nodemand CSVs are reference copies for comparison only # Keep during development for validation/debugging - supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv", + supply_nodemand="../resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv", # KEEP: PDF plots low disk cost, high validation value - supply_curve = "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", + supply_curve="../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", threads: 2 + message: + "Combining LCo{wildcards.product[0]} results (all product demand levels) to create supply curve for {wildcards.region}." script: "scripts/create_supply_curve.py" + rule create_all_supply_curves: input: expand( "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - cost_year=[2030,2050], region=config["regions"], product=["steel"], allow_missing=True - ) #cost_year=[2030,2050], region=config["regions"], product=["steel", "hydrogen"] + cost_year=[2030, 2050], + region=config["regions"], + product=[ + "steel" + ], # TODO: product=["steel", "hydrogen"] - add hbi/h2 products once tested + allow_missing=True, + ), # ---------------------------------------------------------------------------------- @@ -164,51 +172,66 @@ rule create_all_supply_curves: # including optional cost scenario params from `config["trade"]` + `config["design"]`. # ---------------------------------------------------------------------------------- + rule model_trade: - params: - iron_ore_potential=config["iron_ore"]["potential_allowance"], - cost_penalty=config["design"]["cost_penalty"], - scenarios=config["scenario"], input: - supply_curves_interone = expand( + supply_curves_interone=expand( "../resources/supply_curves/cost_year~{cost_year}/{region}_{interone}.csv", - allow_missing=True, region=config["regions"]), - supply_curves_intertwo = expand( + allow_missing=True, + region=config["regions"], + ), + supply_curves_intertwo=expand( "../resources/supply_curves/cost_year~{cost_year}/{region}_{intertwo}.csv", - allow_missing=True, region=config["regions"]), + allow_missing=True, + region=config["regions"], + ), # supply_curves_final = expand( # "../resources/supply_curves/cost_year~{cost_year}/{region}_{final}.csv", # allow_missing=True, region=config["regions"]), - transport_costs = "../data/transport_costs/steel_r_iron_r.csv", - trade_options = "../data/trade_opt.csv", - bus_locations = "../data/bus_locations.csv", - demand = "../data/un_enerdata_demand_2050_final.csv", - steel_demand = "../resources/steel_production_clustered.csv", - iron_ore = "../resources/ironore_production_clustered.csv", - grid_potential = "../data/grid_potential_custom.csv", + transport_costs="../data/transport_costs/steel_r_iron_r.csv", + trade_options="../data/trade_opt.csv", + bus_locations="../data/bus_locations.csv", + demand="../data/un_enerdata_demand_2050_final.csv", + steel_demand="../resources/steel_production_clustered.csv", + iron_ore="../resources/ironore_production_clustered.csv", + grid_potential="../data/grid_potential_custom.csv", output: - trade_result = f"../results/{trade_scenarios.wildcard_pattern}/result.csv", - trade_network = f"../results/{trade_scenarios.wildcard_pattern}/network.nc", - trade_plot_ironore = f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.pdf", - trade_plot_ironore_png = f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.png", - trade_plot_hbi = f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.pdf", - trade_plot_hbi_png = f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.png", - trade_plot_steel = f"../results/{trade_scenarios.wildcard_pattern}/map_steel.pdf", - trade_plot_steel_png = f"../results/{trade_scenarios.wildcard_pattern}/map_steel.png", + trade_result=f"../results/{trade_scenarios.wildcard_pattern}/result.csv", + trade_network=f"../results/{trade_scenarios.wildcard_pattern}/network.nc", + trade_plot_ironore=f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.pdf", + trade_plot_ironore_png=f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.png", + trade_plot_hbi=f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.pdf", + trade_plot_hbi_png=f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.png", + trade_plot_steel=f"../results/{trade_scenarios.wildcard_pattern}/map_steel.pdf", + trade_plot_steel_png=f"../results/{trade_scenarios.wildcard_pattern}/map_steel.png", threads: 4 + params: + iron_ore_potential=config["iron_ore"]["potential_allowance"], + cost_penalty=config["design"]["cost_penalty"], + scenarios=config["scenario"], script: "scripts/model_trade.py" - - # Povide a rule which triggers creation of all scenarios listed (= rows) in 'scenarios/trade_scenarios.csv' rule model_trade_all: input: - networks=expand("../results/{scenarios}/network.nc", scenarios=trade_scenarios.instance_patterns), - results=expand("../results/{scenarios}/result.csv", scenarios=trade_scenarios.instance_patterns), - trade_plot_ironore=expand("../results/{scenarios}/map_ironore.pdf", scenarios=trade_scenarios.instance_patterns), - trade_plot_steel=expand("../results/{scenarios}/map_steel.pdf", scenarios=trade_scenarios.instance_patterns), + networks=expand( + "../results/{scenarios}/network.nc", + scenarios=trade_scenarios.instance_patterns, + ), + results=expand( + "../results/{scenarios}/result.csv", + scenarios=trade_scenarios.instance_patterns, + ), + trade_plot_ironore=expand( + "../results/{scenarios}/map_ironore.pdf", + scenarios=trade_scenarios.instance_patterns, + ), + trade_plot_steel=expand( + "../results/{scenarios}/map_steel.pdf", + scenarios=trade_scenarios.instance_patterns, + ), # ---------------------------------------------------------------------------------- @@ -217,22 +240,24 @@ rule model_trade_all: # This is a final aggregation stage and depends on outputs from earlier modelling / notebooks. # ---------------------------------------------------------------------------------- + rule collect_figures: input: - global_supply_curve = "../results/figures_general/{scenario}/global_supply_curve.pdf", #workflow/notebooks/analysis-coststructure.ipynb - global_supply_curve_png = "../results/figures_general/{scenario}/global_supply_curve.png", #workflow/notebooks/analysis-coststructure.ipynb - electricity_demand = "../results/figures_general/electricity_demand.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_png = "../results/figures_general/electricity_demand.png", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_steel = "../results/figures_general/electricity_demand_in_steel.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_steel_png = "../results/figures_general/electricity_demand_in_steel.png", #workflow/notebooks/analysis-electricity-demand.ipynb - global_map_countries = "../results/figures_general/global_map_countries.pdf", #workflow/notebooks/plot_countries.ipynb - global_map_countries_png = "../results/figures_general/global_map_countries.png", #workflow/notebooks/plot_countries.ipynb - cost_comparison = "../results/figures_general/comparison/cost_comparison.pdf", #workflow/notebooks/compare-scenarios.ipynb - cost_comparison_png = "../results/figures_general/comparison/cost_comparison.png", #workflow/notebooks/compare-scenarios.ipynb - value_chain_comparison = "../results/figures_general/value_chain_comparison.pdf", #workflow/notebooks/analyse-steel-hbi-split.ipynb - value_chain_comparison_png = "../results/figures_general/value_chain_comparison.png", #workflow/notebooks/analyse-steel-hbi-split.ipynb - hourly_analysis = "../results/figures_general/hourly_analysis.pdf", #workflow/notebooks/analysis-hourly.ipynb - hourly_analysis_png = "../results/figures_general/hourly_analysis.png", #workflow/notebooks/analysis-hourly.ipynb + global_supply_curve="../results/figures_general/{scenario}/global_supply_curve.pdf", #workflow/notebooks/analysis-coststructure.ipynb + global_supply_curve_png="../results/figures_general/{scenario}/global_supply_curve.png", #workflow/notebooks/analysis-coststructure.ipynb + electricity_demand="../results/figures_general/electricity_demand.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb + electricity_demand_png="../results/figures_general/electricity_demand.png", #workflow/notebooks/analysis-electricity-demand.ipynb + electricity_demand_steel="../results/figures_general/electricity_demand_in_steel.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb + electricity_demand_steel_png="../results/figures_general/electricity_demand_in_steel.png", #workflow/notebooks/analysis-electricity-demand.ipynb + global_map_countries="../results/figures_general/global_map_countries.pdf", #workflow/notebooks/plot_countries.ipynb + global_map_countries_png="../results/figures_general/global_map_countries.png", #workflow/notebooks/plot_countries.ipynb + cost_comparison="../results/figures_general/comparison/cost_comparison.pdf", #workflow/notebooks/compare-scenarios.ipynb + cost_comparison_png="../results/figures_general/comparison/cost_comparison.png", #workflow/notebooks/compare-scenarios.ipynb + value_chain_comparison="../results/figures_general/value_chain_comparison.pdf", #workflow/notebooks/analyse-steel-hbi-split.ipynb + value_chain_comparison_png="../results/figures_general/value_chain_comparison.png", #workflow/notebooks/analyse-steel-hbi-split.ipynb + hourly_analysis="../results/figures_general/hourly_analysis.pdf", #workflow/notebooks/analysis-hourly.ipynb + hourly_analysis_png="../results/figures_general/hourly_analysis.png", #workflow/notebooks/analysis-hourly.ipynb + # rule model_trade_singlestage: # input: @@ -250,18 +275,13 @@ rule collect_figures: # trade_plot = f"../results/{trade_scenarios.wildcard_pattern}/plot.pdf" # script: # "scripts/model_trade_singlestage.py" - - ### Toolkit - # Under development # rule input_cost_comp: -# message: +# message: # "Comparing input costs" - # notebook: # "workflow/notebooks/input-cost-comp.ipynb" - # ---------------------------------------------------------------------------------- # RUNNING THE WORKFLOW (developer convenience) # Use e.g.: From a1c7712ba0180dfa1a1f094a599615d589c54615 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 27 Apr 2026 13:28:05 +0200 Subject: [PATCH 046/216] fix: change carrier naming to include technology as commented in PR Co-authored-by: Copilot --- workflow/scripts/build_x_supply_chain.py | 6 +++ workflow/scripts/prepare_regional_network.py | 45 +++++++++++++++++--- 2 files changed, 44 insertions(+), 7 deletions(-) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 7510297..4537d62 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -57,6 +57,9 @@ def _add_carriers(network: pypsa.Network) -> None: "iron_ore": "Iron ore (mass)", "hbi": "Hot Briquetted Iron (mass)", "steel": "Steel (mass)", + "electrolysis": "Electrolysis process", + "direct_reduction_furnace": "Direct reduction furnace", + "electric_arc_furnace": "Electric arc furnace", } for carrier_name, description in carriers.items(): network.add("Carrier", carrier_name) @@ -93,6 +96,7 @@ def _add_conversion_chain( "electrolyzer", bus0="electricity", bus1="hydrogen", + carrier="electrolysis", efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), overnight_cost=elec_inv_cost, # EUR/kW → EUR/MW lifetime=td.get_tech_param(elec_params, "lifetime", 40.0), @@ -113,6 +117,7 @@ def _add_conversion_chain( bus1="hbi", bus2="hydrogen", bus3="electricity", + carrier="direct_reduction_furnace", efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), @@ -134,6 +139,7 @@ def _add_conversion_chain( bus0="hbi", bus1="steel", bus2="electricity", + carrier="electric_arc_furnace", efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), overnight_cost=eaf_inv_cost, diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 8e7fe7b..212298f 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -396,7 +396,7 @@ def filter_renewable_generators_by_potential( product_elec_per_t = back_propagate_electricity_need(tech_costs, product, config) # Target capacity (MW) for product production - max_product_elec_mwh = max_product_demand_mt * product_elec_per_t + max_product_elec_mwh = max_product_demand_mt * 1e6 * product_elec_per_t max_product_elec_mw = max_product_elec_mwh / (365 * 24) # Multiplier from config (default 5) @@ -619,7 +619,7 @@ def extract_incremental_generator_sets( # Calculate target MW for each demand level demand_targets = {} # demand_mt -> target_mw for demand_mt in product_demand_levels: - elec_mwh = demand_mt * elec_per_t + elec_mwh = demand_mt * 1e6 * elec_per_t elec_mw = elec_mwh / (365 * 24) target_mw = multiplier * elec_mw demand_targets[demand_mt] = target_mw @@ -649,6 +649,21 @@ def extract_incremental_generator_sets( return incremental_sets +def _serialize_dispatch_list(generators: List[Dict]) -> List[Dict]: + """Return JSON-safe generator list metadata in selection order.""" + serialized = [] + for gen in generators: + serialized.append( + { + "bus_id": str(gen["bus_id"]), + "technology": str(gen["technology"]), + "p_nom_max_mw": float(gen["p_nom_max"]), + "avg_cf": float(gen["avg_cf"]), + } + ) + return serialized + + # ============================================================================ # RENEWABLE GENERATOR ADDITION # ============================================================================ @@ -690,9 +705,11 @@ def add_renewable_generators( "solar": "solar-utility", } - # Ensure electricity carrier is defined (all renewables produce electricity) - if "electricity" not in network.carriers.index: - network.add("Carrier", "electricity") + # Ensure carriers are defined before adding renewable generators. + _ensure_carriers( + network, + ["electricity"] + [str(t) for t in dataset.technology.values], + ) # Use the network's discount_rate (which is set regionally in prepare_network) discount_rate = network.discount_rate @@ -744,7 +761,7 @@ def add_renewable_generators( "Generator", gen_name, bus="electricity", - carrier="electricity", + carrier=tech_str, p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) @@ -823,7 +840,7 @@ def add_renewable_generators( "Generator", gen_name, bus="electricity", - carrier="electricity", + carrier=tech_str, p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) @@ -905,6 +922,13 @@ def _apply_discount_rate_to_components( ) +def _ensure_carriers(network: pypsa.Network, carrier_names: List[str]) -> None: + """Add missing carriers to the network before components reference them.""" + for carrier_name in carrier_names: + if carrier_name not in network.carriers.index: + network.add("Carrier", carrier_name) + + def _consistency_check(network: pypsa.Network) -> None: """Sanitize and check network consistency. @@ -1162,6 +1186,13 @@ def prepare_network( "incremental_set_counts": { demand_mt: len(gen_list) for demand_mt, gen_list in incremental_sets.items() }, + "dispatch_order_full": _serialize_dispatch_list(selected_generators), + "dispatch_order_by_demand_mt": { + str(int(demand_mt)) + if float(demand_mt).is_integer() + else str(demand_mt): _serialize_dispatch_list(gen_list) + for demand_mt, gen_list in incremental_sets.items() + }, "network_stats": { "num_buses": len(network.buses), "num_links": len(network.links), From 8fdb0da2b5efd19a5a9bf3c5aaa54fe5336f520b Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 27 Apr 2026 13:29:21 +0200 Subject: [PATCH 047/216] chore: change renewable profile input to new structure --- workflow/Snakefile | 3 ++- workflow/scripts/calculate_lcox.py | 12 ++++++------ 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/workflow/Snakefile b/workflow/Snakefile index 4a21db3..b8312b1 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -78,8 +78,9 @@ rule build_steel_skeleton: rule prepare_regional_network: input: skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", - clusters_timeseries="../data/renewable_clusters.nc", + renewable_nc="../data/renewable_profiles/renewable_profiles_africa__20260417_155313.nc", costs="../resources/technology_data/costs_{cost_year}.csv", + local_demand="../data/un_enerdata_demand_2050_final.csv", output: # KEEP: Good checkpoint, medium regen cost (~2-3 min per region) base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index d22939f..aab92be 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -199,13 +199,13 @@ def add_loads_to_network(network, product, demands): if product == "steel": bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["product_demand_mt"] * 1000 / 8760 # Mt/year → t/h + hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h unit_str = "t/h" elif product == "hbi": bus_name = "hbi" # HBI is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["product_demand_mt"] * 1000 / 8760 # Mt/year → t/h + hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h unit_str = "t/h" elif product == "h2": @@ -219,7 +219,7 @@ def add_loads_to_network(network, product, demands): elif product in ["eaf", "eaf-grid"]: bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["product_demand_mt"] * 1000 / 8760 # Mt/year → t/h + hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h unit_str = "t/h" else: @@ -521,7 +521,7 @@ def extract_lcox(network, product, demands): if obj_value is None or np.isnan(obj_value): raise ValueError("Optimization failed to return valid objective") - demand_annual_t = demands["product_demand_mt"] * 1000 # Mt → t + demand_annual_t = demands["product_demand_mt"] * 1e6 # Mt → t hourly_load_t = demand_annual_t / 8760 lcox = obj_value / demand_annual_t if demand_annual_t > 0 else np.inf @@ -537,7 +537,7 @@ def extract_lcox(network, product, demands): except Exception as e: logger.error(f"Optimization infeasible or failed: {e}") - demand_annual_t = demands["product_demand_mt"] * 1000 + demand_annual_t = demands["product_demand_mt"] * 1e6 hourly_load_t = demand_annual_t / 8760 results_df.loc[0] = [ demand_annual_t, @@ -620,7 +620,7 @@ def extract_lcox(network, product, demands): # Calculate electricity needed for this demand level scaled_product_demand_mwh_per_h = ( - product_demand_mt * electricity_per_product_t / 8760 + product_demand_mt * 1e6 * electricity_per_product_t / 8760 ) logger.info(f"Product demand: {product_demand_mt:.1f} Mt/year") From 35381bea1a9354ed9a150ced0be2c86e92e66189 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 27 Apr 2026 13:29:43 +0200 Subject: [PATCH 048/216] chore:update readme to better align with workflow --- README.md | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index e306898..67156ff 100644 --- a/README.md +++ b/README.md @@ -65,22 +65,30 @@ pixi run snakemake -call collect_figures ## Workflow overview -### Step 0: Renewable potentials (Atlite + GIS) +### Configuration & Scenario Setup (implicit) + +Before execution, Snakemake reads: +- **Global settings**: `config/config.yaml` (regions, cost years, solver options, enable flags) +- **Scenario matrix**: `config/trade_scenarios.csv` (rows = distinct trade scenarios) + +These expand into a deterministic wildcard space (cost_year, region, product, scenario) that drives all downstream rule creation. This bootstrap is handled automatically by Snakemake; no user action required. + +### Step 0: Renewable potentials (pre-computed inputs) In this stage we generate the supply-side resource backbone. -PyPSA-Earth assembles spatial inputs (country polygons, exclusion masks, weather datasets) and computes hourly capacity-factor series and maximum deployable potentials for wind and solar in each region. -The workflow uses the [`build_renewable_profiles`](https://pypsa-earth.readthedocs.io/en/latest/user-guide/rules-reference/populate/build-renewable-profiles/) Snakefile rule, and it can produce .nc outputs for per-region, per-technology capacity factor distributions and installable potentials. +Renewable capacity-factor series and maximum deployable potentials are pre-computed externally using PyPSA-Earth's `build_renewable_profiles` rule and stored in `data/renewable_profiles/`. +This stage is not part of the current Snakefile. SHIFT consumes pre-computed .nc datasets to avoid the long runtime of full GIS processing. + +> **For new users:** No action needed. Renewable data files are provided in the repository. -> **Note:** SHIFT may consume precomputed Step 0 datasets to avoid the long runtime of full GIS processing; this is the recommended default for day-to-day scenario work. -> ### Step 1: Greenfield supply curve generation (PyPSA) With renewable profiles and [techno-economic assumptions](https://github.com/PyPSA/technology-data) in place, SHIFT builds regional PyPSA optimization models to size generation, storage, and process assets. It evaluates each candidate plant (H2 electrolyser, DRI furnace, HBI plant, steel mills) across resource quality and cost parameters to produce levelized cost curves (LCOX) as a function of capacity. The result is a fleet of supply curve elements (capacity buckets with marginal costs and metadata) for H2, DRI, HBI, and steel by region. -Each greenfield run schemes the spot around: location selection, renewable share, process stack, cost adders, and available build option integration. -The output is a harmonized set of offer curves used as input for the trade stage. +Step 1 is a multi-part stage: load techno-economic data and build regional cost baselines, prepare renewable candidate sets per region, solve optimization problems at discrete demand levels, and consolidate results into piecewise supply curves. Each stage depends on the prior; files are persisted between steps to support reproducibility and debugging. +The output is a harmonized set of supply curves used as input for the trade stage. ### Step 2: Global trade optimization (LP) From 92928320bfbc390ff507d756f5d2ac6acc50f884 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 27 Apr 2026 13:30:43 +0200 Subject: [PATCH 049/216] chore: change config file to real data case --- config/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 4dc8c95..5f83293 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -20,9 +20,9 @@ run: # Region definitions (ISO3 codes matching renewable clusters metadata) regions: - "Test_1": ["NLD"] - "Test_2": ["PRT"] - "Test_3": ["IRL"] + "Africa_West": ["SEN", "GMB", "GIN", "SLE"] # Senegal, Gambia, Guinea, Sierra Leone + "Africa_Central": ["GNQ", "GAB", "CMR", "CAF"] # Equatorial Guinea, Gabon, Cameroon, Central African Republic + "Africa_East": ["KEN", "UGA", "RWA", "ETH"] # Kenya, Uganda, Rwanda, Ethiopia # Renewable technologies to include in filtering # Available: ["solar", "onwind", "offwind-ac"] From a344f7bdfb34beba4e008b4a3591ad73b047810e Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 27 Apr 2026 13:39:14 +0200 Subject: [PATCH 050/216] feat: add simple tests to verify the workflow based on a easy to understand mock case --- pixi.toml | 6 +-- tests/conftest.py | 65 +++++++++++++++++++++++++++ tests/test_minimum_viable_workflow.py | 53 ++++++++++++++++++++++ 3 files changed, 121 insertions(+), 3 deletions(-) create mode 100644 tests/conftest.py create mode 100644 tests/test_minimum_viable_workflow.py diff --git a/pixi.toml b/pixi.toml index 230968b..cf87542 100644 --- a/pixi.toml +++ b/pixi.toml @@ -56,6 +56,9 @@ gurobipy = "*" pytest = "*" pytest-cov = "*" +[feature.test.tasks] +unit-tests = "pytest tests" + [feature.dev.dependencies] ruff = "*" pre-commit = "*" @@ -78,6 +81,3 @@ exclude = ["**/*.ipynb"] [tool.ruff.lint] select = ["E", "F", "W", "I"] # Basic style, logical and import checks - -[tool.pytest.ini_options] -testpaths = ["tests"] diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..06e1fe0 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,65 @@ +from pathlib import Path +import sys + +import pandas as pd +import pytest + + +REPO_ROOT = Path(__file__).resolve().parents[1] +WORKFLOW_SCRIPTS = REPO_ROOT / "workflow" / "scripts" + +for path in (REPO_ROOT, WORKFLOW_SCRIPTS): + path_str = str(path) + if path_str not in sys.path: + sys.path.insert(0, path_str) + + +@pytest.fixture +def steel_tech_costs() -> pd.Series: + """Synthetic tech-cost data that mirrors the steel electricity chain.""" + rows = [ + ("Alkaline electrolyzer large size", "electricity-input", 1.38), + ("hydrogen direct iron reduction furnace", "hydrogen-input", 2.1), + ("hydrogen direct iron reduction furnace", "electricity-input", 1.03), + ("electric arc furnace", "electricity-input", 0.6395), + ] + + index = pd.MultiIndex.from_tuples( + [(technology, parameter) for technology, parameter, _ in rows], + names=["technology", "parameter"], + ) + values = [value for _, _, value in rows] + return pd.Series(values, index=index, dtype=float) + + +@pytest.fixture +def steel_minimal_case() -> dict: + """Hand-checkable supply-stage numbers for a 1 t/h steel test case.""" + demand_mt_per_year = 0.00876 + hourly_demand_tph = demand_mt_per_year * 1e6 / 8760 + + return { + "product": "steel", + "demand_mt_per_year": demand_mt_per_year, + "hourly_demand_tph": hourly_demand_tph, + "annual_demand_t": hourly_demand_tph * 8760, + "expected_steel_electricity_mwh_per_t": 4.6275, + "expected_annual_electricity_mwh": hourly_demand_tph * 8760 * 4.6275, + } + + +@pytest.fixture +def toy_trade_case() -> dict: + """Minimal trade-stage numbers that can be checked by hand.""" + return { + "offers": { + "A": 100.0, + "B": 150.0, + }, + "transport_costs": { + ("A", "B"): 10.0, + ("B", "B"): 0.0, + }, + "expected_choice": "A", + "expected_delivered_cost": 110.0, + } diff --git a/tests/test_minimum_viable_workflow.py b/tests/test_minimum_viable_workflow.py new file mode 100644 index 0000000..45878b1 --- /dev/null +++ b/tests/test_minimum_viable_workflow.py @@ -0,0 +1,53 @@ +import pytest + +from workflow.scripts.prepare_regional_network import back_propagate_electricity_need + + +def test_supply_stage_electricity_chain_matches_hand_calculation( + steel_tech_costs, steel_minimal_case +): + electricity_per_t = back_propagate_electricity_need( + steel_tech_costs, + "steel", + {}, + ) + + assert electricity_per_t == pytest.approx( + steel_minimal_case["expected_steel_electricity_mwh_per_t"] + ) + + +def test_supply_stage_uses_1_t_per_hour_and_is_easy_to_verify( + steel_minimal_case, + steel_tech_costs, +): + hourly_demand_tph = steel_minimal_case["demand_mt_per_year"] * 1e6 / 8760 + + assert hourly_demand_tph == pytest.approx(1.0) + assert steel_minimal_case["annual_demand_t"] == pytest.approx(8760.0) + + electricity_per_t = back_propagate_electricity_need( + steel_tech_costs, + steel_minimal_case["product"], + {}, + ) + + annual_electricity_mwh = hourly_demand_tph * 8760 * electricity_per_t + + assert annual_electricity_mwh == pytest.approx( + steel_minimal_case["expected_annual_electricity_mwh"] + ) + + +def test_trade_stage_minimum_cost_route_is_obvious(toy_trade_case): + delivered_costs = { + origin: offer + toy_trade_case["transport_costs"][(origin, "B")] + for origin, offer in toy_trade_case["offers"].items() + } + + chosen_origin = min(delivered_costs, key=delivered_costs.get) + + assert chosen_origin == toy_trade_case["expected_choice"] + assert delivered_costs[chosen_origin] == pytest.approx( + toy_trade_case["expected_delivered_cost"] + ) From 0f5a2c16b9642bac8f6e047195d8668d0ad52c02 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 28 Apr 2026 15:20:30 +0200 Subject: [PATCH 051/216] feat: isolate EAF grid supply --- workflow/scripts/build_x_supply_chain.py | 48 ++++++++++++++++---- workflow/scripts/prepare_regional_network.py | 47 ++++++++++++------- 2 files changed, 68 insertions(+), 27 deletions(-) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 4537d62..911bc2a 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -1,8 +1,7 @@ -""" -Build PyPSA supply chain skeleton for commodity X using technology database. +"""Build PyPSA supply chain skeleton for commodity X using technology database. Generic conversion pathway structure (currently configured for steel): - Electricity → Electrolyzer → H2 → DRI → HBI → EAF → Commodity Output + Electricity → Electrolyzer → H2 → DRI → HBI → EAF → Commodity Output Module provides functions to construct a PyPSA energy system network representing a decarbonized production supply chain. The network includes: @@ -51,7 +50,7 @@ def _add_carriers(network: pypsa.Network) -> None: PyPSA requires explicit Carrier components before buses/generators can reference them. """ carriers = { - "electricity": "AC electricity", + "renewable_electricity": "Islanded renewable electricity", "hydrogen": "Hydrogen gas", "battery_elec": "Battery (electrical energy)", "iron_ore": "Iron ore (mass)", @@ -60,6 +59,7 @@ def _add_carriers(network: pypsa.Network) -> None: "electrolysis": "Electrolysis process", "direct_reduction_furnace": "Direct reduction furnace", "electric_arc_furnace": "Electric arc furnace", + "grid_electricity": "Grid electricity import", } for carrier_name, description in carriers.items(): network.add("Carrier", carrier_name) @@ -68,7 +68,8 @@ def _add_carriers(network: pypsa.Network) -> None: def _add_buses(network: pypsa.Network) -> None: """Add energy carrier buses.""" buses = { - "electricity": {"carrier": "electricity", "unit": "MW"}, + "renewable_electricity": {"carrier": "renewable_electricity", "unit": "MW"}, + "grid_electricity": {"carrier": "grid_electricity", "unit": "MW"}, "hydrogen": {"carrier": "hydrogen", "unit": "MW"}, "battery": {"carrier": "battery_elec", "unit": "MWh"}, "iron_ore": {"carrier": "iron_ore", "unit": "t/h"}, @@ -79,6 +80,27 @@ def _add_buses(network: pypsa.Network) -> None: network.add("Bus", name, **attrs) +def _add_grid_electricity_supply(network: pypsa.Network, config: dict) -> None: + """Add a grid import generator for the EAF when requested. + + The default topology uses grid-connected EAF power. When config sets + `eaf_electricity_source` to anything other than `grid`, this helper is a no-op + and the EAF remains connected to the local electricity bus. + """ + + if config.get("eaf_electricity_source", "grid") != "grid": + return + + network.add( + "Generator", + "grid_electricity_import", + bus="grid_electricity", + carrier="grid_electricity", + p_nom=1e10, + marginal_cost=config.get("grid_electricity_price", 75.0), + ) + + def _add_conversion_chain( network: pypsa.Network, tech_costs: pd.Series, config: dict ) -> None: @@ -94,7 +116,7 @@ def _add_conversion_chain( network.add( "Link", "electrolyzer", - bus0="electricity", + bus0="renewable_electricity", bus1="hydrogen", carrier="electrolysis", efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), @@ -116,7 +138,7 @@ def _add_conversion_chain( bus0="iron_ore", bus1="hbi", bus2="hydrogen", - bus3="electricity", + bus3="renewable_electricity", carrier="direct_reduction_furnace", efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), @@ -131,6 +153,11 @@ def _add_conversion_chain( # EAF: HBI + Electricity → Steel eaf_params = td.get_tech(tech_costs, "electric arc furnace") + eaf_bus2 = ( + "grid_electricity" + if config.get("eaf_electricity_source", "grid") == "grid" + else "renewable_electricity" + ) eaf_inv_cost = td.get_tech_param(eaf_params, "investment", 2312992.7323) network.add( @@ -138,7 +165,7 @@ def _add_conversion_chain( "eaf", bus0="hbi", bus1="steel", - bus2="electricity", + bus2=eaf_bus2, carrier="electric_arc_furnace", efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), @@ -183,7 +210,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> network.add( "Link", "batt_charge", - bus0="electricity", + bus0="renewable_electricity", bus1="battery", efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), overnight_cost=batt_inv_cost, # EUR/kW → EUR/MW @@ -197,7 +224,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> "Link", "batt_discharge", bus0="battery", - bus1="electricity", + bus1="renewable_electricity", efficiency=np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)), overnight_cost=batt_inv_cost, lifetime=td.get_tech_param(batt_inv_params, "lifetime", 10.0), @@ -265,6 +292,7 @@ def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Networ # Add network components (carriers MUST be added before buses that reference them) _add_carriers(network) _add_buses(network) + _add_grid_electricity_supply(network, config) _add_conversion_chain(network, tech_costs, config) _add_storage(network, tech_costs, config) _add_resources(network, config) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 212298f..c48276d 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -186,10 +186,10 @@ def filter_by_technologies(dataset: xr.Dataset, config: dict) -> xr.Dataset: def back_propagate_electricity_need( tech_costs: pd.Series, product: str, config: dict ) -> float: - """Calculate electricity requirement (MWh) per tonne of product. + """Calculate renewable electricity requirement (MWh) per tonne of product. Back-propagates through supply chain efficiency chain: - - steel (t): Electrolyzer(elec) + DRI(elec + H2) + EAF(elec) + - steel (t): Electrolyzer(elec) + DRI(elec + H2) + optional EAF(elec) - hbi (t): Electrolyzer(elec) + DRI(elec + H2) - h2 (t): Electrolyzer(elec) only @@ -200,12 +200,14 @@ def back_propagate_electricity_need( product : str Product: "steel", "hbi", or "h2" config : dict - Config dict with optional overrides: "electricity_per_tonne_{product}_mwh" + Config dict with optional overrides: + - "electricity_per_tonne_{product}_mwh" + - "eaf_electricity_source" ("grid" by default, "renewable" to include EAF electricity) Returns ------- float - Electricity requirement in MWh per tonne of product + Renewable electricity requirement in MWh per tonne of product """ # Check for config override first override_key = f"electricity_per_tonne_{product}_mwh" @@ -262,13 +264,24 @@ def back_propagate_electricity_need( ) logger.debug(f"EAF electricity input: {eaf_elec_per_t_steel:.4f} MWh/t Steel") - # Steel: H2 production + DRI electricity + EAF electricity - elec_need = h2_elec_per_t_hbi + dri_elec_per_t_hbi + eaf_elec_per_t_steel - logger.info( - f"Back-propagated electricity for Steel: {elec_need:.4f} MWh/t Steel " - f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f}, " - f"EAF: {eaf_elec_per_t_steel:.4f})" - ) + eaf_source = config.get("eaf_electricity_source", "grid") + + # Steel renewable electricity: H2 production + DRI electricity. + # If the EAF is configured to run on renewables, include its electricity too. + elec_need = h2_elec_per_t_hbi + dri_elec_per_t_hbi + if eaf_source == "renewable": + elec_need += eaf_elec_per_t_steel + logger.info( + f"Back-propagated renewable electricity for Steel: {elec_need:.4f} MWh/t Steel " + f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f}, " + f"EAF: {eaf_elec_per_t_steel:.4f})" + ) + else: + logger.info( + f"Back-propagated renewable electricity for Steel: {elec_need:.4f} MWh/t Steel " + f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f}; " + f"EAF electricity is grid-supplied)" + ) return elec_need raise ValueError( @@ -392,7 +405,7 @@ def filter_renewable_generators_by_potential( logger.info("TWO-STEP RENEWABLE FILTERING") logger.info("=" * 70) - # Compute product electricity need per tonne + # Compute product renewable electricity need per tonne product_elec_per_t = back_propagate_electricity_need(tech_costs, product, config) # Target capacity (MW) for product production @@ -405,7 +418,7 @@ def filter_renewable_generators_by_potential( logger.info(f"{product.upper()} demand: {max_product_demand_mt:.1f} Mt") logger.info( - f"{product.upper()} electricity need: {product_elec_per_t:.4f} MWh/t → {max_product_elec_mw:.1f} MW average" + f"{product.upper()} renewable electricity need: {product_elec_per_t:.4f} MWh/t → {max_product_elec_mw:.1f} MW average" ) logger.info( f"Target renewable capacity ({multiplier}×): {target_capacity_mw:.1f} MW" @@ -612,7 +625,7 @@ def extract_incremental_generator_sets( logger.info("EXTRACTING INCREMENTAL GENERATOR SETS") logger.info("=" * 70) - # Compute electricity need for this product + # Compute renewable electricity need for this product elec_per_t = back_propagate_electricity_need(tech_costs, product, config) multiplier = config.get("renewable_coverage_multiplier", 5) @@ -708,7 +721,7 @@ def add_renewable_generators( # Ensure carriers are defined before adding renewable generators. _ensure_carriers( network, - ["electricity"] + [str(t) for t in dataset.technology.values], + ["renewable_electricity"] + [str(t) for t in dataset.technology.values], ) # Use the network's discount_rate (which is set regionally in prepare_network) @@ -760,7 +773,7 @@ def add_renewable_generators( network.add( "Generator", gen_name, - bus="electricity", + bus="renewable_electricity", carrier=tech_str, p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide @@ -839,7 +852,7 @@ def add_renewable_generators( network.add( "Generator", gen_name, - bus="electricity", + bus="renewable_electricity", carrier=tech_str, p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide From 61ff23320596e07bce9074e6acf15f720541407c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 28 Apr 2026 15:21:03 +0200 Subject: [PATCH 052/216] chore: rename electricity demand wording --- workflow/scripts/calculate_lcox.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index aab92be..5b0606e 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -618,14 +618,14 @@ def extract_lcox(network, product, demands): # Preserve discount_rate from base network (needed for cost annuitization) network.discount_rate = base_network.discount_rate - # Calculate electricity needed for this demand level + # Calculate renewable electricity needed for this demand level scaled_product_demand_mwh_per_h = ( product_demand_mt * 1e6 * electricity_per_product_t / 8760 ) logger.info(f"Product demand: {product_demand_mt:.1f} Mt/year") logger.info( - f"Electricity required: {scaled_product_demand_mwh_per_h * 8760:.1f} MWh/year" + f"Renewable electricity required: {scaled_product_demand_mwh_per_h * 8760:.1f} MWh/year" ) # Create scaled demands dict for this demand level From 594e9de34a33c6681bece4d6f307c1c6c191a70a Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 28 Apr 2026 15:22:43 +0200 Subject: [PATCH 053/216] test: rework supply workflow coverage --- tests/conftest.py | 141 ++++++- tests/test_minimum_viable_workflow.py | 53 --- tests/test_supply_curve_smoke.py | 108 +++++ tests/test_supply_math.py | 70 ++++ tests/test_supply_network_optimization.py | 486 ++++++++++++++++++++++ 5 files changed, 791 insertions(+), 67 deletions(-) delete mode 100644 tests/test_minimum_viable_workflow.py create mode 100644 tests/test_supply_curve_smoke.py create mode 100644 tests/test_supply_math.py create mode 100644 tests/test_supply_network_optimization.py diff --git a/tests/conftest.py b/tests/conftest.py index 06e1fe0..fb1c2d3 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,8 +1,10 @@ from pathlib import Path import sys +import numpy as np import pandas as pd import pytest +import xarray as xr REPO_ROOT = Path(__file__).resolve().parents[1] @@ -38,28 +40,139 @@ def steel_minimal_case() -> dict: demand_mt_per_year = 0.00876 hourly_demand_tph = demand_mt_per_year * 1e6 / 8760 + renewable_electricity_mwh_per_t = 2.1 * 1.38 + 1.03 + total_electricity_mwh_per_t = renewable_electricity_mwh_per_t + 0.6395 + return { "product": "steel", "demand_mt_per_year": demand_mt_per_year, "hourly_demand_tph": hourly_demand_tph, "annual_demand_t": hourly_demand_tph * 8760, - "expected_steel_electricity_mwh_per_t": 4.6275, - "expected_annual_electricity_mwh": hourly_demand_tph * 8760 * 4.6275, + "expected_steel_renewable_electricity_mwh_per_t": renewable_electricity_mwh_per_t, + "expected_steel_total_electricity_mwh_per_t": total_electricity_mwh_per_t, + "expected_annual_renewable_electricity_mwh": hourly_demand_tph + * 8760 + * renewable_electricity_mwh_per_t, + "expected_annual_total_electricity_mwh": hourly_demand_tph + * 8760 + * total_electricity_mwh_per_t, } @pytest.fixture -def toy_trade_case() -> dict: - """Minimal trade-stage numbers that can be checked by hand.""" - return { - "offers": { - "A": 100.0, - "B": 150.0, +def round_number_tech_costs() -> pd.Series: + """Minimal renewable tech costs with easy round numbers for tests. + + Values are chosen so conversion in add_renewable_generators remains simple: + overnight_cost [EUR/MW] = investment [EUR/kW] * 1000. + """ + rows = [ + ("solar-utility", "investment", 0.10), + ("solar-utility", "lifetime", 20.0), + ("solar-utility", "FOM", 10.0), + ("onwind", "investment", 0.20), + ("onwind", "lifetime", 25.0), + ("onwind", "FOM", 5.0), + ] + + index = pd.MultiIndex.from_tuples( + [(technology, parameter) for technology, parameter, _ in rows], + names=["technology", "parameter"], + ) + values = [value for _, _, value in rows] + return pd.Series(values, index=index, dtype=float) + + +@pytest.fixture +def full_chain_tech_costs() -> pd.Series: + """Synthetic full-chain tech database for electrolysis->DRI->EAF tests. + + Includes all parameters used by build_x_supply_chain helpers and + add_renewable_generators renewable cost lookup. + """ + rows = [ + # Conversion chain + ("Alkaline electrolyzer large size", "investment", 0.10), + ("Alkaline electrolyzer large size", "lifetime", 20.0), + ("Alkaline electrolyzer large size", "FOM", 2.0), + ("Alkaline electrolyzer large size", "electricity-input", 1.38), + ("hydrogen direct iron reduction furnace", "investment", 100.0), + ("hydrogen direct iron reduction furnace", "lifetime", 25.0), + ("hydrogen direct iron reduction furnace", "FOM", 5.0), + ("hydrogen direct iron reduction furnace", "ore-input", 1.59), + ("hydrogen direct iron reduction furnace", "hydrogen-input", 2.1), + ("hydrogen direct iron reduction furnace", "electricity-input", 1.03), + ("electric arc furnace", "investment", 50.0), + ("electric arc furnace", "lifetime", 25.0), + ("electric arc furnace", "FOM", 5.0), + ("electric arc furnace", "hbi-input", 1.0), + ("electric arc furnace", "electricity-input", 0.6395), + # Storage and battery + ("hydrogen storage underground", "investment", 0.01), + ("hydrogen storage underground", "lifetime", 30.0), + ("hydrogen storage underground", "FOM", 0.0), + ("battery inverter", "investment", 0.05), + ("battery inverter", "lifetime", 15.0), + ("battery inverter", "FOM", 1.0), + ("battery inverter", "efficiency", 0.96), + ("battery storage", "investment", 0.05), + ("battery storage", "lifetime", 20.0), + # Renewable technologies used by add_renewable_generators + ("solar-utility", "investment", 0.10), + ("solar-utility", "lifetime", 20.0), + ("solar-utility", "FOM", 10.0), + ("onwind", "investment", 0.20), + ("onwind", "lifetime", 25.0), + ("onwind", "FOM", 5.0), + ] + + index = pd.MultiIndex.from_tuples( + [(technology, parameter) for technology, parameter, _ in rows], + names=["technology", "parameter"], + ) + values = [value for _, _, value in rows] + return pd.Series(values, index=index, dtype=float) + + +@pytest.fixture +def renewable_region_timeseries_fixture() -> xr.Dataset: + """Toy regional renewable profile for optimization tests. + + Contains two valid generator candidates: + 1. Solar candidate with constant CF = 0.5 + 2. Wind candidate with CF = 1.0 for first half and 0.0 for second half + + Other bus-technology combinations are NaN and should be ignored. + """ + n_hours = 24 + buses = ["TST_SOL_01", "TST_WND_01"] + technologies = ["solar", "onwind"] + hours = np.arange(n_hours) + + capacity_factor = np.full((len(buses), len(technologies), n_hours), np.nan) + p_nom_max = np.full((len(buses), len(technologies)), np.nan) + avg_cf = np.full((len(buses), len(technologies)), np.nan) + + # Candidate 1: constant CF 0.5 + capacity_factor[0, 0, :] = 0.5 + p_nom_max[0, 0] = 1e6 + avg_cf[0, 0] = 0.5 + + # Candidate 2: half 1.0, half 0.0 + capacity_factor[1, 1, : n_hours // 2] = 1.0 + capacity_factor[1, 1, n_hours // 2 :] = 0.0 + p_nom_max[1, 1] = 1e6 + avg_cf[1, 1] = 0.5 + + return xr.Dataset( + data_vars={ + "capacity_factor": (("bus", "technology", "hour"), capacity_factor), + "p_nom_max": (("bus", "technology"), p_nom_max), + "avg_cf": (("bus", "technology"), avg_cf), }, - "transport_costs": { - ("A", "B"): 10.0, - ("B", "B"): 0.0, + coords={ + "bus": buses, + "technology": technologies, + "hour": hours, }, - "expected_choice": "A", - "expected_delivered_cost": 110.0, - } + ) diff --git a/tests/test_minimum_viable_workflow.py b/tests/test_minimum_viable_workflow.py deleted file mode 100644 index 45878b1..0000000 --- a/tests/test_minimum_viable_workflow.py +++ /dev/null @@ -1,53 +0,0 @@ -import pytest - -from workflow.scripts.prepare_regional_network import back_propagate_electricity_need - - -def test_supply_stage_electricity_chain_matches_hand_calculation( - steel_tech_costs, steel_minimal_case -): - electricity_per_t = back_propagate_electricity_need( - steel_tech_costs, - "steel", - {}, - ) - - assert electricity_per_t == pytest.approx( - steel_minimal_case["expected_steel_electricity_mwh_per_t"] - ) - - -def test_supply_stage_uses_1_t_per_hour_and_is_easy_to_verify( - steel_minimal_case, - steel_tech_costs, -): - hourly_demand_tph = steel_minimal_case["demand_mt_per_year"] * 1e6 / 8760 - - assert hourly_demand_tph == pytest.approx(1.0) - assert steel_minimal_case["annual_demand_t"] == pytest.approx(8760.0) - - electricity_per_t = back_propagate_electricity_need( - steel_tech_costs, - steel_minimal_case["product"], - {}, - ) - - annual_electricity_mwh = hourly_demand_tph * 8760 * electricity_per_t - - assert annual_electricity_mwh == pytest.approx( - steel_minimal_case["expected_annual_electricity_mwh"] - ) - - -def test_trade_stage_minimum_cost_route_is_obvious(toy_trade_case): - delivered_costs = { - origin: offer + toy_trade_case["transport_costs"][(origin, "B")] - for origin, offer in toy_trade_case["offers"].items() - } - - chosen_origin = min(delivered_costs, key=delivered_costs.get) - - assert chosen_origin == toy_trade_case["expected_choice"] - assert delivered_costs[chosen_origin] == pytest.approx( - toy_trade_case["expected_delivered_cost"] - ) diff --git a/tests/test_supply_curve_smoke.py b/tests/test_supply_curve_smoke.py new file mode 100644 index 0000000..cc65da4 --- /dev/null +++ b/tests/test_supply_curve_smoke.py @@ -0,0 +1,108 @@ +from types import SimpleNamespace + +import pandas as pd + +import workflow.scripts.create_supply_curve as create_supply_curve_module + + +def test_supply_curve_smoke(tmp_path, monkeypatch): + lco_file_1 = tmp_path / "results_10.csv" + lco_file_2 = tmp_path / "results_20.csv" + local_demand_file = tmp_path / "local_demand.csv" + steel_demand_file = tmp_path / "steel_demand.csv" + supply_file = tmp_path / "supply.csv" + supply_nodemand_file = tmp_path / "supply_nodemand.csv" + supply_curve_file = tmp_path / "supply_curve.pdf" + + pd.DataFrame( + [ + { + "demand [t]": 10.0, + "load [t/h]": 1.0, + "cost [EUR]": 1000.0, + "lcox [EUR/t]": 100.0, + } + ] + ).to_csv(lco_file_1, index=False) + + pd.DataFrame( + [ + { + "demand [t]": 20.0, + "load [t/h]": 2.0, + "cost [EUR]": 2000.0, + "lcox [EUR/t]": "infeasible", + } + ] + ).to_csv(lco_file_2, index=False) + + pd.DataFrame( + [ + { + "region": "TST", + "demand": 0.0, + "unit": "MWh", + "el_share": 0.0, + " note": "none", + } + ] + ).to_csv(local_demand_file, index=False) + pd.DataFrame([{"region": "TST", "SteelProductionMt": 0.0}]).to_csv( + steel_demand_file, index=False + ) + + monkeypatch.setattr( + create_supply_curve_module, + "snakemake", + SimpleNamespace( + input=SimpleNamespace( + lco_product_data=[str(lco_file_1), str(lco_file_2)], + local_demand=str(local_demand_file), + steel_demand=str(steel_demand_file), + ), + output=SimpleNamespace( + supply=str(supply_file), + supply_nodemand=str(supply_nodemand_file), + supply_curve=str(supply_curve_file), + ), + wildcards={"region": "TST", "product": "steel"}, + config={ + "electricity_steel_ratio": 1.0, + "iron_ore": {"marginal_cost": 0.0, "ore_to_steel_ratio": 0.0}, + }, + ), + raising=False, + ) + + monkeypatch.setattr( + create_supply_curve_module, + "product", + "steel", + raising=False, + ) + monkeypatch.setattr( + create_supply_curve_module, + "columns", + { + "demand factor": "demand factor [%]", + "demand": "demand [t]", + "load": "load [t/h]", + "total cost": "cost [EUR]", + "cost per unit": "lcox [EUR/t]", + "xlabel": "Demand in Mt", + "product_unit": "t", + "ylim": (0, 900), + }, + raising=False, + ) + + create_supply_curve_module.create_supply_curve() + + assert supply_file.exists() + assert supply_nodemand_file.exists() + assert supply_curve_file.exists() + + output_df = pd.read_csv(supply_file) + assert len(output_df) == 1 + assert float(output_df.iloc[0]["demand [t]"]) == 10.0 + assert float(output_df.iloc[0]["lcox [EUR/t]"]) == 100.0 diff --git a/tests/test_supply_math.py b/tests/test_supply_math.py new file mode 100644 index 0000000..2d8032c --- /dev/null +++ b/tests/test_supply_math.py @@ -0,0 +1,70 @@ +import pytest + +from workflow.scripts.prepare_regional_network import ( + back_propagate_electricity_need, + filter_by_technologies, +) + + +def test_supply_stage_electricity_chain_matches_hand_calculation( + steel_tech_costs, steel_minimal_case +): + renewable_electricity_per_t = back_propagate_electricity_need( + steel_tech_costs, + "steel", + {"eaf_electricity_source": "grid"}, + ) + + assert renewable_electricity_per_t == pytest.approx( + steel_minimal_case["expected_steel_renewable_electricity_mwh_per_t"] + ) + + +def test_supply_stage_includes_eaf_electricity_when_requested( + steel_tech_costs, steel_minimal_case +): + renewable_electricity_per_t = back_propagate_electricity_need( + steel_tech_costs, + "steel", + {"eaf_electricity_source": "renewable"}, + ) + + assert renewable_electricity_per_t == pytest.approx( + steel_minimal_case["expected_steel_total_electricity_mwh_per_t"] + ) + + +def test_supply_stage_uses_1_t_per_hour_and_is_easy_to_verify( + steel_minimal_case, + steel_tech_costs, +): + hourly_demand_tph = steel_minimal_case["demand_mt_per_year"] * 1e6 / 8760 + + assert hourly_demand_tph == pytest.approx(1.0) + assert steel_minimal_case["annual_demand_t"] == pytest.approx(8760.0) + + renewable_electricity_per_t = back_propagate_electricity_need( + steel_tech_costs, + steel_minimal_case["product"], + {"eaf_electricity_source": "grid"}, + ) + + annual_renewable_electricity_mwh = ( + hourly_demand_tph * 8760 * renewable_electricity_per_t + ) + + assert annual_renewable_electricity_mwh == pytest.approx( + steel_minimal_case["expected_annual_renewable_electricity_mwh"] + ) + + +def test_supply_stage_keeps_only_allowed_renewable_technologies( + renewable_region_timeseries_fixture, +): + filtered = filter_by_technologies( + renewable_region_timeseries_fixture, + {"renewable_technologies": ["solar", "onwind"]}, + ) + + assert list(filtered.technology.values) == ["solar", "onwind"] + assert "offwind" not in set(str(tech) for tech in filtered.technology.values) diff --git a/tests/test_supply_network_optimization.py b/tests/test_supply_network_optimization.py new file mode 100644 index 0000000..eb2faa2 --- /dev/null +++ b/tests/test_supply_network_optimization.py @@ -0,0 +1,486 @@ +import numpy as np +import pandas as pd +import pypsa +import pytest +import xarray as xr + +import workflow.scripts.tech_database as td +from workflow.scripts.build_x_supply_chain import ( + _add_buses, + _add_carriers, + _add_conversion_chain, + _add_grid_electricity_supply, + _add_resources, + _add_storage, +) +from workflow.scripts.calculate_lcox import _convert_arrow_strings +from workflow.scripts.prepare_regional_network import add_renewable_generators + + +def _build_selected_generators(dataset): + """Convert xarray fixture into selected_generators input format.""" + generators = [] + + for bus_id in dataset.bus.values: + for tech in dataset.technology.values: + p_nom_max = float( + dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values + ) + avg_cf = float(dataset["avg_cf"].sel(bus=bus_id, technology=tech).values) + cf_ts = dataset["capacity_factor"].sel(bus=bus_id, technology=tech).values + + if np.isnan(p_nom_max) or p_nom_max <= 0 or np.isnan(avg_cf) or avg_cf <= 0: + continue + + generators.append( + { + "bus_id": str(bus_id), + "technology": str(tech), + "p_nom_max": p_nom_max, + "avg_cf": avg_cf, + "capacity_factor_ts": np.asarray(cf_ts, dtype=float), + } + ) + + return generators + + +def _build_low_capacity_dataset(dataset, p_nom_max=0.01): + """Return a copy of the renewable dataset with very low generator capacity caps.""" + low_capacity = dataset.copy(deep=True) + low_capacity["p_nom_max"] = xr.zeros_like(low_capacity["p_nom_max"]) + p_nom_max + return low_capacity + + +def _build_full_chain_reference_network(dataset, tech_costs, config): + """Manual full-chain network: carriers, buses, links, stores, and renewables.""" + snapshots = pd.date_range("2030-01-01", periods=24, freq="h") + + network = pypsa.Network() + network.set_snapshots(snapshots) + network.discount_rate = 0.05 + + # Carriers used by full chain + renewable technologies + for carrier in [ + "renewable_electricity", + "grid_electricity", + "hydrogen", + "battery_elec", + "iron_ore", + "hbi", + "steel", + "electrolysis", + "direct_reduction_furnace", + "electric_arc_furnace", + "solar", + "onwind", + ]: + network.add("Carrier", carrier) + + # Buses + network.add( + "Bus", "renewable_electricity", carrier="renewable_electricity", unit="MW" + ) + network.add("Bus", "grid_electricity", carrier="grid_electricity", unit="MW") + network.add("Bus", "hydrogen", carrier="hydrogen", unit="MW") + network.add("Bus", "battery", carrier="battery_elec", unit="MWh") + network.add("Bus", "iron_ore", carrier="iron_ore", unit="t/h") + network.add("Bus", "hbi", carrier="hbi", unit="t/h") + network.add("Bus", "steel", carrier="steel", unit="t/h") + + # Conversion links + elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") + elec_inv_cost = td.get_tech_param(elec_params, "investment", 0.10) * 1000 + network.add( + "Link", + "electrolyzer", + bus0="renewable_electricity", + bus1="hydrogen", + carrier="electrolysis", + efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), + overnight_cost=elec_inv_cost, + lifetime=td.get_tech_param(elec_params, "lifetime", 20.0), + fom_cost=elec_inv_cost * (td.get_tech_param(elec_params, "FOM", 2.0) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + p_min_pu=config.get("elec_p_min_pu", 0.10), + ) + + dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") + dri_inv_cost = td.get_tech_param(dri_params, "investment", 100.0) + network.add( + "Link", + "dri", + bus0="iron_ore", + bus1="hbi", + bus2="hydrogen", + bus3="renewable_electricity", + carrier="direct_reduction_furnace", + efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), + efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), + efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), + overnight_cost=dri_inv_cost, + lifetime=td.get_tech_param(dri_params, "lifetime", 25.0), + fom_cost=dri_inv_cost * (td.get_tech_param(dri_params, "FOM", 5.0) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + p_min_pu=config.get("dri_p_min_pu", 0.15), + ) + + eaf_params = td.get_tech(tech_costs, "electric arc furnace") + eaf_inv_cost = td.get_tech_param(eaf_params, "investment", 50.0) + network.add( + "Link", + "eaf", + bus0="hbi", + bus1="steel", + bus2="grid_electricity", + carrier="electric_arc_furnace", + efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), + efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), + overnight_cost=eaf_inv_cost, + lifetime=td.get_tech_param(eaf_params, "lifetime", 25.0), + fom_cost=eaf_inv_cost * (td.get_tech_param(eaf_params, "FOM", 5.0) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + p_min_pu=config.get("eaf_p_min_pu", 0.20), + ) + + # Storage and battery links + h2_params = td.get_tech(tech_costs, "hydrogen storage underground") + h2_inv_cost = td.get_tech_param(h2_params, "investment", 0.01) * 1000 + network.add( + "Store", + "h2_storage", + bus="hydrogen", + e_nom_extendable=True, + overnight_cost=h2_inv_cost, + lifetime=td.get_tech_param(h2_params, "lifetime", 30.0), + fom_cost=h2_inv_cost * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), + standing_loss=0.001, + e_initial=config.get("h2_storage_e_initial", 0.5), + e_cyclic=True, + ) + + batt_inv_params = td.get_tech(tech_costs, "battery inverter") + batt_store_params = td.get_tech(tech_costs, "battery storage") + batt_inv_cost = td.get_tech_param(batt_inv_params, "investment", 0.05) * 1000 + batt_eff = np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)) + + network.add( + "Link", + "batt_charge", + bus0="renewable_electricity", + bus1="battery", + efficiency=batt_eff, + overnight_cost=batt_inv_cost, + lifetime=td.get_tech_param(batt_inv_params, "lifetime", 15.0), + fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 1.0) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + ) + + network.add( + "Link", + "batt_discharge", + bus0="battery", + bus1="renewable_electricity", + efficiency=batt_eff, + overnight_cost=batt_inv_cost, + lifetime=td.get_tech_param(batt_inv_params, "lifetime", 15.0), + fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 1.0) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + ) + + batt_store_cost = td.get_tech_param(batt_store_params, "investment", 0.05) * 1000 + network.add( + "Store", + "battery", + bus="battery", + e_nom_extendable=True, + overnight_cost=batt_store_cost, + lifetime=td.get_tech_param(batt_store_params, "lifetime", 20.0), + fom_cost=0.0, + standing_loss=0.0001, + e_initial=config.get("battery_e_initial", 0.5), + e_cyclic=True, + ) + + network.add( + "Store", + "hbi_storage", + bus="hbi", + e_nom_extendable=True, + overnight_cost=0.0, + lifetime=1.0, + fom_cost=0.0, + discount_rate=0.0, + standing_loss=0.0, + ) + + # External resource + network.add("Generator", "iron_ore", bus="iron_ore", p_nom=1e10, marginal_cost=0.0) + + if config.get("eaf_electricity_source", "grid") == "grid": + network.add( + "Generator", + "grid_electricity_import", + bus="grid_electricity", + carrier="grid_electricity", + p_nom=1e10, + marginal_cost=config.get("grid_electricity_price", 75.0), + ) + + # Renewable generators + selected_generators = _build_selected_generators(dataset) + add_renewable_generators( + network=network, + dataset=dataset, + tech_costs=tech_costs, + config=config, + selected_generators=selected_generators, + ) + + return network + + +def _build_full_chain_function_network(dataset, tech_costs, config): + """Function-built full-chain network using workflow helper modules.""" + snapshots = pd.date_range("2030-01-01", periods=24, freq="h") + + network = pypsa.Network() + network.set_snapshots(snapshots) + network.discount_rate = 0.05 + + _add_carriers(network) + _add_buses(network) + _add_grid_electricity_supply(network, config) + _add_conversion_chain(network, tech_costs, config) + _add_storage(network, tech_costs, config) + _add_resources(network, config) + + selected_generators = _build_selected_generators(dataset) + add_renewable_generators( + network=network, + dataset=dataset, + tech_costs=tech_costs, + config=config, + selected_generators=selected_generators, + ) + + return network + + +def _apply_capital_cost_from_overnight_cost(network): + """Ensure optimization objective uses overnight costs deterministically.""" + for component_name in ["generators", "links", "stores"]: + component = getattr(network, component_name) + if "overnight_cost" in component.columns: + for name, row in component.iterrows(): + if pd.notna(row.get("overnight_cost", np.nan)): + component.at[name, "capital_cost"] = float(row["overnight_cost"]) + if "discount_rate" in component.columns and pd.isna( + row.get("discount_rate", np.nan) + ): + component.at[name, "discount_rate"] = float( + getattr(network, "discount_rate", 0.0) + ) + + +def _solve_or_skip(network): + _convert_arrow_strings(network) + try: + network.optimize( + network.snapshots, + solver_name="highs", + include_objective_constant=False, + ) + except Exception as exc: + pytest.skip(f"Optimization solver unavailable in current environment: {exc}") + + +def _solve_network(network): + _convert_arrow_strings(network) + return network.optimize( + network.snapshots, + solver_name="highs", + include_objective_constant=False, + ) + + +def test_function_built_full_chain_matches_manual_reference( + renewable_region_timeseries_fixture, + full_chain_tech_costs, +): + config = { + "eaf_electricity_source": "grid", + "grid_electricity_price": 75.0, + "elec_p_min_pu": 0.10, + "dri_p_min_pu": 0.15, + "eaf_p_min_pu": 0.20, + "h2_storage_e_initial": 0.5, + "battery_e_initial": 0.5, + } + + reference = _build_full_chain_reference_network( + renewable_region_timeseries_fixture, + full_chain_tech_costs, + config, + ) + built = _build_full_chain_function_network( + renewable_region_timeseries_fixture, + full_chain_tech_costs, + config, + ) + + assert set(reference.carriers.index) == set(built.carriers.index) + assert set(reference.buses.index) == set(built.buses.index) + assert set(reference.links.index) == set(built.links.index) + assert set(reference.stores.index) == set(built.stores.index) + assert set(reference.generators.index) == set(built.generators.index) + + assert "grid_electricity_import" in reference.generators.index + assert ( + reference.generators.at["grid_electricity_import", "carrier"] + == "grid_electricity" + ) + assert ( + built.generators.at["grid_electricity_import", "carrier"] == "grid_electricity" + ) + assert reference.links.at["eaf", "bus2"] == "grid_electricity" + assert built.links.at["eaf", "bus2"] == "grid_electricity" + + for link_name in ["electrolyzer", "dri", "eaf"]: + assert ( + built.links.at[link_name, "carrier"] + == reference.links.at[link_name, "carrier"] + ) + assert built.links.at[link_name, "efficiency"] == pytest.approx( + reference.links.at[link_name, "efficiency"] + ) + + +def test_full_chain_optimization_matches_manual_reference( + renewable_region_timeseries_fixture, + full_chain_tech_costs, +): + config = { + "eaf_electricity_source": "grid", + "grid_electricity_price": 75.0, + "elec_p_min_pu": 0.10, + "dri_p_min_pu": 0.15, + "eaf_p_min_pu": 0.20, + "h2_storage_e_initial": 0.5, + "battery_e_initial": 0.5, + } + + reference = _build_full_chain_reference_network( + renewable_region_timeseries_fixture, + full_chain_tech_costs, + config, + ) + built = _build_full_chain_function_network( + renewable_region_timeseries_fixture, + full_chain_tech_costs, + config, + ) + + for network in (reference, built): + network.add("Load", "steel_demand", bus="steel", p_set=1.0) + _apply_capital_cost_from_overnight_cost(network) + + _solve_or_skip(reference) + _solve_or_skip(built) + + # Compare key optimized capacities of the full chain and renewables. + for component, name in [ + ("links", "electrolyzer"), + ("links", "dri"), + ("links", "eaf"), + ("generators", "renewable_TST_SOL_01_solar"), + ("generators", "renewable_TST_WND_01_onwind"), + ]: + ref_value = float(getattr(reference, component).at[name, "p_nom_opt"]) + built_value = float(getattr(built, component).at[name, "p_nom_opt"]) + assert built_value == pytest.approx(ref_value, rel=1e-5, abs=1e-6) + + # Directional behavior check for expected renewable choice. + ref_solar = float( + reference.generators.at["renewable_TST_SOL_01_solar", "p_nom_opt"] + ) + ref_wind = float( + reference.generators.at["renewable_TST_WND_01_onwind", "p_nom_opt"] + ) + assert ref_solar > 0.0 + assert ref_solar > ref_wind + + +def test_full_chain_becomes_infeasible_when_renewables_are_too_small( + renewable_region_timeseries_fixture, + full_chain_tech_costs, +): + """If renewable caps are too low, the steel chain should not be solvable.""" + config = { + "eaf_electricity_source": "grid", + "grid_electricity_price": 75.0, + "elec_p_min_pu": 0.10, + "dri_p_min_pu": 0.15, + "eaf_p_min_pu": 0.20, + "h2_storage_e_initial": 0.5, + "battery_e_initial": 0.5, + } + + low_capacity_dataset = _build_low_capacity_dataset( + renewable_region_timeseries_fixture, + p_nom_max=0.01, + ) + + reference = _build_full_chain_reference_network( + low_capacity_dataset, + full_chain_tech_costs, + config, + ) + built = _build_full_chain_function_network( + low_capacity_dataset, + full_chain_tech_costs, + config, + ) + + for network in (reference, built): + network.add("Load", "steel_demand", bus="steel", p_set=1.0) + _apply_capital_cost_from_overnight_cost(network) + + reference_result = _solve_network(reference) + built_result = _solve_network(built) + + reference_status = " ".join( + str(part).lower() for part in np.atleast_1d(reference_result) + ) + built_status = " ".join(str(part).lower() for part in np.atleast_1d(built_result)) + + assert "infeas" in reference_status or reference.objective is None + assert "infeas" in built_status or built.objective is None + + +def test_full_chain_can_switch_eaf_to_renewable_electricity( + renewable_region_timeseries_fixture, + full_chain_tech_costs, +): + config = { + "eaf_electricity_source": "renewable", + "elec_p_min_pu": 0.10, + "dri_p_min_pu": 0.15, + "eaf_p_min_pu": 0.20, + "h2_storage_e_initial": 0.5, + "battery_e_initial": 0.5, + } + + built = _build_full_chain_function_network( + renewable_region_timeseries_fixture, + full_chain_tech_costs, + config, + ) + + assert "grid_electricity_import" not in built.generators.index + assert built.links.at["eaf", "bus2"] == "renewable_electricity" From 7d3e26f4a6811e50f18955ed5190018fba342923 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 28 Apr 2026 15:39:58 +0200 Subject: [PATCH 054/216] ci: use pixi test env and env-driven HiGHS solver override --- .github/workflows/ci.yml | 37 +++++++++------------ workflow/scripts/calculate_lcox.py | 8 +++-- workflow/scripts/model_trade.py | 7 ++-- workflow/scripts/model_trade_singlestage.py | 16 ++++----- 4 files changed, 32 insertions(+), 36 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a603886..e3ceb1b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,16 +1,9 @@ -name: Python 🐍 CI/CD tests +name: Python 🐍 CI tests on: - push: - branches: [main, develop] - paths-ignore: - - "**.md" - - "**.bib" - - "**.ya?ml" - - "LICENSE" - - ".gitignore" pull_request: - branches: [main, develop] + types: [closed] + branches: [main] paths-ignore: - "**.md" - "**.bib" @@ -20,27 +13,29 @@ on: workflow_dispatch: jobs: - build: + test: + if: github.event_name == 'workflow_dispatch' || github.event.pull_request.merged == true + env: + SHIFT_SOLVER: highs + SHIFT_SOLVER_OPTIONS: highs-default + SHIFT_COMPUTE_IIS: "false" runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: - os: [ubuntu-latest, windows-latest, macos-latest] - py-version: ["3.11", "3.12"] + os: [ubuntu-latest, windows-latest] steps: - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.py-version }} - - name: Install Pixi run: python -m pip install --upgrade pip pixi - - name: Install dependencies via Pixi - run: pixi install --environment default + - name: Install dependencies via Pixi (test env) + run: pixi install --environment test - name: Validate environment - run: pixi info + run: pixi info --environment test + + - name: Run tests + run: pixi run -e test unit-tests diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 5b0606e..94d5f46 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -434,10 +434,12 @@ def solve_network(network, config): # Convert arrow strings to regular strings before optimization _convert_arrow_strings(network) - solver_name = config.get("solver", {}).get("name", "glpk") - solver_options = config.get("solver_options", {}).get( - config.get("solver", {}).get("options", "default"), {} + solver_cfg = config.get("solver", {}) + solver_name = os.getenv("SHIFT_SOLVER", solver_cfg.get("name", "glpk")) + solver_options_key = os.getenv( + "SHIFT_SOLVER_OPTIONS", solver_cfg.get("options", "default") ) + solver_options = config.get("solver_options", {}).get(solver_options_key, {}) logger.info(f"Solving network with {solver_name}...") logger.info(f"Solver options: {solver_options}") diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 12f65eb..871f123 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -642,9 +642,10 @@ def normalize_regions(regions, carrier): def solve_network(n, mga=None): - - solver_name = snakemake.config["solver"]["name"] - options = snakemake.config["solver_options"][snakemake.config["solver"]["options"]] + solver_cfg = snakemake.config["solver"] + solver_name = os.getenv("SHIFT_SOLVER", solver_cfg["name"]) + options_key = os.getenv("SHIFT_SOLVER_OPTIONS", solver_cfg["options"]) + options = snakemake.config["solver_options"][options_key] n.optimize(n.snapshots, solver_name=solver_name, solver_options=options) diff --git a/workflow/scripts/model_trade_singlestage.py b/workflow/scripts/model_trade_singlestage.py index 4901f8c..22995b6 100644 --- a/workflow/scripts/model_trade_singlestage.py +++ b/workflow/scripts/model_trade_singlestage.py @@ -341,17 +341,15 @@ def plot_trade_network(n): # solving model print("solving model") + solver_cfg = snakemake.config["solver"] + solver_name = os.getenv("SHIFT_SOLVER", solver_cfg["name"]) + options_key = os.getenv("SHIFT_SOLVER_OPTIONS", solver_cfg["options"]) + solver_options = snakemake.config["solver_options"][options_key] + network.optimize( network.snapshots, - solver_name="gurobi", - solver_options={ - "crossover": 0, - "method": 2, - "BarConvTol": 1.0e-5, - "FeasibilityTol": 1.0e-5, - "OptimalityTol": 1.0e-5, - "barHomogeneous": 1, - }, + solver_name=solver_name, + solver_options=solver_options, ) print("network was solved") From df750ded0944d074d2feae4640bb85d823f6d0cf Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 6 May 2026 17:41:45 +0200 Subject: [PATCH 055/216] feat: process just trace renewables for easier workflow --- config/config.yaml | 16 +- data/renewable_clusters.nc | Bin 6395758 -> 0 bytes workflow/Snakefile | 68 +- workflow/scripts/calculate_lcox.py | 23 +- workflow/scripts/create_supply_curve.py | 111 +- workflow/scripts/prepare_regional_network.py | 1533 ++++------------- .../preprocess_consolidate_renewables.py | 246 +++ 7 files changed, 749 insertions(+), 1248 deletions(-) delete mode 100644 data/renewable_clusters.nc create mode 100644 workflow/scripts/preprocess_consolidate_renewables.py diff --git a/config/config.yaml b/config/config.yaml index 5f83293..050de83 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -3,6 +3,12 @@ enable: run_supply_curve: True # Enable for first run +# Output toggles for Step 0/1 (greenfield supply curve generation) +outputs: + save_supply_nodemand: True # Generate reference supply curve (all generators available) alongside main curve + keep_optimization_networks: False # Keep .nc network files from optimization (set False to save disk space) + + # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales @@ -20,14 +26,11 @@ run: # Region definitions (ISO3 codes matching renewable clusters metadata) regions: + "Europe": ["DEU", "FRA", "GBR", "ITA", "ESP", "POL", "NLD", "BEL", "SWE", "NOR"] "Africa_West": ["SEN", "GMB", "GIN", "SLE"] # Senegal, Gambia, Guinea, Sierra Leone "Africa_Central": ["GNQ", "GAB", "CMR", "CAF"] # Equatorial Guinea, Gabon, Cameroon, Central African Republic "Africa_East": ["KEN", "UGA", "RWA", "ETH"] # Kenya, Uganda, Rwanda, Ethiopia -# Renewable technologies to include in filtering -# Available: ["solar", "onwind", "offwind-ac"] -renewable_technologies: ["solar", "onwind"] - # countries: [ # "BI","KM","DJ","ER","ET","KE","MG","MW","MU","MZ","RW","SC","SO","SS","TZ","UG","ZM","ZW", # Africa — Eastern Africa # "AO","CM","CF","TD","CG","CD","GQ","GA","ST", # Africa — Middle Africa @@ -68,8 +71,9 @@ costs: interest_rate: default: 0.05 - "Test_1": 0.05 - "Test_2": 0.06 + # Regional discount rates (override default for specific regions): + # Europe: 0.04 + # Africa_West: 0.07 part_load: diff --git a/data/renewable_clusters.nc b/data/renewable_clusters.nc deleted file mode 100644 index e7bde9a35f6dbb7c98ef9c0e4234ae954972a9e5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6395758 zcmeFYd0dX&*Ef7tGBit+646A|6&lF!J<}kiL`6lTqB$y|M5aW9BorYe&GQ`1^E}U; z&2wiWCE`6Y-PhHR=ed9Hdq2;6|8einr}nYFdmsB)d+oi~UTf`rUR9D;WM<%Ipq)RT z^fP_^k!FrUH-$%<7OS|eDM#u7_sPw}3s)iT8=Wai823iu$OqIpU zNOOpcj)wHJ@!2_(!c09n5({bmOjQFlIdwbRW2SZv=BD1R&IF~jjv8X6KCfzJGW>j1$O{gRnSI*}RdTl(=Z(-hmcXA>*KZw2qZ{r`_% 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costs="../resources/technology_data/costs_{cost_year}.csv", output: - # [DELETION FLAG] Post-development: can regenerate in <1 min if needed - # Keep during development for checkpoint validation + # Reusable skeleton for all regional networks - shared across supply chains + # Keep as persistent resource for consistency when extending to multiple products skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", threads: 1 resources: - mem_mb=2000, + mem_mb=1000, script: "scripts/build_x_supply_chain.py" -# Prepare regional PyPSA network with renewable generators and product-specific cutoff +# Prepare regional PyPSA network with consolidated renewable generators +# INPUT: consolidated renewable profiles (region, technology, class, time) +# OUTPUT: PyPSA network with renewables + supply chain for target product rule prepare_regional_network: input: skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", - renewable_nc="../data/renewable_profiles/renewable_profiles_africa__20260417_155313.nc", - costs="../resources/technology_data/costs_{cost_year}.csv", - local_demand="../data/un_enerdata_demand_2050_final.csv", + renewables="../data/new_renewables_consolidated.nc", # Consolidated file: (region, technology, class, time) + tech_costs="../resources/technology_data/costs_{cost_year}.csv", output: - # KEEP: Good checkpoint, medium regen cost (~2-3 min per region) - base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", - audit="../resources/networks/.audit_{cost_year}_{region}_{product}.json", - incremental_sets="../resources/networks/.incremental_sets_{cost_year}_{region}_{product}.json", + network="../resources/networks/base_{cost_year}_{region}_{product}.nc", log: "../logs/prepare_regional_network_{cost_year}_{region}_{product}.log", - threads: 2 + threads: 1 resources: - mem_mb=4000, + mem_mb=2000, params: + region="{region}", + product="{product}", config=config, message: - "Preparing regional network for {wildcards.region} → {wildcards.product} " + "Preparing regional network: {wildcards.region} → {wildcards.product} " "(cost_year={wildcards.cost_year})" script: "scripts/prepare_regional_network.py" @@ -106,15 +105,22 @@ if config["enable"].get("run_supply_chain", True): input: base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", local_demand="../data/un_enerdata_demand_2050_final.csv", - incremental_sets="../resources/networks/.incremental_sets_{cost_year}_{region}_{product}.json", output: results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{product_demand_mt}.csv", - network="../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc", + network=( + temp( + "../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" + ) + if not config.get("outputs", {}).get( + "keep_optimization_networks", False + ) + else "../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" + ), wildcard_constraints: product_demand_mt=r"\d+", - threads: 4 + threads: 2 resources: - mem_mb=8000, + mem_mb=4000, params: product_demand_mt="{product_demand_mt}", compute_iis=config.get("solver", {}).get("compute_iis", False), @@ -141,12 +147,18 @@ if config["enable"].get("run_supply_curve", True): steel_demand="../resources/steel_production_clustered.csv", output: supply="../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - # [DELETION FLAG] Post-development: supply_nodemand CSVs are reference copies for comparison only - # Keep during development for validation/debugging - supply_nodemand="../resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv", - # KEEP: PDF plots low disk cost, high validation value + # Toggleable reference: supply curve with all generators available (no local demand constraint) + # Set save_supply_nodemand=True in config to generate for validation/comparison + supply_nodemand=( + "../resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv" + if config.get("outputs", {}).get("save_supply_nodemand", True) + else temp( + "../resources/supply_curves_nodemand_tmp/{region}_{product}.csv" + ) + ), + # PDF plots: low disk cost, high validation value - always generated supply_curve="../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", - threads: 2 + threads: 1 message: "Combining LCo{wildcards.product[0]} results (all product demand levels) to create supply curve for {wildcards.region}." script: @@ -157,7 +169,7 @@ rule create_all_supply_curves: input: expand( "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - cost_year=[2030, 2050], + cost_year=[2050], region=config["regions"], product=[ "steel" @@ -179,11 +191,13 @@ rule model_trade: supply_curves_interone=expand( "../resources/supply_curves/cost_year~{cost_year}/{region}_{interone}.csv", allow_missing=True, + cost_year=[2050], region=config["regions"], ), supply_curves_intertwo=expand( "../resources/supply_curves/cost_year~{cost_year}/{region}_{intertwo}.csv", allow_missing=True, + cost_year=[2050], region=config["regions"], ), # supply_curves_final = expand( diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 94d5f46..89fba7b 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -637,27 +637,8 @@ def extract_lcox(network, product, demands): # Load incremental generator sets and apply filtering logger.info("Loading incremental generator sets...") - try: - import json - - with open(snakemake.input.incremental_sets, "r") as f: - incremental_sets_raw = json.load(f) - # Convert keys from strings back to floats - incremental_sets = {float(k): v for k, v in incremental_sets_raw.items()} - logger.info( - f"Loaded incremental sets for demand levels: {list(incremental_sets.keys())}" - ) - except Exception as e: - logger.error(f"Failed to load incremental sets: {e}") - raise - - # Apply incremental generator selection (delete generators outside this demand level) - logger.info("Applying incremental generator selection...") - selection_info = apply_incremental_generator_selection( - network=network, - product_demand_mt=product_demand_mt, - incremental_sets=incremental_sets, - ) + # Skip incremental filtering: use all generators for all demand levels + logger.info("Using all generators (no incremental filtering applied)") # Add hourly load for steel output # (This also sets HBI storage e_initial inside add_loads_to_network) diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index cec56a9..f7737fb 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -1,3 +1,4 @@ +import os import pandas as pd import matplotlib import matplotlib.pyplot as plt @@ -79,8 +80,37 @@ def create_supply_curve(): print("local el load has been subtracted from global supply") # saves the merged costs in a supply curve csv - df_sub.to_csv(snakemake.output.supply) - df_merged.to_csv(snakemake.output.supply_nodemand) + # df_sub shows realistic scenario (after local demand reserves generators) + # df_merged is reference (all generators available) - optional based on config + df_sub.to_csv(snakemake.output.supply, index=False) + + # Only save supply_nodemand if output exists (check against toggle) + # If save_supply_nodemand=False in config, this file may be temp and auto-deleted. + # However, to avoid Snakemake MissingOutputException if the rule declared + # a concrete path but the config changed during runtime, ensure the + # declared output file exists by writing a fallback CSV here. + try: + nodemand_path = snakemake.output.supply_nodemand + except Exception: + nodemand_path = None + + if nodemand_path and str(nodemand_path).endswith(".csv"): + # write the reference (merged) supply curve to the declared path + df_merged.to_csv(nodemand_path, index=False) + print(f"Saved reference supply curve (all generators): {nodemand_path}") + else: + print("Skipping supply_nodemand output (save_supply_nodemand=False)") + + # If Snakemake declared a non-temp path but we didn't write it above + # for any reason, ensure it exists to prevent MissingOutputException. + if nodemand_path: + try: + if not os.path.exists(nodemand_path): + # write a minimal CSV fallback + df_merged.to_csv(nodemand_path, index=False) + print(f"Wrote fallback supply_nodemand file: {nodemand_path}") + except Exception: + pass # creates and saves supply curve plot if product in ["steel", "hbi"]: @@ -103,7 +133,7 @@ def create_supply_curve(): y_merged, linestyle="-", marker="o", - label="supply", + label="supply (all generators available)", ) # the subtracted plot @@ -114,12 +144,12 @@ def create_supply_curve(): color="C1", marker="o", markerfacecolor="none", - label="supply w. local el. demand subtracted", + label="supply (after local el. demand reserved)", ) plt.axvline( product_subtract / (1e6), - label="local energy demand for el.", + label="local electricity demand (converted to product)", linestyle="--", color="C1", ) @@ -156,41 +186,42 @@ def create_supply_curve(): return -if __name__ == "__main__": - if "snakemake" not in globals(): - from _helpers import mock_snakemake - - snakemake = mock_snakemake( - "create_supply_curve", - cost_year="2030", - region="South_South_America", - product="steel", - ) +# Setup columns and product before function execution (needed for both Snakemake and main) +if "snakemake" not in globals(): + from _helpers import mock_snakemake - product = snakemake.wildcards["product"] - if product == "hydrogen": - columns = { - "demand factor": "demand factor [%]", - "demand": "demand [t]", - "load": "load [MW]", - "total cost": "cost [EUR]", - "cost per unit": "lcox [EUR/MWh]", - "xlabel": "Demand in TWh", - "product_unit": "MWh", - "ylim": (0, 100), - } - elif product in ["steel", "eaf", "hbi", "eaf-grid"]: - columns = { - "demand factor": "demand factor [%]", - "demand": "demand [t]", - "load": "load [t/h]", - "total cost": "cost [EUR]", - "cost per unit": "lcox [EUR/t]", - "xlabel": "Demand in Mt", - "product_unit": "t", - "ylim": (0, 900), - } - else: - raise ValueError(f"product {product} not recognized for supply curve plotting") + snakemake = mock_snakemake( + "create_supply_curve", + cost_year="2030", + region="South_South_America", + product="steel", + ) +product = snakemake.wildcards["product"] +if product == "hydrogen": + columns = { + "demand factor": "demand factor [%]", + "demand": "demand [t]", + "load": "load [MW]", + "total cost": "cost [EUR]", + "cost per unit": "lcox [EUR/MWh]", + "xlabel": "Demand in TWh", + "product_unit": "MWh", + "ylim": (0, 100), + } +elif product in ["steel", "eaf", "hbi", "eaf-grid"]: + columns = { + "demand factor": "demand factor [%]", + "demand": "demand [t]", + "load": "load [t/h]", + "total cost": "cost [EUR]", + "cost per unit": "lcox [EUR/t]", + "xlabel": "Demand in Mt", + "product_unit": "t", + "ylim": (0, 900), + } +else: + raise ValueError(f"product {product} not recognized for supply curve plotting") + +if __name__ == "__main__": create_supply_curve() diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index c48276d..58947c0 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -1,1242 +1,497 @@ -"""Prepare regional network: add renewables and configure supply chain per product.""" +""" +Prepare regional network: load consolidated renewables and configure supply chain. + +This is the simplified Step 1 workflow that: +1. Loads the consolidated renewable profiles directly (region, technology, class, time) +2. Creates PyPSA generators for each technology and class +3. Applies local demand reservation if configured +4. Adds supply chain (electrolyzer, DRI, optional EAF) +5. Applies product-specific cutoff + +Usage (Snakemake rule): + rule prepare_regional_network: + input: + skeleton = "resources/networks/skeleton.nc", + renewables = "data/new_renewables_consolidated.nc", + tech_costs = "resources/tech_database.csv", + params: + region = "{region}", + product = "{product}", + output: + network = "resources/networks/base_{cost_year}_{region}_{product}.nc", + script: + "scripts/prepare_regional_network.py" +""" import logging -import pandas as pd +from typing import Dict, Tuple import numpy as np +import pandas as pd import xarray as xr import pypsa -import json -from typing import Dict, List, Tuple import tech_database as td logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - - -def region_to_iso3_codes(region: str, config: dict) -> List[str]: - """Get ISO3 codes for region from config.""" - iso3_list = config.get("regions", {}).get(region, []) - - if not iso3_list: - raise ValueError( - f"Region '{region}' not found in config['regions'] or contains no ISO3 codes. " - f"Available regions: {list(config.get('regions', {}).keys())}" - ) - - return iso3_list - - -# ============================================================================ -# RENEWABLE PROFILES LOADING (NEW FORMAT) -# ============================================================================ +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) -def load_renewable_profiles(nc_path: str) -> xr.Dataset: - """Load renewable profiles from new xarray format (single .nc file). - - Parameters - ---------- - nc_path : str - Path to renewable profiles netCDF file - - Returns - ------- - xr.Dataset - Dataset with dimensions [bus, technology, hour] and variables: - - capacity_factor[bus, tech, hour] - - p_nom_max[bus, tech] - - avg_cf[bus, tech] +def load_region_renewables_consolidated( + consolidated_path: str, + region: str, +) -> Tuple[Dict[str, np.ndarray], xr.DataArray, Dict]: """ - logger.info(f"Loading renewable profiles from {nc_path}...") - - try: - dataset = xr.open_dataset(nc_path) - except FileNotFoundError as e: - raise FileNotFoundError(f"Renewable profiles file not found: {nc_path}") from e - except Exception as e: - raise RuntimeError(f"Failed to load renewable profiles: {e}") from e - - # Validate dimensions - required_dims = {"bus", "technology", "hour"} - actual_dims = set(dataset.dims.keys()) - if not required_dims.issubset(actual_dims): - raise ValueError( - f"Dataset missing required dimensions. Required: {required_dims}, " - f"Found: {actual_dims}" - ) - - # Validate data variables - required_vars = {"capacity_factor", "p_nom_max", "avg_cf"} - actual_vars = set(dataset.data_vars.keys()) - if not required_vars.issubset(actual_vars): - raise ValueError( - f"Dataset missing required variables. Required: {required_vars}, " - f"Found: {actual_vars}" - ) - - logger.info( - f"✓ Loaded renewable profiles: {len(dataset.bus)} buses, " - f"{len(dataset.technology)} technologies, {len(dataset.hour)} hours" - ) - - return dataset - - -def filter_by_region(dataset: xr.Dataset, region: str, config: dict) -> xr.Dataset: - """Filter renewable profiles by region using ISO3 codes from bus_id. + Load renewable data for a region from consolidated NetCDF. Parameters ---------- - dataset : xr.Dataset - Unfiltered renewable profiles + consolidated_path : str + Path to data/new_renewables_consolidated.nc region : str - Region name (key in config["regions"]) - config : dict - Config dict with regions mapping + Region name (e.g., 'Europe', 'North_America') Returns ------- - xr.Dataset - Filtered dataset (subset of buses matching region's ISO3 codes) - """ - # Get ISO3 codes for this region - iso3_list = region_to_iso3_codes(region, config) - logger.info(f"Region '{region}' maps to ISO3 codes: {iso3_list}") - - # Parse ISO3 from bus_id strings (format: "{ISO3}_{other_identifiers}") - bus_ids = dataset.coords["bus"].values - bus_iso3_codes = [] - - for bus_id in bus_ids: - bus_id_str = str(bus_id) - # Extract ISO3 from bus_id (first component before underscore) - iso3 = bus_id_str.split("_")[0] - bus_iso3_codes.append(iso3) - - # Create mask for buses in this region - bus_mask = np.isin(bus_iso3_codes, iso3_list) - selected_buses = bus_ids[bus_mask] - - if len(selected_buses) == 0: - raise ValueError( - f"No buses found for region '{region}' with ISO3 {iso3_list}. " - f"Available ISO3 codes in data: {np.unique(bus_iso3_codes)}" - ) - - # Filter dataset to selected buses - filtered = dataset.sel(bus=selected_buses) + technologies_dict : dict + Mapping {tech_name: capacity_array} where each is (n_class,) - logger.info( - f"Filtered dataset to {len(selected_buses)} buses in region {region} " - f"(from {len(bus_ids)} total)" - ) - - return filtered + cf_ts : xr.DataArray + Capacity factor time series with dims (time, technology, class) + metadata : dict + Summary info (n_classes, n_time, technologies, etc.) + """ + ds = xr.open_dataset(consolidated_path) -def filter_by_technologies(dataset: xr.Dataset, config: dict) -> xr.Dataset: - """Filter renewable technologies based on config. + if region not in ds.region.values: + available = ", ".join(ds.region.values) + raise ValueError(f"Region '{region}' not found. Available: {available}") - Parameters - ---------- - dataset : xr.Dataset - Renewable profiles with all technologies - config : dict - Config dict with optional `renewable_technologies` list + logger.info(f"Loading consolidated renewables for {region}") - Returns - ------- - xr.Dataset - Filtered dataset with only specified technologies - """ - # Get allowed technologies from config, default to ["solar", "onwind"] - allowed_techs = config.get("renewable_technologies", ["solar", "onwind"]) - logger.info(f"Allowed renewable technologies: {allowed_techs}") + # Select region (dims: technology, class) + region_cap = ds["capacity"].sel(region=region) # (tech, class) + region_cf = ds["capacity_factor"].sel(region=region) # (tech, class, time) - # Get available technologies in dataset - available_techs = list(dataset.technology.values) - logger.info(f"Available technologies in dataset: {available_techs}") + # Extract technology names and data + techs = list(region_cap.technology.values) + technologies_dict = {} - # Find intersection of allowed and available - techs_to_keep = [t for t in available_techs if str(t) in allowed_techs] + for tech in techs: + cap = region_cap.sel(technology=tech).values # (class,) + technologies_dict[tech] = cap + logger.info(f" {tech}: {len(cap)} sites, {cap.sum():.0f} MW total") - if not techs_to_keep: - raise ValueError( - f"No renewable technologies available after filtering. " - f"Allowed: {allowed_techs}, Available: {available_techs}" - ) + # Capacity factor time series (keep full structure for now) + cf_ts = region_cf # (tech, class, time) - # Filter dataset - filtered = dataset.sel(technology=techs_to_keep) + metadata = { + "region": region, + "n_classes": region_cap.sizes["class"], + "n_time": region_cf.sizes["time"], + "n_technologies": len(techs), + "technologies": techs, + "time_start": pd.Timestamp(ds["time"].values[0]), + "time_end": pd.Timestamp(ds["time"].values[-1]), + "total_capacity_mw": float(region_cap.sum().values), + } logger.info( - f"Filtered to {len(techs_to_keep)} technologies: {techs_to_keep} " - f"(from {len(available_techs)} available)" + f" Total capacity: {metadata['total_capacity_mw']:.0f} MW, " + f"{metadata['n_time']} timesteps, {metadata['n_technologies']} technologies" ) - return filtered - - -# ============================================================================ -# ELECTRICITY BACK-PROPAGATION -# ============================================================================ - - -def back_propagate_electricity_need( - tech_costs: pd.Series, product: str, config: dict -) -> float: - """Calculate renewable electricity requirement (MWh) per tonne of product. - - Back-propagates through supply chain efficiency chain: - - steel (t): Electrolyzer(elec) + DRI(elec + H2) + optional EAF(elec) - - hbi (t): Electrolyzer(elec) + DRI(elec + H2) - - h2 (t): Electrolyzer(elec) only + ds.close() + return technologies_dict, cf_ts, metadata - Parameters - ---------- - tech_costs : pd.Series - Technology cost database (MultiIndex by [tech_name, parameter]) - product : str - Product: "steel", "hbi", or "h2" - config : dict - Config dict with optional overrides: - - "electricity_per_tonne_{product}_mwh" - - "eaf_electricity_source" ("grid" by default, "renewable" to include EAF electricity) - Returns - ------- - float - Renewable electricity requirement in MWh per tonne of product +def reserve_top_sites_by_capacity( + technologies_dict: Dict[str, np.ndarray], + cf_ts: xr.DataArray, + reserve_capacity_mw: float, +) -> Dict[str, np.ndarray]: """ - # Check for config override first - override_key = f"electricity_per_tonne_{product}_mwh" - if override_key in config: - elec_need = config[override_key] - logger.info( - f"Using config override for {product}: " - f"{override_key} = {elec_need:.4f} MWh/t" - ) - return elec_need - - # Extract efficiencies from tech database - # Electrolyzer: Electricity → H2 - elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") - elec_mwh_per_mwh_h2 = td.get_tech_param(elec_params, "electricity-input", 1.38) - logger.debug( - f"Electrolyzer electricity input: {elec_mwh_per_mwh_h2:.4f} MWh/MWh H2" - ) + Reserve top sites (highest capacity) for local demand. - if product == "h2": - # H2 only: just electrolyzer electricity - # Note: 1 MWh H2 ≈ 1 t H2 for energy accounting (MWh/MWh = MWh/t in energy terms) - elec_need = elec_mwh_per_mwh_h2 - logger.info( - f"Back-propagated electricity for H2: {elec_need:.4f} MWh/t H2 " - f"(electrolyzer only)" - ) - return elec_need - - # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI - dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") - h2_per_t_hbi = td.get_tech_param(dri_params, "hydrogen-input", 2.1) - dri_elec_per_t_hbi = td.get_tech_param(dri_params, "electricity-input", 1.03) - logger.debug(f"DRI hydrogen input: {h2_per_t_hbi:.4f} t H2/t HBI") - logger.debug(f"DRI electricity input: {dri_elec_per_t_hbi:.4f} MWh/t HBI") - - # Electricity for H2 production (via electrolyzer) - h2_elec_per_t_hbi = h2_per_t_hbi * elec_mwh_per_mwh_h2 - - if product == "hbi": - # HBI: H2 production + DRI electricity - elec_need = h2_elec_per_t_hbi + dri_elec_per_t_hbi - logger.info( - f"Back-propagated electricity for HBI: {elec_need:.4f} MWh/t HBI " - f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f})" - ) - return elec_need - - if product == "steel": - # EAF: HBI + Electricity → Steel - eaf_params = td.get_tech(tech_costs, "electric arc furnace") - eaf_elec_per_t_steel = td.get_tech_param( - eaf_params, "electricity-input", 0.6395 - ) - logger.debug(f"EAF electricity input: {eaf_elec_per_t_steel:.4f} MWh/t Steel") - - eaf_source = config.get("eaf_electricity_source", "grid") - - # Steel renewable electricity: H2 production + DRI electricity. - # If the EAF is configured to run on renewables, include its electricity too. - elec_need = h2_elec_per_t_hbi + dri_elec_per_t_hbi - if eaf_source == "renewable": - elec_need += eaf_elec_per_t_steel - logger.info( - f"Back-propagated renewable electricity for Steel: {elec_need:.4f} MWh/t Steel " - f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f}, " - f"EAF: {eaf_elec_per_t_steel:.4f})" - ) - else: - logger.info( - f"Back-propagated renewable electricity for Steel: {elec_need:.4f} MWh/t Steel " - f"(H2 production: {h2_elec_per_t_hbi:.4f}, DRI: {dri_elec_per_t_hbi:.4f}; " - f"EAF electricity is grid-supplied)" - ) - return elec_need - - raise ValueError( - f"Product '{product}' not recognized. Choose from: 'h2', 'hbi', 'steel'" - ) - - -# ============================================================================ -# TWO-STEP RENEWABLE FILTERING -# ============================================================================ - - -def load_local_demand(local_demand_path: str, region: str, config: dict) -> float: - """Load local electricity demand for region from CSV file. - - Parameters - ---------- - local_demand_path : str - Path to local demand CSV (columns: region, demand, el_share or similar) - region : str - Region name - config : dict - Configuration dict - - Returns - ------- - float - Local electricity demand in MWh/year. Returns 0 if region not found (with warning). + Returns dict of same structure as technologies_dict, with NaN for non-reserved sites. """ - try: - df = pd.read_csv(local_demand_path) - except FileNotFoundError: - logger.warning( - f"Local demand file not found: {local_demand_path}. Using 0 MWh/year." - ) - return 0.0 - except Exception as e: - logger.warning(f"Failed to load local demand: {e}. Using 0 MWh/year.") - return 0.0 - - # Look for region in dataframe (try common column names) - region_col = None - for col in ["region", "Region", "name", "Name"]: - if col in df.columns: - region_col = col + # Flatten all capacities with (tech, site) index + reserved = {} + total_reserved_mw = 0 + + all_sites = [] + for tech, caps in technologies_dict.items(): + for site_idx, cap in enumerate(caps): + all_sites.append((tech, site_idx, cap)) + + # Sort by capacity descending + all_sites.sort(key=lambda x: x[2], reverse=True) + + # Reserve until target capacity + reserved_set = set() + for tech, site_idx, cap in all_sites: + if total_reserved_mw >= reserve_capacity_mw: break + reserved_set.add((tech, site_idx)) + total_reserved_mw += cap - if region_col is None: - logger.warning( - f"Could not find region column in {local_demand_path}. Using 0 MWh/year." - ) - return 0.0 - - # Find demand value for this region - region_data = df[df[region_col] == region] + # Create reserved arrays (copy dict structure, mask non-reserved with NaN) + for tech, caps in technologies_dict.items(): + reserved_array = np.full_like(caps, np.nan, dtype=np.float32) + for site_idx, cap in enumerate(caps): + if (tech, site_idx) in reserved_set: + reserved_array[site_idx] = cap + reserved[tech] = reserved_array - if region_data.empty: - logger.warning( - f"Region '{region}' not found in local demand data. " - f"Available regions: {df[region_col].unique()}. Using 0 MWh/year." - ) - return 0.0 - - # Look for demand column (try common names) - demand_col = None - for col in ["demand", "Demand", "demand_mwh", "demand_MWh", "el_demand"]: - if col in df.columns: - demand_col = col - break - - if demand_col is None: - logger.warning( - f"Could not find demand column in {local_demand_path}. Using 0 MWh/year." - ) - return 0.0 - - demand_mwh = float(region_data[demand_col].iloc[0]) logger.info( - f"Loaded local electricity demand for {region}: {demand_mwh:.1f} MWh/year" + f"Reserved {len(reserved_set)} sites, {total_reserved_mw:.0f} MW for local demand" ) - - return demand_mwh + return reserved -def filter_renewable_generators_by_potential( - dataset: xr.Dataset, +def add_renewable_generators( + network: pypsa.Network, region: str, - product: str, + technologies_dict: Dict[str, np.ndarray], + cf_ts: xr.DataArray, tech_costs: pd.Series, config: dict, - local_demand_mwh: float, - max_product_demand_mt: float, -) -> Tuple[List[Dict], Dict]: - """Two-step renewable filtering: reserve for domestic + select for product (per-tech). - - Parameters - ---------- - dataset : xr.Dataset - Filtered renewable profiles (already region + tech filtered) - region : str - Region name - product : str - Product (steel, hbi, h2) - tech_costs : pd.Series - Technology cost database - config : dict - Configuration dict - local_demand_mwh : float - Local electricity demand in MWh/year - max_product_demand_mt : float - Maximum product demand in Mt/year (for capacity target) - - Returns - ------- - tuple - (selected_generators, filter_audit) - - selected_generators: List of dicts with bus, tech, p_nom_max, avg_cf - - filter_audit: Dict with filtering statistics + reserved_techs: Dict[str, np.ndarray] = None, +) -> Dict: """ - logger.info("=" * 70) - logger.info("TWO-STEP RENEWABLE FILTERING") - logger.info("=" * 70) - - # Compute product renewable electricity need per tonne - product_elec_per_t = back_propagate_electricity_need(tech_costs, product, config) - - # Target capacity (MW) for product production - max_product_elec_mwh = max_product_demand_mt * 1e6 * product_elec_per_t - max_product_elec_mw = max_product_elec_mwh / (365 * 24) - - # Multiplier from config (default 5) - multiplier = config.get("renewable_coverage_multiplier", 5) - target_capacity_mw = multiplier * max_product_elec_mw - - logger.info(f"{product.upper()} demand: {max_product_demand_mt:.1f} Mt") - logger.info( - f"{product.upper()} renewable electricity need: {product_elec_per_t:.4f} MWh/t → {max_product_elec_mw:.1f} MW average" - ) - logger.info( - f"Target renewable capacity ({multiplier}×): {target_capacity_mw:.1f} MW" - ) - logger.info( - f"Local demand: {local_demand_mwh:.1f} MWh/year → {local_demand_mwh / (365 * 24):.1f} MW average" - ) + Add renewable generators to PyPSA network. - # STEP 1: Build bus-tech candidate list with all data - candidates = [] - for bus_id in dataset.bus.values: - for tech in dataset.technology.values: - p_nom_max = float( - dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values - ) - avg_cf = float(dataset["avg_cf"].sel(bus=bus_id, technology=tech).values) - - # Skip invalid combos - if np.isnan(p_nom_max) or p_nom_max <= 0 or np.isnan(avg_cf) or avg_cf <= 0: - continue - - candidates.append( - { - "bus_id": str(bus_id), - "technology": str(tech), - "p_nom_max": p_nom_max, - "avg_cf": avg_cf, - "capacity_factor_ts": dataset["capacity_factor"] - .sel(bus=bus_id, technology=tech) - .values, - "potential": p_nom_max * avg_cf, # Quality metric (MW × CF) - } - ) - - if not candidates: - logger.warning("No valid bus-tech candidates after filtering!") - return [], {} + For each technology and class (site), creates an extendable generator on the + electricity bus with capacity ceiling from technologies_dict and time series from cf_ts. - logger.info(f"Total candidates: {len(candidates)}") - - # STEP 1: DOMESTIC RESERVATION - logger.info("-" * 70) - logger.info("STEP 1: RESERVE FOR DOMESTIC DEMAND") - logger.info("-" * 70) - - # Sort globally by avg_cf (descending) for domestic reservation - candidates_sorted_cf = sorted(candidates, key=lambda x: x["avg_cf"], reverse=True) - - local_demand_mw = local_demand_mwh / (365 * 24) - reserved_generators = [] - reserved_capacity_mw = 0 - - for candidate in candidates_sorted_cf: - if reserved_capacity_mw >= local_demand_mw: - break - reserved_generators.append(candidate) - reserved_capacity_mw += candidate["p_nom_max"] - - logger.info( - f"Reserved {len(reserved_generators)} bus-tech combos for domestic demand" - ) - logger.info( - f"Reserved capacity: {reserved_capacity_mw:.1f} MW (target: {local_demand_mw:.1f} MW)" - ) - - # Create set of reserved IDs for filtering - reserved_ids = {(g["bus_id"], g["technology"]) for g in reserved_generators} - - # STEP 2: STEEL SELECTION (PER-TECHNOLOGY) - logger.info("-" * 70) - logger.info("STEP 2: SELECT FOR STEEL PRODUCTION (PER-TECHNOLOGY)") - logger.info("-" * 70) - - # Get non-reserved candidates - non_reserved = [ - c for c in candidates if (c["bus_id"], c["technology"]) not in reserved_ids - ] - - # Calculate per-technology potential shares - tech_potentials = {} - total_potential = 0 - for candidate in non_reserved: - tech = candidate["technology"] - potential = candidate["potential"] - tech_potentials[tech] = tech_potentials.get(tech, 0) + potential - total_potential += potential - - if total_potential <= 0: - logger.warning("Total potential <= 0 in non-reserved generators!") - total_potential = 1.0 # Avoid division by zero - - logger.info(f"Total non-reserved potential: {total_potential:.1f} MW·CF") - - # Per-tech targets (proportional to potential) - tech_targets = {} - for tech, pot in tech_potentials.items(): - share = pot / total_potential - target_mw = share * target_capacity_mw - tech_targets[tech] = target_mw - logger.info(f" {tech}: {share * 100:.1f}% share → target {target_mw:.1f} MW") - - # Select from each tech by avg_cf - selected_generators = [] - selected_capacity_by_tech = {} - - for tech in sorted(tech_targets.keys()): - target_mw = tech_targets[tech] - - # Get candidates for this tech, sort by avg_cf - tech_candidates = sorted( - [c for c in non_reserved if c["technology"] == tech], - key=lambda x: x["avg_cf"], - reverse=True, - ) - - tech_selected = [] - tech_capacity = 0 - - for candidate in tech_candidates: - if tech_capacity >= target_mw: - break - selected_generators.append(candidate) - tech_selected.append(candidate) - tech_capacity += candidate["p_nom_max"] - - selected_capacity_by_tech[tech] = tech_capacity - logger.info( - f" {tech}: Selected {len(tech_selected)} generators, " - f"{tech_capacity:.1f} MW (target: {target_mw:.1f} MW)" - ) - - total_selected_capacity = sum(selected_capacity_by_tech.values()) - - logger.info("-" * 70) - logger.info("FILTERING COMPLETE") - logger.info("-" * 70) - logger.info(f"Total generators selected: {len(selected_generators)}") - logger.info(f"Total capacity selected: {total_selected_capacity:.1f} MW") - logger.info( - f"Coverage ratio: {total_selected_capacity / target_capacity_mw:.2f}× target" - ) - - # Build audit info - filter_audit = { - "region": region, - "product": product, - "local_demand_mwh": local_demand_mwh, - "max_product_demand_mt": max_product_demand_mt, - "product_elec_need_mwh_per_t": product_elec_per_t, - "multiplier": multiplier, - "target_capacity_mw": target_capacity_mw, - "n_reserved_generators": len(reserved_generators), - "reserved_capacity_mw": reserved_capacity_mw, - "n_selected_generators": len(selected_generators), - "selected_capacity_mw": total_selected_capacity, - "selected_by_tech": selected_capacity_by_tech, - "coverage_ratio": total_selected_capacity / target_capacity_mw - if target_capacity_mw > 0 - else 0, - } - - logger.info("=" * 70) - - return selected_generators, filter_audit - - -# ============================================================================ -# EXTRACT INCREMENTAL GENERATOR SETS -# ============================================================================ - - -def extract_incremental_generator_sets( - selected_generators: List[Dict], - product_demand_levels: List[float], - product: str, - tech_costs: pd.Series, - config: dict, -) -> Dict[float, List[Dict]]: - """Extract incremental generator subsets for each demand level. - - Given a ranked list of selected generators (from max demand), extracts smaller - subsets for each demand level. Each subset is a proper subset of the next. + If reserved_techs provided, marks reserved sites with local_priority=True tag. Parameters ---------- - selected_generators : List[Dict] - Ranked list from filter_renewable_generators_by_potential (at max demand) - product_demand_levels : List[float] - Product demand levels in Mt/year (e.g., [10, 200, 1000]) - product : str - Product (steel, hbi, h2) + network : pypsa.Network + PyPSA network to add generators to + region : str + Region name (for bus and generator naming) + technologies_dict : dict + {tech_name: capacity_array} where capacity_array is (n_classes,) + cf_ts : xr.DataArray + Capacity factor time series with dims (technology, class, time) tech_costs : pd.Series Technology cost database config : dict Configuration dict + reserved_techs : dict, optional + {tech_name: reserved_capacity_array} (NaN for non-reserved) Returns ------- - dict - Mapping: {demand_mt: [selected_generators_for_that_level]} - Sets are nested: 10 Mt ⊆ 200 Mt ⊆ 1000 Mt + audit_dict : dict + Statistics: n_generators_added, total_capacity_mw, etc. """ - logger.info("=" * 70) - logger.info("EXTRACTING INCREMENTAL GENERATOR SETS") - logger.info("=" * 70) - - # Compute renewable electricity need for this product - elec_per_t = back_propagate_electricity_need(tech_costs, product, config) - multiplier = config.get("renewable_coverage_multiplier", 5) - - # Calculate target MW for each demand level - demand_targets = {} # demand_mt -> target_mw - for demand_mt in product_demand_levels: - elec_mwh = demand_mt * 1e6 * elec_per_t - elec_mw = elec_mwh / (365 * 24) - target_mw = multiplier * elec_mw - demand_targets[demand_mt] = target_mw - logger.info(f"Demand {demand_mt:.1f} Mt → Target {target_mw:.1f} MW") - - # For each demand level, select generators up to its target - incremental_sets = {} - for demand_mt in sorted(product_demand_levels): - target_mw = demand_targets[demand_mt] - - # Accumulate generators until reaching target - subset = [] - accumulated_mw = 0 - for gen in selected_generators: - if accumulated_mw >= target_mw: - break - subset.append(gen) - accumulated_mw += gen["p_nom_max"] - - incremental_sets[demand_mt] = subset - logger.info( - f" {demand_mt:.1f} Mt: {len(subset)} generators, " - f"{accumulated_mw:.1f} MW (target {target_mw:.1f} MW)" - ) - - logger.info("=" * 70) - return incremental_sets - - -def _serialize_dispatch_list(generators: List[Dict]) -> List[Dict]: - """Return JSON-safe generator list metadata in selection order.""" - serialized = [] - for gen in generators: - serialized.append( - { - "bus_id": str(gen["bus_id"]), - "technology": str(gen["technology"]), - "p_nom_max_mw": float(gen["p_nom_max"]), - "avg_cf": float(gen["avg_cf"]), - } - ) - return serialized - + n_added = 0 + total_p_nom_max = 0 + n_reserved = 0 -# ============================================================================ -# RENEWABLE GENERATOR ADDITION -# ============================================================================ + # Ensure electricity bus exists + elec_bus = "renewable_electricity" + if elec_bus not in network.buses.index: + network.add("Bus", elec_bus, carrier="AC", v_nom=1) + # Get discount rate + discount_rate = network.discount_rate if hasattr(network, "discount_rate") else 0.07 -def add_renewable_generators( - network: pypsa.Network, - dataset: xr.Dataset, - tech_costs: pd.Series, - config: dict, - selected_generators: List[Dict] = None, -) -> Dict: - """Add renewable generators from xarray dataset or pre-filtered list to electricity bus. - - Parameters - ---------- - network : pypsa.Network - PyPSA network to add generators to - dataset : xr.Dataset - Filtered renewable profiles with dimensions [bus, technology, hour] - tech_costs : pd.Series - Technology cost parameters - config : dict - Configuration dict - selected_generators : List[Dict], optional - Pre-filtered list from filter_renewable_generators_by_potential(). - If provided, ONLY these generators are added (faster). - If None, all valid combinations from dataset are added. - - Returns - ------- - dict - Audit info with counts and statistics - """ - # Map technology names to database keys for cost lookup - tech_database_map = { - "onwind": "onwind", - "offwind-ac": "offwind", - "solar": "solar-utility", + # Map consolidated file tech names to database keys + tech_db_map = { + "windonshore": "onwind", + "windoffshore": "offwind", + "pvplant": "solar-utility", } - # Ensure carriers are defined before adding renewable generators. - _ensure_carriers( - network, - ["renewable_electricity"] + [str(t) for t in dataset.technology.values], - ) - - # Use the network's discount_rate (which is set regionally in prepare_network) - discount_rate = network.discount_rate - - # Validate data quality - n_total_combos = len(dataset.bus) * len(dataset.technology) - n_valid_combos = 0 - n_added_generators = 0 - total_p_nom_max = 0 - iso3_set = set() + for tech, capacities in technologies_dict.items(): + cf_data = cf_ts.sel(technology=tech).values # (class, time) - # If pre-filtered generators provided, use fast path - if selected_generators is not None: - logger.info( - f"Adding {len(selected_generators)} pre-filtered generators (fast path)" - ) - for gen_dict in selected_generators: - bus_id = gen_dict["bus_id"] - tech_str = gen_dict["technology"] - p_nom_max = gen_dict["p_nom_max"] - cf_timeseries = gen_dict["capacity_factor_ts"] - avg_cf = gen_dict["avg_cf"] - - # Extract ISO3 from bus_id - iso3 = str(bus_id).split("_")[0] - iso3_set.add(iso3) - - # Handle timeseries NaNs - if isinstance(cf_timeseries, np.ndarray): - cf_timeseries = np.nan_to_num(cf_timeseries, nan=0.0) - else: - cf_timeseries = np.zeros(8760) - - # Get technology parameters from database - db_tech_name = tech_database_map.get(tech_str, tech_str) + # Get technology cost parameters from database + db_tech_name = tech_db_map.get(tech, tech) + try: tech_params = td.get_tech(tech_costs, db_tech_name) - - overnight_cost = ( - td.get_tech_param(tech_params, "investment", 0) * 1000 - ) # EUR/kW → EUR/MW - lifetime = td.get_tech_param(tech_params, "lifetime", 20) + # investment is EUR/kW, convert to EUR/MW by multiplying by 1000 + overnight_cost = td.get_tech_param(tech_params, "investment", 0) * 1000 fom_pct = td.get_tech_param(tech_params, "FOM", 0) fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 + lifetime = td.get_tech_param(tech_params, "lifetime", 20) + except Exception as e: + logger.warning( + f"Could not load costs for {db_tech_name}: {e}, using defaults" + ) + overnight_cost, fom_cost, lifetime = 0, 0, 20 - gen_name = f"renewable_{bus_id}_{tech_str}" + # Add generator for each site (class) + for site_idx, p_nom_max in enumerate(capacities): + if np.isnan(p_nom_max) or p_nom_max <= 0: + continue + + gen_name = f"renewable_{region}_{tech}_{site_idx}" + is_reserved = False + + # Check if this site is reserved for local demand + if reserved_techs is not None and tech in reserved_techs: + reserved_cap = reserved_techs[tech][site_idx] + if not np.isnan(reserved_cap) and reserved_cap > 0: + is_reserved = True + n_reserved += 1 - # Add generator + # Get time series for this site + p_max_pu = cf_data[site_idx, :] # (time,) + + # Add generator (extendable with ceiling) network.add( "Generator", gen_name, - bus="renewable_electricity", - carrier=tech_str, + bus=elec_bus, + carrier=tech, p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) - p_max_pu=cf_timeseries, # Hourly capacity factor (0-1) + p_max_pu=p_max_pu, # Hourly capacity factor (0-1) overnight_cost=overnight_cost, discount_rate=discount_rate, lifetime=lifetime, fom_cost=fom_cost, + tags={ + "technology": tech, + "region": region, + "local_priority": is_reserved, + "site_id": site_idx, + }, ) - n_added_generators += 1 + n_added += 1 total_p_nom_max += p_nom_max - logger.debug( - f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " - f"avg_cf={avg_cf:.3f}, overnight_cost={overnight_cost:.1f} EUR/MW" - ) - - n_valid_combos = len(selected_generators) - else: - # Original slow path: iterate through all xarray combos - logger.info( - f"Adding generators from full dataset ({n_total_combos} combos, slow path)" - ) - for bus_id in dataset.bus.values: - # Extract ISO3 from bus_id - iso3 = str(bus_id).split("_")[0] - iso3_set.add(iso3) - - for tech in dataset.technology.values: - tech_str = str(tech) - - # Extract data for this bus-tech combination - p_nom_max = float( - dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values - ) - avg_cf = float( - dataset["avg_cf"].sel(bus=bus_id, technology=tech).values - ) - cf_timeseries = ( - dataset["capacity_factor"].sel(bus=bus_id, technology=tech).values - ) - - # Skip invalid combinations (NaN or ≤0) - if ( - np.isnan(p_nom_max) - or p_nom_max <= 0 - or np.isnan(avg_cf) - or avg_cf <= 0 - ): - continue - - n_valid_combos += 1 - - # Handle timeseries NaNs - if isinstance(cf_timeseries, np.ndarray): - cf_timeseries = np.nan_to_num(cf_timeseries, nan=0.0) - else: - cf_timeseries = np.zeros(8760) - - # Get technology parameters from database - db_tech_name = tech_database_map.get(tech_str, tech_str) - tech_params = td.get_tech(tech_costs, db_tech_name) - - overnight_cost = ( - td.get_tech_param(tech_params, "investment", 0) * 1000 - ) # EUR/kW → EUR/MW - lifetime = td.get_tech_param(tech_params, "lifetime", 20) - fom_pct = td.get_tech_param(tech_params, "FOM", 0) - fom_cost = overnight_cost * (fom_pct / 100) if overnight_cost > 0 else 0 - - gen_name = f"renewable_{bus_id}_{tech_str}" - - # Add generator - network.add( - "Generator", - gen_name, - bus="renewable_electricity", - carrier=tech_str, - p_nom_extendable=True, - p_nom=0, # Start with no capacity; optimization will decide - p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) - p_max_pu=cf_timeseries, # Hourly capacity factor (0-1) - overnight_cost=overnight_cost, - discount_rate=discount_rate, - lifetime=lifetime, - fom_cost=fom_cost, - ) - - n_added_generators += 1 - total_p_nom_max += p_nom_max - - logger.debug( - f"Added generator {gen_name}: p_nom_max={p_nom_max:.1f} MW, " - f"avg_cf={avg_cf:.3f}, overnight_cost={overnight_cost:.1f} EUR/MW" - ) - - # Build audit info - coverage_pct = (n_valid_combos / n_total_combos * 100) if n_total_combos > 0 else 0 + n_sites_added = sum(~np.isnan(capacities)) + logger.info(f"Added {n_sites_added} {tech} generators for {region}") logger.info( - f"Added {n_added_generators} renewable generators to network " - f"({n_valid_combos}/{n_total_combos} valid combos, {coverage_pct:.1f}% coverage)" + f"Total generators added: {n_added}, ceiling capacity: {total_p_nom_max:.0f} MW" ) - logger.info(f"Total p_nom_max capacity: {total_p_nom_max:.1f} MW") - - if coverage_pct < 50: - logger.warning( - f"Low data coverage: {coverage_pct:.1f}% valid combos. " - f"Consider checking data source." - ) + if n_reserved > 0: + logger.info(f" Reserved sites (local_priority): {n_reserved}") return { - "n_generators_added": n_added_generators, - "n_valid_bus_tech_combos": n_valid_combos, - "n_total_bus_tech_combos": n_total_combos, - "coverage_pct": coverage_pct, - "total_p_nom_max_mw": total_p_nom_max, - "iso3_codes": sorted(list(iso3_set)), - "technologies": sorted([str(t) for t in dataset.technology.values]), + "n_generators_added": n_added, + "total_capacity_mw": total_p_nom_max, + "n_reserved": n_reserved, } -def _apply_discount_rate_to_components( - network: pypsa.Network, discount_rate: float -) -> None: - """Apply regional discount_rate to all cost-bearing components. - - Links, stores, and generators with costs must have discount_rate set for annualization. +def back_propagate_electricity_need( + tech_costs: pd.Series, + product: str, + config: dict, +) -> float: """ - # Apply to links - for link_name, link_row in network.links.iterrows(): - has_cost = ( - pd.notna(link_row.get("overnight_cost")) and link_row["overnight_cost"] >= 0 - ) - if has_cost: - network.links.at[link_name, "discount_rate"] = discount_rate - - # Apply to stores - for store_name, store_row in network.stores.iterrows(): - has_cost = ( - pd.notna(store_row.get("overnight_cost")) - and store_row["overnight_cost"] > 0 - ) - if has_cost: - network.stores.at[store_name, "discount_rate"] = discount_rate - - # Apply to generators - for gen_name, gen_row in network.generators.iterrows(): - has_cost = ( - pd.notna(gen_row.get("overnight_cost")) and gen_row["overnight_cost"] >= 0 - ) - if has_cost: - network.generators.at[gen_name, "discount_rate"] = discount_rate - - logger.debug( - f"Applied discount_rate={discount_rate:.4f} to all cost-bearing components" - ) + Calculate renewable electricity requirement (MWh) per tonne of product. + Paths: + - steel (t): Electrolyzer(elec) + DRI(elec + H2) + optional EAF(elec) + - hbi (t): Electrolyzer(elec) + DRI(elec + H2) + - h2 (t): Electrolyzer(elec) only + """ + # Check for config override + override_key = f"electricity_per_tonne_{product}_mwh" + if override_key in config: + value = config[override_key] + logger.info(f"Using config override: {product} requires {value} MWh/t") + return value -def _ensure_carriers(network: pypsa.Network, carrier_names: List[str]) -> None: - """Add missing carriers to the network before components reference them.""" - for carrier_name in carrier_names: - if carrier_name not in network.carriers.index: - network.add("Carrier", carrier_name) - + # Electrolyzer: Electricity → H2 + elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") + elec_mwh_per_mwh_h2 = td.get_tech_param(elec_params, "electricity-input", 1.38) -def _consistency_check(network: pypsa.Network) -> None: - """Sanitize and check network consistency. + if product == "h2": + return elec_mwh_per_mwh_h2 - PyPSA's sanitize() method automatically adds missing carriers and fixes consistency issues. - """ - logger.info("Running network consistency check...") + # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI + dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") + h2_per_t_hbi = td.get_tech_param(dri_params, "hydrogen-input", 2.1) + dri_elec_per_t_hbi = td.get_tech_param(dri_params, "electricity-input", 1.03) + h2_elec_per_t_hbi = h2_per_t_hbi * elec_mwh_per_mwh_h2 - # PyPSA's built-in consistency check - try: - network.sanitize() - logger.info("Network passed consistency check (sanitized)") - except Exception as e: - logger.warning(f"Network sanitization warning: {e}") + if product == "hbi": + return h2_elec_per_t_hbi + dri_elec_per_t_hbi + if product == "steel": + # Add EAF if configured, otherwise just HBI path + eaf_source = config.get("eaf_electricity_source", "grid") + if eaf_source == "renewable": + eaf_params = td.get_tech(tech_costs, "electric arc furnace") + eaf_elec_per_t_steel = td.get_tech_param( + eaf_params, "electricity-input", 0.5 + ) + return h2_elec_per_t_hbi + dri_elec_per_t_hbi + eaf_elec_per_t_steel + else: + return h2_elec_per_t_hbi + dri_elec_per_t_hbi -# ============================================================================ -# SUPPLY CHAIN PRODUCT CUTOFF -# ============================================================================ + raise ValueError(f"Product '{product}' not recognized. Choose: h2, hbi, steel") def apply_product_cutoff(network: pypsa.Network, product: str) -> None: - """Remove supply chain stages after target product (h2/hbi/steel).""" + """Remove supply chain components after the target product.""" if product == "h2": - logger.info("Product cutoff: Keeping electrolyzer only (H2 output)") - - # Remove conversion stages after hydrogen - links_to_remove = ["dri", "eaf"] - buses_to_remove = ["iron_ore", "hbi", "steel"] - stores_to_remove = ( - ["hbi_storage"] if "hbi_storage" in network.stores.index else [] - ) - generators_to_remove = ["iron_ore"] - - for link_name in links_to_remove: - if link_name in network.links.index: - network.remove("Link", link_name) - logger.debug(f"Removed link: {link_name}") - - for bus_name in buses_to_remove: - try: - network.remove("Bus", bus_name) - logger.debug(f"Removed bus: {bus_name}") - except ValueError: - logger.debug( - f"Bus {bus_name} not found (already removed or not present)" - ) - - for store_name in stores_to_remove: - if store_name in network.stores.index: - network.remove("Store", store_name) - logger.debug(f"Removed store: {store_name}") - - for gen_name in generators_to_remove: - if gen_name in network.generators.index: - network.remove("Generator", gen_name) - logger.debug(f"Removed generator: {gen_name}") - + # Keep only: electricity -> electrolyzer -> H2 storage + # Remove: DRI, HBI, EAF, steel + components_to_remove = [ + ("Link", "link_dri_furnace"), + ("Link", "link_eaf"), + ("Store", "store_hbi"), + ("Store", "store_steel"), + ] elif product == "hbi": - logger.info("Product cutoff: Keeping electrolyzer + DRI (HBI output)") - - # Remove stages after HBI - links_to_remove = ["eaf"] - buses_to_remove = ["steel"] - generators_to_remove = [] - - for link_name in links_to_remove: - if link_name in network.links.index: - network.remove("Link", link_name) - logger.debug(f"Removed link: {link_name}") - - for bus_name in buses_to_remove: - try: - network.remove("Bus", bus_name) - logger.debug(f"Removed bus: {bus_name}") - except ValueError: - logger.debug(f"Bus {bus_name} not found") - - for gen_name in generators_to_remove: - if gen_name in network.generators.index: - network.remove("Generator", gen_name) - + # Keep: electricity -> electrolyzer -> H2 -> DRI -> HBI + # Remove: EAF, steel + components_to_remove = [ + ("Link", "link_eaf"), + ("Store", "store_steel"), + ] elif product == "steel": - logger.info("Product cutoff: Keeping full supply chain (Steel output)") - # No removal; keep all stages - pass - + # Keep all: electricity -> electrolyzer -> H2 -> DRI -> HBI -> EAF -> steel + components_to_remove = [] else: - raise ValueError( - f"Product '{product}' not recognized. Choose from: 'h2', 'hbi', 'steel'" - ) + raise ValueError(f"Product '{product}' not recognized") - -# ============================================================================ -# MAIN ORCHESTRATION -# ============================================================================ + for comp_type, comp_name in components_to_remove: + if comp_name in getattr(network, comp_type.lower() + "s", {}).index: + logger.info(f"Removing {comp_type} {comp_name}") + network.remove(comp_type, comp_name) def prepare_network( skeleton_network_path: str, - renewable_nc_path: str, + consolidated_renewables_path: str, tech_costs_path: str, region: str, product: str, - config: dict, - local_demand_path: str = None, -) -> Tuple[pypsa.Network, Dict, Dict[float, List[Dict]]]: - """Prepare network with incremental renewable filtering. - - Workflow: - 1. Load skeleton + renewables (filtered by region + tech) - 2. Call filter_renewable_generators_by_potential() with MAX demand - 3. Extract incremental subsets for each demand level (10 ⊆ 200 ⊆ 1000) - 4. Build FULL network with max demand set - 5. Apply product cutoff - 6. Return: (network, audit_info, incremental_sets) + cost_year: int = 2030, + config: dict = None, +) -> Tuple[pypsa.Network, Dict]: + """ + Prepare regional network with consolidated renewables. Parameters ---------- skeleton_network_path : str - Path to skeleton network - renewable_nc_path : str - Path to renewable profiles .nc file (NEW xarray format) + Path to base network topology + consolidated_renewables_path : str + Path to consolidated renewables NetCDF tech_costs_path : str - Path to technology costs CSV + Path to technology cost database region : str Region name product : str - Product (h2, hbi, or steel) + Target product (h2, hbi, steel) + cost_year : int + Cost year for technology parameters config : dict - Configuration dict with: - - steel_demand_levels: [10, 200, 1000] Mt/year (applies to any product) - - renewable_technologies: ["solar", "onwind"] - - renewable_coverage_multiplier: 5 - local_demand_path : str, optional - Path to local electricity demand CSV (columns: region, demand_mwh) + Configuration dict Returns ------- - tuple - (network, audit_info, incremental_sets) - - network: PyPSA Network with full max-demand renewable set - - audit_info: Dict with filtering + network stats - - incremental_sets: {demand_mt: [selected_generators]} nested subsets + network : pypsa.Network + Prepared PyPSA network + audit_dict : dict + Summary statistics """ + if config is None: + config = {} + logger.info("=" * 70) - logger.info(f"Preparing network for region={region}, product={product}") + logger.info(f"Preparing network: region={region}, product={product}") logger.info("=" * 70) - # Step 0: Extract demand levels and max demand (config uses 'steel_demand_levels' for backward compat) - product_demand_levels = config.get("steel_demand_levels", [10, 200, 1000]) - max_product_demand_mt = max(product_demand_levels) - logger.info(f"Demand levels: {product_demand_levels} Mt/year") - logger.info(f"Max demand: {max_product_demand_mt} Mt/year") - - # Step 1: Load skeleton network + # Load skeleton logger.info("Loading skeleton network...") network = pypsa.Network(skeleton_network_path) - network.name = f"Skeleton-{region}-{product}" - logger.info( - f"Skeleton loaded: {len(network.buses)} buses, " - f"{len(network.links)} links, {len(network.stores)} stores" - ) + network.name = f"base_{cost_year}_{region}_{product}" - # Set snapshots - cost_year = None - if "snakemake" in globals(): - cost_year = getattr(snakemake.wildcards, "cost_year", None) + # Set region-specific discount rate + interest_rates = config.get("interest_rate", {}) - if cost_year is not None: - network.set_snapshots( - pd.date_range(f"{cost_year}-01-01", periods=8760, freq="h") + # Get region-specific rate, or fall back to default + if isinstance(interest_rates.get(region), dict): + # Handle legacy component-level structure (flatten to use default) + discount_rate = interest_rates[region].get( + "default", interest_rates.get("default", 0.07) ) - logger.info(f"Set snapshots for cost_year={cost_year}") else: - raise ValueError( - "cost_year must be defined in snakemake wildcards for prepare_network" - ) + discount_rate = interest_rates.get(region, interest_rates.get("default", 0.07)) - # Step 1b: Set interest rate (discount rate) - interest_rates = config.get("interest_rate", {}) - discount_rate = interest_rates.get(region, interest_rates.get("default", 0.07)) network.discount_rate = discount_rate - logger.info(f"Set discount rate: {discount_rate:.4f} for region {region}") + logger.info(f"Region {region}: discount_rate = {discount_rate}") - # Step 2: Load tech costs - logger.info("Loading technology costs...") - tech_costs = td.load_tech_costs(tech_costs_path) + # Apply regional discount rate to all links and stores with costs + for link_name in network.links.index: + if network.links.loc[link_name, "overnight_cost"] > 0: + network.links.loc[link_name, "discount_rate"] = discount_rate - # Step 3: Load renewable profiles (NEW FORMAT) - logger.info(f"Loading renewable profiles for region {region}...") - renewable_dataset = load_renewable_profiles(renewable_nc_path) + for store_name in network.stores.index: + if network.stores.loc[store_name, "overnight_cost"] > 0: + network.stores.loc[store_name, "discount_rate"] = discount_rate - # Step 3b: Filter by region - renewable_dataset = filter_by_region(renewable_dataset, region, config) - - # Step 3c: Filter by allowed technologies - renewable_dataset = filter_by_technologies(renewable_dataset, config) - - # Step 4: LOAD LOCAL DEMAND for renewable filtering - local_demand_mwh = 0.0 - if local_demand_path: - local_demand_mwh = load_local_demand(local_demand_path, region, config) - else: - logger.warning("local_demand_path not provided, using 0 MWh/year for filtering") + # Load tech costs + logger.info("Loading technology costs...") + tech_costs = td.load_tech_costs(tech_costs_path) - # Step 5: FILTER RENEWABLES (with MAX demand) - logger.info("Filtering renewable generators (max demand set)...") - selected_generators, filter_audit = filter_renewable_generators_by_potential( - dataset=renewable_dataset, - region=region, - product=product, - tech_costs=tech_costs, - config=config, - local_demand_mwh=local_demand_mwh, - max_product_demand_mt=max_product_demand_mt, + # Load consolidated renewables for region + logger.info("Loading consolidated renewables...") + techs_dict, cf_ts, metadata = load_region_renewables_consolidated( + consolidated_renewables_path, region ) - # Step 6: EXTRACT INCREMENTAL GENERATOR SETS - logger.info("Extracting incremental generator sets...") - incremental_sets = extract_incremental_generator_sets( - selected_generators=selected_generators, - product_demand_levels=product_demand_levels, - product=product, - tech_costs=tech_costs, - config=config, - ) + # Apply local demand reservation if configured + reserved_techs = None + reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) + if reserve_capacity_mw > 0: + logger.info(f"Applying local demand reservation: {reserve_capacity_mw} MW") + reserved_techs = reserve_top_sites_by_capacity( + techs_dict, cf_ts, reserve_capacity_mw + ) - # Step 7: Add renewable generators (FULL SET from filtering) - logger.info("Adding renewable generators to network (full max-demand set)...") + # Add renewable generators + logger.info("Adding renewable generators...") gen_audit = add_renewable_generators( - network=network, - dataset=renewable_dataset, - tech_costs=tech_costs, - config=config, - selected_generators=selected_generators, # Pre-filtered list (fast path) + network, region, techs_dict, cf_ts, tech_costs, config, reserved_techs ) - # Step 8: Apply product-specific cutoff + # Apply product cutoff logger.info(f"Applying product cutoff for {product}...") - apply_product_cutoff(network=network, product=product) + apply_product_cutoff(network, product) - # Step 9: Build audit info + # Build audit info audit_info = { "region": region, "product": product, + "cost_year": cost_year, "discount_rate": discount_rate, - "iso3_list": gen_audit["iso3_codes"], - "num_buses_in_region": len(renewable_dataset.bus), - "num_technologies": len(gen_audit["technologies"]), - "num_generators_added": gen_audit["n_generators_added"], - "data_coverage_pct": gen_audit["coverage_pct"], - "total_renewable_p_nom_max_mw": gen_audit["total_p_nom_max_mw"], - "technologies": gen_audit["technologies"], - "filter_audit": filter_audit, - "incremental_set_counts": { - demand_mt: len(gen_list) for demand_mt, gen_list in incremental_sets.items() - }, - "dispatch_order_full": _serialize_dispatch_list(selected_generators), - "dispatch_order_by_demand_mt": { - str(int(demand_mt)) - if float(demand_mt).is_integer() - else str(demand_mt): _serialize_dispatch_list(gen_list) - for demand_mt, gen_list in incremental_sets.items() - }, + "renewables_metadata": metadata, + "generators_audit": gen_audit, "network_stats": { - "num_buses": len(network.buses), - "num_links": len(network.links), - "num_generators": len(network.generators), - "num_stores": len(network.stores), + "n_buses": len(network.buses), + "n_generators": len(network.generators), + "n_links": len(network.links), + "n_stores": len(network.stores), }, } - logger.info("Network preparation complete:") - logger.info(f" - Region: {region} (ISO3: {audit_info['iso3_list']})") - logger.info(f" - Buses in region: {audit_info['num_buses_in_region']}") - logger.info(f" - Generators added: {audit_info['num_generators_added']}") - logger.info(f" - Data coverage: {audit_info['data_coverage_pct']:.1f}%") - logger.info( - f" - Total p_nom_max: {audit_info['total_renewable_p_nom_max_mw']:.1f} MW" - ) - logger.info(f" - Incremental sets: {audit_info['incremental_set_counts']}") - logger.info( - f" - Network: {audit_info['network_stats']['num_buses']} buses, " - f"{audit_info['network_stats']['num_generators']} generators" - ) - - # Apply regional discount_rate to all cost-bearing components - _apply_discount_rate_to_components(network, discount_rate) - - # Run consistency check - _consistency_check(network) - + logger.info("=" * 70) + logger.info("Network prepared successfully:") + logger.info(f" - Buses: {len(network.buses)}") + logger.info(f" - Generators: {len(network.generators)}") + logger.info(f" - Capacity: {gen_audit['total_capacity_mw']:.0f} MW") logger.info("=" * 70) - return network, audit_info, incremental_sets + return network, audit_info # ============================================================================ @@ -1244,84 +499,54 @@ def prepare_network( # ============================================================================ if __name__ == "__main__": - # Handle Snakemake or mock invocation - if "snakemake" not in globals(): - # For testing: mock Snakemake - - class MockSnakemake: - """Mock Snakemake object for testing.""" - - def __init__(self): - self.input = { - "skeleton": "../resources/networks/skeleton_2030.nc", - "renewable_nc": "../data/renewable_profiles/renewable_profiles_EU__20260420_142941.nc", - "costs": "../resources/technology_data/costs_2030.csv", - "local_demand": "../data/local_demand.csv", - } - self.output = { - "base_network": "test_base_network.nc", - "audit": "test_audit.json", - "incremental_sets": "test_incremental_sets.json", - } - self.wildcards = { - "region": "EU", - "product": "steel", - "cost_year": "2030", - } - self.config = { - "regions": { - "EU": ["DEU", "FRA", "ITA", "NLD"], - "Africa": ["EGY", "ZAF"], - }, - "renewable_technologies": ["solar", "onwind"], - "renewable_coverage_multiplier": 5, - "steel_demand_levels": [10, 200, 1000], - "interest_rate": {"default": 0.07}, - } - - snakemake = MockSnakemake() + # Check if running from Snakemake + if "snakemake" in dir(): + # Snakemake inputs/outputs + skeleton_path = snakemake.input.skeleton + renewables_path = snakemake.input.renewables + tech_costs_path = snakemake.input.tech_costs + + region = snakemake.params.region + product = snakemake.params.product + cost_year = ( + snakemake.wildcards.cost_year + if hasattr(snakemake.wildcards, "cost_year") + else 2030 + ) + + output_path = snakemake.output[0] + + # Load config (if available) + config_dict = snakemake.config if "snakemake" in dir() else {} + else: + # Fallback for manual execution + import sys + + if len(sys.argv) > 1: + skeleton_path = sys.argv[1] + renewables_path = sys.argv[2] + tech_costs_path = sys.argv[3] + region = sys.argv[4] + product = sys.argv[5] + output_path = sys.argv[6] + cost_year = int(sys.argv[7]) if len(sys.argv) > 7 else 2030 + config_dict = {} + else: + raise ValueError("Provide paths and region/product as arguments") # Prepare network - try: - local_demand_path = None - if hasattr(snakemake.input, "local_demand") and snakemake.input.local_demand: - local_demand_path = snakemake.input.local_demand - - network, audit_info, incremental_sets = prepare_network( - skeleton_network_path=snakemake.input.skeleton, - renewable_nc_path=snakemake.input.renewable_nc, - tech_costs_path=snakemake.input.costs, - region=snakemake.wildcards.region, - product=snakemake.wildcards.product, - config=snakemake.config, - local_demand_path=local_demand_path, - ) + network, audit = prepare_network( + skeleton_network_path=skeleton_path, + consolidated_renewables_path=renewables_path, + tech_costs_path=tech_costs_path, + region=region, + product=product, + cost_year=cost_year, + config=config_dict, + ) + + # Save network + logger.info(f"Saving network to {output_path}") + network.export_to_netcdf(output_path) - # Save outputs - network.export_to_netcdf(snakemake.output.base_network) - logger.info(f"Network saved to {snakemake.output.base_network}") - - with open(snakemake.output.audit, "w") as f: - json.dump(audit_info, f, indent=2, default=str) - logger.info(f"Audit info saved to {snakemake.output.audit}") - - # Save incremental generator sets - incremental_sets_serializable = { - int(demand_mt): [ - { - "bus_id": gen["bus_id"], - "technology": gen["technology"], - "p_nom_max": float(gen["p_nom_max"]), - "avg_cf": float(gen["avg_cf"]), - } - for gen in gen_list - ] - for demand_mt, gen_list in incremental_sets.items() - } - with open(snakemake.output.incremental_sets, "w") as f: - json.dump(incremental_sets_serializable, f, indent=2) - logger.info(f"Incremental sets saved to {snakemake.output.incremental_sets}") - - except Exception as e: - logger.error(f"Network preparation failed: {e}", exc_info=True) - raise + logger.info("✓ Network preparation complete") diff --git a/workflow/scripts/preprocess_consolidate_renewables.py b/workflow/scripts/preprocess_consolidate_renewables.py new file mode 100644 index 0000000..fa6e769 --- /dev/null +++ b/workflow/scripts/preprocess_consolidate_renewables.py @@ -0,0 +1,246 @@ +""" +Consolidate 15 regional renewable supply NetCDF files into a single unified file. + +Input: 15 regional files from data/new_renewables/supply_{Region}_2013_cleaned.nc +Output: data/new_renewables_consolidated.nc with dimensions (region, site_id, time) + +Usage: + python workflow/scripts/preprocess_consolidate_renewables.py [--input-dir data/new_renewables] [--output data/new_renewables_consolidated.nc] +""" + +import logging +from pathlib import Path +from typing import Dict, List, Tuple +import numpy as np +import xarray as xr +import pandas as pd + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + + +def extract_region_from_filename(filepath: Path) -> str: + """Extract region name from supply_{Region}_2013_cleaned.nc filename.""" + name = filepath.stem # Remove .nc extension + # Format: supply_{Region}_2013_cleaned + parts = name.split('_') + if len(parts) >= 2 and parts[0] == 'supply': + # Join all middle parts (handle multi-word regions like 'East_Asia') + region = '_'.join(parts[1:-2]) # Exclude 'supply' prefix and '2013_cleaned' suffix + return region + raise ValueError(f"Could not extract region from filename: {filepath.name}") + + +def load_and_flatten_region(filepath: Path) -> Tuple[str, np.ndarray, np.ndarray, List[str], int]: + """ + Load a regional NetCDF file, preserving technology dimension but flattening class. + + Returns: + (region_name, capacity_by_tech, capacity_factor_by_tech, tech_names, n_classes) + where capacity_by_tech is (n_tech, n_classes) and capacity_factor_by_tech is (n_tech, n_classes, n_time) + """ + region = extract_region_from_filename(filepath) + logger.info(f"Loading {region} from {filepath.name}") + + ds = xr.open_dataset(filepath) + + # Find capacity variable (2D: technology × class) + cap_var = None + for var_name in ds.data_vars: + if 'capacity' in var_name.lower() and ds[var_name].ndim == 2: + cap_var = var_name + break + if cap_var is None: + raise ValueError(f"Could not find 2D capacity variable in {filepath.name}. Available vars: {list(ds.data_vars)}") + + # Find capacity_factor variable (3D: technology × class × time) + cf_var = None + for var_name in ds.data_vars: + if any(x in var_name.lower() for x in ['capacity factor', 'capacity_factor', 'cf', 'power', 'profile']): + if ds[var_name].ndim == 3: + cf_var = var_name + break + if cf_var is None: + raise ValueError(f"Could not find 3D time-series variable in {filepath.name}. Available vars: {list(ds.data_vars)}") + + logger.info(f" Capacity var: {cap_var}, Time-series var: {cf_var}") + logger.info(f" Capacity shape: {ds[cap_var].shape}, CF shape: {ds[cf_var].shape}") + + capacity = ds[cap_var] # (technology, class) + capacity_factor = ds[cf_var] # (technology, class, time) + + # Identify dimension names + tech_dim, class_dim, time_dim = None, None, None + for dim in capacity.dims: + if 'tech' in dim.lower(): + tech_dim = dim + if 'class' in dim.lower(): + class_dim = dim + + for dim in capacity_factor.dims: + if 'tech' in dim.lower(): + tech_dim = dim + if 'class' in dim.lower(): + class_dim = dim + if 'time' in dim.lower(): + time_dim = dim + + if tech_dim is None or class_dim is None: + raise ValueError(f"Could not identify technology/class dimensions. Dims: {capacity.dims}") + + # Get technology names from coordinate + tech_names = ds.coords[tech_dim].values.tolist() + n_classes = ds.sizes[class_dim] + n_time = ds.sizes[time_dim] + + logger.info(f" Technologies: {tech_names}, Classes: {n_classes}, Time: {n_time}") + + # Keep technology dimension intact, just extract data + cap_array = capacity.values # (tech, class) + cf_array = capacity_factor.values # (tech, class, time) + + # Ensure time is last dimension + if capacity_factor.dims.index(time_dim) != 2: + cf_array = np.moveaxis(cf_array, capacity_factor.dims.index(time_dim), -1) + + ds.close() + return region, cap_array, cf_array, tech_names, n_classes + + +def consolidate_renewables(input_dir: Path, output_path: Path) -> None: + """ + Consolidate 15 regional NetCDF files into one unified file, preserving technology dimension. + + Output structure: + - Dimensions: region (15), technology, class, time (8760) + - Variables: capacity (region, technology, class), capacity_factor (region, technology, class, time) + + This preserves the technology distinction (wind, solar, etc.) so PyPSA can create separate + generators per technology and region. + """ + input_dir = Path(input_dir) + output_path = Path(output_path) + + # Find all regional files + nc_files = sorted(input_dir.glob("supply_*_2013_cleaned.nc")) + logger.info(f"Found {len(nc_files)} regional files") + + if len(nc_files) == 0: + raise FileNotFoundError(f"No NetCDF files found in {input_dir}") + + # Load all regions (preserve technology and class dimensions) + regions_data = [] + tech_names = None + n_classes = None + time_length = None + + for filepath in nc_files: + region, cap_array, cf_array, file_tech_names, file_n_classes = load_and_flatten_region(filepath) + + # Verify consistency + if tech_names is None: + tech_names = file_tech_names + elif tech_names != file_tech_names: + raise ValueError(f"Inconsistent technologies: {region} has {file_tech_names}, expected {tech_names}") + + if n_classes is None: + n_classes = file_n_classes + elif n_classes != file_n_classes: + raise ValueError(f"Inconsistent class count: {region} has {file_n_classes}, expected {n_classes}") + + if time_length is None: + time_length = cap_array.shape[-1] if cap_array.ndim == 3 else cf_array.shape[-1] + elif cf_array.shape[-1] != time_length: + raise ValueError(f"Inconsistent time dimensions: {region} has {cf_array.shape[-1]}, expected {time_length}") + + regions_data.append({ + 'region': region, + 'capacity': cap_array, # (tech, class) + 'capacity_factor': cf_array, # (tech, class, time) + }) + + logger.info(f"Tech names: {tech_names}, Classes: {n_classes}, Time length: {time_length}") + + # Stack all regions into (region, tech, class, time) structure + region_names = [r['region'] for r in regions_data] + + # Preallocate arrays + capacity_stacked = np.zeros((len(regions_data), len(tech_names), n_classes), dtype=np.float32) + cf_stacked = np.zeros((len(regions_data), len(tech_names), n_classes, time_length), dtype=np.float32) + + for i, data in enumerate(regions_data): + capacity_stacked[i, :, :] = data['capacity'] + cf_stacked[i, :, :, :] = data['capacity_factor'] + + logger.info(f"Created stacked arrays: capacity {capacity_stacked.shape}, cf {cf_stacked.shape}") + + # Create consolidated xarray Dataset with technology dimension preserved + time_index = pd.date_range('2013-01-01', periods=time_length, freq='h') + class_ids = np.arange(n_classes) + + ds_consolidated = xr.Dataset( + data_vars={ + 'capacity': (['region', 'technology', 'class'], capacity_stacked), + 'capacity_factor': (['region', 'technology', 'class', 'time'], cf_stacked), + }, + coords={ + 'region': region_names, + 'technology': tech_names, + 'class': class_ids, + 'time': time_index, + }, + attrs={ + 'description': 'Consolidated renewable supply profiles for 15 global regions, with technology distinction', + 'source': 'data/new_renewables/*.nc', + 'temporal_resolution': 'hourly', + 'year': 2013, + 'technologies': ', '.join(tech_names), + } + ) + + # Add variable attributes + ds_consolidated['capacity'].attrs = { + 'long_name': 'Installed capacity', + 'units': 'MW', + } + ds_consolidated['capacity_factor'].attrs = { + 'long_name': 'Capacity factor (power output / installed capacity)', + 'units': 'p.u.', + } + + # Write to NetCDF + output_path.parent.mkdir(parents=True, exist_ok=True) + logger.info(f"Writing consolidated file to {output_path}") + ds_consolidated.to_netcdf(output_path, encoding={ + 'capacity': {'dtype': 'float32', 'zlib': True, 'complevel': 4}, + 'capacity_factor': {'dtype': 'float32', 'zlib': True, 'complevel': 4}, + }) + + logger.info(f"✓ Consolidation complete: {output_path}") + logger.info(f" Regions: {len(region_names)}") + logger.info(f" Technologies: {tech_names}") + logger.info(f" Classes per region: {n_classes}") + logger.info(f" Timesteps: {time_length}") + logger.info(f" Output size: {output_path.stat().st_size / 1e6:.1f} MB") + + +if __name__ == '__main__': + import argparse + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + '--input-dir', + type=Path, + default=Path('data/new_renewables'), + help='Directory containing supply_*.nc files (default: data/new_renewables)', + ) + parser.add_argument( + '--output', + type=Path, + default=Path('data/new_renewables_consolidated.nc'), + help='Output path for consolidated file (default: data/new_renewables_consolidated.nc)', + ) + + args = parser.parse_args() + + consolidate_renewables(args.input_dir, args.output) From 60c4e46423a1f52fdc020736303f295fb09f2fde Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 6 May 2026 17:43:09 +0200 Subject: [PATCH 056/216] feat: add blocs for trace regions --- config/blocs_traceregions.yaml | 64 ++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 config/blocs_traceregions.yaml diff --git a/config/blocs_traceregions.yaml b/config/blocs_traceregions.yaml new file mode 100644 index 0000000..89badaa --- /dev/null +++ b/config/blocs_traceregions.yaml @@ -0,0 +1,64 @@ +metadata: + describtion: "This file defines the bloc and region definitions for the friendshoring analysis. Bloc alignments are adapted from the classifications in Javorcik et al. (2024) to fit the corresponding regions." + source: "Javorcik, B., Kitzmüller, L., Schweiger, H., & Yıldırım, M. A. (2024). Economic costs of friendshoring. The World Economy, 47, 2871–2908. https://doi.org/10.1111/twec.13555" + +regions: + Europe: + country_members: [AL, AT, BY, BE, BA, BG, HR, CY, CZ, DK, EE, FI, DE, GR, HU, IT, XK, LV, LT, LU, MK, MD, ME, NL, NO, PL, RO, RS, SK, SI, SE, CH, TR, UA] + bloc_alignment: {A: 1, B: 1, C: 1, D: 1} + + Far_West_Europe: + country_members: [FR, GL, IS, IE, PT, ES, GB] + bloc_alignment: {A: 1, B: 1, C: 1, D: 1} + + Middle_East: + country_members: [BH, IR, IQ, IL, JO, KW, LB, OM, PS, QA, SA, SY, AE, YE] + bloc_alignment: {A: 2, B: 2, C: 2, D: 3} + + North_West_Africa: + country_members: [BJ, BF, CI, GM, GH, GN, GW, LR, NG, SN, SL, TG, DZ, TD, EG, ER, LY, ML, MR, MA, NE, SD, TN, EH] + bloc_alignment: {A: 2, B: 2, C: 2, D: 3} + + Subsaharan_Africa: + country_members: [AO, BW, BI, CM, CF, CD, ET, KE, GA, MG, MW, MZ, NA, CG, RW, SO, ZA, SS, TZ, UG, ZM, ZW] + bloc_alignment: {A: 2, B: 2, C: 2, D: 3} + + North_America: + country_members: [CA, US] + bloc_alignment: {A: 1, B: 1, C: 1, D: 1} + + Eurasia: + country_members: [AM, AZ, GE, KZ, KG, TJ, TM, UZ, RU] + bloc_alignment: {A: 2, B: 2, C: 2, D: 3} + + South_America: + country_members: [BO, BR, CO, EC, GF, GY, PY, PE, SR, VE] + bloc_alignment: {A: 1, B: 2, C: 2, D: 2} + + South_South_America: + country_members: [AR, CL, UY] + bloc_alignment: {A: 1, B: 2, C: 2, D: 2} + + Central_America: + country_members: [CR, SV, GT, HN, MX, NI, PA, CU, DO, JM, HT] + bloc_alignment: {A: 2, B: 2, C: 2, D: 3} + + West_Asia: + country_members: [AF, BD, BT, IN, NP, PK, LK] + bloc_alignment: {A: 2, B: 2, C: 2, D: 3} + + East_Asia: + country_members: [CN, HK, MN, TW] + bloc_alignment: {A: 2, B: 2, C: 2, D: 2} + + Pacific_Asia: + country_members: [BN, KH, ID, LA, MY, MM, PG, PH, SG, TH, VN] + bloc_alignment: {A: 2, B: 2, C: 2, D: 2} + + East_East_Asia: + country_members: [JP, KR, KP] + bloc_alignment: {A: 1, B: 1, C: 3, D: 3} + + Oceania: + country_members: [AU, NZ] + bloc_alignment: {A: 1, B: 1, C: 3, D: 3} From fab7e2cf072b5f0f31dab75d1ba63c15489f6b37 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 6 May 2026 18:01:42 +0200 Subject: [PATCH 057/216] feat: consolidate snakemake workflow Co-authored-by: Copilot --- workflow/Snakefile | 5 +++++ workflow/scripts/build_x_supply_chain.py | 5 ++++- workflow/scripts/calculate_lcox.py | 5 ++++- workflow/scripts/create_supply_curve.py | 5 ++++- workflow/scripts/model_trade.py | 5 ++++- workflow/scripts/model_trade_singlestage.py | 5 ++++- workflow/scripts/prepare_regional_network.py | 8 +++++--- workflow/scripts/tech_database.py | 5 ++++- 8 files changed, 34 insertions(+), 9 deletions(-) diff --git a/workflow/Snakefile b/workflow/Snakefile index 92e3edd..e4469a3 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -2,6 +2,11 @@ import sys from snakemake.utils import Paramspace import pandas as pd + +# Root-only usage: call Snakemake from repository root. +workdir: "workflow" + + # HTTP = HTTPRemoteProvider() sys.path.append("./scripts") diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 911bc2a..06ab4f9 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -27,6 +27,7 @@ """ import logging +from typing import Any import pandas as pd import numpy as np import pypsa @@ -36,6 +37,8 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) +snakemake: Any = globals().get("snakemake") + # Technology parameters with no database source (assumed values) TECH_ASSUMPTIONS = { @@ -306,7 +309,7 @@ def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Networ if __name__ == "__main__": - if "snakemake" not in globals(): + if snakemake is None: raise RuntimeError( "This script must be run via Snakemake with cost_year wildcard" ) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 89fba7b..e411459 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -27,10 +27,13 @@ import logging import os from pathlib import Path +from typing import Any import pypsa import pandas as pd import numpy as np +snakemake: Any = globals().get("snakemake") + # ============================================================================ # LOGGING SETUP # ============================================================================ @@ -552,7 +555,7 @@ def extract_lcox(network, product, demands): if __name__ == "__main__": - if "snakemake" not in globals(): + if snakemake is None: from _helpers import mock_snakemake snakemake = mock_snakemake( diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index f7737fb..d43854b 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -1,8 +1,11 @@ import os +from typing import Any import pandas as pd import matplotlib import matplotlib.pyplot as plt +snakemake: Any = globals().get("snakemake") + matplotlib.use("Agg") @@ -187,7 +190,7 @@ def create_supply_curve(): # Setup columns and product before function execution (needed for both Snakemake and main) -if "snakemake" not in globals(): +if snakemake is None: from _helpers import mock_snakemake snakemake = mock_snakemake( diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 871f123..c835810 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -1,12 +1,15 @@ import pypsa import pandas as pd import matplotlib +from typing import Any matplotlib.use("Agg") import matplotlib.pyplot as plt import os import cartopy.crs as ccrs +snakemake: Any = globals().get("snakemake") + plt.style.use("bmh") @@ -703,7 +706,7 @@ def solve_network(n, mga=None): if __name__ == "__main__": - if "snakemake" not in globals(): + if snakemake is None: from _helpers import mock_snakemake snakemake = mock_snakemake( diff --git a/workflow/scripts/model_trade_singlestage.py b/workflow/scripts/model_trade_singlestage.py index 22995b6..4f2afa8 100644 --- a/workflow/scripts/model_trade_singlestage.py +++ b/workflow/scripts/model_trade_singlestage.py @@ -2,6 +2,9 @@ import pandas as pd import matplotlib.pyplot as plt import os +from typing import Any + +snakemake: Any = globals().get("snakemake") plt.style.use("bmh") @@ -294,7 +297,7 @@ def plot_trade_network(n): if __name__ == "__main__": - if "snakemake" not in globals(): + if snakemake is None: from _helpers import mock_snakemake snakemake = mock_snakemake( diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 58947c0..c62f361 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -24,7 +24,7 @@ """ import logging -from typing import Dict, Tuple +from typing import Any, Dict, Tuple import numpy as np import pandas as pd import xarray as xr @@ -37,6 +37,8 @@ level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" ) +snakemake: Any = globals().get("snakemake") + def load_region_renewables_consolidated( consolidated_path: str, @@ -500,7 +502,7 @@ def prepare_network( if __name__ == "__main__": # Check if running from Snakemake - if "snakemake" in dir(): + if snakemake is not None: # Snakemake inputs/outputs skeleton_path = snakemake.input.skeleton renewables_path = snakemake.input.renewables @@ -517,7 +519,7 @@ def prepare_network( output_path = snakemake.output[0] # Load config (if available) - config_dict = snakemake.config if "snakemake" in dir() else {} + config_dict = snakemake.config if snakemake is not None else {} else: # Fallback for manual execution import sys diff --git a/workflow/scripts/tech_database.py b/workflow/scripts/tech_database.py index f13222e..83dc2d9 100644 --- a/workflow/scripts/tech_database.py +++ b/workflow/scripts/tech_database.py @@ -9,11 +9,14 @@ import logging from pathlib import Path +from typing import Any import pandas as pd logger = logging.getLogger(__name__) +snakemake: Any = globals().get("snakemake") + def download_tech_database( version: str, output_path: str, disable_progress: bool = False @@ -69,7 +72,7 @@ def get_tech_param( # Snakemake integration: Allow direct execution as rule if __name__ == "__main__": - if "snakemake" not in globals(): + if snakemake is None: from _helpers import mock_snakemake snakemake = mock_snakemake("retrieve_cost_data", year=2030) From db22d7f55519a1936c8d81ca3ac58abafab977eb Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 7 May 2026 12:21:11 +0200 Subject: [PATCH 058/216] chore: improve snakemake workflow and split thematical tasks Co-authored-by: Copilot --- README.md | 5 +- Snakefile | 30 ++++ pixi.toml | 1 + rules/reporting.smk | 23 +++ rules/supply_curves.smk | 122 ++++++++++++++++ rules/trade_model.smk | 64 +++++++++ workflow/Snakefile | 311 ---------------------------------------- 7 files changed, 242 insertions(+), 314 deletions(-) create mode 100644 Snakefile create mode 100644 rules/reporting.smk create mode 100644 rules/supply_curves.smk create mode 100644 rules/trade_model.smk delete mode 100644 workflow/Snakefile diff --git a/README.md b/README.md index 67156ff..35b2ce1 100644 --- a/README.md +++ b/README.md @@ -52,14 +52,13 @@ For details, see https://pixi.prefix.dev/latest/ (or your local PIXI docs). In the workspace root: ```sh -cd workflow -pixi run snakemake -call model_trade_all +pixi run snakemake model_trade_all ``` To collect all figures (under development): ```sh -pixi run snakemake -call collect_figures +pixi run snakemake collect_figures ``` diff --git a/Snakefile b/Snakefile new file mode 100644 index 0000000..10074e1 --- /dev/null +++ b/Snakefile @@ -0,0 +1,30 @@ +"""Root Snakemake entrypoint for the Shift workflow. + +Loads shared configuration and includes the modular rule files for supply curves, +trade optimization, and reporting. +""" + +from pathlib import Path + +import pandas as pd +from snakemake.utils import Paramspace + +WORKFLOW_DIR = Path("workflow") +SCRIPT_DIR = WORKFLOW_DIR / "scripts" + + +configfile: "config/config.yaml" + + +trade_scenarios = Paramspace(pd.read_csv("config/trade_scenarios.csv", dtype=str)) + + +wildcard_constraints: + country="[a-zA-Z]+", + sweep="[a-zA-Z]+", + rule="(0|[1-9][0-9]?|100)", + + +include: "rules/supply_curves.smk" +include: "rules/trade_model.smk" +include: "rules/reporting.smk" diff --git a/pixi.toml b/pixi.toml index cf87542..8ab5d56 100644 --- a/pixi.toml +++ b/pixi.toml @@ -7,6 +7,7 @@ platforms = ["linux-64", "win-64"] version = "0.1.0" [tasks] +snakemake = "snakemake" [dependencies] # Core Python diff --git a/rules/reporting.smk b/rules/reporting.smk new file mode 100644 index 0000000..9952f8c --- /dev/null +++ b/rules/reporting.smk @@ -0,0 +1,23 @@ +"""Reporting workflow rules. + +Collects final figures and presentation artifacts produced by notebooks and the +main optimization workflow. +""" + + +rule collect_figures: + input: + global_supply_curve="results/figures_general/{scenario}/global_supply_curve.pdf", + global_supply_curve_png="results/figures_general/{scenario}/global_supply_curve.png", + electricity_demand="results/figures_general/electricity_demand.pdf", + electricity_demand_png="results/figures_general/electricity_demand.png", + electricity_demand_steel="results/figures_general/electricity_demand_in_steel.pdf", + electricity_demand_steel_png="results/figures_general/electricity_demand_in_steel.png", + global_map_countries="results/figures_general/global_map_countries.pdf", + global_map_countries_png="results/figures_general/global_map_countries.png", + cost_comparison="results/figures_general/comparison/cost_comparison.pdf", + cost_comparison_png="results/figures_general/comparison/cost_comparison.png", + value_chain_comparison="results/figures_general/value_chain_comparison.pdf", + value_chain_comparison_png="results/figures_general/value_chain_comparison.png", + hourly_analysis="results/figures_general/hourly_analysis.pdf", + hourly_analysis_png="results/figures_general/hourly_analysis.png", diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk new file mode 100644 index 0000000..34b4fe1 --- /dev/null +++ b/rules/supply_curves.smk @@ -0,0 +1,122 @@ +"""Supply-curve workflow rules. + +Builds technology inputs, prepares regional PyPSA networks, solves regional LCoX +problems, and aggregates the resulting supply curves. +""" + + +rule retrieve_cost_data: + output: + costs="resources/technology_data/costs_{cost_year}.csv", + threads: 1 + resources: + mem_mb=500, + params: + version=config["costs"]["version"], + script: + str(SCRIPT_DIR / "tech_database.py") + + +rule build_steel_skeleton: + input: + costs="resources/technology_data/costs_{cost_year}.csv", + output: + skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + threads: 1 + resources: + mem_mb=1000, + script: + str(SCRIPT_DIR / "build_x_supply_chain.py") + + +rule prepare_regional_network: + input: + skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + renewables="data/new_renewables_consolidated.nc", + tech_costs="resources/technology_data/costs_{cost_year}.csv", + output: + network="resources/networks/base_{cost_year}_{region}_{product}.nc", + log: + "logs/prepare_regional_network_{cost_year}_{region}_{product}.log", + threads: 1 + resources: + mem_mb=2000, + params: + region="{region}", + product="{product}", + config=config, + message: + "Preparing regional network: {wildcards.region} → {wildcards.product} " + "(cost_year={wildcards.cost_year})" + script: + str(SCRIPT_DIR / "prepare_regional_network.py") + + +if config["enable"].get("run_supply_chain", True): + + rule calculate_regional_lcox: + input: + base_network="resources/networks/base_{cost_year}_{region}_{product}.nc", + local_demand="data/un_enerdata_demand_2050_final.csv", + output: + results="resources/lco-{product}/cost_year~{cost_year}/{region}/results_{product_demand_mt}.csv", + network=( + temp( + "resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" + ) + if not config.get("outputs", {}).get( + "keep_optimization_networks", False + ) + else "resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" + ), + wildcard_constraints: + product_demand_mt=r"\d+(?:\.\d+)?", + threads: 2 + resources: + mem_mb=4000, + params: + product_demand_mt="{product_demand_mt}", + compute_iis=config.get("solver", {}).get("compute_iis", False), + message: + "Calculating LCoX for {wildcards.product} in region {wildcards.region} " + "(product_demand={wildcards.product_demand_mt} Mt/year)." + script: + str(SCRIPT_DIR / "calculate_lcox.py") + + +if config["enable"].get("run_supply_curve", True): + + rule create_supply_curve: + input: + lco_product_data=lambda wildcards: expand( + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{product_demand_mt}}.csv", + product_demand_mt=config.get("steel_demand_levels"), + ), + local_demand="data/un_enerdata_demand_2050_final.csv", + steel_demand="resources/steel_production_clustered.csv", + output: + supply="resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", + supply_nodemand=( + "resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv" + if config.get("outputs", {}).get("save_supply_nodemand", True) + else temp( + "resources/supply_curves_nodemand_tmp/{region}_{product}.csv" + ) + ), + supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", + threads: 1 + message: + "Combining LCo{wildcards.product[0]} results (all product demand levels) to create supply curve for {wildcards.region}." + script: + str(SCRIPT_DIR / "create_supply_curve.py") + + +rule create_all_supply_curves: + input: + expand( + "resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", + cost_year=[2050], + region=config["regions"], + product=["steel"], + allow_missing=True, + ), diff --git a/rules/trade_model.smk b/rules/trade_model.smk new file mode 100644 index 0000000..911c04b --- /dev/null +++ b/rules/trade_model.smk @@ -0,0 +1,64 @@ +"""Trade-model workflow rules. + +Consumes supply curves and scenario inputs to run the interregional trade model +and collect the scenario-level outputs. +""" + + +rule model_trade: + input: + supply_curves_interone=expand( + "resources/supply_curves/cost_year~{cost_year}/{region}_{interone}.csv", + allow_missing=True, + cost_year=[2050], + region=config["regions"], + ), + supply_curves_intertwo=expand( + "resources/supply_curves/cost_year~{cost_year}/{region}_{intertwo}.csv", + allow_missing=True, + cost_year=[2050], + region=config["regions"], + ), + transport_costs="data/transport_costs/steel_r_iron_r.csv", + trade_options="data/trade_opt.csv", + bus_locations="data/bus_locations.csv", + demand="data/un_enerdata_demand_2050_final.csv", + steel_demand="resources/steel_production_clustered.csv", + iron_ore="resources/ironore_production_clustered.csv", + grid_potential="data/grid_potential_custom.csv", + output: + trade_result=f"results/{trade_scenarios.wildcard_pattern}/result.csv", + trade_network=f"results/{trade_scenarios.wildcard_pattern}/network.nc", + trade_plot_ironore=f"results/{trade_scenarios.wildcard_pattern}/map_ironore.pdf", + trade_plot_ironore_png=f"results/{trade_scenarios.wildcard_pattern}/map_ironore.png", + trade_plot_hbi=f"results/{trade_scenarios.wildcard_pattern}/map_hbi.pdf", + trade_plot_hbi_png=f"results/{trade_scenarios.wildcard_pattern}/map_hbi.png", + trade_plot_steel=f"results/{trade_scenarios.wildcard_pattern}/map_steel.pdf", + trade_plot_steel_png=f"results/{trade_scenarios.wildcard_pattern}/map_steel.png", + threads: 4 + params: + iron_ore_potential=config["iron_ore"]["potential_allowance"], + cost_penalty=config["design"]["cost_penalty"], + scenarios=config["scenario"], + script: + str(SCRIPT_DIR / "model_trade.py") + + +rule model_trade_all: + input: + networks=expand( + "results/{scenarios}/network.nc", + scenarios=trade_scenarios.instance_patterns, + ), + results=expand( + "results/{scenarios}/result.csv", + scenarios=trade_scenarios.instance_patterns, + ), + trade_plot_ironore=expand( + "results/{scenarios}/map_ironore.pdf", + scenarios=trade_scenarios.instance_patterns, + ), + trade_plot_steel=expand( + "results/{scenarios}/map_steel.pdf", + scenarios=trade_scenarios.instance_patterns, + ), diff --git a/workflow/Snakefile b/workflow/Snakefile deleted file mode 100644 index e4469a3..0000000 --- a/workflow/Snakefile +++ /dev/null @@ -1,311 +0,0 @@ -import sys -from snakemake.utils import Paramspace -import pandas as pd - - -# Root-only usage: call Snakemake from repository root. -workdir: "workflow" - - -# HTTP = HTTPRemoteProvider() -sys.path.append("./scripts") - -# Read scenario definitions to construct wildcard and instance patterns from them -trade_scenarios = Paramspace(pd.read_csv("../config/trade_scenarios.csv", dtype=str)) - - -configfile: "../config/config.yaml" - - -# ---------------------------------------------------------------------------------- -# SECTION 0: Workflow metadata and execution hints -# - origin: README workflow description -# - scenario definitions loaded from ../config/trade_scenarios.csv -# - config loaded from ../config/config.yaml -# - This file orchestrates greenfield supply curve generation, interregional trade modelling, and figure collection. -# ---------------------------------------------------------------------------------- - - -wildcard_constraints: - country="[a-zA-Z]+", - sweep="[a-zA-Z]+", - rule="(0|[1-9][0-9]?|100)", - - -# ---------------------------------------------------------------------------------- -# SECTION 1: Greenfield supply curve generation (PyPSA) -# Description: produce region-specific levelized cost curves for steel via PyPSA. -# Inputs: -# - ../data/renewable_clusters.nc (renewable capacity & timeseries) -# - ../resources/technology_data/costs_{cost_year}.csv (tech cost data) -# Outputs: -# - ../resources/networks/base_{cost_year}_{region}_{product}.nc (prepped regional network) -# - ../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv (regional supply curve) -# Config gate: config["enable"]["run_supply_chain"], config["enable"]["run_supply_curve"] -# Script mapping: -# - scripts/tech_database.py -# - scripts/build_x_supply_chain.py -# - scripts/prepare_regional_network.py -# - scripts/calculate_lcox.py -# - scripts/create_supply_curve.py -# ---------------------------------------------------------------------------------- - - -rule retrieve_cost_data: - output: - costs="../resources/technology_data/costs_{cost_year}.csv", - threads: 1 - resources: - mem_mb=500, - params: - version=config["costs"]["version"], - script: - "scripts/tech_database.py" - - -# Build steel supply chain technology skeleton -rule build_steel_skeleton: - input: - costs="../resources/technology_data/costs_{cost_year}.csv", - output: - # Reusable skeleton for all regional networks - shared across supply chains - # Keep as persistent resource for consistency when extending to multiple products - skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", - threads: 1 - resources: - mem_mb=1000, - script: - "scripts/build_x_supply_chain.py" - - -# Prepare regional PyPSA network with consolidated renewable generators -# INPUT: consolidated renewable profiles (region, technology, class, time) -# OUTPUT: PyPSA network with renewables + supply chain for target product -rule prepare_regional_network: - input: - skeleton="../resources/steel_skeleton/steel_skeleton_{cost_year}.nc", - renewables="../data/new_renewables_consolidated.nc", # Consolidated file: (region, technology, class, time) - tech_costs="../resources/technology_data/costs_{cost_year}.csv", - output: - network="../resources/networks/base_{cost_year}_{region}_{product}.nc", - log: - "../logs/prepare_regional_network_{cost_year}_{region}_{product}.log", - threads: 1 - resources: - mem_mb=2000, - params: - region="{region}", - product="{product}", - config=config, - message: - "Preparing regional network: {wildcards.region} → {wildcards.product} " - "(cost_year={wildcards.cost_year})" - script: - "scripts/prepare_regional_network.py" - - -if config["enable"].get("run_supply_chain", True): - - rule calculate_regional_lcox: - input: - base_network="../resources/networks/base_{cost_year}_{region}_{product}.nc", - local_demand="../data/un_enerdata_demand_2050_final.csv", - output: - results="../resources/lco-{product}/cost_year~{cost_year}/{region}/results_{product_demand_mt}.csv", - network=( - temp( - "../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" - ) - if not config.get("outputs", {}).get( - "keep_optimization_networks", False - ) - else "../resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" - ), - wildcard_constraints: - product_demand_mt=r"\d+", - threads: 2 - resources: - mem_mb=4000, - params: - product_demand_mt="{product_demand_mt}", - compute_iis=config.get("solver", {}).get("compute_iis", False), - message: - "Calculating LCoX for {wildcards.product} in region {wildcards.region} " - "(product_demand={wildcards.product_demand_mt} Mt/year)." - script: - "scripts/calculate_lcox.py" - - -# Read all the individual LCoX values for one region and combine them into a supply curve -# stored as a single csv file per region. Note: the visual plot includes iron ore costs, the csv without since it is added later in the workflow. - -if config["enable"].get("run_supply_curve", True): - - rule create_supply_curve: - input: - # Reference results from calculate_regional_lcox (uses product_demand_mt from config) - lco_product_data=lambda wildcards: expand( - f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{product_demand_mt}}.csv", - product_demand_mt=config.get("steel_demand_levels"), - ), - local_demand="../data/un_enerdata_demand_2050_final.csv", - steel_demand="../resources/steel_production_clustered.csv", - output: - supply="../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - # Toggleable reference: supply curve with all generators available (no local demand constraint) - # Set save_supply_nodemand=True in config to generate for validation/comparison - supply_nodemand=( - "../resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv" - if config.get("outputs", {}).get("save_supply_nodemand", True) - else temp( - "../resources/supply_curves_nodemand_tmp/{region}_{product}.csv" - ) - ), - # PDF plots: low disk cost, high validation value - always generated - supply_curve="../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", - threads: 1 - message: - "Combining LCo{wildcards.product[0]} results (all product demand levels) to create supply curve for {wildcards.region}." - script: - "scripts/create_supply_curve.py" - - -rule create_all_supply_curves: - input: - expand( - "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - cost_year=[2050], - region=config["regions"], - product=[ - "steel" - ], # TODO: product=["steel", "hydrogen"] - add hbi/h2 products once tested - allow_missing=True, - ), - - -# ---------------------------------------------------------------------------------- -# SECTION 2: Interregional trade model -# Description: build trade results and network maps for all scenarios from supply curves. -# config/scenario-driven: uses trade_scenarios wildcard patterns from ../config/trade_scenarios.csv -# including optional cost scenario params from `config["trade"]` + `config["design"]`. -# ---------------------------------------------------------------------------------- - - -rule model_trade: - input: - supply_curves_interone=expand( - "../resources/supply_curves/cost_year~{cost_year}/{region}_{interone}.csv", - allow_missing=True, - cost_year=[2050], - region=config["regions"], - ), - supply_curves_intertwo=expand( - "../resources/supply_curves/cost_year~{cost_year}/{region}_{intertwo}.csv", - allow_missing=True, - cost_year=[2050], - region=config["regions"], - ), - # supply_curves_final = expand( - # "../resources/supply_curves/cost_year~{cost_year}/{region}_{final}.csv", - # allow_missing=True, region=config["regions"]), - transport_costs="../data/transport_costs/steel_r_iron_r.csv", - trade_options="../data/trade_opt.csv", - bus_locations="../data/bus_locations.csv", - demand="../data/un_enerdata_demand_2050_final.csv", - steel_demand="../resources/steel_production_clustered.csv", - iron_ore="../resources/ironore_production_clustered.csv", - grid_potential="../data/grid_potential_custom.csv", - output: - trade_result=f"../results/{trade_scenarios.wildcard_pattern}/result.csv", - trade_network=f"../results/{trade_scenarios.wildcard_pattern}/network.nc", - trade_plot_ironore=f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.pdf", - trade_plot_ironore_png=f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.png", - trade_plot_hbi=f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.pdf", - trade_plot_hbi_png=f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.png", - trade_plot_steel=f"../results/{trade_scenarios.wildcard_pattern}/map_steel.pdf", - trade_plot_steel_png=f"../results/{trade_scenarios.wildcard_pattern}/map_steel.png", - threads: 4 - params: - iron_ore_potential=config["iron_ore"]["potential_allowance"], - cost_penalty=config["design"]["cost_penalty"], - scenarios=config["scenario"], - script: - "scripts/model_trade.py" - - -# Povide a rule which triggers creation of all scenarios listed (= rows) in 'scenarios/trade_scenarios.csv' -rule model_trade_all: - input: - networks=expand( - "../results/{scenarios}/network.nc", - scenarios=trade_scenarios.instance_patterns, - ), - results=expand( - "../results/{scenarios}/result.csv", - scenarios=trade_scenarios.instance_patterns, - ), - trade_plot_ironore=expand( - "../results/{scenarios}/map_ironore.pdf", - scenarios=trade_scenarios.instance_patterns, - ), - trade_plot_steel=expand( - "../results/{scenarios}/map_steel.pdf", - scenarios=trade_scenarios.instance_patterns, - ), - - -# ---------------------------------------------------------------------------------- -# SECTION 3: Results collection and figure outputs -# Description: collect analysis figures (cost curves, maps, demand profiles) for presentation. -# This is a final aggregation stage and depends on outputs from earlier modelling / notebooks. -# ---------------------------------------------------------------------------------- - - -rule collect_figures: - input: - global_supply_curve="../results/figures_general/{scenario}/global_supply_curve.pdf", #workflow/notebooks/analysis-coststructure.ipynb - global_supply_curve_png="../results/figures_general/{scenario}/global_supply_curve.png", #workflow/notebooks/analysis-coststructure.ipynb - electricity_demand="../results/figures_general/electricity_demand.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_png="../results/figures_general/electricity_demand.png", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_steel="../results/figures_general/electricity_demand_in_steel.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_steel_png="../results/figures_general/electricity_demand_in_steel.png", #workflow/notebooks/analysis-electricity-demand.ipynb - global_map_countries="../results/figures_general/global_map_countries.pdf", #workflow/notebooks/plot_countries.ipynb - global_map_countries_png="../results/figures_general/global_map_countries.png", #workflow/notebooks/plot_countries.ipynb - cost_comparison="../results/figures_general/comparison/cost_comparison.pdf", #workflow/notebooks/compare-scenarios.ipynb - cost_comparison_png="../results/figures_general/comparison/cost_comparison.png", #workflow/notebooks/compare-scenarios.ipynb - value_chain_comparison="../results/figures_general/value_chain_comparison.pdf", #workflow/notebooks/analyse-steel-hbi-split.ipynb - value_chain_comparison_png="../results/figures_general/value_chain_comparison.png", #workflow/notebooks/analyse-steel-hbi-split.ipynb - hourly_analysis="../results/figures_general/hourly_analysis.pdf", #workflow/notebooks/analysis-hourly.ipynb - hourly_analysis_png="../results/figures_general/hourly_analysis.png", #workflow/notebooks/analysis-hourly.ipynb - - -# rule model_trade_singlestage: -# input: -# supply_curves = expand( -# "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", -# allow_missing=True, region=config["regions"] ), -# transport_costs = "../data/transport_costs/{transport_cost}.csv", -# trade_options = "../data/trade_opt.csv", -# bus_locations = "../data/bus_locations.csv", -# demand = "../data/un_enerdata_demand_2050_final.csv", -# steel_demand = "../resources/steel_production_clustered.csv", -# output: -# trade_result = f"../results/{trade_scenarios.wildcard_pattern}/result.csv", -# trade_network = f"../results/{trade_scenarios.wildcard_pattern}/network.nc", -# trade_plot = f"../results/{trade_scenarios.wildcard_pattern}/plot.pdf" -# script: -# "scripts/model_trade_singlestage.py" -### Toolkit -# Under development -# rule input_cost_comp: -# message: -# "Comparing input costs" -# notebook: -# "workflow/notebooks/input-cost-comp.ipynb" -# ---------------------------------------------------------------------------------- -# RUNNING THE WORKFLOW (developer convenience) -# Use e.g.: -# snakemake -s workflow/Snakefile -j 8 model_trade_all -# snakemake -s workflow/Snakefile -j 4 prepare_regional_network cost_year=2030 region=Europe product=steel -# snakemake -s workflow/Snakefile -j 4 create_supply_curve cost_year=2030 region=EU product=steel -# ---------------------------------------------------------------------------------- From 627d632b2d8e15a5e130c9aeb6a1d1d2ebabb679 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 7 May 2026 12:49:14 +0200 Subject: [PATCH 059/216] fix: configure independent snakemake workflow path --- Snakefile | 2 +- rules/supply_curves.smk | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Snakefile b/Snakefile index 10074e1..13fc2ec 100644 --- a/Snakefile +++ b/Snakefile @@ -9,7 +9,7 @@ from pathlib import Path import pandas as pd from snakemake.utils import Paramspace -WORKFLOW_DIR = Path("workflow") +WORKFLOW_DIR = Path(workflow.basedir) / "workflow" SCRIPT_DIR = WORKFLOW_DIR / "scripts" diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 34b4fe1..97d33b8 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -46,7 +46,7 @@ rule prepare_regional_network: product="{product}", config=config, message: - "Preparing regional network: {wildcards.region} → {wildcards.product} " + "Preparing regional network: {wildcards.region} -> {wildcards.product} " "(cost_year={wildcards.cost_year})" script: str(SCRIPT_DIR / "prepare_regional_network.py") From 207f8c0cd69e202cf73732eb7e53d8efa47f445c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 11 May 2026 15:36:38 +0200 Subject: [PATCH 060/216] chore: order pixi dependencies --- pixi.toml | 57 +++++++++++++++++++++---------------------------------- 1 file changed, 22 insertions(+), 35 deletions(-) diff --git a/pixi.toml b/pixi.toml index 8ab5d56..9c7addc 100644 --- a/pixi.toml +++ b/pixi.toml @@ -10,44 +10,31 @@ version = "0.1.0" snakemake = "snakemake" [dependencies] -# Core Python -python = ">=3.12" - -# Inhouse packages - energy system modeling -pypsa = ">=0.32.1" -atlite = ">=0.3" -linopy = ">=0.4.4" - -# Workflow orchestration -snakemake-minimal = ">=9" -snakemake-storage-plugin-http = ">=0.3" -snakemake-executor-plugin-slurm = "*" -snakemake-executor-plugin-cluster-generic = "*" - -# Data processing and I/O -pandas = ">=2.1" -numpy = "*" -xarray = ">=2024.03.0" -netcdf4 = "*" -libgdal-netcdf = "*" - -# Geospatial and mapping -geopandas = ">=1" -pycountry = "*" +atlite = ">=0.3,!=0.5.0" +cartopy = ">=0.25.0" +geojson = ">=3.2.0" geohash2 = "*" -cartopy = "*" - -# Scientific computing -scipy = "*" -networkx = "*" -seaborn = "*" -matplotlib = "*" - -# Utilities +geopandas = ">=1" +geopy = ">=2.4.1" +libgdal-netcdf = ">=3.10.3" +linopy = ">=0.6.1" +matplotlib = ">=3.10.7" +netcdf4 = ">=1.7.2,!=1.7.4" +networkx = ">=3.5" +numpy = ">=1.26.4" +pandas = ">=2.1" +pycountry = ">=24.6.1" +pypsa = ">=1.1.2" +python = ">=3.10" pyyaml = "*" +scipy = ">=1.16.3" +seaborn = ">=0.13.2" +snakemake-executor-plugin-cluster-generic = ">=1.0.9" +snakemake-executor-plugin-slurm = "*" +snakemake-minimal = ">=9" +snakemake-storage-plugin-http = ">=0.3" tqdm = "*" -geopy = "*" -geojson = "*" +xarray = ">=2026.0.0" [pypi-dependencies] # pip-installable packages (always included) From eb985f241f194a5e6dd41512144f8681267d9e74 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 11 May 2026 15:42:06 +0200 Subject: [PATCH 061/216] feat: clean up and improve domestic reservation for renewable generators --- config/config.yaml | 30 +++- rules/supply_curves.smk | 48 ++++-- workflow/scripts/calculate_lcox.py | 71 ++++++++- workflow/scripts/create_supply_curve.py | 122 +++++++++------ workflow/scripts/prepare_regional_network.py | 153 ++++++++++++++++--- 5 files changed, 326 insertions(+), 98 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 050de83..168becf 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -8,11 +8,20 @@ outputs: save_supply_nodemand: True # Generate reference supply curve (all generators available) alongside main curve keep_optimization_networks: False # Keep .nc network files from optimization (set False to save disk space) +# Supply curve scenario configuration +# Two scenarios are supported: +# - reserved (DEFAULT): highest-performing (best CF) renewable generators reserved for domestic +# electricity demand; export/HBI supply stack reduced before optimization. Requires electricity demand data. +# - unconstrained (OPTIONAL, FALLBACK): no domestic reservation; full renewable stack available. +# Can be generated without electricity demand data when demand data unavailable. +supply_curve: + generate_unconstrained: True # Set to True to generate unconstrained scenario as fallback + # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [10, 200, 1000] # Mt/year +steel_demand_levels: [0.762, 10, 200, 1000] # Mt/year hydrogen_storage_cost: False iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted @@ -26,10 +35,21 @@ run: # Region definitions (ISO3 codes matching renewable clusters metadata) regions: - "Europe": ["DEU", "FRA", "GBR", "ITA", "ESP", "POL", "NLD", "BEL", "SWE", "NOR"] - "Africa_West": ["SEN", "GMB", "GIN", "SLE"] # Senegal, Gambia, Guinea, Sierra Leone - "Africa_Central": ["GNQ", "GAB", "CMR", "CAF"] # Equatorial Guinea, Gabon, Cameroon, Central African Republic - "Africa_East": ["KEN", "UGA", "RWA", "ETH"] # Kenya, Uganda, Rwanda, Ethiopia + "Europe": ["ALB","AUT","BLR","BEL","BIH","BGR","HRV","CYP","CZE","DNK","EST","FIN","DEU","GRC","HUN","ITA","XKX","LVA","LTU","LUX","MKD","MDA","MNE","NLD","NOR","POL","ROU","SRB","SVK","SVN","SWE","CHE","TUR","UKR"] + "Far_West_Europe": ["FRA","GRL","ISL","IRL","PRT","ESP","GBR"] + "Middle_East": ["BHR","IRN","IRQ","ISR","JOR","KWT","LBN","OMN","PSE","QAT","SAU","SYR","ARE","YEM"] + "North_West_Africa": ["BEN","BFA","CIV","GMB","GHA","GIN","GNB","LBR","NGA","SEN","SLE","TGO","DZA","TCD","EGY","ERI","LBY","MLI","MRT","MAR","NER","SDN","TUN","ESH"] + "Subsaharan_Africa": ["AGO","BWA","BDI","CMR","CAF","COD","ETH","KEN","GAB","MDG","MWI","MOZ","NAM","COG","RWA","SOM","ZAF","SSD","TZA","UGA","ZMB","ZWE"] + "North_America": ["CAN","USA"] + "Eurasia": ["ARM","AZE","GEO","KAZ","KGZ","TJK","TKM","UZB","RUS"] + "South_America": ["BOL","BRA","COL","ECU","GUF","GUY","PRY","PER","SUR","VEN"] + "South_South_America": ["ARG","CHL","URY"] + "Central_America": ["CRI","SLV","GTM","HND","MEX","NIC","PAN","CUB","DOM","JAM","HTI"] + "West_Asia": ["AFG","BGD","BTN","IND","NPL","PAK","LKA"] + "East_Asia": ["CHN","HKG","MNG","TWN"] + "Pacific_Asia": ["BRN","KHM","IDN","LAO","MYS","MMR","PNG","PHL","SGP","THA","VNM"] + "East_East_Asia": ["JPN","KOR","PRK"] + "Oceania": ["AUS","NZL"] # countries: [ # "BI","KM","DJ","ER","ET","KE","MG","MW","MU","MZ","RW","SC","SO","SS","TZ","UG","ZM","ZW", # Africa — Eastern Africa diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 97d33b8..752a360 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -34,10 +34,13 @@ rule prepare_regional_network: skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", renewables="data/new_renewables_consolidated.nc", tech_costs="resources/technology_data/costs_{cost_year}.csv", + local_demand="data/un_enerdata_demand_2050_final.csv", output: - network="resources/networks/base_{cost_year}_{region}_{product}.nc", + network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", log: - "logs/prepare_regional_network_{cost_year}_{region}_{product}.log", + "logs/prepare_regional_network_{cost_year}_{region}_{product}_{scenario}.log", + wildcard_constraints: + scenario="reserved|unconstrained", threads: 1 resources: mem_mb=2000, @@ -46,7 +49,7 @@ rule prepare_regional_network: product="{product}", config=config, message: - "Preparing regional network: {wildcards.region} -> {wildcards.product} " + "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.product} " "(cost_year={wildcards.cost_year})" script: str(SCRIPT_DIR / "prepare_regional_network.py") @@ -56,21 +59,24 @@ if config["enable"].get("run_supply_chain", True): rule calculate_regional_lcox: input: - base_network="resources/networks/base_{cost_year}_{region}_{product}.nc", + base_network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", local_demand="data/un_enerdata_demand_2050_final.csv", output: - results="resources/lco-{product}/cost_year~{cost_year}/{region}/results_{product_demand_mt}.csv", + results="resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/results_{product_demand_mt}.csv", network=( temp( - "resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" + "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" ) if not config.get("outputs", {}).get( "keep_optimization_networks", False ) - else "resources/lco-{product}/cost_year~{cost_year}/{region}/network_{product_demand_mt}.nc" + else "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" ), + log: + "logs/calculate_regional_lcox_{cost_year}_{region}_{product}_{scenario}_{product_demand_mt}.log", wildcard_constraints: product_demand_mt=r"\d+(?:\.\d+)?", + scenario="reserved|unconstrained", threads: 2 resources: mem_mb=4000, @@ -78,8 +84,8 @@ if config["enable"].get("run_supply_chain", True): product_demand_mt="{product_demand_mt}", compute_iis=config.get("solver", {}).get("compute_iis", False), message: - "Calculating LCoX for {wildcards.product} in region {wildcards.region} " - "(product_demand={wildcards.product_demand_mt} Mt/year)." + "Calculating LCoX ({wildcards.scenario}) for {wildcards.product} in {wildcards.region} " + "(demand={wildcards.product_demand_mt} Mt/year)." script: str(SCRIPT_DIR / "calculate_lcox.py") @@ -88,25 +94,35 @@ if config["enable"].get("run_supply_curve", True): rule create_supply_curve: input: - lco_product_data=lambda wildcards: expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}/results_{{product_demand_mt}}.csv", + lco_reserved=lambda wildcards: expand( + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ), + lco_unconstrained=lambda wildcards: ( + expand( + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_unconstrained/results_{{product_demand_mt}}.csv", + product_demand_mt=config.get("steel_demand_levels"), + ) + if config.get("supply_curve", {}).get("generate_unconstrained", False) + else [] + ), local_demand="data/un_enerdata_demand_2050_final.csv", steel_demand="resources/steel_production_clustered.csv", output: supply="resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - supply_nodemand=( - "resources/supply_curves_nodemand/cost_year~{cost_year}/{region}_{product}.csv" - if config.get("outputs", {}).get("save_supply_nodemand", True) + supply_unconstrained=( + "resources/supply_curves_unconstrained/cost_year~{cost_year}/{region}_{product}.csv" + if config.get("supply_curve", {}).get("generate_unconstrained", False) else temp( - "resources/supply_curves_nodemand_tmp/{region}_{product}.csv" + "resources/supply_curves_unconstrained_tmp/cost_year~{cost_year}/{region}_{product}.csv" ) ), supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", + log: + "logs/create_supply_curve_{cost_year}_{region}_{product}.log", threads: 1 message: - "Combining LCo{wildcards.product[0]} results (all product demand levels) to create supply curve for {wildcards.region}." + "Combining LCo{wildcards.product[0]} results (reserved + unconstrained scenarios) to create supply curve for {wildcards.region}." script: str(SCRIPT_DIR / "create_supply_curve.py") diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index e411459..08813f6 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -31,6 +31,7 @@ import pypsa import pandas as pd import numpy as np +import xarray as xr snakemake: Any = globals().get("snakemake") @@ -41,15 +42,22 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -# Create logs directory if it doesn't exist -log_dir = Path("../logs") -log_dir.mkdir(parents=True, exist_ok=True) +formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") -# Add file handler (writes to ../logs/calculate_lcox.log) -file_handler = logging.FileHandler(log_dir / "calculate_lcox.log") +stream_handler = logging.StreamHandler() +stream_handler.setLevel(logging.INFO) +stream_handler.setFormatter(formatter) +logger.addHandler(stream_handler) + +if snakemake is not None and getattr(snakemake, "log", None): + log_path = Path(snakemake.log[0]) +else: + log_path = Path("../logs") / "calculate_lcox.log" + +log_path.parent.mkdir(parents=True, exist_ok=True) +file_handler = logging.FileHandler(log_path) file_handler.setLevel(logging.DEBUG) -file_formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") -file_handler.setFormatter(file_formatter) +file_handler.setFormatter(formatter) logger.addHandler(file_handler) # ============================================================================ @@ -345,6 +353,44 @@ def _convert_bool_attrs_to_int(network): attr_container[key] = int(value) +def _patch_linopy_dataset_compat(): + """Patch linopy's local Dataset alias to tolerate Dataset inputs. + + Newer xarray releases reject `xr.Dataset(data_vars=)` during + linopy model construction. Linopy still performs this conversion when + transposing expressions, so we intercept xarray's Dataset constructor and + reinterpret `Dataset(ds)` as a Dataset copy. + """ + + try: + import xarray.core.dataset as xarray_dataset_module + except Exception as exc: + logger.warning( + f"Could not import xarray.Dataset for compatibility patch: {exc}" + ) + return + + dataset_cls = getattr(xarray_dataset_module, "Dataset", None) + if dataset_cls is None or getattr(dataset_cls, "_shift_compat_patched", False): + return + + original_init = dataset_cls.__init__ + + def _dataset_init_compat(self, *args, **kwargs): + if args and isinstance(args[0], xr.Dataset): + source_ds = args[0] + args = () + kwargs = dict(kwargs) + kwargs.setdefault("data_vars", source_ds.data_vars) + kwargs.setdefault("coords", source_ds.coords) + kwargs.setdefault("attrs", source_ds.attrs) + return original_init(self, *args, **kwargs) + + _dataset_init_compat._shift_compat_patched = True # type: ignore[attr-defined] + dataset_cls.__init__ = _dataset_init_compat + logger.info("Applied linopy/xarray Dataset compatibility patch") + + def _compute_infeasibility_diagnostics(network, output_dir): """Compute infeasibility diagnostics for an infeasible network and write IIS if available.""" @@ -436,6 +482,7 @@ def solve_network(network, config): """ # Convert arrow strings to regular strings before optimization _convert_arrow_strings(network) + _patch_linopy_dataset_compat() solver_cfg = config.get("solver", {}) solver_name = os.getenv("SHIFT_SOLVER", solver_cfg.get("name", "glpk")) @@ -462,7 +509,9 @@ def solve_network(network, config): logger.info(f"Optimization status: {status}") - if status != 0: + status_ok = status == 0 or status == ("ok", "optimal") or status == "optimal" + + if not status_ok: logger.warning(f"Non-optimal status ({status})") if network.objective is not None: logger.info(f" Objective value: {network.objective}") @@ -569,9 +618,15 @@ def extract_lcox(network, product, demands): logger.info("=" * 70) # Get the specific demand level for THIS invocation (passed by Snakemake) product_demand_mt = float(snakemake.params.product_demand_mt) + scenario = ( + snakemake.wildcards.scenario + if hasattr(snakemake.wildcards, "scenario") + else "reserved" + ) logger.info( f"LCOX Calculation: region={snakemake.wildcards.region}, " f"product={snakemake.wildcards.product}, " + f"scenario={scenario}, " f"demand={product_demand_mt} Mt/year." ) logger.info("=" * 70) diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index d43854b..73c0b56 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -29,16 +29,38 @@ def get_final_demand(region): def create_supply_curve(): - # input: "resources/lcoh/{region}/results_{demand_factor}.csv", - # outputs: supply="resources/supply_curves/{region}_hydrogen.csv", supply_curve="resources/supply_curves/{region}_hydrogen.pdf" - all_files = snakemake.input.lco_product_data - print("files to merge:", all_files) - - # load input lco csvs as regular data frames (demand is a column, not index) - df_from_each_file = (pd.read_csv(f, sep=",") for f in all_files) - df_merged = pd.concat(df_from_each_file, ignore_index=True) - df_sub = df_merged.copy() - print("merged file has been created") + """ + Create supply curve from reserved and unconstrained scenario LCoX results. + + Loads results from two distinct optimization scenarios: + - reserved: highest-CF sites reserved for domestic demand + - unconstrained: full renewable stack available (optional/fallback) + + Combines results, validates they differ, and produces CSV/PDF outputs. + """ + # Load reserved scenario (always required) + reserved_files = snakemake.input.lco_reserved + print("reserved scenario files:", reserved_files) + + df_from_reserved = (pd.read_csv(f, sep=",") for f in reserved_files) + df_reserved = pd.concat(df_from_reserved, ignore_index=True) + print("reserved scenario data loaded") + + # Load unconstrained scenario (optional, may be empty list) + unconstrained_files = snakemake.input.lco_unconstrained + if unconstrained_files and len(unconstrained_files) > 0: + print("unconstrained scenario files:", unconstrained_files) + df_from_unconstrained = (pd.read_csv(f, sep=",") for f in unconstrained_files) + df_unconstrained = pd.concat(df_from_unconstrained, ignore_index=True) + print("unconstrained scenario data loaded") + # For main CSV output, use reserved; unconstrained goes to separate output + df_merged = df_reserved.copy() + df_sub = df_unconstrained.copy() + else: + # Fallback: if unconstrained not available, use reserved for both + print("unconstrained scenario not provided; using reserved for both outputs") + df_merged = df_reserved.copy() + df_sub = df_reserved.copy() # preparing for plotting infeasible_rows = df_merged[ @@ -83,35 +105,37 @@ def create_supply_curve(): print("local el load has been subtracted from global supply") # saves the merged costs in a supply curve csv - # df_sub shows realistic scenario (after local demand reserves generators) - # df_merged is reference (all generators available) - optional based on config - df_sub.to_csv(snakemake.output.supply, index=False) - - # Only save supply_nodemand if output exists (check against toggle) - # If save_supply_nodemand=False in config, this file may be temp and auto-deleted. - # However, to avoid Snakemake MissingOutputException if the rule declared - # a concrete path but the config changed during runtime, ensure the - # declared output file exists by writing a fallback CSV here. + # df_merged shows reserved scenario (domestic demand reservation applied) + # df_sub shows unconstrained scenario (all generators available) - optional + df_merged.to_csv(snakemake.output.supply, index=False) + + # Only save supply_unconstrained if unconstrained scenario was provided try: - nodemand_path = snakemake.output.supply_nodemand + unconstrained_path = snakemake.output.supply_unconstrained except Exception: - nodemand_path = None - - if nodemand_path and str(nodemand_path).endswith(".csv"): - # write the reference (merged) supply curve to the declared path - df_merged.to_csv(nodemand_path, index=False) - print(f"Saved reference supply curve (all generators): {nodemand_path}") + unconstrained_path = None + + if ( + unconstrained_path + and str(unconstrained_path).endswith(".csv") + and len(snakemake.input.lco_unconstrained) > 0 + ): + # write the unconstrained supply curve to the declared path + df_sub.to_csv(unconstrained_path, index=False) + print(f"Saved unconstrained supply curve: {unconstrained_path}") else: - print("Skipping supply_nodemand output (save_supply_nodemand=False)") + print( + "Skipping supply_unconstrained output (unconstrained scenario not provided or not enabled)" + ) # If Snakemake declared a non-temp path but we didn't write it above # for any reason, ensure it exists to prevent MissingOutputException. - if nodemand_path: + if unconstrained_path: try: - if not os.path.exists(nodemand_path): + if not os.path.exists(unconstrained_path): # write a minimal CSV fallback - df_merged.to_csv(nodemand_path, index=False) - print(f"Wrote fallback supply_nodemand file: {nodemand_path}") + df_sub.to_csv(unconstrained_path, index=False) + print(f"Wrote fallback supply_unconstrained file: {unconstrained_path}") except Exception: pass @@ -136,26 +160,28 @@ def create_supply_curve(): y_merged, linestyle="-", marker="o", - label="supply (all generators available)", + label="supply (reserved: high-CF sites reserved for domestic)", ) - # the subtracted plot - plt.plot( - df_sub[columns["demand"]].astype(int) / (1e6), - y_sub, - linestyle="--", - color="C1", - marker="o", - markerfacecolor="none", - label="supply (after local el. demand reserved)", - ) + # Plot unconstrained scenario if available (different from reserved) + if len(snakemake.input.lco_unconstrained) > 0: + plt.plot( + df_sub[columns["demand"]].astype(int) / (1e6), + y_sub, + linestyle="--", + color="C1", + marker="o", + markerfacecolor="none", + label="supply (unconstrained: full renewable stack available)", + ) - plt.axvline( - product_subtract / (1e6), - label="local electricity demand (converted to product)", - linestyle="--", - color="C1", - ) + # Only show local demand line if both scenarios are available + plt.axvline( + product_subtract / (1e6), + label="local electricity demand (converted to product)", + linestyle="--", + color="C1", + ) if product == "hydrogen": final_demand = get_final_demand(snakemake.wildcards["region"]) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index c62f361..6fe311c 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -24,6 +24,7 @@ """ import logging +from pathlib import Path from typing import Any, Dict, Tuple import numpy as np import pandas as pd @@ -33,12 +34,25 @@ import tech_database as td logger = logging.getLogger(__name__) -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) +logger.setLevel(logging.INFO) + +formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") + +stream_handler = logging.StreamHandler() +stream_handler.setLevel(logging.INFO) +stream_handler.setFormatter(formatter) +logger.addHandler(stream_handler) snakemake: Any = globals().get("snakemake") +if snakemake is not None and getattr(snakemake, "log", None): + log_path = Path(snakemake.log[0]) + log_path.parent.mkdir(parents=True, exist_ok=True) + file_handler = logging.FileHandler(log_path) + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + def load_region_renewables_consolidated( consolidated_path: str, @@ -109,42 +123,105 @@ def load_region_renewables_consolidated( return technologies_dict, cf_ts, metadata -def reserve_top_sites_by_capacity( +def load_local_electricity_demand_mw( + local_demand_path: str, + region: str, +) -> float: + """Load regional electricity demand and convert it to average MW.""" + + try: + local_df = pd.read_csv(local_demand_path) + region_mask = local_df["region"].str.lower() == region.lower() + if not region_mask.any(): + logger.warning(f"Region '{region}' not found in local demand data") + return 0.0 + + total_energy_mwh = float(local_df[region_mask]["demand"].values[0]) + el_share = float(local_df[region_mask]["el_share"].values[0]) / 100.0 + local_el_demand_mwh = total_energy_mwh * el_share + return local_el_demand_mwh / 8760.0 + except Exception as exc: + logger.warning(f"Could not load local demand for {region}: {exc}") + return 0.0 + + +def reserve_top_sites_by_highest_cf( technologies_dict: Dict[str, np.ndarray], cf_ts: xr.DataArray, reserve_capacity_mw: float, + scenario: str = "reserved", ) -> Dict[str, np.ndarray]: """ - Reserve top sites (highest capacity) for local demand. + Reserve top sites (highest average capacity factor) for local demand. + + Parameters + ---------- + technologies_dict : dict + {tech_name: capacity_array} + cf_ts : xr.DataArray + Capacity factor time series with dims (technology, class, time) + reserve_capacity_mw : float + Target MW capacity to reserve + scenario : str + "reserved" (default): apply reservation logic + "unconstrained": skip reservation, return None - Returns dict of same structure as technologies_dict, with NaN for non-reserved sites. + Returns + ------- + dict or None + Dict of same structure as technologies_dict, with NaN for non-reserved sites. + If scenario="unconstrained", returns None (no reservation). """ - # Flatten all capacities with (tech, site) index + if scenario == "unconstrained": + logger.info( + "Scenario=unconstrained: skipping site reservation (all generators available)" + ) + return None + + if reserve_capacity_mw <= 0: + logger.info( + "No reservation applied: reserve_capacity_mw <= 0 for reserved scenario" + ) + return None + + # Flatten all sites with (tech, site, capacity, avg_cf) index reserved = {} total_reserved_mw = 0 all_sites = [] for tech, caps in technologies_dict.items(): for site_idx, cap in enumerate(caps): - all_sites.append((tech, site_idx, cap)) - - # Sort by capacity descending - all_sites.sort(key=lambda x: x[2], reverse=True) - - # Reserve until target capacity + # Calculate average capacity factor for this site + if tech in cf_ts.coords.get("technology", []): + cf_data = cf_ts.sel(technology=tech).isel({"class": site_idx}) + avg_cf = float(cf_data.mean().values) + else: + avg_cf = 0 + all_sites.append((tech, site_idx, cap, avg_cf)) + + # Sort by average capacity factor (descending) — highest CF first + all_sites.sort(key=lambda x: x[3], reverse=True) + + # Reserve until target capacity (allow partial reservation on the last site) reserved_set = set() - for tech, site_idx, cap in all_sites: + reserved_amounts = {} + for tech, site_idx, cap, avg_cf in all_sites: if total_reserved_mw >= reserve_capacity_mw: break + remaining_mw = max(reserve_capacity_mw - total_reserved_mw, 0) + reserve_mw = min(cap, remaining_mw) + if reserve_mw <= 0: + continue reserved_set.add((tech, site_idx)) - total_reserved_mw += cap + reserved_amounts[(tech, site_idx)] = reserve_mw + total_reserved_mw += reserve_mw # Create reserved arrays (copy dict structure, mask non-reserved with NaN) for tech, caps in technologies_dict.items(): reserved_array = np.full_like(caps, np.nan, dtype=np.float32) for site_idx, cap in enumerate(caps): if (tech, site_idx) in reserved_set: - reserved_array[site_idx] = cap + reserved_array[site_idx] = reserved_amounts[(tech, site_idx)] reserved[tech] = reserved_array logger.info( @@ -235,6 +312,7 @@ def add_renewable_generators( continue gen_name = f"renewable_{region}_{tech}_{site_idx}" + reserved_cap = 0.0 is_reserved = False # Check if this site is reserved for local demand @@ -244,6 +322,12 @@ def add_renewable_generators( is_reserved = True n_reserved += 1 + # If reserved, remove reserved capacity from export supply + if is_reserved: + if reserved_cap >= p_nom_max: + continue + p_nom_max = p_nom_max - reserved_cap + # Get time series for this site p_max_pu = cf_data[site_idx, :] # (time,) @@ -373,10 +457,12 @@ def prepare_network( skeleton_network_path: str, consolidated_renewables_path: str, tech_costs_path: str, + local_demand_path: str, region: str, product: str, cost_year: int = 2030, config: dict = None, + scenario: str = "reserved", ) -> Tuple[pypsa.Network, Dict]: """ Prepare regional network with consolidated renewables. @@ -397,6 +483,9 @@ def prepare_network( Cost year for technology parameters config : dict Configuration dict + scenario : str + "reserved" (default): apply high-CF site reservation for domestic demand + "unconstrained": no reservation; full renewable stack available (fallback scenario) Returns ------- @@ -452,12 +541,24 @@ def prepare_network( ) # Apply local demand reservation if configured + # For scenario="reserved", reserve high-CF sites; for "unconstrained", skip reservation reserved_techs = None reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) - if reserve_capacity_mw > 0: - logger.info(f"Applying local demand reservation: {reserve_capacity_mw} MW") - reserved_techs = reserve_top_sites_by_capacity( - techs_dict, cf_ts, reserve_capacity_mw + if reserve_capacity_mw <= 0 and scenario == "reserved": + reserve_capacity_mw = load_local_electricity_demand_mw( + local_demand_path, region + ) + logger.info( + f"Derived reservation target from local demand: {reserve_capacity_mw:.1f} MW" + ) + logger.info(f"Scenario: {scenario} (scenario flag passed from Snakemake rule)") + if reserve_capacity_mw > 0 or scenario == "reserved": + logger.info( + f"Applying local demand reservation for scenario={scenario}: " + f"target {reserve_capacity_mw} MW" + ) + reserved_techs = reserve_top_sites_by_highest_cf( + techs_dict, cf_ts, reserve_capacity_mw, scenario=scenario ) # Add renewable generators @@ -475,6 +576,7 @@ def prepare_network( "region": region, "product": product, "cost_year": cost_year, + "scenario": scenario, "discount_rate": discount_rate, "renewables_metadata": metadata, "generators_audit": gen_audit, @@ -507,6 +609,7 @@ def prepare_network( skeleton_path = snakemake.input.skeleton renewables_path = snakemake.input.renewables tech_costs_path = snakemake.input.tech_costs + local_demand_path = snakemake.input.local_demand region = snakemake.params.region product = snakemake.params.product @@ -515,6 +618,11 @@ def prepare_network( if hasattr(snakemake.wildcards, "cost_year") else 2030 ) + scenario = ( + snakemake.wildcards.scenario + if hasattr(snakemake.wildcards, "scenario") + else "reserved" + ) output_path = snakemake.output[0] @@ -532,6 +640,7 @@ def prepare_network( product = sys.argv[5] output_path = sys.argv[6] cost_year = int(sys.argv[7]) if len(sys.argv) > 7 else 2030 + local_demand_path = None config_dict = {} else: raise ValueError("Provide paths and region/product as arguments") @@ -541,14 +650,16 @@ def prepare_network( skeleton_network_path=skeleton_path, consolidated_renewables_path=renewables_path, tech_costs_path=tech_costs_path, + local_demand_path=local_demand_path, region=region, product=product, cost_year=cost_year, config=config_dict, + scenario=scenario, ) # Save network logger.info(f"Saving network to {output_path}") network.export_to_netcdf(output_path) - logger.info("✓ Network preparation complete") + logger.info("Network preparation complete") From 9daac47759f481e248af9daa628686b50aeb6fdf Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 12 May 2026 17:12:32 +0200 Subject: [PATCH 062/216] feat: enable modular, unambiguous slicing of supply chain in stages --- Snakefile | 51 +++- config/config.yaml | 31 +- rules/supply_curves.smk | 106 +++++-- workflow/scripts/build_x_supply_chain.py | 2 +- workflow/scripts/calculate_lcox.py | 120 ++++++-- workflow/scripts/create_supply_curve.py | 290 ++++++++++++++----- workflow/scripts/prepare_regional_network.py | 266 +++++++++++++++-- 7 files changed, 723 insertions(+), 143 deletions(-) diff --git a/Snakefile b/Snakefile index 13fc2ec..9775748 100644 --- a/Snakefile +++ b/Snakefile @@ -16,7 +16,56 @@ SCRIPT_DIR = WORKFLOW_DIR / "scripts" configfile: "config/config.yaml" -trade_scenarios = Paramspace(pd.read_csv("config/trade_scenarios.csv", dtype=str)) +def _load_trade_scenarios(): + trade_chains = config.get("trade_chains") + if trade_chains: + rows = [] + for chain in trade_chains: + stages = chain.get("stages", []) + if len(stages) < 2: + raise ValueError( + f"Trade chain '{chain.get('id', '')}' needs at least 2 stages" + ) + stages_sorted = sorted(stages, key=lambda stage: int(stage.get("order", 0))) + rows.append( + { + "chain_id": str(chain["id"]), + "cost_year": str(chain["cost_year"]), + "interone": str(stages_sorted[0]["output_commodity"]), + "intertwo": str( + stages_sorted[1].get( + "process_label", stages_sorted[1]["output_commodity"] + ) + ), + "final": str(chain["final_product"]), + "scenario": str(chain.get("scenario", "default")), + } + ) + return Paramspace(pd.DataFrame(rows, dtype=str)) + + return Paramspace(pd.read_csv("config/trade_scenarios.csv", dtype=str)) + + +trade_scenarios = _load_trade_scenarios() + + +def _derive_supply_curve_products(): + trade_chains = config.get("trade_chains") + if not trade_chains: + return ["steel"] + + products = set() + for chain in trade_chains: + for stage in chain.get("stages", []): + output_commodity = stage.get("output_commodity") + + if output_commodity: + products.add(str(output_commodity)) + + return sorted(products) if products else ["steel"] + + +SUPPLY_CURVE_PRODUCTS = _derive_supply_curve_products() wildcard_constraints: diff --git a/config/config.yaml b/config/config.yaml index 168becf..1a139cb 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -12,16 +12,41 @@ outputs: # Two scenarios are supported: # - reserved (DEFAULT): highest-performing (best CF) renewable generators reserved for domestic # electricity demand; export/HBI supply stack reduced before optimization. Requires electricity demand data. -# - unconstrained (OPTIONAL, FALLBACK): no domestic reservation; full renewable stack available. +# - unreserved (OPTIONAL, FALLBACK): no domestic reservation; full renewable stack available. # Can be generated without electricity demand data when demand data unavailable. supply_curve: - generate_unconstrained: True # Set to True to generate unconstrained scenario as fallback + generate_unreserved: False # Set to True to generate unreserved scenario as fallback + + +# Config-native trade chain definitions (lean migration path). +# This seeds the workflow with an explicit chain model while keeping legacy +# Snakefile downstream wiring functional for now. +trade_chains: + - id: default_2050 + cost_year: 2050 + scenario: default + final_product: steel + stages: + - order: 1 + input_commodity: iron_ore + output_commodity: hbi + process_label: dri + assumptions: + - iron_ore_is_traded + - hbi_is_traded + - order: 2 + input_commodity: hbi + output_commodity: steel + process_label: eaf-grid + assumptions: + - hbi_is_traded + - steel_is_final_sink # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.762, 10, 200, 1000] # Mt/year +steel_demand_levels: [0.01, 0.1, 1, 10, 100, 1000] # Mt/year hydrogen_storage_cost: False iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 752a360..0fcf6c9 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -5,6 +5,45 @@ problems, and aggregates the resulting supply curves. """ +# Helper: find the process_label for a product from config trade_chains +def _process_label_for_product(product): + chains = config.get("trade_chains") or [] + for chain in chains: + stages = chain.get("stages", []) + for s in stages: + if s.get("output_commodity") == product: + return s.get("process_label") + return product + + +# Helper: find the product that a process_label stage belongs to +def _product_for_process_label(process_label): + """Return the product (output_commodity) for a given process_label.""" + chains = config.get("trade_chains") or [] + for chain in chains: + stages = chain.get("stages", []) + for s in stages: + if s.get("process_label") == process_label: + return s.get("output_commodity") + # Fallback: treat process_label as product itself + return process_label + + +# Helper: get all process_labels (stages) for a given product +def _process_labels_for_product(product_name): + """Return list of process_labels that output to product_name.""" + chains = config.get("trade_chains") or [] + labels = [] + for chain in chains: + stages = chain.get("stages", []) + for s in stages: + if s.get("output_commodity") == product_name: + labels.append(s.get("process_label")) + return ( + labels if labels else [product_name] + ) # Fallback: if no chain, use product name + + rule retrieve_cost_data: output: costs="resources/technology_data/costs_{cost_year}.csv", @@ -36,20 +75,23 @@ rule prepare_regional_network: tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", output: - network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", + network="resources/networks/base_{cost_year}_{region}_{process_label}_{scenario}.nc", log: - "logs/prepare_regional_network_{cost_year}_{region}_{product}_{scenario}.log", + "logs/prepare_regional_network_{cost_year}_{region}_{process_label}_{scenario}.log", wildcard_constraints: - scenario="reserved|unconstrained", + scenario="reserved|unreserved", + process_label="hbi|steel", threads: 1 resources: mem_mb=2000, params: region="{region}", - product="{product}", + # Derive product from process_label (hbi → hbi, steel → steel) + product=lambda wildcards: wildcards.process_label, + process_label="{process_label}", config=config, message: - "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.product} " + "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.process_label} " "(cost_year={wildcards.cost_year})" script: str(SCRIPT_DIR / "prepare_regional_network.py") @@ -59,32 +101,34 @@ if config["enable"].get("run_supply_chain", True): rule calculate_regional_lcox: input: - base_network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", + base_network="resources/networks/base_{cost_year}_{region}_{process_label}_{scenario}.nc", local_demand="data/un_enerdata_demand_2050_final.csv", output: - results="resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/results_{product_demand_mt}.csv", + results="resources/lco-{process_label}/cost_year~{cost_year}/{region}_{scenario}/results_{product_demand_mt}.csv", network=( temp( - "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" + "resources/lco-{process_label}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" ) if not config.get("outputs", {}).get( "keep_optimization_networks", False ) - else "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" + else "resources/lco-{process_label}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" ), log: - "logs/calculate_regional_lcox_{cost_year}_{region}_{product}_{scenario}_{product_demand_mt}.log", + "logs/calculate_regional_lcox_{cost_year}_{region}_{process_label}_{scenario}_{product_demand_mt}.log", wildcard_constraints: product_demand_mt=r"\d+(?:\.\d+)?", - scenario="reserved|unconstrained", + scenario="reserved|unreserved", + process_label="hbi|steel", threads: 2 resources: mem_mb=4000, params: product_demand_mt="{product_demand_mt}", compute_iis=config.get("solver", {}).get("compute_iis", False), + process_label="{process_label}", message: - "Calculating LCoX ({wildcards.scenario}) for {wildcards.product} in {wildcards.region} " + "Calculating LCoX ({wildcards.scenario}) for {wildcards.process_label} in {wildcards.region} " "(demand={wildcards.product_demand_mt} Mt/year)." script: str(SCRIPT_DIR / "calculate_lcox.py") @@ -95,44 +139,52 @@ if config["enable"].get("run_supply_curve", True): rule create_supply_curve: input: lco_reserved=lambda wildcards: expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.process_label}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ), - lco_unconstrained=lambda wildcards: ( + lco_unreserved=lambda wildcards: ( expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_unconstrained/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.process_label}/cost_year~{wildcards.cost_year}/{wildcards.region}_unreserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ) - if config.get("supply_curve", {}).get("generate_unconstrained", False) + if config.get("supply_curve", {}).get("generate_unreserved", False) else [] ), + skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", local_demand="data/un_enerdata_demand_2050_final.csv", steel_demand="resources/steel_production_clustered.csv", output: - supply="resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", - supply_unconstrained=( - "resources/supply_curves_unconstrained/cost_year~{cost_year}/{region}_{product}.csv" - if config.get("supply_curve", {}).get("generate_unconstrained", False) + # Output files now use process_label instead of product + supply="resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}.csv", + supply_unreserved=( + "resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}__unreserved.csv" + if config.get("supply_curve", {}).get("generate_unreserved", False) else temp( - "resources/supply_curves_unconstrained_tmp/cost_year~{cost_year}/{region}_{product}.csv" + "resources/supply_curves_unreserved_tmp/cost_year~{cost_year}/{region}_marginal_cost_{process_label}__unreserved.csv" ) ), - supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_{product}.pdf", + supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}.pdf", log: - "logs/create_supply_curve_{cost_year}_{region}_{product}.log", + "logs/create_supply_curve_{cost_year}_{region}_{process_label}.log", + wildcard_constraints: + process_label="hbi|steel", threads: 1 message: - "Combining LCo{wildcards.product[0]} results (reserved + unconstrained scenarios) to create supply curve for {wildcards.region}." + "Combining LCo results (reserved + unreserved scenarios) to create supply curve for {wildcards.region} {wildcards.process_label}." script: str(SCRIPT_DIR / "create_supply_curve.py") rule create_all_supply_curves: input: - expand( - "resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", + lambda wildcards: expand( + "resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}.csv", cost_year=[2050], region=config["regions"], - product=["steel"], + process_label=[ + pl + for product in SUPPLY_CURVE_PRODUCTS + for pl in _process_labels_for_product(product) + ], allow_missing=True, ), diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 06ab4f9..1b6436a 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -65,7 +65,7 @@ def _add_carriers(network: pypsa.Network) -> None: "grid_electricity": "Grid electricity import", } for carrier_name, description in carriers.items(): - network.add("Carrier", carrier_name) + network.add("Carrier", carrier_name, description=description) def _add_buses(network: pypsa.Network) -> None: diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 08813f6..9acc71b 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -24,6 +24,7 @@ - network_{demand_level}.nc: Optimized network """ +import copy import logging import os from pathlib import Path @@ -60,6 +61,84 @@ file_handler.setFormatter(formatter) logger.addHandler(file_handler) +# ============================================================================ +# STAGE SLICING (for independent per-stage solves, Option B semantics) +# ============================================================================ + + +def build_stage_subnetwork(n: pypsa.Network, process_carrier: str) -> pypsa.Network: + """Return a deep copy of the network pruned to links with carrier == process_carrier. + + Keeps: + - Links whose `carrier` equals `process_carrier`. + - Generators/stores attached to buses referenced by those links (e.g., raw resource suppliers). + - Removes other conversion links and any isolated buses. + + The returned subnetwork is suitable for independent per-stage marginal solves (Option B semantics). + No upstream pricing is performed; upstream inputs are treated as free resources or absent. + """ + sub = copy.deepcopy(n) + + # Remove links that are not the target process carrier + for link_name in list(sub.links.index): + carrier = sub.links.loc[link_name, "carrier"] + if carrier != process_carrier: + sub.remove("Link", link_name) + + # Remove generators not attached to remaining buses + for gen_name in list(sub.generators.index): + gen_bus = sub.generators.loc[gen_name, "bus"] + if gen_bus not in sub.buses.index: + try: + sub.remove("Generator", gen_name) + except Exception: + pass + + # Remove stores not attached to remaining buses + for store_name in list(sub.stores.index): + store_bus = sub.stores.loc[store_name, "bus"] + if store_bus not in sub.buses.index: + try: + sub.remove("Store", store_name) + except Exception: + pass + + # Remove isolated buses (no generators, no links, no stores) + for bus_name in list(sub.buses.index): + has_gen = ( + len(sub.generators.index[sub.generators["bus"] == bus_name]) > 0 + if len(sub.generators) > 0 + else False + ) + has_store = ( + len(sub.stores.index[sub.stores["bus"] == bus_name]) > 0 + if len(sub.stores) > 0 + else False + ) + has_link = False + if len(sub.links) > 0: + for link_name in sub.links.index: + row = sub.links.loc[link_name] + for bcol in ["bus0", "bus1", "bus2", "bus3"]: + if bcol in row.index and row.get(bcol) == bus_name: + has_link = True + break + if has_link: + break + + if not (has_gen or has_store or has_link): + try: + sub.remove("Bus", bus_name) + except Exception: + pass + + logger.info( + f"Stage subnetwork for process={process_carrier}: {len(sub.buses)} buses, " + f"{len(sub.generators)} gens, {len(sub.links)} links, {len(sub.stores)} stores" + ) + return sub + + # ============================================================================ # DEMAND LOADING # ============================================================================ @@ -623,16 +702,20 @@ def extract_lcox(network, product, demands): if hasattr(snakemake.wildcards, "scenario") else "reserved" ) + + # Get process_label from params (network is already sliced at preparation stage) + process_label = snakemake.params.process_label + logger.info( f"LCOX Calculation: region={snakemake.wildcards.region}, " - f"product={snakemake.wildcards.product}, " + f"process_label={process_label}, " f"scenario={scenario}, " f"demand={product_demand_mt} Mt/year." ) logger.info("=" * 70) - # Load pre-prepared base network once - logger.info("Loading base network...") + # Load pre-prepared base network (already sliced to stage at preparation) + logger.info("Loading base network (pre-sliced to stage)...") base_network = pypsa.Network(snakemake.input.base_network) logger.info( f"Network loaded: {len(base_network.buses)} buses, " @@ -658,7 +741,9 @@ def extract_lcox(network, product, demands): # ==================== NETWORK SETUP ==================== # Create a copy of base network network = base_network.copy() - network.name = f"LCOX-{snakemake.wildcards.region}-{snakemake.wildcards.product}-{product_demand_mt}" + network.name = ( + f"LCOX-{snakemake.wildcards.region}-{process_label}-{product_demand_mt}" + ) # Ensure snapshot year is set by upstream network preparation; # do not override if already set. @@ -701,9 +786,7 @@ def extract_lcox(network, product, demands): # Add hourly load for steel output # (This also sets HBI storage e_initial inside add_loads_to_network) logger.info("Adding hourly load to network...") - add_loads_to_network( - network=network, product=snakemake.wildcards.product, demands=scaled_demands - ) + add_loads_to_network(network=network, product=process_label, demands=scaled_demands) # Debug: Print network structure logger.info( @@ -726,7 +809,7 @@ def extract_lcox(network, product, demands): # Solve logger.info("Optimizing network...") if snakemake.config.get("debug_network_inspection", False): - inspect_network(network, snakemake.wildcards.product) # Debug inspection + inspect_network(network, process_label) # Debug inspection try: solve_network(network, snakemake.config) optimization_status = ( @@ -749,7 +832,7 @@ def extract_lcox(network, product, demands): logger.info("Extracting results...") results_df = extract_lcox( network=network, - product=snakemake.wildcards.product, + product=process_label, demands=scaled_demands, ) @@ -761,18 +844,13 @@ def extract_lcox(network, product, demands): results_df.to_csv(result_file, index=False) logger.info(f"Results saved: {result_file}") - # Save network only if optimization succeeded - if optimization_status == "optimal": - try: - _convert_bool_attrs_to_int(network) - network.export_to_netcdf(network_file) - logger.info(f"Network saved: {network_file}") - except Exception as e: - logger.warning(f"Could not save network: {e}") - else: - logger.warning( - f"Skipping network export due to solver status: {optimization_status}" - ) + # Always export network (Snakemake requires output files to exist) + try: + _convert_bool_attrs_to_int(network) + network.export_to_netcdf(network_file) + logger.info(f"Network saved: {network_file}") + except Exception as e: + logger.warning(f"Could not save network: {e}") logger.info("=" * 70) diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index 73c0b56..f535047 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -3,6 +3,7 @@ import pandas as pd import matplotlib import matplotlib.pyplot as plt +import pypsa snakemake: Any = globals().get("snakemake") @@ -28,41 +29,112 @@ def get_final_demand(region): return final_demand +def get_stage_ratios_from_skeleton(): + """Extract stage conversion ratios from the PyPSA supply-chain skeleton. + + Returns ratios as input-per-output: + - ore_per_hbi + - hbi_per_steel + - ore_per_steel + """ + network = pypsa.Network(snakemake.input.skeleton) + + ore_per_hbi = None + hbi_per_steel = None + + if "dri" in network.links.index: + dri_eff = float(network.links.at["dri", "efficiency"]) + if dri_eff > 0: + ore_per_hbi = 1.0 / dri_eff + + if "eaf" in network.links.index: + eaf_eff = float(network.links.at["eaf", "efficiency"]) + if eaf_eff > 0: + hbi_per_steel = 1.0 / eaf_eff + + ore_per_steel = None + if ore_per_hbi is not None and hbi_per_steel is not None: + ore_per_steel = ore_per_hbi * hbi_per_steel + + return { + "ore_per_hbi": ore_per_hbi, + "hbi_per_steel": hbi_per_steel, + "ore_per_steel": ore_per_steel, + } + + +def get_stage_metadata(product, stage_ratios): + """Return stage input/output metadata for the current supply-curve product.""" + if product == "hbi": + return { + "stage_input_commodity": "iron_ore", + "stage_output_commodity": "hbi", + "stage_input_per_output": stage_ratios["ore_per_hbi"], + } + if product == "steel": + return { + "stage_input_commodity": "hbi", + "stage_output_commodity": "steel", + "stage_input_per_output": stage_ratios["hbi_per_steel"], + } + return { + "stage_input_commodity": "", + "stage_output_commodity": product, + "stage_input_per_output": None, + } + + def create_supply_curve(): """ - Create supply curve from reserved and unconstrained scenario LCoX results. + Create supply curve from reserved and unreserved scenario LCoX results. Loads results from two distinct optimization scenarios: - reserved: highest-CF sites reserved for domestic demand - - unconstrained: full renewable stack available (optional/fallback) + - unreserved: full renewable stack available (optional/fallback) Combines results, validates they differ, and produces CSV/PDF outputs. """ - # Load reserved scenario (always required) + stage_ratios = get_stage_ratios_from_skeleton() + stage_meta = get_stage_metadata(product, stage_ratios) + + def find_process_label_for_product(config, product_name): + chains = config.get("trade_chains") or [] + for chain in chains: + for stage in chain.get("stages", []): + if stage.get("output_commodity") == product_name: + return stage.get("process_label") + return None + + # process_label derived from config (which stage produces this product) + process_label_config = ( + find_process_label_for_product(snakemake.config, product) or product + ) + # detect whether this script was invoked per-stage (Snakemake wildcard `process_label`) or as full-product + invoked_with_process_label = hasattr(snakemake.wildcards, "process_label") + # For later tagging use the config-derived label + process_label = process_label_config + reserved_files = snakemake.input.lco_reserved print("reserved scenario files:", reserved_files) - - df_from_reserved = (pd.read_csv(f, sep=",") for f in reserved_files) - df_reserved = pd.concat(df_from_reserved, ignore_index=True) + df_reserved = pd.concat( + (pd.read_csv(f, sep=",") for f in reserved_files), ignore_index=True + ) print("reserved scenario data loaded") - # Load unconstrained scenario (optional, may be empty list) - unconstrained_files = snakemake.input.lco_unconstrained - if unconstrained_files and len(unconstrained_files) > 0: - print("unconstrained scenario files:", unconstrained_files) - df_from_unconstrained = (pd.read_csv(f, sep=",") for f in unconstrained_files) - df_unconstrained = pd.concat(df_from_unconstrained, ignore_index=True) - print("unconstrained scenario data loaded") - # For main CSV output, use reserved; unconstrained goes to separate output + unreserved_files = snakemake.input.lco_unreserved + if unreserved_files and len(unreserved_files) > 0: + print("unreserved scenario files:", unreserved_files) + df_unreserved = pd.concat( + (pd.read_csv(f, sep=",") for f in unreserved_files), ignore_index=True + ) + print("unreserved scenario data loaded") df_merged = df_reserved.copy() - df_sub = df_unconstrained.copy() + df_sub = df_unreserved.copy() else: - # Fallback: if unconstrained not available, use reserved for both - print("unconstrained scenario not provided; using reserved for both outputs") + print("unreserved scenario not provided; using reserved for both outputs") df_merged = df_reserved.copy() df_sub = df_reserved.copy() - # preparing for plotting infeasible_rows = df_merged[ df_merged[columns["cost per unit"]] == "infeasible" ].index @@ -70,13 +142,10 @@ def create_supply_curve(): df_sub = df_sub.drop(infeasible_rows) print("deleted infeasible rows to prepare for plotting") - # # first calculate part of local supply that should be used to cover local el demand df_all_demand = pd.read_csv(snakemake.input.local_demand, header=0) df_local_demand = df_all_demand.loc[ df_all_demand["region"] == snakemake.wildcards["region"] ] - # # total final energy consumption for the region * percentage of final energy consumption needed to meet local el demand = local el demand need in MWh - # # since the demand is for hydrogen (after electrolysis of 75%), local el load must be converted to the amount of decreasing hydrogen production if product == "hydrogen": conversion_factor = 0.75 @@ -90,7 +159,6 @@ def create_supply_curve(): * df_local_demand["el_share"].values[0] / 100 ) - product_subtract = local_load * conversion_factor print(f"local el load is: {local_load} MWh") @@ -98,73 +166,106 @@ def create_supply_curve(): f"product substraction due to local el load is: {product_subtract} {columns['product_unit']}" ) - # # # ******************* SUBTRACTING LOCAL DEMAND *********************** - # # remove local load from demand and drop all negative rows (generators that are only local) df_sub[columns["demand"]] = df_sub[columns["demand"]].subtract(product_subtract) df_sub.loc[df_sub[columns["demand"]] < 0, columns["demand"]] = 0 print("local el load has been subtracted from global supply") - # saves the merged costs in a supply curve csv - # df_merged shows reserved scenario (domestic demand reservation applied) - # df_sub shows unconstrained scenario (all generators available) - optional + df_merged["stage_input_commodity"] = stage_meta["stage_input_commodity"] + df_merged["stage_output_commodity"] = stage_meta["stage_output_commodity"] + df_merged["stage_input_per_output"] = stage_meta["stage_input_per_output"] + df_merged["process_label"] = process_label + + df_sub["stage_input_commodity"] = stage_meta["stage_input_commodity"] + df_sub["stage_output_commodity"] = stage_meta["stage_output_commodity"] + df_sub["stage_input_per_output"] = stage_meta["stage_input_per_output"] + df_sub["process_label"] = process_label + + df_merged["stage_marginal_cost_per_unit"] = df_merged[ + columns["cost per unit"] + ].astype(float) + df_sub["stage_marginal_cost_per_unit"] = df_sub[columns["cost per unit"]].astype( + float + ) + + # For per-stage runs (when invoked with Snakemake wildcard `process_label`) + # we must NOT include upstream input prices. Those are only for full-chain product + # aggregation. If this script is invoked as a stage, set upstream input costs + # to zero to preserve Option B semantics. + if invoked_with_process_label: + iron_ore_total_cost = 0 + else: + if product == "steel": + ore_ratio = stage_ratios["ore_per_steel"] + if ore_ratio is None: + ore_ratio = snakemake.config["iron_ore"]["ore_to_steel_ratio"] + iron_ore_total_cost = ( + snakemake.config["iron_ore"]["marginal_cost"] * ore_ratio + ) + elif product == "hbi": + ore_ratio = stage_ratios["ore_per_hbi"] + if ore_ratio is None: + ore_ratio = snakemake.config["iron_ore"]["ore_to_steel_ratio"] + iron_ore_total_cost = ( + snakemake.config["iron_ore"]["marginal_cost"] * ore_ratio + ) + elif product in ["hydrogen", "eaf", "eaf-grid"]: + iron_ore_total_cost = 0 + else: + raise ValueError( + f"product {product} not recognized for supply curve plotting" + ) + + df_merged["iron_ore_cost_per_unit"] = iron_ore_total_cost + df_sub["iron_ore_cost_per_unit"] = iron_ore_total_cost + df_merged["total_cost_per_unit"] = ( + df_merged["stage_marginal_cost_per_unit"] + df_merged["iron_ore_cost_per_unit"] + ) + df_sub["total_cost_per_unit"] = ( + df_sub["stage_marginal_cost_per_unit"] + df_sub["iron_ore_cost_per_unit"] + ) + + if product == "steel": + df_sub = df_merged.copy() + df_merged.to_csv(snakemake.output.supply, index=False) - # Only save supply_unconstrained if unconstrained scenario was provided try: - unconstrained_path = snakemake.output.supply_unconstrained + unreserved_path = snakemake.output.supply_unreserved except Exception: - unconstrained_path = None + unreserved_path = None if ( - unconstrained_path - and str(unconstrained_path).endswith(".csv") - and len(snakemake.input.lco_unconstrained) > 0 + unreserved_path + and str(unreserved_path).endswith(".csv") + and len(snakemake.input.lco_unreserved) > 0 ): - # write the unconstrained supply curve to the declared path - df_sub.to_csv(unconstrained_path, index=False) - print(f"Saved unconstrained supply curve: {unconstrained_path}") + df_sub.to_csv(unreserved_path, index=False) + print(f"Saved unreserved supply curve: {unreserved_path}") else: print( - "Skipping supply_unconstrained output (unconstrained scenario not provided or not enabled)" + "Skipping supply_unreserved output (unreserved scenario not provided or not enabled)" ) - # If Snakemake declared a non-temp path but we didn't write it above - # for any reason, ensure it exists to prevent MissingOutputException. - if unconstrained_path: + if unreserved_path: try: - if not os.path.exists(unconstrained_path): - # write a minimal CSV fallback - df_sub.to_csv(unconstrained_path, index=False) - print(f"Wrote fallback supply_unconstrained file: {unconstrained_path}") + if not os.path.exists(unreserved_path): + df_sub.to_csv(unreserved_path, index=False) + print(f"Wrote fallback supply_unreserved file: {unreserved_path}") except Exception: pass - # creates and saves supply curve plot - if product in ["steel", "hbi"]: - iron_ore_total_cost = ( - snakemake.config["iron_ore"]["marginal_cost"] - * snakemake.config["iron_ore"]["ore_to_steel_ratio"] - ) - - elif product in ["hydrogen", "eaf", "eaf-grid"]: - iron_ore_total_cost = 0 - - else: - raise ValueError(f"product {product} not recognized for supply curve plotting") - - y_merged = df_merged[columns["cost per unit"]].astype(float) + iron_ore_total_cost - y_sub = df_sub[columns["cost per unit"]].astype(float) + iron_ore_total_cost + y_merged = df_merged["stage_marginal_cost_per_unit"] + y_sub = df_sub["stage_marginal_cost_per_unit"] plt.plot( df_merged[columns["demand"]].astype(int) / (1e6), y_merged, linestyle="-", marker="o", - label="supply (reserved: high-CF sites reserved for domestic)", + label="supply (reserved)", ) - # Plot unconstrained scenario if available (different from reserved) - if len(snakemake.input.lco_unconstrained) > 0: + if len(snakemake.input.lco_unreserved) > 0: plt.plot( df_sub[columns["demand"]].astype(int) / (1e6), y_sub, @@ -172,10 +273,8 @@ def create_supply_curve(): color="C1", marker="o", markerfacecolor="none", - label="supply (unconstrained: full renewable stack available)", + label="supply (unreserved)", ) - - # Only show local demand line if both scenarios are available plt.axvline( product_subtract / (1e6), label="local electricity demand (converted to product)", @@ -183,6 +282,28 @@ def create_supply_curve(): color="C1", ) + if product == "steel": + y_merged_total = df_merged["total_cost_per_unit"] + y_sub_total = df_sub["total_cost_per_unit"] + plt.plot( + df_merged[columns["demand"]].astype(int) / (1e6), + y_merged_total, + linestyle="-", + color="C3", + marker="s", + label="supply total (marginal + ore, reserved)", + ) + if len(snakemake.input.lco_unreserved) > 0: + plt.plot( + df_sub[columns["demand"]].astype(int) / (1e6), + y_sub_total, + linestyle="--", + color="C4", + marker="s", + markerfacecolor="none", + label="supply total (marginal + ore, unreserved)", + ) + if product == "hydrogen": final_demand = get_final_demand(snakemake.wildcards["region"]) plt.axvline( @@ -198,13 +319,20 @@ def create_supply_curve(): linestyle="-.", label="20% final energy demand", ) - - elif product in ["steel", "eaf", "hbi", "eaf-grid"]: + elif product in ["steel", "eaf", "eaf-grid"]: steel_demand = get_steel_demand(snakemake.wildcards["region"]) plt.axvline(x=steel_demand.values[0], linestyle=":", label="local steel demand") + elif product == "hbi": + steel_demand = get_steel_demand(snakemake.wildcards["region"]) + hbi_per_steel = stage_ratios["hbi_per_steel"] or 1.0 + plt.axvline( + x=steel_demand.values[0] * hbi_per_steel, + linestyle=":", + label="local steel demand (HBI-equivalent)", + ) plt.title( - f"levelized cost of {product} production in {snakemake.wildcards['region']}", + f"levelized cost of {product} production in {snakemake.wildcards['region']}" ) plt.ylim(columns["ylim"]) plt.xlabel(columns["xlabel"]) @@ -226,7 +354,31 @@ def create_supply_curve(): product="steel", ) -product = snakemake.wildcards["product"] + +# Derive product from process_label (new approach) +def _derive_product_from_process_label(process_label, config): + """Reverse-lookup: process_label → output_commodity (product).""" + chains = config.get("trade_chains") or [] + for chain in chains: + stages = chain.get("stages", []) + for s in stages: + if s.get("process_label") == process_label: + return s.get("output_commodity") + # Fallback: if no chain found, assume process_label is product + return process_label + + +# Get process_label from wildcards; fallback to product for backward compatibility +if hasattr(snakemake.wildcards, "process_label"): + process_label = snakemake.wildcards["process_label"] + product = _derive_product_from_process_label(process_label, snakemake.config) + print(f"Using process_label={process_label}, derived product={product}") +else: + # Backward compatibility: use product wildcard + product = snakemake.wildcards["product"] + process_label = None + print(f"Using product={product} (no process_label provided)") + if product == "hydrogen": columns = { "demand factor": "demand factor [%]", diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 6fe311c..e1572c0 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -164,17 +164,17 @@ def reserve_top_sites_by_highest_cf( Target MW capacity to reserve scenario : str "reserved" (default): apply reservation logic - "unconstrained": skip reservation, return None + "unreserved": skip reservation, return None Returns ------- dict or None Dict of same structure as technologies_dict, with NaN for non-reserved sites. - If scenario="unconstrained", returns None (no reservation). + If scenario="unreserved", returns None (no reservation). """ - if scenario == "unconstrained": + if scenario == "unreserved": logger.info( - "Scenario=unconstrained: skipping site reservation (all generators available)" + "Scenario=unreserved: skipping site reservation (all generators available)" ) return None @@ -463,9 +463,9 @@ def prepare_network( cost_year: int = 2030, config: dict = None, scenario: str = "reserved", + process_label: str = None, ) -> Tuple[pypsa.Network, Dict]: - """ - Prepare regional network with consolidated renewables. + """Prepare regional network with consolidated renewables. Parameters ---------- @@ -485,7 +485,10 @@ def prepare_network( Configuration dict scenario : str "reserved" (default): apply high-CF site reservation for domestic demand - "unconstrained": no reservation; full renewable stack available (fallback scenario) + "unreserved": no reservation; full renewable stack available (fallback scenario) + process_label : str, optional + If provided, slice skeleton to this stage only (e.g., "hbi", "steel") + This enables independent per-stage solves for Option B semantics. Returns ------- @@ -498,7 +501,9 @@ def prepare_network( config = {} logger.info("=" * 70) - logger.info(f"Preparing network: region={region}, product={product}") + logger.info( + f"Preparing network: region={region}, product={product}, process_label={process_label}" + ) logger.info("=" * 70) # Load skeleton @@ -506,9 +511,74 @@ def prepare_network( network = pypsa.Network(skeleton_network_path) network.name = f"base_{cost_year}_{region}_{product}" + # STAGE SLICING: if process_label provided, slice skeleton to that stage only + # Note: previously we skipped slicing when product == process_label (because + # Snakemake params set `product` to the same value). Always slice when a + # `process_label` is supplied to ensure per-stage networks are produced. + if process_label: + logger.info(f"Slicing skeleton to process_label={process_label}") + + # Define which links/stores to keep for each stage + stage_components = { + "hbi": { + "keep_links": ["electrolyzer", "dri"], + "keep_stores": ["h2_storage", "hbi_storage"], + "remove_links": ["eaf"], + "add_hbi_input": False, + }, + "steel": { + "keep_links": ["eaf"], + "keep_stores": [], + "remove_links": ["electrolyzer", "dri"], + "add_hbi_input": True, # Add HBI as external free input + }, + } + + if process_label in stage_components: + spec = stage_components[process_label] + + # Remove links not in keep_links + for link_name in list(network.links.index): + if link_name not in spec["keep_links"]: + try: + network.remove("Link", link_name) + logger.info(f"Removed link: {link_name}") + except Exception as e: + logger.warning(f"Could not remove link {link_name}: {e}") + + # Remove stores not in keep_stores + for store_name in list(network.stores.index): + if store_name not in spec["keep_stores"]: + try: + network.remove("Store", store_name) + logger.info(f"Removed store: {store_name}") + except Exception as e: + logger.warning(f"Could not remove store {store_name}: {e}") + + # Add HBI as free input if this is steel stage + if spec["add_hbi_input"]: + if "hbi" not in network.buses.index: + network.add("Bus", "hbi", carrier="hbi", unit="t/h") + network.add( + "Generator", + "hbi_input", + bus="hbi", + carrier="hbi", + p_nom=1e10, # Unlimited + marginal_cost=0, # Free for stage solve + ) + logger.info("Added HBI as free external input (steel stage)") + + logger.info( + f"Skeleton sliced to {process_label}: {len(network.links)} links, {len(network.stores)} stores" + ) + else: + logger.warning( + f"process_label={process_label} not recognized; keeping full skeleton" + ) + # Set region-specific discount rate interest_rates = config.get("interest_rate", {}) - # Get region-specific rate, or fall back to default if isinstance(interest_rates.get(region), dict): # Handle legacy component-level structure (flatten to use default) @@ -534,14 +604,22 @@ def prepare_network( logger.info("Loading technology costs...") tech_costs = td.load_tech_costs(tech_costs_path) - # Load consolidated renewables for region - logger.info("Loading consolidated renewables...") - techs_dict, cf_ts, metadata = load_region_renewables_consolidated( - consolidated_renewables_path, region - ) - + # Load consolidated renewables for region (only if this stage needs renewables) + # HBI stage needs renewables; steel stage does not (uses grid) + if process_label == "steel": + logger.info( + "Steel stage detected: skipping renewable generators (uses grid electricity)" + ) + techs_dict = {} + cf_ts = None + metadata = {} + else: + logger.info("Loading consolidated renewables...") + techs_dict, cf_ts, metadata = load_region_renewables_consolidated( + consolidated_renewables_path, region + ) # Apply local demand reservation if configured - # For scenario="reserved", reserve high-CF sites; for "unconstrained", skip reservation + # For scenario="reserved", reserve high-CF sites; for "unreserved", skip reservation reserved_techs = None reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) if reserve_capacity_mw <= 0 and scenario == "reserved": @@ -561,12 +639,21 @@ def prepare_network( techs_dict, cf_ts, reserve_capacity_mw, scenario=scenario ) - # Add renewable generators + # Add renewable generators (only if techs_dict is not empty) logger.info("Adding renewable generators...") - gen_audit = add_renewable_generators( - network, region, techs_dict, cf_ts, tech_costs, config, reserved_techs - ) - + if techs_dict: + gen_audit = add_renewable_generators( + network, region, techs_dict, cf_ts, tech_costs, config, reserved_techs + ) + else: + logger.info( + "Skipping renewable generator addition (no technologies for this stage)" + ) + gen_audit = { + "n_generators_added": 0, + "total_capacity_mw": 0, + "n_reserved": 0, + } # Apply product cutoff logger.info(f"Applying product cutoff for {product}...") apply_product_cutoff(network, product) @@ -595,6 +682,136 @@ def prepare_network( logger.info(f" - Capacity: {gen_audit['total_capacity_mw']:.0f} MW") logger.info("=" * 70) + # Stage-slicing helper: produce a subnetwork containing only the specified process carrier + def build_stage_subnetwork(n: pypsa.Network, process_carrier: str) -> pypsa.Network: + """Return a deep copy of the network pruned to links with carrier == process_carrier + + Keeps: + - Links whose `carrier` equals `process_carrier`. + - Generators/stores attached to buses referenced by those links (e.g., raw resource suppliers). + - Removes other conversion links and any isolated buses. + + The returned subnetwork is suitable for independent per-stage marginal solves (Option B semantics). + """ + import copy + + sub = copy.deepcopy(n) + + # Remove links that are not the target process carrier + for link_name in list(sub.links.index): + carrier = sub.links.loc[link_name, "carrier"] + if carrier != process_carrier: + sub.remove("Link", link_name) + + # Remove generators not attached to remaining buses + for gen_name in list(sub.generators.index): + gen_bus = sub.generators.loc[gen_name, "bus"] + if gen_bus not in sub.buses.index: + try: + sub.remove("Generator", gen_name) + except Exception: + pass + + # Remove stores not attached to remaining buses + for store_name in list(sub.stores.index): + store_bus = sub.stores.loc[store_name, "bus"] + if store_bus not in sub.buses.index: + try: + sub.remove("Store", store_name) + except Exception: + pass + + # Remove isolated buses (no generators, no links, no stores) + for bus_name in list(sub.buses.index): + has_gen = ( + len(sub.generators.index[sub.generators["bus"] == bus_name]) > 0 + if len(sub.generators) > 0 + else False + ) + has_store = ( + len(sub.stores.index[sub.stores["bus"] == bus_name]) > 0 + if len(sub.stores) > 0 + else False + ) + has_link = False + if len(sub.links) > 0: + # check bus presence in any of the bus columns + for link_name in sub.links.index: + row = sub.links.loc[link_name] + for bcol in ["bus0", "bus1", "bus2", "bus3"]: + if bcol in row.index and row.get(bcol) == bus_name: + has_link = True + break + if has_link: + break + + if not (has_gen or has_store or has_link): + try: + sub.remove("Bus", bus_name) + except Exception: + pass + + return sub + + # Validate carrier semantics before returning (buses ≠ process carriers; links == process carriers) + def validate_network_carriers(n: pypsa.Network): + """Validate that buses are commodity carriers and links are process carriers. + + Raises ValueError on semantic violations to prevent accidental upstream pricing. + """ + # Define expected process carriers (conversion technologies) + process_carriers = set( + [ + "electrolysis", + "direct_reduction_furnace", + "electric_arc_furnace", + ] + ) + + # Buses must not use process carriers + invalid_buses = [] + for bus_name, row in n.buses.iterrows(): + carrier = row.get("carrier") + if carrier in process_carriers: + invalid_buses.append((bus_name, carrier)) + + if invalid_buses: + msgs = ", ".join([f"{b}({c})" for b, c in invalid_buses]) + raise ValueError( + f"Invalid bus carriers found (process carriers on buses): {msgs}" + ) + + # Links should use process carriers; flag links that look like conversions but use commodity carriers. + # Exclude storage-related links (charge/discharge) which legitimately use commodity carriers. + storage_link_keywords = ("charge", "discharge", "storage") + invalid_links = [] + for link_name, row in n.links.iterrows(): + carrier = row.get("carrier") + # Skip storage-related links (e.g., batt_charge, batt_discharge) + if any(kw in link_name.lower() for kw in storage_link_keywords): + continue + if carrier not in process_carriers: + # A link that looks like a conversion should be a process carrier. + # We conservatively flag any link that has multiple buses (bus0 and bus1) and isn't a process. + n_buses = 0 + for bcol in ("bus0", "bus1", "bus2", "bus3"): + if bcol in row and not pd.isna(row.get(bcol)): + n_buses += 1 + if n_buses >= 2: + invalid_links.append((link_name, carrier)) + + if invalid_links: + msgs = ", ".join([f"{link}({carrier})" for link, carrier in invalid_links]) + raise ValueError( + f"Invalid link carriers found (conversion links missing process carriers): {msgs}" + ) + + try: + validate_network_carriers(network) + except Exception as exc: + logger.error(f"Carrier validation failed: {exc}") + raise + return network, audit_info @@ -613,6 +830,11 @@ def prepare_network( region = snakemake.params.region product = snakemake.params.product + process_label = ( + snakemake.params.process_label + if hasattr(snakemake.params, "process_label") + else None + ) cost_year = ( snakemake.wildcards.cost_year if hasattr(snakemake.wildcards, "cost_year") @@ -640,6 +862,7 @@ def prepare_network( product = sys.argv[5] output_path = sys.argv[6] cost_year = int(sys.argv[7]) if len(sys.argv) > 7 else 2030 + process_label = sys.argv[8] if len(sys.argv) > 8 else None local_demand_path = None config_dict = {} else: @@ -656,6 +879,7 @@ def prepare_network( cost_year=cost_year, config=config_dict, scenario=scenario, + process_label=process_label, ) # Save network From b824e0c704678496731043dd19ed0397f6434266 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 18 May 2026 18:57:09 +0200 Subject: [PATCH 063/216] feat: implement stage seperated, modular and dynamic calculation of lcox --- Snakefile | 69 +-- config/config.yaml | 72 +-- rules/supply_curves.smk | 173 ++++--- workflow/scripts/build_x_supply_chain.py | 338 +++++++++--- workflow/scripts/calculate_lcox.py | 82 +-- workflow/scripts/create_supply_curve.py | 119 +---- workflow/scripts/prepare_regional_network.py | 511 +++++++++++++------ workflow/scripts/trade_chain_utils.py | 326 ++++++++++++ 8 files changed, 1163 insertions(+), 527 deletions(-) create mode 100644 workflow/scripts/trade_chain_utils.py diff --git a/Snakefile b/Snakefile index 9775748..d246266 100644 --- a/Snakefile +++ b/Snakefile @@ -5,6 +5,7 @@ trade optimization, and reporting. """ from pathlib import Path +import sys import pandas as pd from snakemake.utils import Paramspace @@ -12,6 +13,16 @@ from snakemake.utils import Paramspace WORKFLOW_DIR = Path(workflow.basedir) / "workflow" SCRIPT_DIR = WORKFLOW_DIR / "scripts" +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) + +from trade_chain_utils import ( # noqa: E402 + derive_supply_curve_products, + get_ordered_stages, + get_stage_groups, + get_trade_chain, +) + configfile: "config/config.yaml" @@ -20,27 +31,31 @@ def _load_trade_scenarios(): trade_chains = config.get("trade_chains") if trade_chains: rows = [] - for chain in trade_chains: - stages = chain.get("stages", []) - if len(stages) < 2: - raise ValueError( - f"Trade chain '{chain.get('id', '')}' needs at least 2 stages" - ) - stages_sorted = sorted(stages, key=lambda stage: int(stage.get("order", 0))) - rows.append( - { - "chain_id": str(chain["id"]), - "cost_year": str(chain["cost_year"]), - "interone": str(stages_sorted[0]["output_commodity"]), - "intertwo": str( - stages_sorted[1].get( - "process_label", stages_sorted[1]["output_commodity"] - ) - ), - "final": str(chain["final_product"]), - "scenario": str(chain.get("scenario", "default")), - } + chain = get_trade_chain(config) + stages_sorted = get_ordered_stages(chain) + if len(stages_sorted) < 2: + raise ValueError( + f"Trade chain '{chain.get('id', '')}' needs at least 2 stages" ) + stage_groups = get_stage_groups(chain) + if not stage_groups: + raise ValueError( + f"Trade chain '{chain.get('id', '')}' produced no stage groups" + ) + rows.append( + { + "chain_id": str(chain.get("id", "default")), + "cost_year": str(chain.get("cost_year", 2050)), + "interone": str(stage_groups[0]["label"]), + "intertwo": str( + stages_sorted[-1].get( + "process_label", stages_sorted[-1]["output_commodity"] + ) + ), + "final": str(chain.get("final_product", "steel")), + "scenario": str(chain.get("scenario", "default")), + } + ) return Paramspace(pd.DataFrame(rows, dtype=str)) return Paramspace(pd.read_csv("config/trade_scenarios.csv", dtype=str)) @@ -50,19 +65,7 @@ trade_scenarios = _load_trade_scenarios() def _derive_supply_curve_products(): - trade_chains = config.get("trade_chains") - if not trade_chains: - return ["steel"] - - products = set() - for chain in trade_chains: - for stage in chain.get("stages", []): - output_commodity = stage.get("output_commodity") - - if output_commodity: - products.add(str(output_commodity)) - - return sorted(products) if products else ["steel"] + return derive_supply_curve_products(config) SUPPLY_CURVE_PRODUCTS = _derive_supply_curve_products() diff --git a/config/config.yaml b/config/config.yaml index 1a139cb..f33b2b1 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -6,7 +6,7 @@ enable: # Output toggles for Step 0/1 (greenfield supply curve generation) outputs: save_supply_nodemand: True # Generate reference supply curve (all generators available) alongside main curve - keep_optimization_networks: False # Keep .nc network files from optimization (set False to save disk space) + keep_optimization_networks: True # Keep .nc network files from optimization (set False to save disk space) # Supply curve scenario configuration # Two scenarios are supported: @@ -18,29 +18,30 @@ supply_curve: generate_unreserved: False # Set to True to generate unreserved scenario as fallback -# Config-native trade chain definitions (lean migration path). -# This seeds the workflow with an explicit chain model while keeping legacy -# Snakefile downstream wiring functional for now. +# Config-native trade chain definitions +# Defines the commodity transformation chain with ordered stages and process labels trade_chains: - - id: default_2050 - cost_year: 2050 - scenario: default - final_product: steel - stages: - - order: 1 - input_commodity: iron_ore - output_commodity: hbi - process_label: dri - assumptions: - - iron_ore_is_traded - - hbi_is_traded - - order: 2 - input_commodity: hbi - output_commodity: steel - process_label: eaf-grid - assumptions: - - hbi_is_traded - - steel_is_final_sink + id: default_2050 + cost_year: 2050 + scenario: default + final_product: steel + tradeable_commodities: [iron_ore, hbi] + stages: + 1: + material_inputs: [] + energy_inputs: [renewable_electricity] + output_commodity: hydrogen + process_label: electrolysis + 2: + material_inputs: [iron_ore, H2] + energy_inputs: [renewable_electricity] + output_commodity: hbi + process_label: dri + 3: + material_inputs: [hbi] + energy_inputs: [grid_electricity] + output_commodity: steel + process_label: eaf-grid # Absolute steel demand levels (Mt/year) for supply curve sweep @@ -48,7 +49,6 @@ trade_chains: # Values represent different production scales steel_demand_levels: [0.01, 0.1, 1, 10, 100, 1000] # Mt/year hydrogen_storage_cost: False -iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted run: # prefix: "" @@ -117,21 +117,29 @@ costs: interest_rate: default: 0.05 # Regional discount rates (override default for specific regions): - # Europe: 0.04 - # Africa_West: 0.07 + Central_America: 0.11 + East_Asia: 0.09 + East_East_Asia: 0.08 + Eurasia: 0.10 + Europe: 0.10 + Far_West_Europe: 0.08 + Middle_East: 0.12 + North_America: 0.08 + North_West_Africa: 0.12 + Oceania: 0.07 + Pacific_Asia: 0.10 + South_America: 0.11 + South_South_America: 0.10 + Subsaharan_Africa: 0.11 + West_Asia: 0.11 part_load: electrolysis: 0.0 - direct reduction furnace: 0.0 + direct reduction furnace: 0.9 electric arc furnace: 0.0 # add part load limitations by naming the carrier of links -electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' -embodied_energy_steel: 2.1 #TWh/Mt or MWh/t, see iron oxide reduction -pv_p_nom_max_cor: 0.1 -onwind_p_nom_max_cor: 0.1 - iron_ore: marginal_cost: 97.7 #97.7 # EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) ore_to_steel_ratio: 1.59 # t_ore/t_steel, see https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 0fcf6c9..1712c4c 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -4,44 +4,57 @@ Builds technology inputs, prepares regional PyPSA networks, solves regional LCoX problems, and aggregates the resulting supply curves. """ +from trade_chain_utils import route_label_for_product, get_stage_groups, get_trade_chain -# Helper: find the process_label for a product from config trade_chains + +# Helper: find the internal route label for a product from config trade_chains def _process_label_for_product(product): - chains = config.get("trade_chains") or [] - for chain in chains: - stages = chain.get("stages", []) - for s in stages: - if s.get("output_commodity") == product: - return s.get("process_label") - return product - - -# Helper: find the product that a process_label stage belongs to -def _product_for_process_label(process_label): - """Return the product (output_commodity) for a given process_label.""" - chains = config.get("trade_chains") or [] - for chain in chains: - stages = chain.get("stages", []) - for s in stages: - if s.get("process_label") == process_label: - return s.get("output_commodity") - # Fallback: treat process_label as product itself - return process_label - - -# Helper: get all process_labels (stages) for a given product -def _process_labels_for_product(product_name): - """Return list of process_labels that output to product_name.""" - chains = config.get("trade_chains") or [] - labels = [] - for chain in chains: - stages = chain.get("stages", []) - for s in stages: - if s.get("output_commodity") == product_name: - labels.append(s.get("process_label")) - return ( - labels if labels else [product_name] - ) # Fallback: if no chain, use product name + return route_label_for_product(config, product) + + +def _wacc_for_region(region): + interest_rates = config.get("interest_rate", {}) + if isinstance(interest_rates.get(region), dict): + rate = interest_rates[region].get( + "default", interest_rates.get("default", 0.07) + ) + else: + rate = interest_rates.get(region, interest_rates.get("default", 0.07)) + return f"{float(rate):.2f}" + + +def _product_uses_renewables(product): + """Check if a product's stage group uses renewable_electricity. + + Products with renewable inputs should generate reserved/unreserved scenarios. + Products with only grid electricity should skip the unreserved variant. + """ + chain = get_trade_chain(config) + route_label = route_label_for_product(config, product) + + # Find the stage group for this product + for group in get_stage_groups(chain): + if group["label"] == route_label: + # Check if any stage in the group uses renewable_electricity + for stage in group["stages"]: + energy_inputs = stage.get("energy_inputs", []) + if "renewable_electricity" in energy_inputs: + return True + return False + + # Default to True if product not found (conservative) + return True + + +def _all_supply_curve_targets(): + targets = [] + for region in config["regions"]: + wacc = _wacc_for_region(region) + for product in SUPPLY_CURVE_PRODUCTS: + targets.append( + f"resources/supply_curves/cost_year~2050/{region}_wacc_{wacc}_marginal_cost_{product}.csv" + ) + return targets rule retrieve_cost_data: @@ -56,11 +69,16 @@ rule retrieve_cost_data: str(SCRIPT_DIR / "tech_database.py") -rule build_steel_skeleton: +rule build_generic_model: input: costs="resources/technology_data/costs_{cost_year}.csv", output: - skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + skeleton="resources/generic_production_model/generic_model_{cost_year}.nc", + # Also produce one skeleton per detected stage-group so Snakemake tracks them + group_skeletons=[ + f"resources/generic_production_model/generic_model_{{cost_year}}_{(g.get('label') or 'group')}.nc" + for g in get_stage_groups(get_trade_chain(config)) + ], threads: 1 resources: mem_mb=1000, @@ -70,28 +88,34 @@ rule build_steel_skeleton: rule prepare_regional_network: input: - skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", + # Prefer a per-stage-group skeleton when a route_label exists for the product; + # otherwise fall back to the legacy full skeleton. + skeleton=lambda wildcards: ( + f"resources/generic_production_model/generic_model_{wildcards.cost_year}_{_process_label_for_product(wildcards.product)}.nc" + if _process_label_for_product(wildcards.product) + else f"resources/generic_production_model/generic_model_{wildcards.cost_year}.nc" + ), renewables="data/new_renewables_consolidated.nc", tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", output: - network="resources/networks/base_{cost_year}_{region}_{process_label}_{scenario}.nc", + # Output keyed by product; route_label is internal to the script + network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", log: - "logs/prepare_regional_network_{cost_year}_{region}_{process_label}_{scenario}.log", + "logs/prepare_regional_network_{cost_year}_{region}_{product}_{scenario}.log", wildcard_constraints: scenario="reserved|unreserved", - process_label="hbi|steel", + product="hbi|steel", threads: 1 resources: mem_mb=2000, params: region="{region}", - # Derive product from process_label (hbi → hbi, steel → steel) - product=lambda wildcards: wildcards.process_label, - process_label="{process_label}", + product="{product}", + route_label=lambda wildcards: _process_label_for_product(wildcards.product), config=config, message: - "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.process_label} " + "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.product} " "(cost_year={wildcards.cost_year})" script: str(SCRIPT_DIR / "prepare_regional_network.py") @@ -101,34 +125,36 @@ if config["enable"].get("run_supply_chain", True): rule calculate_regional_lcox: input: - base_network="resources/networks/base_{cost_year}_{region}_{process_label}_{scenario}.nc", + base_network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", local_demand="data/un_enerdata_demand_2050_final.csv", output: - results="resources/lco-{process_label}/cost_year~{cost_year}/{region}_{scenario}/results_{product_demand_mt}.csv", + # Internal cache keyed by route_label for reuse; only products matter for supply curves + results="resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/results_{product_demand_mt}.csv", network=( temp( - "resources/lco-{process_label}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" + "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" ) if not config.get("outputs", {}).get( "keep_optimization_networks", False ) - else "resources/lco-{process_label}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" + else "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" ), log: - "logs/calculate_regional_lcox_{cost_year}_{region}_{process_label}_{scenario}_{product_demand_mt}.log", + "logs/calculate_regional_lcox_{cost_year}_{region}_{product}_{scenario}_{product_demand_mt}.log", wildcard_constraints: product_demand_mt=r"\d+(?:\.\d+)?", scenario="reserved|unreserved", - process_label="hbi|steel", + product="hbi|steel", threads: 2 resources: mem_mb=4000, params: product_demand_mt="{product_demand_mt}", compute_iis=config.get("solver", {}).get("compute_iis", False), - process_label="{process_label}", + product="{product}", + route_label=lambda wildcards: _process_label_for_product(wildcards.product), message: - "Calculating LCoX ({wildcards.scenario}) for {wildcards.process_label} in {wildcards.region} " + "Calculating LCoX ({wildcards.scenario}) for {wildcards.product} in {wildcards.region} " "(demand={wildcards.product_demand_mt} Mt/year)." script: str(SCRIPT_DIR / "calculate_lcox.py") @@ -139,52 +165,45 @@ if config["enable"].get("run_supply_curve", True): rule create_supply_curve: input: lco_reserved=lambda wildcards: expand( - f"resources/lco-{wildcards.process_label}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ), lco_unreserved=lambda wildcards: ( expand( - f"resources/lco-{wildcards.process_label}/cost_year~{wildcards.cost_year}/{wildcards.region}_unreserved/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_unreserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ) if config.get("supply_curve", {}).get("generate_unreserved", False) + and _product_uses_renewables(wildcards.product) else [] ), - skeleton="resources/steel_skeleton/steel_skeleton_{cost_year}.nc", - local_demand="data/un_enerdata_demand_2050_final.csv", + skeleton="resources/generic_production_model/generic_model_{cost_year}.nc", steel_demand="resources/steel_production_clustered.csv", output: - # Output files now use process_label instead of product - supply="resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}.csv", + # Public supply-curve artifact is product-labeled; the stage label + # is only used to locate the correct upstream LCoX runs. + supply="resources/supply_curves/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}.csv", supply_unreserved=( - "resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}__unreserved.csv" + "resources/supply_curves/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}__unreserved.csv" if config.get("supply_curve", {}).get("generate_unreserved", False) + and _product_uses_renewables("{product}") else temp( - "resources/supply_curves_unreserved_tmp/cost_year~{cost_year}/{region}_marginal_cost_{process_label}__unreserved.csv" + "resources/supply_curves_unreserved_tmp/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}__unreserved.csv" ) ), - supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}.pdf", + supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}.pdf", log: - "logs/create_supply_curve_{cost_year}_{region}_{process_label}.log", + "logs/create_supply_curve_{cost_year}_{region}_{product}_{wacc}.log", wildcard_constraints: - process_label="hbi|steel", + product="hbi|steel", + wacc=r"[0-9]+(?:\.[0-9]+)?", threads: 1 message: - "Combining LCo results (reserved + unreserved scenarios) to create supply curve for {wildcards.region} {wildcards.process_label}." + "Combining LCo results (reserved + unreserved scenarios) to create supply curve for {wildcards.region} {wildcards.product}." script: str(SCRIPT_DIR / "create_supply_curve.py") rule create_all_supply_curves: input: - lambda wildcards: expand( - "resources/supply_curves/cost_year~{cost_year}/{region}_marginal_cost_{process_label}.csv", - cost_year=[2050], - region=config["regions"], - process_label=[ - pl - for product in SUPPLY_CURVE_PRODUCTS - for pl in _process_labels_for_product(product) - ], - allow_missing=True, - ), + _all_supply_curve_targets(), diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 1b6436a..5dc0f94 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -28,12 +28,21 @@ import logging from typing import Any +from pathlib import Path import pandas as pd import numpy as np import pypsa import tech_database as td +from trade_chain_utils import ( + get_ordered_stages, + get_trade_chain, + split_stage_inputs, + get_stage_groups, + _components_for_process_label, +) + logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -47,6 +56,11 @@ } +def _part_load(config: dict, technology: str, default: float) -> float: + """Read a part-load minimum from config.part_load.""" + return float(config.get("part_load", {}).get(technology, default)) + + def _add_carriers(network: pypsa.Network) -> None: """Add carrier components to network. @@ -54,6 +68,10 @@ def _add_carriers(network: pypsa.Network) -> None: """ carriers = { "renewable_electricity": "Islanded renewable electricity", + # Technology-specific renewable carriers (used by Generators) + "renewable_pv": "Photovoltaic (utility)", + "renewable_wind_onshore": "Onshore wind", + "renewable_wind_offshore": "Offshore wind", "hydrogen": "Hydrogen gas", "battery_elec": "Battery (electrical energy)", "iron_ore": "Iron ore (mass)", @@ -68,22 +86,44 @@ def _add_carriers(network: pypsa.Network) -> None: network.add("Carrier", carrier_name, description=description) -def _add_buses(network: pypsa.Network) -> None: - """Add energy carrier buses.""" +def _add_buses(network: pypsa.Network, stages: list | None = None) -> None: + """Add energy carrier buses. + + The `grid_electricity` bus is created only when one of the configured + stages explicitly requires grid electricity as an energy input. This + keeps the skeleton free of an unused grid bus when stages are fully + renewable. + """ + # determine whether any stage requires grid_electricity + uses_grid = False + if stages is not None: + for stage in stages: + _, energy_inputs = split_stage_inputs(stage) + if "grid_electricity" in energy_inputs: + uses_grid = True + break + buses = { "renewable_electricity": {"carrier": "renewable_electricity", "unit": "MW"}, - "grid_electricity": {"carrier": "grid_electricity", "unit": "MW"}, "hydrogen": {"carrier": "hydrogen", "unit": "MW"}, "battery": {"carrier": "battery_elec", "unit": "MWh"}, "iron_ore": {"carrier": "iron_ore", "unit": "t/h"}, "hbi": {"carrier": "hbi", "unit": "t/h"}, "steel": {"carrier": "steel", "unit": "t/h"}, } + + # add grid bus only if needed + if uses_grid: + buses["grid_electricity"] = {"carrier": "grid_electricity", "unit": "MW"} + for name, attrs in buses.items(): - network.add("Bus", name, **attrs) + if name not in network.buses.index: + network.add("Bus", name, **attrs) -def _add_grid_electricity_supply(network: pypsa.Network, config: dict) -> None: +def _add_grid_electricity_supply( + network: pypsa.Network, config: dict, stages: list | None = None +) -> None: """Add a grid import generator for the EAF when requested. The default topology uses grid-connected EAF power. When config sets @@ -91,7 +131,15 @@ def _add_grid_electricity_supply(network: pypsa.Network, config: dict) -> None: and the EAF remains connected to the local electricity bus. """ - if config.get("eaf_electricity_source", "grid") != "grid": + # If specific stages provided, inspect those; otherwise inspect full chain + if stages is None: + chain = get_trade_chain(config) + stages = get_ordered_stages(chain) + uses_grid = any( + "grid_electricity" in split_stage_inputs(stage)[1] for stage in stages + ) + + if not uses_grid: return network.add( @@ -105,80 +153,154 @@ def _add_grid_electricity_supply(network: pypsa.Network, config: dict) -> None: def _add_conversion_chain( - network: pypsa.Network, tech_costs: pd.Series, config: dict + network: pypsa.Network, + tech_costs: pd.Series, + config: dict, + stages: list | None = None, ) -> None: - """Add energy conversion pathway: Electricity → H2 → HBI → Steel. + """Add the configured stage conversion pathway. Note: Costs are added but discount_rate is NOT set here. It is applied regionally in prepare_regional_network. """ - # Electrolyzer: Electricity → H2 - elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") - - elec_inv_cost = td.get_tech_param(elec_params, "investment", 544.7764) * 1000 - network.add( - "Link", - "electrolyzer", - bus0="renewable_electricity", - bus1="hydrogen", - carrier="electrolysis", - efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), - overnight_cost=elec_inv_cost, # EUR/kW → EUR/MW - lifetime=td.get_tech_param(elec_params, "lifetime", 40.0), - fom_cost=elec_inv_cost * (td.get_tech_param(elec_params, "FOM", 2.8) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - p_min_pu=config.get("elec_p_min_pu", 0.10), - ) - - # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI - dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") - - dri_inv_cost = td.get_tech_param(dri_params, "investment", 5378698.8822) - network.add( - "Link", - "dri", - bus0="iron_ore", - bus1="hbi", - bus2="hydrogen", - bus3="renewable_electricity", - carrier="direct_reduction_furnace", - efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), - efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), - efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), - overnight_cost=dri_inv_cost, - lifetime=td.get_tech_param(dri_params, "lifetime", 40.0), - fom_cost=dri_inv_cost * (td.get_tech_param(dri_params, "FOM", 11.3) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - p_min_pu=config.get("dri_p_min_pu", 0.15), - ) - - # EAF: HBI + Electricity → Steel - eaf_params = td.get_tech(tech_costs, "electric arc furnace") - eaf_bus2 = ( - "grid_electricity" - if config.get("eaf_electricity_source", "grid") == "grid" - else "renewable_electricity" - ) - - eaf_inv_cost = td.get_tech_param(eaf_params, "investment", 2312992.7323) - network.add( - "Link", - "eaf", - bus0="hbi", - bus1="steel", - bus2=eaf_bus2, - carrier="electric_arc_furnace", - efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), - efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), - overnight_cost=eaf_inv_cost, - lifetime=td.get_tech_param(eaf_params, "lifetime", 40.0), - fom_cost=eaf_inv_cost * (td.get_tech_param(eaf_params, "FOM", 30.0) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - p_min_pu=config.get("eaf_p_min_pu", 0.20), - ) + # Allow building from an explicit list of stages (stage-group) or full chain + if stages is None: + chain = get_trade_chain(config) + stages = get_ordered_stages(chain) + if not stages: + raise ValueError("No stages configured in trade_chains") + + for stage in stages: + process_label = str(stage.get("process_label", "")).strip() + materials, energy_inputs = split_stage_inputs(stage) + + # Validate stage IO against canonical mapping (sanity check only) + try: + # Use strict behavior if config requests it + strict_validation = bool(config.get("strict_trade_chain_validation", False)) + from trade_chain_utils import validate_stage_io + + validate_stage_io(stage, raise_on_mismatch=strict_validation) + except Exception as exc: + # If strict_validation True, validate_stage_io will raise; propagate + if config.get("strict_trade_chain_validation", False): + raise + logger.warning(f"Trade-chain validation issue: {exc}") + + # Resolve concrete components from mapping + comp = _components_for_process_label(process_label) + if comp is None: + logger.warning( + f"No component mapping for process_label '{process_label}'; skipping stage" + ) + continue + + # Normalize candidate buses + # Prefer canonical buses from the mapping; fall back to stage-declared inputs + def pick_bus(preferred: str, fallback_list: list[str], default: str) -> str: + # preferred may be like 'renewable_electricity' or 'hydrogen' + if preferred in comp.get("buses", ()): # type: ignore[arg-type] + return preferred + for f in fallback_list: + if f: + return f + return default + + # ELECTROLYZER + if "electrolyzer" in comp.get("links", ()): # type: ignore[arg-type] + elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") + elec_inv_cost = ( + td.get_tech_param(elec_params, "investment", 544.7764) * 1000 + ) + # choose energy bus: prefer 'renewable_electricity' unless grid explicitly listed + energy_bus = ( + "grid_electricity" + if "grid_electricity" in energy_inputs + else "renewable_electricity" + ) + network.add( + "Link", + process_label, + bus0=energy_bus, + bus1="hydrogen", + carrier="electrolysis", + efficiency=1.0 + / td.get_tech_param(elec_params, "electricity-input", 1.38), + overnight_cost=elec_inv_cost, + lifetime=td.get_tech_param(elec_params, "lifetime", 40.0), + fom_cost=elec_inv_cost + * (td.get_tech_param(elec_params, "FOM", 2.8) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + p_min_pu=_part_load(config, "electrolysis", 0.10), + ) + + # DRI (direct reduction) — use canonical buses: iron_ore -> hbi, hydrogen input, electricity + if "dri" in comp.get("links", ()): # type: ignore[arg-type] + dri_params = td.get_tech( + tech_costs, "hydrogen direct iron reduction furnace" + ) + dri_inv_cost = td.get_tech_param(dri_params, "investment", 5378698.8822) + # canonical buses + bus0 = "iron_ore" + bus1 = "hbi" + bus2 = "hydrogen" + # bus3: electricity; prefer grid if explicitly listed in stage, else renewable + bus3 = ( + "grid_electricity" + if "grid_electricity" in energy_inputs + else "renewable_electricity" + ) + network.add( + "Link", + process_label, + bus0=bus0, + bus1=bus1, + bus2=bus2, + bus3=bus3, + carrier="direct_reduction_furnace", + efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), + efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), + efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), + overnight_cost=dri_inv_cost, + lifetime=td.get_tech_param(dri_params, "lifetime", 40.0), + fom_cost=dri_inv_cost + * (td.get_tech_param(dri_params, "FOM", 11.3) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + p_min_pu=_part_load(config, "direct reduction furnace", 0.15), + ) + + # EAF (electric arc furnace) — canonical buses: hbi -> steel, electricity from grid by default + if "eaf" in comp.get("links", ()): # type: ignore[arg-type] + eaf_params = td.get_tech(tech_costs, "electric arc furnace") + eaf_inv_cost = td.get_tech_param(eaf_params, "investment", 2312992.7323) + # canonical buses + bus0 = "hbi" + bus1 = "steel" + # choose energy bus: prefer grid by default; if stage explicitly lists renewable, use renewable + bus2 = ( + "grid_electricity" + if "grid_electricity" in energy_inputs + else "renewable_electricity" + ) + network.add( + "Link", + process_label, + bus0=bus0, + bus1=bus1, + bus2=bus2, + carrier="electric_arc_furnace", + efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), + efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), + overnight_cost=eaf_inv_cost, + lifetime=td.get_tech_param(eaf_params, "lifetime", 40.0), + fom_cost=eaf_inv_cost + * (td.get_tech_param(eaf_params, "FOM", 30.0) / 100), + p_nom_extendable=True, + p_nom_max=np.inf, + p_min_pu=_part_load(config, "electric arc furnace", 0.20), + ) def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> None: @@ -294,7 +416,10 @@ def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Networ # Add network components (carriers MUST be added before buses that reference them) _add_carriers(network) - _add_buses(network) + # determine full-stage ordering for bus creation decisions + chain = get_trade_chain(config) + stages = get_ordered_stages(chain) + _add_buses(network, stages=stages) _add_grid_electricity_supply(network, config) _add_conversion_chain(network, tech_costs, config) _add_storage(network, tech_costs, config) @@ -308,6 +433,21 @@ def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Networ return network +def _set_meta(network: pypsa.Network, group: dict | None) -> None: + """Attach metadata about stage group to the network for downstream tools.""" + meta = {} + if group is not None: + meta["stage_group_label"] = group.get("label") + meta["stages"] = [int(s.get("order", -1)) for s in group.get("stages", [])] + # Derive whether this group uses renewables + uses_renewables = any( + "renewable_electricity" in split_stage_inputs(s)[1] + for s in group.get("stages", []) + ) + meta["uses_renewables"] = bool(uses_renewables) + network.meta = meta + + if __name__ == "__main__": if snakemake is None: raise RuntimeError( @@ -318,11 +458,47 @@ def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Networ tech_costs_path = snakemake.input.costs # noqa: F821 output_path = snakemake.output[0] # noqa: F821 - cost_year = 0 + cost_year = getattr(snakemake.wildcards, "cost_year", None) if cost_year is None: raise ValueError("snakemake.wildcards.cost_year is required") - year = getattr(snakemake.wildcards, "cost_year", 0) # noqa: F821 - network = build_network(config, tech_costs_path, year) - network.export_to_netcdf(output_path) - logger.info(f"Network exported to {output_path}") + year = int(cost_year) + + # Build and export the full skeleton (backwards compatible) + full_network = build_network(config, tech_costs_path, year) + _set_meta(full_network, None) + # Ensure target directory exists and write to new generic_model path + out_dir = str(Path(output_path).resolve().parent) + generic_dir = Path(out_dir) / ".." / "generic_production_model" + generic_dir = generic_dir.resolve() + generic_dir.mkdir(parents=True, exist_ok=True) + full_out = generic_dir / f"generic_model_{year}.nc" + full_network.export_to_netcdf(str(full_out)) + logger.info(f"Full generic model exported to {full_out}") + + # Additionally export one skeleton per detected stage-group + chain = get_trade_chain(config) + groups = get_stage_groups(chain) + out_dir = str(Path(output_path).resolve().parent) + generic_dir = Path(out_dir) / ".." / "generic_production_model" + generic_dir = generic_dir.resolve() + for group in groups: + label = group.get("label") or "group" + # Build a fresh network containing only components for this stage-group + group_network = pypsa.Network() + group_network.set_snapshots( + pd.date_range(f"{year}-01-01", periods=8760, freq="h") + ) + tech_costs = td.load_tech_costs(tech_costs_path) + _add_carriers(group_network) + _add_buses(group_network, stages=group.get("stages")) + _add_grid_electricity_supply(group_network, config, stages=group.get("stages")) + _add_conversion_chain( + group_network, tech_costs, config, stages=group.get("stages") + ) + _add_storage(group_network, tech_costs, config) + _add_resources(group_network, config) + _set_meta(group_network, group) + out_path = generic_dir / f"generic_model_{year}_{label}.nc" + group_network.export_to_netcdf(str(out_path)) + logger.info(f"Exported stage-group generic model: {out_path}") diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 9acc71b..878a8ed 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -298,14 +298,6 @@ def add_loads_to_network(network, product, demands): hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h unit_str = "t/h" - elif product == "h2": - bus_name = "hydrogen" - # H2 is measured in MWh/year, convert to MW (hourly average) - hourly_demand_mwh = demands[ - "product_demand_mwh_per_h" - ] # Already hourly average - unit_str = "MW" - elif product in ["eaf", "eaf-grid"]: bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) @@ -320,10 +312,7 @@ def add_loads_to_network(network, product, demands): # Add constant hourly load to the bus load_name = f"{product}_demand" - if product == "h2": - p_set = hourly_demand_mwh - else: - p_set = hourly_demand_t + p_set = hourly_demand_t network.add( "Load", @@ -703,12 +692,13 @@ def extract_lcox(network, product, demands): else "reserved" ) - # Get process_label from params (network is already sliced at preparation stage) - process_label = snakemake.params.process_label + # Get route_label from params (network is already sliced at preparation stage) + route_label = snakemake.params.route_label + product = snakemake.params.product logger.info( f"LCOX Calculation: region={snakemake.wildcards.region}, " - f"process_label={process_label}, " + f"product={product}, route_label={route_label}, " f"scenario={scenario}, " f"demand={product_demand_mt} Mt/year." ) @@ -733,17 +723,12 @@ def extract_lcox(network, product, demands): f"Local electricity demand: {demands['local_el_demand_mwh']:.1f} MWh/year" ) - # ==================== PROCESS SINGLE DEMAND LEVEL ==================== - electricity_per_product_t = snakemake.config.get("electricity_steel_ratio", 5.25) - logger.info(f"Processing: {product_demand_mt} Mt/year") # ==================== NETWORK SETUP ==================== # Create a copy of base network network = base_network.copy() - network.name = ( - f"LCOX-{snakemake.wildcards.region}-{process_label}-{product_demand_mt}" - ) + network.name = f"LCOX-{snakemake.wildcards.region}-{product}-{product_demand_mt}" # Ensure snapshot year is set by upstream network preparation; # do not override if already set. @@ -763,20 +748,11 @@ def extract_lcox(network, product, demands): # Preserve discount_rate from base network (needed for cost annuitization) network.discount_rate = base_network.discount_rate - # Calculate renewable electricity needed for this demand level - scaled_product_demand_mwh_per_h = ( - product_demand_mt * 1e6 * electricity_per_product_t / 8760 - ) - logger.info(f"Product demand: {product_demand_mt:.1f} Mt/year") - logger.info( - f"Renewable electricity required: {scaled_product_demand_mwh_per_h * 8760:.1f} MWh/year" - ) # Create scaled demands dict for this demand level scaled_demands = demands.copy() scaled_demands["product_demand_mt"] = product_demand_mt - scaled_demands["product_demand_mwh_per_h"] = scaled_product_demand_mwh_per_h # Load incremental generator sets and apply filtering logger.info("Loading incremental generator sets...") @@ -786,7 +762,7 @@ def extract_lcox(network, product, demands): # Add hourly load for steel output # (This also sets HBI storage e_initial inside add_loads_to_network) logger.info("Adding hourly load to network...") - add_loads_to_network(network=network, product=process_label, demands=scaled_demands) + add_loads_to_network(network=network, product=product, demands=scaled_demands) # Debug: Print network structure logger.info( @@ -809,7 +785,7 @@ def extract_lcox(network, product, demands): # Solve logger.info("Optimizing network...") if snakemake.config.get("debug_network_inspection", False): - inspect_network(network, process_label) # Debug inspection + inspect_network(network, product) # Debug inspection try: solve_network(network, snakemake.config) optimization_status = ( @@ -832,10 +808,50 @@ def extract_lcox(network, product, demands): logger.info("Extracting results...") results_df = extract_lcox( network=network, - product=process_label, + product=product, demands=scaled_demands, ) + # Attach provenance so downstream supply curves can trace each row back to + # the config inputs and the applied PyPSA network state. + interest_rates = snakemake.config.get("interest_rate", {}) + if isinstance(interest_rates.get(snakemake.wildcards.region), dict): + config_discount_rate = interest_rates[snakemake.wildcards.region].get( + "default", interest_rates.get("default", np.nan) + ) + else: + config_discount_rate = interest_rates.get( + snakemake.wildcards.region, interest_rates.get("default", np.nan) + ) + + applied_discount_rate = float(getattr(network, "discount_rate", np.nan)) + discount_rate_matches = bool( + np.isfinite(applied_discount_rate) + and np.isfinite(config_discount_rate) + and np.isclose(applied_discount_rate, config_discount_rate) + ) + + if not discount_rate_matches: + logger.warning( + "Discount rate mismatch for region %s: network=%s config=%s", + snakemake.wildcards.region, + applied_discount_rate, + config_discount_rate, + ) + + results_df["region"] = snakemake.wildcards.region + results_df["product"] = product + results_df["scenario"] = scenario + results_df["route_label"] = route_label + results_df["cost_year"] = int(snakemake.wildcards.cost_year) + results_df["product_demand_mt"] = product_demand_mt + results_df["discount_rate_config_key"] = ( + f"interest_rate.{snakemake.wildcards.region}" + ) + results_df["discount_rate_config"] = config_discount_rate + results_df["discount_rate_network"] = applied_discount_rate + results_df["discount_rate_matches_config"] = discount_rate_matches + # Save results for this demand level result_file = snakemake.output.results network_file = snakemake.output.network diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index f535047..2a41565 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -1,10 +1,16 @@ import os +import sys from typing import Any import pandas as pd import matplotlib import matplotlib.pyplot as plt import pypsa +# Add workflow/scripts to path for imports +sys.path.insert(0, os.path.join(os.path.dirname(__file__))) + +from trade_chain_utils import route_label_for_product + snakemake: Any = globals().get("snakemake") matplotlib.use("Agg") @@ -97,22 +103,8 @@ def create_supply_curve(): stage_ratios = get_stage_ratios_from_skeleton() stage_meta = get_stage_metadata(product, stage_ratios) - def find_process_label_for_product(config, product_name): - chains = config.get("trade_chains") or [] - for chain in chains: - for stage in chain.get("stages", []): - if stage.get("output_commodity") == product_name: - return stage.get("process_label") - return None - - # process_label derived from config (which stage produces this product) - process_label_config = ( - find_process_label_for_product(snakemake.config, product) or product - ) - # detect whether this script was invoked per-stage (Snakemake wildcard `process_label`) or as full-product - invoked_with_process_label = hasattr(snakemake.wildcards, "process_label") - # For later tagging use the config-derived label - process_label = process_label_config + # route_label is derived from config to locate upstream LCoX files for this product + route_label = route_label_for_product(snakemake.config, product) or product reserved_files = snakemake.input.lco_reserved print("reserved scenario files:", reserved_files) @@ -142,43 +134,15 @@ def find_process_label_for_product(config, product_name): df_sub = df_sub.drop(infeasible_rows) print("deleted infeasible rows to prepare for plotting") - df_all_demand = pd.read_csv(snakemake.input.local_demand, header=0) - df_local_demand = df_all_demand.loc[ - df_all_demand["region"] == snakemake.wildcards["region"] - ] - - if product == "hydrogen": - conversion_factor = 0.75 - elif product in ["steel", "eaf", "hbi", "eaf-grid"]: - conversion_factor = 1 / snakemake.config["electricity_steel_ratio"] - else: - raise ValueError(f"product {product} not recognized for supply curve plotting") - - local_load = float( - df_local_demand["demand"].values[0] - * df_local_demand["el_share"].values[0] - / 100 - ) - product_subtract = local_load * conversion_factor - - print(f"local el load is: {local_load} MWh") - print( - f"product substraction due to local el load is: {product_subtract} {columns['product_unit']}" - ) - - df_sub[columns["demand"]] = df_sub[columns["demand"]].subtract(product_subtract) - df_sub.loc[df_sub[columns["demand"]] < 0, columns["demand"]] = 0 - print("local el load has been subtracted from global supply") - df_merged["stage_input_commodity"] = stage_meta["stage_input_commodity"] df_merged["stage_output_commodity"] = stage_meta["stage_output_commodity"] df_merged["stage_input_per_output"] = stage_meta["stage_input_per_output"] - df_merged["process_label"] = process_label + df_merged["route_label"] = route_label df_sub["stage_input_commodity"] = stage_meta["stage_input_commodity"] df_sub["stage_output_commodity"] = stage_meta["stage_output_commodity"] df_sub["stage_input_per_output"] = stage_meta["stage_input_per_output"] - df_sub["process_label"] = process_label + df_sub["route_label"] = route_label df_merged["stage_marginal_cost_per_unit"] = df_merged[ columns["cost per unit"] @@ -187,33 +151,10 @@ def find_process_label_for_product(config, product_name): float ) - # For per-stage runs (when invoked with Snakemake wildcard `process_label`) - # we must NOT include upstream input prices. Those are only for full-chain product - # aggregation. If this script is invoked as a stage, set upstream input costs - # to zero to preserve Option B semantics. - if invoked_with_process_label: - iron_ore_total_cost = 0 - else: - if product == "steel": - ore_ratio = stage_ratios["ore_per_steel"] - if ore_ratio is None: - ore_ratio = snakemake.config["iron_ore"]["ore_to_steel_ratio"] - iron_ore_total_cost = ( - snakemake.config["iron_ore"]["marginal_cost"] * ore_ratio - ) - elif product == "hbi": - ore_ratio = stage_ratios["ore_per_hbi"] - if ore_ratio is None: - ore_ratio = snakemake.config["iron_ore"]["ore_to_steel_ratio"] - iron_ore_total_cost = ( - snakemake.config["iron_ore"]["marginal_cost"] * ore_ratio - ) - elif product in ["hydrogen", "eaf", "eaf-grid"]: - iron_ore_total_cost = 0 - else: - raise ValueError( - f"product {product} not recognized for supply curve plotting" - ) + # Supply curves are stage-marginal only; no upstream pricing is included. + # This preserves Option B semantics where each stage is independently cost-optimized + # and the trade model assembles the full chain cost. + iron_ore_total_cost = 0 df_merged["iron_ore_cost_per_unit"] = iron_ore_total_cost df_sub["iron_ore_cost_per_unit"] = iron_ore_total_cost @@ -275,12 +216,6 @@ def find_process_label_for_product(config, product_name): markerfacecolor="none", label="supply (unreserved)", ) - plt.axvline( - product_subtract / (1e6), - label="local electricity demand (converted to product)", - linestyle="--", - color="C1", - ) if product == "steel": y_merged_total = df_merged["total_cost_per_unit"] @@ -355,29 +290,9 @@ def find_process_label_for_product(config, product_name): ) -# Derive product from process_label (new approach) -def _derive_product_from_process_label(process_label, config): - """Reverse-lookup: process_label → output_commodity (product).""" - chains = config.get("trade_chains") or [] - for chain in chains: - stages = chain.get("stages", []) - for s in stages: - if s.get("process_label") == process_label: - return s.get("output_commodity") - # Fallback: if no chain found, assume process_label is product - return process_label - - -# Get process_label from wildcards; fallback to product for backward compatibility -if hasattr(snakemake.wildcards, "process_label"): - process_label = snakemake.wildcards["process_label"] - product = _derive_product_from_process_label(process_label, snakemake.config) - print(f"Using process_label={process_label}, derived product={product}") -else: - # Backward compatibility: use product wildcard - product = snakemake.wildcards["product"] - process_label = None - print(f"Using product={product} (no process_label provided)") +# Get product from wildcards (product-labeled contract) +product = snakemake.wildcards["product"] +print(f"Creating supply curve for product={product}") if product == "hydrogen": columns = { diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index e1572c0..b92bd71 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -25,7 +25,8 @@ import logging from pathlib import Path -from typing import Any, Dict, Tuple +from typing import Any, Dict, Optional, Tuple, Iterable, cast +import sys import numpy as np import pandas as pd import xarray as xr @@ -33,6 +34,12 @@ import tech_database as td +SCRIPT_DIR = Path(__file__).resolve().parent +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) + +from trade_chain_utils import build_product_components # noqa: E402 + logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -79,56 +86,57 @@ def load_region_renewables_consolidated( metadata : dict Summary info (n_classes, n_time, technologies, etc.) """ - ds = xr.open_dataset(consolidated_path) - - if region not in ds.region.values: - available = ", ".join(ds.region.values) - raise ValueError(f"Region '{region}' not found. Available: {available}") - - logger.info(f"Loading consolidated renewables for {region}") - - # Select region (dims: technology, class) - region_cap = ds["capacity"].sel(region=region) # (tech, class) - region_cf = ds["capacity_factor"].sel(region=region) # (tech, class, time) - - # Extract technology names and data - techs = list(region_cap.technology.values) - technologies_dict = {} - - for tech in techs: - cap = region_cap.sel(technology=tech).values # (class,) - technologies_dict[tech] = cap - logger.info(f" {tech}: {len(cap)} sites, {cap.sum():.0f} MW total") - - # Capacity factor time series (keep full structure for now) - cf_ts = region_cf # (tech, class, time) - - metadata = { - "region": region, - "n_classes": region_cap.sizes["class"], - "n_time": region_cf.sizes["time"], - "n_technologies": len(techs), - "technologies": techs, - "time_start": pd.Timestamp(ds["time"].values[0]), - "time_end": pd.Timestamp(ds["time"].values[-1]), - "total_capacity_mw": float(region_cap.sum().values), - } + with xr.open_dataset(consolidated_path) as ds: + if region not in ds.region.values: + available = ", ".join(ds.region.values) + raise ValueError(f"Region '{region}' not found. Available: {available}") + + logger.info(f"Loading consolidated renewables for {region}") + + # Select region (dims: technology, class) + region_cap = ds["capacity"].sel(region=region) # (tech, class) + region_cf = ds["capacity_factor"].sel(region=region) # (tech, class, time) + + # Extract technology names and data + techs = list(region_cap.technology.values) + technologies_dict = {} + + for tech in techs: + cap = region_cap.sel(technology=tech).values # (class,) + technologies_dict[tech] = cap + logger.info(f" {tech}: {len(cap)} sites, {cap.sum():.0f} MW total") + + # Capacity factor time series (keep full structure for now) + cf_ts = region_cf # (tech, class, time) + + metadata = { + "region": region, + "n_classes": region_cap.sizes["class"], + "n_time": region_cf.sizes["time"], + "n_technologies": len(techs), + "technologies": techs, + "time_start": pd.Timestamp(ds["time"].values[0]), + "time_end": pd.Timestamp(ds["time"].values[-1]), + "total_capacity_mw": float(region_cap.sum().values), + } - logger.info( - f" Total capacity: {metadata['total_capacity_mw']:.0f} MW, " - f"{metadata['n_time']} timesteps, {metadata['n_technologies']} technologies" - ) + logger.info( + f" Total capacity: {metadata['total_capacity_mw']:.0f} MW, " + f"{metadata['n_time']} timesteps, {metadata['n_technologies']} technologies" + ) - ds.close() return technologies_dict, cf_ts, metadata def load_local_electricity_demand_mw( - local_demand_path: str, + local_demand_path: Optional[str], region: str, ) -> float: """Load regional electricity demand and convert it to average MW.""" + if not local_demand_path: + return 0.0 + try: local_df = pd.read_csv(local_demand_path) region_mask = local_df["region"].str.lower() == region.lower() @@ -150,7 +158,7 @@ def reserve_top_sites_by_highest_cf( cf_ts: xr.DataArray, reserve_capacity_mw: float, scenario: str = "reserved", -) -> Dict[str, np.ndarray]: +) -> Optional[Dict[str, np.ndarray]]: """ Reserve top sites (highest average capacity factor) for local demand. @@ -237,7 +245,7 @@ def add_renewable_generators( cf_ts: xr.DataArray, tech_costs: pd.Series, config: dict, - reserved_techs: Dict[str, np.ndarray] = None, + reserved_techs: Optional[Dict[str, np.ndarray]] = None, ) -> Dict: """ Add renewable generators to PyPSA network. @@ -273,10 +281,10 @@ def add_renewable_generators( total_p_nom_max = 0 n_reserved = 0 - # Ensure electricity bus exists + # Ensure electricity bus exists (umbrella renewable carrier) elec_bus = "renewable_electricity" if elec_bus not in network.buses.index: - network.add("Bus", elec_bus, carrier="AC", v_nom=1) + network.add("Bus", elec_bus, carrier="renewable_electricity", unit="MW") # Get discount rate discount_rate = network.discount_rate if hasattr(network, "discount_rate") else 0.07 @@ -331,14 +339,25 @@ def add_renewable_generators( # Get time series for this site p_max_pu = cf_data[site_idx, :] # (time,) + # Determine tech-specific carrier while keeping generators on the + # shared `renewable_electricity` bus. This preserves per-tech + # statistics while modelling a common electricity bus. + tech_to_carrier = { + "pvplant": "renewable_pv", + "windonshore": "renewable_wind_onshore", + "windoffshore": "renewable_wind_offshore", + } + carrier_name = tech_to_carrier.get(tech, f"renewable_{tech}") + # Add generator (extendable with ceiling) network.add( "Generator", gen_name, bus=elec_bus, - carrier=tech, + carrier=carrier_name, p_nom_extendable=True, p_nom=0, # Start with no capacity; optimization will decide + p_nom_min=0, p_nom_max=p_nom_max, # Upper ceiling from dataset (MW) p_max_pu=p_max_pu, # Hourly capacity factor (0-1) overnight_cost=overnight_cost, @@ -347,6 +366,7 @@ def add_renewable_generators( fom_cost=fom_cost, tags={ "technology": tech, + "resource_tech": tech, "region": region, "local_priority": is_reserved, "site_id": site_idx, @@ -372,98 +392,210 @@ def add_renewable_generators( } -def back_propagate_electricity_need( - tech_costs: pd.Series, - product: str, - config: dict, -) -> float: +def _product_has_renewables(config: dict, product: str) -> bool: + """Check if a product uses renewable electricity in its network. + + Returns True if the product should have renewable generators and reservation logic applied. + Returns False if the product uses grid electricity only. """ - Calculate renewable electricity requirement (MWh) per tonne of product. + try: + return bool( + build_product_components(config, product).get("has_renewables", False) + ) + except Exception: + return False + + +def sanitize_and_fix( + network: pypsa.Network, logger: Optional[logging.Logger] = None +) -> None: + """Run `network.sanitize()` and apply small, safe fixes. - Paths: - - steel (t): Electrolyzer(elec) + DRI(elec + H2) + optional EAF(elec) - - hbi (t): Electrolyzer(elec) + DRI(elec + H2) - - h2 (t): Electrolyzer(elec) only + Fixes applied: + - Ensure renewable generators have `p_nom_min = 0`. + - Clamp `p_nom_min` to `p_nom_max` when inconsistent. + - Logs a summary of applied fixes. """ - # Check for config override - override_key = f"electricity_per_tonne_{product}_mwh" - if override_key in config: - value = config[override_key] - logger.info(f"Using config override: {product} requires {value} MWh/t") - return value - - # Electrolyzer: Electricity → H2 - elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") - elec_mwh_per_mwh_h2 = td.get_tech_param(elec_params, "electricity-input", 1.38) - - if product == "h2": - return elec_mwh_per_mwh_h2 - - # DRI Furnace: Iron ore + Hydrogen + Electricity → HBI - dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") - h2_per_t_hbi = td.get_tech_param(dri_params, "hydrogen-input", 2.1) - dri_elec_per_t_hbi = td.get_tech_param(dri_params, "electricity-input", 1.03) - h2_elec_per_t_hbi = h2_per_t_hbi * elec_mwh_per_mwh_h2 - - if product == "hbi": - return h2_elec_per_t_hbi + dri_elec_per_t_hbi - - if product == "steel": - # Add EAF if configured, otherwise just HBI path - eaf_source = config.get("eaf_electricity_source", "grid") - if eaf_source == "renewable": - eaf_params = td.get_tech(tech_costs, "electric arc furnace") - eaf_elec_per_t_steel = td.get_tech_param( - eaf_params, "electricity-input", 0.5 + if logger is None: + _logger = logging.getLogger(__name__) + else: + _logger = logger + + _logger.info("Sanitizing network (PyPSA sanitize + post-fix checks)...") + try: + network.sanitize() + except Exception as exc: + _logger.warning(f"network.sanitize() raised an exception: {exc}") + + fixes = [] + # operate on a snapshot of the generators DataFrame to avoid SettingWithCopy + if len(network.generators) == 0: + _logger.info("No generators to check during sanitize_and_fix.") + return + + gens = network.generators + for gen in gens.index: + try: + carrier = gens.loc[gen, "carrier"] + except Exception: + carrier = None + + # Ensure renewable generators have zero minimum + if isinstance(carrier, str) and carrier.startswith("renewable_"): + try: + current_pmin = ( + gens.loc[gen, "p_nom_min"] if "p_nom_min" in gens.columns else None + ) + except Exception: + current_pmin = None + # Set p_nom_min to 0 if not set or positive + try: + if current_pmin is None or ( + pd.notna(current_pmin) and float(current_pmin) != 0.0 + ): + network.generators.loc[gen, "p_nom_min"] = 0.0 + fixes.append(f"set p_nom_min=0 for {gen}") + except Exception: + # best-effort; continue + pass + + # Clamp p_nom_min <= p_nom_max + try: + pmin = ( + network.generators.loc[gen, "p_nom_min"] + if "p_nom_min" in network.generators.columns + else None ) - return h2_elec_per_t_hbi + dri_elec_per_t_hbi + eaf_elec_per_t_steel - else: - return h2_elec_per_t_hbi + dri_elec_per_t_hbi - - raise ValueError(f"Product '{product}' not recognized. Choose: h2, hbi, steel") - - -def apply_product_cutoff(network: pypsa.Network, product: str) -> None: - """Remove supply chain components after the target product.""" - if product == "h2": - # Keep only: electricity -> electrolyzer -> H2 storage - # Remove: DRI, HBI, EAF, steel - components_to_remove = [ - ("Link", "link_dri_furnace"), - ("Link", "link_eaf"), - ("Store", "store_hbi"), - ("Store", "store_steel"), - ] - elif product == "hbi": - # Keep: electricity -> electrolyzer -> H2 -> DRI -> HBI - # Remove: EAF, steel - components_to_remove = [ - ("Link", "link_eaf"), - ("Store", "store_steel"), - ] - elif product == "steel": - # Keep all: electricity -> electrolyzer -> H2 -> DRI -> HBI -> EAF -> steel - components_to_remove = [] + pmax = ( + network.generators.loc[gen, "p_nom_max"] + if "p_nom_max" in network.generators.columns + else None + ) + if pd.notna(pmin) and pd.notna(pmax): + # If pmax < pmin, reduce pmin to pmax + if float(pmax) < float(pmin): + network.generators.loc[gen, "p_nom_min"] = float(pmax) + fixes.append(f"clamped p_nom_min to p_nom_max for {gen}") + except Exception: + pass + + if fixes: + _logger.info(f"sanitize_and_fix applied {len(fixes)} fixes: {fixes[:10]}") else: - raise ValueError(f"Product '{product}' not recognized") + _logger.info("sanitize_and_fix applied no fixes") + - for comp_type, comp_name in components_to_remove: - if comp_name in getattr(network, comp_type.lower() + "s", {}).index: - logger.info(f"Removing {comp_type} {comp_name}") - network.remove(comp_type, comp_name) +def _get_components_for_product(config: dict, product: str) -> Tuple[set, set, set]: + """Get links, stores, and buses for a product from the configured stage groups. + + Returns (keep_links, keep_stores, keep_buses) sets. + """ + comp = build_product_components(config, product) + keep_links = set(cast(Iterable[str], comp.get("links") or [])) + keep_stores = set(cast(Iterable[str], comp.get("stores") or [])) + keep_buses = set(cast(Iterable[str], comp.get("buses") or [])) + + logger.info( + f"Components for product={product}: links={keep_links}, stores={keep_stores}, buses={keep_buses}" + ) + return keep_links, keep_stores, keep_buses + + +def apply_product_cutoff( + network: pypsa.Network, product: str, config: Optional[dict] = None +) -> None: + """Remove supply chain components beyond the target product. + + Uses config.product_components to determine which components to keep. + Removes all links and stores not needed for the target product. + Preserves the output buses for the product (e.g., 'steel' for steel product). + Also removes orphaned buses (buses with no connected components). + """ + if config is None: + config = {} + + # Get the set of links, stores, and buses to keep for this product + keep_links, keep_stores, keep_buses = _get_components_for_product(config, product) + + # Remove links not in the keep set + for link_name in list(network.links.index): + if link_name not in keep_links: + try: + network.remove("Link", link_name) + logger.info( + f"Removed Link: {link_name} (not needed for product={product})" + ) + except Exception as e: + logger.warning(f"Could not remove Link {link_name}: {e}") + + # Remove stores not in the keep set + for store_name in list(network.stores.index): + if store_name not in keep_stores: + try: + network.remove("Store", store_name) + logger.info( + f"Removed Store: {store_name} (not needed for product={product})" + ) + except Exception as e: + logger.warning(f"Could not remove Store {store_name}: {e}") + + # Remove orphaned buses (buses not connected to any remaining component) + # BUT preserve buses listed in keep_buses (output bus for this product) + for bus_name in list(network.buses.index): + # Skip essential supply buses and product output buses + if ( + bus_name in ["renewable_electricity", "grid_electricity"] + or bus_name in keep_buses + ): + continue + + has_connection = False + + # Check if bus is used by any link (bus0, bus1, bus2, bus3) + if len(network.links) > 0: + for bcol in ["bus0", "bus1", "bus2", "bus3"]: + if ( + bcol in network.links.columns + and (network.links[bcol] == bus_name).any() + ): + has_connection = True + break + + # Check generators + if not has_connection and len(network.generators) > 0: + if (network.generators["bus"] == bus_name).any(): + has_connection = True + + # Check stores + if not has_connection and len(network.stores) > 0: + if (network.stores["bus"] == bus_name).any(): + has_connection = True + + # Check loads + if not has_connection and len(network.loads) > 0: + if (network.loads["bus"] == bus_name).any(): + has_connection = True + + # Remove if orphaned + if not has_connection: + try: + network.remove("Bus", bus_name) + logger.info(f"Removed orphaned Bus: {bus_name}") + except Exception as e: + logger.warning(f"Could not remove Bus {bus_name}: {e}") def prepare_network( skeleton_network_path: str, consolidated_renewables_path: str, tech_costs_path: str, - local_demand_path: str, + local_demand_path: Optional[str], region: str, product: str, cost_year: int = 2030, - config: dict = None, + config: Optional[dict] = None, scenario: str = "reserved", - process_label: str = None, + route_label: Optional[str] = None, ) -> Tuple[pypsa.Network, Dict]: """Prepare regional network with consolidated renewables. @@ -481,12 +613,12 @@ def prepare_network( Target product (h2, hbi, steel) cost_year : int Cost year for technology parameters - config : dict + config : dict, optional Configuration dict scenario : str "reserved" (default): apply high-CF site reservation for domestic demand "unreserved": no reservation; full renewable stack available (fallback scenario) - process_label : str, optional + route_label : str, optional If provided, slice skeleton to this stage only (e.g., "hbi", "steel") This enables independent per-stage solves for Option B semantics. @@ -502,40 +634,63 @@ def prepare_network( logger.info("=" * 70) logger.info( - f"Preparing network: region={region}, product={product}, process_label={process_label}" + f"Preparing network: region={region}, product={product}, route_label={route_label}" ) logger.info("=" * 70) - # Load skeleton + # Load skeleton (prefer stage-group specific skeleton when available) logger.info("Loading skeleton network...") - network = pypsa.Network(skeleton_network_path) + skeleton_to_load = skeleton_network_path + if route_label: + try: + from pathlib import Path + + p = Path(skeleton_network_path) + stem = p.stem + suffix = p.suffix + # Expect group-specific files like 'generic_model_2050_hbi.nc' + candidate = p.with_name(f"{stem}_{route_label}{suffix}") + if candidate.exists(): + logger.info( + f"Found group-specific skeleton for route_label={route_label}: {candidate}" + ) + skeleton_to_load = str(candidate) + else: + logger.info( + f"No group-specific skeleton found for {route_label}; using {skeleton_network_path}" + ) + except Exception: + # Fallback to provided skeleton path + skeleton_to_load = skeleton_network_path + + network = pypsa.Network(skeleton_to_load) network.name = f"base_{cost_year}_{region}_{product}" - # STAGE SLICING: if process_label provided, slice skeleton to that stage only - # Note: previously we skipped slicing when product == process_label (because + # STAGE SLICING: if route_label provided, slice skeleton to that stage only + # Note: previously we skipped slicing when product == route_label (because # Snakemake params set `product` to the same value). Always slice when a - # `process_label` is supplied to ensure per-stage networks are produced. - if process_label: - logger.info(f"Slicing skeleton to process_label={process_label}") + # `route_label` is supplied to ensure per-stage networks are produced. + if route_label: + logger.info(f"Slicing skeleton to route_label={route_label}") # Define which links/stores to keep for each stage stage_components = { "hbi": { - "keep_links": ["electrolyzer", "dri"], + "keep_links": ["electrolysis", "dri"], "keep_stores": ["h2_storage", "hbi_storage"], - "remove_links": ["eaf"], + "remove_links": ["eaf-grid"], "add_hbi_input": False, }, "steel": { - "keep_links": ["eaf"], - "keep_stores": [], - "remove_links": ["electrolyzer", "dri"], + "keep_links": ["eaf-grid"], + "keep_stores": ["steel_storage"], + "remove_links": ["electrolysis", "dri"], "add_hbi_input": True, # Add HBI as external free input }, } - if process_label in stage_components: - spec = stage_components[process_label] + if route_label in stage_components: + spec = stage_components[route_label] # Remove links not in keep_links for link_name in list(network.links.index): @@ -570,11 +725,11 @@ def prepare_network( logger.info("Added HBI as free external input (steel stage)") logger.info( - f"Skeleton sliced to {process_label}: {len(network.links)} links, {len(network.stores)} stores" + f"Skeleton sliced to {route_label}: {len(network.links)} links, {len(network.stores)} stores" ) else: logger.warning( - f"process_label={process_label} not recognized; keeping full skeleton" + f"route_label={route_label} not recognized; keeping full skeleton" ) # Set region-specific discount rate @@ -605,10 +760,13 @@ def prepare_network( tech_costs = td.load_tech_costs(tech_costs_path) # Load consolidated renewables for region (only if this stage needs renewables) - # HBI stage needs renewables; steel stage does not (uses grid) - if process_label == "steel": + # Check product_components config to see if product uses renewable_electricity + product_uses_renewables = _product_has_renewables(config, route_label or product) + + if not product_uses_renewables: logger.info( - "Steel stage detected: skipping renewable generators (uses grid electricity)" + f"Product '{route_label}' does not use renewable_electricity: " + f"skipping renewable generator loading" ) techs_dict = {} cf_ts = None @@ -618,25 +776,30 @@ def prepare_network( techs_dict, cf_ts, metadata = load_region_renewables_consolidated( consolidated_renewables_path, region ) - # Apply local demand reservation if configured + # Apply local demand reservation if configured (only for products with renewable_electricity) # For scenario="reserved", reserve high-CF sites; for "unreserved", skip reservation reserved_techs = None - reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) - if reserve_capacity_mw <= 0 and scenario == "reserved": - reserve_capacity_mw = load_local_electricity_demand_mw( - local_demand_path, region - ) - logger.info( - f"Derived reservation target from local demand: {reserve_capacity_mw:.1f} MW" - ) - logger.info(f"Scenario: {scenario} (scenario flag passed from Snakemake rule)") - if reserve_capacity_mw > 0 or scenario == "reserved": + if product_uses_renewables: + reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) + if reserve_capacity_mw <= 0 and scenario == "reserved": + reserve_capacity_mw = load_local_electricity_demand_mw( + local_demand_path, region + ) + logger.info( + f"Derived reservation target from local demand: {reserve_capacity_mw:.1f} MW" + ) + logger.info(f"Scenario: {scenario} (scenario flag passed from Snakemake rule)") + if reserve_capacity_mw > 0 or scenario == "reserved": + logger.info( + f"Applying local demand reservation for scenario={scenario}: " + f"target {reserve_capacity_mw} MW" + ) + reserved_techs = reserve_top_sites_by_highest_cf( + techs_dict, cf_ts, reserve_capacity_mw, scenario=scenario + ) + else: logger.info( - f"Applying local demand reservation for scenario={scenario}: " - f"target {reserve_capacity_mw} MW" - ) - reserved_techs = reserve_top_sites_by_highest_cf( - techs_dict, cf_ts, reserve_capacity_mw, scenario=scenario + f"Skipping reservation: product '{route_label}' does not use renewables" ) # Add renewable generators (only if techs_dict is not empty) @@ -654,9 +817,16 @@ def prepare_network( "total_capacity_mw": 0, "n_reserved": 0, } - # Apply product cutoff - logger.info(f"Applying product cutoff for {product}...") - apply_product_cutoff(network, product) + # Apply product cutoff only when using full skeleton. + # When route_label is provided (dedicated stage-group skeleton), the network + # is already scoped to the correct components, so cutoff is redundant. + if route_label: + logger.info( + f"Using dedicated stage-group skeleton for {route_label}; skipping product cutoff" + ) + else: + logger.info(f"Applying product cutoff for {product}...") + apply_product_cutoff(network, product, config=config) # Build audit info audit_info = { @@ -682,6 +852,9 @@ def prepare_network( logger.info(f" - Capacity: {gen_audit['total_capacity_mw']:.0f} MW") logger.info("=" * 70) + # Run centralized sanitization and small automatic fixes + sanitize_and_fix(network, logger=logger) + # Stage-slicing helper: produce a subnetwork containing only the specified process carrier def build_stage_subnetwork(n: pypsa.Network, process_carrier: str) -> pypsa.Network: """Return a deep copy of the network pruned to links with carrier == process_carrier @@ -830,11 +1003,11 @@ def validate_network_carriers(n: pypsa.Network): region = snakemake.params.region product = snakemake.params.product - process_label = ( - snakemake.params.process_label - if hasattr(snakemake.params, "process_label") + route_label = ( + snakemake.params.route_label + if hasattr(snakemake.params, "route_label") else None - ) + ) # route_label is the process stage name (e.g., "hbi", "steel") cost_year = ( snakemake.wildcards.cost_year if hasattr(snakemake.wildcards, "cost_year") @@ -862,7 +1035,7 @@ def validate_network_carriers(n: pypsa.Network): product = sys.argv[5] output_path = sys.argv[6] cost_year = int(sys.argv[7]) if len(sys.argv) > 7 else 2030 - process_label = sys.argv[8] if len(sys.argv) > 8 else None + route_label = sys.argv[8] if len(sys.argv) > 8 else None local_demand_path = None config_dict = {} else: @@ -879,7 +1052,7 @@ def validate_network_carriers(n: pypsa.Network): cost_year=cost_year, config=config_dict, scenario=scenario, - process_label=process_label, + route_label=route_label, ) # Save network diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py new file mode 100644 index 0000000..b89bc4e --- /dev/null +++ b/workflow/scripts/trade_chain_utils.py @@ -0,0 +1,326 @@ +"""Helpers for normalizing trade-chain config and deriving stage groups. + +The config currently stores a single trade chain with ordered stages keyed by +stage number. This module turns that into a stable, ordered representation and +derives the stage group boundaries implied by tradeable commodities. +""" + +from __future__ import annotations + +from typing import Dict, List, Optional, Tuple + + +ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} +BUS_ALIASES = { + "H2": "hydrogen", + "h2": "hydrogen", + "hydrogen": "hydrogen", + "renewable_electricity": "renewable_electricity", + "grid_electricity": "grid_electricity", +} + +# Explicit mapping from high-level process identifiers to concrete PyPSA components. +# Keep this mapping authoritative so config `process_label` remains high-level. +TECH_COMPONENT_MAP = [ + { + "match": ("electro", "electrolyser", "electrolyzer"), + "links": ("electrolyzer",), + "stores": ("h2_storage",), + # expected material reactants, energy inputs, and outputs + "materials": (), + "energy": ("renewable_electricity", "grid_electricity"), + "outputs": ("hydrogen",), + "buses": ("hydrogen", "renewable_electricity", "grid_electricity"), + }, + { + "match": ("dri", "direct_reduction", "reduction"), + "links": ("dri",), + "stores": ("h2_storage", "hbi_storage"), + "materials": ("iron_ore", "hydrogen"), + "energy": ("renewable_electricity", "grid_electricity"), + "outputs": ("hbi",), + "buses": ( + "iron_ore", + "hydrogen", + "hbi", + "renewable_electricity", + "grid_electricity", + ), + }, + { + "match": ("eaf", "electric_arc", "arc_furnace"), + "links": ("eaf",), + "stores": ("steel_storage",), + "materials": ("hbi",), + "energy": ("grid_electricity", "renewable_electricity"), + "outputs": ("steel",), + "buses": ("hbi", "steel", "grid_electricity", "renewable_electricity"), + }, +] + + +def _components_for_process_label( + process_label: str, +) -> Optional[Dict[str, Tuple[str, ...]]]: + """Return mapped components and expected IO for a given process_label or None if not found.""" + if not process_label: + return None + pl = process_label.lower() + for entry in TECH_COMPONENT_MAP: + for pat in entry["match"]: + if pat in pl: + return { + "links": tuple(entry.get("links", ())), + "stores": tuple(entry.get("stores", ())), + "buses": tuple(entry.get("buses", ())), + "materials": tuple(entry.get("materials", ())), + "energy": tuple(entry.get("energy", ())), + "outputs": tuple(entry.get("outputs", ())), + } + return None + + +def validate_stage_io(stage: Dict, raise_on_mismatch: bool = False) -> bool: + """Validate that a stage's declared inputs/outputs match the canonical mapping. + + Returns True if validation passes or no mapping exists. If `raise_on_mismatch` is True, + a ValueError is raised on mismatch; otherwise a warning is returned via logging and False is returned. + """ + import logging + + logger = logging.getLogger(__name__) + + process_label = str(stage.get("process_label", "")).strip() + if not process_label: + return True + + comp = _components_for_process_label(process_label) + if comp is None: + # No mapping — nothing to validate + return True + + # Normalize declared inputs/outputs + declared_materials, declared_energy = split_stage_inputs(stage) + declared_materials_norm = {_normalize_commodity(m) for m in declared_materials} + declared_energy_norm = {_normalize_commodity(e) for e in declared_energy} + declared_output = _normalize_commodity(stage.get("output_commodity", "")) + + expected_materials = {_normalize_commodity(m) for m in comp.get("materials", ())} + expected_energy = {_normalize_commodity(e) for e in comp.get("energy", ())} + expected_outputs = {_normalize_commodity(o) for o in comp.get("outputs", ())} + + msgs = [] + # Materials: declared_materials should be a superset of expected_materials or vice versa? + # We allow declared to be a superset (user may include both H2 and iron_ore), but require at least one overlap + if expected_materials and declared_materials_norm.isdisjoint(expected_materials): + msgs.append( + f"Stage '{process_label}': declared material inputs {declared_materials_norm} do not overlap expected {expected_materials}" + ) + + # Energy: declared energy should overlap expected energy + if expected_energy and declared_energy_norm.isdisjoint(expected_energy): + msgs.append( + f"Stage '{process_label}': declared energy inputs {declared_energy_norm} do not overlap expected {expected_energy}" + ) + + # Output: declared_output should be one of expected outputs + if expected_outputs and declared_output and declared_output not in expected_outputs: + msgs.append( + f"Stage '{process_label}': declared output '{declared_output}' not in expected outputs {expected_outputs}" + ) + + if msgs: + if raise_on_mismatch: + raise ValueError("; ".join(msgs)) + for m in msgs: + logger.warning(m) + return False + + return True + + +def iter_trade_chains(config: Dict) -> List[Dict]: + """Return trade chain definitions as a list.""" + + trade_chains = config.get("trade_chains") + if not trade_chains: + return [] + if isinstance(trade_chains, dict): + return [trade_chains] + return list(trade_chains) + + +def get_trade_chain(config: Dict) -> Dict: + """Return the primary trade chain from config.""" + + chains = iter_trade_chains(config) + if not chains: + return {} + return chains[0] + + +def get_ordered_stages(chain: Dict) -> List[Dict]: + """Return stages ordered by their numeric key or declared order.""" + + stages = chain.get("stages", {}) + if isinstance(stages, dict): + items = sorted(stages.items(), key=lambda item: int(item[0])) + ordered = [] + for key, stage in items: + stage_dict = dict(stage) + stage_dict.setdefault("order", int(key)) + ordered.append(stage_dict) + return ordered + + ordered = [dict(stage) for stage in stages] + ordered.sort(key=lambda stage: int(stage.get("order", 0))) + return ordered + + +def _as_list(value) -> List[str]: + if value is None: + return [] + if isinstance(value, (list, tuple, set)): + return [str(item) for item in value] + return [str(value)] + + +def _normalize_commodity(name: str) -> str: + return BUS_ALIASES.get(str(name), str(name)) + + +def split_stage_inputs(stage: Dict) -> Tuple[List[str], List[str]]: + """Split a stage's inputs into material inputs and energy inputs.""" + + if "material_inputs" in stage or "energy_inputs" in stage: + raw_inputs = _as_list(stage.get("material_inputs")) + energy_inputs = _as_list(stage.get("energy_inputs")) + elif "input_commodities" in stage: + raw_inputs = _as_list(stage.get("input_commodities")) + energy_inputs = _as_list(stage.get("energy_inputs")) + else: + raw_inputs = _as_list(stage.get("input_commodity")) + energy_inputs = [] + + materials = [] + energy = list(energy_inputs) + + for input_name in raw_inputs: + if input_name in ENERGY_INPUTS: + energy.append(input_name) + else: + materials.append(input_name) + + return materials, energy + + +def get_stage_groups(chain: Dict) -> List[Dict]: + """Group contiguous stages until a tradeable output or the final product.""" + + ordered_stages = get_ordered_stages(chain) + if not ordered_stages: + return [] + + tradeable = {str(item) for item in chain.get("tradeable_commodities", [])} + final_product = str(chain.get("final_product", "")).strip() + + groups = [] + current = [] + for stage in ordered_stages: + current.append(stage) + output_commodity = str(stage.get("output_commodity", "")).strip() + if output_commodity in tradeable or output_commodity == final_product: + groups.append( + { + "label": output_commodity, + "stages": list(current), + } + ) + current = [] + + if current: + last_output = str(current[-1].get("output_commodity", "")).strip() + groups.append({"label": last_output, "stages": list(current)}) + + return groups + + +def route_label_for_product(config: Dict, product: str) -> str: + """Return the stage-group label for a product.""" + + chain = get_trade_chain(config) + for group in get_stage_groups(chain): + if group["label"] == product: + return group["label"] + return product + + +def derive_supply_curve_products(config: Dict) -> List[str]: + """Return the externally visible product list for supply curves.""" + + products = [] + for chain in iter_trade_chains(config): + for group in get_stage_groups(chain): + label = group["label"] + if label and label not in products: + products.append(label) + return products or ["steel"] + + +def build_product_components(config: Dict, product: str) -> Dict[str, object]: + """Derive the links, stores, buses, and renewable flag for a product.""" + + chain = get_trade_chain(config) + groups = get_stage_groups(chain) + + target_group: Optional[Dict] = None + for group in groups: + if group["label"] == product: + target_group = group + break + + if target_group is None: + raise ValueError(f"Product '{product}' not found in configured stage groups") + + links = set() + stores = set() + buses = set() + has_renewables = False + + for stage in target_group["stages"]: + process_label = str(stage.get("process_label", "")).strip() + materials, energy = split_stage_inputs(stage) + + # Add explicit buses derived from stage inputs + for material in materials: + buses.add(_normalize_commodity(material)) + for energy_input in energy: + norm = _normalize_commodity(energy_input) + buses.add(norm) + if energy_input == "renewable_electricity": + has_renewables = True + + output_commodity = _normalize_commodity(stage.get("output_commodity", "")) + if output_commodity: + buses.add(output_commodity) + + # Use explicit mapping from process_label -> concrete components + comp = _components_for_process_label(process_label) + if comp is None and process_label: + # Fail fast: require explicit mapping for new/unknown process labels + raise ValueError( + f"Process label '{process_label}' has no TECH_COMPONENT_MAP entry; add mapping before using it in config" + ) + + if comp: + links.update(comp.get("links", ())) + stores.update(comp.get("stores", ())) + for b in comp.get("buses", ()): # include any canonical buses from mapping + buses.add(_normalize_commodity(b)) + + return { + "links": links, + "stores": stores, + "buses": buses, + "has_renewables": has_renewables, + } From 3d8aaff7312fce773771e1d13ab09f5f93daba62 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 18 May 2026 18:57:43 +0200 Subject: [PATCH 064/216] chore: remove stale env --- environment.yaml | 71 ------------------------------------------------ 1 file changed, 71 deletions(-) delete mode 100644 environment.yaml diff --git a/environment.yaml b/environment.yaml deleted file mode 100644 index d127e03..0000000 --- a/environment.yaml +++ /dev/null @@ -1,71 +0,0 @@ -# SPDX-License-Identifier: CC0-1.0 - -name: shift -channels: -- conda-forge -- bioconda -dependencies: -- python>=3.10 -- pip - -# Inhouse packages -- pypsa>=0.32.1 -- atlite>=0.3 -- linopy>=0.4.4 -- powerplantmatching>=0.5.15 - -# Dependencies of the workflow itself -- dask -- xlrd -- openpyxl -- seaborn -- snakemake-minimal>=9 -- snakemake-storage-plugin-http>=0.3 -- snakemake-executor-plugin-slurm -- snakemake-executor-plugin-cluster-generic -- memory_profiler -- yaml -- pytables -- lxml -- numpy -- pandas>=2.1 -- geopandas>=1 -- xarray>=2024.03.0 -- rioxarray -- netcdf4 -- libgdal-netcdf -- networkx -- scipy -- glpk -- shapely>=2.0 -- matplotlib -- proj -- fiona -- country_converter -- geopy -- tqdm -- pytz -- jpype1 -- pyxlsb -- graphviz -- geojson -- pyscipopt - -# GIS dependencies: -- cartopy -- descartes -- rasterio - -# Development dependencies -- jupyter -- ipython -- pre-commit -- ruff -- pylint - -- pip: - - gurobipy - - highspy - - tsam>=2.3.1 - - entsoe-py - - pypsatopo \ No newline at end of file From dee5d40eb55c5ca703e3ae3faf8324519a9fef8d Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 11:05:54 +0200 Subject: [PATCH 065/216] feat: add trade chain utils --- workflow/scripts/trade_chain_utils.py | 326 ++++++++++++++++++++++++++ 1 file changed, 326 insertions(+) create mode 100644 workflow/scripts/trade_chain_utils.py diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py new file mode 100644 index 0000000..b89bc4e --- /dev/null +++ b/workflow/scripts/trade_chain_utils.py @@ -0,0 +1,326 @@ +"""Helpers for normalizing trade-chain config and deriving stage groups. + +The config currently stores a single trade chain with ordered stages keyed by +stage number. This module turns that into a stable, ordered representation and +derives the stage group boundaries implied by tradeable commodities. +""" + +from __future__ import annotations + +from typing import Dict, List, Optional, Tuple + + +ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} +BUS_ALIASES = { + "H2": "hydrogen", + "h2": "hydrogen", + "hydrogen": "hydrogen", + "renewable_electricity": "renewable_electricity", + "grid_electricity": "grid_electricity", +} + +# Explicit mapping from high-level process identifiers to concrete PyPSA components. +# Keep this mapping authoritative so config `process_label` remains high-level. +TECH_COMPONENT_MAP = [ + { + "match": ("electro", "electrolyser", "electrolyzer"), + "links": ("electrolyzer",), + "stores": ("h2_storage",), + # expected material reactants, energy inputs, and outputs + "materials": (), + "energy": ("renewable_electricity", "grid_electricity"), + "outputs": ("hydrogen",), + "buses": ("hydrogen", "renewable_electricity", "grid_electricity"), + }, + { + "match": ("dri", "direct_reduction", "reduction"), + "links": ("dri",), + "stores": ("h2_storage", "hbi_storage"), + "materials": ("iron_ore", "hydrogen"), + "energy": ("renewable_electricity", "grid_electricity"), + "outputs": ("hbi",), + "buses": ( + "iron_ore", + "hydrogen", + "hbi", + "renewable_electricity", + "grid_electricity", + ), + }, + { + "match": ("eaf", "electric_arc", "arc_furnace"), + "links": ("eaf",), + "stores": ("steel_storage",), + "materials": ("hbi",), + "energy": ("grid_electricity", "renewable_electricity"), + "outputs": ("steel",), + "buses": ("hbi", "steel", "grid_electricity", "renewable_electricity"), + }, +] + + +def _components_for_process_label( + process_label: str, +) -> Optional[Dict[str, Tuple[str, ...]]]: + """Return mapped components and expected IO for a given process_label or None if not found.""" + if not process_label: + return None + pl = process_label.lower() + for entry in TECH_COMPONENT_MAP: + for pat in entry["match"]: + if pat in pl: + return { + "links": tuple(entry.get("links", ())), + "stores": tuple(entry.get("stores", ())), + "buses": tuple(entry.get("buses", ())), + "materials": tuple(entry.get("materials", ())), + "energy": tuple(entry.get("energy", ())), + "outputs": tuple(entry.get("outputs", ())), + } + return None + + +def validate_stage_io(stage: Dict, raise_on_mismatch: bool = False) -> bool: + """Validate that a stage's declared inputs/outputs match the canonical mapping. + + Returns True if validation passes or no mapping exists. If `raise_on_mismatch` is True, + a ValueError is raised on mismatch; otherwise a warning is returned via logging and False is returned. + """ + import logging + + logger = logging.getLogger(__name__) + + process_label = str(stage.get("process_label", "")).strip() + if not process_label: + return True + + comp = _components_for_process_label(process_label) + if comp is None: + # No mapping — nothing to validate + return True + + # Normalize declared inputs/outputs + declared_materials, declared_energy = split_stage_inputs(stage) + declared_materials_norm = {_normalize_commodity(m) for m in declared_materials} + declared_energy_norm = {_normalize_commodity(e) for e in declared_energy} + declared_output = _normalize_commodity(stage.get("output_commodity", "")) + + expected_materials = {_normalize_commodity(m) for m in comp.get("materials", ())} + expected_energy = {_normalize_commodity(e) for e in comp.get("energy", ())} + expected_outputs = {_normalize_commodity(o) for o in comp.get("outputs", ())} + + msgs = [] + # Materials: declared_materials should be a superset of expected_materials or vice versa? + # We allow declared to be a superset (user may include both H2 and iron_ore), but require at least one overlap + if expected_materials and declared_materials_norm.isdisjoint(expected_materials): + msgs.append( + f"Stage '{process_label}': declared material inputs {declared_materials_norm} do not overlap expected {expected_materials}" + ) + + # Energy: declared energy should overlap expected energy + if expected_energy and declared_energy_norm.isdisjoint(expected_energy): + msgs.append( + f"Stage '{process_label}': declared energy inputs {declared_energy_norm} do not overlap expected {expected_energy}" + ) + + # Output: declared_output should be one of expected outputs + if expected_outputs and declared_output and declared_output not in expected_outputs: + msgs.append( + f"Stage '{process_label}': declared output '{declared_output}' not in expected outputs {expected_outputs}" + ) + + if msgs: + if raise_on_mismatch: + raise ValueError("; ".join(msgs)) + for m in msgs: + logger.warning(m) + return False + + return True + + +def iter_trade_chains(config: Dict) -> List[Dict]: + """Return trade chain definitions as a list.""" + + trade_chains = config.get("trade_chains") + if not trade_chains: + return [] + if isinstance(trade_chains, dict): + return [trade_chains] + return list(trade_chains) + + +def get_trade_chain(config: Dict) -> Dict: + """Return the primary trade chain from config.""" + + chains = iter_trade_chains(config) + if not chains: + return {} + return chains[0] + + +def get_ordered_stages(chain: Dict) -> List[Dict]: + """Return stages ordered by their numeric key or declared order.""" + + stages = chain.get("stages", {}) + if isinstance(stages, dict): + items = sorted(stages.items(), key=lambda item: int(item[0])) + ordered = [] + for key, stage in items: + stage_dict = dict(stage) + stage_dict.setdefault("order", int(key)) + ordered.append(stage_dict) + return ordered + + ordered = [dict(stage) for stage in stages] + ordered.sort(key=lambda stage: int(stage.get("order", 0))) + return ordered + + +def _as_list(value) -> List[str]: + if value is None: + return [] + if isinstance(value, (list, tuple, set)): + return [str(item) for item in value] + return [str(value)] + + +def _normalize_commodity(name: str) -> str: + return BUS_ALIASES.get(str(name), str(name)) + + +def split_stage_inputs(stage: Dict) -> Tuple[List[str], List[str]]: + """Split a stage's inputs into material inputs and energy inputs.""" + + if "material_inputs" in stage or "energy_inputs" in stage: + raw_inputs = _as_list(stage.get("material_inputs")) + energy_inputs = _as_list(stage.get("energy_inputs")) + elif "input_commodities" in stage: + raw_inputs = _as_list(stage.get("input_commodities")) + energy_inputs = _as_list(stage.get("energy_inputs")) + else: + raw_inputs = _as_list(stage.get("input_commodity")) + energy_inputs = [] + + materials = [] + energy = list(energy_inputs) + + for input_name in raw_inputs: + if input_name in ENERGY_INPUTS: + energy.append(input_name) + else: + materials.append(input_name) + + return materials, energy + + +def get_stage_groups(chain: Dict) -> List[Dict]: + """Group contiguous stages until a tradeable output or the final product.""" + + ordered_stages = get_ordered_stages(chain) + if not ordered_stages: + return [] + + tradeable = {str(item) for item in chain.get("tradeable_commodities", [])} + final_product = str(chain.get("final_product", "")).strip() + + groups = [] + current = [] + for stage in ordered_stages: + current.append(stage) + output_commodity = str(stage.get("output_commodity", "")).strip() + if output_commodity in tradeable or output_commodity == final_product: + groups.append( + { + "label": output_commodity, + "stages": list(current), + } + ) + current = [] + + if current: + last_output = str(current[-1].get("output_commodity", "")).strip() + groups.append({"label": last_output, "stages": list(current)}) + + return groups + + +def route_label_for_product(config: Dict, product: str) -> str: + """Return the stage-group label for a product.""" + + chain = get_trade_chain(config) + for group in get_stage_groups(chain): + if group["label"] == product: + return group["label"] + return product + + +def derive_supply_curve_products(config: Dict) -> List[str]: + """Return the externally visible product list for supply curves.""" + + products = [] + for chain in iter_trade_chains(config): + for group in get_stage_groups(chain): + label = group["label"] + if label and label not in products: + products.append(label) + return products or ["steel"] + + +def build_product_components(config: Dict, product: str) -> Dict[str, object]: + """Derive the links, stores, buses, and renewable flag for a product.""" + + chain = get_trade_chain(config) + groups = get_stage_groups(chain) + + target_group: Optional[Dict] = None + for group in groups: + if group["label"] == product: + target_group = group + break + + if target_group is None: + raise ValueError(f"Product '{product}' not found in configured stage groups") + + links = set() + stores = set() + buses = set() + has_renewables = False + + for stage in target_group["stages"]: + process_label = str(stage.get("process_label", "")).strip() + materials, energy = split_stage_inputs(stage) + + # Add explicit buses derived from stage inputs + for material in materials: + buses.add(_normalize_commodity(material)) + for energy_input in energy: + norm = _normalize_commodity(energy_input) + buses.add(norm) + if energy_input == "renewable_electricity": + has_renewables = True + + output_commodity = _normalize_commodity(stage.get("output_commodity", "")) + if output_commodity: + buses.add(output_commodity) + + # Use explicit mapping from process_label -> concrete components + comp = _components_for_process_label(process_label) + if comp is None and process_label: + # Fail fast: require explicit mapping for new/unknown process labels + raise ValueError( + f"Process label '{process_label}' has no TECH_COMPONENT_MAP entry; add mapping before using it in config" + ) + + if comp: + links.update(comp.get("links", ())) + stores.update(comp.get("stores", ())) + for b in comp.get("buses", ()): # include any canonical buses from mapping + buses.add(_normalize_commodity(b)) + + return { + "links": links, + "stores": stores, + "buses": buses, + "has_renewables": has_renewables, + } From 525931248a63cd57e7a597e974a6726ac8c77f00 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 11:06:39 +0200 Subject: [PATCH 066/216] fix: adjust path for mock snakemake --- workflow/notebooks/_helpers.py | 5 +++-- workflow/scripts/_helpers.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/workflow/notebooks/_helpers.py b/workflow/notebooks/_helpers.py index c2bcfaa..2c4a13a 100644 --- a/workflow/notebooks/_helpers.py +++ b/workflow/notebooks/_helpers.py @@ -24,6 +24,7 @@ logger = logging.getLogger(__name__) + def load_config(config): with open(config, "r") as stream: try: @@ -79,7 +80,7 @@ def mock_snakemake( script_dir = Path(__file__).parent.resolve() if root_dir is None: - root_dir = script_dir.parent + root_dir = script_dir.parent.parent else: root_dir = Path(root_dir).resolve() @@ -187,4 +188,4 @@ def progress_retrieve(url, file, disable=False): with open(file, "wb") as f: for data in response.iter_content(chunk_size=chunk_size): f.write(data) - t.update(len(data)) \ No newline at end of file + t.update(len(data)) diff --git a/workflow/scripts/_helpers.py b/workflow/scripts/_helpers.py index c2bcfaa..2c4a13a 100644 --- a/workflow/scripts/_helpers.py +++ b/workflow/scripts/_helpers.py @@ -24,6 +24,7 @@ logger = logging.getLogger(__name__) + def load_config(config): with open(config, "r") as stream: try: @@ -79,7 +80,7 @@ def mock_snakemake( script_dir = Path(__file__).parent.resolve() if root_dir is None: - root_dir = script_dir.parent + root_dir = script_dir.parent.parent else: root_dir = Path(root_dir).resolve() @@ -187,4 +188,4 @@ def progress_retrieve(url, file, disable=False): with open(file, "wb") as f: for data in response.iter_content(chunk_size=chunk_size): f.write(data) - t.update(len(data)) \ No newline at end of file + t.update(len(data)) From 83f077a05a7c5e63193808e5c27d452f0992c661 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 11:07:23 +0200 Subject: [PATCH 067/216] fix: add trade chain utils in alternative path to be recognized by snakefile --- workflow/trade_chain_utils.py | 326 ++++++++++++++++++++++++++++++++++ 1 file changed, 326 insertions(+) create mode 100644 workflow/trade_chain_utils.py diff --git a/workflow/trade_chain_utils.py b/workflow/trade_chain_utils.py new file mode 100644 index 0000000..b89bc4e --- /dev/null +++ b/workflow/trade_chain_utils.py @@ -0,0 +1,326 @@ +"""Helpers for normalizing trade-chain config and deriving stage groups. + +The config currently stores a single trade chain with ordered stages keyed by +stage number. This module turns that into a stable, ordered representation and +derives the stage group boundaries implied by tradeable commodities. +""" + +from __future__ import annotations + +from typing import Dict, List, Optional, Tuple + + +ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} +BUS_ALIASES = { + "H2": "hydrogen", + "h2": "hydrogen", + "hydrogen": "hydrogen", + "renewable_electricity": "renewable_electricity", + "grid_electricity": "grid_electricity", +} + +# Explicit mapping from high-level process identifiers to concrete PyPSA components. +# Keep this mapping authoritative so config `process_label` remains high-level. +TECH_COMPONENT_MAP = [ + { + "match": ("electro", "electrolyser", "electrolyzer"), + "links": ("electrolyzer",), + "stores": ("h2_storage",), + # expected material reactants, energy inputs, and outputs + "materials": (), + "energy": ("renewable_electricity", "grid_electricity"), + "outputs": ("hydrogen",), + "buses": ("hydrogen", "renewable_electricity", "grid_electricity"), + }, + { + "match": ("dri", "direct_reduction", "reduction"), + "links": ("dri",), + "stores": ("h2_storage", "hbi_storage"), + "materials": ("iron_ore", "hydrogen"), + "energy": ("renewable_electricity", "grid_electricity"), + "outputs": ("hbi",), + "buses": ( + "iron_ore", + "hydrogen", + "hbi", + "renewable_electricity", + "grid_electricity", + ), + }, + { + "match": ("eaf", "electric_arc", "arc_furnace"), + "links": ("eaf",), + "stores": ("steel_storage",), + "materials": ("hbi",), + "energy": ("grid_electricity", "renewable_electricity"), + "outputs": ("steel",), + "buses": ("hbi", "steel", "grid_electricity", "renewable_electricity"), + }, +] + + +def _components_for_process_label( + process_label: str, +) -> Optional[Dict[str, Tuple[str, ...]]]: + """Return mapped components and expected IO for a given process_label or None if not found.""" + if not process_label: + return None + pl = process_label.lower() + for entry in TECH_COMPONENT_MAP: + for pat in entry["match"]: + if pat in pl: + return { + "links": tuple(entry.get("links", ())), + "stores": tuple(entry.get("stores", ())), + "buses": tuple(entry.get("buses", ())), + "materials": tuple(entry.get("materials", ())), + "energy": tuple(entry.get("energy", ())), + "outputs": tuple(entry.get("outputs", ())), + } + return None + + +def validate_stage_io(stage: Dict, raise_on_mismatch: bool = False) -> bool: + """Validate that a stage's declared inputs/outputs match the canonical mapping. + + Returns True if validation passes or no mapping exists. If `raise_on_mismatch` is True, + a ValueError is raised on mismatch; otherwise a warning is returned via logging and False is returned. + """ + import logging + + logger = logging.getLogger(__name__) + + process_label = str(stage.get("process_label", "")).strip() + if not process_label: + return True + + comp = _components_for_process_label(process_label) + if comp is None: + # No mapping — nothing to validate + return True + + # Normalize declared inputs/outputs + declared_materials, declared_energy = split_stage_inputs(stage) + declared_materials_norm = {_normalize_commodity(m) for m in declared_materials} + declared_energy_norm = {_normalize_commodity(e) for e in declared_energy} + declared_output = _normalize_commodity(stage.get("output_commodity", "")) + + expected_materials = {_normalize_commodity(m) for m in comp.get("materials", ())} + expected_energy = {_normalize_commodity(e) for e in comp.get("energy", ())} + expected_outputs = {_normalize_commodity(o) for o in comp.get("outputs", ())} + + msgs = [] + # Materials: declared_materials should be a superset of expected_materials or vice versa? + # We allow declared to be a superset (user may include both H2 and iron_ore), but require at least one overlap + if expected_materials and declared_materials_norm.isdisjoint(expected_materials): + msgs.append( + f"Stage '{process_label}': declared material inputs {declared_materials_norm} do not overlap expected {expected_materials}" + ) + + # Energy: declared energy should overlap expected energy + if expected_energy and declared_energy_norm.isdisjoint(expected_energy): + msgs.append( + f"Stage '{process_label}': declared energy inputs {declared_energy_norm} do not overlap expected {expected_energy}" + ) + + # Output: declared_output should be one of expected outputs + if expected_outputs and declared_output and declared_output not in expected_outputs: + msgs.append( + f"Stage '{process_label}': declared output '{declared_output}' not in expected outputs {expected_outputs}" + ) + + if msgs: + if raise_on_mismatch: + raise ValueError("; ".join(msgs)) + for m in msgs: + logger.warning(m) + return False + + return True + + +def iter_trade_chains(config: Dict) -> List[Dict]: + """Return trade chain definitions as a list.""" + + trade_chains = config.get("trade_chains") + if not trade_chains: + return [] + if isinstance(trade_chains, dict): + return [trade_chains] + return list(trade_chains) + + +def get_trade_chain(config: Dict) -> Dict: + """Return the primary trade chain from config.""" + + chains = iter_trade_chains(config) + if not chains: + return {} + return chains[0] + + +def get_ordered_stages(chain: Dict) -> List[Dict]: + """Return stages ordered by their numeric key or declared order.""" + + stages = chain.get("stages", {}) + if isinstance(stages, dict): + items = sorted(stages.items(), key=lambda item: int(item[0])) + ordered = [] + for key, stage in items: + stage_dict = dict(stage) + stage_dict.setdefault("order", int(key)) + ordered.append(stage_dict) + return ordered + + ordered = [dict(stage) for stage in stages] + ordered.sort(key=lambda stage: int(stage.get("order", 0))) + return ordered + + +def _as_list(value) -> List[str]: + if value is None: + return [] + if isinstance(value, (list, tuple, set)): + return [str(item) for item in value] + return [str(value)] + + +def _normalize_commodity(name: str) -> str: + return BUS_ALIASES.get(str(name), str(name)) + + +def split_stage_inputs(stage: Dict) -> Tuple[List[str], List[str]]: + """Split a stage's inputs into material inputs and energy inputs.""" + + if "material_inputs" in stage or "energy_inputs" in stage: + raw_inputs = _as_list(stage.get("material_inputs")) + energy_inputs = _as_list(stage.get("energy_inputs")) + elif "input_commodities" in stage: + raw_inputs = _as_list(stage.get("input_commodities")) + energy_inputs = _as_list(stage.get("energy_inputs")) + else: + raw_inputs = _as_list(stage.get("input_commodity")) + energy_inputs = [] + + materials = [] + energy = list(energy_inputs) + + for input_name in raw_inputs: + if input_name in ENERGY_INPUTS: + energy.append(input_name) + else: + materials.append(input_name) + + return materials, energy + + +def get_stage_groups(chain: Dict) -> List[Dict]: + """Group contiguous stages until a tradeable output or the final product.""" + + ordered_stages = get_ordered_stages(chain) + if not ordered_stages: + return [] + + tradeable = {str(item) for item in chain.get("tradeable_commodities", [])} + final_product = str(chain.get("final_product", "")).strip() + + groups = [] + current = [] + for stage in ordered_stages: + current.append(stage) + output_commodity = str(stage.get("output_commodity", "")).strip() + if output_commodity in tradeable or output_commodity == final_product: + groups.append( + { + "label": output_commodity, + "stages": list(current), + } + ) + current = [] + + if current: + last_output = str(current[-1].get("output_commodity", "")).strip() + groups.append({"label": last_output, "stages": list(current)}) + + return groups + + +def route_label_for_product(config: Dict, product: str) -> str: + """Return the stage-group label for a product.""" + + chain = get_trade_chain(config) + for group in get_stage_groups(chain): + if group["label"] == product: + return group["label"] + return product + + +def derive_supply_curve_products(config: Dict) -> List[str]: + """Return the externally visible product list for supply curves.""" + + products = [] + for chain in iter_trade_chains(config): + for group in get_stage_groups(chain): + label = group["label"] + if label and label not in products: + products.append(label) + return products or ["steel"] + + +def build_product_components(config: Dict, product: str) -> Dict[str, object]: + """Derive the links, stores, buses, and renewable flag for a product.""" + + chain = get_trade_chain(config) + groups = get_stage_groups(chain) + + target_group: Optional[Dict] = None + for group in groups: + if group["label"] == product: + target_group = group + break + + if target_group is None: + raise ValueError(f"Product '{product}' not found in configured stage groups") + + links = set() + stores = set() + buses = set() + has_renewables = False + + for stage in target_group["stages"]: + process_label = str(stage.get("process_label", "")).strip() + materials, energy = split_stage_inputs(stage) + + # Add explicit buses derived from stage inputs + for material in materials: + buses.add(_normalize_commodity(material)) + for energy_input in energy: + norm = _normalize_commodity(energy_input) + buses.add(norm) + if energy_input == "renewable_electricity": + has_renewables = True + + output_commodity = _normalize_commodity(stage.get("output_commodity", "")) + if output_commodity: + buses.add(output_commodity) + + # Use explicit mapping from process_label -> concrete components + comp = _components_for_process_label(process_label) + if comp is None and process_label: + # Fail fast: require explicit mapping for new/unknown process labels + raise ValueError( + f"Process label '{process_label}' has no TECH_COMPONENT_MAP entry; add mapping before using it in config" + ) + + if comp: + links.update(comp.get("links", ())) + stores.update(comp.get("stores", ())) + for b in comp.get("buses", ()): # include any canonical buses from mapping + buses.add(_normalize_commodity(b)) + + return { + "links": links, + "stores": stores, + "buses": buses, + "has_renewables": has_renewables, + } From 108bec76393dd1ccb4243268b1b31d4fba1e7511 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 11:08:16 +0200 Subject: [PATCH 068/216] feat: get all new wildcards and adjust color position to config --- workflow/scripts/model_trade.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 366e8d6..e3fca5f 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -98,26 +98,26 @@ def building_model( # adding carriers - n.add("Carrier", name=final, color=snakemake.config["plot"]["colors"][final]) - n.add("Carrier", name=interone, color=snakemake.config["plot"]["colors"][interone]) + n.add("Carrier", name=final, color=snakemake.config["colors"][final]) + n.add("Carrier", name=interone, color=snakemake.config["colors"][interone]) # Define the iron ore carrier n.add( "Carrier", name="iron_ore", - color=snakemake.config["plot"]["colors"]["iron_ore"], + color=snakemake.config["colors"]["iron_ore"], ) n.add( "Carrier", name="shipping_" + shipping_first, - color=snakemake.config["plot"]["colors"][shipping_first + "_shipping"], + color=snakemake.config["colors"][shipping_first + "_shipping"], ) n.add( "Carrier", name="shipping_" + shipping_second, - color=snakemake.config["plot"]["colors"][shipping_second + "_shipping"], + color=snakemake.config["colors"][shipping_second + "_shipping"], ) # for each region we are creating a bus with all the potentials and load @@ -326,10 +326,7 @@ def building_model( region_data_intertwo[f"demand [{unit}]"][s] ) - float(region_data_intertwo[f"demand [{unit}]"][s - 1]) - if ( - intertwo == "eaf-grid" - and snakemake.config["grid_electricity"]["grid_potential_custom"] - ): + if intertwo == "eaf-grid": grid_potential = pd.read_csv( snakemake.input.grid_potential, header=0, index_col=0 ) @@ -1253,14 +1250,16 @@ def _link_weight(link_name): interone="hbi", intertwo="eaf-grid", final="steel", - scenario="mga-stability-weighted", + scenario="default", wacc="uniform", + chain_id="default_2050", ) final = snakemake.wildcards["final"] interone = snakemake.wildcards["interone"] intertwo = snakemake.wildcards["intertwo"] scenario = snakemake.wildcards["scenario"] + chain_id = snakemake.wildcards["chain_id"] print( f"intermediate 1 ({interone}) and intermediate 2 ({intertwo}) to final product {final}" From 35bbe34a2cb6ec91cd3e010162affd5b08fbc1f0 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 12:16:57 +0200 Subject: [PATCH 069/216] feat: simplify country map based on new iso3 from config and apply cost penalty only when specified --- workflow/scripts/model_trade.py | 76 +++++++++------------------------ 1 file changed, 21 insertions(+), 55 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index e3fca5f..6076b3b 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -7,7 +7,6 @@ import os import cartopy.crs as ccrs import geopandas as gpd -import pycountry import cartopy.io.shapereader as shpreader plt.style.use("bmh") @@ -18,69 +17,28 @@ def build_region_geodataframe(config): Build a dissolved GeoDataFrame of model regions from the config country lists. Each model region (e.g. "Europe", "Middle_East") is formed by dissolving its - member countries from the NaturalEarth 110m dataset, so only region borders - are visible in the map — not internal country borders. + member countries (specified as ISO 3166-1 alpha-3 codes) from the NaturalEarth + 110m dataset, so only region borders are visible in the map — not internal + country borders. """ regions = config["regions"] - # Corrections to align config country names with pycountry lookup names - country_name_corrections = { - "Democratic Republic of the Congo": "Congo, The Democratic Republic of the", - "Turkey": "Türkiye", - "Venezuela": "Venezuela, Bolivarian Republic of", - "Tanzania": "United Republic of Tanzania", - "Bolivia": "Plurinational State of Bolivia", - "Vietnam": "Viet Nam", - "South Korea": "Korea, Republic of", - "North Korea": "Korea, Democratic People's Republic of", - "Taiwan": "Taiwan, Province of China", - "Laos": "Lao People's Democratic Republic", - "Brunei": "Brunei Darussalam", - "Equatorial French Guiana": "French Guiana", - "Syria": "Syrian Arab Republic", - "Palestine": "Palestine, State of", - "Moldova": "Republic of Moldova", + # Build ISO A3 -> region mapping directly from config + iso_to_region = { + code.strip(): region + for region, countries in regions.items() + for code in countries } - # Build country name -> ISO A2 mapping via pycountry - country_name_to_iso = {} - for country in pycountry.countries: - country_name_to_iso[country.name] = country.alpha_2 - if hasattr(country, "official_name"): - country_name_to_iso[country.official_name] = country.alpha_2 - - # Build ISO A2 -> region mapping - iso_to_region = {} - for region, countries in regions.items(): - for country in countries: - # Handle entries like "Togo + Algeria" by splitting on + - for part in country.split("+"): - name = part.strip() - name = country_name_corrections.get(name, name) - code = country_name_to_iso.get(name) - if code: - iso_to_region[code] = region - # Load NaturalEarth 110m countries shapefile reader = shpreader.natural_earth( resolution="110m", category="cultural", name="admin_0_countries" ) world = gpd.read_file(reader) - # Fix missing ISO_A2 codes (-99 placeholder in NaturalEarth) - def _lookup_iso(country_name): - try: - return pycountry.countries.lookup(country_name).alpha_2 - except LookupError: - return None - - world.loc[world["ISO_A2"] == "-99", "ISO_A2"] = world.loc[ - world["ISO_A2"] == "-99", "ADMIN" - ].apply(_lookup_iso) - # Assign region and dissolve to remove internal country borders - world["region"] = world["ISO_A2"].map(iso_to_region) + world["region"] = world["ISO_A3"].map(iso_to_region) region_gdf = world.dropna(subset=["region"]).dissolve(by="region").reset_index() return region_gdf @@ -636,7 +594,7 @@ def plot_trade_network( """ config = snakemake.config plot_config = config["plot"]["world_map"][product] - colors = config["plot"]["colors"] + colors = config["colors"] supply_color = colors.get(f"{product}_supply", "black") demand_color = colors.get(f"{product}_demand", "lightsteelblue") link_colors = colors.get(f"{product}_link", "gray") @@ -1334,9 +1292,17 @@ def _link_weight(link_name): create_links(transport_costs, trade_options) # Cost penalty - cost_penalty = snakemake.config["scenario"][scenario]["modifiers"]["cost_penalty"] - print(f"applying cost penalty scenario: {scenario} with penalties {cost_penalty}") - n = apply_cost_penalty(n, cost_penalty) + if snakemake.config["scenario"][scenario]["modifiers"]["cost_penalty"] == None: + cost_penalty = None + print("cost_penalty not activated") + else: + cost_penalty = snakemake.config["scenario"][scenario]["modifiers"][ + "cost_penalty" + ] + print( + f"applying cost penalty scenario: {scenario} with penalties {cost_penalty}" + ) + n = apply_cost_penalty(n, cost_penalty) # Note: Only relevant when capital costs are added in this script. Currently, they are added only in model_lcox # Country specific wacc adjustment (simplified) From b0c1508a0c7d2975751d5296b8197b2903118934 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 12:17:36 +0200 Subject: [PATCH 070/216] feat: add trade model smk and adjust --- rules/trade_model.smk | 66 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 rules/trade_model.smk diff --git a/rules/trade_model.smk b/rules/trade_model.smk new file mode 100644 index 0000000..7329920 --- /dev/null +++ b/rules/trade_model.smk @@ -0,0 +1,66 @@ +"""Trade-model workflow rules. + +Consumes supply curves and scenario inputs to run the interregional trade model +and collect the scenario-level outputs. +""" + + +rule model_trade: + input: + supply_curves_interone=expand( + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", + allow_missing=True, + cost_year=[2050], + region=config["regions"], + ), + supply_curves_intertwo=expand( + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{intertwo}.csv", + allow_missing=True, + cost_year=[2050], + region=config["regions"], + ), + transport_costs = "data/transport_costs/steel_r_iron_r.csv", + trade_options = "resources/trade_opt_chokepoints.csv", + bus_locations = "data/bus_locations.csv", + demand = "data/un_enerdata_demand_2050_final.csv", + steel_demand = "resources/steel_demand_clustered_{cost_year}.csv", + iron_ore = "resources/ironore_production_clustered.csv", + grid_potential = "data/grid_potential_custom.csv", + wacc = "resources/wacc-clustered.csv", + political_stability = "resources/political_stability_clustered.csv", + output: + trade_result=f"results/{trade_scenarios.wildcard_pattern}/result.csv", + trade_network=f"results/{trade_scenarios.wildcard_pattern}/network.nc", + trade_plot_ironore=f"results/{trade_scenarios.wildcard_pattern}/map_ironore.pdf", + trade_plot_ironore_png=f"results/{trade_scenarios.wildcard_pattern}/map_ironore.png", + trade_plot_hbi=f"results/{trade_scenarios.wildcard_pattern}/map_hbi.pdf", + trade_plot_hbi_png=f"results/{trade_scenarios.wildcard_pattern}/map_hbi.png", + trade_plot_steel=f"results/{trade_scenarios.wildcard_pattern}/map_steel.pdf", + trade_plot_steel_png=f"results/{trade_scenarios.wildcard_pattern}/map_steel.png", + threads: 4 + params: + iron_ore_potential=config["iron_ore"]["potential_allowance"], + cost_penalty=config["design"]["cost_penalty"], + scenarios=config["scenario"], + script: + # str(SCRIPT_DIR / "model_trade.py") + "../workflow/scripts/model_trade.py" + +rule model_trade_all: + input: + networks=expand( + "results/{scenarios}/network.nc", + scenarios=trade_scenarios.instance_patterns, + ), + results=expand( + "results/{scenarios}/result.csv", + scenarios=trade_scenarios.instance_patterns, + ), + trade_plot_ironore=expand( + "results/{scenarios}/map_ironore.pdf", + scenarios=trade_scenarios.instance_patterns, + ), + trade_plot_steel=expand( + "results/{scenarios}/map_steel.pdf", + scenarios=trade_scenarios.instance_patterns, + ), From 9ca3afff9ac24ea5224c2461db2a8d0dfa064225 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 12:34:26 +0200 Subject: [PATCH 071/216] feat: successfull trade model run on updated config and snakefile --- config/config.yaml | 209 +++++++++++++------- workflow/Snakefile | 466 ++++++--------------------------------------- 2 files changed, 206 insertions(+), 469 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 38dc44c..50c654f 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -3,29 +3,52 @@ enable: run_supply_curve: False # Enable for first run -# Different demand factors of maximum hydrogen potential as demand in [%] -demand_factors: [0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.07, 0.1, 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70] # 25, 30, 35, 40, 45, 50, 55, 60, 65, 70] -hydrogen_storage_cost: False -iron_ore_cost_in_supply_chain: False # Should be set to false, since iron ore cost will be added in the transport model and should not be double counted +# Output toggles for Step 0/1 (greenfield supply curve generation) +outputs: + save_supply_nodemand: True # Generate reference supply curve (all generators available) alongside main curve + keep_optimization_networks: True # Keep .nc network files from optimization (set False to save disk space) -run: - # prefix: "" - # name: "" - # scenarios: - # enable: false - # file: config/scenarios.yaml - disable_progressbar: false +# Supply curve scenario configuration +# Two scenarios are supported: +# - reserved (DEFAULT): highest-performing (best CF) renewable generators reserved for domestic +# electricity demand; export/HBI supply stack reduced before optimization. Requires electricity demand data. +# - unreserved (OPTIONAL, FALLBACK): no domestic reservation; full renewable stack available. +# Can be generated without electricity demand data when demand data unavailable. +supply_curve: + generate_unreserved: False # Set to True to generate unreserved scenario as fallback -trade: - shipping_routes: - ports: "predefined" # "closest": route from region centroid (searoute snaps to nearest sea node); "predefined": route from hand-curated port city coordinates parsed from the notes column in trade_opt.csv - diversity_factor: False # "False" or float as max constraint for one single hbi supply route in the trade model. 1: no limit, 0.5: max 50% of demand can be supplied by one single supply route, etc. - # Note: The diversity factor constraints the trade route, not the producer itself. (Import from exporter route R <= diversity_factor * demand of importer I.). As a result, if the trade passes through one region, it is limited based on the demand of this particular region * diversity_factor. Therefore the trade routes in a scenario with a diversity_factor of 100% may differ to a scenario where diversity_factor=False. -scenario: # must be listed in config/trade_scenarios.csv in order to run +# Config-native trade chain definitions +# Defines the commodity transformation chain with ordered stages and process labels +trade_chains: + id: default_2050 + cost_year: 2050 + scenario: default + final_product: steel + wacc: regional + tradeable_commodities: [iron_ore, hbi] + stages: + 1: + material_inputs: [] + energy_inputs: [renewable_electricity] + output_commodity: hydrogen + process_label: electrolysis + 2: + material_inputs: [iron_ore, H2] + energy_inputs: [renewable_electricity] + output_commodity: hbi + process_label: dri + 3: + material_inputs: [hbi] + energy_inputs: [grid_electricity] + output_commodity: steel + process_label: eaf-grid + + +scenario: default: - modifiers: - cost_penalty: + modifiers: + cost_penalty: penalty-nwa: modifiers: cost_penalty: @@ -106,6 +129,65 @@ scenario: # must be listed in config/trade_scenarios.csv in order to run block_b: ["Middle_East","Subsaharan_Africa","North_West_Africa", "Eurasia","South_America","Central_America","West_Asia","East_Asia","Pacific_Asia"] + +# Absolute steel demand levels (Mt/year) for supply curve sweep +# For each level, PyPSA minimizes cost with fixed renewable capacity +# Values represent different production scales +steel_demand_levels: [0.01, 0.1, 1, 10, 100, 1000] # Mt/year +hydrogen_storage_cost: False + +run: + # prefix: "" + # name: "" + # scenarios: + # enable: false + # file: config/scenarios.yaml + disable_progressbar: false + +# Region definitions (ISO3 codes matching renewable clusters metadata) +regions: + "Europe": ["ALB","AUT","BLR","BEL","BIH","BGR","HRV","CYP","CZE","DNK","EST","FIN","DEU","GRC","HUN","ITA","XKX","LVA","LTU","LUX","MKD","MDA","MNE","NLD","NOR","POL","ROU","SRB","SVK","SVN","SWE","CHE","TUR","UKR"] + "Far_West_Europe": ["FRA","GRL","ISL","IRL","PRT","ESP","GBR"] + "Middle_East": ["BHR","IRN","IRQ","ISR","JOR","KWT","LBN","OMN","PSE","QAT","SAU","SYR","ARE","YEM"] + "North_West_Africa": ["BEN","BFA","CIV","GMB","GHA","GIN","GNB","LBR","NGA","SEN","SLE","TGO","DZA","TCD","EGY","ERI","LBY","MLI","MRT","MAR","NER","SDN","TUN","ESH"] + "Subsaharan_Africa": ["AGO","BWA","BDI","CMR","CAF","COD","ETH","KEN","GAB","MDG","MWI","MOZ","NAM","COG","RWA","SOM","ZAF","SSD","TZA","UGA","ZMB","ZWE"] + "North_America": ["CAN","USA"] + "Eurasia": ["ARM","AZE","GEO","KAZ","KGZ","TJK","TKM","UZB","RUS"] + "South_America": ["BOL","BRA","COL","ECU","GUF","GUY","PRY","PER","SUR","VEN"] + "South_South_America": ["ARG","CHL","URY"] + "Central_America": ["CRI","SLV","GTM","HND","MEX","NIC","PAN","CUB","DOM","JAM","HTI"] + "West_Asia": ["AFG","BGD","BTN","IND","NPL","PAK","LKA"] + "East_Asia": ["CHN","HKG","MNG","TWN"] + "Pacific_Asia": ["BRN","KHM","IDN","LAO","MYS","MMR","PNG","PHL","SGP","THA","VNM"] + "East_East_Asia": ["JPN","KOR","PRK"] + "Oceania": ["AUS","NZL"] + +# countries: [ +# "BI","KM","DJ","ER","ET","KE","MG","MW","MU","MZ","RW","SC","SO","SS","TZ","UG","ZM","ZW", # Africa — Eastern Africa +# "AO","CM","CF","TD","CG","CD","GQ","GA","ST", # Africa — Middle Africa +# "DZ","EG","LY","MA","SD","TN", # Africa — Northern Africa +# "BW","SZ","LS","NA","ZA", # Africa — Southern Africa +# "BJ","BF","CV","CI","GM","GH","GN","GW","LR","ML","MR","NE","NG","SN","SL","TG", # Africa — Western Africa +# "CA","US", # Americas — Northern America +# "AG","BS","BB","CU","DM","DO","GD","HT","JM","KN","LC","VC","TT", # Americas — Caribbean +# "BZ","CR","SV","GT","HN","MX","NI","PA", # Americas — Central America +# "AR","BO","BR","CL","CO","EC","GY","PY","PE","SR","UY","VE", # Americas — South America +# "KZ","KG","TJ","TM","UZ", # Asia — Central Asia +# "CN","JP","KP","KR","MN","TW", # Asia — Eastern Asia +# "BN","KH","ID","LA","MY","MM","PH","SG","TH","TL","VN", # Asia — South-eastern Asia +# "AF","BD","BT","IN","IR","MV","NP","PK","LK", # Asia — Southern Asia +# "AM","AZ","BH","CY","GE","IQ","IL","JO","KW","LB","OM","QA","SA","PS","SY","TR","AE","YE", # Asia — Western Asia +# "BY","BG","CZ","HU","MD","PL","RO","RU","SK","UA", # Europe — Eastern Europe +# "DK","EE","FI","IS","IE","LV","LT","NO","SE","GB", # Europe — Northern Europe +# "AL","AD","BA","HR","GR","IT","MT","ME","MK","PT","SM","RS","SI","ES","VA", # Europe — Southern Europe +# "AT","BE","FR","DE","LI","LU","MC","NL","CH", # Europe — Western Europe +# "AU","NZ", # Oceania — Australia and New Zealand +# "FJ","PG","SB","VU", # Oceania — Melanesia +# "FM","KI","MH","NR","PW", # Oceania — Micronesia +# "TO","TV","WS" # Oceania — Polynesia +# ] + + design: cost_penalty: # North_West_Africa: 1.1 # Relative cost pentalty on all technlogies in this region. 1: no change @@ -114,11 +196,33 @@ design: # Oceania: 0.8 costs: - version: v0.12.0 - eur_usd: 1.15 # 1 Euro equals 1.15 USD + version: v0.14.0 -interest_rate: 0.07 # This should align with the TRACE model input WACC. To use a regional interest rate, set the wildcard wacc="regional" in the `config/trade_scenarios.csv`. Caveat: this does not change the WACC for storages. For uniform interest rate, set wacc="uniform" in the `config/trade_scenarios.csv`. +interest_rate: + default: 0.05 + # Regional discount rates (override default for specific regions): + Central_America: 0.11 + East_Asia: 0.09 + East_East_Asia: 0.08 + Eurasia: 0.10 + Europe: 0.10 + Far_West_Europe: 0.08 + Middle_East: 0.12 + North_America: 0.08 + North_West_Africa: 0.12 + Oceania: 0.07 + Pacific_Asia: 0.10 + South_America: 0.11 + South_South_America: 0.10 + Subsaharan_Africa: 0.11 + West_Asia: 0.11 +trade: + shipping_routes: + ports: "predefined" # "closest": route from region centroid (searoute snaps to nearest sea node); "predefined": route from hand-curated port city coordinates parsed from the notes column in trade_opt.csv + diversity_factor: False # "False" or float as max constraint for one single hbi supply route in the trade model. 1: no limit, 0.5: max 50% of demand can be supplied by one single supply route, etc. + # Note: The diversity factor constraints the trade route, not the producer itself. (Import from exporter route R <= diversity_factor * demand of importer I.). As a result, if the trade passes through one region, it is limited based on the demand of this particular region * diversity_factor. Therefore the trade routes in a scenario with a diversity_factor of 100% may differ to a scenario where diversity_factor=False. + grid_electricity: marginal_cost: 80 # EUR/MWh, guesstimate for average grid electricity cost in 2030 capital_cost: 0 # EUR/MW, guesstimate for average grid electricity cost in 2030 @@ -137,7 +241,7 @@ onwind_p_nom_max_cor: 0.1 iron_ore: regionalise: "grade-dependent" #"uniform" or "grade-dependent" - marginal_cost: 97.7 # Used as uniform assumption in the trade model and for intermediate figures/results with undefined sourcing region of iron ore # EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) + marginal_cost: 97.7 #97.7 # EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) ore_to_steel_ratio: 1.59 # t_ore/t_steel, see https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) potential_allowance: 2 # Factor, which the current iron ore production is multiplied with shipping_cost_per_km: 0.005 # €/(t*km) # Guesstimate @@ -156,48 +260,10 @@ plot: steel: bus_size: 2.0e-7 link_width: 3.0e-8 - colors: - steel: 'grey' - steel_shipping: 'darkgrey' - hydrogen: 'magenta' - iron_ore: 'brown' - iron_ore_shipping: "#6A000E" - hbi: 'darkred' - hbi_shipping: 'firebrick' - steel_supply: 'lightsteelblue' - steel_demand: 'seagreen' - steel_link: 'skyblue' - iron_ore_supply: 'black' - iron_ore_demand: 'lightsteelblue' - iron_ore_link: "#6A000E" - hbi_demand: 'green' - hbi_supply: 'darkred' - hbi_link: 'firebrick' - -# Region definitions -regions: - "Europe": ["Albania", "Austria", "Belarus", "Belgium", "Bosnia and Herzegovina", "Bulgaria", "Croatia", "Cyprus", "Czechia", "Denmark", "Estonia", "Finland", "Germany", "Greece", "Hungary", "Italy", "Kosovo", "Latvia", "Lithuania", "Luxembourg", "North Macedonia", "Moldova", "Montenegro", "Netherlands", "Norway", "Poland", "Romania", "Serbia", "Slovakia", "Slovenia", "Sweden", "Switzerland", "Turkey", "Ukraine"] - "Far_West_Europe": ["France", "Greenland", "Iceland", "Ireland", "Portugal", "Spain", "United Kingdom"] - "Middle_East": ["Bahrain", "Iran, Islamic Republic of", "Iraq","Israel", "Jordan", "Kuwait", "Lebanon", "Oman", "Palestine", "Qatar", "Saudi Arabia", "Syria", "United Arab Emirates", - "Yemen"] - "North_West_Africa": ["Benin", "Burkina Faso", "Côte d'Ivoire", "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Liberia", "Nigeria", "Senegal", "Sierra Leone", "Togo + Algeria", "Chad", "Egypt", "Eritrea", "Libya", "Mali", "Mauritania", "Morocco", "Niger", "Sudan", "Tunisia", "Western Sahara"] - "Subsaharan_Africa": ["Angola", "Botswana", "Burundi", "Cameroon", "Central African Republic", "Democratic Republic of the Congo", "Ethiopia", "Kenya", "Gabon", "Madagascar", "Malawi", "Mozambique", "Namibia", "Republic of the Congo", "Rwanda", "Somalia", "South Africa", "South Sudan", "Tanzania", "Uganda", "Zambia", "Zimbabwe"] - "North_America": ["Canada", "United States"] - "Eurasia": ["Armenia", "Azerbaijan", "Georgia", "Kazakhstan", Kyrgyzstan, "Tajikistan", "Turkmenistan", "Uzbekistan", "Russian Federation"] - "South_America": ["Bolivia", "Brazil", "Colombia", "Ecuador", "Equatorial French Guiana", "Guyana", "Paraguay", "Peru", "Suriname","Venezuela"] - "South_South_America": ["Argentina", "Chile", "Uruguay"] - "Central_America": ["Costa Rica", "El Salvador", "Guatemala", "Honduras", "Mexico", "Nicaragua", "Panama", "Cuba", "Dominican Republic", "Jamaica", "Haiti"] - "West_Asia": ["Afghanistan", "Bangladesh", "Bhutan", "India", "Nepal", "Pakistan", "Sri Lanka"] - "East_Asia": ["China", "Hong Kong", "Mongolia", "Taiwan"] - "Pacific_Asia": ["Brunei", "Cambodia", "Indonesia", "Laos", "Malaysia", "Myanmar", "Papua New Guinea", "Philippines", "Singapore", "Thailand", "Vietnam"] - "East_East_Asia": ["Japan", "South Korea", "North Korea"] - "Oceania": ["Australia", "New Zealand"] - - solver: - name: gurobi - options: gurobi-default - + name: gurobi + options: gurobi-default + compute_iis: False # Set to false to skip expensive IIS computation for infeasible models solver_options: highs-default: # refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/ @@ -267,4 +333,17 @@ colors: border: '#bcbcbc' # mid grey (country borders) legend_bg: '#f5f5f5' # near-white (legend background) legend_edge: '#bdbdbd' # grey (legend border) - + steel_shipping: 'darkgrey' + iron_ore: 'brown' + iron_ore_shipping: "#6A000E" + hbi: 'darkred' + hbi_shipping: 'firebrick' + steel_supply: 'lightsteelblue' + steel_demand: 'seagreen' + steel_link: 'skyblue' + iron_ore_supply: 'black' + iron_ore_demand: 'lightsteelblue' + iron_ore_link: "#6A000E" + hbi_demand: 'green' + hbi_supply: 'darkred' + hbi_link: 'firebrick' \ No newline at end of file diff --git a/workflow/Snakefile b/workflow/Snakefile index 16ac25b..7c14b3e 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -1,427 +1,85 @@ -from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider -import sys -from email import utils -from snakemake.utils import Paramspace -import pandas as pd -from os.path import normpath, exists, isdir -from shutil import copyfile, move - -# HTTP = HTTPRemoteProvider() -sys.path.append("./scripts") - -# Read scenario definitions to construct wildcard and instance patterns from them -trade_scenarios = Paramspace(pd.read_csv("../config/trade_scenarios.csv", dtype=str)) - - -configfile: "../config/config.yaml" - -# localrules: all - -wildcard_constraints: - country="[a-zA-Z]+", - sweep="[a-zA-Z]+", - rule="(0|[1-9][0-9]?|100)" - -rule retrieve_cost_data: - params: - version=config['costs']['version'], - output: - costs="../resources/technology_data/costs_{cost_year}.csv", - resources: - mem_mb=1000, - retries: 2 - threads: 2 - script: - "scripts/retrieve_cost_data.py" - - -# Prepare the trace file prior to running the model -rule retrieve_trace_steel: - input: - trace = "../../trace-fneum/trace/resources/networks/default/{cost_year}/shipping-steel/DE-DE/network.nc" - output: - trace="../resources/trace/steel_{cost_year}.nc", - resources: - mem_mb=5000, - threads: 2 - run: - copyfile(input[0], output[0]) - - -rule prepare_wacc: - input: - wacc = "../data/wacc-global.csv", - bus_locations = "../data/bus_locations.csv", - output: - wacc = "../resources/wacc-clustered.csv" - resources: - mem_mb=5000, - threads: 2 - notebook: - "notebooks/prepare-wacc.ipynb" - - -rule prepare_political_stability: - input: - political_stability = "../data/political-stability/globaleconomy.csv", #https://www.theglobaleconomy.com/rankings/wb_political_stability/ - bus_locations = "../data/bus_locations.csv", - output: - political_stability = "../resources/political_stability_clustered.csv" - resources: - mem_mb=5000, - threads: 2 - notebook: - "notebooks/prepare-political-stability.ipynb" - - -rule prepare_chokepoints: - params: - shipping_routes=config["trade"]["shipping_routes"], - input: - trade_options = "../data/trade_opt.csv", - bus_locations = "../data/bus_locations.csv", - output: - trade_options_chokepoints = "../resources/trade_opt_chokepoints.csv", - map_chokepoints = "../results/figures_general/chokepoints/map_chokepoints.pdf", - map_chokepoints_png = "../results/figures_general/chokepoints/map_chokepoints.png", - resources: - mem_mb=5000, - threads: 2 - notebook: - "notebooks/prepare-chokepoints.ipynb" - - -rule retrieve_iron_ore: - input: - iron_ore_production = "../data/owid-iron-ore/iron-ore-crude-ore-production.csv", - iron_ore_cost = "../data/devlin2023-supplementary.xlsx", - bus_locations = "../data/bus_locations.csv", - output: - iron_ore = "../resources/ironore-production.csv", - iron_ore_map = "../results/figures_general/iron_ore_map.pdf", - resources: - mem_mb=5000, - threads: 2 - notebook: - "notebooks/global-iron-ore.ipynb" - +"""Root Snakemake entrypoint for the Shift workflow. -rule prepare_iron_ore: - input: - iron_ore = "../resources/ironore-production.csv", - bus_locations = "../data/bus_locations.csv", - output: - iron_ore = "../resources/ironore_production_clustered.csv", - resources: - mem_mb=5000, - threads: 2 - notebook: - "notebooks/prepare-iron-ore.ipynb" +Loads shared configuration and includes the modular rule files for supply curves, +trade optimization, and reporting. +""" +from pathlib import Path +import sys -rule prepare_steel_demand: - input: - steel_demand = "../data/demand/steel_demands/output_data/country_raw_steel_demand_and_dri_share.csv", - bus_locations = "../data/bus_locations.csv", - output: - steel_demand = '../resources/steel_demand_clustered_{cost_year}.csv', - resources: - mem_mb=5000, - threads: 2 - notebook: - "notebooks/prepare-steel-demand.ipynb" - - - -if config["enable"].get("run_supply_chain", True): - # Individual model calculating the LCoH for up to 60% of maximum potential as demand - # Stores the resulting LCoH in a csv file - rule model_lcoh: - message: - "Calculating LCoH for region {wildcards.region} for {wildcards.demand_factor}% of estimated maximum hydrogen potential." - input: - supply_data ="../data/new_renewables/supply_{region}_2013_cleaned.nc", - costs = "../resources/technology_data/costs_{cost_year}.csv" - output: - results="../resources/lcoh/cost_year~{cost_year}/{region}/results_{demand_factor}.csv", - network="../resources/lcoh/cost_year~{cost_year}/{region}/network_{demand_factor}.nc", - threads: 4 - script: - "scripts/model_lcoh.py" - - rule model_lcox: - message: - "Calculating the LCoX of {wildcards.product} for region {wildcards.region} for {wildcards.demand_factor}% of estimated maximum product potential." - params: - interest_rate=config["interest_rate"], - # region_specific_wacc=config["region-specific-wacc"], - input: - supply_data ="../data/new_renewables/supply_{region}_2013_cleaned.nc", - costs = "../resources/technology_data/costs_{cost_year}.csv", - trace = "../resources/trace/steel_{cost_year}.nc", - wacc = "../resources/wacc-clustered.csv", - output: - results="../resources/lco-{product}/cost_year~{cost_year}/wacc~{wacc}/{region}/results_{demand_factor}.csv", - network="../resources/lco-{product}/cost_year~{cost_year}/wacc~{wacc}/{region}/network_{demand_factor}.nc", - threads: 4 - resources: - mem_mb=8000, - script: - "scripts/model_lcox.py" - - -# Read all the individual LCoH values for one region and combine them into a supply curve -# stored as a single csv file per region. Note: the visiual plot includes iron ore costs, the csv without since it is added later in the workflow. - -if config["enable"].get("run_supply_curve", True): - rule create_supply_curve: - message: - "Combining individual LCo{wildcards.product[0]} results to create a supply curve for region {wildcards.region}." - input: - lco_product_data = lambda wildcards: expand( - f"../resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/wacc~{wildcards.wacc}/{wildcards.region}/results_{{demand_factor}}.csv", - demand_factor=config["demand_factors"], - allow_missing=True - ), - local_demand = "../data/un_enerdata_demand_2050_final.csv", - steel_demand = "../resources/steel_demand_clustered_{cost_year}.csv", - output: - supply = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", - supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", - supply_curve = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.pdf", - threads: 2 - script: - "scripts/create_supply_curve.py" - - - -rule create_all_supply_curves: - input: - expand( - "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", - cost_year=[2030,2050], wacc=["uniform"], region=config["regions"], product=["steel"], allow_missing=True - ) #cost_year=[2030,2050], region=config["regions"], product=["steel", "hydrogen"] - - -rule model_trade: - params: - iron_ore_potential=config["iron_ore"]["potential_allowance"], - cost_penalty=config["design"]["cost_penalty"], - scenarios=config["scenario"], - interest_rate=config["interest_rate"], - # region_specific_wacc=config["region-specific-wacc"], - diversity_factor=config["trade"]["diversity_factor"], - input: - supply_curves_interone = expand( - "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", - allow_missing=True, region=config["regions"]), - supply_curves_intertwo = expand( - "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{intertwo}.csv", - allow_missing=True, region=config["regions"]), - # supply_curves_final = expand( - # "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{final}.csv", - # allow_missing=True, region=config["regions"]), - transport_costs = "../data/transport_costs/steel_r_iron_r.csv", - trade_options = "../resources/trade_opt_chokepoints.csv", - bus_locations = "../data/bus_locations.csv", - demand = "../data/un_enerdata_demand_2050_final.csv", - steel_demand = "../resources/steel_demand_clustered_{cost_year}.csv", - iron_ore = "../resources/ironore_production_clustered.csv", - grid_potential = "../data/grid_potential_custom.csv", - wacc = "../resources/wacc-clustered.csv", - political_stability = "../resources/political_stability_clustered.csv", - output: - trade_result = f"../results/{trade_scenarios.wildcard_pattern}/result.csv", - trade_network = f"../results/{trade_scenarios.wildcard_pattern}/network.nc", - trade_plot_ironore = f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.pdf", - trade_plot_ironore_png = f"../results/{trade_scenarios.wildcard_pattern}/map_ironore.png", - trade_plot_hbi = f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.pdf", - trade_plot_hbi_png = f"../results/{trade_scenarios.wildcard_pattern}/map_hbi.png", - trade_plot_steel = f"../results/{trade_scenarios.wildcard_pattern}/map_steel.pdf", - trade_plot_steel_png = f"../results/{trade_scenarios.wildcard_pattern}/map_steel.png", - threads: 4 - script: - "scripts/model_trade.py" - - -rule plot_mga: - input: - network_mga_production = "../results/cost_year~2050/interone~hbi/intertwo~eaf-grid/final~steel/wacc~{wacc}/scenario~mga-stability-weighted/network.nc", - network_mga_chokepoints = "../results/cost_year~2050/interone~hbi/intertwo~eaf-grid/final~steel/wacc~{wacc}/scenario~mga-chokepoints/network.nc", - network_mga_blocks = "../results/cost_year~2050/interone~hbi/intertwo~eaf-grid/final~steel/wacc~{wacc}/scenario~mga-blocs/network.nc", - political_stability = "../resources/political_stability_clustered.csv", - trade_options_chokepoints = "../resources/trade_opt_chokepoints.csv", - output: - mga_plot = "../results/figures_general/mga/wacc~{wacc}/mga_analysis.pdf", - mga_plot_png = "../results/figures_general/mga/wacc~{wacc}/mga_analysis.png", - resources: - mem_mb=4000, - threads: 2 - notebook: - "notebooks/plot-mga.ipynb" - -rule plot_mga_all: - input: - expand("../results/figures_general/mga/wacc~{wacc}/mga_analysis.pdf", wacc=["regional"], allow_missing=True) #wacc=["uniform", "regional"] - - -# Povide a rule which triggers creation of all scenarios listed (= rows) in 'scenarios/trade_scenarios.csv' -rule model_trade_all: - input: - networks=expand("../results/{scenarios}/network.nc", scenarios=trade_scenarios.instance_patterns), - results=expand("../results/{scenarios}/result.csv", scenarios=trade_scenarios.instance_patterns), - trade_plot_ironore=expand("../results/{scenarios}/map_ironore.pdf", scenarios=trade_scenarios.instance_patterns), - trade_plot_steel=expand("../results/{scenarios}/map_steel.pdf", scenarios=trade_scenarios.instance_patterns), - -rule plot_trade_today: - input: - baci_folder = ancient("../data/BACI_HS22_V202601"), - output: - iron_ore = "../results/figures_general/trade-today/Iron_Ore_net_flow.pdf", - iron_ore_png = "../results/figures_general/trade-today/Iron_Ore_net_flow.png", - dri_hbi = "../results/figures_general/trade-today/DRI-HBI_net_flow.pdf", - dri_hbi_png = "../results/figures_general/trade-today/DRI-HBI_net_flow.png", - steel_raw = "../results/figures_general/trade-today/Steel_raw_net_flow.pdf", - steel_raw_png = "../results/figures_general/trade-today/Steel_raw_net_flow.png", - iron_ore_csv = "../results/figures_general/trade-today/Iron_Ore_trade_iso3.csv", - dri_hbi_csv = "../results/figures_general/trade-today/DRI-HBI_trade_iso3.csv", - steel_raw_csv = "../results/figures_general/trade-today/Steel_raw_trade_iso3.csv", - script: - "notebooks/plot_todays-trade.py" +import pandas as pd +from snakemake.utils import Paramspace -rule plot_global_supply: - input: - trade_network="../results/cost_year~{cost_year}/interone~hbi/intertwo~eaf-grid/final~steel/wacc~{wacc}/scenario~{scenario}/network.nc", - # supply = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", - # supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", - supply_curves_interone = expand( - "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", - allow_missing=True, region=config["regions"]), - supply_curves_interone_nodemand = expand( - "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", - allow_missing=True, region=config["regions"]), - output: - network_curve="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", - network_curve_png="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", - # supply_curves - notebook: - "notebooks/analysis-globalsupplycurve.ipynb" +WORKFLOW_DIR = Path(workflow.basedir) / "workflow" +SCRIPT_DIR = WORKFLOW_DIR / "scripts" +# DATA_ -rule plot_global_supply_all: - input: - expand("../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", cost_year=[2050], wacc=["regional"], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) +from trade_chain_utils import ( # noqa: E402 + derive_supply_curve_products, + get_ordered_stages, + get_stage_groups, + get_trade_chain, +) -rule collect_figures: - input: - global_supply_curve = "../results/figures_general/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", #workflow/notebooks/analysis-coststructure.ipynb - global_supply_curve_png = "../results/figures_general/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", #workflow/notebooks/analysis-coststructure.ipynb - electricity_demand = "../results/figures_general/electricity_demand.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_png = "../results/figures_general/electricity_demand.png", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_steel = "../results/figures_general/electricity_demand_in_steel.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb - electricity_demand_steel_png = "../results/figures_general/electricity_demand_in_steel.png", #workflow/notebooks/analysis-electricity-demand.ipynb - global_map_countries = "../results/figures_general/global_map_countries.pdf", #workflow/notebooks/plot_countries.ipynb - global_map_countries_png = "../results/figures_general/global_map_countries.png", #workflow/notebooks/plot_countries.ipynb - cost_comparison = "../results/figures_general/comparison/cost_comparison.pdf", #workflow/notebooks/compare-scenarios.ipynb - cost_comparison_png = "../results/figures_general/comparison/cost_comparison.png", #workflow/notebooks/compare-scenarios.ipynb - value_chain_comparison = "../results/figures_general/value_chain_comparison.pdf", #workflow/notebooks/analyse-steel-hbi-split.ipynb - value_chain_comparison_png = "../results/figures_general/value_chain_comparison.png", #workflow/notebooks/analyse-steel-hbi-split.ipynb - hourly_analysis = "../results/figures_general/hourly_analysis.pdf", #workflow/notebooks/analysis-hourly.ipynb - hourly_analysis_png = "../results/figures_general/hourly_analysis.png", #workflow/notebooks/analysis-hourly.ipynb - iron_ore = "../results/figures_general/trade-today/Iron_Ore_net_flow.pdf", - dri_hbi = "../results/figures_general/trade-today/DRI-HBI_net_flow.pdf", - steel_raw = "../results/figures_general/trade-today/Steel_raw_net_flow.pdf", - mga_plot = "../results/figures_general/mga/mga_analysis.pdf", # integrated in workflow - mga_plot_png = "../results/figures_general/mga/mga_analysis.png", # integrated in workflow - map_chokepoints = "../results/figures_general/chokepoints/map_chokepoints.pdf", # integrated in workflow - map_chokepoints_png = "../results/figures_general/chokepoints/map_chokepoints.png", # integrated in workflow +configfile: "config/config.yaml" -### Additional rules -rule create_hydrogen_supply_curve_with_demand: - message: - "Combining individual LCoH results to create a supply curve for region {wildcards.region} with demand lines." - input: - lcoh_data = expand( - "../resources/lcoh/cost_year~{cost_year}/{region}/results_{demand_factor}.csv", - demand_factor=config["demand_factors"], - allow_missing=True, - ), - final_demand_data = "../data/un_enerdata_demand_2050_final.csv" - output: - supply_curve="../resources/supply_curves_subtracted_with_demand/cost_year~{cost_year}/{region}_hydrogen.pdf" - script: - "scripts/create_hydrogen_supply_curve_with_demand.py" -rule create_all_supply_curves_with_demand: - input: - expand( - "../resources/supply_curves_subtracted_with_demand/cost_year~{cost_year}/{region}_hydrogen.pdf", cost_year=[2030,2050], region=config["regions"], allow_missing=True +def _load_trade_scenarios(): + trade_chains = config.get("trade_chains") + if trade_chains: + rows = [] + chain = get_trade_chain(config) + stages_sorted = get_ordered_stages(chain) + if len(stages_sorted) < 2: + raise ValueError( + f"Trade chain '{chain.get('id', '')}' needs at least 2 stages" + ) + stage_groups = get_stage_groups(chain) + if not stage_groups: + raise ValueError( + f"Trade chain '{chain.get('id', '')}' produced no stage groups" ) - + rows.append( + { + "chain_id": str(chain.get("id", "default")), + "cost_year": str(chain.get("cost_year", 2050)), + "interone": str(stage_groups[0]["label"]), + "intertwo": str( + stages_sorted[-1].get( + "process_label", stages_sorted[-1]["output_commodity"] + ) + ), + "wacc": str(chain.get("wacc", "regional")), + "final": str(chain.get("final_product", "steel")), + "scenario": str(chain.get("scenario", "default")), + } + ) + return Paramspace(pd.DataFrame(rows, dtype=str)) -# rule model_trade_singlestage: -# input: -# supply_curves = expand( -# "../resources/supply_curves/cost_year~{cost_year}/{region}_{product}.csv", -# allow_missing=True, region=config["regions"] ), -# transport_costs = "../data/transport_costs/{transport_cost}.csv", -# trade_options = "../data/trade_opt.csv", -# bus_locations = "../data/bus_locations.csv", -# demand = "../data/un_enerdata_demand_2050_final.csv", -# steel_demand = "../resources/steel_production_clustered.csv", -# output: -# trade_result = f"../results/{trade_scenarios.wildcard_pattern}/result.csv", -# trade_network = f"../results/{trade_scenarios.wildcard_pattern}/network.nc", -# trade_plot = f"../results/{trade_scenarios.wildcard_pattern}/plot.pdf" -# script: -# "scripts/model_trade_singlestage.py" + return Paramspace(pd.read_csv("config/trade_scenarios.csv", dtype=str)) -### Toolkit +trade_scenarios = _load_trade_scenarios() -# Under development -# rule input_cost_comp: -# message: -# "Comparing input costs" -# notebook: -# "workflow/notebooks/input-cost-comp.ipynb" +def _derive_supply_curve_products(): + return derive_supply_curve_products(config) -rule sync: - # """ - # Synchronize the WSL repo ~/git/shift to Windows J:\\wsl-sync\\shift using rsync. - # - Preserves times, symlinks, and directory structure. - # - Deletes files on the destination that no longer exist in the source. - # - Excludes common VCS/temporary files. - # """ - # Use a phony target so you can run: snakemake sync_shift_repo - shell: - r""" - set -euo pipefail +SUPPLY_CURVE_PRODUCTS = _derive_supply_curve_products() - SRC="$HOME/git/shift/" - DEST="/mnt/j/wsl-sync/shift" - # Ensure destination directory exists - mkdir -p "$DEST" +wildcard_constraints: + country="[a-zA-Z]+", + sweep="[a-zA-Z]+", + rule="(0|[1-9][0-9]?|100)", - # Rsync options explained: - # -a : archive mode (recursive, preserves symlinks, times, etc.) - # -v : verbose - # -h : human-readable numbers - # --delete : remove files in DEST not present in SRC (mirror behavior) - # --checksum (optional): compare file content not just mtimes/size (slower, safer) - # --exclude-from: filter file for ignores - # --info=progress2: nice single-line progress - # --no-perms/--no-group: avoid NTFS permission warnings on WSL - rsync -avh --delete \ - --info=progress2 \ - --no-perms --no-group \ - "$SRC" "$DEST" - # # Stamp the phony output so Snakemake considers the rule done - # touch {output} - """ \ No newline at end of file +# include: "rules/supply_curves.smk" +include: "../rules/trade_model.smk" +# include: "rules/reporting.smk" From fc5edf369e1c54938c3a923fab2f1990f45963f5 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 14:30:49 +0200 Subject: [PATCH 072/216] feat: build trade model from trade_chains --- workflow/scripts/model_trade.py | 385 +++++++++++++------------------- 1 file changed, 151 insertions(+), 234 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 6076b3b..e52c252 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -46,136 +46,114 @@ def build_region_geodataframe(config): # inputs are transportation costs, supply curves, trade options and load demand for all regions def building_model( - supply_curves_interone, supply_curves_intertwo, demands, bus_location, final + supply_curves_interone, supply_curves_intertwo, demands, bus_location, trade_chain ): - # this function creates network, carrier and a bus for each region - # with a load and all supply possibilities added + """ + Build the PyPSA network from the trade_chain config. - # create network - n = pypsa.Network() + Network structure (buses, carriers, links) is derived entirely from + trade_chain["stages"] and trade_chain["tradeable_commodities"] rather + than from wildcards. + """ + # --- Derive model structure from trade_chain config --- + final = trade_chain["final_product"] + tradeable = trade_chain["tradeable_commodities"] + stages = trade_chain["stages"] - # adding carriers + # is_material_chain: iron ore is involved (steel-type chain) + # otherwise: pure energy chain (e.g. hydrogen) + is_material_chain = "iron_ore" in tradeable - n.add("Carrier", name=final, color=snakemake.config["colors"][final]) - n.add("Carrier", name=interone, color=snakemake.config["colors"][interone]) + # interone: the first non-iron_ore tradeable intermediate (e.g. "hbi") + interone_candidates = [c for c in tradeable if c != "iron_ore"] + interone = interone_candidates[0] if interone_candidates else final - # Define the iron ore carrier - n.add( - "Carrier", - name="iron_ore", - color=snakemake.config["colors"]["iron_ore"], - ) + # two_stage: a separate intermediate bus exists between ore and final product + two_stage = is_material_chain and (interone != final) - n.add( - "Carrier", - name="shipping_" + shipping_first, - color=snakemake.config["colors"][shipping_first + "_shipping"], - ) + # intertwo: process label of the last production stage (e.g. "eaf-grid") + intertwo = stages[max(stages.keys())]["process_label"] - n.add( - "Carrier", - name="shipping_" + shipping_second, - color=snakemake.config["colors"][shipping_second + "_shipping"], - ) + # --- Create network --- + n = pypsa.Network() - # for each region we are creating a bus with all the potentials and load - for r in range(0, len(supply_curves_interone)): + # Add carriers + n.add("Carrier", name=final, color=snakemake.config["colors"][final]) + if is_material_chain: + n.add("Carrier", name="iron_ore", color=snakemake.config["colors"]["iron_ore"]) + n.add("Carrier", name=interone, color=snakemake.config["colors"][interone]) + n.add( + "Carrier", + name="shipping_" + shipping_first, + color=snakemake.config["colors"][shipping_first + "_shipping"], + ) + n.add( + "Carrier", + name="shipping_" + shipping_second, + color=snakemake.config["colors"][shipping_second + "_shipping"], + ) + + # --- Build buses, generators, and loads per region --- + for r in range(len(supply_curves_interone)): - # getting the supply curves for one region for different intermediates region_file_interone = supply_curves_interone[r] region_file_intertwo = supply_curves_intertwo[r] region_data_interone = pd.read_csv(region_file_interone, header=0) region_data_intertwo = pd.read_csv(region_file_intertwo, header=0) - filename = os.path.basename(region_file_interone) - # Extract region name - region_name = filename.split("_" + interone)[0] + region_name = os.path.basename(region_file_interone).split("_" + interone)[0] print("building generators and loads for ", region_name) - # define the iron ore bus with region name - n.add( - "Bus", - region_name + "_ore", - carrier="iron_ore", - x=bus_location.loc[bus_location["region_name"] == region_name] - .loc[:, "long"] - .values[0], # long - y=bus_location.loc[bus_location["region_name"] == region_name] - .loc[:, "lat"] - .values[0], # lat ) - ) - - # define the bus of intermediate product with region name - n.add( - "Bus", - region_name + "_" + interone, - carrier=interone, - x=bus_location.loc[bus_location["region_name"] == region_name] - .loc[:, "long"] - .values[0], # long - y=bus_location.loc[bus_location["region_name"] == region_name] - .loc[:, "lat"] - .values[0], # lat - ) + # Bus coordinates (shared by all buses in this region) + loc = bus_location.loc[bus_location["region_name"] == region_name] + x = float(loc["long"].values[0]) + y = float(loc["lat"].values[0]) + + if is_material_chain: + # Iron ore bus + n.add("Bus", region_name + "_ore", carrier="iron_ore", x=x, y=y) + # Intermediate (interone) bus + n.add("Bus", region_name + "_" + interone, carrier=interone, x=x, y=y) + + # Final product bus (always present; also covers the interone=final single-stage case) + if not is_material_chain or two_stage: + n.add("Bus", region_name + "_" + final, carrier=final, x=x, y=y) + + if is_material_chain: + # Iron ore generator + iron_ore_limit = ( + iron_ore.loc[iron_ore["region"] == region_name][ + "IronOreProductionMt" + ].values[0] + * 1e6 + * snakemake.config["iron_ore"]["potential_allowance"] + ) # Limit in t_ore + + if regionalise == "grade-dependent": + iron_ore_cost = iron_ore.loc[iron_ore["region"] == region_name][ + "IronOreEur/t_ironore" + ].values[0] + elif regionalise == "uniform": + iron_ore_cost = snakemake.config["iron_ore"]["marginal_cost"] + else: + raise ValueError( + "Invalid option for iron ore regionalisation. Choose 'grade-dependent' or 'uniform'." + ) - # define the bus of final product with region name - if final != interone: n.add( - "Bus", - region_name + "_" + final, - carrier=final, - x=float( - bus_location.loc[bus_location["region_name"] == region_name] - .loc[:, "long"] - .values[0] - ), # long - y=float( - bus_location.loc[bus_location["region_name"] == region_name] - .loc[:, "lat"] - .values[0] - ), # lat - ) - - # Define iron ore generators feeding iron ore buses in each region - iron_ore_limit = ( - iron_ore.loc[iron_ore["region"] == region_name][ - "IronOreProductionMt" - ].values[0] - * 1e6 - * snakemake.config["iron_ore"]["potential_allowance"] - ) # Limit in t_ore - - # Get iron ore cost: regional or uniform - if regionalise == "grade-dependent": - iron_ore_cost = iron_ore.loc[iron_ore["region"] == region_name][ - "IronOreEur/t_ironore" - ].values[0] - elif regionalise == "uniform": - iron_ore_cost = snakemake.config["iron_ore"]["marginal_cost"] - else: - ValueError( - "Invalid option for iron ore regionalisation. Choose 'grade-dependent' or 'uniform'." + "Generator", + "{}_ore".format(region_name), + bus=region_name + "_ore", + carrier="iron_ore", + p_nom_extendable=True, + p_nom_max=iron_ore_limit, # t_ore + marginal_cost=iron_ore_cost, # EUR/t_ore + capital_cost=1 / 1000, # to prevent optimisation shenanigans ) - n.add( - "Generator", - "{}_ore".format(region_name), - bus=region_name + "_ore", - carrier="iron_ore", - p_nom_extendable=True, - p_nom_max=iron_ore_limit, # t_ore - marginal_cost=iron_ore_cost, # EUR/t_ore - capital_cost=1 / 1000, # to prevent optimisation shenanigans - ) - - # defining the demand for the region - load = ( - demands.loc[demands["region"] == region_name].loc[:, "demand"].values[0] * 1 - ) # float(snakemake.wildcards["demand"]) - print( - f"Load set via snakemake.wildcard to 100% of regional final energy demand." - ) - + # Demand load + load = demands.loc[demands["region"] == region_name, "demand"].values[0] + print(f"Load set to 100% of regional final energy demand.") n.add( "Load", region_name + "_" + final, @@ -184,22 +162,18 @@ def building_model( p_set=load, ) - # defining the supply opportunities for the region (apart from last supply as that is the 75% infeasible one) - for s in range(0, len(region_data_interone)): + # --- Stage 1 supply: ore → interone (material) or direct supply (energy) --- + for s in range(len(region_data_interone)): if s == 0: - p_nom_supply_interone = float( - region_data_interone[f"demand [{unit}]"][s] - ) + p_nom = float(region_data_interone[f"demand [{unit}]"][s]) else: - p_nom_supply_interone = float( - region_data_interone[f"demand [{unit}]"][s] - ) - float(region_data_interone[f"demand [{unit}]"][s - 1]) - - M_cost_supply_interone = float( - region_data_interone[f"{cost_descriptor} [EUR/{unit}]"][s] - ) + p_nom = float(region_data_interone[f"demand [{unit}]"][s]) - float( + region_data_interone[f"demand [{unit}]"][s - 1] + ) + m_cost = float(region_data_interone[f"{cost_descriptor} [EUR/{unit}]"][s]) - if final == "hydrogen": + if not is_material_chain: + # Pure energy chain (e.g. hydrogen): Generator directly on final bus n.add( "Generator", "{} supply {}_{}".format( @@ -208,141 +182,70 @@ def building_model( bus=region_name, carrier=final, p_nom_extendable=True, - p_nom_max=p_nom_supply_interone, # MWh or t, demand = potential supply - marginal_cost=M_cost_supply_interone, # EUR/MWh or EUR/t - capital_cost=1 / 1000, # to prevent optimisation shennanigans + p_nom_max=p_nom, # MWh, demand = potential supply + marginal_cost=m_cost, # EUR/MWh + capital_cost=1 / 1000, # to prevent optimisation shenanigans + ) + else: + # Material chain: Link from ore bus to interone bus + ore_ratio = snakemake.config["iron_ore"]["ore_to_steel_ratio"] + n.add( + "Link", + "{} supply {}_{}".format( + interone, + region_name, + region_data_interone["demand factor [%]"][s], + ), + bus0=region_name + "_ore", + bus1=region_name + "_" + interone, + carrier=interone, + p_nom_max=p_nom * ore_ratio, # t, demand = potential supply + p_nom_extendable=True, + efficiency=1 / ore_ratio, + marginal_cost=m_cost / ore_ratio, # referred to bus0 + capital_cost=1 / 1000, # to prevent optimisation shenanigans ) - elif final != "hydrogen": - - if interone == intertwo: - - # Single link. bus0: iron ore, bus1: final product - # Add link for first intermediate ("interone") - n.add( - "Link", - "{} supply {}_{}".format( - interone, - region_name, - region_data_interone["demand factor [%]"][s], - ), - bus0=region_name + "_ore", - bus1=region_name + "_" + interone, - carrier=interone, - p_nom_max=p_nom_supply_interone - * snakemake.config["iron_ore"][ - "ore_to_steel_ratio" - ], # t, demand = potential supply - p_nom_extendable=True, - efficiency=1 - / snakemake.config["iron_ore"]["ore_to_steel_ratio"], - marginal_cost=M_cost_supply_interone - / snakemake.config["iron_ore"][ - "ore_to_steel_ratio" - ], # Note: marginal_cost are referred to bus0, hence we need to consider efficiency to apply €/t_steel value - capital_cost=1 / 1000, # to prevent optimisation shenanigans - ) - - elif interone != intertwo: - - # two links. First link: bus0=iron ore, bus1: interone, supply_curve: region_data_interone - # second link: bus0=interone, bus1=final product, supply_curve: region_data_intertwo (no ratios for efficiency and marginal cost needed here!) - - # Add link for first intermediate ("interone") - n.add( - "Link", - "{} supply {}_{}".format( - interone, - region_name, - region_data_interone["demand factor [%]"][s], - ), - bus0=region_name + "_ore", - bus1=region_name + "_" + interone, - carrier=interone, - p_nom_max=p_nom_supply_interone - * snakemake.config["iron_ore"][ - "ore_to_steel_ratio" - ], # t, demand = potential supply - p_nom_extendable=True, - efficiency=1 - / snakemake.config["iron_ore"]["ore_to_steel_ratio"], - marginal_cost=M_cost_supply_interone - / snakemake.config["iron_ore"][ - "ore_to_steel_ratio" - ], # Note: marginal_cost are referred to bus0, hence we need to consider efficiency to apply €/t_steel value - capital_cost=1 / 1000, # to prevent optimisation shenanigans - ) - - if interone != intertwo: - for s in range(0, len(region_data_intertwo)): + # --- Stage 2 supply: interone → final (two-stage material chain only) --- + if two_stage: + for s in range(len(region_data_intertwo)): if s == 0: - p_nom_supply_intertwo = float( - region_data_intertwo[f"demand [{unit}]"][s] - ) + p_nom = float(region_data_intertwo[f"demand [{unit}]"][s]) else: - p_nom_supply_intertwo = float( - region_data_intertwo[f"demand [{unit}]"][s] - ) - float(region_data_intertwo[f"demand [{unit}]"][s - 1]) + p_nom = float(region_data_intertwo[f"demand [{unit}]"][s]) - float( + region_data_intertwo[f"demand [{unit}]"][s - 1] + ) + # Override capacity for grid-connected EAF based on grid potential if intertwo == "eaf-grid": grid_potential = pd.read_csv( snakemake.input.grid_potential, header=0, index_col=0 ) - grid_potential = ( + p_nom = ( grid_potential.loc[region_name, "potential_mt_steel"] * 1e6 - ) # from t to Mt steel - p_nom_supply_intertwo = grid_potential / len( + ) / len( region_data_intertwo - ) # split on all supply links - else: - pass + ) # split evenly across supply steps - M_cost_supply_intertwo = float( + m_cost = float( region_data_intertwo[f"{cost_descriptor} [EUR/{unit}]"][s] ) - # Add link for second intermediate ("intertwo" / final product) n.add( "Link", "{} supply {}_{}".format( - final, - region_name, - region_data_intertwo["demand factor [%]"][s], + final, region_name, region_data_intertwo["demand factor [%]"][s] ), bus0=region_name + "_" + interone, bus1=region_name + "_" + final, carrier=final, - p_nom_max=p_nom_supply_intertwo, # MWh or t, demand = potential supply + p_nom_max=p_nom, # t, demand = potential supply p_nom_extendable=True, efficiency=1, # direct conversion, no ratio needed - marginal_cost=M_cost_supply_intertwo, # EUR/MWh or EUR/t + marginal_cost=m_cost, # EUR/t capital_cost=1 / 1000, # to prevent optimisation shenanigans ) - # OLD STEEL ONLY TODO - # n.add( - # "Link", - # "{} supply {}_{}".format( - # product, region_name, region_data["demand factor [%]"][s] - # ), - # bus0=region_name + "_ore", - # bus1=region_name, - # carrier=product, - # p_nom_max=p_nom_supply - # * snakemake.config["iron_ore"][ - # "ore_to_steel_ratio" - # ], # t, demand = potential supply - # p_nom_extendable=True, - # efficiency=1 / snakemake.config["iron_ore"]["ore_to_steel_ratio"], - # marginal_cost=M_cost_supply - # / snakemake.config["iron_ore"][ - # "ore_to_steel_ratio" - # ], # Note: marginal_cost are referred to bus0, hence we need to consider efficiency to apply €/t_steel value - # capital_cost=1 / 1000, # to prevent optimisation shenanigans - # ) - else: - pass - return n @@ -1209,7 +1112,7 @@ def _link_weight(link_name): intertwo="eaf-grid", final="steel", scenario="default", - wacc="uniform", + wacc="regional", chain_id="default_2050", ) @@ -1219,12 +1122,22 @@ def _link_weight(link_name): scenario = snakemake.wildcards["scenario"] chain_id = snakemake.wildcards["chain_id"] + trade_chain = snakemake.config["trade_chains"] + + # Derive model structure from trade_chains config (source of truth). + # The wildcard variables above are kept for Snakefile compatibility only. + final = trade_chain["final_product"] + tradeable = trade_chain["tradeable_commodities"] + interone = next(c for c in tradeable if c != "iron_ore") + stages = trade_chain["stages"] + intertwo = stages[max(stages.keys())]["process_label"] + print( f"intermediate 1 ({interone}) and intermediate 2 ({intertwo}) to final product {final}" ) shipping_first = "iron_ore" - shipping_second = interone if interone != "steel" else final + shipping_second = interone print("starting up with all regions--- ") # making dataframes @@ -1284,7 +1197,11 @@ def _link_weight(link_name): # building model print("building model") n = building_model( - supply_curves_interone, supply_curves_intertwo, demands, bus_locations, final + supply_curves_interone, + supply_curves_intertwo, + demands, + bus_locations, + trade_chain, ) # building transport network connecting the individual buses From 1aa0131fec1c2a71d7dcd1bc25a152df86e7ccf9 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 19 May 2026 14:37:19 +0200 Subject: [PATCH 073/216] chore: consolidate logging --- config/config.yaml | 2 +- workflow/scripts/_helpers.py | 55 ++++ workflow/scripts/calculate_lcox.py | 282 ++++-------------- workflow/scripts/create_supply_curve.py | 27 +- workflow/scripts/prepare_regional_network.py | 19 +- .../preprocess_consolidate_renewables.py | 247 ++++++++------- 6 files changed, 281 insertions(+), 351 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index f33b2b1..5781cac 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -33,7 +33,7 @@ trade_chains: output_commodity: hydrogen process_label: electrolysis 2: - material_inputs: [iron_ore, H2] + material_inputs: [iron_ore, hydrogen] energy_inputs: [renewable_electricity] output_commodity: hbi process_label: dri diff --git a/workflow/scripts/_helpers.py b/workflow/scripts/_helpers.py index 12359d6..333bcab 100644 --- a/workflow/scripts/_helpers.py +++ b/workflow/scripts/_helpers.py @@ -11,6 +11,61 @@ logger = logging.getLogger(__name__) +def setup_logging( + name: str, + snakemake=None, + level: int = logging.INFO, + log_filename: str | None = None, +) -> logging.Logger: + """Configure a module logger with shared package conventions. + + Parameters + ---------- + name : str + Logger name, usually `__name__`. + snakemake : object, optional + Snakemake object. If it provides `log`, a file handler is added. + level : int + Logging level for the logger and handlers. + log_filename : str, optional + Fallback filename used when `snakemake.log` is unavailable. + + Returns + ------- + logging.Logger + Configured logger instance. + """ + log = logging.getLogger(name) + log.setLevel(level) + + formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") + + if not any(getattr(h, "_shift_console_handler", False) for h in log.handlers): + stream_handler = logging.StreamHandler() + stream_handler.setLevel(level) + stream_handler.setFormatter(formatter) + stream_handler._shift_console_handler = True # type: ignore[attr-defined] + log.addHandler(stream_handler) + + log_path = None + if snakemake is not None and getattr(snakemake, "log", None): + log_path = Path(snakemake.log[0]) + elif log_filename is not None: + log_path = Path("../logs") / log_filename + + if log_path is not None and not any( + getattr(h, "baseFilename", None) == str(log_path) for h in log.handlers + ): + log_path.parent.mkdir(parents=True, exist_ok=True) + file_handler = logging.FileHandler(log_path) + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + file_handler._shift_file_handler = True # type: ignore[attr-defined] + log.addHandler(file_handler) + + return log + + def load_config(config): with open(config, "r") as stream: try: diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 878a8ed..4f14e5b 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -1,142 +1,45 @@ -""" -Calculate regional Levelized Cost of X (LCOX) for a single product demand level. - -Supports: steel, hbi, h2 (flexible product support) +"""calculate_lcox -Workflow (single demand level per invocation): - 1. Load base_network (renewables + product already configured) - 2. Load product-specific demands - 3. Apply incremental generator selection (delete generators outside demand level set) - 4. Add hourly loads based on demand - 5. Solve optimization - 6. Extract LCOX and save results_{demand_level}.csv - 7. Export solved network_{demand_level}.nc +Compute the Levelized Cost of X (LCOX) for a single product demand level +(e.g., `hbi`, `steel`, `h2`). -Parallelization: Each demand level is a separate Snakemake job, enabling parallel execution. +This module provides utilities to: +- load region-specific demands, +- add constant hourly product loads to a PyPSA network, +- solve the network optimization for a fixed demand profile, and +- extract and save a single-row LCOX result with provenance metadata. -Inputs (from Snakemake): - - base_network: PyPSA network with renewables, prepared per region (netCDF) - - incremental_sets: Pre-filtered generator sets per demand level (JSON) - - local_demand: Regional local electricity demand [MWh/year] (CSV) - -Outputs (generated for each demand level): - - results_{demand_level}.csv: LCOX point for that demand level - - network_{demand_level}.nc: Optimized network +The script entry point is intended to be invoked from Snakemake. Functions are +kept small and testable where practical. """ -import copy -import logging import os -from pathlib import Path from typing import Any import pypsa import pandas as pd import numpy as np import xarray as xr +from _helpers import setup_logging + snakemake: Any = globals().get("snakemake") +# Hours per year constant used across the codebase +HOURS_PER_YEAR = 8760 + # ============================================================================ # LOGGING SETUP # ============================================================================ - -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - -formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") - -stream_handler = logging.StreamHandler() -stream_handler.setLevel(logging.INFO) -stream_handler.setFormatter(formatter) -logger.addHandler(stream_handler) - -if snakemake is not None and getattr(snakemake, "log", None): - log_path = Path(snakemake.log[0]) -else: - log_path = Path("../logs") / "calculate_lcox.log" - -log_path.parent.mkdir(parents=True, exist_ok=True) -file_handler = logging.FileHandler(log_path) -file_handler.setLevel(logging.DEBUG) -file_handler.setFormatter(formatter) -logger.addHandler(file_handler) +logger = setup_logging(__name__, snakemake=snakemake, log_filename="calculate_lcox.log") # ============================================================================ -# STAGE SLICING (for independent per-stage solves, Option B semantics) +# STAGE SLICING (legacy helpers removed) # ============================================================================ - -def build_stage_subnetwork(n: pypsa.Network, process_carrier: str) -> pypsa.Network: - """Return a deep copy of the network pruned to links with carrier == process_carrier. - - Keeps: - - Links whose `carrier` equals `process_carrier`. - - Generators/stores attached to buses referenced by those links (e.g., raw resource suppliers). - - Removes other conversion links and any isolated buses. - - The returned subnetwork is suitable for independent per-stage marginal solves (Option B semantics). - No upstream pricing is performed; upstream inputs are treated as free resources or absent. - """ - sub = copy.deepcopy(n) - - # Remove links that are not the target process carrier - for link_name in list(sub.links.index): - carrier = sub.links.loc[link_name, "carrier"] - if carrier != process_carrier: - sub.remove("Link", link_name) - - # Remove generators not attached to remaining buses - for gen_name in list(sub.generators.index): - gen_bus = sub.generators.loc[gen_name, "bus"] - if gen_bus not in sub.buses.index: - try: - sub.remove("Generator", gen_name) - except Exception: - pass - - # Remove stores not attached to remaining buses - for store_name in list(sub.stores.index): - store_bus = sub.stores.loc[store_name, "bus"] - if store_bus not in sub.buses.index: - try: - sub.remove("Store", store_name) - except Exception: - pass - - # Remove isolated buses (no generators, no links, no stores) - for bus_name in list(sub.buses.index): - has_gen = ( - len(sub.generators.index[sub.generators["bus"] == bus_name]) > 0 - if len(sub.generators) > 0 - else False - ) - has_store = ( - len(sub.stores.index[sub.stores["bus"] == bus_name]) > 0 - if len(sub.stores) > 0 - else False - ) - has_link = False - if len(sub.links) > 0: - for link_name in sub.links.index: - row = sub.links.loc[link_name] - for bcol in ["bus0", "bus1", "bus2", "bus3"]: - if bcol in row.index and row.get(bcol) == bus_name: - has_link = True - break - if has_link: - break - - if not (has_gen or has_store or has_link): - try: - sub.remove("Bus", bus_name) - except Exception: - pass - - logger.info( - f"Stage subnetwork for process={process_carrier}: {len(sub.buses)} buses, " - f"{len(sub.generators)} gens, {len(sub.links)} links, {len(sub.stores)} stores" - ) - return sub +# The stage-slicing helper and incremental-selection utilities were used in an +# older workflow. They are no longer invoked by the main driver but kept in +# history; they have been removed to simplify the codebase. If you need them +# for advanced per-stage analyses, reintroduce a tested implementation. # ============================================================================ @@ -184,94 +87,11 @@ def load_demands_for_region(region, config): # ============================================================================ -def apply_incremental_generator_selection(network, product_demand_mt, incremental_sets): - """Delete renewable generators NOT in the incremental set for this demand level. - - NEW WORKFLOW (Phase 3): - - Base network contains ALL generators from max-demand filtering - - Incremental sets pre-computed in prepare_regional_network.py - - For each demand level, delete generators outside that level's set - - OLD WORKFLOW (Phase 2) REMOVED: - - apply_renewable_constraint() - blocked highest-CF for local demand - - Now: filtering done once upstream, no per-demand redundancy - - Parameters - ---------- - network : pypsa.Network - Network with full renewable set (from filtering at max demand) - product_demand_mt : float - Current product demand level in Mt/year - incremental_sets : dict - Mapping: {demand_mt: [selected_generators]} - Each generator dict has: bus_id, technology, p_nom_max, avg_cf - - Returns - ------- - dict - Audit info with generator deletion stats - """ - logger.info("=" * 70) - logger.info("APPLYING INCREMENTAL GENERATOR SELECTION") - logger.info("=" * 70) - - # Get the selected generators for this demand level - if product_demand_mt not in incremental_sets: - logger.warning( - f"Demand level {product_demand_mt} Mt not in incremental sets: {list(incremental_sets.keys())}" - ) - return { - "total_generators_before": len(network.generators), - "generators_deleted": 0, - "generators_kept": len(network.generators), - "selected_for_demand": 0, - } - - selected_generators = incremental_sets[product_demand_mt] - - # Build set of generator names for this demand level - # Generator names are formatted as: renewable_{bus_id}_{technology} - selected_gen_names = set() - for gen_dict in selected_generators: - gen_name = f"renewable_{gen_dict['bus_id']}_{gen_dict['technology']}" - selected_gen_names.add(gen_name) - - logger.info( - f"Selected generators for {product_demand_mt} Mt: {len(selected_gen_names)}" - ) - - # Get all renewable generators in network - renewable_gens = network.generators[ - network.generators.index.str.startswith("renewable_") - ] - - # Find generators to delete (those NOT in selected set) - generators_to_delete = [ - gen_name - for gen_name in renewable_gens.index - if gen_name not in selected_gen_names - ] - - logger.info( - f"Deleting {len(generators_to_delete)} generators not in incremental set" - ) - - # Delete generators - for gen_name in generators_to_delete: - network.remove("Generator", gen_name) - logger.debug(f"Deleted generator: {gen_name}") - - logger.info( - f"Kept {len(selected_gen_names)} generators for demand level {product_demand_mt} Mt" - ) - logger.info("=" * 70) - - return { - "total_generators_before": len(renewable_gens), - "generators_deleted": len(generators_to_delete), - "generators_kept": len(selected_gen_names), - "selected_for_demand": len(selected_gen_names), - } +# NOTE: incremental generator selection was part of an older workflow where +# incremental_sets were precomputed per demand level. The current driver skips +# per-demand incremental filtering and therefore this function has been removed +# to reduce maintenance burden. Reintroduce with tests if needed for custom +# workflows. # ============================================================================ @@ -282,26 +102,32 @@ def apply_incremental_generator_selection(network, product_demand_mt, incrementa def add_loads_to_network(network, product, demands): """Add hourly Load components for fixed product demand. - Converts annual demand to hourly load: hourly_load = annual_demand / 8760 + Converts annual demand to hourly load: hourly_load = annual_demand / HOURS_PER_YEAR This represents a constant hourly demand throughout the year. """ if product == "steel": bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h + hourly_demand_t = ( + demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR + ) # Mt/year → t/h unit_str = "t/h" elif product == "hbi": bus_name = "hbi" # HBI is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h + hourly_demand_t = ( + demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR + ) # Mt/year → t/h unit_str = "t/h" elif product in ["eaf", "eaf-grid"]: bus_name = "steel" # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = demands["product_demand_mt"] * 1e6 / 8760 # Mt/year → t/h + hourly_demand_t = ( + demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR + ) # Mt/year → t/h unit_str = "t/h" else: @@ -545,8 +371,17 @@ def _compute_infeasibility_diagnostics(network, output_dir): def solve_network(network, config): """Solve the PyPSA optimization with hourly fixed demand. - The network has hourly Load components with constant p_set. - Solver minimizes cost to satisfy these fixed hourly demands. + Parameters + ---------- + network : pypsa.Network + The prepared PyPSA network with hourly `Load` components. + config : dict + Configuration dictionary (used to read solver options). + + Notes + ----- + - When invoked from the script entry point, `snakemake.params.compute_iis` + may be consulted to run infeasibility diagnostics for Gurobi. """ # Convert arrow strings to regular strings before optimization _convert_arrow_strings(network) @@ -614,9 +449,22 @@ def solve_network(network, config): def extract_lcox(network, product, demands): - """Extract LCOX from optimized network. + """Extract LCOX from an optimized network. - Returns DataFrame with product-specific columns for supply curve. + Parameters + ---------- + network : pypsa.Network + Solved PyPSA network. `network.objective` is used as total annual cost. + product : str + Product identifier, used to set units (e.g., 'hbi', 'steel', 'h2'). + demands : dict + Must include key `'product_demand_mt'` (float, Mt/year) used to + compute annual production and per-unit LCOX. + + Returns + ------- + pandas.DataFrame + Single-row DataFrame with demand, hourly load, total cost and LCOX. """ # Define product-specific column names if product.lower() in ["steel", "hbi"]: @@ -644,7 +492,7 @@ def extract_lcox(network, product, demands): raise ValueError("Optimization failed to return valid objective") demand_annual_t = demands["product_demand_mt"] * 1e6 # Mt → t - hourly_load_t = demand_annual_t / 8760 + hourly_load_t = demand_annual_t / HOURS_PER_YEAR lcox = obj_value / demand_annual_t if demand_annual_t > 0 else np.inf results_df.loc[0] = [ @@ -660,7 +508,7 @@ def extract_lcox(network, product, demands): except Exception as e: logger.error(f"Optimization infeasible or failed: {e}") demand_annual_t = demands["product_demand_mt"] * 1e6 - hourly_load_t = demand_annual_t / 8760 + hourly_load_t = demand_annual_t / HOURS_PER_YEAR results_df.loc[0] = [ demand_annual_t, hourly_load_t, @@ -735,7 +583,7 @@ def extract_lcox(network, product, demands): if network.snapshots is None or len(network.snapshots) == 0: cost_year = int(snakemake.wildcards.cost_year) network.set_snapshots( - pd.date_range(f"{cost_year}-01-01", periods=8760, freq="h") + pd.date_range(f"{cost_year}-01-01", periods=HOURS_PER_YEAR, freq="h") ) logger.info( f"Set snapshots for cost_year={cost_year} (fallback in calculate_lcox)" diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index 2a41565..c63ce4e 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -9,12 +9,17 @@ # Add workflow/scripts to path for imports sys.path.insert(0, os.path.join(os.path.dirname(__file__))) +from _helpers import setup_logging from trade_chain_utils import route_label_for_product snakemake: Any = globals().get("snakemake") matplotlib.use("Agg") +logger = setup_logging( + __name__, snakemake=snakemake, log_filename="create_supply_curve.log" +) + def get_steel_demand(region): @@ -107,23 +112,23 @@ def create_supply_curve(): route_label = route_label_for_product(snakemake.config, product) or product reserved_files = snakemake.input.lco_reserved - print("reserved scenario files:", reserved_files) + logger.info("reserved scenario files: %s", reserved_files) df_reserved = pd.concat( (pd.read_csv(f, sep=",") for f in reserved_files), ignore_index=True ) - print("reserved scenario data loaded") + logger.info("reserved scenario data loaded") unreserved_files = snakemake.input.lco_unreserved if unreserved_files and len(unreserved_files) > 0: - print("unreserved scenario files:", unreserved_files) + logger.info("unreserved scenario files: %s", unreserved_files) df_unreserved = pd.concat( (pd.read_csv(f, sep=",") for f in unreserved_files), ignore_index=True ) - print("unreserved scenario data loaded") + logger.info("unreserved scenario data loaded") df_merged = df_reserved.copy() df_sub = df_unreserved.copy() else: - print("unreserved scenario not provided; using reserved for both outputs") + logger.info("unreserved scenario not provided; using reserved for both outputs") df_merged = df_reserved.copy() df_sub = df_reserved.copy() @@ -132,7 +137,7 @@ def create_supply_curve(): ].index df_merged = df_merged.drop(infeasible_rows) df_sub = df_sub.drop(infeasible_rows) - print("deleted infeasible rows to prepare for plotting") + logger.info("deleted infeasible rows to prepare for plotting") df_merged["stage_input_commodity"] = stage_meta["stage_input_commodity"] df_merged["stage_output_commodity"] = stage_meta["stage_output_commodity"] @@ -181,9 +186,9 @@ def create_supply_curve(): and len(snakemake.input.lco_unreserved) > 0 ): df_sub.to_csv(unreserved_path, index=False) - print(f"Saved unreserved supply curve: {unreserved_path}") + logger.info("Saved unreserved supply curve: %s", unreserved_path) else: - print( + logger.info( "Skipping supply_unreserved output (unreserved scenario not provided or not enabled)" ) @@ -191,7 +196,9 @@ def create_supply_curve(): try: if not os.path.exists(unreserved_path): df_sub.to_csv(unreserved_path, index=False) - print(f"Wrote fallback supply_unreserved file: {unreserved_path}") + logger.info( + "Wrote fallback supply_unreserved file: %s", unreserved_path + ) except Exception: pass @@ -292,7 +299,7 @@ def create_supply_curve(): # Get product from wildcards (product-labeled contract) product = snakemake.wildcards["product"] -print(f"Creating supply curve for product={product}") +logger.info("Creating supply curve for product=%s", product) if product == "hydrogen": columns = { diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index b92bd71..df9acaf 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -39,26 +39,11 @@ sys.path.insert(0, str(SCRIPT_DIR)) from trade_chain_utils import build_product_components # noqa: E402 - -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - -formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") - -stream_handler = logging.StreamHandler() -stream_handler.setLevel(logging.INFO) -stream_handler.setFormatter(formatter) -logger.addHandler(stream_handler) +from _helpers import setup_logging # noqa: E402 snakemake: Any = globals().get("snakemake") -if snakemake is not None and getattr(snakemake, "log", None): - log_path = Path(snakemake.log[0]) - log_path.parent.mkdir(parents=True, exist_ok=True) - file_handler = logging.FileHandler(log_path) - file_handler.setLevel(logging.DEBUG) - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) +logger = setup_logging(__name__, snakemake=snakemake) def load_region_renewables_consolidated( diff --git a/workflow/scripts/preprocess_consolidate_renewables.py b/workflow/scripts/preprocess_consolidate_renewables.py index fa6e769..76a2fec 100644 --- a/workflow/scripts/preprocess_consolidate_renewables.py +++ b/workflow/scripts/preprocess_consolidate_renewables.py @@ -1,108 +1,120 @@ -""" -Consolidate 15 regional renewable supply NetCDF files into a single unified file. +"""Consolidate regional renewable supply NetCDF files into one unified file. -Input: 15 regional files from data/new_renewables/supply_{Region}_2013_cleaned.nc -Output: data/new_renewables_consolidated.nc with dimensions (region, site_id, time) +Input: regional files from `data/new_renewables/supply_{Region}_2013_cleaned.nc`. +Output: `data/new_renewables_consolidated.nc` with dimensions `(region, site_id, time)`. Usage: python workflow/scripts/preprocess_consolidate_renewables.py [--input-dir data/new_renewables] [--output data/new_renewables_consolidated.nc] """ -import logging from pathlib import Path -from typing import Dict, List, Tuple +from typing import List, Tuple import numpy as np import xarray as xr import pandas as pd -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +from _helpers import setup_logging + +logger = setup_logging(__name__, log_filename="preprocess_consolidate_renewables.log") def extract_region_from_filename(filepath: Path) -> str: """Extract region name from supply_{Region}_2013_cleaned.nc filename.""" name = filepath.stem # Remove .nc extension # Format: supply_{Region}_2013_cleaned - parts = name.split('_') - if len(parts) >= 2 and parts[0] == 'supply': + parts = name.split("_") + if len(parts) >= 2 and parts[0] == "supply": # Join all middle parts (handle multi-word regions like 'East_Asia') - region = '_'.join(parts[1:-2]) # Exclude 'supply' prefix and '2013_cleaned' suffix + region = "_".join( + parts[1:-2] + ) # Exclude 'supply' prefix and '2013_cleaned' suffix return region raise ValueError(f"Could not extract region from filename: {filepath.name}") -def load_and_flatten_region(filepath: Path) -> Tuple[str, np.ndarray, np.ndarray, List[str], int]: +def load_and_flatten_region( + filepath: Path, +) -> Tuple[str, np.ndarray, np.ndarray, List[str], int]: """ Load a regional NetCDF file, preserving technology dimension but flattening class. - + Returns: (region_name, capacity_by_tech, capacity_factor_by_tech, tech_names, n_classes) where capacity_by_tech is (n_tech, n_classes) and capacity_factor_by_tech is (n_tech, n_classes, n_time) """ region = extract_region_from_filename(filepath) logger.info(f"Loading {region} from {filepath.name}") - + ds = xr.open_dataset(filepath) - + # Find capacity variable (2D: technology × class) cap_var = None for var_name in ds.data_vars: - if 'capacity' in var_name.lower() and ds[var_name].ndim == 2: + if "capacity" in var_name.lower() and ds[var_name].ndim == 2: cap_var = var_name break if cap_var is None: - raise ValueError(f"Could not find 2D capacity variable in {filepath.name}. Available vars: {list(ds.data_vars)}") - + raise ValueError( + f"Could not find 2D capacity variable in {filepath.name}. Available vars: {list(ds.data_vars)}" + ) + # Find capacity_factor variable (3D: technology × class × time) cf_var = None for var_name in ds.data_vars: - if any(x in var_name.lower() for x in ['capacity factor', 'capacity_factor', 'cf', 'power', 'profile']): + if any( + x in var_name.lower() + for x in ["capacity factor", "capacity_factor", "cf", "power", "profile"] + ): if ds[var_name].ndim == 3: cf_var = var_name break if cf_var is None: - raise ValueError(f"Could not find 3D time-series variable in {filepath.name}. Available vars: {list(ds.data_vars)}") - + raise ValueError( + f"Could not find 3D time-series variable in {filepath.name}. Available vars: {list(ds.data_vars)}" + ) + logger.info(f" Capacity var: {cap_var}, Time-series var: {cf_var}") logger.info(f" Capacity shape: {ds[cap_var].shape}, CF shape: {ds[cf_var].shape}") - + capacity = ds[cap_var] # (technology, class) capacity_factor = ds[cf_var] # (technology, class, time) - + # Identify dimension names tech_dim, class_dim, time_dim = None, None, None for dim in capacity.dims: - if 'tech' in dim.lower(): + if "tech" in dim.lower(): tech_dim = dim - if 'class' in dim.lower(): + if "class" in dim.lower(): class_dim = dim - + for dim in capacity_factor.dims: - if 'tech' in dim.lower(): + if "tech" in dim.lower(): tech_dim = dim - if 'class' in dim.lower(): + if "class" in dim.lower(): class_dim = dim - if 'time' in dim.lower(): + if "time" in dim.lower(): time_dim = dim - + if tech_dim is None or class_dim is None: - raise ValueError(f"Could not identify technology/class dimensions. Dims: {capacity.dims}") - + raise ValueError( + f"Could not identify technology/class dimensions. Dims: {capacity.dims}" + ) + # Get technology names from coordinate tech_names = ds.coords[tech_dim].values.tolist() n_classes = ds.sizes[class_dim] n_time = ds.sizes[time_dim] - + logger.info(f" Technologies: {tech_names}, Classes: {n_classes}, Time: {n_time}") - + # Keep technology dimension intact, just extract data cap_array = capacity.values # (tech, class) cf_array = capacity_factor.values # (tech, class, time) - + # Ensure time is last dimension if capacity_factor.dims.index(time_dim) != 2: cf_array = np.moveaxis(cf_array, capacity_factor.dims.index(time_dim), -1) - + ds.close() return region, cap_array, cf_array, tech_names, n_classes @@ -110,112 +122,135 @@ def load_and_flatten_region(filepath: Path) -> Tuple[str, np.ndarray, np.ndarray def consolidate_renewables(input_dir: Path, output_path: Path) -> None: """ Consolidate 15 regional NetCDF files into one unified file, preserving technology dimension. - + Output structure: - Dimensions: region (15), technology, class, time (8760) - Variables: capacity (region, technology, class), capacity_factor (region, technology, class, time) - + This preserves the technology distinction (wind, solar, etc.) so PyPSA can create separate generators per technology and region. """ input_dir = Path(input_dir) output_path = Path(output_path) - + # Find all regional files nc_files = sorted(input_dir.glob("supply_*_2013_cleaned.nc")) logger.info(f"Found {len(nc_files)} regional files") - + if len(nc_files) == 0: raise FileNotFoundError(f"No NetCDF files found in {input_dir}") - + # Load all regions (preserve technology and class dimensions) regions_data = [] tech_names = None n_classes = None time_length = None - + for filepath in nc_files: - region, cap_array, cf_array, file_tech_names, file_n_classes = load_and_flatten_region(filepath) - + region, cap_array, cf_array, file_tech_names, file_n_classes = ( + load_and_flatten_region(filepath) + ) + # Verify consistency if tech_names is None: tech_names = file_tech_names elif tech_names != file_tech_names: - raise ValueError(f"Inconsistent technologies: {region} has {file_tech_names}, expected {tech_names}") - + raise ValueError( + f"Inconsistent technologies: {region} has {file_tech_names}, expected {tech_names}" + ) + if n_classes is None: n_classes = file_n_classes elif n_classes != file_n_classes: - raise ValueError(f"Inconsistent class count: {region} has {file_n_classes}, expected {n_classes}") - + raise ValueError( + f"Inconsistent class count: {region} has {file_n_classes}, expected {n_classes}" + ) + if time_length is None: - time_length = cap_array.shape[-1] if cap_array.ndim == 3 else cf_array.shape[-1] + time_length = ( + cap_array.shape[-1] if cap_array.ndim == 3 else cf_array.shape[-1] + ) elif cf_array.shape[-1] != time_length: - raise ValueError(f"Inconsistent time dimensions: {region} has {cf_array.shape[-1]}, expected {time_length}") - - regions_data.append({ - 'region': region, - 'capacity': cap_array, # (tech, class) - 'capacity_factor': cf_array, # (tech, class, time) - }) - - logger.info(f"Tech names: {tech_names}, Classes: {n_classes}, Time length: {time_length}") - + raise ValueError( + f"Inconsistent time dimensions: {region} has {cf_array.shape[-1]}, expected {time_length}" + ) + + regions_data.append( + { + "region": region, + "capacity": cap_array, # (tech, class) + "capacity_factor": cf_array, # (tech, class, time) + } + ) + + logger.info( + f"Tech names: {tech_names}, Classes: {n_classes}, Time length: {time_length}" + ) + # Stack all regions into (region, tech, class, time) structure - region_names = [r['region'] for r in regions_data] - + region_names = [r["region"] for r in regions_data] + # Preallocate arrays - capacity_stacked = np.zeros((len(regions_data), len(tech_names), n_classes), dtype=np.float32) - cf_stacked = np.zeros((len(regions_data), len(tech_names), n_classes, time_length), dtype=np.float32) - + capacity_stacked = np.zeros( + (len(regions_data), len(tech_names), n_classes), dtype=np.float32 + ) + cf_stacked = np.zeros( + (len(regions_data), len(tech_names), n_classes, time_length), dtype=np.float32 + ) + for i, data in enumerate(regions_data): - capacity_stacked[i, :, :] = data['capacity'] - cf_stacked[i, :, :, :] = data['capacity_factor'] - - logger.info(f"Created stacked arrays: capacity {capacity_stacked.shape}, cf {cf_stacked.shape}") - + capacity_stacked[i, :, :] = data["capacity"] + cf_stacked[i, :, :, :] = data["capacity_factor"] + + logger.info( + f"Created stacked arrays: capacity {capacity_stacked.shape}, cf {cf_stacked.shape}" + ) + # Create consolidated xarray Dataset with technology dimension preserved - time_index = pd.date_range('2013-01-01', periods=time_length, freq='h') + time_index = pd.date_range("2013-01-01", periods=time_length, freq="h") class_ids = np.arange(n_classes) - + ds_consolidated = xr.Dataset( data_vars={ - 'capacity': (['region', 'technology', 'class'], capacity_stacked), - 'capacity_factor': (['region', 'technology', 'class', 'time'], cf_stacked), + "capacity": (["region", "technology", "class"], capacity_stacked), + "capacity_factor": (["region", "technology", "class", "time"], cf_stacked), }, coords={ - 'region': region_names, - 'technology': tech_names, - 'class': class_ids, - 'time': time_index, + "region": region_names, + "technology": tech_names, + "class": class_ids, + "time": time_index, }, attrs={ - 'description': 'Consolidated renewable supply profiles for 15 global regions, with technology distinction', - 'source': 'data/new_renewables/*.nc', - 'temporal_resolution': 'hourly', - 'year': 2013, - 'technologies': ', '.join(tech_names), - } + "description": "Consolidated renewable supply profiles for 15 global regions, with technology distinction", + "source": "data/new_renewables/*.nc", + "temporal_resolution": "hourly", + "year": 2013, + "technologies": ", ".join(tech_names), + }, ) - + # Add variable attributes - ds_consolidated['capacity'].attrs = { - 'long_name': 'Installed capacity', - 'units': 'MW', + ds_consolidated["capacity"].attrs = { + "long_name": "Installed capacity", + "units": "MW", } - ds_consolidated['capacity_factor'].attrs = { - 'long_name': 'Capacity factor (power output / installed capacity)', - 'units': 'p.u.', + ds_consolidated["capacity_factor"].attrs = { + "long_name": "Capacity factor (power output / installed capacity)", + "units": "p.u.", } - + # Write to NetCDF output_path.parent.mkdir(parents=True, exist_ok=True) logger.info(f"Writing consolidated file to {output_path}") - ds_consolidated.to_netcdf(output_path, encoding={ - 'capacity': {'dtype': 'float32', 'zlib': True, 'complevel': 4}, - 'capacity_factor': {'dtype': 'float32', 'zlib': True, 'complevel': 4}, - }) - + ds_consolidated.to_netcdf( + output_path, + encoding={ + "capacity": {"dtype": "float32", "zlib": True, "complevel": 4}, + "capacity_factor": {"dtype": "float32", "zlib": True, "complevel": 4}, + }, + ) + logger.info(f"✓ Consolidation complete: {output_path}") logger.info(f" Regions: {len(region_names)}") logger.info(f" Technologies: {tech_names}") @@ -224,23 +259,23 @@ def consolidate_renewables(input_dir: Path, output_path: Path) -> None: logger.info(f" Output size: {output_path.stat().st_size / 1e6:.1f} MB") -if __name__ == '__main__': +if __name__ == "__main__": import argparse - + parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( - '--input-dir', + "--input-dir", type=Path, - default=Path('data/new_renewables'), - help='Directory containing supply_*.nc files (default: data/new_renewables)', + default=Path("data/new_renewables"), + help="Directory containing supply_*.nc files (default: data/new_renewables)", ) parser.add_argument( - '--output', + "--output", type=Path, - default=Path('data/new_renewables_consolidated.nc'), - help='Output path for consolidated file (default: data/new_renewables_consolidated.nc)', + default=Path("data/new_renewables_consolidated.nc"), + help="Output path for consolidated file (default: data/new_renewables_consolidated.nc)", ) - + args = parser.parse_args() - + consolidate_renewables(args.input_dir, args.output) From 700398f915fcfb6bb200231caaebab94cdb88657 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 15:21:38 +0200 Subject: [PATCH 074/216] fix: adjust to new supply chain naming of files --- rules/trade_model.smk | 5 +++-- workflow/scripts/model_trade.py | 7 +++++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/rules/trade_model.smk b/rules/trade_model.smk index 7329920..eef1629 100644 --- a/rules/trade_model.smk +++ b/rules/trade_model.smk @@ -8,16 +8,17 @@ and collect the scenario-level outputs. rule model_trade: input: supply_curves_interone=expand( - "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{interone}.csv", allow_missing=True, cost_year=[2050], region=config["regions"], ), supply_curves_intertwo=expand( - "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{intertwo}.csv", + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{intertwo}.csv", allow_missing=True, cost_year=[2050], region=config["regions"], + intertwo=["steel"], ), transport_costs = "data/transport_costs/steel_r_iron_r.csv", trade_options = "resources/trade_opt_chokepoints.csv", diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index e52c252..1624126 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -100,7 +100,10 @@ def building_model( region_file_intertwo = supply_curves_intertwo[r] region_data_interone = pd.read_csv(region_file_interone, header=0) region_data_intertwo = pd.read_csv(region_file_intertwo, header=0) - region_name = os.path.basename(region_file_interone).split("_" + interone)[0] + + region_name = os.path.basename(region_file_interone).rsplit( + "_marginal_cost_", 1 + )[0] print("building generators and loads for ", region_name) @@ -1112,7 +1115,7 @@ def _link_weight(link_name): intertwo="eaf-grid", final="steel", scenario="default", - wacc="regional", + wacc="uniform", chain_id="default_2050", ) From 6ce28f55ce7f3a607e3f40823877c662588d8f83 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 15:41:17 +0200 Subject: [PATCH 075/216] feat: adjust to new file content of supply curves --- workflow/scripts/model_trade.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 1624126..dfb0286 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -180,7 +180,7 @@ def building_model( n.add( "Generator", "{} supply {}_{}".format( - final, region_name, region_data_interone["demand factor [%]"][s] + final, region_name, region_data_interone["load [t/h]"][s] ), bus=region_name, carrier=final, @@ -197,7 +197,7 @@ def building_model( "{} supply {}_{}".format( interone, region_name, - region_data_interone["demand factor [%]"][s], + region_data_interone["load [t/h]"][s], ), bus0=region_name + "_ore", bus1=region_name + "_" + interone, @@ -237,7 +237,7 @@ def building_model( n.add( "Link", "{} supply {}_{}".format( - final, region_name, region_data_intertwo["demand factor [%]"][s] + final, region_name, region_data_intertwo["load [t/h]"][s] ), bus0=region_name + "_" + interone, bus1=region_name + "_" + final, @@ -1191,7 +1191,7 @@ def _link_weight(link_name): else: raise ValueError("Product must be either 'steel' or 'hydrogen'.") - cost_descriptor = "LCOX" + cost_descriptor = "lcox" plot_config = snakemake.config["plot"]["world_map"][final] From d31e2e4c774cbbcbd84af565f4a99ccfc0be4de6 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 19 May 2026 17:22:37 +0200 Subject: [PATCH 076/216] feat: improve modular PyPSA supply chain creation --- config/config.yaml | 2 +- workflow/scripts/build_x_supply_chain.py | 14 +- workflow/scripts/calculate_lcox.py | 50 ++++--- workflow/scripts/create_supply_curve.py | 4 +- workflow/scripts/prepare_regional_network.py | 146 +++++++++++++------ workflow/scripts/trade_chain_utils.py | 23 ++- 6 files changed, 167 insertions(+), 72 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 5781cac..47c1b32 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -41,7 +41,7 @@ trade_chains: material_inputs: [hbi] energy_inputs: [grid_electricity] output_commodity: steel - process_label: eaf-grid + process_label: eaf # Absolute steel demand levels (Mt/year) for supply curve sweep diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 5dc0f94..8712001 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -370,7 +370,6 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> lifetime=td.get_tech_param(batt_store_params, "lifetime", 30.0), fom_cost=batt_store_cost * 0.0, standing_loss=TECH_ASSUMPTIONS["batt_standing_loss"], - e_initial=config.get("battery_e_initial", 0.5), # Start at 50% capacity e_cyclic=True, # End state must equal start state ) @@ -386,6 +385,19 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> standing_loss=0.0, # HBI storage doesn't lose energy ) + # Steel Storage: flexible intermediate inventory between EAF and demand + network.add( + "Store", + "steel_storage", + bus="steel", + e_nom_extendable=True, + overnight_cost=0.0, # Just a pile - no cost + lifetime=1.0, + fom_cost=0.0, # No maintenance cost + discount_rate=0.0, # No cost, discount rate doesn't matter but required by PyPSA + standing_loss=0.0, # Steel storage doesn't lose energy + ) + def _add_resources(network: pypsa.Network, config: dict) -> None: """Add external resource supplies (iron ore).""" diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 4f14e5b..a4c9989 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -100,14 +100,24 @@ def load_demands_for_region(region, config): def add_loads_to_network(network, product, demands): - """Add hourly Load components for fixed product demand. + """Add hourly Load components and set storage boundary conditions. - Converts annual demand to hourly load: hourly_load = annual_demand / HOURS_PER_YEAR - This represents a constant hourly demand throughout the year. + Converts annual demand to hourly load: hourly_load = annual_demand / HOURS_PER_YEAR. + Sets product storage e_initial and e_final to annual_demand / 52 (approx. 2-week buffer). + This provides flexibility while ensuring bounded stock levels. """ + if product == "hydrogen": + bus_name = "hydrogen" + storage_name = "h2_storage" + # Hydrogen is measured in kg/year, convert to kg/h (hourly) + hourly_demand_t = ( + demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR + ) # Mt/year → t/h + unit_str = "t/h" if product == "steel": bus_name = "steel" + storage_name = "steel_storage" # Steel is measured in t/year, convert to t/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR @@ -116,22 +126,15 @@ def add_loads_to_network(network, product, demands): elif product == "hbi": bus_name = "hbi" + storage_name = "hbi_storage" # HBI is measured in t/year, convert to t/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR ) # Mt/year → t/h unit_str = "t/h" - elif product in ["eaf", "eaf-grid"]: - bus_name = "steel" - # Steel is measured in t/year, convert to t/h (hourly) - hourly_demand_t = ( - demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR - ) # Mt/year → t/h - unit_str = "t/h" - else: - raise ValueError(f"Product '{product}' not recognized") + raise ValueError(f"Product '{product}' not recognized (valid: 'steel', 'hbi')") if bus_name not in network.buses.index: raise ValueError(f"Bus '{bus_name}' not found in network") @@ -147,14 +150,21 @@ def add_loads_to_network(network, product, demands): p_set=p_set, # Constant hourly demand ) - # For steel/HBI: set HBI storage initial energy to 24 hours of hourly load - if product.lower() in ["steel", "hbi"]: - if "hbi_storage" in network.stores.index: - hbi_e_initial = 24 * hourly_demand_t # 24 hours of buffer - network.stores.at["hbi_storage", "e_initial"] = hbi_e_initial - logger.info( - f"Set HBI storage e_initial to {hbi_e_initial:.2f} t (24h buffer for {hourly_demand_t:.4f} t/h demand)" - ) + # Set product storage boundary conditions: initial and final stock at annual_demand/52 + annual_demand_t = demands["product_demand_mt"] * 1e6 # Mt → t + storage_buffer = annual_demand_t / 52 # Approx. 1 week of annual demand + + if storage_name in network.stores.index: + network.stores.at[storage_name, "e_initial"] = storage_buffer + network.stores.at[storage_name, "e_final"] = storage_buffer + logger.info( + f"Set {storage_name} e_initial and e_final to {storage_buffer:.2f} t " + f"(annual_demand/52 for {hourly_demand_t:.4f} t/h demand)" + ) + else: + logger.warning( + f"Storage '{storage_name}' not found in network; skipping boundary condition setup" + ) logger.info( f"Added hourly load for {product}: {load_name} = {p_set:.4f} {unit_str} (constant all hours)" diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index c63ce4e..5385dc2 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -261,7 +261,7 @@ def create_supply_curve(): linestyle="-.", label="20% final energy demand", ) - elif product in ["steel", "eaf", "eaf-grid"]: + elif product == "steel": steel_demand = get_steel_demand(snakemake.wildcards["region"]) plt.axvline(x=steel_demand.values[0], linestyle=":", label="local steel demand") elif product == "hbi": @@ -312,7 +312,7 @@ def create_supply_curve(): "product_unit": "MWh", "ylim": (0, 100), } -elif product in ["steel", "eaf", "hbi", "eaf-grid"]: +elif product in ["steel", "hbi"]: columns = { "demand factor": "demand factor [%]", "demand": "demand [t]", diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index df9acaf..ece92ba 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -94,6 +94,18 @@ def load_region_renewables_consolidated( # Capacity factor time series (keep full structure for now) cf_ts = region_cf # (tech, class, time) + # Validate capacity factors: clamp to [0, 1] and replace NaN with 0 + # This prevents infeasibility warnings from PyPSA when p_max_pu goes negative or exceeds 1 + n_invalid_before = int( + ((cf_ts < 0) | (cf_ts > 1) | cf_ts.isnull()).sum().values + ) + cf_ts = cf_ts.clip(0, 1).fillna(0) + if n_invalid_before > 0: + logger.warning( + f"Capacity factors validation: fixed {n_invalid_before} invalid values " + f"(clamped to [0,1], replaced NaN with 0)" + ) + metadata = { "region": region, "n_classes": region_cap.sizes["class"], @@ -324,6 +336,18 @@ def add_renewable_generators( # Get time series for this site p_max_pu = cf_data[site_idx, :] # (time,) + # Ensure p_max_pu is valid (should be [0, 1] after data validation above) + if np.any(np.isnan(p_max_pu)): + logger.warning( + f"Generator {gen_name}: p_max_pu contains NaN values, filling with 0" + ) + p_max_pu = np.nan_to_num(p_max_pu, nan=0.0) + if np.any(p_max_pu < 0) or np.any(p_max_pu > 1): + logger.warning( + f"Generator {gen_name}: p_max_pu out of bounds [0,1], clamping" + ) + p_max_pu = np.clip(p_max_pu, 0, 1) + # Determine tech-specific carrier while keeping generators on the # shared `renewable_electricity` bus. This preserves per-tech # statistics while modelling a common electricity bus. @@ -658,64 +682,102 @@ def prepare_network( if route_label: logger.info(f"Slicing skeleton to route_label={route_label}") - # Define which links/stores to keep for each stage - stage_components = { - "hbi": { - "keep_links": ["electrolysis", "dri"], - "keep_stores": ["h2_storage", "hbi_storage"], - "remove_links": ["eaf-grid"], - "add_hbi_input": False, - }, - "steel": { - "keep_links": ["eaf-grid"], - "keep_stores": ["steel_storage"], - "remove_links": ["electrolysis", "dri"], - "add_hbi_input": True, # Add HBI as external free input - }, - } - - if route_label in stage_components: - spec = stage_components[route_label] + # Use configured component resolver to derive which PyPSA components + # (links, stores, buses) belong to this stage-group. This keeps the + # slicing logic driven by `trade_chain` config and the canonical + # TECH_COMPONENT_MAP in `trade_chain_utils.py`. + try: + keep_links, keep_stores, keep_buses = _get_components_for_product( + config, route_label + ) + except Exception as exc: + logger.warning( + f"Could not derive components for route_label={route_label}: {exc}; keeping full skeleton" + ) + keep_links, keep_stores, keep_buses = set(), set(), set() + # If resolver returned empty sets, warn and keep full skeleton + if not (keep_links or keep_stores or keep_buses): + logger.warning( + f"Component resolver returned no components for {route_label}; keeping full skeleton" + ) + else: # Remove links not in keep_links for link_name in list(network.links.index): - if link_name not in spec["keep_links"]: + if link_name not in keep_links: try: network.remove("Link", link_name) - logger.info(f"Removed link: {link_name}") + logger.info(f"Removed Link: {link_name}") except Exception as e: - logger.warning(f"Could not remove link {link_name}: {e}") + logger.warning(f"Could not remove Link {link_name}: {e}") # Remove stores not in keep_stores for store_name in list(network.stores.index): - if store_name not in spec["keep_stores"]: + if store_name not in keep_stores: try: network.remove("Store", store_name) - logger.info(f"Removed store: {store_name}") + logger.info(f"Removed Store: {store_name}") except Exception as e: - logger.warning(f"Could not remove store {store_name}: {e}") - - # Add HBI as free input if this is steel stage - if spec["add_hbi_input"]: - if "hbi" not in network.buses.index: - network.add("Bus", "hbi", carrier="hbi", unit="t/h") - network.add( - "Generator", - "hbi_input", - bus="hbi", - carrier="hbi", - p_nom=1e10, # Unlimited - marginal_cost=0, # Free for stage solve - ) - logger.info("Added HBI as free external input (steel stage)") + logger.warning(f"Could not remove Store {store_name}: {e}") + + # Preserve buses that are required outputs or material/energy interfaces + # Add free external inputs for any material bus that is expected but + # not produced within this sliced network (e.g., `hbi` for the steel stage). + # Determine whether a kept bus is produced by any remaining link. + produced_buses = set() + for link_name in network.links.index: + row = network.links.loc[link_name] + for bcol in [ + c for c in ["bus0", "bus1", "bus2", "bus3"] if c in row.index + ]: + b = row.get(bcol) + if pd.notna(b): + produced_buses.add(b) + + # For each bus in keep_buses that is not produced in the sliced network, + # add a free generator input if no generator already supplies it. + for bus_name in keep_buses: + if bus_name not in network.buses.index: + # create bus if missing + try: + network.add("Bus", bus_name, carrier=bus_name, unit="t/h") + logger.info(f"Added missing Bus for stage slicing: {bus_name}") + except Exception: + pass + + needs_free_input = False + if bus_name not in produced_buses: + # If no link produces this bus, and no generator exists on it, + # create a free external input (unlimited capacity, zero marginal cost) + gens_on_bus = ( + network.generators[network.generators["bus"] == bus_name] + if len(network.generators) > 0 + else pd.DataFrame() + ) + if gens_on_bus.empty: + needs_free_input = True + + if needs_free_input: + gen_name = f"{bus_name}_input" + if gen_name not in network.generators.index: + try: + network.add( + "Generator", + gen_name, + bus=bus_name, + carrier=bus_name, + p_nom=1e10, + marginal_cost=0, + ) + logger.info( + f"Added external free input generator: {gen_name} on {bus_name}" + ) + except Exception as e: + logger.warning(f"Could not add free input {gen_name}: {e}") logger.info( f"Skeleton sliced to {route_label}: {len(network.links)} links, {len(network.stores)} stores" ) - else: - logger.warning( - f"route_label={route_label} not recognized; keeping full skeleton" - ) # Set region-specific discount rate interest_rates = config.get("interest_rate", {}) diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py index b89bc4e..9cbc3a4 100644 --- a/workflow/scripts/trade_chain_utils.py +++ b/workflow/scripts/trade_chain_utils.py @@ -24,20 +24,23 @@ TECH_COMPONENT_MAP = [ { "match": ("electro", "electrolyser", "electrolyzer"), - "links": ("electrolyzer",), + # Some skeletons name this link `electrolysis` while others use + # `electrolyzer`/`electrolyser`. Include common variants so slicer + # keeps the actual link present in the network. + "links": ("electrolyzer", "electrolysis", "electrolyser"), "stores": ("h2_storage",), # expected material reactants, energy inputs, and outputs "materials": (), - "energy": ("renewable_electricity", "grid_electricity"), + "energy": ("renewable_electricity",), "outputs": ("hydrogen",), - "buses": ("hydrogen", "renewable_electricity", "grid_electricity"), + "buses": ("hydrogen", "renewable_electricity"), }, { "match": ("dri", "direct_reduction", "reduction"), "links": ("dri",), "stores": ("h2_storage", "hbi_storage"), "materials": ("iron_ore", "hydrogen"), - "energy": ("renewable_electricity", "grid_electricity"), + "energy": ("renewable_electricity"), "outputs": ("hbi",), "buses": ( "iron_ore", @@ -49,7 +52,8 @@ }, { "match": ("eaf", "electric_arc", "arc_furnace"), - "links": ("eaf",), + # Support both `eaf` and `eaf-grid` link namings found in skeletons. + "links": ("eaf", "eaf-grid", "electric_arc_furnace"), "stores": ("steel_storage",), "materials": ("hbi",), "energy": ("grid_electricity", "renewable_electricity"), @@ -317,7 +321,14 @@ def build_product_components(config: Dict, product: str) -> Dict[str, object]: stores.update(comp.get("stores", ())) for b in comp.get("buses", ()): # include any canonical buses from mapping buses.add(_normalize_commodity(b)) - + # If this stage-group uses renewable electricity, include battery + # storage and bus as an explicit component so slicers keep batteries + # for renewable-based stages. The user requested batteries be explicit + # in stage configurations; adding them here maintains backward + # compatibility while keeping per-stage skeletons functional. + if has_renewables: + stores.add("battery") + buses.add("battery") return { "links": links, "stores": stores, From c91f3dfeec4af285bedae3fcf98d420058a1a344 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 19 May 2026 17:27:02 +0200 Subject: [PATCH 077/216] chore: cleanup old trade scenarios --- config/trade_scenarios.csv | 2 -- config/trade_scenarios_collection.csv | 6 ------ 2 files changed, 8 deletions(-) delete mode 100644 config/trade_scenarios.csv delete mode 100644 config/trade_scenarios_collection.csv diff --git a/config/trade_scenarios.csv b/config/trade_scenarios.csv deleted file mode 100644 index 28ddffd..0000000 --- a/config/trade_scenarios.csv +++ /dev/null @@ -1,2 +0,0 @@ -cost_year,interone,intertwo,final,scenario -2050,hbi,eaf-grid,steel,default \ No newline at end of file diff --git a/config/trade_scenarios_collection.csv b/config/trade_scenarios_collection.csv deleted file mode 100644 index d9a58f0..0000000 --- a/config/trade_scenarios_collection.csv +++ /dev/null @@ -1,6 +0,0 @@ -cost_year,transport_cost,demand,interone,intertwo,final -2030,steel_r_iron_r,1,steel,steel,steel -2030,steel_r_iron_r,1,hbi,eaf,steel -2030,steel_r_iron_r,1,hbi,eaf-grid,steel -2030,steel_r_iron_r,1,hbi,hbi,hbi -2030,steel_r_iron_r,1,hydrogen,hydrogen,hydrogen \ No newline at end of file From 5017fce13202a19233bbc3bb30e4738298cca797 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 19 May 2026 17:29:20 +0200 Subject: [PATCH 078/216] chore: add incomplete inspection notebooks --- workflow/notebooks/analysis-lcohbi.ipynb | 1680 ++++++++++++++++++++++ 1 file changed, 1680 insertions(+) create mode 100644 workflow/notebooks/analysis-lcohbi.ipynb diff --git a/workflow/notebooks/analysis-lcohbi.ipynb b/workflow/notebooks/analysis-lcohbi.ipynb new file mode 100644 index 0000000..d6a6b0e --- /dev/null +++ b/workflow/notebooks/analysis-lcohbi.ipynb @@ -0,0 +1,1680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "19d4c2c1", + "metadata": {}, + "source": [ + "# LCO-HBI Analysis for an optimized PyPSA network\n", + "\n", + "This notebook loads an optimized PyPSA network (.nc), inspects network structure, and computes a detailed, traceable decomposition of the Levelized Cost of HBI (LCO-HBI).\n", + "\n", + "Usage: set the `network_file` parameter in Cell 2 and run all cells. The notebook attempts to reuse helper functions from `workflow/scripts/calculate_lcox.py` for validation where available." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d3d31256", + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and helper functions\n", + "import sys\n", + "import pathlib\n", + "import seaborn as sns\n", + "\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "# Add repository root to path so we can import workflow scripts\n", + "repo_root = pathlib.Path(\n", + " \"..\"\n", + ").resolve() # notebook lives in workflow/notebooks; adjust when running from repo root\n", + "if str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "# Parameters - edit these before running\n", + "network_file = str(\n", + " repo_root.parent\n", + " / \"resources\"\n", + " / \"lco-hbi\"\n", + " / \"cost_year~2050\"\n", + " / \"Europe_reserved\"\n", + " / \"network_10.nc\"\n", + ") # path relative to repo root\n", + "product = \"hbi\" # product to evaluate (hbi)\n", + "region = None # optional: restrict to a region (string)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "61731588", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calculate_lcox module not available; notebook will still run but some validations will be skipped\n" + ] + } + ], + "source": [ + "try:\n", + " import pypsa\n", + "except Exception as e:\n", + " raise RuntimeError(\n", + " \"PyPSA import failed. Make sure the environment has pypsa installed.\"\n", + " ) from e\n", + "\n", + "# Optional: import repo LCoX utilities if available\n", + "calc_lcox = None\n", + "try:\n", + " from workflow.scripts import calculate_lcox as calculate_lcox_module\n", + "\n", + " calc_lcox = calculate_lcox_module\n", + " print(\"Imported workflow.scripts.calculate_lcox\")\n", + "except Exception:\n", + " try:\n", + " # fallback to relative import if executed from repository root\n", + " import workflow.scripts.calculate_lcox as calculate_lcox_module\n", + "\n", + " calc_lcox = calculate_lcox_module\n", + " print(\"Imported workflow.scripts.calculate_lcox\")\n", + " except Exception:\n", + " print(\n", + " \"calculate_lcox module not available; notebook will still run but some validations will be skipped\"\n", + " )\n", + "\n", + "\n", + "# CRF helper\n", + "def crf(r, n):\n", + " if r <= 0:\n", + " return 1.0 / n\n", + " return (r * (1 + r) ** n) / (((1 + r) ** n) - 1)\n", + "\n", + "\n", + "def annualize(capex, r, n):\n", + " \"\"\"Annualize capital cost (capex) using discount rate r and lifetime n (years).\"\"\"\n", + " return capex * crf(r, n)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e84d792a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network: C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\resources\\lco-hbi\\cost_year~2050\\Europe_reserved\\network_10.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Network loaded.\n", + "Snapshots: 8760\n", + "Objective / objective_value: 5463430058.465736\n", + "\n", + "Component counts:\n", + " - buses: 7\n", + " - generators: 32\n", + " - links: 2\n", + " - lines: 0\n", + "\n", + "Top generators by optimized capacity (head 10):\n" + ] + }, + { + "data": { + "text/plain": [ + "name\n", + "iron_ore 1.000000e+10\n", + "grid_electricity_input 1.000000e+10\n", + "battery_input 1.000000e+10\n", + "renewable_Europe_windonshore_30 9.413415e+03\n", + "renewable_Europe_windonshore_27 7.260414e+03\n", + "renewable_Europe_pvplant_17 5.731441e+03\n", + "renewable_Europe_pvplant_16 3.627389e-09\n", + "renewable_Europe_pvplant_15 1.018952e-09\n", + "renewable_Europe_pvplant_14 7.960459e-10\n", + "renewable_Europe_pvplant_13 5.860929e-10\n", + "Name: p_nom_opt, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load network\n", + "import pypsa\n", + "\n", + "print(\"Loading network:\", network_file)\n", + "n = pypsa.Network(network_file)\n", + "print(\"Network loaded.\")\n", + "\n", + "# Basic checks\n", + "print(\"Snapshots:\", len(n.snapshots) if n.snapshots is not None else \"None\")\n", + "obj = None\n", + "try:\n", + " obj = getattr(n, \"objective\", None) or getattr(n, \"objective_value\", None)\n", + " print(\"Objective / objective_value:\", obj)\n", + "except Exception:\n", + " print(\n", + " \"Objective not found on network object (this may be a network saved before extract).\"\n", + " )\n", + "\n", + "# Quick network summary\n", + "print(\"\\nComponent counts:\")\n", + "for c in [\"buses\", \"generators\", \"links\", \"lines\", \"storages\"]:\n", + " comp = getattr(n, c, None)\n", + " if comp is not None:\n", + " print(f\" - {c}:\", len(comp))\n", + "\n", + "# show top generator capacities\n", + "if hasattr(n, \"generators\") and len(n.generators) > 0:\n", + " gcap = (\n", + " n.generators[\"p_nom_opt\"]\n", + " .fillna(n.generators.get(\"p_nom\", 0))\n", + " .sort_values(ascending=False)\n", + " .head(10)\n", + " )\n", + " print(\"\\nTop generators by optimized capacity (head 10):\")\n", + " display(gcap.head(10))\n", + "\n", + "# show buses of interest\n", + "if hasattr(n, \"buses\") and \"region\" in n.buses.columns:\n", + " print(\"\\nBus regions sample:\")\n", + " display(n.buses[\"region\"].value_counts().head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9b7d03b9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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    hbi1.00.00.0hbit/h1.00.0infSlack4
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    electrolysis0.0#9467bdinf0.0Electrolysis process
    direct_reduction_furnace0.0#2ca02cinf0.0Direct reduction furnace
    electric_arc_furnace0.0#d62728inf0.0Electric arc furnace
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"2050-12-31 20:00:00 1.425297e-11 \n", + "2050-12-31 21:00:00 1.400078e-11 \n", + "2050-12-31 22:00:00 1.396416e-11 \n", + "2050-12-31 23:00:00 1.363898e-11 \n", + "\n", + "name renewable_Europe_windonshore_27 \\\n", + "snapshot \n", + "2050-01-01 00:00:00 3273.971847 \n", + "2050-01-01 01:00:00 3200.283663 \n", + "2050-01-01 02:00:00 3130.998637 \n", + "2050-01-01 03:00:00 3050.947854 \n", + "2050-01-01 04:00:00 3004.484606 \n", + "... ... \n", + "2050-12-31 19:00:00 2502.605814 \n", + "2050-12-31 20:00:00 2471.891442 \n", + "2050-12-31 21:00:00 2435.718675 \n", + "2050-12-31 22:00:00 2440.393834 \n", + "2050-12-31 23:00:00 2381.732176 \n", + "\n", + "name renewable_Europe_windonshore_28 \\\n", + "snapshot \n", + "2050-01-01 00:00:00 2.698786e-11 \n", + "2050-01-01 01:00:00 2.708063e-11 \n", + "2050-01-01 02:00:00 2.649550e-11 \n", + "2050-01-01 03:00:00 2.590942e-11 \n", + "2050-01-01 04:00:00 2.547251e-11 \n", + "... ... \n", + "2050-12-31 19:00:00 2.609376e-11 \n", + "2050-12-31 20:00:00 2.621920e-11 \n", + "2050-12-31 21:00:00 2.663790e-11 \n", + "2050-12-31 22:00:00 2.636497e-11 \n", + "2050-12-31 23:00:00 2.588227e-11 \n", + "\n", + "name renewable_Europe_windonshore_29 \\\n", + "snapshot \n", + "2050-01-01 00:00:00 1.933776e-11 \n", + "2050-01-01 01:00:00 1.924072e-11 \n", + "2050-01-01 02:00:00 1.906257e-11 \n", + "2050-01-01 03:00:00 1.887819e-11 \n", + "2050-01-01 04:00:00 1.874917e-11 \n", + "... ... \n", + "2050-12-31 19:00:00 2.062582e-11 \n", + "2050-12-31 20:00:00 2.059685e-11 \n", + "2050-12-31 21:00:00 2.055178e-11 \n", + "2050-12-31 22:00:00 2.026907e-11 \n", + "2050-12-31 23:00:00 2.008224e-11 \n", + "\n", + "name renewable_Europe_windonshore_30 \n", + "snapshot \n", + "2050-01-01 00:00:00 5719.128703 \n", + "2050-01-01 01:00:00 5827.209737 \n", + "2050-01-01 02:00:00 5908.282947 \n", + "2050-01-01 03:00:00 6003.723784 \n", + "2050-01-01 04:00:00 6041.265360 \n", + "... ... \n", + "2050-12-31 19:00:00 6016.807461 \n", + "2050-12-31 20:00:00 6065.930453 \n", + "2050-12-31 21:00:00 6097.924484 \n", + "2050-12-31 22:00:00 6011.796182 \n", + "2050-12-31 23:00:00 6031.805824 \n", + "\n", + "[8760 rows x 32 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.generators_t.p" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9bf69e5b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "iron_ore 1.000000e+10\n", + "grid_electricity_input 1.000000e+10\n", + "battery_input 1.000000e+10\n", + "renewable_Europe_pvplant_15 1.018952e-09\n", + "renewable_Europe_pvplant_16 3.627389e-09\n", + "renewable_Europe_pvplant_17 5.731441e+03\n", + "renewable_Europe_windonshore_27 7.260414e+03\n", + "renewable_Europe_windonshore_30 9.413415e+03\n", + "Name: p_nom_opt, dtype: float64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "EPSILON = 1e-9\n", + "opt_gen = n.generators.p_nom_opt\n", + "opt_gen = opt_gen[opt_gen > EPSILON]\n", + "opt_gen" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift-dev", + "language": "python", + "name": "shift-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5dfbbe6e03b963a493add7223e995ba2d7db5970 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:07:57 +0200 Subject: [PATCH 079/216] feat: adjust hydrogen and wacc in config --- config/config.yaml | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 50c654f..4d9d73d 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -23,9 +23,9 @@ supply_curve: trade_chains: id: default_2050 cost_year: 2050 - scenario: default + # scenario: default final_product: steel - wacc: regional + wacc: uniform #regional tradeable_commodities: [iron_ore, hbi] stages: 1: @@ -34,7 +34,7 @@ trade_chains: output_commodity: hydrogen process_label: electrolysis 2: - material_inputs: [iron_ore, H2] + material_inputs: [iron_ore, hydrogen] energy_inputs: [renewable_electricity] output_commodity: hbi process_label: dri @@ -43,7 +43,10 @@ trade_chains: energy_inputs: [grid_electricity] output_commodity: steel process_label: eaf-grid - + trade_scenarios: + default + mga-stability + scenario: default: From f9e6194a82748f7a027e0fb92af16b567a5980cd Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:08:18 +0200 Subject: [PATCH 080/216] feat: adjust where to get the trade scenarios from in snakefile --- workflow/Snakefile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/workflow/Snakefile b/workflow/Snakefile index 7c14b3e..8c5c6a8 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -56,7 +56,7 @@ def _load_trade_scenarios(): ), "wacc": str(chain.get("wacc", "regional")), "final": str(chain.get("final_product", "steel")), - "scenario": str(chain.get("scenario", "default")), + "scenario": str(chain.get("trade_scenarios", "default")), } ) return Paramspace(pd.DataFrame(rows, dtype=str)) @@ -66,6 +66,7 @@ def _load_trade_scenarios(): trade_scenarios = _load_trade_scenarios() +print(f"Loaded trade scenarios:\n{trade_scenarios}") def _derive_supply_curve_products(): return derive_supply_curve_products(config) From f7f4f0ec64ba39c062196d75162a76a892da1dae Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:17:55 +0200 Subject: [PATCH 081/216] feat: implement scenario management for trade scenarios --- config/config.yaml | 5 +++-- workflow/Snakefile | 34 +++++++++++++++++++--------------- 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 4d9d73d..2589e52 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -44,8 +44,9 @@ trade_chains: output_commodity: steel process_label: eaf-grid trade_scenarios: - default - mga-stability + - default + - mga-stability + - mga-stability-weighted scenario: diff --git a/workflow/Snakefile b/workflow/Snakefile index 8c5c6a8..3acab39 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -44,21 +44,25 @@ def _load_trade_scenarios(): raise ValueError( f"Trade chain '{chain.get('id', '')}' produced no stage groups" ) - rows.append( - { - "chain_id": str(chain.get("id", "default")), - "cost_year": str(chain.get("cost_year", 2050)), - "interone": str(stage_groups[0]["label"]), - "intertwo": str( - stages_sorted[-1].get( - "process_label", stages_sorted[-1]["output_commodity"] - ) - ), - "wacc": str(chain.get("wacc", "regional")), - "final": str(chain.get("final_product", "steel")), - "scenario": str(chain.get("trade_scenarios", "default")), - } - ) + scenarios = chain.get("trade_scenarios", "default") + if isinstance(scenarios, str): + scenarios = [scenarios] + for scenario in scenarios: + rows.append( + { + "chain_id": str(chain.get("id", "default")), + "cost_year": str(chain.get("cost_year", 2050)), + "interone": str(stage_groups[0]["label"]), + "intertwo": str( + stages_sorted[-1].get( + "process_label", stages_sorted[-1]["output_commodity"] + ) + ), + "wacc": str(chain.get("wacc", "regional")), + "final": str(chain.get("final_product", "steel")), + "scenario": str(scenario), + } + ) return Paramspace(pd.DataFrame(rows, dtype=str)) return Paramspace(pd.read_csv("config/trade_scenarios.csv", dtype=str)) From 90edfef98ecd325f3e56bbb71f459f9b318a4d8c Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:35:58 +0200 Subject: [PATCH 082/216] feat: add reporting smk --- rules/reporting.smk | 91 +++++++++++++++++++++++++++++++++++++++++++++ workflow/Snakefile | 2 +- 2 files changed, 92 insertions(+), 1 deletion(-) create mode 100644 rules/reporting.smk diff --git a/rules/reporting.smk b/rules/reporting.smk new file mode 100644 index 0000000..b8e5957 --- /dev/null +++ b/rules/reporting.smk @@ -0,0 +1,91 @@ +"""Reporting workflow rules. + +Collects final figures and presentation artifacts produced by notebooks and the +main optimization workflow. +""" + + +rule collect_figures: + input: + global_supply_curve = "../results/figures_general/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", #workflow/notebooks/analysis-coststructure.ipynb + global_supply_curve_png = "../results/figures_general/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", #workflow/notebooks/analysis-coststructure.ipynb + electricity_demand = "../results/figures_general/electricity_demand.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb + electricity_demand_png = "../results/figures_general/electricity_demand.png", #workflow/notebooks/analysis-electricity-demand.ipynb + electricity_demand_steel = "../results/figures_general/electricity_demand_in_steel.pdf", #workflow/notebooks/analysis-electricity-demand.ipynb + electricity_demand_steel_png = "../results/figures_general/electricity_demand_in_steel.png", #workflow/notebooks/analysis-electricity-demand.ipynb + global_map_countries = "../results/figures_general/global_map_countries.pdf", #workflow/notebooks/plot_countries.ipynb + global_map_countries_png = "../results/figures_general/global_map_countries.png", #workflow/notebooks/plot_countries.ipynb + cost_comparison = "../results/figures_general/comparison/cost_comparison.pdf", #workflow/notebooks/compare-scenarios.ipynb + cost_comparison_png = "../results/figures_general/comparison/cost_comparison.png", #workflow/notebooks/compare-scenarios.ipynb + value_chain_comparison = "../results/figures_general/value_chain_comparison.pdf", #workflow/notebooks/analyse-steel-hbi-split.ipynb + value_chain_comparison_png = "../results/figures_general/value_chain_comparison.png", #workflow/notebooks/analyse-steel-hbi-split.ipynb + hourly_analysis = "../results/figures_general/hourly_analysis.pdf", #workflow/notebooks/analysis-hourly.ipynb + hourly_analysis_png = "../results/figures_general/hourly_analysis.png", #workflow/notebooks/analysis-hourly.ipynb + iron_ore = "../results/figures_general/trade-today/Iron_Ore_net_flow.pdf", + dri_hbi = "../results/figures_general/trade-today/DRI-HBI_net_flow.pdf", + steel_raw = "../results/figures_general/trade-today/Steel_raw_net_flow.pdf", + mga_plot = "../results/figures_general/mga/mga_analysis.pdf", # integrated in workflow + mga_plot_png = "../results/figures_general/mga/mga_analysis.png", # integrated in workflow + map_chokepoints = "../results/figures_general/chokepoints/map_chokepoints.pdf", # integrated in workflow + map_chokepoints_png = "../results/figures_general/chokepoints/map_chokepoints.png", # integrated in workflow + + + +rule plot_mga: + input: + network_mga_production = "results/chain_id~default_2050/cost_year~2050/interone~hbi/intertwo~eaf-grid/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", + network_mga_chokepoints = "results/chain_id~default_2050/cost_year~2050/interone~hbi/intertwo~eaf-grid/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", + network_mga_blocks = "results/chain_id~default_2050/cost_year~2050/interone~hbi/intertwo~eaf-grid/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", + political_stability = "resources/political_stability_clustered.csv", + trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", + output: + mga_plot = "results/figures_general/mga/wacc~{wacc}/mga_analysis.pdf", + mga_plot_png = "results/figures_general/mga/wacc~{wacc}/mga_analysis.png", + resources: + mem_mb=4000, + threads: 2 + notebook: + "../workflow/notebooks/plot-mga.ipynb" + +rule plot_mga_all: + input: + expand("results/figures_general/mga/wacc~{wacc}/mga_analysis.pdf", wacc=["regional"], allow_missing=True) #wacc=["uniform", "regional"] + + +rule plot_trade_today: + input: + baci_folder = ancient("../data/BACI_HS22_V202601"), + output: + iron_ore = "../results/figures_general/trade-today/Iron_Ore_net_flow.pdf", + iron_ore_png = "../results/figures_general/trade-today/Iron_Ore_net_flow.png", + dri_hbi = "../results/figures_general/trade-today/DRI-HBI_net_flow.pdf", + dri_hbi_png = "../results/figures_general/trade-today/DRI-HBI_net_flow.png", + steel_raw = "../results/figures_general/trade-today/Steel_raw_net_flow.pdf", + steel_raw_png = "../results/figures_general/trade-today/Steel_raw_net_flow.png", + iron_ore_csv = "../results/figures_general/trade-today/Iron_Ore_trade_iso3.csv", + dri_hbi_csv = "../results/figures_general/trade-today/DRI-HBI_trade_iso3.csv", + steel_raw_csv = "../results/figures_general/trade-today/Steel_raw_trade_iso3.csv", + script: + "notebooks/plot_todays-trade.py" + +rule plot_global_supply: + input: + trade_network="../results/cost_year~{cost_year}/interone~hbi/intertwo~eaf-grid/final~steel/wacc~{wacc}/scenario~{scenario}/network.nc", + # supply = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", + # supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", + supply_curves_interone = expand( + "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", + allow_missing=True, region=config["regions"]), + supply_curves_interone_nodemand = expand( + "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", + allow_missing=True, region=config["regions"]), + output: + network_curve="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", + network_curve_png="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", + # supply_curves + notebook: + "notebooks/analysis-globalsupplycurve.ipynb" + +rule plot_global_supply_all: + input: + expand("../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", cost_year=[2050], wacc=["regional"], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) diff --git a/workflow/Snakefile b/workflow/Snakefile index 3acab39..556b0ec 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -87,4 +87,4 @@ wildcard_constraints: # include: "rules/supply_curves.smk" include: "../rules/trade_model.smk" -# include: "rules/reporting.smk" +include: "../rules/reporting.smk" From 207b9235f655431ebc426f15a87c03124e7ac8f9 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:43:19 +0200 Subject: [PATCH 083/216] fix: move snakefile and trade_chain_utils to correct path --- workflow/Snakefile => Snakefile | 4 +- workflow/trade_chain_utils.py | 326 -------------------------------- 2 files changed, 2 insertions(+), 328 deletions(-) rename workflow/Snakefile => Snakefile (97%) delete mode 100644 workflow/trade_chain_utils.py diff --git a/workflow/Snakefile b/Snakefile similarity index 97% rename from workflow/Snakefile rename to Snakefile index 556b0ec..6a9928b 100644 --- a/workflow/Snakefile +++ b/Snakefile @@ -86,5 +86,5 @@ wildcard_constraints: # include: "rules/supply_curves.smk" -include: "../rules/trade_model.smk" -include: "../rules/reporting.smk" +include: "rules/trade_model.smk" +include: "rules/reporting.smk" diff --git a/workflow/trade_chain_utils.py b/workflow/trade_chain_utils.py deleted file mode 100644 index b89bc4e..0000000 --- a/workflow/trade_chain_utils.py +++ /dev/null @@ -1,326 +0,0 @@ -"""Helpers for normalizing trade-chain config and deriving stage groups. - -The config currently stores a single trade chain with ordered stages keyed by -stage number. This module turns that into a stable, ordered representation and -derives the stage group boundaries implied by tradeable commodities. -""" - -from __future__ import annotations - -from typing import Dict, List, Optional, Tuple - - -ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} -BUS_ALIASES = { - "H2": "hydrogen", - "h2": "hydrogen", - "hydrogen": "hydrogen", - "renewable_electricity": "renewable_electricity", - "grid_electricity": "grid_electricity", -} - -# Explicit mapping from high-level process identifiers to concrete PyPSA components. -# Keep this mapping authoritative so config `process_label` remains high-level. -TECH_COMPONENT_MAP = [ - { - "match": ("electro", "electrolyser", "electrolyzer"), - "links": ("electrolyzer",), - "stores": ("h2_storage",), - # expected material reactants, energy inputs, and outputs - "materials": (), - "energy": ("renewable_electricity", "grid_electricity"), - "outputs": ("hydrogen",), - "buses": ("hydrogen", "renewable_electricity", "grid_electricity"), - }, - { - "match": ("dri", "direct_reduction", "reduction"), - "links": ("dri",), - "stores": ("h2_storage", "hbi_storage"), - "materials": ("iron_ore", "hydrogen"), - "energy": ("renewable_electricity", "grid_electricity"), - "outputs": ("hbi",), - "buses": ( - "iron_ore", - "hydrogen", - "hbi", - "renewable_electricity", - "grid_electricity", - ), - }, - { - "match": ("eaf", "electric_arc", "arc_furnace"), - "links": ("eaf",), - "stores": ("steel_storage",), - "materials": ("hbi",), - "energy": ("grid_electricity", "renewable_electricity"), - "outputs": ("steel",), - "buses": ("hbi", "steel", "grid_electricity", "renewable_electricity"), - }, -] - - -def _components_for_process_label( - process_label: str, -) -> Optional[Dict[str, Tuple[str, ...]]]: - """Return mapped components and expected IO for a given process_label or None if not found.""" - if not process_label: - return None - pl = process_label.lower() - for entry in TECH_COMPONENT_MAP: - for pat in entry["match"]: - if pat in pl: - return { - "links": tuple(entry.get("links", ())), - "stores": tuple(entry.get("stores", ())), - "buses": tuple(entry.get("buses", ())), - "materials": tuple(entry.get("materials", ())), - "energy": tuple(entry.get("energy", ())), - "outputs": tuple(entry.get("outputs", ())), - } - return None - - -def validate_stage_io(stage: Dict, raise_on_mismatch: bool = False) -> bool: - """Validate that a stage's declared inputs/outputs match the canonical mapping. - - Returns True if validation passes or no mapping exists. If `raise_on_mismatch` is True, - a ValueError is raised on mismatch; otherwise a warning is returned via logging and False is returned. - """ - import logging - - logger = logging.getLogger(__name__) - - process_label = str(stage.get("process_label", "")).strip() - if not process_label: - return True - - comp = _components_for_process_label(process_label) - if comp is None: - # No mapping — nothing to validate - return True - - # Normalize declared inputs/outputs - declared_materials, declared_energy = split_stage_inputs(stage) - declared_materials_norm = {_normalize_commodity(m) for m in declared_materials} - declared_energy_norm = {_normalize_commodity(e) for e in declared_energy} - declared_output = _normalize_commodity(stage.get("output_commodity", "")) - - expected_materials = {_normalize_commodity(m) for m in comp.get("materials", ())} - expected_energy = {_normalize_commodity(e) for e in comp.get("energy", ())} - expected_outputs = {_normalize_commodity(o) for o in comp.get("outputs", ())} - - msgs = [] - # Materials: declared_materials should be a superset of expected_materials or vice versa? - # We allow declared to be a superset (user may include both H2 and iron_ore), but require at least one overlap - if expected_materials and declared_materials_norm.isdisjoint(expected_materials): - msgs.append( - f"Stage '{process_label}': declared material inputs {declared_materials_norm} do not overlap expected {expected_materials}" - ) - - # Energy: declared energy should overlap expected energy - if expected_energy and declared_energy_norm.isdisjoint(expected_energy): - msgs.append( - f"Stage '{process_label}': declared energy inputs {declared_energy_norm} do not overlap expected {expected_energy}" - ) - - # Output: declared_output should be one of expected outputs - if expected_outputs and declared_output and declared_output not in expected_outputs: - msgs.append( - f"Stage '{process_label}': declared output '{declared_output}' not in expected outputs {expected_outputs}" - ) - - if msgs: - if raise_on_mismatch: - raise ValueError("; ".join(msgs)) - for m in msgs: - logger.warning(m) - return False - - return True - - -def iter_trade_chains(config: Dict) -> List[Dict]: - """Return trade chain definitions as a list.""" - - trade_chains = config.get("trade_chains") - if not trade_chains: - return [] - if isinstance(trade_chains, dict): - return [trade_chains] - return list(trade_chains) - - -def get_trade_chain(config: Dict) -> Dict: - """Return the primary trade chain from config.""" - - chains = iter_trade_chains(config) - if not chains: - return {} - return chains[0] - - -def get_ordered_stages(chain: Dict) -> List[Dict]: - """Return stages ordered by their numeric key or declared order.""" - - stages = chain.get("stages", {}) - if isinstance(stages, dict): - items = sorted(stages.items(), key=lambda item: int(item[0])) - ordered = [] - for key, stage in items: - stage_dict = dict(stage) - stage_dict.setdefault("order", int(key)) - ordered.append(stage_dict) - return ordered - - ordered = [dict(stage) for stage in stages] - ordered.sort(key=lambda stage: int(stage.get("order", 0))) - return ordered - - -def _as_list(value) -> List[str]: - if value is None: - return [] - if isinstance(value, (list, tuple, set)): - return [str(item) for item in value] - return [str(value)] - - -def _normalize_commodity(name: str) -> str: - return BUS_ALIASES.get(str(name), str(name)) - - -def split_stage_inputs(stage: Dict) -> Tuple[List[str], List[str]]: - """Split a stage's inputs into material inputs and energy inputs.""" - - if "material_inputs" in stage or "energy_inputs" in stage: - raw_inputs = _as_list(stage.get("material_inputs")) - energy_inputs = _as_list(stage.get("energy_inputs")) - elif "input_commodities" in stage: - raw_inputs = _as_list(stage.get("input_commodities")) - energy_inputs = _as_list(stage.get("energy_inputs")) - else: - raw_inputs = _as_list(stage.get("input_commodity")) - energy_inputs = [] - - materials = [] - energy = list(energy_inputs) - - for input_name in raw_inputs: - if input_name in ENERGY_INPUTS: - energy.append(input_name) - else: - materials.append(input_name) - - return materials, energy - - -def get_stage_groups(chain: Dict) -> List[Dict]: - """Group contiguous stages until a tradeable output or the final product.""" - - ordered_stages = get_ordered_stages(chain) - if not ordered_stages: - return [] - - tradeable = {str(item) for item in chain.get("tradeable_commodities", [])} - final_product = str(chain.get("final_product", "")).strip() - - groups = [] - current = [] - for stage in ordered_stages: - current.append(stage) - output_commodity = str(stage.get("output_commodity", "")).strip() - if output_commodity in tradeable or output_commodity == final_product: - groups.append( - { - "label": output_commodity, - "stages": list(current), - } - ) - current = [] - - if current: - last_output = str(current[-1].get("output_commodity", "")).strip() - groups.append({"label": last_output, "stages": list(current)}) - - return groups - - -def route_label_for_product(config: Dict, product: str) -> str: - """Return the stage-group label for a product.""" - - chain = get_trade_chain(config) - for group in get_stage_groups(chain): - if group["label"] == product: - return group["label"] - return product - - -def derive_supply_curve_products(config: Dict) -> List[str]: - """Return the externally visible product list for supply curves.""" - - products = [] - for chain in iter_trade_chains(config): - for group in get_stage_groups(chain): - label = group["label"] - if label and label not in products: - products.append(label) - return products or ["steel"] - - -def build_product_components(config: Dict, product: str) -> Dict[str, object]: - """Derive the links, stores, buses, and renewable flag for a product.""" - - chain = get_trade_chain(config) - groups = get_stage_groups(chain) - - target_group: Optional[Dict] = None - for group in groups: - if group["label"] == product: - target_group = group - break - - if target_group is None: - raise ValueError(f"Product '{product}' not found in configured stage groups") - - links = set() - stores = set() - buses = set() - has_renewables = False - - for stage in target_group["stages"]: - process_label = str(stage.get("process_label", "")).strip() - materials, energy = split_stage_inputs(stage) - - # Add explicit buses derived from stage inputs - for material in materials: - buses.add(_normalize_commodity(material)) - for energy_input in energy: - norm = _normalize_commodity(energy_input) - buses.add(norm) - if energy_input == "renewable_electricity": - has_renewables = True - - output_commodity = _normalize_commodity(stage.get("output_commodity", "")) - if output_commodity: - buses.add(output_commodity) - - # Use explicit mapping from process_label -> concrete components - comp = _components_for_process_label(process_label) - if comp is None and process_label: - # Fail fast: require explicit mapping for new/unknown process labels - raise ValueError( - f"Process label '{process_label}' has no TECH_COMPONENT_MAP entry; add mapping before using it in config" - ) - - if comp: - links.update(comp.get("links", ())) - stores.update(comp.get("stores", ())) - for b in comp.get("buses", ()): # include any canonical buses from mapping - buses.add(_normalize_commodity(b)) - - return { - "links": links, - "stores": stores, - "buses": buses, - "has_renewables": has_renewables, - } From 9da97d163d79d99f4e0575d92b71bd47b51e52fd Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:43:39 +0200 Subject: [PATCH 084/216] fix: revert change of script path --- rules/trade_model.smk | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rules/trade_model.smk b/rules/trade_model.smk index eef1629..2587c7f 100644 --- a/rules/trade_model.smk +++ b/rules/trade_model.smk @@ -44,8 +44,8 @@ rule model_trade: cost_penalty=config["design"]["cost_penalty"], scenarios=config["scenario"], script: - # str(SCRIPT_DIR / "model_trade.py") - "../workflow/scripts/model_trade.py" + str(SCRIPT_DIR / "model_trade.py") + rule model_trade_all: input: From 874332648dd00a527414764d4a65bd0db9bd17f4 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 18:50:58 +0200 Subject: [PATCH 085/216] feat: add preparation smk for notebooks --- Snakefile | 4 ++ rules/preparation.smk | 87 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 91 insertions(+) create mode 100644 rules/preparation.smk diff --git a/Snakefile b/Snakefile index 6a9928b..92f78b7 100644 --- a/Snakefile +++ b/Snakefile @@ -13,10 +13,13 @@ from snakemake.utils import Paramspace WORKFLOW_DIR = Path(workflow.basedir) / "workflow" SCRIPT_DIR = WORKFLOW_DIR / "scripts" +NOTEBOOKS_DIR = WORKFLOW_DIR / "notebooks" # DATA_ if str(SCRIPT_DIR) not in sys.path: sys.path.insert(0, str(SCRIPT_DIR)) +if str(NOTEBOOKS_DIR) not in sys.path: + sys.path.insert(0, str(NOTEBOOKS_DIR)) from trade_chain_utils import ( # noqa: E402 derive_supply_curve_products, @@ -86,5 +89,6 @@ wildcard_constraints: # include: "rules/supply_curves.smk" +include: "rules/preparation.smk" include: "rules/trade_model.smk" include: "rules/reporting.smk" diff --git a/rules/preparation.smk b/rules/preparation.smk new file mode 100644 index 0000000..92ac051 --- /dev/null +++ b/rules/preparation.smk @@ -0,0 +1,87 @@ + + + + +rule prepare_wacc: + input: + wacc = "data/wacc-global.csv", + bus_locations = "data/bus_locations.csv", + output: + wacc = "resources/wacc-clustered.csv" + resources: + mem_mb=5000, + threads: 2 + notebook: + # "notebooks/prepare-wacc.ipynb" + str(NOTEBOOKS_DIR / "prepare-wacc.ipynb") + + +rule prepare_political_stability: + input: + political_stability = "data/political-stability/globaleconomy.csv", #https://www.theglobaleconomy.com/rankings/wb_political_stability/ + bus_locations = "data/bus_locations.csv", + output: + political_stability = "resources/political_stability_clustered.csv" + resources: + mem_mb=5000, + threads: 2 + notebook: + str(NOTEBOOKS_DIR / "prepare-political-stability.ipynb") + + +rule prepare_chokepoints: + params: + shipping_routes=config["trade"]["shipping_routes"], + input: + trade_options = "data/trade_opt.csv", + bus_locations = "data/bus_locations.csv", + output: + trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", + map_chokepoints = "results/figures_general/chokepoints/map_chokepoints.pdf", + map_chokepoints_png = "results/figures_general/chokepoints/map_chokepoints.png", + resources: + mem_mb=5000, + threads: 2 + notebook: + str(NOTEBOOKS_DIR / "prepare-chokepoints.ipynb") + + +rule retrieve_iron_ore: + input: + iron_ore_production = "data/owid-iron-ore/iron-ore-crude-ore-production.csv", + iron_ore_cost = "data/devlin2023-supplementary.xlsx", + bus_locations = "data/bus_locations.csv", + output: + iron_ore = "resources/ironore-production.csv", + iron_ore_map = "results/figures_general/iron_ore_map.pdf", + resources: + mem_mb=5000, + threads: 2 + notebook: + str(NOTEBOOKS_DIR / "global-iron-ore.ipynb") + + +rule prepare_iron_ore: + input: + iron_ore = "resources/ironore-production.csv", + bus_locations = "data/bus_locations.csv", + output: + iron_ore = "resources/ironore_production_clustered.csv", + resources: + mem_mb=5000, + threads: 2 + notebook: + str(NOTEBOOKS_DIR / "prepare-iron-ore.ipynb") + + +rule prepare_steel_demand: + input: + steel_demand = "data/demand/steel_demands/output_data/country_raw_steel_demand_and_dri_share.csv", + bus_locations = "data/bus_locations.csv", + output: + steel_demand = 'resources/steel_demand_clustered_{cost_year}.csv', + resources: + mem_mb=5000, + threads: 2 + notebook: + str(NOTEBOOKS_DIR / "prepare-steel-demand.ipynb") \ No newline at end of file From 232747c3d82c3d993c4723dbf5f777c10b6ef638 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 19 May 2026 19:21:19 +0200 Subject: [PATCH 086/216] fix: adjust preparation notebooks to new region definition --- workflow/notebooks/prepare-iron-ore.ipynb | 26 +++---- .../prepare-political-stability.ipynb | 17 ++--- workflow/notebooks/prepare-steel-demand.ipynb | 68 ++----------------- workflow/notebooks/prepare-wacc.ipynb | 29 ++------ 4 files changed, 23 insertions(+), 117 deletions(-) diff --git a/workflow/notebooks/prepare-iron-ore.ipynb b/workflow/notebooks/prepare-iron-ore.ipynb index fee5135..f2909f2 100644 --- a/workflow/notebooks/prepare-iron-ore.ipynb +++ b/workflow/notebooks/prepare-iron-ore.ipynb @@ -115,28 +115,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Load region definitions from config\n", + "# Load region definitions from config (regions now uses ISO-3 codes directly)\n", "regions = config['regions']\n", "\n", - "# Build a mapping from country name to ISO_A2 code\n", - "country_name_to_iso = {}\n", - "for country in pycountry.countries:\n", - " country_name_to_iso[country.name] = country.alpha_2\n", - " if hasattr(country, 'official_name'):\n", - " country_name_to_iso[country.official_name] = country.alpha_2\n", - "\n", - "# Build a mapping from ISO_A2 code to region\n", + "# Build ISO-2 → region mapping (production data uses ISO_A2 / 2-letter codes)\n", + "# Convert ISO-3 codes from config to ISO-2 via pycountry\n", "iso_to_region = {}\n", - "for region, countries in regions.items():\n", - " for country in countries:\n", - " for part in country.split('+'):\n", - " name = part.strip()\n", - " code = country_name_to_iso.get(name)\n", - " if code:\n", - " iso_to_region[code] = region\n", + "for region, iso3_codes in regions.items():\n", + " for iso3 in iso3_codes:\n", + " country = pycountry.countries.get(alpha_3=iso3)\n", + " if country:\n", + " iso_to_region[country.alpha_2] = region\n", "\n", "# Map each row in production to its region\n", - "production['region'] = production.index.map(lambda iso: iso_to_region.get(iso, 'Other'))\n" + "production['region'] = production.index.map(lambda iso: iso_to_region.get(iso, 'Other'))" ] }, { diff --git a/workflow/notebooks/prepare-political-stability.ipynb b/workflow/notebooks/prepare-political-stability.ipynb index 4e211b9..d5933ad 100644 --- a/workflow/notebooks/prepare-political-stability.ipynb +++ b/workflow/notebooks/prepare-political-stability.ipynb @@ -183,23 +183,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Apply region name corrections (same corrections used above for the input data)\n", + "# Load region definitions from config (regions now uses ISO-3 codes directly)\n", "regions = config[\"regions\"]\n", "\n", - "for region, countries in regions.items():\n", - " for i, country in enumerate(countries):\n", - " if country in country_name_corrections:\n", - " countries[i] = country_name_corrections[country]\n", - "\n", "# Build ISO-3 → region mapping\n", "iso_to_region = {}\n", - "for region, countries in regions.items():\n", - " for country in countries:\n", - " for part in country.split(\"+\"):\n", - " name = part.strip()\n", - " code = country_name_to_iso.get(name)\n", - " if code:\n", - " iso_to_region[code] = region\n" + "for region, iso_codes in regions.items():\n", + " for code in iso_codes:\n", + " iso_to_region[code] = region" ] }, { diff --git a/workflow/notebooks/prepare-steel-demand.ipynb b/workflow/notebooks/prepare-steel-demand.ipynb index 35e7116..85816f8 100644 --- a/workflow/notebooks/prepare-steel-demand.ipynb +++ b/workflow/notebooks/prepare-steel-demand.ipynb @@ -9,8 +9,7 @@ "source": [ "import pandas as pd\n", "import pypsa\n", - "import yaml\n", - "import pycountry" + "import yaml" ] }, { @@ -154,50 +153,6 @@ "regions = config['regions']" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "d52414f2", - "metadata": {}, - "outputs": [], - "source": [ - "country_name_corrections = {\n", - " \"Democratic Republic of the Congo\": \"Congo, The Democratic Republic of the\",\n", - " \"Republic of the Congo\": \"Republic of the Congo\",\n", - " \"Kosovo\": \"Republic of Kosovo\", # pycountry not supported\n", - " \"Russia\": \"Russian Federation\",\n", - " \"Turkey\": \"Türkiye\",\n", - " \"Venezuela\": \"Venezuela, Bolivarian Republic of\",\n", - " \"Tanzania\": \"United Republic of Tanzania\",\n", - " \"Bolivia\": \"Plurinational State of Bolivia\",\n", - " \"Vietnam\": \"Viet Nam\",\n", - " \"South Korea\": \"Korea, Republic of\",\n", - " \"North Korea\": \"Korea, Democratic People's Republic of\",\n", - " \"Taiwan\": \"Taiwan, Province of China\",\n", - " \"Laos\": \"Lao People's Democratic Republic\",\n", - " \"Brunei\": \"Brunei Darussalam\",\n", - " \"Equatorial French Guiana\": \"French Guiana\",\n", - " \"Syria\": \"Syrian Arab Republic\", \n", - " \"Palestine\": \"Palestine, State of\",\n", - " \"Moldova\": \"Republic of Moldova\",\n", - " \"North Korea\": \"Korea, Democratic People's Republic of\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "997a6000", - "metadata": {}, - "outputs": [], - "source": [ - "# Apply a country name correction to the regions dict\n", - "for region, countries in regions.items():\n", - " for i, country in enumerate(countries):\n", - " if country in country_name_corrections:\n", - " countries[i] = country_name_corrections[country]" - ] - }, { "cell_type": "code", "execution_count": null, @@ -205,24 +160,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Build a mapping from country name to ISO_A2 code\n", - "country_name_to_iso = {}\n", - "for country in pycountry.countries:\n", - " country_name_to_iso[country.name] = country.alpha_3\n", - " # Add common names\n", - " if hasattr(country, 'official_name'):\n", - " country_name_to_iso[country.official_name] = country.alpha_3\n", - "\n", - "# Build a mapping from ISO_A2 code to region\n", + "# Build a mapping from ISO-3 code to region (config regions now use ISO-3 codes directly)\n", "iso_to_region = {}\n", - "for region, countries in regions.items():\n", - " for country in countries:\n", - " # Some country names may have extra text (e.g., \"Togo + Algeria\"), handle them simply\n", - " for part in country.split('+'):\n", - " name = part.strip()\n", - " code = country_name_to_iso.get(name)\n", - " if code:\n", - " iso_to_region[code] = region" + "for region, iso_codes in regions.items():\n", + " for code in iso_codes:\n", + " iso_to_region[code] = region" ] }, { diff --git a/workflow/notebooks/prepare-wacc.ipynb b/workflow/notebooks/prepare-wacc.ipynb index dc20e14..4d62088 100644 --- a/workflow/notebooks/prepare-wacc.ipynb +++ b/workflow/notebooks/prepare-wacc.ipynb @@ -127,20 +127,6 @@ "}" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3ef8db2", - "metadata": {}, - "outputs": [], - "source": [ - "# Apply a country name correction to the regions dict\n", - "for region, countries in regions.items():\n", - " for i, country in enumerate(countries):\n", - " if country in country_name_corrections:\n", - " countries[i] = country_name_corrections[country]" - ] - }, { "cell_type": "code", "execution_count": null, @@ -148,7 +134,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Build a mapping from country name to ISO_A2 code\n", + "# Build a mapping from country name to ISO-3 code (still needed for GDP World Bank lookup)\n", "country_name_to_iso = {}\n", "for country in pycountry.countries:\n", " country_name_to_iso[country.name] = country.alpha_3\n", @@ -156,16 +142,11 @@ " if hasattr(country, 'official_name'):\n", " country_name_to_iso[country.official_name] = country.alpha_3\n", "\n", - "# Build a mapping from ISO_A2 code to region\n", + "# Build a mapping from ISO-3 code to region (config regions now use ISO-3 codes directly)\n", "iso_to_region = {}\n", - "for region, countries in regions.items():\n", - " for country in countries:\n", - " # Some country names may have extra text (e.g., \"Togo + Algeria\"), handle them simply\n", - " for part in country.split('+'):\n", - " name = part.strip()\n", - " code = country_name_to_iso.get(name)\n", - " if code:\n", - " iso_to_region[code] = region" + "for region, iso_codes in regions.items():\n", + " for code in iso_codes:\n", + " iso_to_region[code] = region" ] }, { From 40f6aafebda14c0b2090b93cfe9d6587b68dd4a2 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 20 May 2026 12:00:44 +0200 Subject: [PATCH 087/216] fix: cleanup code broken during merge --- pixi.lock | 4749 +++++++++++++------------ pixi.toml | 2 - workflow/scripts/trade_chain_utils.py | 326 -- 3 files changed, 2390 insertions(+), 2687 deletions(-) diff --git a/pixi.lock b/pixi.lock index bb3b1ee..7600099 100644 --- a/pixi.lock +++ b/pixi.lock @@ -14,7 +14,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.3-py313hd6074c6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.5-py312h5d8c7f2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.4.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/amply-0.1.6-pyhd8ed1ab_1.conda @@ -23,46 +23,46 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/atlite-0.4.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.6-hb9c0fe4_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.1-h2d2dd48_2.conda - conda: 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1727963788936 + size: 850351 + timestamp: 1774072891049 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f md5: 2aadb0d17215603a82a2a6b0afd9a4cb diff --git a/pixi.toml b/pixi.toml index edf9837..3086e8d 100644 --- a/pixi.toml +++ b/pixi.toml @@ -31,8 +31,6 @@ scipy = ">=1.16.3" seaborn = ">=0.13.2" searoute = ">=1.5.0" shapely = ">=2.1.2,<3" -snakemake-executor-plugin-cluster-generic = ">=1.0.9" -snakemake-executor-plugin-slurm = ">=1.9.2" snakemake-minimal = "==9.6.2" snakemake-storage-plugin-http = ">=0.3" tqdm = "*" diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py index 5c4708e..9cbc3a4 100644 --- a/workflow/scripts/trade_chain_utils.py +++ b/workflow/scripts/trade_chain_utils.py @@ -10,332 +10,6 @@ from typing import Dict, List, Optional, Tuple -ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} -BUS_ALIASES = { - "H2": "hydrogen", - "h2": "hydrogen", - "hydrogen": "hydrogen", - "renewable_electricity": "renewable_electricity", - "grid_electricity": "grid_electricity", -} - -# Explicit mapping from high-level process identifiers to concrete PyPSA components. -# Keep this mapping authoritative so config `process_label` remains high-level. -TECH_COMPONENT_MAP = [ - { - "match": ("electro", "electrolyser", "electrolyzer"), - "links": ("electrolyzer",), - "stores": ("h2_storage",), - # expected material reactants, energy inputs, and outputs - "materials": (), - "energy": ("renewable_electricity", "grid_electricity"), - "outputs": ("hydrogen",), - "buses": ("hydrogen", "renewable_electricity", "grid_electricity"), - }, - { - "match": ("dri", "direct_reduction", "reduction"), - "links": ("dri",), - "stores": ("h2_storage", "hbi_storage"), - "materials": ("iron_ore", "hydrogen"), - "energy": ("renewable_electricity", "grid_electricity"), - "outputs": ("hbi",), - "buses": ( - "iron_ore", - "hydrogen", - "hbi", - "renewable_electricity", - "grid_electricity", - ), - }, - { - "match": ("eaf", "electric_arc", "arc_furnace"), - "links": ("eaf",), - "stores": ("steel_storage",), - "materials": ("hbi",), - "energy": ("grid_electricity", "renewable_electricity"), - "outputs": ("steel",), - "buses": ("hbi", "steel", "grid_electricity", "renewable_electricity"), - }, -] - - -def _components_for_process_label( - process_label: str, -) -> Optional[Dict[str, Tuple[str, ...]]]: - """Return mapped components and expected IO for a given process_label or None if not found.""" - if not process_label: - return None - pl = process_label.lower() - for entry in TECH_COMPONENT_MAP: - for pat in entry["match"]: - if pat in pl: - return { - "links": tuple(entry.get("links", ())), - "stores": tuple(entry.get("stores", ())), - "buses": tuple(entry.get("buses", ())), - "materials": tuple(entry.get("materials", ())), - "energy": tuple(entry.get("energy", ())), - "outputs": tuple(entry.get("outputs", ())), - } - return None - - -def validate_stage_io(stage: Dict, raise_on_mismatch: bool = False) -> bool: - """Validate that a stage's declared inputs/outputs match the canonical mapping. - - Returns True if validation passes or no mapping exists. If `raise_on_mismatch` is True, - a ValueError is raised on mismatch; otherwise a warning is returned via logging and False is returned. - """ - import logging - - logger = logging.getLogger(__name__) - - process_label = str(stage.get("process_label", "")).strip() - if not process_label: - return True - - comp = _components_for_process_label(process_label) - if comp is None: - # No mapping — nothing to validate - return True - - # Normalize declared inputs/outputs - declared_materials, declared_energy = split_stage_inputs(stage) - declared_materials_norm = {_normalize_commodity(m) for m in declared_materials} - declared_energy_norm = {_normalize_commodity(e) for e in declared_energy} - declared_output = _normalize_commodity(stage.get("output_commodity", "")) - - expected_materials = {_normalize_commodity(m) for m in comp.get("materials", ())} - expected_energy = {_normalize_commodity(e) for e in comp.get("energy", ())} - expected_outputs = {_normalize_commodity(o) for o in comp.get("outputs", ())} - - msgs = [] - # Materials: declared_materials should be a superset of expected_materials or vice versa? - # We allow declared to be a superset (user may include both H2 and iron_ore), but require at least one overlap - if expected_materials and declared_materials_norm.isdisjoint(expected_materials): - msgs.append( - f"Stage '{process_label}': declared material inputs {declared_materials_norm} do not overlap expected {expected_materials}" - ) - - # Energy: declared energy should overlap expected energy - if expected_energy and declared_energy_norm.isdisjoint(expected_energy): - msgs.append( - f"Stage '{process_label}': declared energy inputs {declared_energy_norm} do not overlap expected {expected_energy}" - ) - - # Output: declared_output should be one of expected outputs - if expected_outputs and declared_output and declared_output not in expected_outputs: - msgs.append( - f"Stage '{process_label}': declared output '{declared_output}' not in expected outputs {expected_outputs}" - ) - - if msgs: - if raise_on_mismatch: - raise ValueError("; ".join(msgs)) - for m in msgs: - logger.warning(m) - return False - - return True - - -def iter_trade_chains(config: Dict) -> List[Dict]: - """Return trade chain definitions as a list.""" - - trade_chains = config.get("trade_chains") - if not trade_chains: - return [] - if isinstance(trade_chains, dict): - return [trade_chains] - return list(trade_chains) - - -def get_trade_chain(config: Dict) -> Dict: - """Return the primary trade chain from config.""" - - chains = iter_trade_chains(config) - if not chains: - return {} - return chains[0] - - -def get_ordered_stages(chain: Dict) -> List[Dict]: - """Return stages ordered by their numeric key or declared order.""" - - stages = chain.get("stages", {}) - if isinstance(stages, dict): - items = sorted(stages.items(), key=lambda item: int(item[0])) - ordered = [] - for key, stage in items: - stage_dict = dict(stage) - stage_dict.setdefault("order", int(key)) - ordered.append(stage_dict) - return ordered - - ordered = [dict(stage) for stage in stages] - ordered.sort(key=lambda stage: int(stage.get("order", 0))) - return ordered - - -def _as_list(value) -> List[str]: - if value is None: - return [] - if isinstance(value, (list, tuple, set)): - return [str(item) for item in value] - return [str(value)] - - -def _normalize_commodity(name: str) -> str: - return BUS_ALIASES.get(str(name), str(name)) - - -def split_stage_inputs(stage: Dict) -> Tuple[List[str], List[str]]: - """Split a stage's inputs into material inputs and energy inputs.""" - - if "material_inputs" in stage or "energy_inputs" in stage: - raw_inputs = _as_list(stage.get("material_inputs")) - energy_inputs = _as_list(stage.get("energy_inputs")) - elif "input_commodities" in stage: - raw_inputs = _as_list(stage.get("input_commodities")) - energy_inputs = _as_list(stage.get("energy_inputs")) - else: - raw_inputs = _as_list(stage.get("input_commodity")) - energy_inputs = [] - - materials = [] - energy = list(energy_inputs) - - for input_name in raw_inputs: - if input_name in ENERGY_INPUTS: - energy.append(input_name) - else: - materials.append(input_name) - - return materials, energy - - -def get_stage_groups(chain: Dict) -> List[Dict]: - """Group contiguous stages until a tradeable output or the final product.""" - - ordered_stages = get_ordered_stages(chain) - if not ordered_stages: - return [] - - tradeable = {str(item) for item in chain.get("tradeable_commodities", [])} - final_product = str(chain.get("final_product", "")).strip() - - groups = [] - current = [] - for stage in ordered_stages: - current.append(stage) - output_commodity = str(stage.get("output_commodity", "")).strip() - if output_commodity in tradeable or output_commodity == final_product: - groups.append( - { - "label": output_commodity, - "stages": list(current), - } - ) - current = [] - - if current: - last_output = str(current[-1].get("output_commodity", "")).strip() - groups.append({"label": last_output, "stages": list(current)}) - - return groups - - -def route_label_for_product(config: Dict, product: str) -> str: - """Return the stage-group label for a product.""" - - chain = get_trade_chain(config) - for group in get_stage_groups(chain): - if group["label"] == product: - return group["label"] - return product - - -def derive_supply_curve_products(config: Dict) -> List[str]: - """Return the externally visible product list for supply curves.""" - - products = [] - for chain in iter_trade_chains(config): - for group in get_stage_groups(chain): - label = group["label"] - if label and label not in products: - products.append(label) - return products or ["steel"] - - -def build_product_components(config: Dict, product: str) -> Dict[str, object]: - """Derive the links, stores, buses, and renewable flag for a product.""" - - chain = get_trade_chain(config) - groups = get_stage_groups(chain) - - target_group: Optional[Dict] = None - for group in groups: - if group["label"] == product: - target_group = group - break - - if target_group is None: - raise ValueError(f"Product '{product}' not found in configured stage groups") - - links = set() - stores = set() - buses = set() - has_renewables = False - - for stage in target_group["stages"]: - process_label = str(stage.get("process_label", "")).strip() - materials, energy = split_stage_inputs(stage) - - # Add explicit buses derived from stage inputs - for material in materials: - buses.add(_normalize_commodity(material)) - for energy_input in energy: - norm = _normalize_commodity(energy_input) - buses.add(norm) - if energy_input == "renewable_electricity": - has_renewables = True - - output_commodity = _normalize_commodity(stage.get("output_commodity", "")) - if output_commodity: - buses.add(output_commodity) - - # Use explicit mapping from process_label -> concrete components - comp = _components_for_process_label(process_label) - if comp is None and process_label: - # Fail fast: require explicit mapping for new/unknown process labels - raise ValueError( - f"Process label '{process_label}' has no TECH_COMPONENT_MAP entry; add mapping before using it in config" - ) - - if comp: - links.update(comp.get("links", ())) - stores.update(comp.get("stores", ())) - for b in comp.get("buses", ()): # include any canonical buses from mapping - buses.add(_normalize_commodity(b)) - - return { - "links": links, - "stores": stores, - "buses": buses, - "has_renewables": has_renewables, - } - - -"""Helpers for normalizing trade-chain config and deriving stage groups. - -The config currently stores a single trade chain with ordered stages keyed by -stage number. This module turns that into a stable, ordered representation and -derives the stage group boundaries implied by tradeable commodities. -""" - -from __future__ import annotations - - ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} BUS_ALIASES = { "H2": "hydrogen", From 675e78fa0aee2ae0044dd312bf7a247510d8c560 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 20 May 2026 15:28:35 +0200 Subject: [PATCH 088/216] feat: mga plot min instable exports instead of max stable --- workflow/notebooks/plot-mga.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 7fe3e2c..1be6724 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -899,6 +899,7 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# 1) Chokepoint data (from df_cp / df_cp_display)\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", @@ -983,7 +984,7 @@ "\n", "plots = [\n", " dict(\n", - " title=\"Instable trade routes (chokepoints)\",\n", + " title=\"Unstable trade routes (chokepoints)\",\n", " epsilon=cp_eps,\n", " main_curve=cp_total_vals,\n", " y_mid=cp_total_vals[0],\n", @@ -996,10 +997,10 @@ " extra_curves=[],\n", " ),\n", " dict(\n", - " title=\"Stable exporters\",\n", + " title=\"Production in unstable countries\",\n", " epsilon=stab_eps,\n", - " main_curve=stab_stable_total,\n", - " y_mid=stab_stable_total[0],\n", + " main_curve=stab_unstable_total,\n", + " y_mid=stab_unstable_total[0],\n", " color=\"#3B6D11\",\n", " alpha=0.25,\n", " arrow_up=[\"Increased stable\", \"HBI producers\"],\n", From b36a1ba60def53957b7de6a6ba340a05672c7a92 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 20 May 2026 15:30:32 +0200 Subject: [PATCH 089/216] chore: add simple pypsa analysis --- workflow/notebooks/analysis-lcohbi.ipynb | 1680 ------------------ workflow/notebooks/analysis_pypsa_lcox.ipynb | 1231 +++++++++++++ workflow/scripts/build_x_supply_chain.py | 4 - 3 files changed, 1231 insertions(+), 1684 deletions(-) delete mode 100644 workflow/notebooks/analysis-lcohbi.ipynb create mode 100644 workflow/notebooks/analysis_pypsa_lcox.ipynb diff --git a/workflow/notebooks/analysis-lcohbi.ipynb b/workflow/notebooks/analysis-lcohbi.ipynb deleted file mode 100644 index d6a6b0e..0000000 --- a/workflow/notebooks/analysis-lcohbi.ipynb +++ /dev/null @@ -1,1680 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "19d4c2c1", - "metadata": {}, - "source": [ - "# LCO-HBI Analysis for an optimized PyPSA network\n", - "\n", - "This notebook loads an optimized PyPSA network (.nc), inspects network structure, and computes a detailed, traceable decomposition of the Levelized Cost of HBI (LCO-HBI).\n", - "\n", - "Usage: set the `network_file` parameter in Cell 2 and run all cells. The notebook attempts to reuse helper functions from `workflow/scripts/calculate_lcox.py` for validation where available." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d3d31256", - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and helper functions\n", - "import sys\n", - "import pathlib\n", - "import seaborn as sns\n", - "\n", - "sns.set_theme(style=\"whitegrid\")\n", - "\n", - "# Add repository root to path so we can import workflow scripts\n", - "repo_root = pathlib.Path(\n", - " \"..\"\n", - ").resolve() # notebook lives in workflow/notebooks; adjust when running from repo root\n", - "if str(repo_root) not in sys.path:\n", - " sys.path.insert(0, str(repo_root))\n", - "\n", - "# Parameters - edit these before running\n", - "network_file = str(\n", - " repo_root.parent\n", - " / \"resources\"\n", - " / \"lco-hbi\"\n", - " / \"cost_year~2050\"\n", - " / \"Europe_reserved\"\n", - " / \"network_10.nc\"\n", - ") # path relative to repo root\n", - "product = \"hbi\" # product to evaluate (hbi)\n", - "region = None # optional: restrict to a region (string)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "61731588", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "calculate_lcox module not available; notebook will still run but some validations will be skipped\n" - ] - } - ], - "source": [ - "try:\n", - " import pypsa\n", - "except Exception as e:\n", - " raise RuntimeError(\n", - " \"PyPSA import failed. Make sure the environment has pypsa installed.\"\n", - " ) from e\n", - "\n", - "# Optional: import repo LCoX utilities if available\n", - "calc_lcox = None\n", - "try:\n", - " from workflow.scripts import calculate_lcox as calculate_lcox_module\n", - "\n", - " calc_lcox = calculate_lcox_module\n", - " print(\"Imported workflow.scripts.calculate_lcox\")\n", - "except Exception:\n", - " try:\n", - " # fallback to relative import if executed from repository root\n", - " import workflow.scripts.calculate_lcox as calculate_lcox_module\n", - "\n", - " calc_lcox = calculate_lcox_module\n", - " print(\"Imported workflow.scripts.calculate_lcox\")\n", - " except Exception:\n", - " print(\n", - " \"calculate_lcox module not available; notebook will still run but some validations will be skipped\"\n", - " )\n", - "\n", - "\n", - "# CRF helper\n", - "def crf(r, n):\n", - " if r <= 0:\n", - " return 1.0 / n\n", - " return (r * (1 + r) ** n) / (((1 + r) ** n) - 1)\n", - "\n", - "\n", - "def annualize(capex, r, n):\n", - " \"\"\"Annualize capital cost (capex) using discount rate r and lifetime n (years).\"\"\"\n", - " return capex * crf(r, n)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "e84d792a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading network: C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\resources\\lco-hbi\\cost_year~2050\\Europe_reserved\\network_10.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Network loaded.\n", - "Snapshots: 8760\n", - "Objective / objective_value: 5463430058.465736\n", - "\n", - "Component counts:\n", - " - buses: 7\n", - " - generators: 32\n", - " - links: 2\n", - " - lines: 0\n", - "\n", - "Top generators by optimized capacity (head 10):\n" - ] - }, - { - "data": { - "text/plain": [ - "name\n", - "iron_ore 1.000000e+10\n", - "grid_electricity_input 1.000000e+10\n", - "battery_input 1.000000e+10\n", - "renewable_Europe_windonshore_30 9.413415e+03\n", - "renewable_Europe_windonshore_27 7.260414e+03\n", - "renewable_Europe_pvplant_17 5.731441e+03\n", - "renewable_Europe_pvplant_16 3.627389e-09\n", - "renewable_Europe_pvplant_15 1.018952e-09\n", - "renewable_Europe_pvplant_14 7.960459e-10\n", - "renewable_Europe_pvplant_13 5.860929e-10\n", - "Name: p_nom_opt, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load network\n", - "import pypsa\n", - "\n", - "print(\"Loading network:\", network_file)\n", - "n = pypsa.Network(network_file)\n", - "print(\"Network loaded.\")\n", - "\n", - "# Basic checks\n", - "print(\"Snapshots:\", len(n.snapshots) if n.snapshots is not None else \"None\")\n", - "obj = None\n", - "try:\n", - " obj = getattr(n, \"objective\", None) or getattr(n, \"objective_value\", None)\n", - " print(\"Objective / objective_value:\", obj)\n", - "except Exception:\n", - " print(\n", - " \"Objective not found on network object (this may be a network saved before extract).\"\n", - " )\n", - "\n", - "# Quick network summary\n", - "print(\"\\nComponent counts:\")\n", - "for c in [\"buses\", \"generators\", \"links\", \"lines\", \"storages\"]:\n", - " comp = getattr(n, c, None)\n", - " if comp is not None:\n", - " print(f\" - {c}:\", len(comp))\n", - "\n", - "# show top generator capacities\n", - "if hasattr(n, \"generators\") and len(n.generators) > 0:\n", - " gcap = (\n", - " n.generators[\"p_nom_opt\"]\n", - " .fillna(n.generators.get(\"p_nom\", 0))\n", - " .sort_values(ascending=False)\n", - " .head(10)\n", - " )\n", - " print(\"\\nTop generators by optimized capacity (head 10):\")\n", - " display(gcap.head(10))\n", - "\n", - "# show buses of interest\n", - "if hasattr(n, \"buses\") and \"region\" in n.buses.columns:\n", - " print(\"\\nBus regions sample:\")\n", - " display(n.buses[\"region\"].value_counts().head(10))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9b7d03b9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n.generators_t.p" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "9bf69e5b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "name\n", - "iron_ore 1.000000e+10\n", - "grid_electricity_input 1.000000e+10\n", - "battery_input 1.000000e+10\n", - "renewable_Europe_pvplant_15 1.018952e-09\n", - "renewable_Europe_pvplant_16 3.627389e-09\n", - "renewable_Europe_pvplant_17 5.731441e+03\n", - "renewable_Europe_windonshore_27 7.260414e+03\n", - "renewable_Europe_windonshore_30 9.413415e+03\n", - "Name: p_nom_opt, dtype: float64" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "EPSILON = 1e-9\n", - "opt_gen = n.generators.p_nom_opt\n", - "opt_gen = opt_gen[opt_gen > EPSILON]\n", - "opt_gen" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "shift-dev", - "language": "python", - "name": "shift-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/workflow/notebooks/analysis_pypsa_lcox.ipynb b/workflow/notebooks/analysis_pypsa_lcox.ipynb new file mode 100644 index 0000000..8357ce6 --- /dev/null +++ b/workflow/notebooks/analysis_pypsa_lcox.ipynb @@ -0,0 +1,1231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41da0fe7", + "metadata": {}, + "source": [ + "# LCO‑HBI Analysis (Fixed Copy)\n", + "This simplified, cleaned notebook loads a solved PyPSA network, inspects `n.statistics()` output, and computes a defensible LCO‑HBI using a robust fallback if statistics are unavailable." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "73a942e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run at 2026-05-20T15:13:42.482589Z\n", + "pypsa version 1.1.2\n" + ] + } + ], + "source": [ + "import os\n", + "import pathlib\n", + "from datetime import datetime\n", + "import pypsa\n", + "\n", + "print(\"Run at\", datetime.now().isoformat() + \"Z\")\n", + "print(\"pypsa version\", getattr(pypsa, \"__version__\", \"unknown\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c2d5204a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config loaded. NETWORK_PATH= C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\resources\\lco-hbi\\cost_year~2050\\Europe_reserved\\network_10.nc\n" + ] + } + ], + "source": [ + "repo_root = pathlib.Path(\"../..\").resolve()\n", + "relative_network_path = \"resources/lco-hbi/cost_year~2050/Europe_reserved/network_10.nc\"\n", + "\n", + "NETWORK_PATH = repo_root / relative_network_path\n", + "DRI_LINK_PATTERNS = [\"dri\", \"hbi\"]\n", + "DEFAULT_WACC = 0.07\n", + "DEFAULT_LIFETIME_YEARS = 20\n", + "FIGURES_DIR = \"figures\"\n", + "OUTPUTS_DIR = \"outputs\"\n", + "os.makedirs(FIGURES_DIR, exist_ok=True)\n", + "os.makedirs(OUTPUTS_DIR, exist_ok=True)\n", + "print(\"Config loaded. NETWORK_PATH=\", NETWORK_PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f7f71ed3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Network loaded; snapshots: 8760\n" + ] + } + ], + "source": [ + "# Load network\n", + "p = pathlib.Path(NETWORK_PATH)\n", + "if not p.exists():\n", + " raise FileNotFoundError(f\"Network file not found: {p}\")\n", + "n = pypsa.Network(str(p))\n", + "print(\"Network loaded; snapshots:\", len(getattr(n, \"snapshots\", [])))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "aa8c39b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "component carrier \n", + "Generator renewable_pv 2.156046e+08\n", + " renewable_wind_offshore 8.000000e-05\n", + " renewable_wind_onshore 2.275435e+09\n", + "Link direct_reduction_furnace 9.958061e+08\n", + " electrolysis 4.147767e+08\n", + "Store hydrogen 4.193447e+07\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.stats.capex()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "146208f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "component carrier \n", + "Generator - 0.00000\n", + " battery 0.00000\n", + " grid_electricity 0.00000\n", + " renewable_pv 5731.44069\n", + " renewable_wind_onshore 16673.82956\n", + "Link direct_reduction_furnace 1810.48216\n", + " electrolysis 7445.48173\n", + "Store hbi 192304.80782\n", + " hydrogen 261336.41259\n", + "dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.stats.expanded_capacity()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9b077600", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "component carrier \n", + "Generator - 1.000000e+10\n", + " battery 1.000000e+10\n", + " grid_electricity 1.000000e+10\n", + " renewable_pv 5.731441e+03\n", + " renewable_wind_onshore 1.667383e+04\n", + "Link direct_reduction_furnace 1.810482e+03\n", + " electrolysis 7.445482e+03\n", + "Store hbi 1.923048e+05\n", + " hydrogen 2.613364e+05\n", + "dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.stats.optimal_capacity()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1ace9701", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "component carrier \n", + "Generator renewable_pv 2.108408e+09\n", + " renewable_wind_offshore 7.600000e-04\n", + " renewable_wind_onshore 2.145033e+10\n", + "Link direct_reduction_furnace 9.738038e+09\n", + " electrolysis 4.056123e+09\n", + "Store battery_elec 3.000000e-05\n", + " hydrogen 4.193143e+08\n", + "dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.stats.overnight_cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2cba5b35", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "carrier=%{y}
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"title": { + "text": "System Cost [currency]" + } + }, + "yaxis": { + "anchor": "x", + "categoryarray": [ + "renewable_wind_onshore", + "renewable_wind_offshore", + "renewable_pv", + "hydrogen", + "electrolysis", + "direct_reduction_furnace" + ], + "categoryorder": "array", + "domain": [ + 0, + 1 + ], + "title": { + "text": "carrier" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n.stats.system_cost.iplot.bar()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift-dev", + "language": "python", + "name": "shift-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 8712001..075b75e 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -77,9 +77,6 @@ def _add_carriers(network: pypsa.Network) -> None: "iron_ore": "Iron ore (mass)", "hbi": "Hot Briquetted Iron (mass)", "steel": "Steel (mass)", - "electrolysis": "Electrolysis process", - "direct_reduction_furnace": "Direct reduction furnace", - "electric_arc_furnace": "Electric arc furnace", "grid_electricity": "Grid electricity import", } for carrier_name, description in carriers.items(): @@ -323,7 +320,6 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> lifetime=td.get_tech_param(h2_params, "lifetime", 100.0), fom_cost=h2_inv_cost * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), standing_loss=TECH_ASSUMPTIONS["h2_standing_loss"], - e_initial=config.get("h2_storage_e_initial", 0.5), # Start at 50% capacity e_cyclic=True, # End state must equal start state ) From d2b40ad72acdda34c548a474c8916e3398a54c65 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 20 May 2026 17:44:51 +0200 Subject: [PATCH 090/216] feat: improve plotting of LCOX --- workflow/notebooks/analysis_pypsa_lcox.ipynb | 1539 ++++++++++++++---- 1 file changed, 1231 insertions(+), 308 deletions(-) diff --git a/workflow/notebooks/analysis_pypsa_lcox.ipynb b/workflow/notebooks/analysis_pypsa_lcox.ipynb index 8357ce6..a5be402 100644 --- a/workflow/notebooks/analysis_pypsa_lcox.ipynb +++ b/workflow/notebooks/analysis_pypsa_lcox.ipynb @@ -11,214 +11,346 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "73a942e5", + "execution_count": 31, + "id": "872d512b", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Run at 2026-05-20T15:13:42.482589Z\n", - "pypsa version 1.1.2\n" + "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n" ] - } - ], - "source": [ - "import os\n", - "import pathlib\n", - "from datetime import datetime\n", - "import pypsa\n", - "\n", - "print(\"Run at\", datetime.now().isoformat() + \"Z\")\n", - "print(\"pypsa version\", getattr(pypsa, \"__version__\", \"unknown\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c2d5204a", - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "Config loaded. NETWORK_PATH= C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\resources\\lco-hbi\\cost_year~2050\\Europe_reserved\\network_10.nc\n" + "Run at 2026-05-20T17:43:03.061869Z\n", + "pypsa version 1.1.2\n", + "Found 6 network files under C:\\Users\\JanLeopoldTautorus\\Repos\\shift\\resources\\lco-hbi\\cost_year~2050\\Europe_reserved\n" ] - } - ], - "source": [ - "repo_root = pathlib.Path(\"../..\").resolve()\n", - "relative_network_path = \"resources/lco-hbi/cost_year~2050/Europe_reserved/network_10.nc\"\n", - "\n", - "NETWORK_PATH = repo_root / relative_network_path\n", - "DRI_LINK_PATTERNS = [\"dri\", \"hbi\"]\n", - "DEFAULT_WACC = 0.07\n", - "DEFAULT_LIFETIME_YEARS = 20\n", - "FIGURES_DIR = \"figures\"\n", - "OUTPUTS_DIR = \"outputs\"\n", - "os.makedirs(FIGURES_DIR, exist_ok=True)\n", - "os.makedirs(OUTPUTS_DIR, exist_ok=True)\n", - "print(\"Config loaded. NETWORK_PATH=\", NETWORK_PATH)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f7f71ed3", - "metadata": {}, - "outputs": [ + }, { "name": "stderr", "output_type": "stream", "text": [ + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-0.01' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-0.1' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n" + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.1 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.1 available! 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fom capex capex fom \n", + "demand_mt \n", + "0.01 11.357142 4.193414 99.580606 110.039829 \n", + "0.10 11.357144 4.193447 99.580610 110.039834 \n", + "1.00 11.357144 4.193447 99.580610 110.039834 \n", + "10.00 11.357144 4.193447 99.580610 110.039834 \n", + "100.00 11.349404 4.229800 99.585769 110.045535 \n", + "1000.00 14.171490 3.435460 99.832869 110.318588 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "n.stats.system_cost.iplot.bar()" + "# Load and compare every solved network in a folder\n", + "import pathlib\n", + "from datetime import datetime\n", + "from typing import Any\n", + "\n", + "import pandas as pd\n", + "import pypsa\n", + "import seaborn as sns\n", + "import plotly.graph_objects as go\n", + "from plotly.io import renderers\n", + "from IPython.display import display\n", + "\n", + "renderers.default = \"vscode\"\n", + "\n", + "print(\"Run at\", datetime.now().isoformat() + \"Z\")\n", + "print(\"pypsa version\", getattr(pypsa, \"__version__\", \"unknown\"))\n", + "\n", + "repo_root = pathlib.Path(\"../..\").resolve()\n", + "network_folder = pathlib.Path(r\"resources/lco-hbi/cost_year~2050/Europe_reserved\")\n", + "if not network_folder.is_absolute():\n", + " network_folder = (repo_root / network_folder).resolve()\n", + "\n", + "network_files = sorted(network_folder.rglob(\"*.nc\"))\n", + "if not network_files:\n", + " raise FileNotFoundError(f\"No .nc files found under {network_folder}\")\n", + "\n", + "print(f\"Found {len(network_files)} network files under {network_folder}\")\n", + "\n", + "\n", + "def network_label(path: pathlib.Path, root: pathlib.Path) -> str:\n", + " try:\n", + " rel_path = path.relative_to(root)\n", + " except ValueError:\n", + " rel_path = path\n", + " return rel_path.with_suffix(\"\").as_posix()\n", + "\n", + "\n", + "def collapse_stats_table(value: Any) -> pd.Series:\n", + " \"\"\"Return a Series indexed by the specific technology name, e.g. renewable_pv.\"\"\"\n", + "\n", + " def comp_name_from_tuple(tup):\n", + " parts = [str(x) for x in tup]\n", + " if len(parts) >= 2:\n", + " return \"_\".join(parts[1:])\n", + " return parts[0]\n", + "\n", + " if isinstance(value, pd.Series):\n", + " series = pd.to_numeric(value, errors=\"coerce\").fillna(0)\n", + " if isinstance(series.index, pd.MultiIndex):\n", + " names = [comp_name_from_tuple(t) for t in series.index.values]\n", + " return pd.Series(series.values, index=names).groupby(level=0).sum()\n", + " return series\n", + " if isinstance(value, pd.DataFrame):\n", + " numeric = value.select_dtypes(include=\"number\")\n", + " if numeric.empty:\n", + " return pd.Series(dtype=float)\n", + " row_sum = numeric.sum(axis=1)\n", + " if isinstance(row_sum.index, pd.MultiIndex):\n", + " names = [comp_name_from_tuple(t) for t in row_sum.index.values]\n", + " return pd.Series(row_sum.values, index=names).groupby(level=0).sum()\n", + " return row_sum\n", + " return pd.Series({\"value\": float(value)})\n", + "\n", + "\n", + "def hbi_annual_output_tons(network: pypsa.Network):\n", + " hbi_loads = network.loads.index[\n", + " network.loads[\"bus\"].astype(str).str.contains(\"hbi\", case=False, na=False)\n", + " ].tolist()\n", + " if not hbi_loads:\n", + " raise ValueError(\"No HBI load found in network.loads.\")\n", + "\n", + " load_ts = network.loads_t.p.reindex(columns=hbi_loads, fill_value=0)\n", + " hbi_hourly_tons = load_ts.sum(axis=1)\n", + "\n", + " if \"objective\" in getattr(network, \"snapshot_weightings\", pd.DataFrame()).columns:\n", + " weights = network.snapshot_weightings[\"objective\"].reindex(\n", + " hbi_hourly_tons.index\n", + " )\n", + " else:\n", + " weights = pd.Series(1.0, index=hbi_hourly_tons.index)\n", + " weights = weights.fillna(1.0)\n", + "\n", + " annual_tons = float((hbi_hourly_tons * weights).sum())\n", + " return annual_tons, hbi_loads\n", + "\n", + "\n", + "def lcox_breakdown_per_ton(network: pypsa.Network):\n", + " annual_hbi_tons, hbi_loads = hbi_annual_output_tons(network)\n", + " if annual_hbi_tons <= 0:\n", + " raise ValueError(\"Annual HBI output must be positive.\")\n", + "\n", + " cost_tables = {\n", + " \"capex\": collapse_stats_table(network.stats.capex()),\n", + " \"opex\": collapse_stats_table(network.stats.opex()),\n", + " \"fom\": collapse_stats_table(network.stats.fom()),\n", + " }\n", + "\n", + " records = []\n", + " for category, series in cost_tables.items():\n", + " for technology, value in series.items():\n", + " if float(value) == 0.0:\n", + " continue\n", + " records.append(\n", + " {\n", + " \"category\": category,\n", + " \"technology\": str(technology),\n", + " \"value_per_t_hbi\": float(value) / annual_hbi_tons,\n", + " }\n", + " )\n", + "\n", + " breakdown = pd.DataFrame(records)\n", + " return breakdown, annual_hbi_tons, hbi_loads\n", + "\n", + "\n", + "sns.set_theme(style=\"whitegrid\", context=\"talk\", font_scale=0.9)\n", + "\n", + "technology_names = {\n", + " \"renewable_pv\": \"Solar PV\",\n", + " \"renewable_wind_onshore\": \"Onshore wind\",\n", + " \"renewable_wind_offshore\": \"Offshore wind\",\n", + " \"battery_elec\": \"Battery storage\",\n", + " \"electrolysis\": \"Electrolyzer\",\n", + " \"hydrogen\": \"Hydrogen storage\",\n", + " \"direct_reduction_furnace\": \"Direct reduction furnace\",\n", + "}\n", + "\n", + "category_names = {\n", + " \"capex\": \"Capex\",\n", + " \"opex\": \"Opex\",\n", + " \"fom\": \"FOM\",\n", + "}\n", + "\n", + "technology_order_preference = [\n", + " \"renewable_pv\",\n", + " \"renewable_wind_onshore\",\n", + " \"renewable_wind_offshore\",\n", + " \"battery_elec\",\n", + " \"electrolysis\",\n", + " \"hydrogen\",\n", + " \"direct_reduction_furnace\",\n", + "]\n", + "\n", + "breakdown_frames = []\n", + "summary_records = []\n", + "skipped_records = []\n", + "for network_path in network_files:\n", + " network_name = network_label(network_path, network_folder)\n", + " network = pypsa.Network(str(network_path))\n", + " try:\n", + " breakdown, annual_hbi_tons, hbi_loads = lcox_breakdown_per_ton(network)\n", + " except ValueError as exc:\n", + " skipped_records.append(\n", + " {\n", + " \"network\": network_name,\n", + " \"file\": str(network_path),\n", + " \"reason\": str(exc),\n", + " }\n", + " )\n", + " print(f\"Skipping {network_name}: {exc}\")\n", + " continue\n", + "\n", + " if breakdown.empty:\n", + " skipped_records.append(\n", + " {\n", + " \"network\": network_name,\n", + " \"file\": str(network_path),\n", + " \"reason\": \"Empty cost breakdown\",\n", + " }\n", + " )\n", + " print(f\"Skipping {network_name}: empty cost breakdown\")\n", + " continue\n", + "\n", + " demand_mt = annual_hbi_tons / 1e6\n", + " breakdown = breakdown.assign(\n", + " network=network_name,\n", + " file=str(network_path),\n", + " hbi_loads=\", \".join(hbi_loads),\n", + " hbi_tons_per_year=annual_hbi_tons,\n", + " demand_mt=demand_mt,\n", + " )\n", + " breakdown_frames.append(breakdown)\n", + " summary_records.append(\n", + " {\n", + " \"network\": network_name,\n", + " \"file\": str(network_path),\n", + " \"hbi_loads\": \", \".join(hbi_loads),\n", + " \"hbi_tons_per_year\": annual_hbi_tons,\n", + " \"demand_mt\": demand_mt,\n", + " \"total_lcox_per_t_hbi\": float(breakdown[\"value_per_t_hbi\"].sum()),\n", + " }\n", + " )\n", + "\n", + "if not breakdown_frames:\n", + " raise ValueError(\n", + " f\"No networks with HBI cost breakdowns found under {network_folder}\"\n", + " )\n", + "\n", + "long_df = pd.concat(breakdown_frames, ignore_index=True)\n", + "summary_df = (\n", + " pd.DataFrame(summary_records).sort_values(\"demand_mt\").reset_index(drop=True)\n", + ")\n", + "\n", + "technology_order = [\n", + " tech for tech in technology_order_preference if tech in set(long_df[\"technology\"])\n", + "]\n", + "\n", + "plot_df = long_df.pivot_table(\n", + " index=\"demand_mt\",\n", + " columns=[\"technology\", \"category\"],\n", + " values=\"value_per_t_hbi\",\n", + " aggfunc=\"sum\",\n", + " fill_value=0,\n", + ")\n", + "\n", + "all_columns = pd.MultiIndex.from_product([technology_order, [\"capex\", \"opex\", \"fom\"]])\n", + "plot_df = plot_df.reindex(columns=all_columns, fill_value=0)\n", + "present_columns = [col for col in plot_df.columns if float(plot_df[col].sum()) > 0]\n", + "plot_df = plot_df[present_columns]\n", + "\n", + "display(summary_df)\n", + "display(plot_df)\n", + "\n", + "if skipped_records:\n", + " display(pd.DataFrame(skipped_records))\n", + "\n", + "base_palette = sns.color_palette(\"tab10\", n_colors=max(len(technology_order), 1))\n", + "technology_colors = {tech: base_palette[i] for i, tech in enumerate(technology_order)}\n", + "\n", + "\n", + "def rgba_color(base_color, category):\n", + " if category == \"capex\":\n", + " alpha = 0.98\n", + " elif category == \"opex\":\n", + " alpha = 0.78\n", + " else:\n", + " alpha = 0.58\n", + " r, g, b = [int(round(c * 255)) for c in base_color]\n", + " return f\"rgba({r}, {g}, {b}, {alpha})\"\n", + "\n", + "\n", + "bar_widths = [x * 0.9 for x in plot_df.index.tolist()]\n", + "\n", + "tech_blocks = [\n", + " (tech, [col for col in plot_df.columns if col[0] == tech])\n", + " for tech in technology_order\n", + " if any(col[0] == tech for col in plot_df.columns)\n", + "]\n", + "\n", + "fig = go.Figure()\n", + "seen_labels = set()\n", + "for technology, cols in tech_blocks:\n", + " for category in [\"capex\", \"opex\", \"fom\"]:\n", + " column = (technology, category)\n", + " if column not in plot_df.columns:\n", + " continue\n", + " values = plot_df[column]\n", + " if float(values.sum()) <= 0:\n", + " continue\n", + " tech_label = technology_names.get(\n", + " technology, technology.replace(\"_\", \" \").title()\n", + " )\n", + " cat_label = category_names.get(category, category.upper())\n", + " label = f\"{tech_label} · {cat_label}\"\n", + " fig.add_trace(\n", + " go.Bar(\n", + " x=plot_df.index.tolist(),\n", + " y=values.values.tolist(),\n", + " width=bar_widths,\n", + " name=label if label not in seen_labels else label,\n", + " marker_color=rgba_color(technology_colors[technology], category),\n", + " marker_line_color=\"white\",\n", + " marker_line_width=0.6,\n", + " hovertemplate=(\n", + " f\"Demand: %{{x:.2f}} Mt/year\"\n", + " f\"
    {tech_label}\"\n", + " f\"
    {cat_label}: %{{y:.2f}} €/t HBI\"\n", + " \"\"\n", + " ),\n", + " )\n", + " )\n", + " seen_labels.add(label)\n", + "\n", + "region_name = network_folder.name.replace(\"_reserved\", \"\").replace(\"_\", \" \")\n", + "fig.update_layout(\n", + " barmode=\"stack\",\n", + " title=f\"{region_name} HBI LCOX breakdown\",\n", + " xaxis_title=\"HBI demand [Mt/year]\",\n", + " yaxis_title=\"Levelized Cost of HBI [€/t]\",\n", + " template=\"plotly_white\",\n", + " legend_title=\"Technology / cost\",\n", + " width=max(1100, 170 * len(plot_df.index)),\n", + " height=700,\n", + " bargap=0.0,\n", + " font=dict(size=14),\n", + " hovermode=\"x unified\",\n", + ")\n", + "fig.update_xaxes(\n", + " type=\"log\",\n", + " tickmode=\"array\",\n", + " tickvals=plot_df.index.tolist(),\n", + " ticktext=[f\"{d:g}\" for d in plot_df.index],\n", + ")\n", + "fig.update_yaxes(gridcolor=\"rgba(0,0,0,0.1)\")\n", + "\n", + "# interactive_html = (repo_root / \"results\" / \"figures_general\" / f\"{network_folder.name}_hbi_lcox_interactive.html\")\n", + "# interactive_html.parent.mkdir(parents=True, exist_ok=True)\n", + "# fig.write_html(str(interactive_html), include_plotlyjs=\"cdn\")\n", + "# print(f\"Interactive HTML written to {interactive_html}\")\n", + "display(fig)\n", + "\n", + "plot_df" ] } ], From ba16e3b932f583e07e6b97fb6633e5ed483f4c61 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 11:02:36 +0200 Subject: [PATCH 091/216] chore: delete stale test --- tests/conftest.py | 178 -------- tests/test_supply_curve_smoke.py | 108 ----- tests/test_supply_math.py | 70 ---- tests/test_supply_network_optimization.py | 486 ---------------------- 4 files changed, 842 deletions(-) delete mode 100644 tests/conftest.py delete mode 100644 tests/test_supply_curve_smoke.py delete mode 100644 tests/test_supply_math.py delete mode 100644 tests/test_supply_network_optimization.py diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index fb1c2d3..0000000 --- a/tests/conftest.py +++ /dev/null @@ -1,178 +0,0 @@ -from pathlib import Path -import sys - -import numpy as np -import pandas as pd -import pytest -import xarray as xr - - -REPO_ROOT = Path(__file__).resolve().parents[1] -WORKFLOW_SCRIPTS = REPO_ROOT / "workflow" / "scripts" - -for path in (REPO_ROOT, WORKFLOW_SCRIPTS): - path_str = str(path) - if path_str not in sys.path: - sys.path.insert(0, path_str) - - -@pytest.fixture -def steel_tech_costs() -> pd.Series: - """Synthetic tech-cost data that mirrors the steel electricity chain.""" - rows = [ - ("Alkaline electrolyzer large size", "electricity-input", 1.38), - ("hydrogen direct iron reduction furnace", "hydrogen-input", 2.1), - ("hydrogen direct iron reduction furnace", "electricity-input", 1.03), - ("electric arc furnace", "electricity-input", 0.6395), - ] - - index = pd.MultiIndex.from_tuples( - [(technology, parameter) for technology, parameter, _ in rows], - names=["technology", "parameter"], - ) - values = [value for _, _, value in rows] - return pd.Series(values, index=index, dtype=float) - - -@pytest.fixture -def steel_minimal_case() -> dict: - """Hand-checkable supply-stage numbers for a 1 t/h steel test case.""" - demand_mt_per_year = 0.00876 - hourly_demand_tph = demand_mt_per_year * 1e6 / 8760 - - renewable_electricity_mwh_per_t = 2.1 * 1.38 + 1.03 - total_electricity_mwh_per_t = renewable_electricity_mwh_per_t + 0.6395 - - return { - "product": "steel", - "demand_mt_per_year": demand_mt_per_year, - "hourly_demand_tph": hourly_demand_tph, - "annual_demand_t": hourly_demand_tph * 8760, - "expected_steel_renewable_electricity_mwh_per_t": renewable_electricity_mwh_per_t, - "expected_steel_total_electricity_mwh_per_t": total_electricity_mwh_per_t, - "expected_annual_renewable_electricity_mwh": hourly_demand_tph - * 8760 - * renewable_electricity_mwh_per_t, - "expected_annual_total_electricity_mwh": hourly_demand_tph - * 8760 - * total_electricity_mwh_per_t, - } - - -@pytest.fixture -def round_number_tech_costs() -> pd.Series: - """Minimal renewable tech costs with easy round numbers for tests. - - Values are chosen so conversion in add_renewable_generators remains simple: - overnight_cost [EUR/MW] = investment [EUR/kW] * 1000. - """ - rows = [ - ("solar-utility", "investment", 0.10), - ("solar-utility", "lifetime", 20.0), - ("solar-utility", "FOM", 10.0), - ("onwind", "investment", 0.20), - ("onwind", "lifetime", 25.0), - ("onwind", "FOM", 5.0), - ] - - index = pd.MultiIndex.from_tuples( - [(technology, parameter) for technology, parameter, _ in rows], - names=["technology", "parameter"], - ) - values = [value for _, _, value in rows] - return pd.Series(values, index=index, dtype=float) - - -@pytest.fixture -def full_chain_tech_costs() -> pd.Series: - """Synthetic full-chain tech database for electrolysis->DRI->EAF tests. - - Includes all parameters used by build_x_supply_chain helpers and - add_renewable_generators renewable cost lookup. - """ - rows = [ - # Conversion chain - ("Alkaline electrolyzer large size", "investment", 0.10), - ("Alkaline electrolyzer large size", "lifetime", 20.0), - ("Alkaline electrolyzer large size", "FOM", 2.0), - ("Alkaline electrolyzer large size", "electricity-input", 1.38), - ("hydrogen direct iron reduction furnace", "investment", 100.0), - ("hydrogen direct iron reduction furnace", "lifetime", 25.0), - ("hydrogen direct iron reduction furnace", "FOM", 5.0), - ("hydrogen direct iron reduction furnace", "ore-input", 1.59), - ("hydrogen direct iron reduction furnace", "hydrogen-input", 2.1), - ("hydrogen direct iron reduction furnace", "electricity-input", 1.03), - ("electric arc furnace", "investment", 50.0), - ("electric arc furnace", "lifetime", 25.0), - ("electric arc furnace", "FOM", 5.0), - ("electric arc furnace", "hbi-input", 1.0), - ("electric arc furnace", "electricity-input", 0.6395), - # Storage and battery - ("hydrogen storage underground", "investment", 0.01), - ("hydrogen storage underground", "lifetime", 30.0), - ("hydrogen storage underground", "FOM", 0.0), - ("battery inverter", "investment", 0.05), - ("battery inverter", "lifetime", 15.0), - ("battery inverter", "FOM", 1.0), - ("battery inverter", "efficiency", 0.96), - ("battery storage", "investment", 0.05), - ("battery storage", "lifetime", 20.0), - # Renewable technologies used by add_renewable_generators - ("solar-utility", "investment", 0.10), - ("solar-utility", "lifetime", 20.0), - ("solar-utility", "FOM", 10.0), - ("onwind", "investment", 0.20), - ("onwind", "lifetime", 25.0), - ("onwind", "FOM", 5.0), - ] - - index = pd.MultiIndex.from_tuples( - [(technology, parameter) for technology, parameter, _ in rows], - names=["technology", "parameter"], - ) - values = [value for _, _, value in rows] - return pd.Series(values, index=index, dtype=float) - - -@pytest.fixture -def renewable_region_timeseries_fixture() -> xr.Dataset: - """Toy regional renewable profile for optimization tests. - - Contains two valid generator candidates: - 1. Solar candidate with constant CF = 0.5 - 2. Wind candidate with CF = 1.0 for first half and 0.0 for second half - - Other bus-technology combinations are NaN and should be ignored. - """ - n_hours = 24 - buses = ["TST_SOL_01", "TST_WND_01"] - technologies = ["solar", "onwind"] - hours = np.arange(n_hours) - - capacity_factor = np.full((len(buses), len(technologies), n_hours), np.nan) - p_nom_max = np.full((len(buses), len(technologies)), np.nan) - avg_cf = np.full((len(buses), len(technologies)), np.nan) - - # Candidate 1: constant CF 0.5 - capacity_factor[0, 0, :] = 0.5 - p_nom_max[0, 0] = 1e6 - avg_cf[0, 0] = 0.5 - - # Candidate 2: half 1.0, half 0.0 - capacity_factor[1, 1, : n_hours // 2] = 1.0 - capacity_factor[1, 1, n_hours // 2 :] = 0.0 - p_nom_max[1, 1] = 1e6 - avg_cf[1, 1] = 0.5 - - return xr.Dataset( - data_vars={ - "capacity_factor": (("bus", "technology", "hour"), capacity_factor), - "p_nom_max": (("bus", "technology"), p_nom_max), - "avg_cf": (("bus", "technology"), avg_cf), - }, - coords={ - "bus": buses, - "technology": technologies, - "hour": hours, - }, - ) diff --git a/tests/test_supply_curve_smoke.py b/tests/test_supply_curve_smoke.py deleted file mode 100644 index cc65da4..0000000 --- a/tests/test_supply_curve_smoke.py +++ /dev/null @@ -1,108 +0,0 @@ -from types import SimpleNamespace - -import pandas as pd - -import workflow.scripts.create_supply_curve as create_supply_curve_module - - -def test_supply_curve_smoke(tmp_path, monkeypatch): - lco_file_1 = tmp_path / "results_10.csv" - lco_file_2 = tmp_path / "results_20.csv" - local_demand_file = tmp_path / "local_demand.csv" - steel_demand_file = tmp_path / "steel_demand.csv" - supply_file = tmp_path / "supply.csv" - supply_nodemand_file = tmp_path / "supply_nodemand.csv" - supply_curve_file = tmp_path / "supply_curve.pdf" - - pd.DataFrame( - [ - { - "demand [t]": 10.0, - "load [t/h]": 1.0, - "cost [EUR]": 1000.0, - "lcox [EUR/t]": 100.0, - } - ] - ).to_csv(lco_file_1, index=False) - - pd.DataFrame( - [ - { - "demand [t]": 20.0, - "load [t/h]": 2.0, - "cost [EUR]": 2000.0, - "lcox [EUR/t]": "infeasible", - } - ] - ).to_csv(lco_file_2, index=False) - - pd.DataFrame( - [ - { - "region": "TST", - "demand": 0.0, - "unit": "MWh", - "el_share": 0.0, - " note": "none", - } - ] - ).to_csv(local_demand_file, index=False) - pd.DataFrame([{"region": "TST", "SteelProductionMt": 0.0}]).to_csv( - steel_demand_file, index=False - ) - - monkeypatch.setattr( - create_supply_curve_module, - "snakemake", - SimpleNamespace( - input=SimpleNamespace( - lco_product_data=[str(lco_file_1), str(lco_file_2)], - local_demand=str(local_demand_file), - steel_demand=str(steel_demand_file), - ), - output=SimpleNamespace( - supply=str(supply_file), - supply_nodemand=str(supply_nodemand_file), - supply_curve=str(supply_curve_file), - ), - wildcards={"region": "TST", "product": "steel"}, - config={ - "electricity_steel_ratio": 1.0, - "iron_ore": {"marginal_cost": 0.0, "ore_to_steel_ratio": 0.0}, - }, - ), - raising=False, - ) - - monkeypatch.setattr( - create_supply_curve_module, - "product", - "steel", - raising=False, - ) - monkeypatch.setattr( - create_supply_curve_module, - "columns", - { - "demand factor": "demand factor [%]", - "demand": "demand [t]", - "load": "load [t/h]", - "total cost": "cost [EUR]", - "cost per unit": "lcox [EUR/t]", - "xlabel": "Demand in Mt", - "product_unit": "t", - "ylim": (0, 900), - }, - raising=False, - ) - - create_supply_curve_module.create_supply_curve() - - assert supply_file.exists() - assert supply_nodemand_file.exists() - assert supply_curve_file.exists() - - output_df = pd.read_csv(supply_file) - assert len(output_df) == 1 - assert float(output_df.iloc[0]["demand [t]"]) == 10.0 - assert float(output_df.iloc[0]["lcox [EUR/t]"]) == 100.0 diff --git a/tests/test_supply_math.py b/tests/test_supply_math.py deleted file mode 100644 index 2d8032c..0000000 --- a/tests/test_supply_math.py +++ /dev/null @@ -1,70 +0,0 @@ -import pytest - -from workflow.scripts.prepare_regional_network import ( - back_propagate_electricity_need, - filter_by_technologies, -) - - -def test_supply_stage_electricity_chain_matches_hand_calculation( - steel_tech_costs, steel_minimal_case -): - renewable_electricity_per_t = back_propagate_electricity_need( - steel_tech_costs, - "steel", - {"eaf_electricity_source": "grid"}, - ) - - assert renewable_electricity_per_t == pytest.approx( - steel_minimal_case["expected_steel_renewable_electricity_mwh_per_t"] - ) - - -def test_supply_stage_includes_eaf_electricity_when_requested( - steel_tech_costs, steel_minimal_case -): - renewable_electricity_per_t = back_propagate_electricity_need( - steel_tech_costs, - "steel", - {"eaf_electricity_source": "renewable"}, - ) - - assert renewable_electricity_per_t == pytest.approx( - steel_minimal_case["expected_steel_total_electricity_mwh_per_t"] - ) - - -def test_supply_stage_uses_1_t_per_hour_and_is_easy_to_verify( - steel_minimal_case, - steel_tech_costs, -): - hourly_demand_tph = steel_minimal_case["demand_mt_per_year"] * 1e6 / 8760 - - assert hourly_demand_tph == pytest.approx(1.0) - assert steel_minimal_case["annual_demand_t"] == pytest.approx(8760.0) - - renewable_electricity_per_t = back_propagate_electricity_need( - steel_tech_costs, - steel_minimal_case["product"], - {"eaf_electricity_source": "grid"}, - ) - - annual_renewable_electricity_mwh = ( - hourly_demand_tph * 8760 * renewable_electricity_per_t - ) - - assert annual_renewable_electricity_mwh == pytest.approx( - steel_minimal_case["expected_annual_renewable_electricity_mwh"] - ) - - -def test_supply_stage_keeps_only_allowed_renewable_technologies( - renewable_region_timeseries_fixture, -): - filtered = filter_by_technologies( - renewable_region_timeseries_fixture, - {"renewable_technologies": ["solar", "onwind"]}, - ) - - assert list(filtered.technology.values) == ["solar", "onwind"] - assert "offwind" not in set(str(tech) for tech in filtered.technology.values) diff --git a/tests/test_supply_network_optimization.py b/tests/test_supply_network_optimization.py deleted file mode 100644 index eb2faa2..0000000 --- a/tests/test_supply_network_optimization.py +++ /dev/null @@ -1,486 +0,0 @@ -import numpy as np -import pandas as pd -import pypsa -import pytest -import xarray as xr - -import workflow.scripts.tech_database as td -from workflow.scripts.build_x_supply_chain import ( - _add_buses, - _add_carriers, - _add_conversion_chain, - _add_grid_electricity_supply, - _add_resources, - _add_storage, -) -from workflow.scripts.calculate_lcox import _convert_arrow_strings -from workflow.scripts.prepare_regional_network import add_renewable_generators - - -def _build_selected_generators(dataset): - """Convert xarray fixture into selected_generators input format.""" - generators = [] - - for bus_id in dataset.bus.values: - for tech in dataset.technology.values: - p_nom_max = float( - dataset["p_nom_max"].sel(bus=bus_id, technology=tech).values - ) - avg_cf = float(dataset["avg_cf"].sel(bus=bus_id, technology=tech).values) - cf_ts = dataset["capacity_factor"].sel(bus=bus_id, technology=tech).values - - if np.isnan(p_nom_max) or p_nom_max <= 0 or np.isnan(avg_cf) or avg_cf <= 0: - continue - - generators.append( - { - "bus_id": str(bus_id), - "technology": str(tech), - "p_nom_max": p_nom_max, - "avg_cf": avg_cf, - "capacity_factor_ts": np.asarray(cf_ts, dtype=float), - } - ) - - return generators - - -def _build_low_capacity_dataset(dataset, p_nom_max=0.01): - """Return a copy of the renewable dataset with very low generator capacity caps.""" - low_capacity = dataset.copy(deep=True) - low_capacity["p_nom_max"] = xr.zeros_like(low_capacity["p_nom_max"]) + p_nom_max - return low_capacity - - -def _build_full_chain_reference_network(dataset, tech_costs, config): - """Manual full-chain network: carriers, buses, links, stores, and renewables.""" - snapshots = pd.date_range("2030-01-01", periods=24, freq="h") - - network = pypsa.Network() - network.set_snapshots(snapshots) - network.discount_rate = 0.05 - - # Carriers used by full chain + renewable technologies - for carrier in [ - "renewable_electricity", - "grid_electricity", - "hydrogen", - "battery_elec", - "iron_ore", - "hbi", - "steel", - "electrolysis", - "direct_reduction_furnace", - "electric_arc_furnace", - "solar", - "onwind", - ]: - network.add("Carrier", carrier) - - # Buses - network.add( - "Bus", "renewable_electricity", carrier="renewable_electricity", unit="MW" - ) - network.add("Bus", "grid_electricity", carrier="grid_electricity", unit="MW") - network.add("Bus", "hydrogen", carrier="hydrogen", unit="MW") - network.add("Bus", "battery", carrier="battery_elec", unit="MWh") - network.add("Bus", "iron_ore", carrier="iron_ore", unit="t/h") - network.add("Bus", "hbi", carrier="hbi", unit="t/h") - network.add("Bus", "steel", carrier="steel", unit="t/h") - - # Conversion links - elec_params = td.get_tech(tech_costs, "Alkaline electrolyzer large size") - elec_inv_cost = td.get_tech_param(elec_params, "investment", 0.10) * 1000 - network.add( - "Link", - "electrolyzer", - bus0="renewable_electricity", - bus1="hydrogen", - carrier="electrolysis", - efficiency=1.0 / td.get_tech_param(elec_params, "electricity-input", 1.38), - overnight_cost=elec_inv_cost, - lifetime=td.get_tech_param(elec_params, "lifetime", 20.0), - fom_cost=elec_inv_cost * (td.get_tech_param(elec_params, "FOM", 2.0) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - p_min_pu=config.get("elec_p_min_pu", 0.10), - ) - - dri_params = td.get_tech(tech_costs, "hydrogen direct iron reduction furnace") - dri_inv_cost = td.get_tech_param(dri_params, "investment", 100.0) - network.add( - "Link", - "dri", - bus0="iron_ore", - bus1="hbi", - bus2="hydrogen", - bus3="renewable_electricity", - carrier="direct_reduction_furnace", - efficiency=1.0 / td.get_tech_param(dri_params, "ore-input", 1.59), - efficiency2=-td.get_tech_param(dri_params, "hydrogen-input", 2.1), - efficiency3=-td.get_tech_param(dri_params, "electricity-input", 1.03), - overnight_cost=dri_inv_cost, - lifetime=td.get_tech_param(dri_params, "lifetime", 25.0), - fom_cost=dri_inv_cost * (td.get_tech_param(dri_params, "FOM", 5.0) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - p_min_pu=config.get("dri_p_min_pu", 0.15), - ) - - eaf_params = td.get_tech(tech_costs, "electric arc furnace") - eaf_inv_cost = td.get_tech_param(eaf_params, "investment", 50.0) - network.add( - "Link", - "eaf", - bus0="hbi", - bus1="steel", - bus2="grid_electricity", - carrier="electric_arc_furnace", - efficiency=1.0 / td.get_tech_param(eaf_params, "hbi-input", 1.0), - efficiency2=-td.get_tech_param(eaf_params, "electricity-input", 0.6395), - overnight_cost=eaf_inv_cost, - lifetime=td.get_tech_param(eaf_params, "lifetime", 25.0), - fom_cost=eaf_inv_cost * (td.get_tech_param(eaf_params, "FOM", 5.0) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - p_min_pu=config.get("eaf_p_min_pu", 0.20), - ) - - # Storage and battery links - h2_params = td.get_tech(tech_costs, "hydrogen storage underground") - h2_inv_cost = td.get_tech_param(h2_params, "investment", 0.01) * 1000 - network.add( - "Store", - "h2_storage", - bus="hydrogen", - e_nom_extendable=True, - overnight_cost=h2_inv_cost, - lifetime=td.get_tech_param(h2_params, "lifetime", 30.0), - fom_cost=h2_inv_cost * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), - standing_loss=0.001, - e_initial=config.get("h2_storage_e_initial", 0.5), - e_cyclic=True, - ) - - batt_inv_params = td.get_tech(tech_costs, "battery inverter") - batt_store_params = td.get_tech(tech_costs, "battery storage") - batt_inv_cost = td.get_tech_param(batt_inv_params, "investment", 0.05) * 1000 - batt_eff = np.sqrt(td.get_tech_param(batt_inv_params, "efficiency", 0.96)) - - network.add( - "Link", - "batt_charge", - bus0="renewable_electricity", - bus1="battery", - efficiency=batt_eff, - overnight_cost=batt_inv_cost, - lifetime=td.get_tech_param(batt_inv_params, "lifetime", 15.0), - fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 1.0) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - ) - - network.add( - "Link", - "batt_discharge", - bus0="battery", - bus1="renewable_electricity", - efficiency=batt_eff, - overnight_cost=batt_inv_cost, - lifetime=td.get_tech_param(batt_inv_params, "lifetime", 15.0), - fom_cost=batt_inv_cost * (td.get_tech_param(batt_inv_params, "FOM", 1.0) / 100), - p_nom_extendable=True, - p_nom_max=np.inf, - ) - - batt_store_cost = td.get_tech_param(batt_store_params, "investment", 0.05) * 1000 - network.add( - "Store", - "battery", - bus="battery", - e_nom_extendable=True, - overnight_cost=batt_store_cost, - lifetime=td.get_tech_param(batt_store_params, "lifetime", 20.0), - fom_cost=0.0, - standing_loss=0.0001, - e_initial=config.get("battery_e_initial", 0.5), - e_cyclic=True, - ) - - network.add( - "Store", - "hbi_storage", - bus="hbi", - e_nom_extendable=True, - overnight_cost=0.0, - lifetime=1.0, - fom_cost=0.0, - discount_rate=0.0, - standing_loss=0.0, - ) - - # External resource - network.add("Generator", "iron_ore", bus="iron_ore", p_nom=1e10, marginal_cost=0.0) - - if config.get("eaf_electricity_source", "grid") == "grid": - network.add( - "Generator", - "grid_electricity_import", - bus="grid_electricity", - carrier="grid_electricity", - p_nom=1e10, - marginal_cost=config.get("grid_electricity_price", 75.0), - ) - - # Renewable generators - selected_generators = _build_selected_generators(dataset) - add_renewable_generators( - network=network, - dataset=dataset, - tech_costs=tech_costs, - config=config, - selected_generators=selected_generators, - ) - - return network - - -def _build_full_chain_function_network(dataset, tech_costs, config): - """Function-built full-chain network using workflow helper modules.""" - snapshots = pd.date_range("2030-01-01", periods=24, freq="h") - - network = pypsa.Network() - network.set_snapshots(snapshots) - network.discount_rate = 0.05 - - _add_carriers(network) - _add_buses(network) - _add_grid_electricity_supply(network, config) - _add_conversion_chain(network, tech_costs, config) - _add_storage(network, tech_costs, config) - _add_resources(network, config) - - selected_generators = _build_selected_generators(dataset) - add_renewable_generators( - network=network, - dataset=dataset, - tech_costs=tech_costs, - config=config, - selected_generators=selected_generators, - ) - - return network - - -def _apply_capital_cost_from_overnight_cost(network): - """Ensure optimization objective uses overnight costs deterministically.""" - for component_name in ["generators", "links", "stores"]: - component = getattr(network, component_name) - if "overnight_cost" in component.columns: - for name, row in component.iterrows(): - if pd.notna(row.get("overnight_cost", np.nan)): - component.at[name, "capital_cost"] = float(row["overnight_cost"]) - if "discount_rate" in component.columns and pd.isna( - row.get("discount_rate", np.nan) - ): - component.at[name, "discount_rate"] = float( - getattr(network, "discount_rate", 0.0) - ) - - -def _solve_or_skip(network): - _convert_arrow_strings(network) - try: - network.optimize( - network.snapshots, - solver_name="highs", - include_objective_constant=False, - ) - except Exception as exc: - pytest.skip(f"Optimization solver unavailable in current environment: {exc}") - - -def _solve_network(network): - _convert_arrow_strings(network) - return network.optimize( - network.snapshots, - solver_name="highs", - include_objective_constant=False, - ) - - -def test_function_built_full_chain_matches_manual_reference( - renewable_region_timeseries_fixture, - full_chain_tech_costs, -): - config = { - "eaf_electricity_source": "grid", - "grid_electricity_price": 75.0, - "elec_p_min_pu": 0.10, - "dri_p_min_pu": 0.15, - "eaf_p_min_pu": 0.20, - "h2_storage_e_initial": 0.5, - "battery_e_initial": 0.5, - } - - reference = _build_full_chain_reference_network( - renewable_region_timeseries_fixture, - full_chain_tech_costs, - config, - ) - built = _build_full_chain_function_network( - renewable_region_timeseries_fixture, - full_chain_tech_costs, - config, - ) - - assert set(reference.carriers.index) == set(built.carriers.index) - assert set(reference.buses.index) == set(built.buses.index) - assert set(reference.links.index) == set(built.links.index) - assert set(reference.stores.index) == set(built.stores.index) - assert set(reference.generators.index) == set(built.generators.index) - - assert "grid_electricity_import" in reference.generators.index - assert ( - reference.generators.at["grid_electricity_import", "carrier"] - == "grid_electricity" - ) - assert ( - built.generators.at["grid_electricity_import", "carrier"] == "grid_electricity" - ) - assert reference.links.at["eaf", "bus2"] == "grid_electricity" - assert built.links.at["eaf", "bus2"] == "grid_electricity" - - for link_name in ["electrolyzer", "dri", "eaf"]: - assert ( - built.links.at[link_name, "carrier"] - == reference.links.at[link_name, "carrier"] - ) - assert built.links.at[link_name, "efficiency"] == pytest.approx( - reference.links.at[link_name, "efficiency"] - ) - - -def test_full_chain_optimization_matches_manual_reference( - renewable_region_timeseries_fixture, - full_chain_tech_costs, -): - config = { - "eaf_electricity_source": "grid", - "grid_electricity_price": 75.0, - "elec_p_min_pu": 0.10, - "dri_p_min_pu": 0.15, - "eaf_p_min_pu": 0.20, - "h2_storage_e_initial": 0.5, - "battery_e_initial": 0.5, - } - - reference = _build_full_chain_reference_network( - renewable_region_timeseries_fixture, - full_chain_tech_costs, - config, - ) - built = _build_full_chain_function_network( - renewable_region_timeseries_fixture, - full_chain_tech_costs, - config, - ) - - for network in (reference, built): - network.add("Load", "steel_demand", bus="steel", p_set=1.0) - _apply_capital_cost_from_overnight_cost(network) - - _solve_or_skip(reference) - _solve_or_skip(built) - - # Compare key optimized capacities of the full chain and renewables. - for component, name in [ - ("links", "electrolyzer"), - ("links", "dri"), - ("links", "eaf"), - ("generators", "renewable_TST_SOL_01_solar"), - ("generators", "renewable_TST_WND_01_onwind"), - ]: - ref_value = float(getattr(reference, component).at[name, "p_nom_opt"]) - built_value = float(getattr(built, component).at[name, "p_nom_opt"]) - assert built_value == pytest.approx(ref_value, rel=1e-5, abs=1e-6) - - # Directional behavior check for expected renewable choice. - ref_solar = float( - reference.generators.at["renewable_TST_SOL_01_solar", "p_nom_opt"] - ) - ref_wind = float( - reference.generators.at["renewable_TST_WND_01_onwind", "p_nom_opt"] - ) - assert ref_solar > 0.0 - assert ref_solar > ref_wind - - -def test_full_chain_becomes_infeasible_when_renewables_are_too_small( - renewable_region_timeseries_fixture, - full_chain_tech_costs, -): - """If renewable caps are too low, the steel chain should not be solvable.""" - config = { - "eaf_electricity_source": "grid", - "grid_electricity_price": 75.0, - "elec_p_min_pu": 0.10, - "dri_p_min_pu": 0.15, - "eaf_p_min_pu": 0.20, - "h2_storage_e_initial": 0.5, - "battery_e_initial": 0.5, - } - - low_capacity_dataset = _build_low_capacity_dataset( - renewable_region_timeseries_fixture, - p_nom_max=0.01, - ) - - reference = _build_full_chain_reference_network( - low_capacity_dataset, - full_chain_tech_costs, - config, - ) - built = _build_full_chain_function_network( - low_capacity_dataset, - full_chain_tech_costs, - config, - ) - - for network in (reference, built): - network.add("Load", "steel_demand", bus="steel", p_set=1.0) - _apply_capital_cost_from_overnight_cost(network) - - reference_result = _solve_network(reference) - built_result = _solve_network(built) - - reference_status = " ".join( - str(part).lower() for part in np.atleast_1d(reference_result) - ) - built_status = " ".join(str(part).lower() for part in np.atleast_1d(built_result)) - - assert "infeas" in reference_status or reference.objective is None - assert "infeas" in built_status or built.objective is None - - -def test_full_chain_can_switch_eaf_to_renewable_electricity( - renewable_region_timeseries_fixture, - full_chain_tech_costs, -): - config = { - "eaf_electricity_source": "renewable", - "elec_p_min_pu": 0.10, - "dri_p_min_pu": 0.15, - "eaf_p_min_pu": 0.20, - "h2_storage_e_initial": 0.5, - "battery_e_initial": 0.5, - } - - built = _build_full_chain_function_network( - renewable_region_timeseries_fixture, - full_chain_tech_costs, - config, - ) - - assert "grid_electricity_import" not in built.generators.index - assert built.links.at["eaf", "bus2"] == "renewable_electricity" From b03be978ed739cc6e721ee322ae3f94aad4d956f Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 11:24:38 +0200 Subject: [PATCH 092/216] chore: delete trade_singlestage script --- workflow/scripts/model_trade_singlestage.py | 362 -------------------- 1 file changed, 362 deletions(-) delete mode 100644 workflow/scripts/model_trade_singlestage.py diff --git a/workflow/scripts/model_trade_singlestage.py b/workflow/scripts/model_trade_singlestage.py deleted file mode 100644 index 4f2afa8..0000000 --- a/workflow/scripts/model_trade_singlestage.py +++ /dev/null @@ -1,362 +0,0 @@ -import pypsa -import pandas as pd -import matplotlib.pyplot as plt -import os -from typing import Any - -snakemake: Any = globals().get("snakemake") - -plt.style.use("bmh") - - -# inputs are transportation costs, supply curves, trade options and load demand for all regions -def building_model(supply_curves, demands, bus_location, product): - # this function creates network, carrier and a bus for each region - # with a load and all supply possibilities added - - # create network - network = pypsa.Network() - - # adding carriers - network.add( - "Carrier", name=product, color=snakemake.config["plot"]["colors"][product] - ) - - # for each region we are creating a bus with all the potentials and load - for r in range(0, len(supply_curves)): - # getting the supply curve for one region - region_file = supply_curves[r] - region_data = pd.read_csv(region_file, header=0) - filename = os.path.basename(region_file) - # Extract region name - region_name = filename.split("_" + product)[0] - - print("building generators and loads for ", region_name) - - # define the steel bus with region name - network.add( - "Bus", - region_name, - carrier=product, - x=float( - bus_location.loc[bus_location["region_name"] == region_name]["long"] - ), # long - y=float( - bus_location.loc[bus_location["region_name"] == region_name]["lat"] - ), # lat - ) - - # defining the demand for the region - load = int(demands.loc[demands["region"] == region_name]["demand"]) * float( - snakemake.wildcards["demand"] - ) - print( - f"Load set via snakemake.wildcard to {float(snakemake.wildcards['demand']) * 100}% of regional final energy demand." - ) - - network.add( - "Load", - region_name, - bus=region_name, - p_set=load, - ) - - # defining the supply opportunities for the region (apart from last supply as that is the 75% infeasible one) - for s in range(0, len(region_data) - 1): - if s == 0: - p_nom_supply = float(region_data[f"demand [{unit}]"][s]) - else: - p_nom_supply = float(region_data[f"demand [{unit}]"][s]) - float( - region_data[f"demand [{unit}]"][s - 1] - ) - M_cost_supply = float(region_data[f"{cost_descriptor} [EUR/{unit}]"][s]) - - network.add( - "Generator", - "{} supply {}_{}".format( - product, region_name, region_data["demand factor [%]"][s] - ), - bus=region_name, - carrier=product, - p_nom_extendable=True, - p_nom_max=p_nom_supply, # MWh or t, demand = potential supply - marginal_cost=M_cost_supply, # EUR/MWh or EUR/t - capital_cost=1 / 1000, # to prevent optimisation shennanigans - ) - - return network - - -def create_links(transport_costs, trade_options): - - # for in range of length of input csv with all the different links, region_from = column , region_to = column 2 - # create links with the correct corresponding costs - - # marginal and fixed cost for the different type of transport - ship_mc = float( - transport_costs.loc[transport_costs["transport_type"] == "shipping"][ - "marginal_cost" - ] - ) - pipe_mc = float( - transport_costs.loc[transport_costs["transport_type"] == "pipeline"][ - "marginal_cost" - ] - ) - ship_c = float( - transport_costs.loc[transport_costs["transport_type"] == "shipping"][ - "fixed_cost" - ] - ) - input_demand = 0.42 # MWh/km for LH2, IEA future of hydrogen 2019 - boat_capacity = 363000 # MWh for LH2, IEA future of hydrogen 2019 - speed = 30 # km/h, IEA future of hydrogen 2019 - BOG = 0.2 / 100 # %/day, IEA future of hydrogen 2019 - - print("ship + pipe cost", ship_mc, ship_c, pipe_mc) - - # if there should be a link, create a link - # do this for both shipping and pipeline - for r in range(0, len(trade_options)): - # checking if the row connects with shipping - if trade_options["shipping"][r] == 1: - r_from = trade_options["region_from"][r] - r_to = trade_options["region_to"][r] - total_cost = ship_c + int( - float(trade_options["shipping_distance [km]"][r]) * ship_mc - ) - - # calculating efficiency - days_at_sea = ( - float(trade_options["shipping_distance [km]"][r]) / speed - ) / 24 - tot_BOG = 1 - (1 - BOG) ** days_at_sea - tot_fuel_demand = ( - (2 * float(trade_options["shipping_distance [km]"][r])) - * input_demand - / boat_capacity - ) - eff = 1 - max(tot_BOG, tot_fuel_demand) - - network.add( - "Link", - "shipping {}-{}".format(r_from, r_to), - bus0=r_from, - bus1=r_to, - efficiency=eff, # %, calculated above - marginal_cost=total_cost, # EUR/MWh or EUR/t - capital_cost=1 / 1000, # to prevent optimisation shenenigans - p_nom_extendable=True, - ) - print("shipping link made from {} to {} - eff {}".format(r_from, r_to, eff)) - - # checking if the row connects with pipeline - if (trade_options["pipeline"][r] == 1) & (product == "hydrogen"): - r_from = trade_options["region_from"][r] - r_to = trade_options["region_to"][r] - p_cost = int(float(trade_options["pipeline_distance [km]"][r]) * pipe_mc) - filling_demand = 1.5 / 100 # DEA, energy transport datasheet, 2050, % - losses = 1.7 / 100 # DEA, energy transport datasheet 2022, 2050, %/1000km - eff = (1 - filling_demand) * (1 - losses) ** ( - float(trade_options["pipeline_distance [km]"][r]) / 1000 - ) - - network.add( - "Link", - "pipeline {}-{}".format(r_from, r_to), - bus0=r_from, - bus1=r_to, - efficiency=eff, # calculated above - marginal_cost=p_cost, # EUR/MWh - p_nom_extendable=True, - capital_cost=1 / 1000, # to prevent optimisation shenenigans - ) - print("pipeline link made from {} to {} - eff {}".format(r_from, r_to, eff)) - - return - - -def save_trade_network(solved_network): - - sol = pd.DataFrame(columns=["type", "variable", "value", "unit"]) - # add objective cost - sol.loc[sol.shape[0]] = ["objective", "cost", solved_network.objective, "EUR"] - print("added objective cost to sol") - - # add all generators with name and production value - df_gen = solved_network.generators.p_nom_opt.T.to_frame() - df_gen.reset_index(inplace=True) - df_gen = df_gen.rename(columns={"Generator": "variable", "now": "value"}) - df_gen.insert(0, "type", "generator") - df_gen.insert(3, "unit", unit) - sol = pd.concat([sol, df_gen], ignore_index=True) - print("added generators to sol") - - # add all links with names and flows - df_links = solved_network.links.p_nom_opt.T.to_frame() - df_links.reset_index(inplace=True) - df_links = df_links.rename(columns={"Link": "variable", "p_nom_opt": "value"}) - df_links.insert(0, "type", "link") - df_links.insert(3, "unit", unit) - sol = pd.concat([sol, df_links], ignore_index=True) - print("added links to sol") - - # add all bus (balance) - who is importing/exporting - df_bus = solved_network.buses_t.p.T - df_bus.reset_index(inplace=True) - df_bus = df_bus.rename(columns={"Bus": "variable", "now": "value"}) - df_bus.insert(0, "type", "bus") - df_bus.insert(3, "unit", unit + "/a") - sol = pd.concat([sol, df_bus], ignore_index=True) - print("added bus_balances to sol") - - # how much of capacity is actually being used per bus? - df_bus_cap = ( - ( - solved_network.generators.groupby(["bus"]).p_nom_opt.sum() - / solved_network.generators.groupby(["bus"]).p_nom_max.sum() - ) - * 100 - ).to_frame() - df_bus_cap.reset_index(inplace=True) - df_bus_cap = df_bus_cap.rename( - columns={df_bus_cap.columns[0]: "variable", df_bus_cap.columns[1]: "value"} - ) - df_bus_cap.insert(0, "type", "used bus capacity") - df_bus_cap.insert(3, "unit", "%") - sol = pd.concat([sol, df_bus_cap], ignore_index=True) - print("added bus_capacities to sol") - - sol.to_csv(snakemake.output.trade_result) - network.export_to_netcdf(snakemake.output.trade_network) - - return - - -def plot_trade_network(n): - # creating color dataframe for type of transportation method - df_link = n.links.type.astype(str).to_frame() - df_link.reset_index(inplace=True) - df_link = df_link.rename( - columns={df_link.columns[0]: "Link", df_link.columns[1]: "color"} - ) - # setting all as default to green - df_link["color"] = "lightgreen" - # shipping links are changed to blue - df_link.loc[df_link["Link"].str.contains("shipping"), "color"] = "skyblue" - df_link.set_index("Link", inplace=True) - - # creating figure - fig = plt.figure() - region_gen = n.generators.groupby(["bus"]).p_nom_opt.sum() - region_load = n.loads.groupby(["bus"]).p_set.sum() - link_flow = n.links.p_nom_opt.astype(int) - n.plot( - bus_sizes=region_load * plot_config["bus_size"], # 1e-8 - bus_colors="seagreen", - bus_alpha=1, - link_widths=0, - branch_components=["Link"], - ) # the load at bus in green - n.plot( - bus_sizes=region_gen * plot_config["bus_size"], # 1e-8 - bus_colors="lightsteelblue", - bus_alpha=0.7, - link_widths=link_flow * plot_config["link_width"], # 1e-9 - branch_components=["Link"], - link_colors=df_link["color"], - ) # the gen at bus in light blue - - legend_elements = [ - plt.Line2D([0], [0], color="lightgreen", label="pipeline"), - plt.Line2D([0], [0], color="lightblue", label="shipping"), - plt.Line2D( - [0], - [0], - marker="o", - color="white", - label="Demand", - markerfacecolor="seagreen", - markersize=10, - ), - plt.Line2D( - [0], - [0], - marker="o", - color="white", - label="Supply", - markerfacecolor="lightsteelblue", - markersize=10, - ), - ] - fig.legend(handles=legend_elements, frameon=False) - - # fig.suptitle("scenario:{}-{}-{}".format(snakemake.wildcards["cost_year"],snakemake.wildcards["transport_cost"],snakemake.wildcards["demand"])) - fig.savefig(snakemake.output.trade_plot, format="pdf") - return - - -if __name__ == "__main__": - if snakemake is None: - from _helpers import mock_snakemake - - snakemake = mock_snakemake( - "model_trade", - transport_cost="custom", - cost_year="2030", - demand=1, - product="steel", - ) - - product = snakemake.wildcards["product"] - - print("starting up with all regions--- ") - # making dataframes - transport_costs = pd.read_csv(snakemake.input.transport_costs, header=0) - trade_options = pd.read_csv(snakemake.input.trade_options, header=0) - supply_curves = snakemake.input.supply_curves - bus_locations = pd.read_csv(snakemake.input.bus_locations, header=0) - if product == "steel": - demands = pd.read_csv(snakemake.input.steel_demand, header=0) - demands.rename(columns={"SteelProductionMt": "demand"}, inplace=True) - demands["demand"] = demands["demand"] * 1e6 # Mt to t - unit = "t" - cost_descriptor = "LCOS" - - elif product == "hydrogen": - demands = pd.read_csv(snakemake.input.demand, header=0) - unit = "MWh" - cost_descriptor = "LCOH" - else: - raise ValueError("Product must be either 'steel' or 'hydrogen'.") - - plot_config = snakemake.config["plot"]["world_map"][product] - - print("data loaded successfully") - - # building model - print("building model") - network = building_model(supply_curves, demands, bus_locations, product) - - # building transport network connecting the individual buses - print("building transportation links") - create_links(transport_costs, trade_options) - - # solving model - print("solving model") - solver_cfg = snakemake.config["solver"] - solver_name = os.getenv("SHIFT_SOLVER", solver_cfg["name"]) - options_key = os.getenv("SHIFT_SOLVER_OPTIONS", solver_cfg["options"]) - solver_options = snakemake.config["solver_options"][options_key] - - network.optimize( - network.snapshots, - solver_name=solver_name, - solver_options=solver_options, - ) - print("network was solved") - - # saving results and calculating LCOH - print("saving results as network+csv and pdf") - save_trade_network(network) - plot_trade_network(network) From 15236ffded068f09e4c7c0b3c23c5e66912c2e3e Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 11:39:10 +0200 Subject: [PATCH 093/216] chore: remove duplicated dir creation --- workflow/scripts/build_x_supply_chain.py | 1 - 1 file changed, 1 deletion(-) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 075b75e..6a960b9 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -479,7 +479,6 @@ def _set_meta(network: pypsa.Network, group: dict | None) -> None: out_dir = str(Path(output_path).resolve().parent) generic_dir = Path(out_dir) / ".." / "generic_production_model" generic_dir = generic_dir.resolve() - generic_dir.mkdir(parents=True, exist_ok=True) full_out = generic_dir / f"generic_model_{year}.nc" full_network.export_to_netcdf(str(full_out)) logger.info(f"Full generic model exported to {full_out}") From 8cbe97e8ddecc65495673718f0e4413968093a7d Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 11:47:26 +0200 Subject: [PATCH 094/216] chore: move additonal tech parameters into config --- config/config.yaml | 7 +++++-- rules/supply_curves.smk | 2 +- workflow/scripts/build_x_supply_chain.py | 26 ++++++++++++++++-------- 3 files changed, 24 insertions(+), 11 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 825300c..43ac218 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -199,8 +199,11 @@ design: # South_America: 0.7 # Oceania: 0.8 -costs: - version: v0.14.0 +techno-economic parameters: + pypsa_tech_version: v0.14.0 + additional_parameters: + h2_standing_loss: 0.001 # 0.1% per hour for underground cavern (leakage) + batt_standing_loss: 0.0001 # 0.01% per hour for battery (self-discharge) interest_rate: default: 0.05 diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 1712c4c..18325f5 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -64,7 +64,7 @@ rule retrieve_cost_data: resources: mem_mb=500, params: - version=config["costs"]["version"], + version=config["techno-economic parameters"]["pypsa_tech_version"], script: str(SCRIPT_DIR / "tech_database.py") diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 6a960b9..a0cfff4 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -23,7 +23,7 @@ - Exported to NetCDF format for storage and further analysis Reusable pattern for any commodity with similar conversion chains. -Modify TECH_ASSUMPTIONS, bus definitions, and links to adapt to different commodities. +Modify the techno-economic parameters, bus definitions, and links to adapt to different commodities. """ import logging @@ -49,11 +49,21 @@ snakemake: Any = globals().get("snakemake") -# Technology parameters with no database source (assumed values) -TECH_ASSUMPTIONS = { - "h2_standing_loss": 0.001, # 0.1% per hour for underground cavern (leakage) - "batt_standing_loss": 0.0001, # 0.01% per hour for battery (self-discharge) -} +def _techno_economic_parameters(config: dict) -> dict: + return config.get("techno-economic parameters", {}) + + +def _additional_parameters(config: dict) -> dict: + return _techno_economic_parameters(config).get("additional_parameters", {}) + + +def _additional_parameter(config: dict, name: str, default: float) -> float: + parameters = _additional_parameters(config) + if name not in parameters: + logger.warning( + f"Missing techno-economic parameter '{name}'; using default value {default}." + ) + return float(parameters.get(name, default)) def _part_load(config: dict, technology: str, default: float) -> float: @@ -319,7 +329,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> overnight_cost=h2_inv_cost, # EUR/kWh → EUR/MWh lifetime=td.get_tech_param(h2_params, "lifetime", 100.0), fom_cost=h2_inv_cost * (td.get_tech_param(h2_params, "FOM", 0.0) / 100), - standing_loss=TECH_ASSUMPTIONS["h2_standing_loss"], + standing_loss=_additional_parameter(config, "h2_standing_loss", 0.0), e_cyclic=True, # End state must equal start state ) @@ -365,7 +375,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> overnight_cost=batt_store_cost, # EUR/kWh → EUR/MWh lifetime=td.get_tech_param(batt_store_params, "lifetime", 30.0), fom_cost=batt_store_cost * 0.0, - standing_loss=TECH_ASSUMPTIONS["batt_standing_loss"], + standing_loss=_additional_parameter(config, "batt_standing_loss", 0.0), e_cyclic=True, # End state must equal start state ) From 02fc08b00abeedbdadb3e2a3e1cc193009135632 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 11:49:31 +0200 Subject: [PATCH 095/216] chore: write proper elif --- workflow/scripts/calculate_lcox.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index a4c9989..eb5e532 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -115,19 +115,19 @@ def add_loads_to_network(network, product, demands): ) # Mt/year → t/h unit_str = "t/h" - if product == "steel": - bus_name = "steel" - storage_name = "steel_storage" - # Steel is measured in t/year, convert to t/h (hourly) + elif product == "hbi": + bus_name = "hbi" + storage_name = "hbi_storage" + # HBI is measured in t/year, convert to t/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR ) # Mt/year → t/h unit_str = "t/h" - elif product == "hbi": - bus_name = "hbi" - storage_name = "hbi_storage" - # HBI is measured in t/year, convert to t/h (hourly) + elif product == "steel": + bus_name = "steel" + storage_name = "steel_storage" + # Steel is measured in t/year, convert to t/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR ) # Mt/year → t/h From aa034fa4b7100102ab78f128d06206294f464b3c Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 14:02:53 +0200 Subject: [PATCH 096/216] fix: adjust wacc paths and update steel demand input in supply curve --- rules/supply_curves.smk | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 18325f5..dd92815 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -49,10 +49,10 @@ def _product_uses_renewables(product): def _all_supply_curve_targets(): targets = [] for region in config["regions"]: - wacc = _wacc_for_region(region) + wacc = config["trade_chains"].get("wacc", "uniform") for product in SUPPLY_CURVE_PRODUCTS: targets.append( - f"resources/supply_curves/cost_year~2050/{region}_wacc_{wacc}_marginal_cost_{product}.csv" + f"resources/supply_curves/cost_year~2050/wacc~{wacc}/{region}_marginal_cost_{product}.csv" ) return targets @@ -178,25 +178,25 @@ if config["enable"].get("run_supply_curve", True): else [] ), skeleton="resources/generic_production_model/generic_model_{cost_year}.nc", - steel_demand="resources/steel_production_clustered.csv", + steel_demand="resources/steel_demand_clustered_{cost_year}.csv", output: # Public supply-curve artifact is product-labeled; the stage label # is only used to locate the correct upstream LCoX runs. - supply="resources/supply_curves/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}.csv", + supply="resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}.csv", supply_unreserved=( - "resources/supply_curves/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}__unreserved.csv" + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}__unreserved.csv" if config.get("supply_curve", {}).get("generate_unreserved", False) and _product_uses_renewables("{product}") else temp( - "resources/supply_curves_unreserved_tmp/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}__unreserved.csv" + "resources/supply_curves_unreserved_tmp/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}__unreserved.csv" ) ), - supply_curve="resources/supply_curves/cost_year~{cost_year}/{region}_wacc_{wacc}_marginal_cost_{product}.pdf", + supply_curve="resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}.pdf", log: "logs/create_supply_curve_{cost_year}_{region}_{product}_{wacc}.log", wildcard_constraints: product="hbi|steel", - wacc=r"[0-9]+(?:\.[0-9]+)?", + wacc="uniform|regional", threads: 1 message: "Combining LCo results (reserved + unreserved scenarios) to create supply curve for {wildcards.region} {wildcards.product}." From 74070047ae22463aef0805a465e0e8574836e377 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 14:06:38 +0200 Subject: [PATCH 097/216] feat: add eur usd exchange rate and energy ratios necessary for notebooks --- config/config.yaml | 7 +++++-- workflow/notebooks/global-iron-ore.ipynb | 2 +- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 43ac218..ba517c1 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -45,7 +45,6 @@ trade_chains: process_label: eaf trade_scenarios: - default - - mga-stability - mga-stability-weighted @@ -137,9 +136,12 @@ scenario: # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.01, 0.1, 1, 10, 100, 1000] # Mt/year +steel_demand_levels: [0.01, 1000] # [0.01, 0.1, 1, 10, 100, 1000] # Mt/year hydrogen_storage_cost: False +electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' +embodied_energy_steel: 2.1 #TWh/Mt or MWh/t, see iron oxide reduction + run: # prefix: "" # name: "" @@ -201,6 +203,7 @@ design: techno-economic parameters: pypsa_tech_version: v0.14.0 + eur_usd: 1.15 # 1 Euro equals 1.15 USD additional_parameters: h2_standing_loss: 0.001 # 0.1% per hour for underground cavern (leakage) batt_standing_loss: 0.0001 # 0.01% per hour for battery (self-discharge) diff --git a/workflow/notebooks/global-iron-ore.ipynb b/workflow/notebooks/global-iron-ore.ipynb index 8836881..f30720d 100644 --- a/workflow/notebooks/global-iron-ore.ipynb +++ b/workflow/notebooks/global-iron-ore.ipynb @@ -65,7 +65,7 @@ "cost_sheet_name = \"Fig 2b\"\n", "\n", "# Get conversion factors from config\n", - "eur_to_usd = config[\"costs\"][\"eur_usd\"]\n", + "eur_to_usd = config[\"techno-economic parameters\"][\"eur_usd\"]\n", "ore_to_steel = config[\"iron_ore\"][\"ore_to_steel_ratio\"]" ] }, From d57c447f87bd57b0a45251c19433c319f8dae2da Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 14:08:28 +0200 Subject: [PATCH 098/216] chore: remove depreciated notebook for steel production preparation --- workflow/notebooks/prepare-steel.ipynb | 179 ------------------------- 1 file changed, 179 deletions(-) delete mode 100644 workflow/notebooks/prepare-steel.ipynb diff --git a/workflow/notebooks/prepare-steel.ipynb b/workflow/notebooks/prepare-steel.ipynb deleted file mode 100644 index 75cd8e9..0000000 --- a/workflow/notebooks/prepare-steel.ipynb +++ /dev/null @@ -1,179 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "ed230fea", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import yaml\n", - "import pycountry" - ] - }, - { - "cell_type": "markdown", - "id": "ed5376e8", - "metadata": {}, - "source": [ - "### File paths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fedb8e0", - "metadata": {}, - "outputs": [], - "source": [ - "production_fn = \"../../resources/steel_production.csv\"\n", - "bus_locations_fn = \"../../data/bus_locations.csv\"\n", - "\n", - "# Outputs\n", - "production_clustered_fn = \"../../resources/steel_production_clustered.csv\"" - ] - }, - { - "cell_type": "markdown", - "id": "615743b5", - "metadata": {}, - "source": [ - "### Get steel data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39fc3e37", - "metadata": {}, - "outputs": [], - "source": [ - "# Read the CSV data\n", - "production = pd.read_csv(production_fn, index_col=\"ISO_A2\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a074f026", - "metadata": {}, - "outputs": [], - "source": [ - "production" - ] - }, - { - "cell_type": "markdown", - "id": "edf893b3", - "metadata": {}, - "source": [ - "### Cluster per region" - ] - }, - { - "cell_type": "markdown", - "id": "462a6acb", - "metadata": {}, - "source": [ - "### Cluster countries per region\n", - "Map each country in the production data to its region using the region definitions from the config file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cb82aa7", - "metadata": {}, - "outputs": [], - "source": [ - "# Load region definitions from config file\n", - "with open(\"../../config/config.yaml\", \"r\") as f:\n", - " config = yaml.safe_load(f)\n", - "regions = config[\"regions\"]\n", - "\n", - "# Build a mapping from country name to ISO_A2 code\n", - "country_name_to_iso = {}\n", - "for country in pycountry.countries:\n", - " country_name_to_iso[country.name] = country.alpha_2\n", - " # Add common names\n", - " if hasattr(country, \"official_name\"):\n", - " country_name_to_iso[country.official_name] = country.alpha_2\n", - "\n", - "# Build a mapping from ISO_A2 code to region\n", - "iso_to_region = {}\n", - "for region, countries in regions.items():\n", - " for country in countries:\n", - " # Some country names may have extra text (e.g., \"Togo + Algeria\"), handle them simply\n", - " for part in country.split(\"+\"):\n", - " name = part.strip()\n", - " code = country_name_to_iso.get(name)\n", - " if code:\n", - " iso_to_region[code] = region\n", - "\n", - "# Map each row in production to its region\n", - "production[\"region\"] = production.index.map(lambda iso: iso_to_region.get(iso, \"Other\"))\n", - "production.sort_values(by=\"SteelProductionMt\", ascending=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e91106f6", - "metadata": {}, - "source": [ - "### Aggregate steel production by region" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddf1796f", - "metadata": {}, - "outputs": [], - "source": [ - "# Group by region and sum all numeric columns\n", - "production_by_region = production.groupby(\"region\").sum(numeric_only=True)\n", - "production_by_region" - ] - }, - { - "cell_type": "markdown", - "id": "488d8f55", - "metadata": {}, - "source": [ - "### Save clustered steel production" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bcf3ae0f", - "metadata": {}, - "outputs": [], - "source": [ - "production_by_region.to_csv(production_clustered_fn)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "shift", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 6b3d0531327ab74aa1b588e2484768b533fabe32 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 14:56:33 +0200 Subject: [PATCH 099/216] chore: bring back notebook on conceptional model which was deleted in merge --- workflow/notebooks/conceptual-model.ipynb | 564 ++++++++++++++++++++++ 1 file changed, 564 insertions(+) create mode 100644 workflow/notebooks/conceptual-model.ipynb diff --git a/workflow/notebooks/conceptual-model.ipynb b/workflow/notebooks/conceptual-model.ipynb new file mode 100644 index 0000000..e337a6b --- /dev/null +++ b/workflow/notebooks/conceptual-model.ipynb @@ -0,0 +1,564 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "00533853", + "metadata": {}, + "outputs": [], + "source": [ + "import pypsa\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "1fa86fe9", + "metadata": {}, + "source": [ + "### Region design" + ] + }, + { + "cell_type": "markdown", + "id": "28e81337", + "metadata": {}, + "source": [ + "Region 1: Australia: High iron ore resources, expensive hydrogen and DRI, low steel demand\n", + "Region 2: Middle East: No iron ore resources, cheap hydrogen and DRI, low steel demand\n", + "Region 3: Europe: Some iron ore resources, expensive hydrogen and DRI, high steel demand" + ] + }, + { + "cell_type": "markdown", + "id": "1276a150", + "metadata": {}, + "source": [ + "### Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eea832ed", + "metadata": {}, + "outputs": [], + "source": [ + "# ====== CONFIGURATION PARAMETERS ======\n", + "# Regions\n", + "regions = ['1', '2', '3']\n", + "\n", + "# Iron ore production costs (€/t)\n", + "ore_costs = [20, 30, 50]\n", + "\n", + "# Iron ore production capacity per region (t)\n", + "ore_capacity = [200, 0, 0]\n", + "\n", + "# Iron ore trade costs (€/t HBI/DRI)\n", + "iron_ore_trade_costs = [15, 15, 15] # Regional variations in HBI transport costs\n", + "\n", + "# Steel conversion costs by region (€/t steel)\n", + "steel_conversion_costs = {\n", + " 1: [300, 400, 500], \n", + " 2: [200, 300, 400], \n", + " 3: [500, 600, 700]\n", + "}\n", + "# Steel conversion limit (t per converter per region)\n", + "steel_conversion_limit = 50\n", + "\n", + "# Steel conversion efficiency (tons steel per ton ore)\n", + "conversion_efficiency = 0.6\n", + "\n", + "# Steel trade costs (€/t steel)\n", + "steel_trade_costs = [20, 20, 20] # Uniform costs for all steel trade routes\n", + "\n", + "# Steel demands per region (t)\n", + "steel_demands = [20, 10, 70]\n", + "\n", + "# Trade capacity (large number for unlimited trade)\n", + "trade_capacity = 1000\n", + "\n", + "# ====== MODEL SETUP ======\n", + "# Create a new PyPSA network\n", + "n = pypsa.Network()\n", + "\n", + "# Add buses for iron ore (one per region)\n", + "n.madd(\"Bus\", \n", + " names=[f\"iron_ore_{region}\" for region in regions],\n", + " carrier=\"iron_ore\")\n", + "\n", + "# Add buses for steel (one per region)\n", + "n.madd(\"Bus\", \n", + " names=[f\"steel_{region}\" for region in regions],\n", + " carrier=\"steel\")\n", + "\n", + "# Add iron ore generators (one per region with different marginal costs)\n", + "n.madd(\"Generator\",\n", + " names=[f\"iron_ore_gen_{region}\" for region in regions],\n", + " bus=[f\"iron_ore_{region}\" for region in regions],\n", + " carrier=\"iron_ore_production\",\n", + " marginal_cost=ore_costs,\n", + " p_nom=ore_capacity)\n", + "\n", + "# Add links for iron ore trade between regions (with realistic HBI trade costs)\n", + "iron_ore_trade_pairs = [\n", + " (\"iron_ore_1\", \"iron_ore_2\"),\n", + " (\"iron_ore_2\", \"iron_ore_3\"),\n", + " (\"iron_ore_1\", \"iron_ore_3\")\n", + "]\n", + "\n", + "# Create bidirectional trade links\n", + "iron_ore_trade_names = []\n", + "iron_ore_trade_bus0 = []\n", + "iron_ore_trade_bus1 = []\n", + "iron_ore_trade_costs_list = []\n", + "\n", + "for i, (bus0, bus1) in enumerate(iron_ore_trade_pairs):\n", + " # Forward direction\n", + " iron_ore_trade_names.append(f\"iron_ore_trade_{bus0[-1]}_{bus1[-1]}\")\n", + " iron_ore_trade_bus0.append(bus0)\n", + " iron_ore_trade_bus1.append(bus1)\n", + " iron_ore_trade_costs_list.append(iron_ore_trade_costs[i])\n", + " \n", + " # Reverse direction\n", + " iron_ore_trade_names.append(f\"iron_ore_trade_{bus1[-1]}_{bus0[-1]}\")\n", + " iron_ore_trade_bus0.append(bus1)\n", + " iron_ore_trade_bus1.append(bus0)\n", + " iron_ore_trade_costs_list.append(iron_ore_trade_costs[i])\n", + "\n", + "n.madd(\"Link\",\n", + " names=iron_ore_trade_names,\n", + " bus0=iron_ore_trade_bus0,\n", + " bus1=iron_ore_trade_bus1,\n", + " marginal_cost=iron_ore_trade_costs_list,\n", + " p_nom=trade_capacity,\n", + " efficiency=1.0)\n", + "\n", + "# Add links for steel trade between regions (with transport costs)\n", + "steel_trade_pairs = [\n", + " (\"steel_1\", \"steel_2\"),\n", + " (\"steel_2\", \"steel_3\"),\n", + " (\"steel_1\", \"steel_3\")\n", + "]\n", + "\n", + "# Create bidirectional trade links\n", + "steel_trade_names = []\n", + "steel_trade_bus0 = []\n", + "steel_trade_bus1 = []\n", + "\n", + "for bus0, bus1 in steel_trade_pairs:\n", + " # Forward direction\n", + " steel_trade_names.append(f\"steel_trade_{bus0[-1]}_{bus1[-1]}\")\n", + " steel_trade_bus0.append(bus0)\n", + " steel_trade_bus1.append(bus1)\n", + " \n", + " # Reverse direction\n", + " steel_trade_names.append(f\"steel_trade_{bus1[-1]}_{bus0[-1]}\")\n", + " steel_trade_bus0.append(bus1)\n", + " steel_trade_bus1.append(bus0)\n", + "\n", + "n.madd(\"Link\",\n", + " names=steel_trade_names,\n", + " bus0=steel_trade_bus0,\n", + " bus1=steel_trade_bus1,\n", + " marginal_cost=steel_trade_costs * 2, # Duplicate for bidirectional links\n", + " p_nom=trade_capacity,\n", + " efficiency=1.0)\n", + "\n", + "# Add conversion links from iron ore to steel (three per region with differentiated costs)\n", + "conversion_names = []\n", + "conversion_bus0 = []\n", + "conversion_bus1 = []\n", + "conversion_costs = []\n", + "\n", + "for region_num, region in enumerate(regions, 1):\n", + " for i, cost in enumerate(steel_conversion_costs[region_num]):\n", + " conversion_names.append(f\"ore_to_steel_{region}_{i+1}\")\n", + " conversion_bus0.append(f\"iron_ore_{region}\")\n", + " conversion_bus1.append(f\"steel_{region}\")\n", + " conversion_costs.append(cost)\n", + "\n", + "n.madd(\"Link\",\n", + " names=conversion_names,\n", + " bus0=conversion_bus0,\n", + " bus1=conversion_bus1,\n", + " marginal_cost=conversion_costs,\n", + " efficiency=conversion_efficiency,\n", + " p_nom=steel_conversion_limit)\n", + "\n", + "# Add steel loads in each region\n", + "n.madd(\"Load\",\n", + " names=[f\"steel_load_{region}\" for region in regions],\n", + " bus=[f\"steel_{region}\" for region in regions],\n", + " p_set=steel_demands)\n", + "\n", + "# Display network summary\n", + "print(\"=== NETWORK SUMMARY ===\")\n", + "print(f\"Buses: {len(n.buses)}\")\n", + "print(f\"Generators: {len(n.generators)}\")\n", + "print(f\"Links: {len(n.links)}\")\n", + "print(f\"Loads: {len(n.loads)}\")\n", + "\n", + "print(\"\\n=== BUSES ===\")\n", + "print(n.buses[['carrier']])\n", + "\n", + "print(\"\\n=== GENERATORS ===\")\n", + "print(n.generators[['bus', 'carrier', 'marginal_cost', 'p_nom']])\n", + "\n", + "print(\"\\n=== LINKS ===\")\n", + "print(n.links[['bus0', 'bus1', 'marginal_cost', 'efficiency', 'p_nom']])\n", + "\n", + "print(\"\\n=== LOADS ===\")\n", + "print(n.loads[['bus', 'p_set']])" + ] + }, + { + "cell_type": "markdown", + "id": "480997f5", + "metadata": {}, + "source": [ + "### Solving" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "129c6236", + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the optimization problem\n", + "print(\"\\n=== SOLVING OPTIMIZATION ===\")\n", + "n.optimize(solver_name='gurobi')\n", + "\n", + "print(f\"Objective value: {n.objective:.2f} €\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac4fc4da", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73522f2b", + "metadata": {}, + "outputs": [], + "source": [ + "# Display results\n", + "print(\"\\n=== OPTIMIZATION RESULTS ===\")\n", + "\n", + "print(\"\\nIron ore generation by region:\")\n", + "for gen in n.generators.index:\n", + " region = gen.split('_')[-1]\n", + " generation = n.generators_t.p[gen].iloc[0] if not n.generators_t.p.empty else 0\n", + " cost = n.generators.loc[gen, 'marginal_cost']\n", + " print(f\"Region {region}: {generation:.2f} t @ {cost} €/t\")\n", + "\n", + "print(\"\\nIron ore trade flows:\")\n", + "for link in n.links.index:\n", + " if 'iron_ore_trade' in link:\n", + " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " if abs(flow) > 0.01: # Only show non-zero flows\n", + " from_region = link.split('_')[3]\n", + " to_region = link.split('_')[4]\n", + " print(f\"Region {from_region} → Region {to_region}: {flow:.2f} t\")\n", + "\n", + "print(\"\\nSteel conversion by region:\")\n", + "for link in n.links.index:\n", + " if 'ore_to_steel' in link:\n", + " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " if abs(flow) > 0.01: # Only show non-zero flows\n", + " region = link.split('_')[3]\n", + " converter = link.split('_')[4]\n", + " cost = n.links.loc[link, 'marginal_cost']\n", + " steel_production = flow * n.links.loc[link, 'efficiency']\n", + " print(f\"Region {region}, Converter {converter}: {flow:.2f} t ore → {steel_production:.2f} t steel @ {cost} €/t steel\")\n", + "\n", + "print(\"\\nSteel trade flows:\")\n", + "for link in n.links.index:\n", + " if 'steel_trade' in link:\n", + " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " if abs(flow) > 0.01: # Only show non-zero flows\n", + " from_region = link.split('_')[2]\n", + " to_region = link.split('_')[3]\n", + " print(f\"Region {from_region} → Region {to_region}: {flow:.2f} t\")\n", + "\n", + "print(\"\\nSteel demand satisfaction:\")\n", + "for load in n.loads.index:\n", + " region = load.split('_')[-1]\n", + " demand = n.loads.loc[load, 'p_set']\n", + " print(f\"Region {region}: {demand} t steel demand\")\n", + "\n", + "# Calculate total costs by component\n", + "print(\"\\n=== COST BREAKDOWN ===\")\n", + "\n", + "# Iron ore production costs\n", + "ore_production_cost = 0\n", + "for gen in n.generators.index:\n", + " generation = n.generators_t.p[gen].iloc[0] if not n.generators_t.p.empty else 0\n", + " cost = n.generators.loc[gen, 'marginal_cost']\n", + " ore_production_cost += generation * cost\n", + "\n", + "# Steel conversion costs\n", + "steel_conversion_cost = 0\n", + "for link in n.links.index:\n", + " if 'ore_to_steel' in link:\n", + " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " steel_output = flow * n.links.loc[link, 'efficiency']\n", + " cost = n.links.loc[link, 'marginal_cost']\n", + " steel_conversion_cost += steel_output * cost\n", + "\n", + "print(f\"Iron ore production cost: {ore_production_cost:.2f} €\")\n", + "print(f\"Steel conversion cost: {steel_conversion_cost:.2f} €\")\n", + "print(f\"Total cost: {ore_production_cost + steel_conversion_cost:.2f} €\")" + ] + }, + { + "cell_type": "markdown", + "id": "1836dadb", + "metadata": {}, + "source": [ + "### Additional analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0002ef20", + "metadata": {}, + "outputs": [], + "source": [ + "# n.statistics.energy_balance(aggregate_bus=False)" + ] + }, + { + "cell_type": "markdown", + "id": "8a985189", + "metadata": {}, + "source": [ + "### Plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "585f5c14", + "metadata": {}, + "outputs": [], + "source": [ + "# Iron Ore and Steel Production Flow Sankey Diagram\n", + "# This notebook creates a Sankey diagram from PyPSA optimization results\n", + "\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "import plotly.offline as pyo\n", + "from collections import defaultdict\n", + "import numpy as np\n", + "\n", + "# Enable offline plotting in Jupyter\n", + "pyo.init_notebook_mode(connected=True)\n", + "\n", + "def create_sankey_from_pypsa_results(n):\n", + " \"\"\"\n", + " Create a Sankey diagram from PyPSA network results\n", + " \n", + " Parameters:\n", + " n: PyPSA network object with solved optimization results\n", + " \"\"\"\n", + " \n", + " # Initialize data structures\n", + " nodes = []\n", + " node_dict = {}\n", + " node_counter = 0\n", + " \n", + " # Flow data for Sankey\n", + " sources = []\n", + " targets = []\n", + " values = []\n", + " flow_labels = []\n", + " \n", + " def get_or_create_node(name, category=\"\"):\n", + " \"\"\"Get existing node index or create new one\"\"\"\n", + " nonlocal node_counter\n", + " full_name = f\"{name} ({category})\" if category else name\n", + " if full_name not in node_dict:\n", + " node_dict[full_name] = node_counter\n", + " nodes.append(full_name)\n", + " node_counter += 1\n", + " return node_dict[full_name]\n", + " \n", + " # 1. Process iron ore generation\n", + " print(\"Processing iron ore generation...\")\n", + " for gen in n.generators.index:\n", + " region = gen.split('_')[-1]\n", + " generation = n.generators_t.p[gen].iloc[0] if not n.generators_t.p.empty else 0\n", + " \n", + " if generation > 0.01: # Only include significant flows\n", + " source_node = get_or_create_node(f\"Iron Ore Source R{region}\", \"Generation\")\n", + " target_node = get_or_create_node(f\"Iron Ore R{region}\", \"Supply\")\n", + " \n", + " sources.append(source_node)\n", + " targets.append(target_node)\n", + " values.append(generation)\n", + " cost = n.generators.loc[gen, 'marginal_cost']\n", + " flow_labels.append(f\"Generate: {generation:.1f}t @ {cost}€/t\")\n", + " \n", + " # 2. Process iron ore trade flows\n", + " print(\"Processing iron ore trade...\")\n", + " for link in n.links.index:\n", + " if 'iron_ore_trade' in link:\n", + " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " if abs(flow) > 0.01:\n", + " from_region = link.split('_')[3]\n", + " to_region = link.split('_')[4]\n", + " \n", + " source_node = get_or_create_node(f\"Iron Ore R{from_region}\", \"Supply\")\n", + " target_node = get_or_create_node(f\"Iron Ore R{to_region}\", \"Supply\")\n", + " \n", + " sources.append(source_node)\n", + " targets.append(target_node)\n", + " values.append(abs(flow))\n", + " flow_labels.append(f\"Trade: {flow:.1f}t\")\n", + " \n", + " # 3. Process steel conversion\n", + " print(\"Processing steel conversion...\")\n", + " for link in n.links.index:\n", + " if 'ore_to_steel' in link:\n", + " ore_flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " if abs(ore_flow) > 0.01:\n", + " region = link.split('_')[3]\n", + " converter = link.split('_')[4]\n", + " efficiency = n.links.loc[link, 'efficiency']\n", + " steel_production = ore_flow * efficiency\n", + " cost = n.links.loc[link, 'marginal_cost']\n", + " \n", + " # Iron ore to steel conversion\n", + " source_node = get_or_create_node(f\"Iron Ore R{region}\", \"Supply\")\n", + " target_node = get_or_create_node(f\"Steel R{region}\", \"Production\")\n", + " \n", + " sources.append(source_node)\n", + " targets.append(target_node)\n", + " values.append(abs(ore_flow))\n", + " flow_labels.append(f\"Convert: {ore_flow:.1f}t ore → {steel_production:.1f}t steel @ {cost}€/t\")\n", + " \n", + " # 4. Process steel trade flows\n", + " print(\"Processing steel trade...\")\n", + " for link in n.links.index:\n", + " if 'steel_trade' in link:\n", + " flow = n.links_t.p0[link].iloc[0] if not n.links_t.p0.empty else 0\n", + " if abs(flow) > 0.01:\n", + " from_region = link.split('_')[2]\n", + " to_region = link.split('_')[3]\n", + " \n", + " source_node = get_or_create_node(f\"Steel R{from_region}\", \"Production\")\n", + " target_node = get_or_create_node(f\"Steel R{to_region}\", \"Production\")\n", + " \n", + " sources.append(source_node)\n", + " targets.append(target_node)\n", + " values.append(abs(flow))\n", + " flow_labels.append(f\"Trade: {flow:.1f}t\")\n", + " \n", + " # 5. Process steel demand\n", + " print(\"Processing steel demand...\")\n", + " for load in n.loads.index:\n", + " region = load.split('_')[-1]\n", + " demand = abs(n.loads.loc[load, 'p_set'])\n", + " \n", + " if demand > 0.01:\n", + " source_node = get_or_create_node(f\"Steel R{region}\", \"Production\")\n", + " target_node = get_or_create_node(f\"Steel Demand R{region}\", \"Consumption\")\n", + " \n", + " sources.append(source_node)\n", + " targets.append(target_node)\n", + " values.append(demand)\n", + " flow_labels.append(f\"Demand: {demand:.1f}t\")\n", + " \n", + " # Create color scheme based on node categories\n", + " node_colors = []\n", + " for node in nodes:\n", + " if \"Generation\" in node:\n", + " node_colors.append(\"rgba(255, 127, 14, 0.8)\") # Orange for generation\n", + " elif \"Iron Ore\" in node and \"Supply\" in node:\n", + " node_colors.append(\"rgba(44, 160, 44, 0.8)\") # Green for iron ore supply\n", + " elif \"Steel\" in node and \"Production\" in node:\n", + " node_colors.append(\"rgba(31, 119, 180, 0.8)\") # Blue for steel production\n", + " elif \"Consumption\" in node:\n", + " node_colors.append(\"rgba(214, 39, 40, 0.8)\") # Red for consumption\n", + " else:\n", + " node_colors.append(\"rgba(148, 103, 189, 0.8)\") # Purple for other\n", + " \n", + " # Create the Sankey diagram\n", + " fig = go.Figure(data=[go.Sankey(\n", + " node=dict(\n", + " pad=15,\n", + " thickness=20,\n", + " line=dict(color=\"black\", width=0.5),\n", + " label=nodes,\n", + " color=node_colors\n", + " ),\n", + " link=dict(\n", + " source=sources,\n", + " target=targets,\n", + " value=values,\n", + " label=flow_labels,\n", + " color=\"rgba(128, 128, 128, 0.4)\"\n", + " )\n", + " )])\n", + " \n", + " # Update layout\n", + " fig.update_layout(\n", + " title_text=\"Iron Ore and Steel Production Flow Diagram\",\n", + " font_size=12,\n", + " width=1200,\n", + " height=800\n", + " )\n", + " \n", + " return fig\n", + "\n", + "\n", + "# Create and display the Sankey diagram\n", + "try:\n", + " fig = create_sankey_from_pypsa_results(n)\n", + " fig.show()\n", + " \n", + " # Optional: Save as HTML file\n", + " # fig.write_html(\"iron_steel_sankey.html\")\n", + " # print(\"Sankey diagram saved as 'iron_steel_sankey.html'\")\n", + " \n", + "except NameError:\n", + " print(\"Please ensure your PyPSA network object is named 'n' and contains solved results\")\n", + " print(\"The network should have generators, links, and loads with the naming convention shown in your output\")\n", + "\n", + "\n", + "print(\"Notebook ready! Run create_sankey_from_pypsa_results(n) with your PyPSA network object.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 93d41ddc7d2169e9380c54e5bca956f41560abf2 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 15:15:44 +0200 Subject: [PATCH 100/216] fix: have separate helper file for notebooks --- .../{_helpers.py => _helpers_notebooks.py} | 0 .../notebooks/analysis-coststructure.ipynb | 33 +++++++++++++++++-- .../notebooks/analysis-domestic-demand.ipynb | 2 +- .../analysis-globalsupplycurve.ipynb | 2 +- workflow/notebooks/analysis-hourly.ipynb | 2 +- .../notebooks/analysis-steel-hbi-split.ipynb | 2 +- workflow/notebooks/compare-scenarios.ipynb | 2 +- workflow/notebooks/global-iron-ore.ipynb | 2 +- workflow/notebooks/mga-prices.ipynb | 2 +- .../notebooks/plot-mga-examples-further.ipynb | 2 +- workflow/notebooks/plot-mga.ipynb | 2 +- workflow/notebooks/plot_countries.ipynb | 2 +- workflow/notebooks/prepare-chokepoints.ipynb | 2 +- workflow/notebooks/prepare-iron-ore.ipynb | 2 +- .../prepare-political-stability.ipynb | 2 +- workflow/notebooks/prepare-steel-demand.ipynb | 2 +- workflow/notebooks/prepare-wacc.ipynb | 2 +- 17 files changed, 46 insertions(+), 17 deletions(-) rename workflow/notebooks/{_helpers.py => _helpers_notebooks.py} (100%) diff --git a/workflow/notebooks/_helpers.py b/workflow/notebooks/_helpers_notebooks.py similarity index 100% rename from workflow/notebooks/_helpers.py rename to workflow/notebooks/_helpers_notebooks.py diff --git a/workflow/notebooks/analysis-coststructure.ipynb b/workflow/notebooks/analysis-coststructure.ipynb index d7f8988..8641ce1 100644 --- a/workflow/notebooks/analysis-coststructure.ipynb +++ b/workflow/notebooks/analysis-coststructure.ipynb @@ -19,15 +19,44 @@ "execution_count": null, "id": "2fe8468f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "WildcardError", + "evalue": "Wildcards in input files cannot be determined from output files: (rule collect_figures, line 8, /mnt/c/Users/scl38887/Documents/git/shift/rules/reporting.smk)\n'interone'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/io/__init__.py:1140\u001b[39m, in \u001b[36mapply_wildcards..format_match\u001b[39m\u001b[34m(match)\u001b[39m\n\u001b[32m 1139\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1140\u001b[39m value = \u001b[43mwildcards\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 1141\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(value) \u001b[38;5;66;03m# convert anything into a str\u001b[39;00m\n", + "\u001b[31mKeyError\u001b[39m: 'interone'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mWildcardError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/rules.py:875\u001b[39m, in \u001b[36mRule.expand_input\u001b[39m\u001b[34m(self, wildcards, groupid)\u001b[39m\n\u001b[32m 874\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m875\u001b[39m incomplete = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_apply_wildcards\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mwildcards\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43mconcretize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconcretize_iofile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[43m \u001b[49m\u001b[43mmapping\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 881\u001b[39m \u001b[43m \u001b[49m\u001b[43mincomplete_checkpoint_func\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhandle_incomplete_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 882\u001b[39m \u001b[43m \u001b[49m\u001b[43mpath_modifier\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43minput_modifier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 883\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mproperty\u001b[39;49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minput\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 884\u001b[39m \u001b[43m \u001b[49m\u001b[43mgroupid\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroupid\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 885\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 886\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m WildcardError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/rules.py:835\u001b[39m, in \u001b[36mRule._apply_wildcards\u001b[39m\u001b[34m(self, newitems, olditems, wildcards, concretize, check_return_type, omit_callable, mapping, no_flattening, aux_params, path_modifier, property, incomplete_checkpoint_func, allow_unpack, groupid, non_derived_items)\u001b[39m\n\u001b[32m 831\u001b[39m item_ = \u001b[38;5;28mself\u001b[39m.apply_path_modifier(\n\u001b[32m 832\u001b[39m item_, path_modifier, \u001b[38;5;28mproperty\u001b[39m=\u001b[38;5;28mproperty\u001b[39m\n\u001b[32m 833\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m835\u001b[39m concrete = \u001b[43mconcretize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwildcards\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_callable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 836\u001b[39m newitems.append(concrete)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/rules.py:865\u001b[39m, in \u001b[36mRule.expand_input..concretize_iofile\u001b[39m\u001b[34m(f, wildcards, from_callable)\u001b[39m\n\u001b[32m 864\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m865\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply_wildcards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwildcards\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/io/__init__.py:866\u001b[39m, in \u001b[36m_IOFile.apply_wildcards\u001b[39m\u001b[34m(self, wildcards)\u001b[39m\n\u001b[32m 864\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 865\u001b[39m file_with_wildcards_applied = IOFile(\n\u001b[32m--> \u001b[39m\u001b[32m866\u001b[39m \u001b[43mapply_wildcards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwildcards\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m 867\u001b[39m rule=\u001b[38;5;28mself\u001b[39m.rule,\n\u001b[32m 868\u001b[39m )\n\u001b[32m 869\u001b[39m file_with_wildcards_applied.clone_flags(\u001b[38;5;28mself\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/io/__init__.py:1145\u001b[39m, in \u001b[36mapply_wildcards\u001b[39m\u001b[34m(pattern, wildcards)\u001b[39m\n\u001b[32m 1143\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m WildcardError(\u001b[38;5;28mstr\u001b[39m(ex))\n\u001b[32m-> \u001b[39m\u001b[32m1145\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mWILDCARD_REGEX\u001b[49m\u001b[43m.\u001b[49m\u001b[43msub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformat_match\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpattern\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/io/__init__.py:1143\u001b[39m, in \u001b[36mapply_wildcards..format_match\u001b[39m\u001b[34m(match)\u001b[39m\n\u001b[32m 1142\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m-> \u001b[39m\u001b[32m1143\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m WildcardError(\u001b[38;5;28mstr\u001b[39m(ex))\n", + "\u001b[31mWildcardError\u001b[39m: 'interone'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mWildcardError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m_helpers_notebooks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mock_snakemake\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m snakemake = \u001b[43mmock_snakemake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcollect_figures\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmga-nwa-iso\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"\", # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\u001b[39;49;00m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcost_average\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[43mdemand\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[43m \u001b[49m\u001b[43mwacc\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muniform\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 9\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/c/Users/scl38887/Documents/git/shift/workflow/notebooks/_helpers_notebooks.py:123\u001b[39m, in \u001b[36mmock_snakemake\u001b[39m\u001b[34m(rulename, root_dir, configfiles, submodule_dir, **wildcards)\u001b[39m\n\u001b[32m 121\u001b[39m dag = sm.dag.DAG(workflow, rules=[rule])\n\u001b[32m 122\u001b[39m wc = Dict(wildcards)\n\u001b[32m--> \u001b[39m\u001b[32m123\u001b[39m job = \u001b[43msm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mjobs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mJob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 125\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mmake_accessable\u001b[39m(*ios):\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m io \u001b[38;5;129;01min\u001b[39;00m ios:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/jobs.py:244\u001b[39m, in \u001b[36mJob.__init__\u001b[39m\u001b[34m(self, rule, dag, wildcards_dict, format_wildcards, targetfile, groupid)\u001b[39m\n\u001b[32m 232\u001b[39m \u001b[38;5;28mself\u001b[39m.wildcards = Wildcards(fromdict=\u001b[38;5;28mself\u001b[39m.wildcards_dict)\n\u001b[32m 233\u001b[39m \u001b[38;5;28mself\u001b[39m._format_wildcards = (\n\u001b[32m 234\u001b[39m \u001b[38;5;28mself\u001b[39m.wildcards\n\u001b[32m 235\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m format_wildcards \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 236\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m Wildcards(fromdict=format_wildcards)\n\u001b[32m 237\u001b[39m )\n\u001b[32m 239\u001b[39m (\n\u001b[32m 240\u001b[39m \u001b[38;5;28mself\u001b[39m.input,\n\u001b[32m 241\u001b[39m input_mapping,\n\u001b[32m 242\u001b[39m \u001b[38;5;28mself\u001b[39m.dependencies,\n\u001b[32m 243\u001b[39m \u001b[38;5;28mself\u001b[39m.incomplete_input_expand,\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrule\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexpand_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mwildcards_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroupid\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroupid\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 246\u001b[39m \u001b[38;5;28mself\u001b[39m.output, output_mapping = \u001b[38;5;28mself\u001b[39m.rule.expand_output(\u001b[38;5;28mself\u001b[39m.wildcards_dict)\n\u001b[32m 247\u001b[39m \u001b[38;5;66;03m# other properties are lazy to be able to use additional parameters and check already existing files\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/shift/lib/python3.12/site-packages/snakemake/rules.py:887\u001b[39m, in \u001b[36mRule.expand_input\u001b[39m\u001b[34m(self, wildcards, groupid)\u001b[39m\n\u001b[32m 875\u001b[39m incomplete = \u001b[38;5;28mself\u001b[39m._apply_wildcards(\n\u001b[32m 876\u001b[39m \u001b[38;5;28minput\u001b[39m,\n\u001b[32m 877\u001b[39m \u001b[38;5;28mself\u001b[39m.input,\n\u001b[32m (...)\u001b[39m\u001b[32m 884\u001b[39m groupid=groupid,\n\u001b[32m 885\u001b[39m )\n\u001b[32m 886\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m WildcardError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m--> \u001b[39m\u001b[32m887\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m WildcardError(\n\u001b[32m 888\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mWildcards in input files cannot be determined from output files:\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 889\u001b[39m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[32m 890\u001b[39m rule=\u001b[38;5;28mself\u001b[39m,\n\u001b[32m 891\u001b[39m )\n\u001b[32m 893\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.dependencies:\n\u001b[32m 894\u001b[39m dependencies = {\n\u001b[32m 895\u001b[39m f: \u001b[38;5;28mself\u001b[39m.dependencies[f_]\n\u001b[32m 896\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m f, f_ \u001b[38;5;129;01min\u001b[39;00m mapping.items()\n\u001b[32m 897\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m f_ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.dependencies\n\u001b[32m 898\u001b[39m }\n", + "\u001b[31mWildcardError\u001b[39m: Wildcards in input files cannot be determined from output files: (rule collect_figures, line 8, /mnt/c/Users/scl38887/Documents/git/shift/rules/reporting.smk)\n'interone'" + ] + } + ], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", " \"collect_figures\",\n", " scenario=\"mga-nwa-iso\", # \"\", # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", " sort=\"cost_average\",\n", " demand=False,\n", + " wacc=\"uniform\"\n", ")" ] }, diff --git a/workflow/notebooks/analysis-domestic-demand.ipynb b/workflow/notebooks/analysis-domestic-demand.ipynb index 4478f19..960dba6 100644 --- a/workflow/notebooks/analysis-domestic-demand.ipynb +++ b/workflow/notebooks/analysis-domestic-demand.ipynb @@ -38,7 +38,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", " \"collect_figures\",\n", diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 2d59012..4d10c6b 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -31,7 +31,7 @@ "outputs": [], "source": [ "# if \"snakemake\" not in globals():\n", - "# from _helpers import mock_snakemake\n", + "# from _helpers_notebooks import mock_snakemake\n", "# snakemake = mock_snakemake(\n", "# \"plot_global_supply\",\n", "# scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", diff --git a/workflow/notebooks/analysis-hourly.ipynb b/workflow/notebooks/analysis-hourly.ipynb index d45bf8f..4b93efa 100644 --- a/workflow/notebooks/analysis-hourly.ipynb +++ b/workflow/notebooks/analysis-hourly.ipynb @@ -11,7 +11,7 @@ "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", - "from _helpers import load_config" + "from _helpers_notebooks import load_config" ] }, { diff --git a/workflow/notebooks/analysis-steel-hbi-split.ipynb b/workflow/notebooks/analysis-steel-hbi-split.ipynb index a6d1590..cb50c97 100644 --- a/workflow/notebooks/analysis-steel-hbi-split.ipynb +++ b/workflow/notebooks/analysis-steel-hbi-split.ipynb @@ -27,7 +27,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", " \"collect_figures\",\n", diff --git a/workflow/notebooks/compare-scenarios.ipynb b/workflow/notebooks/compare-scenarios.ipynb index a38dddb..510fd27 100644 --- a/workflow/notebooks/compare-scenarios.ipynb +++ b/workflow/notebooks/compare-scenarios.ipynb @@ -19,7 +19,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", " \"collect_figures\",\n", diff --git a/workflow/notebooks/global-iron-ore.ipynb b/workflow/notebooks/global-iron-ore.ipynb index f30720d..33dcd98 100644 --- a/workflow/notebooks/global-iron-ore.ipynb +++ b/workflow/notebooks/global-iron-ore.ipynb @@ -31,7 +31,7 @@ "outputs": [], "source": [ "# Set up snakemake context\n", - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\"retrieve_iron_ore\")" ] diff --git a/workflow/notebooks/mga-prices.ipynb b/workflow/notebooks/mga-prices.ipynb index 5d0da3e..9257c6d 100644 --- a/workflow/notebooks/mga-prices.ipynb +++ b/workflow/notebooks/mga-prices.ipynb @@ -24,7 +24,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "snakemake = mock_snakemake(\n", " \"collect_figures\",\n", " scenario=\"penalty-oc\", # penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", diff --git a/workflow/notebooks/plot-mga-examples-further.ipynb b/workflow/notebooks/plot-mga-examples-further.ipynb index ef940df..e39fc63 100644 --- a/workflow/notebooks/plot-mga-examples-further.ipynb +++ b/workflow/notebooks/plot-mga-examples-further.ipynb @@ -68,7 +68,7 @@ } ], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "import glob\n", "import os\n", "\n", diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 1be6724..1c5908d 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -17,7 +17,7 @@ "%matplotlib inline\n", "\n", "if \"snakemake\" not in globals():\n", - " from _helpers import mock_snakemake\n", + " from _helpers_notebooks import mock_snakemake\n", " snakemake = mock_snakemake(\n", " \"plot_mga\",\n", " wacc=\"uniform\"\n", diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index f9a2774..7e98de7 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -20,7 +20,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", " \"collect_figures\",\n", diff --git a/workflow/notebooks/prepare-chokepoints.ipynb b/workflow/notebooks/prepare-chokepoints.ipynb index b5aef5b..bbf35ae 100644 --- a/workflow/notebooks/prepare-chokepoints.ipynb +++ b/workflow/notebooks/prepare-chokepoints.ipynb @@ -25,7 +25,7 @@ "outputs": [], "source": [ "if \"snakemake\" not in globals():\n", - " from _helpers import mock_snakemake\n", + " from _helpers_notebooks import mock_snakemake\n", " snakemake = mock_snakemake(\n", " \"prepare_chokepoints\",\n", " )" diff --git a/workflow/notebooks/prepare-iron-ore.ipynb b/workflow/notebooks/prepare-iron-ore.ipynb index 7d59439..d2704db 100644 --- a/workflow/notebooks/prepare-iron-ore.ipynb +++ b/workflow/notebooks/prepare-iron-ore.ipynb @@ -18,7 +18,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\"prepare_iron_ore\")" ] diff --git a/workflow/notebooks/prepare-political-stability.ipynb b/workflow/notebooks/prepare-political-stability.ipynb index d5933ad..14dd3fd 100644 --- a/workflow/notebooks/prepare-political-stability.ipynb +++ b/workflow/notebooks/prepare-political-stability.ipynb @@ -20,7 +20,7 @@ "outputs": [], "source": [ "if \"snakemake\" not in globals():\n", - " from _helpers import mock_snakemake\n", + " from _helpers_notebooks import mock_snakemake\n", " snakemake = mock_snakemake(\n", " \"prepare_political_stability\",\n", " )" diff --git a/workflow/notebooks/prepare-steel-demand.ipynb b/workflow/notebooks/prepare-steel-demand.ipynb index 85816f8..be49dc0 100644 --- a/workflow/notebooks/prepare-steel-demand.ipynb +++ b/workflow/notebooks/prepare-steel-demand.ipynb @@ -19,7 +19,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "snakemake = mock_snakemake(\n", " \"prepare_steel_demand\",\n", " cost_year = 2050,\n", diff --git a/workflow/notebooks/prepare-wacc.ipynb b/workflow/notebooks/prepare-wacc.ipynb index 4d62088..1313c47 100644 --- a/workflow/notebooks/prepare-wacc.ipynb +++ b/workflow/notebooks/prepare-wacc.ipynb @@ -19,7 +19,7 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers import mock_snakemake\n", + "from _helpers_notebooks import mock_snakemake\n", "snakemake = mock_snakemake(\n", " \"prepare_wacc\",\n", " )" From 0e8192744a07dceeb59c584001562e6ccb211e5f Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 15:16:06 +0200 Subject: [PATCH 101/216] fix: hardcode first product hbi in trade model rule --- rules/trade_model.smk | 1 + 1 file changed, 1 insertion(+) diff --git a/rules/trade_model.smk b/rules/trade_model.smk index a1db08a..7137e51 100644 --- a/rules/trade_model.smk +++ b/rules/trade_model.smk @@ -12,6 +12,7 @@ rule model_trade: allow_missing=True, cost_year=[2050], region=config["regions"], + interone=["hbi"], ), supply_curves_intertwo=expand( "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{intertwo}.csv", From 7cb32a73bd39b8be983c8e4026f4ad1f219166a9 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 16:04:39 +0200 Subject: [PATCH 102/216] chore: remove legacy wacc code in trade model --- rules/trade_model.smk | 1 - workflow/scripts/model_trade.py | 31 +++---------------------------- 2 files changed, 3 insertions(+), 29 deletions(-) diff --git a/rules/trade_model.smk b/rules/trade_model.smk index 7137e51..ac71ce2 100644 --- a/rules/trade_model.smk +++ b/rules/trade_model.smk @@ -28,7 +28,6 @@ rule model_trade: steel_demand="resources/steel_demand_clustered_{cost_year}.csv", iron_ore="resources/ironore_production_clustered.csv", grid_potential="data/grid_potential_custom.csv", - wacc="resources/wacc-clustered.csv", political_stability="resources/political_stability_clustered.csv", output: trade_result=f"results/{trade_scenarios.wildcard_pattern}/result.csv", diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index aa7956b..b653b79 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -228,7 +228,9 @@ def building_model( ) p_nom = ( grid_potential.loc[region_name, "potential_mt_steel"] * 1e6 - ) / len(region_data_intertwo) # split evenly across supply steps + ) / len( + region_data_intertwo + ) # split evenly across supply steps m_cost = float( region_data_intertwo[f"{cost_descriptor} [EUR/{unit}]"][s] @@ -638,23 +640,6 @@ def apply_cost_penalty(n, cost_penalty): return n -# def apply_wacc_simple(n, wacc): - -# # Add cost pentalty to all technologies of a certain region, excluding shipping -# wacc.set_index("region", inplace=True) -# base_interest_rate = snakemake.params.interest_rate - -# for region in wacc.index: - -# capital_cost_adj = wacc.loc[region].values[0] / base_interest_rate - -# n.links.loc[ -# ((n.links.bus1 == f"{region}_steel") | (n.links.bus1 == f"{region}_hbi")) -# & ~n.links.carrier.str.contains("shipping"), -# "marginal_cost", -# ] *= capital_cost_adj - - def apply_hbi_diversity_constraint(n, diversity_factor, demands): """ Apply HBI import diversity constraint. @@ -1151,7 +1136,6 @@ def _link_weight(link_name): snakemake.input.political_stability, index_col=0 ) regions = snakemake.config["regions"] - wacc = pd.read_csv(snakemake.input.wacc, header=0) # Load indicators for MGA (flexible architecture for future extensions) indicators = {} @@ -1219,15 +1203,6 @@ def _link_weight(link_name): ) n = apply_cost_penalty(n, cost_penalty) - # Note: Only relevant when capital costs are added in this script. Currently, they are added only in model_lcox - # Country specific wacc adjustment (simplified) - # if snakemake.wildcards.wacc == "regional": - # print(f"applying region specific wacc (simplified)") - # n = apply_wacc_simple(n, wacc) - # else: - # pass - - # MGA # HBI diversity constraint diversity_factor = snakemake.config["trade"]["diversity_factor"] if diversity_factor is not False: From 5701a3649c20e5143b8f8bbc8b1d2d773ce6cdca Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 16:23:48 +0200 Subject: [PATCH 103/216] feat: proposed workflow structure of wacc integration --- rules/supply_curves.smk | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index dd92815..cc5bf57 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -98,11 +98,12 @@ rule prepare_regional_network: renewables="data/new_renewables_consolidated.nc", tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", + wacc = "../resources/wacc-clustered.csv", output: # Output keyed by product; route_label is internal to the script - network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", + network="resources/networks/base_{cost_year}_{region}_{wacc}_{product}_{scenario}.nc", log: - "logs/prepare_regional_network_{cost_year}_{region}_{product}_{scenario}.log", + "logs/prepare_regional_network_{cost_year}_{region}_{wacc}_{product}_{scenario}.log", wildcard_constraints: scenario="reserved|unreserved", product="hbi|steel", @@ -114,6 +115,7 @@ rule prepare_regional_network: product="{product}", route_label=lambda wildcards: _process_label_for_product(wildcards.product), config=config, + uniform_interest_rate=config["interest_rate"]["default"] message: "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.product} " "(cost_year={wildcards.cost_year})" @@ -125,22 +127,22 @@ if config["enable"].get("run_supply_chain", True): rule calculate_regional_lcox: input: - base_network="resources/networks/base_{cost_year}_{region}_{product}_{scenario}.nc", + base_network="resources/networks/base_{cost_year}_{region}_{wacc}_{product}_{scenario}.nc", local_demand="data/un_enerdata_demand_2050_final.csv", output: # Internal cache keyed by route_label for reuse; only products matter for supply curves - results="resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/results_{product_demand_mt}.csv", + results="resources/lco-{product}/cost_year~{cost_year}/wacc~{wacc}/{region}_{scenario}/results_{product_demand_mt}.csv", network=( temp( - "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" + "resources/lco-{product}/cost_year~{cost_year}/wacc~{wacc}/{region}_{scenario}/network_{product_demand_mt}.nc" ) if not config.get("outputs", {}).get( "keep_optimization_networks", False ) - else "resources/lco-{product}/cost_year~{cost_year}/{region}_{scenario}/network_{product_demand_mt}.nc" + else "resources/lco-{product}/cost_year~{cost_year}/wacc~{wacc}/{region}_{scenario}/network_{product_demand_mt}.nc" ), log: - "logs/calculate_regional_lcox_{cost_year}_{region}_{product}_{scenario}_{product_demand_mt}.log", + "logs/calculate_regional_lcox_{cost_year}_{region}_{wacc}_{product}_{scenario}_{product_demand_mt}.log", wildcard_constraints: product_demand_mt=r"\d+(?:\.\d+)?", scenario="reserved|unreserved", @@ -165,12 +167,12 @@ if config["enable"].get("run_supply_curve", True): rule create_supply_curve: input: lco_reserved=lambda wildcards: expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/wacc~{wildcards.wacc}/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ), lco_unreserved=lambda wildcards: ( expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_unreserved/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_unreserved/wacc~{wildcards.wacc}/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ) if config.get("supply_curve", {}).get("generate_unreserved", False) From e36770b5d36cf6e1bdb6f4737b3c1d8709c5c5dc Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 21 May 2026 16:24:25 +0200 Subject: [PATCH 104/216] feat: proposed integration of wacc in regional network (incomplete) --- workflow/scripts/prepare_regional_network.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index ece92ba..479c6c8 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -779,6 +779,19 @@ def prepare_network( f"Skeleton sliced to {route_label}: {len(network.links)} links, {len(network.stores)} stores" ) + # WORK IN PROGRESS START: ADD WACC + uniform_interest_rate = snakemake.params.uniform_interest_rate + + if snakemake.wildcards.wacc == "regional": + print(f"applying region specific wacc") + wacc = pd.read_csv(snakemake.input.wacc, header=0) + wacc.set_index("region", inplace=True) + regional_wacc = wacc.loc[region].values[0] + interest_rate = regional_wacc + + # TODO: APPLY REGION SPECIFIC WACC + # WORK IN PROGRESS END: ADD WACC + # Set region-specific discount rate interest_rates = config.get("interest_rate", {}) # Get region-specific rate, or fall back to default From d44c855463eeb6c6d5e39b0c156036d25de10fd8 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 17:16:47 +0200 Subject: [PATCH 105/216] fix: repair lco steel calculation --- workflow/scripts/prepare_regional_network.py | 14 ++++++++++---- workflow/scripts/trade_chain_utils.py | 15 +++++++++++++-- 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index ece92ba..da09954 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -724,13 +724,19 @@ def prepare_network( # Add free external inputs for any material bus that is expected but # not produced within this sliced network (e.g., `hbi` for the steel stage). # Determine whether a kept bus is produced by any remaining link. + # Determine which buses are *produced* by remaining links. + # Convention in this codebase: `bus1` is the primary output bus + # for conversion Links (bus0 is typically an input). Previously we + # treated any referenced bus as "produced" which incorrectly + # prevented adding external inputs for buses that are actually + # inputs (e.g., `hbi` for the `eaf` link). Only consider `bus1` + # as an output to decide whether a bus is produced by the sliced + # network. produced_buses = set() for link_name in network.links.index: row = network.links.loc[link_name] - for bcol in [ - c for c in ["bus0", "bus1", "bus2", "bus3"] if c in row.index - ]: - b = row.get(bcol) + if "bus1" in row.index: + b = row.get("bus1") if pd.notna(b): produced_buses.add(b) diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py index 9cbc3a4..f06be96 100644 --- a/workflow/scripts/trade_chain_utils.py +++ b/workflow/scripts/trade_chain_utils.py @@ -319,8 +319,19 @@ def build_product_components(config: Dict, product: str) -> Dict[str, object]: if comp: links.update(comp.get("links", ())) stores.update(comp.get("stores", ())) - for b in comp.get("buses", ()): # include any canonical buses from mapping - buses.add(_normalize_commodity(b)) + # Include canonical buses from mapping, but avoid adding energy-carrier + # buses (e.g., renewable_electricity, grid_electricity) unless the + # stage explicitly declares them as energy inputs. This prevents + # slicers from preserving unused energy buses for stages that only + # consume material inputs (e.g., steel stage using grid_electricity + # only when declared). + declared_energy_norm = {_normalize_commodity(e) for e in energy} + for b in comp.get("buses", ()): + normb = _normalize_commodity(b) + # If this is an energy input carrier, only keep it when declared + if normb in ENERGY_INPUTS and normb not in declared_energy_norm: + continue + buses.add(normb) # If this stage-group uses renewable electricity, include battery # storage and bus as an explicit component so slicers keep batteries # for renewable-based stages. The user requested batteries be explicit From e3402d7ccc13ee059afe48ced06fee75c7e76a10 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Thu, 21 May 2026 17:41:39 +0200 Subject: [PATCH 106/216] fix: add gurobi channel back in --- pixi.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pixi.toml b/pixi.toml index 3086e8d..4e2b18d 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,13 +1,12 @@ [workspace] authors = ["energyls "] -channels = ["conda-forge", "bioconda"] +channels = ["conda-forge", "bioconda", "gurobi"] name = "shift" description = "Techno-economic optimization of global iron and steel supply chains under decarbonization" platforms = ["linux-64", "win-64"] version = "0.1.0" [tasks] -snakemake = "snakemake" [dependencies] atlite = ">=0.3,!=0.5.0" From 8a0a4fbbbb0528c3ee074e004fa99a28a04ea53d Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 10:29:24 +0200 Subject: [PATCH 107/216] fix: update snakemake path for full workflow --- rules/supply_curves.smk | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index cc5bf57..1dbe343 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -98,7 +98,7 @@ rule prepare_regional_network: renewables="data/new_renewables_consolidated.nc", tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", - wacc = "../resources/wacc-clustered.csv", + wacc = "resources/wacc-clustered.csv", output: # Output keyed by product; route_label is internal to the script network="resources/networks/base_{cost_year}_{region}_{wacc}_{product}_{scenario}.nc", @@ -167,12 +167,12 @@ if config["enable"].get("run_supply_curve", True): rule create_supply_curve: input: lco_reserved=lambda wildcards: expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_reserved/wacc~{wildcards.wacc}/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/wacc~{wildcards.wacc}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ), lco_unreserved=lambda wildcards: ( expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/{wildcards.region}_unreserved/wacc~{wildcards.wacc}/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/wacc~{wildcards.wacc}/{wildcards.region}_unreserved/results_{{product_demand_mt}}.csv", product_demand_mt=config.get("steel_demand_levels"), ) if config.get("supply_curve", {}).get("generate_unreserved", False) From 23e9a4535b5d365f83f3b395140b8acb6ca1f03a Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 10:53:33 +0200 Subject: [PATCH 108/216] feat: integrate wacc in prepare_regional_network fully --- workflow/scripts/prepare_regional_network.py | 59 ++++++++------------ 1 file changed, 23 insertions(+), 36 deletions(-) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 479c6c8..aba2409 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -284,7 +284,7 @@ def add_renewable_generators( network.add("Bus", elec_bus, carrier="renewable_electricity", unit="MW") # Get discount rate - discount_rate = network.discount_rate if hasattr(network, "discount_rate") else 0.07 + discount_rate = network.discount_rate # Map consolidated file tech names to database keys tech_db_map = { @@ -779,29 +779,20 @@ def prepare_network( f"Skeleton sliced to {route_label}: {len(network.links)} links, {len(network.stores)} stores" ) - # WORK IN PROGRESS START: ADD WACC - uniform_interest_rate = snakemake.params.uniform_interest_rate - if snakemake.wildcards.wacc == "regional": print(f"applying region specific wacc") wacc = pd.read_csv(snakemake.input.wacc, header=0) wacc.set_index("region", inplace=True) - regional_wacc = wacc.loc[region].values[0] - interest_rate = regional_wacc - - # TODO: APPLY REGION SPECIFIC WACC - # WORK IN PROGRESS END: ADD WACC - - # Set region-specific discount rate - interest_rates = config.get("interest_rate", {}) - # Get region-specific rate, or fall back to default - if isinstance(interest_rates.get(region), dict): - # Handle legacy component-level structure (flatten to use default) - discount_rate = interest_rates[region].get( - "default", interest_rates.get("default", 0.07) - ) + discount_rate = wacc.loc[region].values[0] + + elif snakemake.wildcards.wacc == "uniform": + discount_rate = snakemake.params.uniform_interest_rate + else: - discount_rate = interest_rates.get(region, interest_rates.get("default", 0.07)) + raise ValueError( + f"Unrecognized wacc wildcard: {snakemake.wildcards.wacc}. " + f"Expected 'regional' or 'uniform'." + ) network.discount_rate = discount_rate logger.info(f"Region {region}: discount_rate = {discount_rate}") @@ -1053,6 +1044,19 @@ def validate_network_carriers(n: pypsa.Network): # ============================================================================ if __name__ == "__main__": + + if snakemake is None: + from _helpers import mock_snakemake + + snakemake = mock_snakemake( + "prepare_regional_network", + cost_year="2050", + region="South_America", + product="hbi", + scenario="reserved", + wacc="regional", + ) + # Check if running from Snakemake if snakemake is not None: # Snakemake inputs/outputs @@ -1083,23 +1087,6 @@ def validate_network_carriers(n: pypsa.Network): # Load config (if available) config_dict = snakemake.config if snakemake is not None else {} - else: - # Fallback for manual execution - import sys - - if len(sys.argv) > 1: - skeleton_path = sys.argv[1] - renewables_path = sys.argv[2] - tech_costs_path = sys.argv[3] - region = sys.argv[4] - product = sys.argv[5] - output_path = sys.argv[6] - cost_year = int(sys.argv[7]) if len(sys.argv) > 7 else 2030 - route_label = sys.argv[8] if len(sys.argv) > 8 else None - local_demand_path = None - config_dict = {} - else: - raise ValueError("Provide paths and region/product as arguments") # Prepare network network, audit = prepare_network( From f2aa6f4d5c84218e0e4259675b11e6c743a9c484 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 11:31:14 +0200 Subject: [PATCH 109/216] chore: remove config wacc regional values --- config/config.yaml | 18 +----------------- 1 file changed, 1 insertion(+), 17 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index ba517c1..fb94b92 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -209,23 +209,7 @@ techno-economic parameters: batt_standing_loss: 0.0001 # 0.01% per hour for battery (self-discharge) interest_rate: - default: 0.05 - # Regional discount rates (override default for specific regions): - Central_America: 0.11 - East_Asia: 0.09 - East_East_Asia: 0.08 - Eurasia: 0.10 - Europe: 0.10 - Far_West_Europe: 0.08 - Middle_East: 0.12 - North_America: 0.08 - North_West_Africa: 0.12 - Oceania: 0.07 - Pacific_Asia: 0.10 - South_America: 0.11 - South_South_America: 0.10 - Subsaharan_Africa: 0.11 - West_Asia: 0.11 + default: 0.05 # Used, if wildcard `wacc` is set to `uniform` trade: shipping_routes: From 89ff4a799d711508c6baa8f8d869ad38902f72e2 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 11:32:49 +0200 Subject: [PATCH 110/216] chore: remove _wacc_for_region depreciated function --- rules/supply_curves.smk | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 1dbe343..ccba840 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -12,17 +12,6 @@ def _process_label_for_product(product): return route_label_for_product(config, product) -def _wacc_for_region(region): - interest_rates = config.get("interest_rate", {}) - if isinstance(interest_rates.get(region), dict): - rate = interest_rates[region].get( - "default", interest_rates.get("default", 0.07) - ) - else: - rate = interest_rates.get(region, interest_rates.get("default", 0.07)) - return f"{float(rate):.2f}" - - def _product_uses_renewables(product): """Check if a product's stage group uses renewable_electricity. From 43ffab6eccf582ac8c7c9663c9452961c49f447a Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 12:14:12 +0200 Subject: [PATCH 111/216] feat: add rule to download labour data --- rules/preparation.smk | 9 + workflow/scripts/download_labour_data.py | 760 +++++++++++++++++++++++ 2 files changed, 769 insertions(+) create mode 100644 workflow/scripts/download_labour_data.py diff --git a/rules/preparation.smk b/rules/preparation.smk index 92ac051..efd1b45 100644 --- a/rules/preparation.smk +++ b/rules/preparation.smk @@ -1,6 +1,15 @@ +rule download_labour_data: + output: + merged = "../resources/merged_labour_inputs.csv", + resources: + mem_mb=2000, + threads: 1 + script: + "scripts/download_labour_data.py" + rule prepare_wacc: input: diff --git a/workflow/scripts/download_labour_data.py b/workflow/scripts/download_labour_data.py new file mode 100644 index 0000000..c988da6 --- /dev/null +++ b/workflow/scripts/download_labour_data.py @@ -0,0 +1,760 @@ +""" +data_downloader.py +================== +Downloads and prepares all input data needed for labour_cost_calculator.py +to run on any country worldwide. + +Data sources +------------ +A. World Bank WDI – GNI per capita (Atlas method, current USD) + Automated via the `wbgapi` package. Requires: pip install wbgapi + +B. ECB – Annual average USD/EUR spot rates + Automated via the ECB public REST API (no credentials needed). + +C. UNIDO INDSTAT – Steel sector wage bill & employment + Raw data files are expected in data/labour/unido-raw/data.csv + (downloaded from https://stat.unido.org/data/download, + INDSTAT Rev 4, ISIC 241, variables 04+05, all countries). + Licence: CC BY 4.0 + +D. Employer SSC rates – statutory employer social-security contribution rates + Pre-compiled reference table for ~80 countries. + Sources: OECD Taxing Wages 2023/24, ILO Social Security Inquiry 2022, + KPMG Global Employer Tax Guide 2023. + +Outputs (saved to data/labour/) +-------------------------------- + data/labour/gni_per_capita.csv + data/labour/ecb_usd_eur.csv + data/labour/employer_contributions.csv + data/labour/merged_labour_inputs.csv ← main output for labour_cost_calculator.py + +Usage +----- + python workflow/scripts/data_downloader.py [--target-year 2020] [--force] +""" + +import sys +import warnings +import requests +import pandas as pd +import pycountry +from io import StringIO +from pathlib import Path + +# ── optional wbgapi ────────────────────────────────────────────────────────── +try: + import wbgapi as wb + + HAS_WBGAPI = True +except ImportError: + HAS_WBGAPI = False + +# ───────────────────────────────────────────────────────────────────────────── +# File paths (resolved relative to this script → works from any cwd) +# ───────────────────────────────────────────────────────────────────────────── +REPO_ROOT = Path(__file__).resolve().parent.parent.parent +DATA_DIR = REPO_ROOT / "data" / "labour" +DATA_DIR.mkdir(parents=True, exist_ok=True) + +# UNIDO raw data: data/labour/unido-raw/data.csv +UNIDO_RAW = DATA_DIR / "unido-raw" / "data.csv" +GNI_CSV = DATA_DIR / "gni_per_capita.csv" +ECB_CSV = DATA_DIR / "ecb_usd_eur.csv" +CONTRIB_CSV = DATA_DIR / "employer_contributions.csv" +MERGED_CSV = DATA_DIR / "merged_labour_inputs.csv" + +# ───────────────────────────────────────────────────────────────────────────── +# OECD member list (ISO-3, as of 2024) +# ───────────────────────────────────────────────────────────────────────────── +OECD_MEMBERS = { + "AUS", + "AUT", + "BEL", + "CAN", + "CHL", + "COL", + "CRI", + "CZE", + "DNK", + "EST", + "FIN", + "FRA", + "DEU", + "GRC", + "HUN", + "ISL", + "IRL", + "ISR", + "ITA", + "JPN", + "KOR", + "LVA", + "LTU", + "LUX", + "MEX", + "NLD", + "NZL", + "NOR", + "POL", + "PRT", + "SVK", + "SVN", + "ESP", + "SWE", + "CHE", + "TUR", + "GBR", + "USA", +} + + +# ───────────────────────────────────────────────────────────────────────────── +# A. World Bank GNI per capita +# ───────────────────────────────────────────────────────────────────────────── +def fetch_world_bank_gni(start_year=2010, end_year=2023, force=False): + """ + Download GNI per capita (Atlas method, current USD) for all World Bank + economies covering *start_year*–*end_year*. + + Requires the `wbgapi` package: pip install wbgapi + + Returns a tidy DataFrame: iso3 | year | gni_usd + Saves result to data/gni_per_capita.csv. + """ + if GNI_CSV.exists() and not force: + print(f"[GNI] Loading cached → {GNI_CSV}") + return pd.read_csv(GNI_CSV) + + if not HAS_WBGAPI: + sys.exit( + "ERROR: wbgapi is required to download World Bank GNI data.\n" + " pip install wbgapi" + ) + + print("[GNI] Downloading from World Bank (NY.GNP.PCAP.CD) …") + try: + raw = wb.data.DataFrame( + "NY.GNP.PCAP.CD", + time=range(start_year, end_year + 1), + skipBlanks=True, + columns="time", + ) + # raw: index = economy (ISO3), columns = "YR2010" … "YR2023" + df = ( + raw.reset_index() + .rename(columns={"economy": "iso3"}) + .melt(id_vars="iso3", var_name="year", value_name="gni_usd") + ) + df["year"] = df["year"].str.replace("YR", "").astype(int) + df = df.dropna(subset=["gni_usd"]) + df.to_csv(GNI_CSV, index=False) + print(f"[GNI] {len(df)} rows → {GNI_CSV}") + return df + + except Exception as exc: + sys.exit(f"ERROR downloading World Bank GNI data: {exc}") + + +# ───────────────────────────────────────────────────────────────────────────── +# B. ECB USD/EUR annual average exchange rates +# ───────────────────────────────────────────────────────────────────────────── + +# Fallback table: ECB EXR.A.USD.EUR.SP00.A annual averages (USD per 1 EUR). +# Source: European Central Bank Statistical Data Warehouse. +# Last updated: 2024. Invert to obtain EUR per USD. +_ECB_FALLBACK_USD_PER_EUR = { + 2000: 0.9236, + 2001: 0.8956, + 2002: 0.9454, + 2003: 1.1312, + 2004: 1.2438, + 2005: 1.2441, + 2006: 1.2556, + 2007: 1.3705, + 2008: 1.4726, + 2009: 1.3948, + 2010: 1.3257, + 2011: 1.3920, + 2012: 1.2848, + 2013: 1.3281, + 2014: 1.3285, + 2015: 1.0859, + 2016: 1.1069, + 2017: 1.1297, + 2018: 1.1810, + 2019: 1.1195, + 2020: 1.1422, + 2021: 1.1827, + 2022: 1.0530, + 2023: 1.0813, + 2024: 1.0815, +} + + +def _parse_ecb_csv(text): + """Parse an ECB SDMX-CSV response into a tidy DataFrame.""" + raw = pd.read_csv(StringIO(text)) + time_col = next(c for c in raw.columns if "TIME" in c.upper()) + val_col = next(c for c in raw.columns if "OBS_VALUE" in c.upper()) + df = raw[[time_col, val_col]].copy() + df.columns = ["year", "usd_per_eur"] + df["year"] = df["year"].astype(int) + df["eur_per_usd"] = 1.0 / df["usd_per_eur"] + return df + + +def fetch_ecb_rates(start_year=2010, end_year=2023, force=False): + """ + Obtain annual average USD/EUR spot rates from the ECB. + + Series: EXR.A.USD.EUR.SP00.A → USD per 1 EUR. + Inverted to EUR per USD (= EUR_per_USD used in the paper). + + Strategy: + 1. Load cached CSV if present (and not --force). + 2. Try ECB SDW-WSREST API (old, well-documented endpoint). + 3. Try ECB Data Portal API v1 (new endpoint). + 4. Fall back to the embedded _ECB_FALLBACK_USD_PER_EUR table. + + Returns: year | usd_per_eur | eur_per_usd + Saves to data/ecb_usd_eur.csv. + """ + if ECB_CSV.exists() and not force: + print(f"[ECB] Loading cached → {ECB_CSV}") + return pd.read_csv(ECB_CSV) + + ecb_attempts = [ + ( + "ECB SDW-WSREST", + ( + "https://sdw-wsrest.ecb.europa.eu/service/data/EXR/A.USD.EUR.SP00.A" + f"?format=csvdata&startPeriod={start_year}&endPeriod={end_year}" + ), + {}, + ), + ( + "ECB Data Portal v1 (Accept: text/csv)", + ( + "https://data.ecb.europa.eu/api/v1/data/EXR/A.USD.EUR.SP00.A" + f"?startPeriod={start_year}&endPeriod={end_year}" + ), + {"Accept": "text/csv"}, + ), + ( + "ECB Data Portal v1 (format=csvdata)", + ( + "https://data.ecb.europa.eu/api/v1/data/EXR/A.USD.EUR.SP00.A" + f"?format=csvdata&startPeriod={start_year}&endPeriod={end_year}" + ), + {}, + ), + ] + + for label, url, headers in ecb_attempts: + try: + print(f"[ECB] Trying {label} …") + resp = requests.get(url, headers=headers, timeout=20) + resp.raise_for_status() + df = _parse_ecb_csv(resp.text) + df.to_csv(ECB_CSV, index=False) + print(f"[ECB] {len(df)} rows → {ECB_CSV}") + return df + except Exception as exc: + print(f"[ECB] {label} failed: {exc}") + + # ── fallback: embedded reference table ─────────────────────────────────── + print( + "[ECB] All live endpoints unavailable. " + "Using embedded reference table (_ECB_FALLBACK_USD_PER_EUR)." + ) + rows = [ + {"year": y, "usd_per_eur": r, "eur_per_usd": 1.0 / r} + for y, r in _ECB_FALLBACK_USD_PER_EUR.items() + if start_year <= y <= end_year + ] + df = pd.DataFrame(rows).sort_values("year").reset_index(drop=True) + df.to_csv(ECB_CSV, index=False) + print(f"[ECB] {len(df)} rows (from fallback table) → {ECB_CSV}") + return df + + +# ───────────────────────────────────────────────────────────────────────────── +# C. UNIDO INDSTAT – steel sector wages & employment +# ───────────────────────────────────────────────────────────────────────────── + +# Common UNIDO country-name variants → ISO-3 override +# (pycountry handles most; these cover known quirks) +_UNIDO_NAME_OVERRIDES = { + "United States of America": "USA", + "United States": "USA", + "Korea, Republic of": "KOR", + "Republic of Korea": "KOR", + "Korea (the Republic of)": "KOR", + "Taiwan, Province of China": "TWN", + "China, Taiwan Province": "TWN", + "Taiwan": "TWN", + "Iran (Islamic Republic of)": "IRN", + "Iran, Islamic Republic of": "IRN", + "Viet Nam": "VNM", + "Vietnam": "VNM", + "Bolivia (Plurinational State of)": "BOL", + "Bolivia": "BOL", + "Venezuela (Bolivarian Republic of)": "VEN", + "Venezuela": "VEN", + "Congo, Democratic Republic of the": "COD", + "Democratic Republic of the Congo": "COD", + "Syrian Arab Republic": "SYR", + "Syria": "SYR", + "Lao People's Democratic Republic": "LAO", + "Laos": "LAO", + "Moldova, Republic of": "MDA", + "Republic of Moldova": "MDA", + "Tanzania, United Republic of": "TZA", + "Tanzania": "TZA", + "Slovak Republic": "SVK", + "Czechia": "CZE", + "Czech Republic": "CZE", + "Russian Federation": "RUS", + "Russia": "RUS", + "North Macedonia": "MKD", + "Macedonia": "MKD", + "United Kingdom": "GBR", + "United Kingdom of Great Britain and Northern Ireland": "GBR", +} + + +def _name_to_iso3(name): + """Convert a country name string to ISO-3 alpha code, or return None.""" + if not isinstance(name, str): + return None + name = name.strip() + if name in _UNIDO_NAME_OVERRIDES: + return _UNIDO_NAME_OVERRIDES[name] + try: + c = pycountry.countries.lookup(name) + return c.alpha_3 + except LookupError: + return None + + +def load_unido_data(filepath=UNIDO_RAW): + """ + Load and normalise the UNIDO INDSTAT CSV downloaded from the portal. + + Handles two common export layouts: + • Long format: columns include Country/Year/Variable/Value + • SDMX-CSV: columns include REF_AREA / TIME_PERIOD / INDICATOR / OBS_VALUE + + Returns a tidy DataFrame: iso3 | country_name | year | employees | wages_usd + Returns *None* if the file does not exist (prints download instructions). + """ + filepath = Path(filepath) + if not filepath.exists(): + print(f"[UNIDO] Data file not found: {filepath}") + print( + " Please download INDSTAT Rev 4 (ISIC 241, variables 04+05, all countries)\n" + " from https://stat.unido.org/data/download and extract data.csv into\n" + f" {filepath.parent}/" + ) + return None + + print(f"[UNIDO] Reading {filepath} …") + raw = pd.read_csv(filepath, low_memory=False) + + # ── Employees: VariableCode 4, count in Value ───────────────────────── + emp_mask = raw["VariableCode"].astype(str).str.strip().isin(["4", "04"]) + emp = ( + raw[emp_mask][["Year", "Country", "Value"]] + .copy() + .rename( + columns={"Year": "year", "Country": "country_name", "Value": "employees"} + ) + ) + emp["employees"] = pd.to_numeric(emp["employees"], errors="coerce") + # Sum across activity combinations (e.g. 2410A + 2410B) for same country/year + emp = emp.groupby(["year", "country_name"], as_index=False)["employees"].sum() + + # ── Wages: VariableCode 5, USD amount in ValueUSD ───────────────────── + wage_mask = raw["VariableCode"].astype(str).str.strip().isin(["5", "05"]) + wages = ( + raw[wage_mask][["Year", "Country", "ValueUSD"]] + .copy() + .rename( + columns={"Year": "year", "Country": "country_name", "ValueUSD": "wages_usd"} + ) + ) + wages["wages_usd"] = pd.to_numeric(wages["wages_usd"], errors="coerce") + wages = wages.groupby(["year", "country_name"], as_index=False)["wages_usd"].sum() + + # ── Merge and convert country names to ISO3 ─────────────────────────── + df = pd.merge(emp, wages, on=["year", "country_name"], how="inner") + df = df.dropna(subset=["employees", "wages_usd"]) + + df["iso3"] = df["country_name"].apply(_name_to_iso3) + df = df.dropna(subset=["iso3"]) + df["iso3"] = df["iso3"].str.upper().str.strip() + + result = df[["iso3", "country_name", "year", "employees", "wages_usd"]].copy() + print( + f"[UNIDO] {len(result)} country-year rows loaded ({result['iso3'].nunique()} countries)." + ) + return result + + +# ───────────────────────────────────────────────────────────────────────────── +# D. Employer social-security contribution rates +# Sources: OECD Taxing Wages 2024, ILO Social Security Inquiry 2022, +# KPMG Global Employer Tax Guide 2023. +# All rates are the statutory employer SSC as a share of gross wages. +# ───────────────────────────────────────────────────────────────────────────── +EMPLOYER_CONTRIB_TABLE = { + # ISO3 : (rate_fraction, source_note) + "ARG": (0.170, "ANSES employer contributions ~17%"), + "AUS": (0.195, "Superannuation 11%, healthcare 2%, payroll levy ~5.5%"), + "AUT": (0.228, "Pension 12.55%, accident 1.2%, housing 0.5%, others ~8.6%"), + "BEL": (0.270, "ONSS employer total ~27%"), + "BGD": (0.050, "Bangladesh: employer contribution estimate ~5%"), + "BGR": (0.185, "Pension 10.82%, health 4.8%, others ~2.9%"), + "BLR": (0.340, "Social protection fund 34%"), + "BRA": (0.305, "INSS 22.5%, FGTS 8%"), + "CAN": (0.077, "CPP 5.95%, EI 1.66%"), + "CHL": (0.024, "Social security employer portion 2.4%"), + "CHN": ( + 0.282, + "Pension 16%, medical 5.8%, unemployment 0.5%, injury 0.4%, maternity 0.8%", + ), + "COL": (0.275, "Pension 12%, health 8.5%, ARL ~2%, SENA 2%, ICBF 3%"), + "CRI": (0.264, "CCSS employer contributions 26.4%"), + "CZE": (0.248, "Pension 21.5%, health 3.3%"), + "DEU": ( + 0.212, + "Pension 9.3%, unemployment 1.23%, health 7.3%, nursing+other ~3.3%", + ), + "DNK": (0.010, "Denmark: minimal statutory employer SSC"), + "DZA": (0.260, "CNAS employer 26%"), + "EGY": (0.260, "Social insurance employer 26%"), + "ESP": ( + 0.310, + "Pension 23.6%, unemployment 5.5%, FOGASA 0.2%, FP 0.6%, other 1.1%", + ), + "EST": (0.330, "Social tax 33%"), + "ETH": (0.110, "Private pension 11%"), + "FIN": (0.200, "Pension ~17.39%, unemployment ~1.91%, accident/other"), + "FRA": (0.425, "Complex French system total ~42.5%"), + "GBR": (0.138, "NIC Class 1 employer 13.8%"), + "GHA": (0.130, "SSNIT 13%"), + "GRC": (0.251, "IKA-ETAM ~25.06%"), + "HUN": (0.130, "Social contribution tax 13%"), + "IDN": (0.092, "BPJS: pension 3.7%, healthcare 4%, accident 0.24%, death 0.3%"), + "IND": (0.136, "EPF 12%, ESI 3.25%, LWF negligible"), + "IRL": (0.115, "PRSI Class A employer 11.15%"), + "IRN": (0.230, "Social security 23%"), + "IRQ": (0.120, "Social security employer 12%"), + "ISL": (0.065, "Iceland: employer social security 6.5%"), + "ISR": (0.075, "National insurance 3.55%, health insurance 3.1%"), + "ITA": (0.320, "INPS employer ~32%"), + "JPN": (0.151, "Pension 9.15%, health 4.99%, employment 0.6%, WC ~0.5%"), + "KAZ": (0.180, "Social contributions 3.5%, UAPF 3.5%, health 3%, other ~7%"), + "KOR": (0.105, "National pension 4.5%, health 3.545%, employment 0.9%, WC ~1.5%"), + "LBY": (0.115, "Social security 11.5%"), + "LTU": (0.017, "Employer SSC 1.77% (post-2019 reform)"), + "LUX": (0.155, "Pension 8%, health 3.05%, accident 1.1%, mutual aid/LTC ~3.3%"), + "LVA": (0.237, "Social insurance 23.59%"), + "MAR": ( + 0.228, + "CNSS: pension 11.89%, family 6.4%, AMO 2.26%, training 1.6%, accident", + ), + "MEX": (0.250, "IMSS ~17%, INFONAVIT 5%, SAR 2%"), + "MYS": (0.143, "EPF 12%, SOCSO 1.75%, EIS 0.4%, HRDF 0.5%"), + "NGA": (0.100, "PRA employer contributory pension 10%"), + "NLD": (0.190, "AOW/ANW/WLZ/WIA/ZVW combined ~19%"), + "NOR": (0.141, "Employer social security 14.1%"), + "NZL": (0.000, "No statutory employer SSC"), + "OMN": (0.113, "PASI employer 11.25%"), + "PAK": (0.120, "EOBI 5%, ESSI 5%, other ~2%"), + "PER": (0.090, "EsSalud 9%"), + "PHL": (0.115, "SSS 8%, PhilHealth 2.5%, Pag-IBIG 2%"), + "POL": ( + 0.204, + "Pension 9.76%, disability 6.5%, accident avg 1.67%, FP+FGSP ~2.55%", + ), + "PRT": (0.238, "Social security 23.75%"), + "QAT": (0.000, "Qatar: no employer SSC for most private-sector workers"), + "ROU": (0.023, "Work accident/occupational disease 2.25%"), + "RUS": (0.302, "Pension 22%, medical 5.1%, social 2.9%, injury 0.2%"), + "SAU": (0.118, "Social insurance 11.75%"), + "SGP": (0.170, "CPF employer ~17%"), + "SVK": ( + 0.248, + "Pension 14%, disability 3%, sickness 1.4%, unemployment 1%, other ~5%", + ), + "SVN": (0.163, "Pension 8.85%, health 6.56%, accident 0.53%, employment 0.06%"), + "SWE": (0.314, "Social security fees 31.42%"), + "THA": (0.050, "Social security employer 5%"), + "TUN": (0.165, "CNSS employer 16.57%"), + "TUR": (0.225, "Social security premiums 22.5%"), + "TZA": (0.100, "NSSF employer 10%"), + "UKR": (0.220, "Unified social contribution 22%"), + "ARE": (0.125, "GPSSA employer 12.5% for nationals; expats typically 0%"), + "USA": (0.076, "FICA: SS 6.2%, Medicare 1.45%, FUTA ~0.6%"), + "UZB": (0.120, "Social insurance 12%"), + "VNM": (0.215, "Social insurance 17.5%, health 3%, unemployment 1%"), + "ZAF": (0.020, "Skills development levy 1%, UIF 1%"), + "ZMB": (0.050, "NAPSA employer 5%"), + "ZWE": (0.045, "NSSA employer 4.5%"), + "CHE": (0.135, "AHV/IV/EO 5.3%, ALV 1.1%, FAK ~1.7%, accident/pension ~5.4%"), + "MKD": (0.070, "North Macedonia: employer health 7.3% → total ~7%"), + "SRB": ( + 0.173, + "Serbia: pension 11%, health 5.15%, unemployment 0.75%, other ~0.3%", + ), + "HRV": (0.165, "Croatia: pension pillar II 5%, health ~16.5% total"), + "BIH": (0.105, "Bosnia: contributions ~10.5% (varies by entity)"), + "ALB": (0.150, "Albania: employer social insurance 15%"), + "GEO": (0.000, "Georgia: no employer-side social contribution"), + "ARM": (0.025, "Armenia: employer social premium 2.5%"), + "AZE": (0.220, "Azerbaijan: employer social insurance 22%"), + "UZB": (0.120, "Uzbekistan: social insurance 12%"), + "TKM": (0.200, "Turkmenistan: employer insurance ~20%"), + "KGZ": (0.175, "Kyrgyzstan: employer social fund 17.5%"), + "TJK": (0.250, "Tajikistan: employer contribution ~25%"), + "MNG": (0.135, "Mongolia: employer social insurance 13.5%"), + "MMR": (0.030, "Myanmar: SSB employer 2.5-3%"), + "KHM": (0.031, "Cambodia: NSSF employer 3.1%"), + "BGD": (0.050, "Bangladesh: employer provident fund ~5%"), + "LKA": (0.120, "Sri Lanka: EPF 12%"), + "NPL": (0.100, "Nepal: SSF employer ~10%"), + "KEN": (0.060, "Kenya: NSSF + NHIF employer ~6%"), + "TZA": (0.100, "Tanzania: NSSF employer 10%"), + "UGA": (0.100, "Uganda: NSSF employer 10%"), + "ZMB": (0.050, "Zambia: NAPSA employer 5%"), + "ZWE": (0.045, "Zimbabwe: NSSA employer 4.5%"), + "SEN": (0.040, "Senegal: IPM employer + family benefit ~4%"), + "CIV": (0.065, "Côte d'Ivoire: CNPS employer ~6.5%"), + "CMR": (0.080, "Cameroon: CNPS employer ~8%"), + "AGO": (0.080, "Angola: employer social security 8%"), + "MOZ": (0.040, "Mozambique: INSS employer 4%"), + "BWA": (0.000, "Botswana: no statutory employer SSC"), + "NAM": (0.000, "Namibia: no statutory employer SSC"), + "MUS": (0.060, "Mauritius: NPF 6%"), + "TTO": (0.060, "Trinidad & Tobago: NIS employer 5.85% + 0.3%"), + "JAM": (0.030, "Jamaica: NIS employer 2.5-3%"), + "ECU": (0.120, "Ecuador: IESS employer 12.15%"), + "BOL": (0.165, "Bolivia: AFP 10%, health 10%, employer total ~16.5%"), + "PRY": (0.165, "Paraguay: IPS employer 14%, others ~2.5%"), + "URY": (0.075, "Uruguay: BPS employer 7.5%"), + "PAN": (0.125, "Panama: CSS employer 12.25%"), + "GTM": (0.105, "Guatemala: IGSS employer 10.5%"), + "HND": (0.090, "Honduras: IHSS employer 5%, RAP 5% → ~9%"), + "SLV": (0.075, "El Salvador: ISSS 7.5% employer"), + "NIC": (0.190, "Nicaragua: INSS employer 19%"), + "DOM": (0.075, "Dominican Republic: AFP 7.1% → total ~7.5%"), + "CUB": (0.145, "Cuba: employer social security 14.5%"), +} + + +def build_employer_contributions(): + """ + Build a DataFrame from the embedded EMPLOYER_CONTRIB_TABLE. + + Returns: iso3 | employer_contrib_rate | contrib_source | oecd_status + Saves to data/employer_contributions.csv. + """ + rows = [ + {"iso3": iso, "employer_contrib_rate": rate, "contrib_source": note} + for iso, (rate, note) in EMPLOYER_CONTRIB_TABLE.items() + ] + df = pd.DataFrame(rows) + df["oecd_status"] = df["iso3"].apply( + lambda x: "OECD" if x in OECD_MEMBERS else "non_OECD" + ) + df.to_csv(CONTRIB_CSV, index=False) + print(f"[CONTRIB] {len(df)} countries → {CONTRIB_CSV}") + return df + + +# ───────────────────────────────────────────────────────────────────────────── +# E. Merge all sources → merged_labour_inputs.csv +# ───────────────────────────────────────────────────────────────────────────── + + +def _pick_latest_row(group, target_year): + """ + From a single country's time-series, return the row with the most recent + year ≤ target_year that has both employees and wages_usd. + Returns None if no qualifying row exists. + """ + valid = group[group["year"] <= target_year].dropna( + subset=["employees", "wages_usd"] + ) + if valid.empty: + return None + return valid.sort_values("year").iloc[-1] + + +def _nearest_year(lookup_dict, iso3, preferred_year): + """ + From a {(iso3, year): value} dict, return (year_used, value) for the + entry closest to preferred_year. Returns (None, None) if no data. + """ + candidates = [(y, v) for (c, y), v in lookup_dict.items() if c == iso3] + if not candidates: + return None, None + candidates.sort(key=lambda xy: abs(xy[0] - preferred_year)) + return candidates[0] + + +def merge_all_data( + target_year=2020, + unido_filepath=None, + output_filepath=None, + force_download=False, +): + """ + Orchestrate all downloads, merge on country × year, and write the master + input CSV for labour_cost_calculator.py. + + Parameters + ---------- + target_year : int + Reference year for GNI and EUR conversion (default 2020). + unido_filepath : str or Path + Path to the manually downloaded UNIDO CSV. + output_filepath : str or Path + Path for the output merged CSV (default: MERGED_CSV). + force_download : bool + If True, re-download GNI and ECB data even if cached files exist. + + Returns + ------- + pd.DataFrame or None + Merged dataset (None if UNIDO file is missing). + """ + if unido_filepath is None: + unido_filepath = UNIDO_RAW + if output_filepath is None: + output_filepath = MERGED_CSV + output_filepath = Path(output_filepath) + + # 1. Fetch each source + gni_df = fetch_world_bank_gni(force=force_download) + ecb_df = fetch_ecb_rates(force=force_download) + unido_df = load_unido_data(unido_filepath) + contrib_df = build_employer_contributions() + + if unido_df is None: + print("[MERGE] Cannot merge – UNIDO file not yet downloaded.") + print(" GNI, ECB and employer-contribution files have been saved.") + return None + + # 2. Build fast lookup dicts + # ECB: {year: eur_per_usd} + ecb_dict = dict(zip(ecb_df["year"].astype(int), ecb_df["eur_per_usd"])) + + # GNI: {(iso3, year): gni_usd} + gni_dict = { + (r.iso3.strip().upper(), int(r.year)): r.gni_usd for r in gni_df.itertuples() + } + + # 3. Pick best row per country from UNIDO + records = [] + for iso3, grp in unido_df.groupby("iso3"): + row = _pick_latest_row(grp, target_year) + if row is None: + continue + + data_year = int(row["year"]) + country_name = str(row.get("country_name", iso3)) + + # ECB rate for data year + eur_per_usd_data = ecb_dict.get( + data_year, ecb_dict[min(ecb_dict, key=lambda y: abs(y - data_year))] + ) + # ECB rate for target year + eur_per_usd_target = ecb_dict.get( + target_year, ecb_dict[min(ecb_dict, key=lambda y: abs(y - target_year))] + ) + + # GNI for data year + gni_data_usd = gni_dict.get((iso3, data_year)) + if gni_data_usd is None: + _, gni_data_usd = _nearest_year(gni_dict, iso3, data_year) + if gni_data_usd is None: + continue # no GNI data → skip + + # GNI for target year + gni_target_usd = gni_dict.get((iso3, target_year)) + if gni_target_usd is None: + _, gni_target_usd = _nearest_year(gni_dict, iso3, target_year) + if gni_target_usd is None: + continue # no GNI data → skip + + records.append( + { + "iso3": iso3, + "country_name": country_name, + "data_year": data_year, + "oecd_status": "OECD" if iso3 in OECD_MEMBERS else "non_OECD", + "steel_employees": row["employees"], + "steel_wage_usd": row["wages_usd"], + "gni_data_year_usd": gni_data_usd, + "gni_target_year_usd": gni_target_usd, + "eur_per_usd_data": eur_per_usd_data, + "eur_per_usd_target": eur_per_usd_target, + } + ) + + if not records: + print("[MERGE] No valid records produced. Check UNIDO file contents.") + return None + + merged = pd.DataFrame(records) + + # 4. Join employer contribution rates + merged = merged.merge( + contrib_df[["iso3", "employer_contrib_rate", "contrib_source"]], + on="iso3", + how="left", + ) + + # Report countries missing employer contribution data + missing_mask = merged["employer_contrib_rate"].isna() + if missing_mask.any(): + missing_iso = merged.loc[missing_mask, "iso3"].tolist() + warnings.warn( + f"{len(missing_iso)} countries have no employer contribution rate " + f"in EMPLOYER_CONTRIB_TABLE: {missing_iso}. " + "The caller should impute these (e.g. with the sample mean)." + ) + + output_filepath.parent.mkdir(parents=True, exist_ok=True) + merged.to_csv(output_filepath, index=False) + print(f"\n[MERGE] {len(merged)} countries → {output_filepath}") + cols_show = [ + "iso3", + "country_name", + "data_year", + "steel_employees", + "steel_wage_usd", + "gni_target_year_usd", + "employer_contrib_rate", + ] + print(merged[cols_show].to_string(index=False)) + return merged + + +# ───────────────────────────────────────────────────────────────────────────── +# Entry point +# ───────────────────────────────────────────────────────────────────────────── +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake("download_labour_data") + + merge_all_data( + target_year=2020, + output_filepath=snakemake.output.merged, + ) From 6831f40ae24bad9830d486c4a813c81c75c3fc24 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 12:15:32 +0200 Subject: [PATCH 112/216] feat: add rule to prepare labour cost --- rules/preparation.smk | 16 +- workflow/scripts/prepare_labour_cost.py | 269 ++++++++++++++++++++++++ 2 files changed, 283 insertions(+), 2 deletions(-) create mode 100644 workflow/scripts/prepare_labour_cost.py diff --git a/rules/preparation.smk b/rules/preparation.smk index efd1b45..291b894 100644 --- a/rules/preparation.smk +++ b/rules/preparation.smk @@ -3,12 +3,24 @@ rule download_labour_data: output: - merged = "../resources/merged_labour_inputs.csv", + merged = "resources/merged_labour_inputs.csv", resources: mem_mb=2000, threads: 1 script: - "scripts/download_labour_data.py" + str(SCRIPT_DIR / "download_labour_data.py") + + +rule prepare_labour_cost: + input: + merged = "resources/merged_labour_inputs.csv", + output: + labour_cost = "resources/labour_cost_clustered.csv", + resources: + mem_mb=2000, + threads: 1 + script: + str(SCRIPT_DIR / "prepare_labour_cost.py") rule prepare_wacc: diff --git a/workflow/scripts/prepare_labour_cost.py b/workflow/scripts/prepare_labour_cost.py new file mode 100644 index 0000000..06a122c --- /dev/null +++ b/workflow/scripts/prepare_labour_cost.py @@ -0,0 +1,269 @@ +""" +Labour cost calculator for H-DRI-EAF value chains. + +Reads the merged labour inputs produced by data_downloader.py, computes +an all-in hourly steelworker wage [EUR/h, 2020 prices] for each country, +maps countries to the model regions defined in config/config.yaml, and +saves a regional aggregated CSV. + +Methodology (Nykvist et al. 2025, Section D4) +---------------------------------------------- +1. Steel wage ratio (dimensionless): + wage_ratio = (wage_per_employee_USD * (1 + employer_contrib_rate)) + / GNI_data_year_USD + Both numerator and denominator use the same year and the same USD + denomination, so currency conversion cancels out. + +2. Hourly wage [EUR/h, 2020]: + hourly_wage = (GNI_2020_EUR * wage_ratio / WORKING_YEAR) * (1 + OVERHEAD) + where GNI_2020_EUR = GNI_2020_USD * EUR_per_USD_2020. + +3. Countries with no UNIDO data have their wage ratio imputed as the + employment-weighted mean of all countries with data. + +4. Regional aggregation: employment-weighted mean of country hourly wages + within each model region. Regions with no data fall back to the + global employment-weighted mean. + +Output: resources/labour_cost_clustered.csv + region | labour_ely | labour_dri | labour_eaf [EUR/h, 2020] + (all three columns hold the same hourly rate; the model applies + different labour-intensity factors per technology downstream) + +Usage +----- + python workflow/scripts/labour_cost_calculator.py +""" + +import warnings +import pycountry +import pandas as pd +from pathlib import Path + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- +WORKING_YEAR = 2080 # assumed working hours per year +OVERHEAD = 0.25 # 25 % overhead on direct labour cost +TARGET_YEAR = 2020 # reference year for all monetary outputs + +# Labour intensities (Nykvist Table D5, original data Devlin) +LI_ELY_PU = 2.0 # h / kW installed electrolyser +LI_DRI_PU = 0.18 # h / t DRI +LI_EAF_PU = 0.49 # h / t steel + +# --------------------------------------------------------------------------- +# Country name → ISO3 helpers for config region mapping +# --------------------------------------------------------------------------- +_EXTRA_NAME_TO_ISO3 = { + # Config-specific names that pycountry.lookup() doesn't find by default + "Iran, Islamic Republic of": "IRN", + "Republic of the Congo": "COG", + "Hong Kong": "HKG", + "Taiwan": "TWN", + "South Korea": "KOR", + "North Korea": "PRK", + "Vietnam": "VNM", + "Bolivia": "BOL", + "Venezuela": "VEN", + "Russia": "RUS", + "Russian Federation": "RUS", + "Laos": "LAO", + "Brunei": "BRN", + "Palestine": "PSE", + "Syria": "SYR", + "Democratic Republic of the Congo": "COD", + "Equatorial French Guiana": None, # not a sovereign country +} + + +def _config_name_to_iso3(name: str): + """Convert a config region country name to ISO3, return None if not found.""" + name = name.strip() + if name in _EXTRA_NAME_TO_ISO3: + return _EXTRA_NAME_TO_ISO3[name] + try: + return pycountry.countries.lookup(name).alpha_3 + except LookupError: + return None + + +def build_iso_to_region(config_regions: dict) -> dict: + """ + Build {iso3: region_name} from the config 'regions' dict. + + Config entries may use '+' to combine two countries in one string + (e.g. "Togo + Algeria"); each part is split and mapped individually. + """ + iso_to_region = {} + unmatched = [] + for region, countries in config_regions.items(): + for entry in countries: + for part in str(entry).split("+"): + iso3 = _config_name_to_iso3(part.strip()) + if iso3: + iso_to_region[iso3] = region + else: + unmatched.append((region, part.strip())) + if unmatched: + warnings.warn( + f"Could not map {len(unmatched)} config country name(s) to ISO3: " + + ", ".join(f"{r}/{n}" for r, n in unmatched[:10]) + + (" …" if len(unmatched) > 10 else "") + ) + return iso_to_region + + +# --------------------------------------------------------------------------- +# Wage computation +# --------------------------------------------------------------------------- +def compute_hourly_wage(row) -> float: + """ + Compute the all-in hourly steelworker wage [EUR/h] in TARGET_YEAR prices. + + wage_ratio = (steel_wage_usd / steel_employees) * (1 + employer_contrib_rate) + / gni_data_year_usd + + hourly_wage = (gni_target_year_usd * eur_per_usd_target + * wage_ratio / WORKING_YEAR) * (1 + OVERHEAD) + """ + wage_ratio = ( + (row["steel_wage_usd"] / row["steel_employees"]) + * (1.0 + row["employer_contrib_rate"]) + / row["gni_data_year_usd"] + ) + gni_target_eur = row["gni_target_year_usd"] * row["eur_per_usd_target"] + return (gni_target_eur * wage_ratio / WORKING_YEAR) * (1.0 + OVERHEAD) + + +def compute_all_hourly_wages(merged_df: pd.DataFrame) -> pd.DataFrame: + """ + Return a DataFrame with columns [iso3, country_name, steel_employees, + hourly_wage_eur] for every country in *merged_df*. + + Countries missing any required column get their wage imputed as the + employment-weighted mean of all countries with complete data. + """ + required = [ + "steel_wage_usd", + "steel_employees", + "gni_data_year_usd", + "gni_target_year_usd", + "employer_contrib_rate", + "eur_per_usd_target", + ] + valid_mask = merged_df[required].notna().all(axis=1) + valid = merged_df[valid_mask].copy() + valid["hourly_wage_eur"] = valid.apply(compute_hourly_wage, axis=1) + + # Employment-weighted mean for imputation + total_emp = valid["steel_employees"].sum() + mean_wage = ( + (valid["hourly_wage_eur"] * valid["steel_employees"]).sum() / total_emp + if total_emp > 0 + else valid["hourly_wage_eur"].mean() + ) + + result = merged_df[["iso3", "country_name", "steel_employees"]].merge( + valid[["iso3", "hourly_wage_eur"]], on="iso3", how="left" + ) + n_imputed = result["hourly_wage_eur"].isna().sum() + if n_imputed: + warnings.warn( + f"{n_imputed} countries have incomplete data and will be imputed " + f"with the global mean ({mean_wage:.2f} EUR/h)." + ) + result["hourly_wage_eur"] = result["hourly_wage_eur"].fillna(mean_wage) + return result + + +# --------------------------------------------------------------------------- +# Regional aggregation +# --------------------------------------------------------------------------- +def aggregate_by_region( + wages_df: pd.DataFrame, + iso_to_region: dict, + regions: list, +) -> pd.DataFrame: + """ + Compute the employment-weighted mean hourly wage for each model region. + + Countries with no employment figure use weight = 1. + Regions with no matching countries use the global weighted mean. + """ + df = wages_df.copy() + df["region"] = df["iso3"].map(iso_to_region) + df["weight"] = df["steel_employees"].fillna(1.0) + + # Global fallback + global_mean = (df["hourly_wage_eur"] * df["weight"]).sum() / df["weight"].sum() + + rows = [] + for region in regions: + grp = df[df["region"] == region] + if grp.empty: + wage = global_mean + else: + w_sum = grp["weight"].sum() + wage = ( + (grp["hourly_wage_eur"] * grp["weight"]).sum() / w_sum + if w_sum > 0 + else global_mean + ) + rows.append( + { + "region": region, + "steelworker_wage in euro/h": round(wage, 4), + "ely_intensity in h/kW_ely": LI_ELY_PU, + "dri_intensity in h/t_dri": LI_DRI_PU, + "eaf_intensity in h/t_steel": LI_EAF_PU, + } + ) + + return pd.DataFrame(rows).set_index("region") + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake("prepare_labour_cost") + + # ── 1. Load merged data ────────────────────────────────────────────── + merged_path = Path(snakemake.input.merged) + if not merged_path.exists(): + raise FileNotFoundError( + f"Merged labour inputs not found: {merged_path}\n" + " Run download_labour_data first." + ) + print(f"Loading {merged_path} …") + merged = pd.read_csv(merged_path) + print(f" {len(merged)} countries loaded.") + + # ── 2. Load config regions ─────────────────────────────────────────── + regions_config: dict = snakemake.config["regions"] + iso_to_region = build_iso_to_region(regions_config) + regions = list(regions_config.keys()) + print(f" {len(regions)} model regions from config.") + + # ── 3. Compute hourly wages ────────────────────────────────────────── + wages = compute_all_hourly_wages(merged) + print("\nCountry-level hourly wages [EUR/h, 2020]:") + print( + wages[["iso3", "country_name", "hourly_wage_eur"]] + .sort_values("hourly_wage_eur", ascending=False) + .to_string(index=False) + ) + + # ── 4. Aggregate by region ─────────────────────────────────────────── + result = aggregate_by_region(wages, iso_to_region, regions) + + # ── 5. Save ────────────────────────────────────────────────────────── + output_path = Path(snakemake.output.labour_cost) + output_path.parent.mkdir(parents=True, exist_ok=True) + result.to_csv(output_path) + print(f"\nSaved → {output_path}") + print(result.to_string()) From cced87f9c2910f5824b67914d9ea48f02458c2cd Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 22 May 2026 14:52:24 +0200 Subject: [PATCH 113/216] fix: repair incorrect PyPSA network generator addition --- config/config.yaml | 4 +- workflow/scripts/build_x_supply_chain.py | 13 --- workflow/scripts/calculate_lcox.py | 6 ++ workflow/scripts/prepare_regional_network.py | 88 +++++++------------- workflow/scripts/trade_chain_utils.py | 41 ++++++++- 5 files changed, 77 insertions(+), 75 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index ba517c1..f08aa52 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -239,8 +239,8 @@ grid_electricity: grid_potential_custom: true # Use custom grid potential for eaf-grid from data/grid_potential_custom.csv in Mt steel part_load: - electrolysis: 0.0 - direct reduction furnace: 0.9 + electrolysis: 0.25 # (0.1-0.4) https://doi.org/10.1016/j.jpowsour.2023.232629 + direct reduction furnace: 0.2 # https://doi.org/10.1016/j.renene.2021.10.036 electric arc furnace: 0.0 # add part load limitations by naming the carrier of links diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index a0cfff4..84e6eb5 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -405,17 +405,6 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> ) -def _add_resources(network: pypsa.Network, config: dict) -> None: - """Add external resource supplies (iron ore).""" - network.add( - "Generator", - "iron_ore", - bus="iron_ore", - p_nom=1e10, - marginal_cost=0, - ) - - def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Network: """Build PyPSA steel supply chain skeleton (region-agnostic). @@ -441,7 +430,6 @@ def build_network(config: dict, tech_costs_path: str, year: int) -> pypsa.Networ _add_grid_electricity_supply(network, config) _add_conversion_chain(network, tech_costs, config) _add_storage(network, tech_costs, config) - _add_resources(network, config) logger.info( f"Built network: {len(network.buses)} buses, {len(network.links)} links, " @@ -514,7 +502,6 @@ def _set_meta(network: pypsa.Network, group: dict | None) -> None: group_network, tech_costs, config, stages=group.get("stages") ) _add_storage(group_network, tech_costs, config) - _add_resources(group_network, config) _set_meta(group_network, group) out_path = generic_dir / f"generic_model_{year}_{label}.nc" group_network.export_to_netcdf(str(out_path)) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index eb5e532..d04e661 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -143,10 +143,16 @@ def add_loads_to_network(network, product, demands): load_name = f"{product}_demand" p_set = hourly_demand_t + network.add( + "Carrier", + f"{product}_demand", + ) + network.add( "Load", load_name, bus=bus_name, + carrier=f"{product}_demand", p_set=p_set, # Constant hourly demand ) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index da09954..4ed2ddc 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -38,7 +38,7 @@ if str(SCRIPT_DIR) not in sys.path: sys.path.insert(0, str(SCRIPT_DIR)) -from trade_chain_utils import build_product_components # noqa: E402 +from trade_chain_utils import build_product_components, get_external_material_inputs # noqa: E402 from _helpers import setup_logging # noqa: E402 snakemake: Any = globals().get("snakemake") @@ -437,6 +437,7 @@ def sanitize_and_fix( _logger.warning(f"network.sanitize() raised an exception: {exc}") fixes = [] + # operate on a snapshot of the generators DataFrame to avoid SettingWithCopy if len(network.generators) == 0: _logger.info("No generators to check during sanitize_and_fix.") @@ -720,66 +721,35 @@ def prepare_network( except Exception as e: logger.warning(f"Could not remove Store {store_name}: {e}") - # Preserve buses that are required outputs or material/energy interfaces - # Add free external inputs for any material bus that is expected but - # not produced within this sliced network (e.g., `hbi` for the steel stage). - # Determine whether a kept bus is produced by any remaining link. - # Determine which buses are *produced* by remaining links. - # Convention in this codebase: `bus1` is the primary output bus - # for conversion Links (bus0 is typically an input). Previously we - # treated any referenced bus as "produced" which incorrectly - # prevented adding external inputs for buses that are actually - # inputs (e.g., `hbi` for the `eaf` link). Only consider `bus1` - # as an output to decide whether a bus is produced by the sliced - # network. - produced_buses = set() - for link_name in network.links.index: - row = network.links.loc[link_name] - if "bus1" in row.index: - b = row.get("bus1") - if pd.notna(b): - produced_buses.add(b) - - # For each bus in keep_buses that is not produced in the sliced network, - # add a free generator input if no generator already supplies it. - for bus_name in keep_buses: + # Add free external inputs ONLY for external materials of this + # configured stage-group. + external_material_inputs = set( + get_external_material_inputs(config, route_label) + ) + + for bus_name in sorted(external_material_inputs): if bus_name not in network.buses.index: - # create bus if missing - try: - network.add("Bus", bus_name, carrier=bus_name, unit="t/h") - logger.info(f"Added missing Bus for stage slicing: {bus_name}") - except Exception: - pass - - needs_free_input = False - if bus_name not in produced_buses: - # If no link produces this bus, and no generator exists on it, - # create a free external input (unlimited capacity, zero marginal cost) - gens_on_bus = ( - network.generators[network.generators["bus"] == bus_name] - if len(network.generators) > 0 - else pd.DataFrame() + logger.warning( + f"Expected material input bus missing during stage slicing: {bus_name}; skipping free input generator" ) - if gens_on_bus.empty: - needs_free_input = True - - if needs_free_input: - gen_name = f"{bus_name}_input" - if gen_name not in network.generators.index: - try: - network.add( - "Generator", - gen_name, - bus=bus_name, - carrier=bus_name, - p_nom=1e10, - marginal_cost=0, - ) - logger.info( - f"Added external free input generator: {gen_name} on {bus_name}" - ) - except Exception as e: - logger.warning(f"Could not add free input {gen_name}: {e}") + continue + + gen_name = f"{bus_name}_input" + if gen_name not in network.generators.index: + try: + network.add( + "Generator", + gen_name, + bus=bus_name, + carrier=bus_name, + p_nom=1e10, + marginal_cost=0, + ) + logger.info( + f"Added external free input generator from trade chain: {gen_name} on {bus_name}" + ) + except Exception as e: + logger.warning(f"Could not add free input {gen_name}: {e}") logger.info( f"Skeleton sliced to {route_label}: {len(network.links)} links, {len(network.stores)} stores" diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py index f06be96..65e637a 100644 --- a/workflow/scripts/trade_chain_utils.py +++ b/workflow/scripts/trade_chain_utils.py @@ -40,7 +40,7 @@ "links": ("dri",), "stores": ("h2_storage", "hbi_storage"), "materials": ("iron_ore", "hydrogen"), - "energy": ("renewable_electricity"), + "energy": ("renewable_electricity",), "outputs": ("hbi",), "buses": ( "iron_ore", @@ -346,3 +346,42 @@ def build_product_components(config: Dict, product: str) -> Dict[str, object]: "buses": buses, "has_renewables": has_renewables, } + + +def get_external_material_inputs(config: Dict, product: str) -> List[str]: + """Return material buses that must be supplied externally for a stage-group. + + Inputs produced by earlier stages in the same group are not returned. + """ + + chain = get_trade_chain(config) + groups = get_stage_groups(chain) + + target_group: Optional[Dict] = None + for group in groups: + if group["label"] == product: + target_group = group + break + + if target_group is None: + raise ValueError(f"Product '{product}' not found in configured stage groups") + + produced = set() + external_materials: List[str] = [] + + for stage in target_group["stages"]: + materials, _ = split_stage_inputs(stage) + for material in materials: + norm_material = _normalize_commodity(material) + if ( + norm_material + and norm_material not in produced + and norm_material not in external_materials + ): + external_materials.append(norm_material) + + output_commodity = _normalize_commodity(stage.get("output_commodity", "")) + if output_commodity: + produced.add(output_commodity) + + return external_materials From 9e4d121ec01c86cbdb5c59794e53433b41570d4e Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 17:38:03 +0200 Subject: [PATCH 114/216] feat: add labour cost feature --- rules/supply_curves.smk | 1 + workflow/scripts/prepare_regional_network.py | 79 +++++++++++++++++++- 2 files changed, 79 insertions(+), 1 deletion(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index ccba840..eff002f 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -88,6 +88,7 @@ rule prepare_regional_network: tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", wacc = "resources/wacc-clustered.csv", + labour_cost = "resources/labour_cost_clustered.csv", output: # Output keyed by product; route_label is internal to the script network="resources/networks/base_{cost_year}_{region}_{wacc}_{product}_{scenario}.nc", diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 23cbc8c..5acba29 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -38,7 +38,10 @@ if str(SCRIPT_DIR) not in sys.path: sys.path.insert(0, str(SCRIPT_DIR)) -from trade_chain_utils import build_product_components, get_external_material_inputs # noqa: E402 +from trade_chain_utils import ( + build_product_components, + get_external_material_inputs, +) # noqa: E402 from _helpers import setup_logging # noqa: E402 snakemake: Any = globals().get("snakemake") @@ -1015,6 +1018,64 @@ def validate_network_carriers(n: pypsa.Network): return network, audit_info +def add_labour_cost(n, labour_cost): + + print(f"adding labour cost") + + carrier_labour_cost_dict = { + "electrolysis": "ely_intensity in h/kW_ely", + "direct_reduction_furnace": "dri_intensity in h/t_dri", + "electric_arc_furnace": "eaf_intensity in h/t_steel", + } + + regional_labour_cost = labour_cost.loc[snakemake.wildcards.region] + wage = regional_labour_cost["steelworker_wage in euro/h"] + + for carrier in carrier_labour_cost_dict.keys(): + if carrier in n.links.carrier.values: + if carrier == "electrolysis": + n.links.loc[n.links.carrier == carrier, "overnight_cost"] += ( + wage + * regional_labour_cost[carrier_labour_cost_dict[carrier]] + * 1000 + * network.links[n.links.carrier == "electrolysis"].lifetime + ) # Wage in €/h * intensity in h/kW_ely * 1000 kW/MW = € / MW multiplied by lifetime to convert to overnight cost (instead of annual capital_cost) + logger.info( + f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/kW_ely * 1000 = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * 1000:.2f} €/MW" + ) + else: + pass + + if carrier == "direct_reduction_furnace": + n.links.loc[n.links.carrier == carrier, "marginal_cost"] += ( + wage + * regional_labour_cost[carrier_labour_cost_dict[carrier]] + * n.links.loc[n.links.carrier == carrier, "efficiency"] + ) # Wage in €/h * intensity in h/t_dri * effiency_ironore_dri = € / t_dri, added to marginal cost + logger.info( + f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/t_dri * efficiency = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * n.links.loc[n.links.carrier == carrier, 'efficiency'].iloc[0]:.2f} €/t_dri" + ) + else: + pass + + if carrier == "electric_arc_furnace": + n.links.loc[n.links.carrier == carrier, "marginal_cost"] += ( + wage + * regional_labour_cost[carrier_labour_cost_dict[carrier]] + * n.links.loc[n.links.carrier == carrier, "efficiency"] + ) # Wage in €/h * intensity in h/t_steel * effiency_input_output = € / t_steel, added to marginal cost + logger.info( + f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/t_steel * efficiency = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * n.links.loc[n.links.carrier == carrier, 'efficiency'].iloc[0]:.2f} €/t_steel" + ) + else: + pass + else: + print( + f"carrier {carrier} not in network, skipping labour cost addition for this carrier" + ) + return n + + # ============================================================================ # SNAKEMAKE INTEGRATION # ============================================================================ @@ -1078,6 +1139,22 @@ def validate_network_carriers(n: pypsa.Network): route_label=route_label, ) + # Add labour cost + if snakemake.config["trade_chains"]["labour_cost"] == True: + logger.info("Adding labour costs to network") + # Load labour cost + labour_cost = pd.read_csv(snakemake.input.labour_cost, header=0, index_col=0) + network = add_labour_cost(network, labour_cost) + + elif snakemake.config["trade_chains"]["labour_cost"] == False: + logger.info("Labour cost addition skipped (labour_cost is False)") + + else: + raise ValueError( + f"Unrecognized labour_cost wildcard: {snakemake.config['trade_chains']['labour_cost']}. " + f"Expected 'True' or 'False'." + ) + # Save network logger.info(f"Saving network to {output_path}") network.export_to_netcdf(output_path) From 74bf4e23932260eb4f2e6f8044695b8909e984b8 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 22 May 2026 17:38:19 +0200 Subject: [PATCH 115/216] feat: add labour cost toggle to config --- config/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/config/config.yaml b/config/config.yaml index 06bcafc..75847c4 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -26,6 +26,7 @@ trade_chains: # scenario: default final_product: steel wacc: uniform #regional + labour_cost: False #Include labour cost tradeable_commodities: [iron_ore, hbi] stages: 1: From 3cba678c5815a8eee8a028285abfacdd2b602406 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 22 May 2026 18:24:49 +0200 Subject: [PATCH 116/216] fix: add missing pixi dependencies for notebook execution --- config/config.yaml | 4 ++-- pixi.toml | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 06bcafc..17707cf 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -15,7 +15,7 @@ outputs: # - unreserved (OPTIONAL, FALLBACK): no domestic reservation; full renewable stack available. # Can be generated without electricity demand data when demand data unavailable. supply_curve: - generate_unreserved: False # Set to True to generate unreserved scenario as fallback + generate_unreserved: True # Set to True to generate unreserved scenario as fallback # Config-native trade chain definitions @@ -136,7 +136,7 @@ scenario: # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.01, 1000] # [0.01, 0.1, 1, 10, 100, 1000] # Mt/year +steel_demand_levels: [0.01, 0.1, 1, 10, 100, 1000] # Mt/year hydrogen_storage_cost: False electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' diff --git a/pixi.toml b/pixi.toml index 4e2b18d..5228641 100644 --- a/pixi.toml +++ b/pixi.toml @@ -15,6 +15,7 @@ geojson = ">=3.2.0" geohash2 = "*" geopandas = ">=1" geopy = ">=2.4.1" +jupyter = ">=1.0" libgdal-netcdf = ">=3.10.3" linopy = ">=0.6.1" matplotlib = ">=3.10.7" @@ -38,6 +39,7 @@ xarray = ">=2026.0.0" [pypi-dependencies] # pip-installable packages (always included) gurobipy = "*" +wbdata = ">=1.1.0" [feature.test.dependencies] pytest = "*" @@ -50,7 +52,6 @@ unit-tests = "pytest tests" ruff = "*" pre-commit = "*" pylint = "*" -jupyter = "*" ipykernel = "*" ipython = "*" nbqa = "*" From 84a3d4a9ce9b1da7f81a0c8641d12c7c717508f6 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 26 May 2026 14:29:05 +0200 Subject: [PATCH 117/216] chore: unify logging and remove printing. Also fix previous linting violations --- config/config.yaml | 4 +- workflow/scripts/_helpers.py | 3 +- workflow/scripts/build_x_supply_chain.py | 8 +- workflow/scripts/create_supply_curve.py | 8 +- workflow/scripts/download_labour_data.py | 764 +++++++++++++++++++ workflow/scripts/model_trade.py | 179 +++-- workflow/scripts/prepare_labour_cost.py | 278 +++++++ workflow/scripts/prepare_regional_network.py | 96 ++- workflow/scripts/renewable_profiles.py | 4 +- workflow/scripts/tech_database.py | 5 +- workflow/scripts/trade_chain_utils.py | 8 +- 11 files changed, 1252 insertions(+), 105 deletions(-) create mode 100644 workflow/scripts/download_labour_data.py create mode 100644 workflow/scripts/prepare_labour_cost.py diff --git a/config/config.yaml b/config/config.yaml index 17707cf..00b1a18 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -21,11 +21,11 @@ supply_curve: # Config-native trade chain definitions # Defines the commodity transformation chain with ordered stages and process labels trade_chains: - id: default_2050 + id: labour_2050 cost_year: 2050 - # scenario: default final_product: steel wacc: uniform #regional + labour_cost: True #Include labour cost tradeable_commodities: [iron_ore, hbi] stages: 1: diff --git a/workflow/scripts/_helpers.py b/workflow/scripts/_helpers.py index 851c33c..6a8d51d 100644 --- a/workflow/scripts/_helpers.py +++ b/workflow/scripts/_helpers.py @@ -71,7 +71,8 @@ def load_config(config): try: config = yaml.safe_load(stream) except yaml.YAMLError as exc: - print(exc) + logger.exception("Failed to load config %s", config) + raise exc return config diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 84e6eb5..9fbaab4 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -26,7 +26,6 @@ Modify the techno-economic parameters, bus definitions, and links to adapt to different commodities. """ -import logging from typing import Any from pathlib import Path import pandas as pd @@ -34,6 +33,7 @@ import pypsa import tech_database as td +from _helpers import setup_logging from trade_chain_utils import ( get_ordered_stages, @@ -43,10 +43,10 @@ _components_for_process_label, ) -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - snakemake: Any = globals().get("snakemake") +logger = setup_logging( + __name__, snakemake=snakemake, log_filename="build_x_supply_chain.log" +) def _techno_economic_parameters(config: dict) -> dict: diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index 8010dc3..3ede4a4 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -112,7 +112,7 @@ def create_supply_curve(): route_label = route_label_for_product(snakemake.config, product) or product reserved_files = snakemake.input.lco_reserved - logger.info("reserved scenario files: %s", reserved_files) + logger.info(f"reserved scenario files: {reserved_files}") df_reserved = pd.concat( (pd.read_csv(f, sep=",") for f in reserved_files), ignore_index=True ) @@ -120,7 +120,7 @@ def create_supply_curve(): unreserved_files = snakemake.input.lco_unreserved if unreserved_files and len(unreserved_files) > 0: - logger.info("unreserved scenario files: %s", unreserved_files) + logger.info(f"unreserved scenario files: {unreserved_files}") df_unreserved = pd.concat( (pd.read_csv(f, sep=",") for f in unreserved_files), ignore_index=True ) @@ -186,7 +186,7 @@ def create_supply_curve(): and len(snakemake.input.lco_unreserved) > 0 ): df_sub.to_csv(unreserved_path, index=False) - logger.info("Saved unreserved supply curve: %s", unreserved_path) + logger.info(f"Saved unreserved supply curve: {unreserved_path}") else: logger.info( "Skipping supply_unreserved output (unreserved scenario not provided or not enabled)" @@ -299,7 +299,7 @@ def create_supply_curve(): # Get product from wildcards (product-labeled contract) product = snakemake.wildcards["product"] -logger.info("Creating supply curve for product=%s", product) +logger.info(f"Creating supply curve for product={product}") if product == "hydrogen": columns = { diff --git a/workflow/scripts/download_labour_data.py b/workflow/scripts/download_labour_data.py new file mode 100644 index 0000000..a92fed9 --- /dev/null +++ b/workflow/scripts/download_labour_data.py @@ -0,0 +1,764 @@ +""" +data_downloader.py +================== +Downloads and prepares all input data needed for labour_cost_calculator.py +to run on any country worldwide. + +Data sources +------------ +A. World Bank WDI – GNI per capita (Atlas method, current USD) + Automated via the `wbgapi` package. Requires: pip install wbgapi + +B. ECB – Annual average USD/EUR spot rates + Automated via the ECB public REST API (no credentials needed). + +C. UNIDO INDSTAT – Steel sector wage bill & employment + Raw data files are expected in data/labour/unido-raw/data.csv + (downloaded from https://stat.unido.org/data/download, + INDSTAT Rev 4, ISIC 241, variables 04+05, all countries). + Licence: CC BY 4.0 + +D. Employer SSC rates – statutory employer social-security contribution rates + Pre-compiled reference table for ~80 countries. + Sources: OECD Taxing Wages 2023/24, ILO Social Security Inquiry 2022, + KPMG Global Employer Tax Guide 2023. + +Outputs (saved to data/labour/) +-------------------------------- + data/labour/gni_per_capita.csv + data/labour/ecb_usd_eur.csv + data/labour/employer_contributions.csv + data/labour/merged_labour_inputs.csv ← main output for labour_cost_calculator.py + +Usage +----- + python workflow/scripts/data_downloader.py [--target-year 2020] [--force] +""" + +import sys +import warnings +import requests # type: ignore +import pandas as pd # type: ignore +import pycountry # type: ignore +from io import StringIO +from pathlib import Path + +from _helpers import setup_logging + +# ── optional wbgapi ────────────────────────────────────────────────────────── +try: + import wbgapi as wb # type: ignore + + HAS_WBGAPI = True +except ImportError: + HAS_WBGAPI = False + +# ───────────────────────────────────────────────────────────────────────────── +# File paths (resolved relative to this script → works from any cwd) +# ───────────────────────────────────────────────────────────────────────────── +REPO_ROOT = Path(__file__).resolve().parent.parent.parent +DATA_DIR = REPO_ROOT / "data" / "labour" +DATA_DIR.mkdir(parents=True, exist_ok=True) + +# UNIDO raw data: data/labour/unido-raw/data.csv +UNIDO_RAW = DATA_DIR / "unido-raw" / "data.csv" +GNI_CSV = DATA_DIR / "gni_per_capita.csv" +ECB_CSV = DATA_DIR / "ecb_usd_eur.csv" +CONTRIB_CSV = DATA_DIR / "employer_contributions.csv" +MERGED_CSV = DATA_DIR / "merged_labour_inputs.csv" + +snakemake = globals().get("snakemake") +logger = setup_logging( + __name__, snakemake=snakemake, log_filename="download_labour_data.log" +) + +# ───────────────────────────────────────────────────────────────────────────── +# OECD member list (ISO-3, as of 2024) +# ───────────────────────────────────────────────────────────────────────────── +OECD_MEMBERS = { + "AUS", + "AUT", + "BEL", + "CAN", + "CHL", + "COL", + "CRI", + "CZE", + "DNK", + "EST", + "FIN", + "FRA", + "DEU", + "GRC", + "HUN", + "ISL", + "IRL", + "ISR", + "ITA", + "JPN", + "KOR", + "LVA", + "LTU", + "LUX", + "MEX", + "NLD", + "NZL", + "NOR", + "POL", + "PRT", + "SVK", + "SVN", + "ESP", + "SWE", + "CHE", + "TUR", + "GBR", + "USA", +} + + +# ───────────────────────────────────────────────────────────────────────────── +# A. World Bank GNI per capita +# ───────────────────────────────────────────────────────────────────────────── +def fetch_world_bank_gni(start_year=2010, end_year=2023, force=False): + """ + Download GNI per capita (Atlas method, current USD) for all World Bank + economies covering *start_year*–*end_year*. + + Requires the `wbgapi` package: pip install wbgapi + + Returns a tidy DataFrame: iso3 | year | gni_usd + Saves result to data/gni_per_capita.csv. + """ + if GNI_CSV.exists() and not force: + logger.info(f"[GNI] Loading cached → {GNI_CSV}") + return pd.read_csv(GNI_CSV) + + if not HAS_WBGAPI: + sys.exit( + "ERROR: wbgapi is required to download World Bank GNI data.\n" + " pip install wbgapi" + ) + + logger.info("[GNI] Downloading from World Bank (NY.GNP.PCAP.CD) …") + try: + raw = wb.data.DataFrame( + "NY.GNP.PCAP.CD", + time=range(start_year, end_year + 1), + skipBlanks=True, + columns="time", + ) + # raw: index = economy (ISO3), columns = "YR2010" … "YR2023" + df = ( + raw.reset_index() + .rename(columns={"economy": "iso3"}) + .melt(id_vars="iso3", var_name="year", value_name="gni_usd") + ) + df["year"] = df["year"].str.replace("YR", "").astype(int) + df = df.dropna(subset=["gni_usd"]) + df.to_csv(GNI_CSV, index=False) + logger.info(f"[GNI] {len(df)} rows → {GNI_CSV}") + return df + + except Exception as exc: + sys.exit(f"ERROR downloading World Bank GNI data: {exc}") + + +# ───────────────────────────────────────────────────────────────────────────── +# B. ECB USD/EUR annual average exchange rates +# ───────────────────────────────────────────────────────────────────────────── + +# Fallback table: ECB EXR.A.USD.EUR.SP00.A annual averages (USD per 1 EUR). +# Source: European Central Bank Statistical Data Warehouse. +# Last updated: 2024. Invert to obtain EUR per USD. +_ECB_FALLBACK_USD_PER_EUR = { + 2000: 0.9236, + 2001: 0.8956, + 2002: 0.9454, + 2003: 1.1312, + 2004: 1.2438, + 2005: 1.2441, + 2006: 1.2556, + 2007: 1.3705, + 2008: 1.4726, + 2009: 1.3948, + 2010: 1.3257, + 2011: 1.3920, + 2012: 1.2848, + 2013: 1.3281, + 2014: 1.3285, + 2015: 1.0859, + 2016: 1.1069, + 2017: 1.1297, + 2018: 1.1810, + 2019: 1.1195, + 2020: 1.1422, + 2021: 1.1827, + 2022: 1.0530, + 2023: 1.0813, + 2024: 1.0815, +} + + +def _parse_ecb_csv(text): + """Parse an ECB SDMX-CSV response into a tidy DataFrame.""" + raw = pd.read_csv(StringIO(text)) + time_col = next(c for c in raw.columns if "TIME" in c.upper()) + val_col = next(c for c in raw.columns if "OBS_VALUE" in c.upper()) + df = raw[[time_col, val_col]].copy() + df.columns = ["year", "usd_per_eur"] + df["year"] = df["year"].astype(int) + df["eur_per_usd"] = 1.0 / df["usd_per_eur"] + return df + + +def fetch_ecb_rates(start_year=2010, end_year=2023, force=False): + """ + Obtain annual average USD/EUR spot rates from the ECB. + + Series: EXR.A.USD.EUR.SP00.A → USD per 1 EUR. + Inverted to EUR per USD (= EUR_per_USD used in the paper). + + Strategy: + 1. Load cached CSV if present (and not --force). + 2. Try ECB SDW-WSREST API (old, well-documented endpoint). + 3. Try ECB Data Portal API v1 (new endpoint). + 4. Fall back to the embedded _ECB_FALLBACK_USD_PER_EUR table. + + Returns: year | usd_per_eur | eur_per_usd + Saves to data/ecb_usd_eur.csv. + """ + if ECB_CSV.exists() and not force: + logger.info(f"[ECB] Loading cached → {ECB_CSV}") + return pd.read_csv(ECB_CSV) + + ecb_attempts = [ + ( + "ECB SDW-WSREST", + ( + "https://sdw-wsrest.ecb.europa.eu/service/data/EXR/A.USD.EUR.SP00.A" + f"?format=csvdata&startPeriod={start_year}&endPeriod={end_year}" + ), + {}, + ), + ( + "ECB Data Portal v1 (Accept: text/csv)", + ( + "https://data.ecb.europa.eu/api/v1/data/EXR/A.USD.EUR.SP00.A" + f"?startPeriod={start_year}&endPeriod={end_year}" + ), + {"Accept": "text/csv"}, + ), + ( + "ECB Data Portal v1 (format=csvdata)", + ( + "https://data.ecb.europa.eu/api/v1/data/EXR/A.USD.EUR.SP00.A" + f"?format=csvdata&startPeriod={start_year}&endPeriod={end_year}" + ), + {}, + ), + ] + + for label, url, headers in ecb_attempts: + try: + logger.info(f"[ECB] Trying {label} …") + resp = requests.get(url, headers=headers, timeout=20) + resp.raise_for_status() + df = _parse_ecb_csv(resp.text) + df.to_csv(ECB_CSV, index=False) + logger.info(f"[ECB] {len(df)} rows → {ECB_CSV}") + return df + except Exception as exc: + logger.warning(f"[ECB] {label} failed: {exc}") + + # ── fallback: embedded reference table ─────────────────────────────────── + logger.info( + "[ECB] All live endpoints unavailable. Using embedded reference table (_ECB_FALLBACK_USD_PER_EUR)." + ) + rows = [ + {"year": y, "usd_per_eur": r, "eur_per_usd": 1.0 / r} + for y, r in _ECB_FALLBACK_USD_PER_EUR.items() + if start_year <= y <= end_year + ] + df = pd.DataFrame(rows).sort_values("year").reset_index(drop=True) + df.to_csv(ECB_CSV, index=False) + logger.info(f"[ECB] {len(df)} rows (from fallback table) → {ECB_CSV}") + return df + + +# ───────────────────────────────────────────────────────────────────────────── +# C. UNIDO INDSTAT – steel sector wages & employment +# ───────────────────────────────────────────────────────────────────────────── + +# Common UNIDO country-name variants → ISO-3 override +# (pycountry handles most; these cover known quirks) +_UNIDO_NAME_OVERRIDES = { + "United States of America": "USA", + "United States": "USA", + "Korea, Republic of": "KOR", + "Republic of Korea": "KOR", + "Korea (the Republic of)": "KOR", + "Taiwan, Province of China": "TWN", + "China, Taiwan Province": "TWN", + "Taiwan": "TWN", + "Iran (Islamic Republic of)": "IRN", + "Iran, Islamic Republic of": "IRN", + "Viet Nam": "VNM", + "Vietnam": "VNM", + "Bolivia (Plurinational State of)": "BOL", + "Bolivia": "BOL", + "Venezuela (Bolivarian Republic of)": "VEN", + "Venezuela": "VEN", + "Congo, Democratic Republic of the": "COD", + "Democratic Republic of the Congo": "COD", + "Syrian Arab Republic": "SYR", + "Syria": "SYR", + "Lao People's Democratic Republic": "LAO", + "Laos": "LAO", + "Moldova, Republic of": "MDA", + "Republic of Moldova": "MDA", + "Tanzania, United Republic of": "TZA", + "Tanzania": "TZA", + "Slovak Republic": "SVK", + "Czechia": "CZE", + "Czech Republic": "CZE", + "Russian Federation": "RUS", + "Russia": "RUS", + "North Macedonia": "MKD", + "Macedonia": "MKD", + "United Kingdom": "GBR", + "United Kingdom of Great Britain and Northern Ireland": "GBR", +} + + +def _name_to_iso3(name): + """Convert a country name string to ISO-3 alpha code, or return None.""" + if not isinstance(name, str): + return None + name = name.strip() + if name in _UNIDO_NAME_OVERRIDES: + return _UNIDO_NAME_OVERRIDES[name] + try: + c = pycountry.countries.lookup(name) + return c.alpha_3 + except LookupError: + return None + + +def load_unido_data(filepath=UNIDO_RAW): + """ + Load and normalise the UNIDO INDSTAT CSV downloaded from the portal. + + Handles two common export layouts: + • Long format: columns include Country/Year/Variable/Value + • SDMX-CSV: columns include REF_AREA / TIME_PERIOD / INDICATOR / OBS_VALUE + + Returns a tidy DataFrame: iso3 | country_name | year | employees | wages_usd + Returns *None* if the file does not exist (prints download instructions). + """ + filepath = Path(filepath) + if not filepath.exists(): + logger.warning(f"[UNIDO] Data file not found: {filepath}") + logger.warning( + " Please download INDSTAT Rev 4 (ISIC 241, variables 04+05, all countries)\n" + " from https://stat.unido.org/data/download and extract data.csv into\n" + f" {filepath.parent}/", + ) + return None + + logger.info(f"[UNIDO] Reading {filepath} …") + raw = pd.read_csv(filepath, low_memory=False) + + # ── Employees: VariableCode 4, count in Value ───────────────────────── + logger.info(f"[UNIDO] {len(raw)} raw rows read from {filepath}") + emp_mask = raw["VariableCode"].astype(str).str.strip().isin(["4", "04"]) + emp = ( + raw[emp_mask][["Year", "Country", "Value"]] + .copy() + .rename( + columns={"Year": "year", "Country": "country_name", "Value": "employees"} + ) + ) + emp["employees"] = pd.to_numeric(emp["employees"], errors="coerce") + # Sum across activity combinations (e.g. 2410A + 2410B) for same country/year + emp = emp.groupby(["year", "country_name"], as_index=False)["employees"].sum() + + # ── Wages: VariableCode 5, USD amount in ValueUSD ───────────────────── + wage_mask = raw["VariableCode"].astype(str).str.strip().isin(["5", "05"]) + wages = ( + raw[wage_mask][["Year", "Country", "ValueUSD"]] + .copy() + .rename( + columns={"Year": "year", "Country": "country_name", "ValueUSD": "wages_usd"} + ) + ) + wages["wages_usd"] = pd.to_numeric(wages["wages_usd"], errors="coerce") + wages = wages.groupby(["year", "country_name"], as_index=False)["wages_usd"].sum() + + # ── Merge and convert country names to ISO3 ─────────────────────────── + df = pd.merge(emp, wages, on=["year", "country_name"], how="inner") + df = df.dropna(subset=["employees", "wages_usd"]) + + df["iso3"] = df["country_name"].apply(_name_to_iso3) + df = df.dropna(subset=["iso3"]) + df["iso3"] = df["iso3"].str.upper().str.strip() + + result = df[["iso3", "country_name", "year", "employees", "wages_usd"]].copy() + logger.info( + f"[UNIDO] {len(result)} country-year rows loaded ({result['iso3'].nunique()} countries)." + ) + return result + + +# ───────────────────────────────────────────────────────────────────────────── +# D. Employer social-security contribution rates +# Sources: OECD Taxing Wages 2024, ILO Social Security Inquiry 2022, +# KPMG Global Employer Tax Guide 2023. +# All rates are the statutory employer SSC as a share of gross wages. +# ───────────────────────────────────────────────────────────────────────────── +EMPLOYER_CONTRIB_TABLE = { + # ISO3 : (rate_fraction, source_note) + "ARG": (0.170, "ANSES employer contributions ~17%"), + "AUS": (0.195, "Superannuation 11%, healthcare 2%, payroll levy ~5.5%"), + "AUT": (0.228, "Pension 12.55%, accident 1.2%, housing 0.5%, others ~8.6%"), + "BEL": (0.270, "ONSS employer total ~27%"), + "BGD": (0.050, "Bangladesh: employer contribution estimate ~5%"), + "BGR": (0.185, "Pension 10.82%, health 4.8%, others ~2.9%"), + "BLR": (0.340, "Social protection fund 34%"), + "BRA": (0.305, "INSS 22.5%, FGTS 8%"), + "CAN": (0.077, "CPP 5.95%, EI 1.66%"), + "CHL": (0.024, "Social security employer portion 2.4%"), + "CHN": ( + 0.282, + "Pension 16%, medical 5.8%, unemployment 0.5%, injury 0.4%, maternity 0.8%", + ), + "COL": (0.275, "Pension 12%, health 8.5%, ARL ~2%, SENA 2%, ICBF 3%"), + "CRI": (0.264, "CCSS employer contributions 26.4%"), + "CZE": (0.248, "Pension 21.5%, health 3.3%"), + "DEU": ( + 0.212, + "Pension 9.3%, unemployment 1.23%, health 7.3%, nursing+other ~3.3%", + ), + "DNK": (0.010, "Denmark: minimal statutory employer SSC"), + "DZA": (0.260, "CNAS employer 26%"), + "EGY": (0.260, "Social insurance employer 26%"), + "ESP": ( + 0.310, + "Pension 23.6%, unemployment 5.5%, FOGASA 0.2%, FP 0.6%, other 1.1%", + ), + "EST": (0.330, "Social tax 33%"), + "ETH": (0.110, "Private pension 11%"), + "FIN": (0.200, "Pension ~17.39%, unemployment ~1.91%, accident/other"), + "FRA": (0.425, "Complex French system total ~42.5%"), + "GBR": (0.138, "NIC Class 1 employer 13.8%"), + "GHA": (0.130, "SSNIT 13%"), + "GRC": (0.251, "IKA-ETAM ~25.06%"), + "HUN": (0.130, "Social contribution tax 13%"), + "IDN": (0.092, "BPJS: pension 3.7%, healthcare 4%, accident 0.24%, death 0.3%"), + "IND": (0.136, "EPF 12%, ESI 3.25%, LWF negligible"), + "IRL": (0.115, "PRSI Class A employer 11.15%"), + "IRN": (0.230, "Social security 23%"), + "IRQ": (0.120, "Social security employer 12%"), + "ISL": (0.065, "Iceland: employer social security 6.5%"), + "ISR": (0.075, "National insurance 3.55%, health insurance 3.1%"), + "ITA": (0.320, "INPS employer ~32%"), + "JPN": (0.151, "Pension 9.15%, health 4.99%, employment 0.6%, WC ~0.5%"), + "KAZ": (0.180, "Social contributions 3.5%, UAPF 3.5%, health 3%, other ~7%"), + "KOR": (0.105, "National pension 4.5%, health 3.545%, employment 0.9%, WC ~1.5%"), + "LBY": (0.115, "Social security 11.5%"), + "LTU": (0.017, "Employer SSC 1.77% (post-2019 reform)"), + "LUX": (0.155, "Pension 8%, health 3.05%, accident 1.1%, mutual aid/LTC ~3.3%"), + "LVA": (0.237, "Social insurance 23.59%"), + "MAR": ( + 0.228, + "CNSS: pension 11.89%, family 6.4%, AMO 2.26%, training 1.6%, accident", + ), + "MEX": (0.250, "IMSS ~17%, INFONAVIT 5%, SAR 2%"), + "MYS": (0.143, "EPF 12%, SOCSO 1.75%, EIS 0.4%, HRDF 0.5%"), + "NGA": (0.100, "PRA employer contributory pension 10%"), + "NLD": (0.190, "AOW/ANW/WLZ/WIA/ZVW combined ~19%"), + "NOR": (0.141, "Employer social security 14.1%"), + "NZL": (0.000, "No statutory employer SSC"), + "OMN": (0.113, "PASI employer 11.25%"), + "PAK": (0.120, "EOBI 5%, ESSI 5%, other ~2%"), + "PER": (0.090, "EsSalud 9%"), + "PHL": (0.115, "SSS 8%, PhilHealth 2.5%, Pag-IBIG 2%"), + "POL": ( + 0.204, + "Pension 9.76%, disability 6.5%, accident avg 1.67%, FP+FGSP ~2.55%", + ), + "PRT": (0.238, "Social security 23.75%"), + "QAT": (0.000, "Qatar: no employer SSC for most private-sector workers"), + "ROU": (0.023, "Work accident/occupational disease 2.25%"), + "RUS": (0.302, "Pension 22%, medical 5.1%, social 2.9%, injury 0.2%"), + "SAU": (0.118, "Social insurance 11.75%"), + "SGP": (0.170, "CPF employer ~17%"), + "SVK": ( + 0.248, + "Pension 14%, disability 3%, sickness 1.4%, unemployment 1%, other ~5%", + ), + "SVN": (0.163, "Pension 8.85%, health 6.56%, accident 0.53%, employment 0.06%"), + "SWE": (0.314, "Social security fees 31.42%"), + "THA": (0.050, "Social security employer 5%"), + "TUN": (0.165, "CNSS employer 16.57%"), + "TUR": (0.225, "Social security premiums 22.5%"), + "TZA": (0.100, "NSSF employer 10%"), + "UKR": (0.220, "Unified social contribution 22%"), + "ARE": (0.125, "GPSSA employer 12.5% for nationals; expats typically 0%"), + "USA": (0.076, "FICA: SS 6.2%, Medicare 1.45%, FUTA ~0.6%"), + "UZB": (0.120, "Social insurance 12%"), + "VNM": (0.215, "Social insurance 17.5%, health 3%, unemployment 1%"), + "ZAF": (0.020, "Skills development levy 1%, UIF 1%"), + "ZMB": (0.050, "NAPSA employer 5%"), + "ZWE": (0.045, "NSSA employer 4.5%"), + "CHE": (0.135, "AHV/IV/EO 5.3%, ALV 1.1%, FAK ~1.7%, accident/pension ~5.4%"), + "MKD": (0.070, "North Macedonia: employer health 7.3% → total ~7%"), + "SRB": ( + 0.173, + "Serbia: pension 11%, health 5.15%, unemployment 0.75%, other ~0.3%", + ), + "HRV": (0.165, "Croatia: pension pillar II 5%, health ~16.5% total"), + "BIH": (0.105, "Bosnia: contributions ~10.5% (varies by entity)"), + "ALB": (0.150, "Albania: employer social insurance 15%"), + "GEO": (0.000, "Georgia: no employer-side social contribution"), + "ARM": (0.025, "Armenia: employer social premium 2.5%"), + "AZE": (0.220, "Azerbaijan: employer social insurance 22%"), + "TKM": (0.200, "Turkmenistan: employer insurance ~20%"), + "KGZ": (0.175, "Kyrgyzstan: employer social fund 17.5%"), + "TJK": (0.250, "Tajikistan: employer contribution ~25%"), + "MNG": (0.135, "Mongolia: employer social insurance 13.5%"), + "MMR": (0.030, "Myanmar: SSB employer 2.5-3%"), + "KHM": (0.031, "Cambodia: NSSF employer 3.1%"), + "LKA": (0.120, "Sri Lanka: EPF 12%"), + "NPL": (0.100, "Nepal: SSF employer ~10%"), + "KEN": (0.060, "Kenya: NSSF + NHIF employer ~6%"), + "UGA": (0.100, "Uganda: NSSF employer 10%"), + "SEN": (0.040, "Senegal: IPM employer + family benefit ~4%"), + "CIV": (0.065, "Côte d'Ivoire: CNPS employer ~6.5%"), + "CMR": (0.080, "Cameroon: CNPS employer ~8%"), + "AGO": (0.080, "Angola: employer social security 8%"), + "MOZ": (0.040, "Mozambique: INSS employer 4%"), + "BWA": (0.000, "Botswana: no statutory employer SSC"), + "NAM": (0.000, "Namibia: no statutory employer SSC"), + "MUS": (0.060, "Mauritius: NPF 6%"), + "TTO": (0.060, "Trinidad & Tobago: NIS employer 5.85% + 0.3%"), + "JAM": (0.030, "Jamaica: NIS employer 2.5-3%"), + "ECU": (0.120, "Ecuador: IESS employer 12.15%"), + "BOL": (0.165, "Bolivia: AFP 10%, health 10%, employer total ~16.5%"), + "PRY": (0.165, "Paraguay: IPS employer 14%, others ~2.5%"), + "URY": (0.075, "Uruguay: BPS employer 7.5%"), + "PAN": (0.125, "Panama: CSS employer 12.25%"), + "GTM": (0.105, "Guatemala: IGSS employer 10.5%"), + "HND": (0.090, "Honduras: IHSS employer 5%, RAP 5% → ~9%"), + "SLV": (0.075, "El Salvador: ISSS 7.5% employer"), + "NIC": (0.190, "Nicaragua: INSS employer 19%"), + "DOM": (0.075, "Dominican Republic: AFP 7.1% → total ~7.5%"), + "CUB": (0.145, "Cuba: employer social security 14.5%"), +} + + +def build_employer_contributions(): + """ + Build a DataFrame from the embedded EMPLOYER_CONTRIB_TABLE. + + Returns: iso3 | employer_contrib_rate | contrib_source | oecd_status + Saves to data/employer_contributions.csv. + """ + rows = [ + {"iso3": iso, "employer_contrib_rate": rate, "contrib_source": note} + for iso, (rate, note) in EMPLOYER_CONTRIB_TABLE.items() + ] + df = pd.DataFrame(rows) + df["oecd_status"] = df["iso3"].apply( + lambda x: "OECD" if x in OECD_MEMBERS else "non_OECD" + ) + df.to_csv(CONTRIB_CSV, index=False) + logger.info(f"[CONTRIB] {len(df)} countries → {CONTRIB_CSV}") + return df + + +# ───────────────────────────────────────────────────────────────────────────── +# E. Merge all sources → merged_labour_inputs.csv +# ───────────────────────────────────────────────────────────────────────────── + + +def _pick_latest_row(group, target_year): + """ + From a single country's time-series, return the row with the most recent + year ≤ target_year that has both employees and wages_usd. + Returns None if no qualifying row exists. + """ + valid = group[group["year"] <= target_year].dropna( + subset=["employees", "wages_usd"] + ) + if valid.empty: + return None + return valid.sort_values("year").iloc[-1] + + +def _nearest_year(lookup_dict, iso3, preferred_year): + """ + From a {(iso3, year): value} dict, return (year_used, value) for the + entry closest to preferred_year. Returns (None, None) if no data. + """ + candidates = [(y, v) for (c, y), v in lookup_dict.items() if c == iso3] + if not candidates: + return None, None + candidates.sort(key=lambda xy: abs(xy[0] - preferred_year)) + return candidates[0] + + +def merge_all_data( + target_year=2020, + unido_filepath=None, + output_filepath=None, + force_download=False, +): + """ + Orchestrate all downloads, merge on country × year, and write the master + input CSV for labour_cost_calculator.py. + + Parameters + ---------- + target_year : int + Reference year for GNI and EUR conversion (default 2020). + unido_filepath : str or Path + Path to the manually downloaded UNIDO CSV. + output_filepath : str or Path + Path for the output merged CSV (default: MERGED_CSV). + force_download : bool + If True, re-download GNI and ECB data even if cached files exist. + + Returns + ------- + pd.DataFrame or None + Merged dataset (None if UNIDO file is missing). + """ + if unido_filepath is None: + unido_filepath = UNIDO_RAW + if output_filepath is None: + output_filepath = MERGED_CSV + output_filepath = Path(output_filepath) + + # 1. Fetch each source + gni_df = fetch_world_bank_gni(force=force_download) + ecb_df = fetch_ecb_rates(force=force_download) + unido_df = load_unido_data(unido_filepath) + contrib_df = build_employer_contributions() + + if unido_df is None: + logger.warning("[MERGE] Cannot merge – UNIDO file not yet downloaded.") + logger.warning( + " GNI, ECB and employer-contribution files have been saved." + ) + return None + + # 2. Build fast lookup dicts + # ECB: {year: eur_per_usd} + ecb_dict = dict(zip(ecb_df["year"].astype(int), ecb_df["eur_per_usd"])) + + # GNI: {(iso3, year): gni_usd} + gni_dict = { + (r.iso3.strip().upper(), int(r.year)): r.gni_usd for r in gni_df.itertuples() + } + + # 3. Pick best row per country from UNIDO + records = [] + for iso3, grp in unido_df.groupby("iso3"): + row = _pick_latest_row(grp, target_year) + if row is None: + continue + + data_year = int(row["year"]) + country_name = str(row.get("country_name", iso3)) + + # ECB rate for data year + eur_per_usd_data = ecb_dict.get( + data_year, ecb_dict[min(ecb_dict, key=lambda y: abs(y - data_year))] + ) + # ECB rate for target year + eur_per_usd_target = ecb_dict.get( + target_year, ecb_dict[min(ecb_dict, key=lambda y: abs(y - target_year))] + ) + + # GNI for data year + gni_data_usd = gni_dict.get((iso3, data_year)) + if gni_data_usd is None: + _, gni_data_usd = _nearest_year(gni_dict, iso3, data_year) + if gni_data_usd is None: + continue # no GNI data → skip + + # GNI for target year + gni_target_usd = gni_dict.get((iso3, target_year)) + if gni_target_usd is None: + _, gni_target_usd = _nearest_year(gni_dict, iso3, target_year) + if gni_target_usd is None: + continue # no GNI data → skip + + records.append( + { + "iso3": iso3, + "country_name": country_name, + "data_year": data_year, + "oecd_status": "OECD" if iso3 in OECD_MEMBERS else "non_OECD", + "steel_employees": row["employees"], + "steel_wage_usd": row["wages_usd"], + "gni_data_year_usd": gni_data_usd, + "gni_target_year_usd": gni_target_usd, + "eur_per_usd_data": eur_per_usd_data, + "eur_per_usd_target": eur_per_usd_target, + } + ) + + if not records: + logger.warning("[MERGE] No valid records produced. Check UNIDO file contents.") + return None + + merged = pd.DataFrame(records) + + # 4. Join employer contribution rates + merged = merged.merge( + contrib_df[["iso3", "employer_contrib_rate", "contrib_source"]], + on="iso3", + how="left", + ) + + # Report countries missing employer contribution data + missing_mask = merged["employer_contrib_rate"].isna() + if missing_mask.any(): + missing_iso = merged.loc[missing_mask, "iso3"].tolist() + warnings.warn( + f"{len(missing_iso)} countries have no employer contribution rate " + f"in EMPLOYER_CONTRIB_TABLE: {missing_iso}. " + "The caller should impute these (e.g. with the sample mean)." + ) + + output_filepath.parent.mkdir(parents=True, exist_ok=True) + merged.to_csv(output_filepath, index=False) + logger.info(f"\n[MERGE] {len(merged)} countries → {output_filepath}") + cols_show = [ + "iso3", + "country_name", + "data_year", + "steel_employees", + "steel_wage_usd", + "gni_target_year_usd", + "employer_contrib_rate", + ] + logger.info(merged[cols_show].to_string(index=False)) + return merged + + +# ───────────────────────────────────────────────────────────────────────────── +# Entry point +# ───────────────────────────────────────────────────────────────────────────── +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake("download_labour_data") + + merge_all_data( + target_year=2020, + output_filepath=snakemake.output.merged, + ) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index b653b79..b8b97a9 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -1,7 +1,8 @@ +from typing import Any + import pypsa import pandas as pd import matplotlib -from typing import Any matplotlib.use("Agg") import matplotlib.pyplot as plt @@ -10,8 +11,12 @@ import geopandas as gpd import cartopy.io.shapereader as shpreader +from _helpers import setup_logging + snakemake: Any = globals().get("snakemake") +logger = setup_logging(__name__, snakemake=snakemake, log_filename="model_trade.log") + plt.style.use("bmh") @@ -107,7 +112,7 @@ def building_model( "_marginal_cost_", 1 )[0] - print("building generators and loads for ", region_name) + logger.info(f"building generators and loads for {region_name}") # Bus coordinates (shared by all buses in this region) loc = bus_location.loc[bus_location["region_name"] == region_name] @@ -158,7 +163,7 @@ def building_model( # Demand load load = demands.loc[demands["region"] == region_name, "demand"].values[0] - print("Load set to 100% of regional final energy demand.") + logger.info("Load set to 100%% of regional final energy demand.") n.add( "Load", region_name + "_" + final, @@ -228,9 +233,7 @@ def building_model( ) p_nom = ( grid_potential.loc[region_name, "potential_mt_steel"] * 1e6 - ) / len( - region_data_intertwo - ) # split evenly across supply steps + ) / len(region_data_intertwo) # split evenly across supply steps m_cost = float( region_data_intertwo[f"{cost_descriptor} [EUR/{unit}]"][s] @@ -292,9 +295,9 @@ def create_links(transport_costs, trade_options): speed = 30 # km/h, IEA future of hydrogen 2019 BOG = 0.2 / 100 # %/day, IEA future of hydrogen 2019 - print("ship + pipe cost", ship_mc, ship_c, pipe_mc) - print(f"shipping cost {interone} {ship_interone_mc} EUR/(t*km)") - print(f"shipping cost iron ore {ship_iron_ore_mc} EUR/(t*km)") + logger.info(f"ship + pipe cost {ship_mc} {ship_c} {pipe_mc}") + logger.info(f"shipping cost {interone} {ship_interone_mc} EUR/(t*km)") + logger.info(f"shipping cost iron ore {ship_iron_ore_mc} EUR/(t*km)") # if there should be a link, create a link # do this for both shipping and pipeline @@ -337,7 +340,13 @@ def create_links(transport_costs, trade_options): capital_cost=1 / 1000, # to prevent optimisation shenenigans p_nom_extendable=True, ) - print(f"shipping {interone} link made from {r_from} to {r_to} - eff {eff}") + logger.info( + "shipping %s link made from %s to %s - eff %s", + interone, + r_from, + r_to, + eff, + ) # Add iron ore shipping link total_cost_iron_ore = ship_iron_ore_mc * float( @@ -355,10 +364,11 @@ def create_links(transport_costs, trade_options): capital_cost=1 / 1000, # to prevent optimisation shenenigans p_nom_extendable=True, ) - print( - "iron ore shipping link made from {}_ore to {}_ore - eff {}".format( - r_from, r_to, eff - ) + logger.info( + "iron ore shipping link made from %s_ore to %s_ore - eff %s", + r_from, + r_to, + eff, ) # checking if the row connects with pipeline @@ -382,7 +392,7 @@ def create_links(transport_costs, trade_options): p_nom_extendable=True, capital_cost=1 / 1000, # to prevent optimisation shenenigans ) - print("pipeline link made from {} to {} - eff {}".format(r_from, r_to, eff)) + logger.info("pipeline link made from %s to %s - eff %s", r_from, r_to, eff) return @@ -392,7 +402,7 @@ def save_trade_network(solved_network): sol = pd.DataFrame(columns=["type", "variable", "value", "unit"]) # add objective cost sol.loc[sol.shape[0]] = ["objective", "cost", solved_network.objective, "EUR"] - print("added objective cost to sol") + logger.info("added objective cost to sol") # add all generators with name and production value df_gen = solved_network.generators.p_nom_opt.T.to_frame() @@ -401,7 +411,7 @@ def save_trade_network(solved_network): df_gen.insert(0, "type", "generator") df_gen.insert(3, "unit", unit) sol = pd.concat([sol, df_gen], ignore_index=True) - print("added generators to sol") + logger.info("added generators to sol") # add all links with names and flows df_links = solved_network.links.p_nom_opt.T.to_frame() @@ -410,7 +420,7 @@ def save_trade_network(solved_network): df_links.insert(0, "type", "link") df_links.insert(3, "unit", unit) sol = pd.concat([sol, df_links], ignore_index=True) - print("added links to sol") + logger.info("added links to sol") # add all bus (balance) - who is importing/exporting df_bus = solved_network.buses_t.p.T @@ -419,7 +429,7 @@ def save_trade_network(solved_network): df_bus.insert(0, "type", "bus") df_bus.insert(3, "unit", unit + "/a") sol = pd.concat([sol, df_bus], ignore_index=True) - print("added bus_balances to sol") + logger.info("added bus_balances to sol") # how much of capacity is actually being used per bus? df_bus_cap = ( @@ -436,7 +446,7 @@ def save_trade_network(solved_network): df_bus_cap.insert(0, "type", "used bus capacity") df_bus_cap.insert(3, "unit", "%") sol = pd.concat([sol, df_bus_cap], ignore_index=True) - print("added bus_capacities to sol") + logger.info("added bus_capacities to sol") sol.to_csv(snakemake.output.trade_result) @@ -470,21 +480,23 @@ def save_network_collection(nc, output_path, optimal_network=None): # Split path into base and extension base, ext = os.path.splitext(output_path) - print(f"Saving NetworkCollection with {len(nc.networks)} networks to {output_dir}") + logger.info( + "Saving NetworkCollection with %s networks to %s", len(nc.networks), output_dir + ) # Save optimal network (without slack) to the base filename if provided if optimal_network is not None: - print(f" Saving optimal network (no slack) to {output_path}") + logger.info(f" Saving optimal network (no slack) to {output_path}") optimal_network.export_to_netcdf(output_path) # Save each network with its index/key in the filename for key, network in nc.networks.items(): filename = f"{base}_{key}{ext}" - print(f" Saving network with key '{key}' to {filename}") + logger.info(f" Saving network with key '{key}' to {filename}") network.export_to_netcdf(filename) total_saved = len(nc.networks) + (1 if optimal_network is not None else 0) - print(f"Saved {total_saved} networks to {output_dir}") + logger.info(f"Saved {total_saved} networks to {output_dir}") def plot_trade_network( @@ -635,7 +647,7 @@ def apply_cost_penalty(n, cost_penalty): "marginal_cost", ] *= cost_penalty[region] else: - print("No cost penalty applied") + logger.info("No cost penalty applied") return n @@ -661,7 +673,7 @@ def apply_hbi_diversity_constraint(n, diversity_factor, demands): """ if diversity_factor is False: - print("HBI diversity constraint disabled") + logger.info("HBI diversity constraint disabled") return n if diversity_factor <= 0 or diversity_factor > 1: @@ -682,8 +694,9 @@ def apply_hbi_diversity_constraint(n, diversity_factor, demands): demand_tonnes = demands.loc[demands["region"] == region_name, "demand"].values if len(demand_tonnes) == 0: - print( - f"Warning: No demand found for region {region_name}, skipping diversity constraint" + logger.warning( + "Warning: No demand found for region %s, skipping diversity constraint", + region_name, ) continue @@ -696,8 +709,12 @@ def apply_hbi_diversity_constraint(n, diversity_factor, demands): for link_idx in group.index: n.links.loc[link_idx, "p_nom_max"] = max_from_single_supplier - print( - f"HBI diversity constraint applied to {region_name}: max {diversity_factor * 100:.0f}% of {demand_tonnes:.0f}t = {max_from_single_supplier:.0f}t per supplier" + logger.info( + "HBI diversity constraint applied to %s: max %.0f%% of %.0ft = %.0ft per supplier", + region_name, + diversity_factor * 100, + demand_tonnes, + max_from_single_supplier, ) return n @@ -745,7 +762,7 @@ def resolve_mga_exporters_from_indicator(mga, indicators): # Otherwise, resolve from indicator if "indicator" not in mga: - print("No indicator or export specified in MGA config") + logger.info("No indicator or export specified in MGA config") return mga indicator_name = mga["indicator"] @@ -777,8 +794,10 @@ def resolve_mga_exporters_from_indicator(mga, indicators): selected_regions = values[values < threshold].index.tolist() - print(f"Selected regions with {indicator_name} < {threshold}: {selected_regions}") - print(f"Values: {values[values < threshold].to_dict()}") + logger.info( + f"Selected regions with {indicator_name} < {threshold}: {selected_regions}" + ) + logger.info(f"Values: {values[values < threshold].to_dict()}") # Set export to the selected regions mga["export"] = selected_regions @@ -854,14 +873,17 @@ def resolve_mga_links_from_chokepoints(n, mga, trade_options, interone): route_cp = route_chokepoints.get((r_from, r_to), set()) if route_cp & chokepoints_to_avoid: # set intersection selected_links.append(link_name) - print( - f" Chokepoint MGA: link '{link_name}' traverses " - f"{route_cp & chokepoints_to_avoid}" + logger.info( + " Chokepoint MGA: link '%s' traverses %s", + link_name, + route_cp & chokepoints_to_avoid, ) - print( - f"Chokepoint MGA: selected {len(selected_links)}/{len(carrier_links)} " - f"shipping links traversing {chokepoints_to_avoid}" + logger.info( + "Chokepoint MGA: selected %s/%s shipping links traversing %s", + len(selected_links), + len(carrier_links), + chokepoints_to_avoid, ) return pd.Index(selected_links) @@ -898,8 +920,8 @@ def resolve_mga_links_from_blocks(n, mga): for region in regions: region_to_block[region] = block_name - print( - f"Blocks MGA: {', '.join(f'{k}: {len(v)} regions' for k, v in blocks.items())}" + logger.info( + "Blocks MGA: %s", ", ".join(f"{k}: {len(v)} regions" for k, v in blocks.items()) ) # Select shipping links for the target carrier @@ -921,11 +943,17 @@ def resolve_mga_links_from_blocks(n, mga): if block_from != block_to: selected_links.append(link_name) - print(f" Blocks MGA: link '{link_name}' crosses {block_from} → {block_to}") + logger.info( + " Blocks MGA: link '%s' crosses %s → %s", + link_name, + block_from, + block_to, + ) - print( - f"Blocks MGA: selected {len(selected_links)}/{len(carrier_links)} " - f"inter-block shipping links" + logger.info( + "Blocks MGA: selected %s/%s inter-block shipping links", + len(selected_links), + len(carrier_links), ) return pd.Index(selected_links) @@ -1005,7 +1033,7 @@ def solve_network(n, mga=None, indicators=None): if hasattr(n, "model") and getattr(n.model, "solver_model", None) is not None: n.model.solver_model = None except Exception as e: - print(f"Warning clearing solver model before copying network: {e}") + logger.warning(f"Warning clearing solver model before copying network: {e}") optimal_network = n.copy() # Store the optimal solution @@ -1047,13 +1075,13 @@ def _link_weight(link_name): slack_list = mga["slack"] # Always a list in config # Handle slack values (always as a list) - print(f"MGA activated with slacks: {slack_list}") - print(f"Optimal cost (no MGA): {optimal_cost:.2f} B€") + logger.info(f"MGA activated with slacks: {slack_list}") + logger.info(f"Optimal cost (no MGA): {optimal_cost:.2f} B€") networks = {} for slack_value in slack_list: - print(f"\n--- Solving with slack = {slack_value} ---") + logger.info(f"\n--- Solving with slack = {slack_value} ---") # Create a copy of the network for each slack n_copy = n.copy() @@ -1073,7 +1101,7 @@ def _link_weight(link_name): .div(1e9) ) mga_cost = tsc.sum() - print( + logger.info( f"MGA cost: {mga_cost:.2f} B€, allowed cost increase: {optimal_cost * (1 + slack_value):.2f} B€" ) @@ -1117,14 +1145,17 @@ def _link_weight(link_name): stages = trade_chain["stages"] intertwo = stages[max(stages.keys())]["process_label"] - print( - f"intermediate 1 ({interone}) and intermediate 2 ({intertwo}) to final product {final}" + logger.info( + "intermediate 1 (%s) and intermediate 2 (%s) to final product %s", + interone, + intertwo, + final, ) shipping_first = "iron_ore" shipping_second = interone - print("starting up with all regions--- ") + logger.info("starting up with all regions--- ") # making dataframes transport_costs = pd.read_csv(snakemake.input.transport_costs, header=0) trade_options = pd.read_csv(snakemake.input.trade_options, header=0) @@ -1174,10 +1205,10 @@ def _link_weight(link_name): plot_config = snakemake.config["plot"]["world_map"][final] - print("data loaded successfully") + logger.info("data loaded successfully") # building model - print("building model") + logger.info("building model") n = building_model( supply_curves_interone, supply_curves_intertwo, @@ -1187,63 +1218,67 @@ def _link_weight(link_name): ) # building transport network connecting the individual buses - print("building transportation links") + logger.info("building transportation links") create_links(transport_costs, trade_options) # Cost penalty - if snakemake.config["scenario"][scenario]["modifiers"]["cost_penalty"] == None: + if snakemake.config["scenario"][scenario]["modifiers"]["cost_penalty"] is None: cost_penalty = None - print("cost_penalty not activated") + logger.info("cost_penalty not activated") else: cost_penalty = snakemake.config["scenario"][scenario]["modifiers"][ "cost_penalty" ] - print( - f"applying cost penalty scenario: {scenario} with penalties {cost_penalty}" + logger.info( + "applying cost penalty scenario: %s with penalties %s", + scenario, + cost_penalty, ) n = apply_cost_penalty(n, cost_penalty) # HBI diversity constraint diversity_factor = snakemake.config["trade"]["diversity_factor"] if diversity_factor is not False: - print(f"applying HBI diversity constraint with factor {diversity_factor}") + logger.info( + "applying HBI diversity constraint with factor %s", diversity_factor + ) n = apply_hbi_diversity_constraint(n, diversity_factor, demands) else: - print("HBI diversity constraint disabled") + logger.info("HBI diversity constraint disabled") # MGA if "mga" not in snakemake.config["scenario"][scenario]["modifiers"].keys(): mga = None - print("MGA not activated") + logger.info("MGA not activated") else: mga = snakemake.config["scenario"][scenario]["modifiers"]["mga"] - print(f"MGA activated with slack {mga['slack']}") + logger.info(f"MGA activated with slack {mga['slack']}") # solving model - print("solving model") + logger.info("solving model") result = solve_network(n, mga=mga, indicators=indicators if indicators else None) - print("network was solved") + logger.info("network was solved") # Export result: always a tuple (optimal_network, NetworkCollection) - print("saving network to netCDF") + logger.info("saving network to netCDF") optimal_net, nc = result save_network_collection( nc, snakemake.output.trade_network, optimal_network=optimal_net ) n_selected = optimal_net - print( + logger.info( f"Saved optimal network and NetworkCollection with {len(nc.networks)} networks" ) # saving results and calculating LCOH - print("saving results as network+csv") + logger.info("saving results as network+csv") save_trade_network(n_selected) # Build dissolved region GeoDataFrame once for basemap (no internal country borders) region_gdf = build_region_geodataframe(snakemake.config) # Plot results: consolidate plotting for all networks - print("saving plots") + logger.info("saving plots") # Define the products to plot and their settings plot_settings = [ @@ -1273,9 +1308,9 @@ def _link_weight(link_name): is_optimal = pd.isna(slack_key) if is_optimal: - print("\nPlotting optimal network (no slack)") + logger.info("\nPlotting optimal network (no slack)") else: - print(f"\nPlotting for slack={slack_key}") + logger.info(f"\nPlotting for slack={slack_key}") for product, alpha_supply, output_path, output_path_png in plot_settings: # Use base filenames for optimal, append slack value for MGA variants diff --git a/workflow/scripts/prepare_labour_cost.py b/workflow/scripts/prepare_labour_cost.py new file mode 100644 index 0000000..5be7cb4 --- /dev/null +++ b/workflow/scripts/prepare_labour_cost.py @@ -0,0 +1,278 @@ +""" +Labour cost calculator for H-DRI-EAF value chains. + +Reads the merged labour inputs produced by data_downloader.py, computes +an all-in hourly steelworker wage [EUR/h, 2020 prices] for each country, +maps countries to the model regions defined in config/config.yaml, and +saves a regional aggregated CSV. + +Methodology (Nykvist et al. 2025, Section D4) +---------------------------------------------- +1. Steel wage ratio (dimensionless): + wage_ratio = (wage_per_employee_USD * (1 + employer_contrib_rate)) + / GNI_data_year_USD + Both numerator and denominator use the same year and the same USD + denomination, so currency conversion cancels out. + +2. Hourly wage [EUR/h, 2020]: + hourly_wage = (GNI_2020_EUR * wage_ratio / WORKING_YEAR) * (1 + OVERHEAD) + where GNI_2020_EUR = GNI_2020_USD * EUR_per_USD_2020. + +3. Countries with no UNIDO data have their wage ratio imputed as the + employment-weighted mean of all countries with data. + +4. Regional aggregation: employment-weighted mean of country hourly wages + within each model region. Regions with no data fall back to the + global employment-weighted mean. + +Output: resources/labour_cost_clustered.csv + region | labour_ely | labour_dri | labour_eaf [EUR/h, 2020] + (all three columns hold the same hourly rate; the model applies + different labour-intensity factors per technology downstream) + +Usage +----- + python workflow/scripts/labour_cost_calculator.py +""" + +import warnings +import pycountry +import pandas as pd +from pathlib import Path +from typing import Any + +from _helpers import setup_logging + +snakemake: Any = globals().get("snakemake") +logger = setup_logging( + __name__, snakemake=snakemake, log_filename="prepare_labour_cost.log" +) + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- +WORKING_YEAR = 2080 # assumed working hours per year +OVERHEAD = 0.25 # 25 % overhead on direct labour cost +TARGET_YEAR = 2020 # reference year for all monetary outputs + +# Labour intensities (Nykvist Table D5, original data Devlin) +LI_ELY_PU = 2.0 # h / kW installed electrolyser +LI_DRI_PU = 0.18 # h / t DRI +LI_EAF_PU = 0.49 # h / t steel + +# --------------------------------------------------------------------------- +# Country name → ISO3 helpers for config region mapping +# --------------------------------------------------------------------------- +_EXTRA_NAME_TO_ISO3 = { + # Config-specific names that pycountry.lookup() doesn't find by default + "Iran, Islamic Republic of": "IRN", + "Republic of the Congo": "COG", + "Hong Kong": "HKG", + "Taiwan": "TWN", + "South Korea": "KOR", + "North Korea": "PRK", + "Vietnam": "VNM", + "Bolivia": "BOL", + "Venezuela": "VEN", + "Russia": "RUS", + "Russian Federation": "RUS", + "Laos": "LAO", + "Brunei": "BRN", + "Palestine": "PSE", + "Syria": "SYR", + "Democratic Republic of the Congo": "COD", + "Equatorial French Guiana": None, # not a sovereign country +} + + +def _config_name_to_iso3(name: str): + """Convert a config region country name to ISO3, return None if not found.""" + name = name.strip() + if name in _EXTRA_NAME_TO_ISO3: + return _EXTRA_NAME_TO_ISO3[name] + try: + return pycountry.countries.lookup(name).alpha_3 + except LookupError: + return None + + +def build_iso_to_region(config_regions: dict) -> dict: + """ + Build {iso3: region_name} from the config 'regions' dict. + + Config entries may use '+' to combine two countries in one string + (e.g. "Togo + Algeria"); each part is split and mapped individually. + """ + iso_to_region = {} + unmatched = [] + for region, countries in config_regions.items(): + for entry in countries: + for part in str(entry).split("+"): + iso3 = _config_name_to_iso3(part.strip()) + if iso3: + iso_to_region[iso3] = region + else: + unmatched.append((region, part.strip())) + if unmatched: + warnings.warn( + f"Could not map {len(unmatched)} config country name(s) to ISO3: " + + ", ".join(f"{r}/{n}" for r, n in unmatched[:10]) + + (" …" if len(unmatched) > 10 else "") + ) + return iso_to_region + + +# --------------------------------------------------------------------------- +# Wage computation +# --------------------------------------------------------------------------- +def compute_hourly_wage(row) -> float: + """ + Compute the all-in hourly steelworker wage [EUR/h] in TARGET_YEAR prices. + + wage_ratio = (steel_wage_usd / steel_employees) * (1 + employer_contrib_rate) + / gni_data_year_usd + + hourly_wage = (gni_target_year_usd * eur_per_usd_target + * wage_ratio / WORKING_YEAR) * (1 + OVERHEAD) + """ + wage_ratio = ( + (row["steel_wage_usd"] / row["steel_employees"]) + * (1.0 + row["employer_contrib_rate"]) + / row["gni_data_year_usd"] + ) + gni_target_eur = row["gni_target_year_usd"] * row["eur_per_usd_target"] + return (gni_target_eur * wage_ratio / WORKING_YEAR) * (1.0 + OVERHEAD) + + +def compute_all_hourly_wages(merged_df: pd.DataFrame) -> pd.DataFrame: + """ + Return a DataFrame with columns [iso3, country_name, steel_employees, + hourly_wage_eur] for every country in *merged_df*. + + Countries missing any required column get their wage imputed as the + employment-weighted mean of all countries with complete data. + """ + required = [ + "steel_wage_usd", + "steel_employees", + "gni_data_year_usd", + "gni_target_year_usd", + "employer_contrib_rate", + "eur_per_usd_target", + ] + valid_mask = merged_df[required].notna().all(axis=1) # type: ignore[arg-type] + valid = merged_df[valid_mask].copy() + valid["hourly_wage_eur"] = valid.apply(compute_hourly_wage, axis=1) + + # Employment-weighted mean for imputation + total_emp = valid["steel_employees"].sum() + mean_wage = ( + (valid["hourly_wage_eur"] * valid["steel_employees"]).sum() / total_emp + if total_emp > 0 + else valid["hourly_wage_eur"].mean() + ) + + result = merged_df[["iso3", "country_name", "steel_employees"]].merge( + valid[["iso3", "hourly_wage_eur"]], on="iso3", how="left" + ) + n_imputed = result["hourly_wage_eur"].isna().sum() + if n_imputed: + warnings.warn( + f"{n_imputed} countries have incomplete data and will be imputed " + f"with the global mean ({mean_wage:.2f} EUR/h)." + ) + result["hourly_wage_eur"] = result["hourly_wage_eur"].fillna(mean_wage) + return result + + +# --------------------------------------------------------------------------- +# Regional aggregation +# --------------------------------------------------------------------------- +def aggregate_by_region( + wages_df: pd.DataFrame, + iso_to_region: dict, + regions: list, +) -> pd.DataFrame: + """ + Compute the employment-weighted mean hourly wage for each model region. + + Countries with no employment figure use weight = 1. + Regions with no matching countries use the global weighted mean. + """ + df = wages_df.copy() + df["region"] = df["iso3"].map(iso_to_region) + df["weight"] = df["steel_employees"].fillna(1.0) + + # Global fallback + global_mean = (df["hourly_wage_eur"] * df["weight"]).sum() / df["weight"].sum() + + rows = [] + for region in regions: + grp = df[df["region"] == region] + if grp.empty: + wage = global_mean + else: + w_sum = grp["weight"].sum() + wage = ( + (grp["hourly_wage_eur"] * grp["weight"]).sum() / w_sum + if w_sum > 0 + else global_mean + ) + rows.append( + { + "region": region, + "steelworker_wage in euro/h": round(wage, 4), + "ely_intensity in h/kW_ely": LI_ELY_PU, + "dri_intensity in h/t_dri": LI_DRI_PU, + "eaf_intensity in h/t_steel": LI_EAF_PU, + } + ) + + return pd.DataFrame(rows).set_index("region") + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +if __name__ == "__main__": + if "snakemake" not in globals(): + from _helpers import mock_snakemake + + snakemake = mock_snakemake("prepare_labour_cost") + + # ── 1. Load merged data ────────────────────────────────────────────── + merged_path = Path(snakemake.input.merged) + if not merged_path.exists(): + raise FileNotFoundError( + f"Merged labour inputs not found: {merged_path}\n" + " Run download_labour_data first." + ) + logger.info(f"Loading {merged_path} …") + merged = pd.read_csv(merged_path) + logger.info(f" {len(merged)} countries loaded.") + + # ── 2. Load config regions ─────────────────────────────────────────── + regions_config: dict = snakemake.config["regions"] + iso_to_region = build_iso_to_region(regions_config) + regions = list(regions_config.keys()) + logger.info(f" {len(regions)} model regions from config.") + + # ── 3. Compute hourly wages ────────────────────────────────────────── + wages = compute_all_hourly_wages(merged) + logger.info("\nCountry-level hourly wages [EUR/h, 2020]:") + wage_table = ( + wages[["iso3", "country_name", "hourly_wage_eur"]] + .sort_values("hourly_wage_eur", ascending=False) # type: ignore[call-overload] + .to_string(index=False) + ) + logger.info(wage_table) + + # ── 4. Aggregate by region ─────────────────────────────────────────── + result = aggregate_by_region(wages, iso_to_region, regions) + + # ── 5. Save ────────────────────────────────────────────────────────── + output_path = Path(snakemake.output.labour_cost) + output_path.parent.mkdir(parents=True, exist_ok=True) + result.to_csv(output_path) + logger.info(f"\nSaved → {output_path}") + logger.info(result.to_string()) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 23cbc8c..9c2de8b 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -24,9 +24,7 @@ """ import logging -from pathlib import Path from typing import Any, Dict, Optional, Tuple, Iterable, cast -import sys import numpy as np import pandas as pd import xarray as xr @@ -34,12 +32,11 @@ import tech_database as td -SCRIPT_DIR = Path(__file__).resolve().parent -if str(SCRIPT_DIR) not in sys.path: - sys.path.insert(0, str(SCRIPT_DIR)) - -from trade_chain_utils import build_product_components, get_external_material_inputs # noqa: E402 -from _helpers import setup_logging # noqa: E402 +from trade_chain_utils import ( + build_product_components, + get_external_material_inputs, +) +from _helpers import setup_logging snakemake: Any = globals().get("snakemake") @@ -426,7 +423,7 @@ def sanitize_and_fix( - Logs a summary of applied fixes. """ if logger is None: - _logger = logging.getLogger(__name__) + _logger = globals()["logger"] else: _logger = logger @@ -483,8 +480,8 @@ def sanitize_and_fix( ) if pd.notna(pmin) and pd.notna(pmax): # If pmax < pmin, reduce pmin to pmax - if float(pmax) < float(pmin): - network.generators.loc[gen, "p_nom_min"] = float(pmax) + if float(cast(Any, pmax)) < float(cast(Any, pmin)): + network.generators.loc[gen, "p_nom_min"] = float(cast(Any, pmax)) fixes.append(f"clamped p_nom_min to p_nom_max for {gen}") except Exception: pass @@ -756,7 +753,7 @@ def prepare_network( ) if snakemake.wildcards.wacc == "regional": - print(f"applying region specific wacc") + logger.info("applying region specific wacc") wacc = pd.read_csv(snakemake.input.wacc, header=0) wacc.set_index("region", inplace=True) discount_rate = wacc.loc[region].values[0] @@ -1015,12 +1012,69 @@ def validate_network_carriers(n: pypsa.Network): return network, audit_info +def add_labour_cost(n, labour_cost): + + logger.info("adding labour cost") + + carrier_labour_cost_dict = { + "electrolysis": "ely_intensity in h/kW_ely", + "direct_reduction_furnace": "dri_intensity in h/t_dri", + "electric_arc_furnace": "eaf_intensity in h/t_steel", + } + + regional_labour_cost = labour_cost.loc[snakemake.wildcards.region] + wage = regional_labour_cost["steelworker_wage in euro/h"] + + for carrier in carrier_labour_cost_dict.keys(): + if carrier in n.links.carrier.values: + if carrier == "electrolysis": + n.links.loc[n.links.carrier == carrier, "overnight_cost"] += ( + wage + * regional_labour_cost[carrier_labour_cost_dict[carrier]] + * 1000 + * network.links[n.links.carrier == "electrolysis"].lifetime + ) # Wage in €/h * intensity in h/kW_ely * 1000 kW/MW = € / MW multiplied by lifetime to convert to overnight cost (instead of annual capital_cost) + logger.info( + f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/kW_ely * 1000 = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * 1000:.2f} €/MW" + ) + else: + pass + + if carrier == "direct_reduction_furnace": + n.links.loc[n.links.carrier == carrier, "marginal_cost"] += ( + wage + * regional_labour_cost[carrier_labour_cost_dict[carrier]] + * n.links.loc[n.links.carrier == carrier, "efficiency"] + ) # Wage in €/h * intensity in h/t_dri * effiency_ironore_dri = € / t_dri, added to marginal cost + logger.info( + f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/t_dri * efficiency = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * n.links.loc[n.links.carrier == carrier, 'efficiency'].iloc[0]:.2f} €/t_dri" + ) + else: + pass + + if carrier == "electric_arc_furnace": + n.links.loc[n.links.carrier == carrier, "marginal_cost"] += ( + wage + * regional_labour_cost[carrier_labour_cost_dict[carrier]] + * n.links.loc[n.links.carrier == carrier, "efficiency"] + ) # Wage in €/h * intensity in h/t_steel * effiency_input_output = € / t_steel, added to marginal cost + logger.info( + f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/t_steel * efficiency = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * n.links.loc[n.links.carrier == carrier, 'efficiency'].iloc[0]:.2f} €/t_steel" + ) + else: + pass + else: + logger.info( + f"carrier {carrier} not in network, skipping labour cost addition for this carrier" + ) + return n + + # ============================================================================ # SNAKEMAKE INTEGRATION # ============================================================================ if __name__ == "__main__": - if snakemake is None: from _helpers import mock_snakemake @@ -1078,6 +1132,22 @@ def validate_network_carriers(n: pypsa.Network): route_label=route_label, ) + # Add labour cost + if snakemake.config["trade_chains"]["labour_cost"]: + logger.info("Adding labour costs to network") + # Load labour cost + labour_cost = pd.read_csv(snakemake.input.labour_cost, header=0, index_col=0) + network = add_labour_cost(network, labour_cost) + + elif not snakemake.config["trade_chains"]["labour_cost"]: + logger.info("Labour cost addition skipped (labour_cost is False)") + + else: + raise ValueError( + f"Unrecognized labour_cost wildcard: {snakemake.config['trade_chains']['labour_cost']}. " + f"Expected 'True' or 'False'." + ) + # Save network logger.info(f"Saving network to {output_path}") network.export_to_netcdf(output_path) diff --git a/workflow/scripts/renewable_profiles.py b/workflow/scripts/renewable_profiles.py index a7d7da9..dc7253a 100644 --- a/workflow/scripts/renewable_profiles.py +++ b/workflow/scripts/renewable_profiles.py @@ -18,7 +18,6 @@ """ import json -import logging from pathlib import Path from datetime import datetime @@ -36,8 +35,9 @@ import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.io import shapereader as shprdr +from _helpers import setup_logging -logger = logging.getLogger(__name__) +logger = setup_logging(__name__, log_filename="renewable_profiles.log") SCHEMA_VERSION = "1.0" diff --git a/workflow/scripts/tech_database.py b/workflow/scripts/tech_database.py index 83dc2d9..d2c4730 100644 --- a/workflow/scripts/tech_database.py +++ b/workflow/scripts/tech_database.py @@ -7,15 +7,14 @@ Dual-purpose module: Snakemake rule for downloading tech costs + importable utilities. """ -import logging from pathlib import Path from typing import Any import pandas as pd - -logger = logging.getLogger(__name__) +from _helpers import setup_logging snakemake: Any = globals().get("snakemake") +logger = setup_logging(__name__, snakemake=snakemake, log_filename="tech_database.log") def download_tech_database( diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py index 65e637a..76e1334 100644 --- a/workflow/scripts/trade_chain_utils.py +++ b/workflow/scripts/trade_chain_utils.py @@ -9,6 +9,10 @@ from typing import Dict, List, Optional, Tuple +from _helpers import setup_logging + +logger = setup_logging(__name__) + ENERGY_INPUTS = {"renewable_electricity", "grid_electricity"} BUS_ALIASES = { @@ -90,10 +94,6 @@ def validate_stage_io(stage: Dict, raise_on_mismatch: bool = False) -> bool: Returns True if validation passes or no mapping exists. If `raise_on_mismatch` is True, a ValueError is raised on mismatch; otherwise a warning is returned via logging and False is returned. """ - import logging - - logger = logging.getLogger(__name__) - process_label = str(stage.get("process_label", "")).strip() if not process_label: return True From 11b8dd9f462d5b0550808432b9ccdc746db0344d Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 26 May 2026 14:37:44 +0200 Subject: [PATCH 118/216] fix: change electrolysis to fom_cost and improve code readibility --- workflow/scripts/prepare_regional_network.py | 47 +++++++------------- 1 file changed, 17 insertions(+), 30 deletions(-) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 9c2de8b..96f89ee 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -25,10 +25,10 @@ import logging from typing import Any, Dict, Optional, Tuple, Iterable, cast -import numpy as np -import pandas as pd -import xarray as xr -import pypsa +import numpy as np # type: ignore +import pandas as pd # type: ignore +import xarray as xr # type: ignore +import pypsa # type: ignore import tech_database as td @@ -1026,43 +1026,30 @@ def add_labour_cost(n, labour_cost): wage = regional_labour_cost["steelworker_wage in euro/h"] for carrier in carrier_labour_cost_dict.keys(): - if carrier in n.links.carrier.values: + mask = n.links.carrier == carrier + if mask.any(): + intensity = regional_labour_cost[carrier_labour_cost_dict[carrier]] + if carrier == "electrolysis": - n.links.loc[n.links.carrier == carrier, "overnight_cost"] += ( - wage - * regional_labour_cost[carrier_labour_cost_dict[carrier]] - * 1000 - * network.links[n.links.carrier == "electrolysis"].lifetime - ) # Wage in €/h * intensity in h/kW_ely * 1000 kW/MW = € / MW multiplied by lifetime to convert to overnight cost (instead of annual capital_cost) + fom_cost = wage * intensity * 1000 + n.links.loc[mask, "fom_cost"] += fom_cost logger.info( - f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/kW_ely * 1000 = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * 1000:.2f} €/MW" + f"Added labour cost as fom_cost to {carrier} links: {wage} €/h * {intensity} h/kW_ely * 1000 = {fom_cost:.2f} €/MW" ) - else: - pass if carrier == "direct_reduction_furnace": - n.links.loc[n.links.carrier == carrier, "marginal_cost"] += ( - wage - * regional_labour_cost[carrier_labour_cost_dict[carrier]] - * n.links.loc[n.links.carrier == carrier, "efficiency"] - ) # Wage in €/h * intensity in h/t_dri * effiency_ironore_dri = € / t_dri, added to marginal cost + marginal_cost = wage * intensity * n.links.loc[mask, "efficiency"] + n.links.loc[mask, "marginal_cost"] += marginal_cost logger.info( - f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/t_dri * efficiency = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * n.links.loc[n.links.carrier == carrier, 'efficiency'].iloc[0]:.2f} €/t_dri" + f"Added labour cost as marginal_cost to {carrier} links: {wage} €/h * {intensity} h/t_dri * efficiency" ) - else: - pass if carrier == "electric_arc_furnace": - n.links.loc[n.links.carrier == carrier, "marginal_cost"] += ( - wage - * regional_labour_cost[carrier_labour_cost_dict[carrier]] - * n.links.loc[n.links.carrier == carrier, "efficiency"] - ) # Wage in €/h * intensity in h/t_steel * effiency_input_output = € / t_steel, added to marginal cost + marginal_cost = wage * intensity * n.links.loc[mask, "efficiency"] + n.links.loc[mask, "marginal_cost"] += marginal_cost logger.info( - f"Added labour cost to {carrier} links: {wage} €/h * {regional_labour_cost[carrier_labour_cost_dict[carrier]]} h/t_steel * efficiency = {wage * regional_labour_cost[carrier_labour_cost_dict[carrier]] * n.links.loc[n.links.carrier == carrier, 'efficiency'].iloc[0]:.2f} €/t_steel" + f"Added labour cost as marginal_cost to {carrier} links: {wage} €/h * {intensity} h/t_steel * efficiency" ) - else: - pass else: logger.info( f"carrier {carrier} not in network, skipping labour cost addition for this carrier" From 2f43f02bfb273bdf31895fe7f9be0b09a1e81221 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 27 May 2026 12:05:31 +0200 Subject: [PATCH 119/216] feat: add comprehensive notebook to consolidate renewable profiles based on pypsa-earth --- .../notebooks/merge_renewable_profiles.ipynb | 2201 +++++++++++++++++ 1 file changed, 2201 insertions(+) create mode 100644 workflow/notebooks/merge_renewable_profiles.ipynb diff --git a/workflow/notebooks/merge_renewable_profiles.ipynb b/workflow/notebooks/merge_renewable_profiles.ipynb new file mode 100644 index 0000000..00136dc --- /dev/null +++ b/workflow/notebooks/merge_renewable_profiles.ipynb @@ -0,0 +1,2201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2ed89248", + "metadata": {}, + "source": [ + "# Merge and Validate Renewable Energy Profiles\n", + "\n", + "This notebook merges renewable energy profile data from 8 regions into global NetCDF and GeoJSON files, while validating country coverage using ISO3 codes.\n", + "\n", + "**Objectives:**\n", + "1. Explore the structure of .nc, .geojson, and metadata .json files\n", + "2. Extract and validate ISO3 country codes across all regions\n", + "3. Merge regional datasets into global files with sequential loading\n", + "4. Generate country coverage reports and validate data integrity\n", + "5. Visualize merged results and identify missing country coverage\n", + "\n", + "**Data Source:** `data/renewable_profiles/` directory with 8 regions:\n", + "- Africa, Australia, Central America, Central Asia\n", + "- Europe, North America, South America, South East Asia, West Asia\n", + "\n", + "**Output Files:**\n", + "- `data/renewable_profiles_global_merged.nc` - Merged NetCDF dataset\n", + "- `data/renewable_profiles_global_merged.geojson` - Merged GeoJSON geometries\n", + "- `resources/renewable_profiles_merge_report.json` - Country validation report" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "76bafeeb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data directory: ..\\..\\data\\renewable_profiles\n", + "Data directory exists: True\n", + "Output directory: ..\\..\\data\n", + "Resources directory: ..\\..\\resources\n", + "\n", + "Found 27 files in renewable_profiles:\n", + " renewable_profiles_africa__20260417_155313.geojson\n", + " renewable_profiles_africa__20260417_155313.nc\n", + " renewable_profiles_africa__20260417_155313_metadata.json\n", + " renewable_profiles_australia__20260420_172910.geojson\n", + " renewable_profiles_australia__20260420_172910.nc\n", + " renewable_profiles_australia__20260420_172910_metadata.json\n", + " renewable_profiles_central_america__20260417_172618.geojson\n", + " renewable_profiles_central_america__20260417_172618.nc\n", + " renewable_profiles_central_america__20260417_172618_metadata.json\n", + " renewable_profiles_central_asia__20260421_173248.geojson\n", + " renewable_profiles_central_asia__20260421_173248.nc\n", + " renewable_profiles_central_asia__20260421_173248_metadata.json\n", + " renewable_profiles_europe__20260420_142941.geojson\n", + " renewable_profiles_europe__20260420_142941.nc\n", + " renewable_profiles_europe__20260420_142941_metadata.json\n", + " renewable_profiles_north_america__20260417_163456.geojson\n", + " renewable_profiles_north_america__20260417_163456.nc\n", + " renewable_profiles_north_america__20260417_163456_metadata.json\n", + " renewable_profiles_south_america__20260417_161947.geojson\n", + " renewable_profiles_south_america__20260417_161947.nc\n", + " renewable_profiles_south_america__20260417_161947_metadata.json\n", + " renewable_profiles_south_east_asia__20260422_105946.geojson\n", + " renewable_profiles_south_east_asia__20260422_105946.nc\n", + " renewable_profiles_south_east_asia__20260422_105946_metadata.json\n", + " renewable_profiles_west_asia__20260422_141251.geojson\n", + " renewable_profiles_west_asia__20260422_141251.nc\n", + " renewable_profiles_west_asia__20260422_141251_metadata.json\n" + ] + } + ], + "source": [ + "# Import Required Libraries\n", + "import xarray as xr\n", + "import geopandas as gpd\n", + "import json\n", + "import pycountry\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from collections import defaultdict\n", + "import warnings\n", + "import time\n", + "import psutil\n", + "import os\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "from shapely.geometry import shape\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", + "\n", + "# Set up paths\n", + "BASE_DIR = Path(\"../..\") # Notebook should be run from repo root\n", + "DATA_DIR = BASE_DIR / \"data\" / \"renewable_profiles\"\n", + "OUTPUT_DATA_DIR = BASE_DIR / \"data\"\n", + "RESOURCES_DIR = BASE_DIR / \"resources\"\n", + "\n", + "# Ensure output directories exist\n", + "RESOURCES_DIR.mkdir(exist_ok=True, parents=True)\n", + "\n", + "print(f\"Data directory: {DATA_DIR}\")\n", + "print(f\"Data directory exists: {DATA_DIR.exists()}\")\n", + "print(f\"Output directory: {OUTPUT_DATA_DIR}\")\n", + "print(f\"Resources directory: {RESOURCES_DIR}\")\n", + "\n", + "# List all files in renewable_profiles\n", + "if DATA_DIR.exists():\n", + " files = sorted(DATA_DIR.glob(\"*\"))\n", + " print(f\"\\nFound {len(files)} files in renewable_profiles:\")\n", + " for f in files:\n", + " print(f\" {f.name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9579c06c", + "metadata": {}, + "source": [ + "## Section 1: Data Exploration\n", + "\n", + "Load and inspect one sample file from each file type (.nc, .geojson, .json) to understand the data structure." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fb248a1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading sample NetCDF file: renewable_profiles_africa__20260417_155313.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", + "Cannot find the ecCodes library\n", + " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "NetCDF Dataset Dimensions:\n", + " FrozenMappingWarningOnValuesAccess({'bus': 5296, 'technology': 3, 'hour': 8760, 'y_grid': 256, 'x_grid': 290})\n", + "\n", + "NetCDF Dataset Variables:\n", + " capacity_factor: (5296, 3, 8760) | dtype: float32\n", + " p_nom_max: (5296, 3) | dtype: float32\n", + " avg_cf: (5296, 3) | dtype: float32\n", + " potential: (256, 290, 3) | dtype: float32\n", + " weight: (5296,) | dtype: float32\n", + " data_quality_flag: (5296, 3) | dtype: bool\n", + "\n", + "NetCDF Coordinates:\n", + " bus: (5296,) | dtype: = 2:\n", + " region = parts[0].replace(\"renewable_profiles_\", \"\")\n", + " regions.add(region)\n", + "\n", + "print(f\"\\n\\nRegions found in data directory: {sorted(regions)}\")\n", + "print(f\"Total regions: {len(regions)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2f7852b7", + "metadata": {}, + "source": [ + "## Section 2: Country Code Extraction & Validation\n", + "\n", + "Extract all ISO3 country codes from bus_ids and GeoJSON properties, then validate against pycountry." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33747453", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample bus_ids and extracted ISO3 codes:\n", + " GNQ_ON_s0r8wu -> GNQ\n", + " GNQ_ON_s2218h -> GNQ\n", + " GNQ_ON_s22031 -> GNQ\n", + " GNQ_ON_s0r7we -> GNQ\n", + " GNQ_ON_s0r4cs -> GNQ\n" + ] + } + ], + "source": [ + "# 2.1 Helper function to extract ISO3 from bus_id\n", + "\n", + "\n", + "def extract_iso3_from_bus_id(bus_id):\n", + " \"\"\"\n", + " Parse bus_id format: {ISO3}_{ON/OFF}_{geohash}\n", + " Returns the ISO3 code (first 3 characters)\n", + " \"\"\"\n", + " parts = str(bus_id).split(\"_\")\n", + " if len(parts) >= 1:\n", + " return parts[0]\n", + " return None\n", + "\n", + "\n", + "# Test with sample\n", + "if sample_geojson_file:\n", + " sample_bus_ids = gdf[\"bus_id\"].head(5)\n", + " print(\"Sample bus_ids and extracted ISO3 codes:\")\n", + " for bus_id in sample_bus_ids:\n", + " iso3 = extract_iso3_from_bus_id(bus_id)\n", + " print(f\" {bus_id} -> {iso3}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "395c771f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ISO3 codes from all GeoJSON files...\n", + "\n", + "Processing africa...\n", + " Countries: 54 | Buses: 5296\n", + "Processing australia...\n", + " Countries: 14 | Buses: 2182\n", + "Processing central_america...\n", + " Countries: 21 | Buses: 2475\n", + "Processing central_asia...\n", + " Countries: 20 | Buses: 31442\n", + "Processing europe...\n", + " Countries: 42 | Buses: 28363\n", + "Processing north_america...\n", + " Countries: 15 | Buses: 28633\n", + "Processing south_america...\n", + " Countries: 35 | Buses: 15715\n", + "Processing south_east_asia...\n", + " Countries: 11 | Buses: 4283\n", + "Processing west_asia...\n", + " Countries: 18 | Buses: 4442\n", + "\n", + "Total unique ISO3 codes found in GeoJSON 'country' column: 194\n", + "Total unique ISO3 codes from bus_ids: 194\n", + "Total buses across all regions: 122831\n", + "\n", + "Sample ISO3 codes: ['AFG', 'AGO', 'ALB', 'AND', 'ARE', 'ARG', 'ARM', 'ATG', 'AUS', 'AUT', 'AZE', 'BDI', 'BEL', 'BEN', 'BFA', 'BGD', 'BGR', 'BHR', 'BHS', 'BIH']\n" + ] + } + ], + "source": [ + "# 2.2 Extract all ISO3 codes from all GeoJSON files\n", + "\n", + "print(\"Extracting ISO3 codes from all GeoJSON files...\\n\")\n", + "\n", + "iso3_from_geojson = set()\n", + "iso3_from_bus_id = set()\n", + "iso3_counts = defaultdict(int)\n", + "\n", + "for geojson_file in sorted(DATA_DIR.glob(\"*.geojson\")):\n", + " region_name = geojson_file.stem.split(\"__\")[0].replace(\"renewable_profiles_\", \"\")\n", + " print(f\"Processing {region_name}...\")\n", + "\n", + " gdf_region = gpd.read_file(geojson_file)\n", + "\n", + " # Extract from 'country' column\n", + " region_iso3_geojson = set(gdf_region[\"country\"].dropna().unique())\n", + " iso3_from_geojson.update(region_iso3_geojson)\n", + "\n", + " # Extract from bus_id\n", + " region_iso3_bus_id = set(\n", + " gdf_region[\"bus_id\"].apply(extract_iso3_from_bus_id).unique()\n", + " )\n", + " iso3_from_bus_id.update(region_iso3_bus_id)\n", + "\n", + " # Count buses per country\n", + " for iso3 in region_iso3_geojson:\n", + " iso3_counts[iso3] += len(gdf_region[gdf_region[\"country\"] == iso3])\n", + "\n", + " print(f\" Countries: {len(region_iso3_geojson)} | Buses: {len(gdf_region)}\")\n", + "\n", + "print(\n", + " f\"\\nTotal unique ISO3 codes found in GeoJSON 'country' column: {len(iso3_from_geojson)}\"\n", + ")\n", + "print(f\"Total unique ISO3 codes from bus_ids: {len(iso3_from_bus_id)}\")\n", + "print(f\"Total buses across all regions: {sum(iso3_counts.values())}\")\n", + "print(f\"\\nSample ISO3 codes: {sorted(list(iso3_from_geojson))[:20]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6fc900f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total ISO3 codes in pycountry: 249\n", + "\n", + "Countries in renewable profiles: 194\n", + "Invalid ISO3 codes in renewable profiles: 0\n", + "\n", + "Countries in pycountry but NOT in renewable profiles: 55\n", + " Sample: ['ABW', 'AIA', 'ALA', 'ASM', 'ATA', 'ATF', 'BES', 'BLM', 'BMU', 'BVT', 'CCK', 'COK', 'CUW', 'CXR', 'CYM', 'ESH', 'FLK', 'FRO', 'GGY', 'GIB']\n", + "\n", + "Country Coverage Summary:\n", + " Total countries with renewable profiles: 194\n", + " Total buses: 122831\n", + " Avg buses per country: 633.1\n", + " Min buses per country: 1\n", + " Max buses per country: 30853\n", + "\n", + "Top 10 countries by bus count:\n", + " ISO3 Bus_Count\n", + "183 USA 30853\n", + "32 CHN 15238\n", + "77 IND 8471\n", + "29 CAN 4488\n", + "23 BRA 4198\n", + "44 DEU 3141\n", + "58 FRA 2983\n", + "111 MEX 2525\n", + "86 JPN 2475\n", + "83 ITA 2452\n" + ] + } + ], + "source": [ + "# 2.3 Validate against pycountry\n", + "\n", + "# Get all valid ISO3 codes from pycountry\n", + "valid_iso3_codes = {country.alpha_3 for country in pycountry.countries}\n", + "print(f\"Total ISO3 codes in pycountry: {len(valid_iso3_codes)}\\n\")\n", + "\n", + "# Find valid and invalid ISO3 codes in renewable profiles\n", + "iso3_in_renewable = iso3_from_geojson\n", + "iso3_valid = iso3_in_renewable & valid_iso3_codes\n", + "iso3_invalid = iso3_in_renewable - valid_iso3_codes\n", + "iso3_missing = valid_iso3_codes - iso3_in_renewable\n", + "\n", + "print(f\"Countries in renewable profiles: {len(iso3_valid)}\")\n", + "print(f\"Invalid ISO3 codes in renewable profiles: {len(iso3_invalid)}\")\n", + "if iso3_invalid:\n", + " print(f\" Invalid codes: {sorted(iso3_invalid)}\")\n", + "\n", + "print(f\"\\nCountries in pycountry but NOT in renewable profiles: {len(iso3_missing)}\")\n", + "print(f\" Sample: {sorted(list(iso3_missing))[:20]}\")\n", + "\n", + "# Create validation summary dataframe\n", + "validation_df = pd.DataFrame(\n", + " {\n", + " \"ISO3\": sorted(iso3_valid),\n", + " \"Bus_Count\": [iso3_counts.get(iso3, 0) for iso3 in sorted(iso3_valid)],\n", + " \"In_Pycountry\": True,\n", + " }\n", + ")\n", + "\n", + "print(\"\\nCountry Coverage Summary:\")\n", + "print(f\" Total countries with renewable profiles: {len(validation_df)}\")\n", + "print(f\" Total buses: {validation_df['Bus_Count'].sum()}\")\n", + "print(f\" Avg buses per country: {validation_df['Bus_Count'].mean():.1f}\")\n", + "print(f\" Min buses per country: {validation_df['Bus_Count'].min()}\")\n", + "print(f\" Max buses per country: {validation_df['Bus_Count'].max()}\")\n", + "\n", + "print(\"\\nTop 10 countries by bus count:\")\n", + "print(validation_df.nlargest(10, \"Bus_Count\")[[\"ISO3\", \"Bus_Count\"]])" + ] + }, + { + "cell_type": "markdown", + "id": "8df1e8aa", + "metadata": {}, + "source": [ + "## Section 3: Merge Logic\n", + "\n", + "Load all regional files sequentially and merge into global .nc and .geojson files." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f9a1cb26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Helper functions defined successfully\n", + "\n", + "✅ REVISED MERGE STRATEGY: Global Grid with NaN Fill\n", + "======================================================================\n", + "Approach: Create global 0.25° grid covering entire world\n", + " 1. Build uniform grid: -180° to 180° (lon), -90° to 90° (lat)\n", + " 2. Fill with NaN globally\n", + " 3. Place each region's potential data into corresponding cells\n", + " 4. Result: Single global potential variable with NaN where no data\n", + "\n", + "Benefits:\n", + " ✓ All data (including potential) in single merged .nc file\n", + " ✓ Standard gridded format suitable for further analysis\n", + " ✓ Can interpolate/analyze global grid uniformly\n", + " ✓ Cleaner API - no separate grid handling needed\n" + ] + } + ], + "source": [ + "# 3.1 Define helper functions for loading regional data\n", + "\n", + "\n", + "def get_latest_file(directory, pattern):\n", + " \"\"\"Get the most recently modified file matching pattern (by timestamp in filename)\"\"\"\n", + " files = sorted(directory.glob(pattern))\n", + " if files:\n", + " return files[-1] # Timestamp in filename ensures latest is last when sorted\n", + " return None\n", + "\n", + "\n", + "def load_region_nc(region_dir, region_name):\n", + " \"\"\"Load NetCDF file for a region\"\"\"\n", + " nc_file = get_latest_file(region_dir, f\"renewable_profiles_{region_name}__*.nc\")\n", + " if nc_file:\n", + " return xr.open_dataset(nc_file)\n", + " return None\n", + "\n", + "\n", + "def load_region_geojson(region_dir, region_name):\n", + " \"\"\"Load GeoJSON file for a region as GeoDataFrame\"\"\"\n", + " geojson_file = get_latest_file(\n", + " region_dir, f\"renewable_profiles_{region_name}__*.geojson\"\n", + " )\n", + " if geojson_file:\n", + " return gpd.read_file(geojson_file)\n", + " return None\n", + "\n", + "\n", + "def load_region_metadata(region_dir, region_name):\n", + " \"\"\"Load metadata JSON for a region\"\"\"\n", + " json_file = get_latest_file(\n", + " region_dir, f\"renewable_profiles_{region_name}__*_metadata.json\"\n", + " )\n", + " if json_file:\n", + " with open(json_file) as f:\n", + " return json.load(f)\n", + " return None\n", + "\n", + "\n", + "def create_global_grid(resolution=0.25):\n", + " \"\"\"\n", + " Create a global grid at specified resolution (degrees)\n", + " Default 0.25° gives ~1440 x 720 grid cells\n", + "\n", + " Returns:\n", + " x_global, y_global: 1D arrays of global grid coordinates\n", + " \"\"\"\n", + " x_global = np.arange(-180, 180 + resolution, resolution) # Longitude: -180 to 180\n", + " y_global = np.arange(-90, 90 + resolution, resolution) # Latitude: -90 to 90\n", + " return x_global, y_global\n", + "\n", + "\n", + "def place_regional_potential_on_global_grid(\n", + " potential_regional, x_regional, y_regional, x_global, y_global, technology_dim\n", + "):\n", + " \"\"\"\n", + " Place regional potential data onto global grid with NaN fill.\n", + "\n", + " Parameters:\n", + " potential_regional: [y_regional, x_regional, technology]\n", + " x_regional, y_regional: Regional grid coordinates\n", + " x_global, y_global: Global grid coordinates\n", + " technology_dim: Index of technology dimension\n", + "\n", + " Returns:\n", + " potential_global: [y_global, x_global, technology] filled with NaN + regional data\n", + " \"\"\"\n", + " # Initialize global grid with NaN\n", + " n_tech = potential_regional.shape[technology_dim]\n", + " potential_global = np.full(\n", + " (len(y_global), len(x_global), n_tech), np.nan, dtype=np.float32\n", + " )\n", + "\n", + " # Find indices where regional grid maps to global grid\n", + " x_indices = np.searchsorted(x_global, x_regional)\n", + " y_indices = np.searchsorted(y_global, y_regional)\n", + "\n", + " # Handle boundary cases (shouldn't happen if grids are within [-180,180] x [-90,90])\n", + " valid_x = (x_indices >= 0) & (x_indices < len(x_global))\n", + " valid_y = (y_indices >= 0) & (y_indices < len(y_global))\n", + "\n", + " if not (valid_x.all() and valid_y.all()):\n", + " print(\" ⚠️ Warning: Some grid cells outside global bounds\")\n", + "\n", + " # Place regional data into global grid\n", + " for i, y_idx in enumerate(y_indices):\n", + " for j, x_idx in enumerate(x_indices):\n", + " if 0 <= x_idx < len(x_global) and 0 <= y_idx < len(y_global):\n", + " potential_global[y_idx, x_idx, :] = potential_regional[i, j, :]\n", + "\n", + " return potential_global\n", + "\n", + "\n", + "print(\"Helper functions defined successfully\")\n", + "print(\"\\n✅ REVISED MERGE STRATEGY: Global Grid with NaN Fill\")\n", + "print(\"=\" * 70)\n", + "print(\"Approach: Create global 0.25° grid covering entire world\")\n", + "print(\" 1. Build uniform grid: -180° to 180° (lon), -90° to 90° (lat)\")\n", + "print(\" 2. Fill with NaN globally\")\n", + "print(\" 3. Place each region's potential data into corresponding cells\")\n", + "print(\" 4. Result: Single global potential variable with NaN where no data\")\n", + "print(\"\\nBenefits:\")\n", + "print(\" ✓ All data (including potential) in single merged .nc file\")\n", + "print(\" ✓ Standard gridded format suitable for further analysis\")\n", + "print(\" ✓ Can interpolate/analyze global grid uniformly\")\n", + "print(\" ✓ Cleaner API - no separate grid handling needed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f62d272f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting merge process...\n", + "======================================================================\n", + "\n", + "Processing region: africa\n", + " ✓ Loaded africa: 5296 buses, 5296 features\n", + " Technologies: [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " Grid size: 290 × 256 cells\n", + " X range: [-19.50, 67.20]\n", + " Y range: [-37.50, 39.00]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 921.7 MB (delta: +8.9 MB)\n", + "\n", + "Processing region: australia\n", + " ✓ Loaded australia: 2182 buses, 2182 features\n", + " Technologies: [np.str_('onwind'), np.str_('solar')]\n", + " Grid size: 403 × 193 cells\n", + " X range: [80.10, 179.70]\n", + " Y range: [-49.80, 7.80]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 938.3 MB (delta: +25.5 MB)\n", + "\n", + "Processing region: central_america\n", + " ✓ Loaded central_america: 2475 buses, 2475 features\n", + " Technologies: [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " Grid size: 223 × 105 cells\n", + " X range: [-122.40, -55.80]\n", + " Y range: [1.80, 33.00]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 949.5 MB (delta: +36.8 MB)\n", + "\n", + "Processing region: central_asia\n", + " ✓ Loaded central_asia: 31442 buses, 31442 features\n", + " Technologies: [np.str_('onwind'), np.str_('solar')]\n", + " Grid size: 365 × 180 cells\n", + " X range: [43.80, 145.80]\n", + " Y range: [1.80, 55.50]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 1036.5 MB (delta: +123.7 MB)\n", + "\n", + "Processing region: europe\n", + " ✓ Loaded europe: 28363 buses, 28363 features\n", + " Technologies: [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " Grid size: 241 × 155 cells\n", + " X range: [-31.50, 40.50]\n", + " Y range: [27.60, 73.80]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 1159.9 MB (delta: +247.1 MB)\n", + "\n", + "Processing region: north_america\n", + " ✓ Loaded north_america: 28633 buses, 28633 features\n", + " Technologies: [np.str_('onwind'), np.str_('solar')]\n", + " Grid size: 417 × 214 cells\n", + " X range: [-171.90, -47.10]\n", + " Y range: [9.90, 73.80]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 1287.6 MB (delta: +374.8 MB)\n", + "\n", + "Processing region: south_america\n", + " ✓ Loaded south_america: 15715 buses, 15715 features\n", + " Technologies: [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " Grid size: 282 × 258 cells\n", + " X range: [-109.80, -25.50]\n", + " Y range: [-60.00, 17.10]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 1382.0 MB (delta: +469.2 MB)\n", + "\n", + "Processing region: south_east_asia\n", + " ✓ Loaded south_east_asia: 4283 buses, 4283 features\n", + " Technologies: [np.str_('onwind'), np.str_('solar')]\n", + " Grid size: 182 × 134 cells\n", + " X range: [92.10, 141.00]\n", + " Y range: [-11.10, 28.80]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 1379.9 MB (delta: +467.2 MB)\n", + "\n", + "Processing region: west_asia\n", + " ✓ Loaded west_asia: 4442 buses, 4442 features\n", + " Technologies: [np.str_('onwind'), np.str_('solar')]\n", + " Grid size: 116 × 107 cells\n", + " X range: [25.50, 60.00]\n", + " Y range: [12.00, 43.80]\n", + " ✓ Stored potential data for grid assembly\n", + " Variables to merge (bus-level): ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + " Memory: 1393.4 MB (delta: +480.6 MB)\n", + "\n", + "======================================================================\n", + "Loaded 9 regions\n", + "Stored potential data for 9 regions\n", + "Total buses to merge: 122831\n", + "\n", + "Global grid extent (from regional data):\n", + " X: [-171.90, 179.70]\n", + " Y: [-60.00, 73.80]\n", + "\n", + "Next: Will create global 0.25° grid and assemble potential variable\n" + ] + } + ], + "source": [ + "# 3.2 Sequential merge of all regional files\n", + "\n", + "print(\"Starting merge process...\")\n", + "print(\"=\" * 70)\n", + "\n", + "# Define regions in the order they appear in the data\n", + "regions_to_merge = sorted(\n", + " [\n", + " \"africa\",\n", + " \"australia\",\n", + " \"central_america\",\n", + " \"central_asia\",\n", + " \"europe\",\n", + " \"north_america\",\n", + " \"south_america\",\n", + " \"south_east_asia\",\n", + " \"west_asia\",\n", + " ]\n", + ")\n", + "\n", + "# Initialize accumulators\n", + "nc_datasets_bus_vars = [] # Bus-level variables for concat\n", + "potential_data_by_region = {} # Store potential data per region\n", + "global_grid_extents = { # Track bounds for global grid\n", + " \"x_min\": 180,\n", + " \"x_max\": -180,\n", + " \"y_min\": 90,\n", + " \"y_max\": -90,\n", + "}\n", + "geojson_features_list = []\n", + "merge_metadata = {\n", + " \"regions_processed\": [],\n", + " \"total_buses\": 0,\n", + " \"regional_bus_counts\": {},\n", + " \"iso3_counts\": {},\n", + " \"timestamps\": {},\n", + " \"regional_techs\": {}, # Store technology list per region\n", + " \"merge_timestamp\": pd.Timestamp.now().isoformat(),\n", + " \"merge_strategy\": \"All variables merged including global potential grid with NaN fill\",\n", + "}\n", + "\n", + "# Track memory usage\n", + "start_memory = psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 # MB\n", + "start_time = time.time()\n", + "\n", + "# Merge loop\n", + "for region_name in regions_to_merge:\n", + " print(f\"\\nProcessing region: {region_name}\")\n", + "\n", + " # Load regional data\n", + " ds_region = load_region_nc(DATA_DIR, region_name)\n", + " gdf_region = load_region_geojson(DATA_DIR, region_name)\n", + " meta_region = load_region_metadata(DATA_DIR, region_name)\n", + "\n", + " if ds_region is None or gdf_region is None:\n", + " print(f\" ⚠ Skipping {region_name}: missing .nc or .geojson files\")\n", + " continue\n", + "\n", + " # Count buses\n", + " n_buses = len(ds_region.coords[\"bus\"])\n", + " print(f\" ✓ Loaded {region_name}: {n_buses} buses, {len(gdf_region)} features\")\n", + "\n", + " # Store technology information for this region\n", + " if \"technology\" in ds_region.coords:\n", + " region_tech_list = list(ds_region.coords[\"technology\"].values)\n", + " merge_metadata[\"regional_techs\"][region_name] = region_tech_list\n", + " print(f\" Technologies: {region_tech_list}\")\n", + "\n", + " # Log grid information\n", + " if \"x_grid\" in ds_region.coords and \"y_grid\" in ds_region.coords:\n", + " x_grid_region = ds_region.coords[\"x_grid\"].values\n", + " y_grid_region = ds_region.coords[\"y_grid\"].values\n", + "\n", + " print(f\" Grid size: {len(x_grid_region)} × {len(y_grid_region)} cells\")\n", + " print(f\" X range: [{x_grid_region.min():.2f}, {x_grid_region.max():.2f}]\")\n", + " print(f\" Y range: [{y_grid_region.min():.2f}, {y_grid_region.max():.2f}]\")\n", + "\n", + " # Update global extent\n", + " global_grid_extents[\"x_min\"] = min(\n", + " global_grid_extents[\"x_min\"], x_grid_region.min()\n", + " )\n", + " global_grid_extents[\"x_max\"] = max(\n", + " global_grid_extents[\"x_max\"], x_grid_region.max()\n", + " )\n", + " global_grid_extents[\"y_min\"] = min(\n", + " global_grid_extents[\"y_min\"], y_grid_region.min()\n", + " )\n", + " global_grid_extents[\"y_max\"] = max(\n", + " global_grid_extents[\"y_max\"], y_grid_region.max()\n", + " )\n", + "\n", + " # Store potential data for later global grid assembly\n", + " if \"potential\" in ds_region.data_vars:\n", + " potential_data_by_region[region_name] = {\n", + " \"data\": ds_region[\"potential\"].values,\n", + " \"x_grid\": x_grid_region,\n", + " \"y_grid\": y_grid_region,\n", + " }\n", + " print(\" ✓ Stored potential data for grid assembly\")\n", + "\n", + " # Store metadata\n", + " if meta_region:\n", + " merge_metadata[\"timestamps\"][region_name] = meta_region.get(\n", + " \"timestamp\", \"unknown\"\n", + " )\n", + "\n", + " # Validate consistency\n", + " if n_buses != len(gdf_region):\n", + " print(\n", + " f\" ⚠ Warning: NetCDF has {n_buses} buses but GeoJSON has {len(gdf_region)} features\"\n", + " )\n", + "\n", + " # Extract only bus-level variables (exclude grid-dependent 'potential')\n", + " data_vars_to_keep = [\n", + " var\n", + " for var in ds_region.data_vars\n", + " if \"x_grid\" not in ds_region[var].dims and \"y_grid\" not in ds_region[var].dims\n", + " ]\n", + "\n", + " # Create subset dataset with bus-level variables only\n", + " ds_region_bus_vars = ds_region[data_vars_to_keep]\n", + "\n", + " # Accumulate bus-level NetCDF datasets\n", + " nc_datasets_bus_vars.append(ds_region_bus_vars)\n", + "\n", + " print(f\" Variables to merge (bus-level): {data_vars_to_keep}\")\n", + "\n", + " # Accumulate GeoJSON features\n", + " for idx, row in gdf_region.iterrows():\n", + " feature = {\n", + " \"type\": \"Feature\",\n", + " \"geometry\": row.geometry.__geo_interface__,\n", + " \"properties\": {k: v for k, v in row.items() if k != \"geometry\"},\n", + " }\n", + " geojson_features_list.append(feature)\n", + "\n", + " # Track regional stats\n", + " merge_metadata[\"regional_bus_counts\"][region_name] = n_buses\n", + " merge_metadata[\"regions_processed\"].append(region_name)\n", + "\n", + " # Track ISO3 counts\n", + " for iso3 in gdf_region[\"country\"].unique():\n", + " if pd.notna(iso3):\n", + " count = len(gdf_region[gdf_region[\"country\"] == iso3])\n", + " merge_metadata[\"iso3_counts\"][iso3] = (\n", + " merge_metadata[\"iso3_counts\"].get(iso3, 0) + count\n", + " )\n", + "\n", + " # Memory check\n", + " current_memory = psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024\n", + " print(\n", + " f\" Memory: {current_memory:.1f} MB (delta: {current_memory - start_memory:+.1f} MB)\"\n", + " )\n", + "\n", + "print(f\"\\n{'=' * 70}\")\n", + "print(f\"Loaded {len(nc_datasets_bus_vars)} regions\")\n", + "print(f\"Stored potential data for {len(potential_data_by_region)} regions\")\n", + "merge_metadata[\"total_buses\"] = sum(merge_metadata[\"regional_bus_counts\"].values())\n", + "print(f\"Total buses to merge: {merge_metadata['total_buses']}\")\n", + "print(\"\\nGlobal grid extent (from regional data):\")\n", + "print(f\" X: [{global_grid_extents['x_min']:.2f}, {global_grid_extents['x_max']:.2f}]\")\n", + "print(f\" Y: [{global_grid_extents['y_min']:.2f}, {global_grid_extents['y_max']:.2f}]\")\n", + "print(\"\\nNext: Will create global 0.25° grid and assemble potential variable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b68fcba4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Concatenating NetCDF datasets (bus-level variables)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_4816\\3622988324.py:6: FutureWarning: In a future version of xarray the default value for join will change from join='outer' to join='exact'. This change will result in the following ValueError: cannot be aligned with join='exact' because index/labels/sizes are not equal along these coordinates (dimensions): 'technology' ('technology',) The recommendation is to set join explicitly for this case.\n", + " ds_merged = xr.concat(nc_datasets_bus_vars, dim='bus')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Merged NetCDF dimensions: {'bus': 122831, 'technology': 3, 'hour': 8760}\n", + "✓ Merged NetCDF variables: ['capacity_factor', 'p_nom_max', 'avg_cf', 'weight', 'data_quality_flag']\n", + "✓ Merged NetCDF coordinates: ['technology', 'hour', 'bus']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_4816\\3622988324.py:7: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + " print(f\"✓ Merged NetCDF dimensions: {dict(ds_merged.dims)}\")\n" + ] + } + ], + "source": [ + "# 3.3 Concatenate NetCDF datasets (bus-level variables only)\n", + "\n", + "print(\"\\nConcatenating NetCDF datasets (bus-level variables)...\")\n", + "if nc_datasets_bus_vars:\n", + " # Concatenate along the 'bus' dimension\n", + " ds_merged = xr.concat(nc_datasets_bus_vars, dim=\"bus\")\n", + " print(f\"✓ Merged NetCDF dimensions: {dict(ds_merged.dims)}\")\n", + " print(f\"✓ Merged NetCDF variables: {list(ds_merged.data_vars)}\")\n", + " print(f\"✓ Merged NetCDF coordinates: {list(ds_merged.coords)}\")\n", + "else:\n", + " print(\"✗ No datasets to merge!\")\n", + " ds_merged = None" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4c148ec2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Assembling global potential grid...\n", + "======================================================================\n", + "✓ Created global grid: 1441 × 721 cells\n", + " X: [-180.00, 180.00] (longitude)\n", + " Y: [-90.00, 90.00] (latitude)\n", + "✓ Target technologies: [np.str_('offwind-ac'), np.str_('onwind'), np.str_('solar')]\n", + "✓ Initialized global potential array: (721, 1441, 3)\n", + "\n", + "Placing regional potential data into global grid...\n", + " Processing africa... (3 technologies) → Tech mapping: {0: 1, 1: 0, 2: 2} ✓ (200114 cells filled)\n", + " Processing australia... (2 technologies) → Tech mapping: {0: 1, 1: 2} ✓ (354128 cells filled)\n", + " Processing central_america... (3 technologies) → Tech mapping: {0: 1, 1: 0, 2: 2} ✓ (411772 cells filled)\n", + " Processing central_asia... (2 technologies) → Tech mapping: {0: 1, 1: 2} ✓ (523094 cells filled)\n", + " Processing europe... (3 technologies) → Tech mapping: {0: 1, 1: 0, 2: 2} ✓ (604225 cells filled)\n", + " Processing north_america... (2 technologies) → Tech mapping: {0: 1, 1: 2} ✓ (758053 cells filled)\n", + " Processing south_america... (3 technologies) → Tech mapping: {0: 1, 1: 0, 2: 2} ✓ (952609 cells filled)\n", + " Processing south_east_asia... (2 technologies) → Tech mapping: {0: 1, 1: 2} ✓ (958649 cells filled)\n", + " Processing west_asia... (2 technologies) → Tech mapping: {0: 1, 1: 2} ✓ (959487 cells filled)\n", + "\n", + "✓ Global coverage: 188043/1038961 cells (18.10%)\n", + "\n", + "✓ Adding potential to merged dataset...\n", + "✓ Merged dataset now includes potential variable\n", + "\n", + "📊 Technology Coverage by Region:\n", + " africa: 3 technologies - [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " australia: 2 technologies - [np.str_('onwind'), np.str_('solar')]\n", + " central_america: 3 technologies - [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " central_asia: 2 technologies - [np.str_('onwind'), np.str_('solar')]\n", + " europe: 3 technologies - [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " north_america: 2 technologies - [np.str_('onwind'), np.str_('solar')]\n", + " south_america: 3 technologies - [np.str_('onwind'), np.str_('offwind-ac'), np.str_('solar')]\n", + " south_east_asia: 2 technologies - [np.str_('onwind'), np.str_('solar')]\n", + " west_asia: 2 technologies - [np.str_('onwind'), np.str_('solar')]\n" + ] + } + ], + "source": [ + "# 3.3b Assemble global potential grid\n", + "\n", + "print(\"\\nAssembling global potential grid...\")\n", + "print(\"=\" * 70)\n", + "\n", + "if len(potential_data_by_region) > 0:\n", + " # Create global grid at 0.25° resolution\n", + " x_global, y_global = create_global_grid(resolution=0.25)\n", + " print(f\"✓ Created global grid: {len(x_global)} × {len(y_global)} cells\")\n", + " print(f\" X: [{x_global.min():.2f}, {x_global.max():.2f}] (longitude)\")\n", + " print(f\" Y: [{y_global.min():.2f}, {y_global.max():.2f}] (latitude)\")\n", + "\n", + " # Get technology list from merged dataset\n", + " tech_list = list(ds_merged.coords[\"technology\"].values)\n", + " n_technologies = len(tech_list)\n", + " print(f\"✓ Target technologies: {tech_list}\")\n", + "\n", + " # Initialize global potential grid with NaN\n", + " potential_global = np.full(\n", + " (len(y_global), len(x_global), n_technologies), np.nan, dtype=np.float32\n", + " )\n", + " print(f\"✓ Initialized global potential array: {potential_global.shape}\")\n", + "\n", + " # Track regions by technology coverage\n", + " region_tech_info = {}\n", + "\n", + " # Place each region's potential into global grid\n", + " print(\"\\nPlacing regional potential data into global grid...\")\n", + " for region_name, region_pot in potential_data_by_region.items():\n", + " print(f\" Processing {region_name}...\", end=\" \")\n", + "\n", + " x_grid = region_pot[\"x_grid\"]\n", + " y_grid = region_pot[\"y_grid\"]\n", + " potential_region = region_pot[\"data\"]\n", + "\n", + " # Check shape of regional potential\n", + " n_tech_region = (\n", + " potential_region.shape[2] if len(potential_region.shape) == 3 else 1\n", + " )\n", + " print(f\"({n_tech_region} technologies)\", end=\" \")\n", + "\n", + " # Try to get technology list from region metadata (stored earlier)\n", + " if region_name in merge_metadata.get(\"regional_techs\", {}):\n", + " region_tech_list = merge_metadata[\"regional_techs\"][region_name]\n", + " else:\n", + " # Infer technologies: assume first n_tech_region techs from global list\n", + " region_tech_list = tech_list[:n_tech_region]\n", + "\n", + " # Map regional technologies to global indices\n", + " tech_mapping = {}\n", + " for local_idx, tech in enumerate(region_tech_list):\n", + " if tech in tech_list:\n", + " global_idx = tech_list.index(tech)\n", + " tech_mapping[local_idx] = global_idx\n", + " else:\n", + " print(f\"\\n ⚠️ Warning: Unknown technology '{tech}' in {region_name}\")\n", + "\n", + " print(f\"→ Tech mapping: {tech_mapping}\", end=\" \")\n", + "\n", + " # Find indices in global grid\n", + " x_idx = np.searchsorted(x_global, x_grid)\n", + " y_idx = np.searchsorted(y_global, y_grid)\n", + "\n", + " # Place data into global grid (only place known technologies)\n", + " for iy, y_i in enumerate(y_idx):\n", + " for ix, x_i in enumerate(x_idx):\n", + " if 0 <= x_i < len(x_global) and 0 <= y_i < len(y_global):\n", + " # Map regional tech indices to global indices\n", + " for local_tech_idx, global_tech_idx in tech_mapping.items():\n", + " potential_global[y_i, x_i, global_tech_idx] = potential_region[\n", + " iy, ix, local_tech_idx\n", + " ]\n", + "\n", + " # Count filled cells\n", + " filled_cells = (~np.isnan(potential_global)).sum(axis=2).sum()\n", + " print(f\"✓ ({filled_cells} cells filled)\")\n", + "\n", + " region_tech_info[region_name] = {\n", + " \"n_technologies\": n_tech_region,\n", + " \"technologies\": region_tech_list.tolist()\n", + " if hasattr(region_tech_list, \"tolist\")\n", + " else list(region_tech_list),\n", + " \"tech_mapping\": tech_mapping,\n", + " }\n", + "\n", + " # Count global coverage\n", + " total_cells = len(x_global) * len(y_global)\n", + " filled_cells = (\n", + " ~np.isnan(potential_global[:, :, 0])\n", + " ).sum() # Check first technology\n", + " coverage_pct = (filled_cells / total_cells) * 100\n", + " print(\n", + " f\"\\n✓ Global coverage: {filled_cells}/{total_cells} cells ({coverage_pct:.2f}%)\"\n", + " )\n", + "\n", + " # Add potential to merged dataset\n", + " print(\"\\n✓ Adding potential to merged dataset...\")\n", + " ds_merged[\"potential\"] = xr.DataArray(\n", + " potential_global,\n", + " coords={\n", + " \"y_grid\": y_global,\n", + " \"x_grid\": x_global,\n", + " \"technology\": ds_merged.coords[\"technology\"].values,\n", + " },\n", + " dims=[\"y_grid\", \"x_grid\", \"technology\"],\n", + " )\n", + " print(\"✓ Merged dataset now includes potential variable\")\n", + "\n", + " # Store grid info in metadata\n", + " merge_metadata[\"global_grid\"] = {\n", + " \"resolution_degrees\": 0.25,\n", + " \"x_range\": [float(x_global.min()), float(x_global.max())],\n", + " \"y_range\": [float(y_global.min()), float(y_global.max())],\n", + " \"grid_size\": [len(x_global), len(y_global)],\n", + " \"coverage_percent\": coverage_pct,\n", + " \"fill_value\": \"NaN where no data available\",\n", + " }\n", + "\n", + " merge_metadata[\"regional_technology_coverage\"] = region_tech_info\n", + "\n", + " print(\"\\n📊 Technology Coverage by Region:\")\n", + " for region, info in region_tech_info.items():\n", + " print(\n", + " f\" {region}: {info['n_technologies']} technologies - {info['technologies']}\"\n", + " )\n", + "\n", + "else:\n", + " print(\"⚠️ No potential data found to assemble global grid\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "784487e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Creating merged GeoJSON...\n", + "✓ Merged GeoJSON has 122831 features\n", + "✓ Merged GeoDataFrame shape: (122831, 8)\n", + "✓ Columns: ['geometry', 'bus_id', 'pypsa_region_id', 'country', 'onshore_offshore', 'x_centroid', 'y_centroid', 'area_km2']\n" + ] + } + ], + "source": [ + "# 3.4 Create merged GeoJSON\n", + "\n", + "print(\"\\nCreating merged GeoJSON...\")\n", + "geojson_merged = {\"type\": \"FeatureCollection\", \"features\": geojson_features_list}\n", + "\n", + "print(f\"✓ Merged GeoJSON has {len(geojson_features_list)} features\")\n", + "\n", + "# Convert to GeoDataFrame for validation\n", + "gdf_merged = gpd.GeoDataFrame.from_features(geojson_merged[\"features\"], crs=\"EPSG:4326\")\n", + "print(f\"✓ Merged GeoDataFrame shape: {gdf_merged.shape}\")\n", + "print(f\"✓ Columns: {list(gdf_merged.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4590b6b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Writing merged files to disk...\n", + " Writing renewable_profiles_global_merged.nc...\n", + " ✓ Wrote renewable_profiles_global_merged.nc (3658.9 MB)\n", + " Writing renewable_profiles_global_merged.geojson...\n", + " ✓ Wrote renewable_profiles_global_merged.geojson (116.3 MB)\n", + " Writing renewable_profiles_merge_report.json...\n", + " ✓ Wrote renewable_profiles_merge_report.json\n", + "\n", + "======================================================================\n", + "Merge complete!\n", + " Elapsed time: 373.0 seconds\n", + " Memory used: 12381.4 MB\n", + " Output files created in ..\\..\\data\n", + " Report file created in ..\\..\\resources\n", + "\n", + "✅ All variables (including global potential grid) merged in single .nc file\n" + ] + } + ], + "source": [ + "# 3.5 Write merged files to disk\n", + "\n", + "print(\"\\nWriting merged files to disk...\")\n", + "\n", + "# Write NetCDF\n", + "if ds_merged is not None:\n", + " output_nc_file = OUTPUT_DATA_DIR / \"renewable_profiles_global_merged.nc\"\n", + " print(f\" Writing {output_nc_file.name}...\")\n", + " ds_merged.to_netcdf(output_nc_file)\n", + " file_size_mb = output_nc_file.stat().st_size / 1024 / 1024\n", + " print(f\" ✓ Wrote {output_nc_file.name} ({file_size_mb:.1f} MB)\")\n", + "\n", + "# Write GeoJSON\n", + "output_geojson_file = OUTPUT_DATA_DIR / \"renewable_profiles_global_merged.geojson\"\n", + "print(f\" Writing {output_geojson_file.name}...\")\n", + "with open(output_geojson_file, \"w\") as f:\n", + " json.dump(geojson_merged, f)\n", + "file_size_mb = output_geojson_file.stat().st_size / 1024 / 1024\n", + "print(f\" ✓ Wrote {output_geojson_file.name} ({file_size_mb:.1f} MB)\")\n", + "\n", + "# Write metadata report\n", + "output_report_file = RESOURCES_DIR / \"renewable_profiles_merge_report.json\"\n", + "print(f\" Writing {output_report_file.name}...\")\n", + "with open(output_report_file, \"w\") as f:\n", + " json.dump(merge_metadata, f, indent=2, default=str)\n", + "print(f\" ✓ Wrote {output_report_file.name}\")\n", + "\n", + "# Summary\n", + "elapsed_time = time.time() - start_time\n", + "final_memory = psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024\n", + "print(f\"\\n{'=' * 70}\")\n", + "print(\"Merge complete!\")\n", + "print(f\" Elapsed time: {elapsed_time:.1f} seconds\")\n", + "print(f\" Memory used: {final_memory - start_memory:.1f} MB\")\n", + "print(f\" Output files created in {OUTPUT_DATA_DIR}\")\n", + "print(f\" Report file created in {RESOURCES_DIR}\")\n", + "print(\"\\n✅ All variables (including global potential grid) merged in single .nc file\")" + ] + }, + { + "cell_type": "markdown", + "id": "660b36ee", + "metadata": {}, + "source": [ + "## Section 4: Validation & Reporting\n", + "\n", + "Validate merge integrity, generate country coverage reports, and visualize results." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "02be0d02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validating merge integrity...\n", + "======================================================================\n", + "✓ Bus count consistency: 122831 == 122831: PASS\n", + "✓ GeoJSON-NetCDF consistency: 122831 features == 122831 buses: PASS\n", + "✓ Technology dimension: 3 == 3: PASS\n", + "✓ Hour dimension: 8760 == 8760: PASS\n", + "✓ Duplicate bus_ids: 121517 unique == 122831 total: FAIL\n", + "\n", + "======================================================================\n" + ] + } + ], + "source": [ + "# 4.1 Validate merge integrity\n", + "\n", + "print(\"Validating merge integrity...\")\n", + "print(\"=\" * 70)\n", + "\n", + "validation_checks = {}\n", + "\n", + "# Check 1: Bus count consistency\n", + "expected_bus_count = sum(merge_metadata[\"regional_bus_counts\"].values())\n", + "actual_bus_count = len(ds_merged.coords[\"bus\"]) if ds_merged is not None else 0\n", + "check1 = expected_bus_count == actual_bus_count\n", + "validation_checks[\"Bus count match\"] = {\n", + " \"passed\": check1,\n", + " \"expected\": expected_bus_count,\n", + " \"actual\": actual_bus_count,\n", + "}\n", + "print(\n", + " f\"✓ Bus count consistency: {expected_bus_count} == {actual_bus_count}: {'PASS' if check1 else 'FAIL'}\"\n", + ")\n", + "\n", + "# Check 2: GeoJSON feature count vs NetCDF bus count\n", + "geojson_count = len(gdf_merged) if gdf_merged is not None else 0\n", + "check2 = geojson_count == actual_bus_count\n", + "validation_checks[\"GeoJSON-NetCDF count match\"] = {\n", + " \"passed\": check2,\n", + " \"geojson_features\": geojson_count,\n", + " \"netcdf_buses\": actual_bus_count,\n", + "}\n", + "print(\n", + " f\"✓ GeoJSON-NetCDF consistency: {geojson_count} features == {actual_bus_count} buses: {'PASS' if check2 else 'FAIL'}\"\n", + ")\n", + "\n", + "# Check 3: Dimension preservation\n", + "if ds_merged is not None:\n", + " check3 = (\n", + " \"technology\" in ds_merged.coords and len(ds_merged.coords[\"technology\"]) == 3\n", + " )\n", + " check4 = \"hour\" in ds_merged.coords and len(ds_merged.coords[\"hour\"]) == 8760\n", + " validation_checks[\"Technology dimension\"] = {\n", + " \"passed\": check3,\n", + " \"expected\": 3,\n", + " \"actual\": len(ds_merged.coords[\"technology\"])\n", + " if \"technology\" in ds_merged.coords\n", + " else 0,\n", + " }\n", + " validation_checks[\"Hour dimension\"] = {\n", + " \"passed\": check4,\n", + " \"expected\": 8760,\n", + " \"actual\": len(ds_merged.coords[\"hour\"]) if \"hour\" in ds_merged.coords else 0,\n", + " }\n", + " print(\n", + " f\"✓ Technology dimension: {3} == {len(ds_merged.coords['technology'])}: {'PASS' if check3 else 'FAIL'}\"\n", + " )\n", + " print(\n", + " f\"✓ Hour dimension: {8760} == {len(ds_merged.coords['hour'])}: {'PASS' if check4 else 'FAIL'}\"\n", + " )\n", + "\n", + "# Check 4: No duplicate bus_ids\n", + "if gdf_merged is not None:\n", + " n_unique = gdf_merged[\"bus_id\"].nunique()\n", + " n_total = len(gdf_merged)\n", + " check5 = n_unique == n_total\n", + " validation_checks[\"No duplicate bus_ids\"] = {\n", + " \"passed\": check5,\n", + " \"total_buses\": n_total,\n", + " \"unique_buses\": n_unique,\n", + " }\n", + " print(\n", + " f\"✓ Duplicate bus_ids: {n_unique} unique == {n_total} total: {'PASS' if check5 else 'FAIL'}\"\n", + " )\n", + "\n", + "print(f\"\\n{'=' * 70}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6695d38e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Understanding merged data structure with global grid...\n", + "======================================================================\n", + "\n", + "📊 MERGED DATASET CONTENT:\n", + "\n", + "Variables in merged .nc:\n", + " ✓ capacity_factor: (122831, 3, 8760) (float32)\n", + " ✓ p_nom_max: (122831, 3) (float32)\n", + " ✓ avg_cf: (122831, 3) (float32)\n", + " ✓ weight: (122831,) (float32)\n", + " ✓ data_quality_flag: (122831, 3) (float64)\n", + " ✓ potential: (721, 1441, 3) (float32)\n", + "\n", + "Dimensions and Coordinates:\n", + " • technology: 3 elements\n", + " • hour: 8760 elements\n", + " • bus: 122831 elements\n", + " • y_grid: 721 elements (global grid, 0.25° resolution)\n", + " • x_grid: 1441 elements (global grid, 0.25° resolution)\n", + "\n", + "✅ KEY FEATURE: Global Potential Grid\n", + " • All regional potential data placed on unified 0.25° global grid\n", + " • Coverage: NaN where no data, actual values where available\n", + " • Extent: -180° to 180° (lon), -90° to 90° (lat)\n", + " • Coverage: 188043/1038961 cells (18.10%)\n", + "\n", + "======================================================================\n", + "\n", + "Generating country coverage report...\n", + "\n", + "Countries in merged data: 194\n", + "\n", + "Countries in pycountry but NOT in renewable profiles: 55\n", + "\n", + "Top 15 countries by bus count:\n", + "ISO3 Bus_Count Valid_Pycountry\n", + " USA 30853 True\n", + " CHN 15238 True\n", + " IND 8471 True\n", + " CAN 4488 True\n", + " BRA 4198 True\n", + " DEU 3141 True\n", + " FRA 2983 True\n", + " MEX 2525 True\n", + " JPN 2475 True\n", + " ITA 2452 True\n", + " ESP 2121 True\n", + " NOR 2050 True\n", + " TUR 2011 True\n", + " UKR 1856 True\n", + " AUS 1749 True\n", + "\n", + "Country Coverage Summary:\n", + " Total countries with data: 194\n", + " Total valid ISO3 codes: 194\n", + " Total invalid ISO3 codes: 0\n", + " Total missing countries: 55\n", + " Coverage: 77.9% of world\n" + ] + } + ], + "source": [ + "# 4.2 Understand merged structure with global grid\n", + "\n", + "print(\"Understanding merged data structure with global grid...\")\n", + "print(\"=\" * 70)\n", + "\n", + "print(\"\\n📊 MERGED DATASET CONTENT:\")\n", + "print(\"\\nVariables in merged .nc:\")\n", + "if ds_merged is not None:\n", + " for var in ds_merged.data_vars:\n", + " shape = ds_merged[var].shape\n", + " dtype = ds_merged[var].dtype\n", + " print(f\" ✓ {var}: {shape} ({dtype})\")\n", + "\n", + "print(\"\\nDimensions and Coordinates:\")\n", + "if ds_merged is not None:\n", + " for coord in ds_merged.coords:\n", + " size = len(ds_merged.coords[coord])\n", + " if coord in [\"x_grid\", \"y_grid\"]:\n", + " print(f\" • {coord}: {size} elements (global grid, 0.25° resolution)\")\n", + " else:\n", + " print(f\" • {coord}: {size} elements\")\n", + "\n", + "print(\"\\n✅ KEY FEATURE: Global Potential Grid\")\n", + "print(\" • All regional potential data placed on unified 0.25° global grid\")\n", + "print(\" • Coverage: NaN where no data, actual values where available\")\n", + "print(\" • Extent: -180° to 180° (lon), -90° to 90° (lat)\")\n", + "if \"potential\" in ds_merged.data_vars:\n", + " potential_var = ds_merged[\"potential\"]\n", + " valid_cells = (~np.isnan(potential_var.values[:, :, 0])).sum()\n", + " total_cells = potential_var.shape[0] * potential_var.shape[1]\n", + " print(\n", + " f\" • Coverage: {valid_cells}/{total_cells} cells ({(valid_cells / total_cells * 100):.2f}%)\"\n", + " )\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "\n", + "# 4.2b Generate country coverage report\n", + "print(\"\\nGenerating country coverage report...\\n\")\n", + "\n", + "# Extract ISO3 codes from merged GeoJSON\n", + "if gdf_merged is not None:\n", + " iso3_merged = set(gdf_merged[\"country\"].dropna().unique())\n", + "\n", + " # Validate against pycountry\n", + " valid_iso3_codes_pycountry = {country.alpha_3 for country in pycountry.countries}\n", + "\n", + " iso3_valid_in_merged = iso3_merged & valid_iso3_codes_pycountry\n", + " iso3_invalid_in_merged = iso3_merged - valid_iso3_codes_pycountry\n", + " iso3_missing_from_merged = valid_iso3_codes_pycountry - iso3_merged\n", + "\n", + " print(f\"Countries in merged data: {len(iso3_valid_in_merged)}\")\n", + " if iso3_invalid_in_merged:\n", + " print(f\" Invalid ISO3 codes found: {sorted(iso3_invalid_in_merged)}\")\n", + "\n", + " print(\n", + " f\"\\nCountries in pycountry but NOT in renewable profiles: {len(iso3_missing_from_merged)}\"\n", + " )\n", + "\n", + " # Create detailed report\n", + " iso3_counts_merged = {}\n", + " for iso3 in iso3_valid_in_merged:\n", + " count = len(gdf_merged[gdf_merged[\"country\"] == iso3])\n", + " iso3_counts_merged[iso3] = count\n", + "\n", + " # Create report dataframe\n", + " report_df = pd.DataFrame(\n", + " {\n", + " \"ISO3\": sorted(iso3_counts_merged.keys()),\n", + " \"Bus_Count\": [\n", + " iso3_counts_merged[iso3] for iso3 in sorted(iso3_counts_merged.keys())\n", + " ],\n", + " }\n", + " )\n", + " report_df[\"Valid_Pycountry\"] = report_df[\"ISO3\"].isin(valid_iso3_codes_pycountry)\n", + " report_df = report_df.sort_values(\"Bus_Count\", ascending=False)\n", + "\n", + " print(\"\\nTop 15 countries by bus count:\")\n", + " print(report_df.head(15).to_string(index=False))\n", + "\n", + " print(\"\\nCountry Coverage Summary:\")\n", + " print(f\" Total countries with data: {len(iso3_counts_merged)}\")\n", + " print(f\" Total valid ISO3 codes: {len(iso3_valid_in_merged)}\")\n", + " print(f\" Total invalid ISO3 codes: {len(iso3_invalid_in_merged)}\")\n", + " print(f\" Total missing countries: {len(iso3_missing_from_merged)}\")\n", + " print(\n", + " f\" Coverage: {len(iso3_valid_in_merged) / len(valid_iso3_codes_pycountry) * 100:.1f}% of world\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b2a8f22e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "MISSING COUNTRIES ANALYSIS\n", + "======================================================================\n", + "\n", + "📊 Analyzing 55 missing countries...\n", + "✓ Downloaded world data from Natural Earth\n", + " ⚠️ Error processing ABW: False\n", + " ⚠️ Error processing AIA: False\n", + " ⚠️ Error processing ALA: False\n", + " ⚠️ Error processing ASM: False\n", + " ⚠️ Error processing BES: False\n", + " ⚠️ Error processing BLM: False\n", + " ⚠️ Error processing BMU: False\n", + " ⚠️ Error processing BVT: False\n", + " ⚠️ Error processing CCK: False\n", + " ⚠️ Error processing COK: False\n", + " ⚠️ Error processing CUW: False\n", + " ⚠️ Error processing CXR: False\n", + " ⚠️ Error processing CYM: False\n", + " ⚠️ Error processing FRO: False\n", + " ⚠️ Error processing GGY: False\n", + " ⚠️ Error processing GIB: False\n", + " ⚠️ Error processing GLP: False\n", + " ⚠️ Error processing GUF: False\n", + " ⚠️ Error processing GUM: False\n", + " ⚠️ Error processing HKG: False\n", + " ⚠️ Error processing HMD: False\n", + " ⚠️ Error processing IMN: False\n", + " ⚠️ Error processing IOT: False\n", + " ⚠️ Error processing JEY: False\n", + " ⚠️ Error processing MAC: False\n", + " ⚠️ Error processing MAF: False\n", + " ⚠️ Error processing MNP: False\n", + " ⚠️ Error processing MSR: False\n", + " ⚠️ Error processing MTQ: False\n", + " ⚠️ Error processing MYT: False\n", + " ⚠️ Error processing NFK: False\n", + " ⚠️ Error processing NIU: False\n", + " ⚠️ Error processing PCN: False\n", + " ⚠️ Error processing PYF: False\n", + " ⚠️ Error processing REU: False\n", + " ⚠️ Error processing SGS: False\n", + " ⚠️ Error processing SHN: False\n", + " ⚠️ Error processing SJM: False\n", + " ⚠️ Error processing SPM: False\n", + " ⚠️ Error processing SXM: False\n", + " ⚠️ Error processing TCA: False\n", + " ⚠️ Error processing TKL: False\n", + " ⚠️ Error processing UMI: False\n", + " ⚠️ Error processing VAT: False\n", + " ⚠️ Error processing VGB: False\n", + " ⚠️ Error processing VIR: False\n", + " ⚠️ Error processing WLF: False\n", + "\n", + "📊 TOP MISSING COUNTRIES (ranked by relevance: population + area):\n", + "ISO3 Country Population Area_km2 Relevance_Score\n", + " RUS Russian Federation 144373535.0 2935.205205 0.743430\n", + " ATA Antarctica 4490.0 6028.836194 0.500016\n", + " GRL Greenland 56225.0 677.509565 0.056384\n", + " PRI Puerto Rico 3193694.0 0.788009 0.011126\n", + " ESH Western Sahara 603253.0 8.603984 0.002803\n", + " NCL New Caledonia 287800.0 2.020406 0.001164\n", + " FLK Falkland Islands (Malvinas) 3398.0 2.128750 0.000188\n", + " ATF French Southern Territories 140.0 1.432928 0.000119\n", + "\n", + "📈 MISSING COUNTRIES SUMMARY:\n", + " Total missing countries: 8\n", + " Countries in world data: 8\n", + " Countries not in world data (territories/special): 0\n", + "\n", + " Population in missing countries: 148,522,535\n", + " Land area in missing countries: 9,657 degrees²\n" + ] + } + ], + "source": [ + "# 4.2c Analyze missing countries by area and population\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"MISSING COUNTRIES ANALYSIS\")\n", + "print(\"=\" * 70)\n", + "\n", + "# Recompute missing countries (in case this cell is run independently)\n", + "if \"iso3_missing_from_merged\" not in locals():\n", + " valid_iso3_codes_pycountry = {country.alpha_3 for country in pycountry.countries}\n", + " if gdf_merged is not None:\n", + " iso3_in_data = set(gdf_merged[\"country\"].dropna().unique())\n", + " iso3_missing_from_merged = valid_iso3_codes_pycountry - iso3_in_data\n", + " else:\n", + " iso3_missing_from_merged = set()\n", + "\n", + "if len(iso3_missing_from_merged) == 0:\n", + " print(\"\\n✅ No missing countries - all countries have renewable profile data!\")\n", + "else:\n", + " print(f\"\\n📊 Analyzing {len(iso3_missing_from_merged)} missing countries...\")\n", + "\n", + " # Try to download Natural Earth data directly\n", + " try:\n", + " url = \"https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip\"\n", + " world = gpd.read_file(url)\n", + " print(\"✓ Downloaded world data from Natural Earth\")\n", + " has_world_data = True\n", + " except Exception as e1:\n", + " print(f\" ⚠️ Could not download Natural Earth data: {e1}\")\n", + " # Fallback: create empty dataframe\n", + " has_world_data = False\n", + " world = None\n", + "\n", + " if has_world_data and world is not None:\n", + " # Create analysis dataframe\n", + " missing_analysis = []\n", + "\n", + " for iso3 in sorted(iso3_missing_from_merged):\n", + " try:\n", + " # Get country from pycountry\n", + " country = pycountry.countries.get(alpha_3=iso3)\n", + " country_name = country.name if country else iso3\n", + "\n", + " # Find in world data using ISO3 code\n", + " world_match = world[world[\"ISO_A3\"] == iso3]\n", + "\n", + " if len(world_match) == 0:\n", + " # Try alternative column names\n", + " world_match = world[world.get(\"iso_a3\", \"\") == iso3]\n", + "\n", + " if len(world_match) > 0:\n", + " row = world_match.iloc[0]\n", + "\n", + " # Extract population\n", + " population = 0\n", + " for pop_col in [\"POP_EST\", \"POPULATION\", \"pop_est\", \"population\"]:\n", + " if pop_col in row and pd.notna(row[pop_col]):\n", + " population = float(row[pop_col])\n", + " break\n", + "\n", + " # Calculate area from geometry\n", + " area_km2 = 0\n", + " try:\n", + " if row.geometry:\n", + " # Convert area from degrees squared to km2 (rough estimate at equator)\n", + " area_km2 = float(row.geometry.area)\n", + " except:\n", + " pass\n", + "\n", + " missing_analysis.append(\n", + " {\n", + " \"ISO3\": iso3,\n", + " \"Country\": country_name,\n", + " \"Population\": population,\n", + " \"Area_km2\": area_km2,\n", + " \"In_World_Data\": True,\n", + " }\n", + " )\n", + " else:\n", + " # Not in world data\n", + " missing_analysis.append(\n", + " {\n", + " \"ISO3\": iso3,\n", + " \"Country\": country_name,\n", + " \"Population\": 0,\n", + " \"Area_km2\": 0,\n", + " \"In_World_Data\": False,\n", + " }\n", + " )\n", + " except Exception as e:\n", + " print(f\" ⚠️ Error processing {iso3}: {e}\")\n", + "\n", + " # Create dataframe and rank\n", + " missing_df = pd.DataFrame(missing_analysis)\n", + "\n", + " # Calculate relevance score (normalized population + normalized area)\n", + " pop_max = missing_df[\"Population\"].max()\n", + " area_max = missing_df[\"Area_km2\"].max()\n", + "\n", + " if pop_max > 0:\n", + " missing_df[\"Pop_Normalized\"] = missing_df[\"Population\"] / pop_max\n", + " else:\n", + " missing_df[\"Pop_Normalized\"] = 0\n", + "\n", + " if area_max > 0:\n", + " missing_df[\"Area_Normalized\"] = missing_df[\"Area_km2\"] / area_max\n", + " else:\n", + " missing_df[\"Area_Normalized\"] = 0\n", + "\n", + " missing_df[\"Relevance_Score\"] = (\n", + " missing_df[\"Pop_Normalized\"] + missing_df[\"Area_Normalized\"]\n", + " ) / 2\n", + " missing_df = missing_df.sort_values(\"Relevance_Score\", ascending=False)\n", + "\n", + " print(\"\\n📊 TOP MISSING COUNTRIES (ranked by relevance: population + area):\")\n", + " display_cols = [\"ISO3\", \"Country\", \"Population\", \"Area_km2\", \"Relevance_Score\"]\n", + " print(missing_df[display_cols].head(30).to_string(index=False))\n", + "\n", + " print(\"\\n📈 MISSING COUNTRIES SUMMARY:\")\n", + " in_world = missing_df[missing_df[\"In_World_Data\"]].shape[0]\n", + " not_in_world = missing_df[~missing_df[\"In_World_Data\"]].shape[0]\n", + " print(f\" Total missing countries: {len(missing_df)}\")\n", + " print(f\" Countries in world data: {in_world}\")\n", + " print(f\" Countries not in world data (territories/special): {not_in_world}\")\n", + "\n", + " if in_world > 0:\n", + " total_missing_pop = missing_df[missing_df[\"In_World_Data\"]][\n", + " \"Population\"\n", + " ].sum()\n", + " total_missing_area = missing_df[missing_df[\"In_World_Data\"]][\n", + " \"Area_km2\"\n", + " ].sum()\n", + " print(f\"\\n Population in missing countries: {total_missing_pop:,.0f}\")\n", + " print(\n", + " f\" Land area in missing countries: {total_missing_area:,.0f} degrees²\"\n", + " )\n", + "\n", + " if not_in_world > 0:\n", + " not_found = missing_df[~missing_df[\"In_World_Data\"]][\"Country\"].tolist()\n", + " print(\n", + " \"\\n ⚠️ Not found in Natural Earth data (may be territories/special regions):\"\n", + " )\n", + " for country in sorted(not_found):\n", + " print(f\" - {country}\")\n", + " else:\n", + " print(\"\\n Using pycountry data (no population/area available)\")\n", + " print(\n", + " f\" Missing countries: {', '.join(sorted(list(iso3_missing_from_merged)[:20]))}\"\n", + " )\n", + " if len(iso3_missing_from_merged) > 20:\n", + " print(f\" ... and {len(iso3_missing_from_merged) - 20} more\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "95d6ce9c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Spot-checking sample records from merged data...\n", + "======================================================================\n", + "\n", + "Sample 1: Country USA\n", + " Bus ID: USA_ON_9tsm40\n", + " Onshore/Offshore: onshore\n", + " Area (km²): 293.78088\n", + " Capacity Factor - Min: 0.0000, Mean: 0.1783, Max: 1.0000\n", + " ✓ Capacity factors in valid range [0,1]: True\n", + " ✓ Geometry valid: True\n", + "\n", + "Sample 2: Country CHN\n", + " Bus ID: CHN_ON_wwgw3k\n", + " Onshore/Offshore: onshore\n", + " Area (km²): 214.79025\n", + " Capacity Factor - Min: 0.0000, Mean: 0.2686, Max: 1.0000\n", + " ✓ Capacity factors in valid range [0,1]: True\n", + " ✓ Geometry valid: True\n", + "\n", + "Sample 3: Country IND\n", + " Bus ID: IND_ON_tdr1vb\n", + " Onshore/Offshore: onshore\n", + " Area (km²): 442.59442\n", + " Capacity Factor - Min: 0.0000, Mean: 0.1180, Max: 0.9840\n", + " ✓ Capacity factors in valid range [0,1]: True\n", + " ✓ Geometry valid: True\n", + "\n", + "======================================================================\n" + ] + } + ], + "source": [ + "# 4.3 Spot-check sample records\n", + "\n", + "print(\"\\nSpot-checking sample records from merged data...\")\n", + "print(\"=\" * 70)\n", + "\n", + "if ds_merged is not None and gdf_merged is not None:\n", + " # Sample 3 random buses from different countries\n", + " sample_countries = (\n", + " report_df[\"ISO3\"].head(5).tolist() if \"report_df\" in locals() else []\n", + " )\n", + "\n", + " for i, country in enumerate(sample_countries[:3]):\n", + " print(f\"\\nSample {i + 1}: Country {country}\")\n", + "\n", + " # Find a bus from this country\n", + " buses_in_country = gdf_merged[gdf_merged[\"country\"] == country][\"bus_id\"].values\n", + " if len(buses_in_country) > 0:\n", + " sample_bus = buses_in_country[0]\n", + " print(f\" Bus ID: {sample_bus}\")\n", + "\n", + " # Get GeoJSON properties\n", + " bus_geojson = gdf_merged[gdf_merged[\"bus_id\"] == sample_bus].iloc[0]\n", + " print(f\" Onshore/Offshore: {bus_geojson.get('onshore_offshore', 'N/A')}\")\n", + " print(f\" Area (km²): {bus_geojson.get('area_km2', 'N/A')}\")\n", + "\n", + " # Get NetCDF data\n", + " if sample_bus in ds_merged.coords[\"bus\"].values:\n", + " bus_data = ds_merged.sel(bus=sample_bus)\n", + "\n", + " # Check capacity factors\n", + " cf_mean = float(bus_data[\"capacity_factor\"].mean())\n", + " cf_min = float(bus_data[\"capacity_factor\"].min())\n", + " cf_max = float(bus_data[\"capacity_factor\"].max())\n", + "\n", + " print(\n", + " f\" Capacity Factor - Min: {cf_min:.4f}, Mean: {cf_mean:.4f}, Max: {cf_max:.4f}\"\n", + " )\n", + " print(\n", + " f\" ✓ Capacity factors in valid range [0,1]: {0 <= cf_min and cf_max <= 1}\"\n", + " )\n", + "\n", + " # Check if geometry is valid\n", + " try:\n", + " geom = bus_geojson.geometry\n", + " print(f\" ✓ Geometry valid: {geom.is_valid}\")\n", + " except:\n", + " print(\" ✗ Geometry invalid\")\n", + "\n", + "print(f\"\\n{'=' * 70}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4954944b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved summary plot to resources/renewable_profiles_merge_summary.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 4.4 Visualize results\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n", + "\n", + "# Plot 1: Top 20 countries by bus count\n", + "if \"report_df\" in locals():\n", + " top20 = report_df.head(20)\n", + " ax = axes[0, 0]\n", + " ax.barh(range(len(top20)), top20[\"Bus_Count\"].values)\n", + " ax.set_yticks(range(len(top20)))\n", + " ax.set_yticklabels(top20[\"ISO3\"].values)\n", + " ax.set_xlabel(\"Bus Count\")\n", + " ax.set_title(\"Top 20 Countries by Bus Count\")\n", + " ax.invert_yaxis()\n", + " ax.grid(axis=\"x\", alpha=0.3)\n", + "\n", + "# Plot 2: Technology distribution (onshore vs offshore)\n", + "if gdf_merged is not None:\n", + " ax = axes[0, 1]\n", + " tech_dist = gdf_merged[\"onshore_offshore\"].value_counts()\n", + " colors = [\"#2ecc71\", \"#3498db\"]\n", + " ax.pie(\n", + " tech_dist.values,\n", + " labels=tech_dist.index,\n", + " autopct=\"%1.1f%%\",\n", + " colors=colors,\n", + " startangle=90,\n", + " )\n", + " ax.set_title(\"Onshore vs Offshore Distribution\")\n", + "\n", + "# Plot 3: Regional distribution (buses per region)\n", + "if \"merge_metadata\" in locals():\n", + " ax = axes[1, 0]\n", + " regions_list = list(merge_metadata[\"regional_bus_counts\"].keys())\n", + " buses_list = list(merge_metadata[\"regional_bus_counts\"].values())\n", + " ax.bar(range(len(regions_list)), buses_list, color=\"#9b59b6\")\n", + " ax.set_xticks(range(len(regions_list)))\n", + " ax.set_xticklabels(regions_list, rotation=45, ha=\"right\")\n", + " ax.set_ylabel(\"Bus Count\")\n", + " ax.set_title(\"Bus Count by Region\")\n", + " ax.grid(axis=\"y\", alpha=0.3)\n", + "\n", + "# Plot 4: Data quality - Buses with data coverage\n", + "if ds_merged is not None and gdf_merged is not None:\n", + " ax = axes[1, 1]\n", + "\n", + " # Count valid capacity factors per bus (non-NaN)\n", + " valid_cf_counts = []\n", + " for bus in ds_merged.coords[\"bus\"].values[\n", + " : min(1000, len(ds_merged.coords[\"bus\"]))\n", + " ]:\n", + " try:\n", + " bus_data = ds_merged.sel(bus=bus)\n", + " valid_cf = (~np.isnan(bus_data[\"capacity_factor\"].values)).sum()\n", + " valid_cf_counts.append(valid_cf)\n", + " except:\n", + " pass\n", + "\n", + " if valid_cf_counts:\n", + " ax.hist(valid_cf_counts, bins=30, color=\"#e74c3c\", edgecolor=\"black\", alpha=0.7)\n", + " ax.set_xlabel(\"Valid Capacity Factor Hours\")\n", + " ax.set_ylabel(\"Bus Count\")\n", + " ax.set_title(\"Data Coverage (Sample of 1000 buses)\")\n", + " ax.axvline(\n", + " np.mean(valid_cf_counts),\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " label=f\"Mean: {np.mean(valid_cf_counts):.0f}\",\n", + " )\n", + " ax.legend()\n", + " ax.grid(axis=\"y\", alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\n", + " RESOURCES_DIR / \"renewable_profiles_merge_summary.png\", dpi=150, bbox_inches=\"tight\"\n", + ")\n", + "print(\"✓ Saved summary plot to resources/renewable_profiles_merge_summary.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "70eea69e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "MERGE & VALIDATION COMPLETE\n", + "======================================================================\n", + "\n", + "📊 MERGE SUMMARY:\n", + " Regions merged: 9\n", + " Total buses: 122831\n", + " Countries with data: 194\n", + " Merge timestamp: 2026-05-27T11:51:25.801922\n", + "\n", + "✅ VALIDATION RESULTS:\n", + " Bus count match: PASS ✓\n", + " GeoJSON-NetCDF count match: PASS ✓\n", + " Technology dimension: PASS ✓\n", + " Hour dimension: PASS ✓\n", + " No duplicate bus_ids: FAIL ✗\n", + "\n", + "📁 OUTPUT FILES:\n", + " • ..\\..\\data\\renewable_profiles_global_merged.nc (116.3 MB)\n", + " • ..\\..\\data\\renewable_profiles_global_merged.geojson\n", + " • ..\\..\\resources\\renewable_profiles_merge_report.json\n", + " • resources/renewable_profiles_merge_summary.png\n", + "\n", + "🌍 COUNTRY COVERAGE:\n", + " Coverage: 194/249 countries (77.9%)\n", + "\n", + "✨ MERGE STRATEGY HIGHLIGHTS\n", + "======================================================================\n", + "Global Grid Approach:\n", + " • Created unified 0.25° global grid covering entire world\n", + " • Placed all regional potential data into corresponding grid cells\n", + " • Filled with NaN where no data available\n", + "\n", + "Result: Single comprehensive .nc file containing:\n", + " ✓ All bus-level variables (capacity_factor, p_nom_max, avg_cf, weight, data_quality_flag)\n", + " ✓ Global potential grid (y_grid, x_grid, technology)\n", + " ✓ All bus geometries + country codes in .geojson\n", + "\n", + "Benefits:\n", + " ✓ Unified dataset - no need to access multiple files\n", + " ✓ Standard gridded format ready for analysis/interpolation\n", + " ✓ Can use numpy/xarray operations on global grid\n", + " ✓ Cleaner API - all data in one place\n", + "\n", + "📝 USING THE MERGED DATA\n", + "======================================================================\n", + "Load and explore:\n", + " ds = xr.open_dataset('data/renewable_profiles_global_merged.nc')\n", + " gdf = gpd.read_file('data/renewable_profiles_global_merged.geojson')\n", + "\n", + "Access bus-level data:\n", + " cf = ds['capacity_factor'] # [bus, technology, hour]\n", + " p_max = ds['p_nom_max'] # [bus, technology]\n", + "\n", + "Access global potential grid:\n", + " pot = ds['potential'] # [y_grid, x_grid, technology]\n", + " pot_data = pot.where(~np.isnan(pot), drop=True) # Drop NaN regions\n", + "\n", + "======================================================================\n" + ] + } + ], + "source": [ + "# 4.5 Final summary and documentation\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"MERGE & VALIDATION COMPLETE\")\n", + "print(\"=\" * 70)\n", + "\n", + "print(\"\\n📊 MERGE SUMMARY:\")\n", + "if \"merge_metadata\" in locals():\n", + " print(f\" Regions merged: {len(merge_metadata['regions_processed'])}\")\n", + " print(f\" Total buses: {merge_metadata['total_buses']}\")\n", + " print(f\" Countries with data: {len(merge_metadata['iso3_counts'])}\")\n", + " print(f\" Merge timestamp: {merge_metadata['merge_timestamp']}\")\n", + "\n", + "print(\"\\n✅ VALIDATION RESULTS:\")\n", + "for check_name, check_result in validation_checks.items():\n", + " status = \"PASS ✓\" if check_result.get(\"passed\", False) else \"FAIL ✗\"\n", + " print(f\" {check_name}: {status}\")\n", + "\n", + "print(\"\\n📁 OUTPUT FILES:\")\n", + "print(f\" • {output_nc_file} ({file_size_mb:.1f} MB)\")\n", + "print(f\" • {output_geojson_file}\")\n", + "print(f\" • {output_report_file}\")\n", + "print(\" • resources/renewable_profiles_merge_summary.png\")\n", + "\n", + "print(\"\\n🌍 COUNTRY COVERAGE:\")\n", + "if \"iso3_valid_in_merged\" in locals():\n", + " print(\n", + " f\" Coverage: {len(iso3_valid_in_merged)}/{len(valid_iso3_codes_pycountry)} countries ({len(iso3_valid_in_merged) / len(valid_iso3_codes_pycountry) * 100:.1f}%)\"\n", + " )\n", + "\n", + "print(\"\\n✨ MERGE STRATEGY HIGHLIGHTS\")\n", + "print(\"=\" * 70)\n", + "print(\"Global Grid Approach:\")\n", + "print(\" • Created unified 0.25° global grid covering entire world\")\n", + "print(\" • Placed all regional potential data into corresponding grid cells\")\n", + "print(\" • Filled with NaN where no data available\")\n", + "print(\"\\nResult: Single comprehensive .nc file containing:\")\n", + "print(\n", + " \" ✓ All bus-level variables (capacity_factor, p_nom_max, avg_cf, weight, data_quality_flag)\"\n", + ")\n", + "print(\" ✓ Global potential grid (y_grid, x_grid, technology)\")\n", + "print(\" ✓ All bus geometries + country codes in .geojson\")\n", + "print(\"\\nBenefits:\")\n", + "print(\" ✓ Unified dataset - no need to access multiple files\")\n", + "print(\" ✓ Standard gridded format ready for analysis/interpolation\")\n", + "print(\" ✓ Can use numpy/xarray operations on global grid\")\n", + "print(\" ✓ Cleaner API - all data in one place\")\n", + "\n", + "print(\"\\n📝 USING THE MERGED DATA\")\n", + "print(\"=\" * 70)\n", + "print(\"Load and explore:\")\n", + "print(\" ds = xr.open_dataset('data/renewable_profiles_global_merged.nc')\")\n", + "print(\" gdf = gpd.read_file('data/renewable_profiles_global_merged.geojson')\")\n", + "print(\"\\nAccess bus-level data:\")\n", + "print(\" cf = ds['capacity_factor'] # [bus, technology, hour]\")\n", + "print(\" p_max = ds['p_nom_max'] # [bus, technology]\")\n", + "print(\"\\nAccess global potential grid:\")\n", + "print(\" pot = ds['potential'] # [y_grid, x_grid, technology]\")\n", + "print(\" pot_data = pot.where(~np.isnan(pot), drop=True) # Drop NaN regions\")\n", + "print(\"\\n\" + \"=\" * 70)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift-dev", + "language": "python", + "name": "shift-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 6b11f1b7566e53d6438cd0ff7b9adfdbd9b10cf3 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 27 May 2026 18:32:43 +0200 Subject: [PATCH 120/216] feat: add capacity factor based clustering of cells in same region --- rules/preparation.smk | 100 ++- workflow/scripts/cluster_renewables.py | 871 +++++++++++++++++++++++++ 2 files changed, 935 insertions(+), 36 deletions(-) create mode 100644 workflow/scripts/cluster_renewables.py diff --git a/rules/preparation.smk b/rules/preparation.smk index 291b894..caa1d10 100644 --- a/rules/preparation.smk +++ b/rules/preparation.smk @@ -3,35 +3,35 @@ rule download_labour_data: output: - merged = "resources/merged_labour_inputs.csv", + merged="resources/merged_labour_inputs.csv", + threads: 1 resources: mem_mb=2000, - threads: 1 script: str(SCRIPT_DIR / "download_labour_data.py") rule prepare_labour_cost: input: - merged = "resources/merged_labour_inputs.csv", + merged="resources/merged_labour_inputs.csv", output: - labour_cost = "resources/labour_cost_clustered.csv", + labour_cost="resources/labour_cost_clustered.csv", + threads: 1 resources: mem_mb=2000, - threads: 1 script: str(SCRIPT_DIR / "prepare_labour_cost.py") rule prepare_wacc: input: - wacc = "data/wacc-global.csv", - bus_locations = "data/bus_locations.csv", + wacc="data/wacc-global.csv", + bus_locations="data/bus_locations.csv", output: - wacc = "resources/wacc-clustered.csv" + wacc="resources/wacc-clustered.csv", + threads: 2 resources: mem_mb=5000, - threads: 2 notebook: # "notebooks/prepare-wacc.ipynb" str(NOTEBOOKS_DIR / "prepare-wacc.ipynb") @@ -39,70 +39,98 @@ rule prepare_wacc: rule prepare_political_stability: input: - political_stability = "data/political-stability/globaleconomy.csv", #https://www.theglobaleconomy.com/rankings/wb_political_stability/ - bus_locations = "data/bus_locations.csv", + political_stability="data/political-stability/globaleconomy.csv", #https://www.theglobaleconomy.com/rankings/wb_political_stability/ + bus_locations="data/bus_locations.csv", output: - political_stability = "resources/political_stability_clustered.csv" + political_stability="resources/political_stability_clustered.csv", + threads: 2 resources: mem_mb=5000, - threads: 2 notebook: str(NOTEBOOKS_DIR / "prepare-political-stability.ipynb") rule prepare_chokepoints: - params: - shipping_routes=config["trade"]["shipping_routes"], input: - trade_options = "data/trade_opt.csv", - bus_locations = "data/bus_locations.csv", + trade_options="data/trade_opt.csv", + bus_locations="data/bus_locations.csv", output: - trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", - map_chokepoints = "results/figures_general/chokepoints/map_chokepoints.pdf", - map_chokepoints_png = "results/figures_general/chokepoints/map_chokepoints.png", + trade_options_chokepoints="resources/trade_opt_chokepoints.csv", + map_chokepoints="results/figures_general/chokepoints/map_chokepoints.pdf", + map_chokepoints_png="results/figures_general/chokepoints/map_chokepoints.png", + threads: 2 resources: mem_mb=5000, - threads: 2 + params: + shipping_routes=config["trade"]["shipping_routes"], notebook: str(NOTEBOOKS_DIR / "prepare-chokepoints.ipynb") rule retrieve_iron_ore: input: - iron_ore_production = "data/owid-iron-ore/iron-ore-crude-ore-production.csv", - iron_ore_cost = "data/devlin2023-supplementary.xlsx", - bus_locations = "data/bus_locations.csv", + iron_ore_production="data/owid-iron-ore/iron-ore-crude-ore-production.csv", + iron_ore_cost="data/devlin2023-supplementary.xlsx", + bus_locations="data/bus_locations.csv", output: - iron_ore = "resources/ironore-production.csv", - iron_ore_map = "results/figures_general/iron_ore_map.pdf", + iron_ore="resources/ironore-production.csv", + iron_ore_map="results/figures_general/iron_ore_map.pdf", + threads: 2 resources: mem_mb=5000, - threads: 2 notebook: str(NOTEBOOKS_DIR / "global-iron-ore.ipynb") rule prepare_iron_ore: input: - iron_ore = "resources/ironore-production.csv", - bus_locations = "data/bus_locations.csv", + iron_ore="resources/ironore-production.csv", + bus_locations="data/bus_locations.csv", output: - iron_ore = "resources/ironore_production_clustered.csv", + iron_ore="resources/ironore_production_clustered.csv", + threads: 2 resources: mem_mb=5000, - threads: 2 notebook: str(NOTEBOOKS_DIR / "prepare-iron-ore.ipynb") rule prepare_steel_demand: input: - steel_demand = "data/demand/steel_demands/output_data/country_raw_steel_demand_and_dri_share.csv", - bus_locations = "data/bus_locations.csv", + steel_demand="data/demand/steel_demands/output_data/country_raw_steel_demand_and_dri_share.csv", + bus_locations="data/bus_locations.csv", output: - steel_demand = 'resources/steel_demand_clustered_{cost_year}.csv', + steel_demand="resources/steel_demand_clustered_{cost_year}.csv", + threads: 2 resources: mem_mb=5000, - threads: 2 notebook: - str(NOTEBOOKS_DIR / "prepare-steel-demand.ipynb") \ No newline at end of file + str(NOTEBOOKS_DIR / "prepare-steel-demand.ipynb") + + +rule cluster_renewables: + """ +Cluster renewable generators from merged profiles for optimization. + +Input: merged renewable profiles (122k+ buses from all regions) +Output: clustered profiles (region, technology, class dimensions) + validation report with quality metrics + +Clustering strategy: +- Filters to onwind + pvplant only (excludes unreliable offshore) +- Extracts 6D temporal features: avg_cf, temporal_std, cv, autocorr_24h, lat, lon +- Applies weighted K-means (0.5 merit-order, 0.3 temporal, 0.2 geospatial) +- Selects representative timeseries per cluster to preserve real patterns +- Cluster naming: Regionname_tech(onwind/solar)_number +""" + input: + merged="data/renewable_profiles_global_merged.nc", + output: + clustered="resources/renewables_clustered.nc", + report="resources/renewables_clustering_report.json", + threads: 4 + resources: + mem_mb=16000, + time_min=60, + script: + str(SCRIPT_DIR / "cluster_renewables.py") diff --git a/workflow/scripts/cluster_renewables.py b/workflow/scripts/cluster_renewables.py new file mode 100644 index 0000000..79de0da --- /dev/null +++ b/workflow/scripts/cluster_renewables.py @@ -0,0 +1,871 @@ +""" +Cluster renewable generators from merged profiles for optimization. + +This script: +1. Loads merged renewable profiles (all 122k+ buses from merged file) +2. Filters to onwind + pvplant (excludes unreliable offshore wind) +3. Maps buses to regions using ISO3 codes and config +4. Extracts 6D temporal features: avg_cf, temporal_std, cv, autocorr_24h, lat, lon +5. Applies weighted K-means clustering (0.5 merit-order, 0.3 temporal, 0.2 geospatial) +6. Selects representative timeseries per cluster to preserve real patterns +7. Outputs clustered NetCDF in expected format: (region, technology, class, time) +8. Generates validation report with quality metrics + +Usage (Snakemake rule): + rule cluster_renewables: + input: + merged = "data/renewable_profiles_global_merged.nc", + config = "config/config.yaml", + output: + clustered = "resources/renewables_clustered.nc", + report = "resources/renewables_clustering_report.json", + script: + "scripts/cluster_renewables.py" +""" + +import logging +import json +from pathlib import Path +from typing import Dict, Tuple, List +from contextlib import contextmanager + +import numpy as np +import pandas as pd +import xarray as xr +from tqdm import tqdm +import joblib +from sklearn.preprocessing import StandardScaler +from sklearn.cluster import KMeans + +# Setup logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +@contextmanager +def tqdm_joblib(tqdm_object): + """ + Context manager to integrate tqdm progress bar with joblib Parallel. + + Usage: + with tqdm_joblib(tqdm(total=n_tasks, desc="Processing")) as pbar: + results = Parallel(n_jobs=4)(delayed(func)(i) for i in range(n_tasks)) + """ + + class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): + def __call__(self, *args, **kwargs): + tqdm_object.update(self.batch_size) + return super().__call__(*args, **kwargs) + + old_batch_callback = joblib.parallel.BatchCompletionCallBack + joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback + try: + yield tqdm_object + finally: + joblib.parallel.BatchCompletionCallBack = old_batch_callback + + +def build_region_map(config: Dict, iso3_to_region: Dict[str, str]) -> Dict[str, str]: + """Build mapping from ISO3 code to region name.""" + iso3_to_region_local = {} + for region_name, iso3_list in config.get("regions", {}).items(): + for iso3 in iso3_list: + iso3_to_region_local[iso3] = region_name + return iso3_to_region_local + + +def extract_iso3_from_bus_id(bus_id: str) -> str: + """Extract ISO3 code from bus_id format: {ISO3}_{ON/OFF}_{geohash}.""" + try: + return bus_id.split("_")[0] + except Exception: + logger.warning(f"Could not extract ISO3 from bus_id: {bus_id}") + return None + + +def load_merged_data(merged_path: str) -> xr.Dataset: + """Load merged renewable profiles.""" + logger.info(f"Loading merged data from {merged_path}") + ds = xr.open_dataset(merged_path) + logger.info(f" Merged data shape: {dict(ds.sizes)}") + logger.info(f" Variables: {list(ds.data_vars)}") + return ds + + +def filter_to_onwind_pv(ds: xr.Dataset) -> xr.Dataset: + """Filter merged data to onwind + solar only (exclude offshore).""" + logger.info("Filtering to onwind + solar (excluding offwind-ac)") + + techs_present = list(ds.coords["technology"].values) + # Actual technology names in merged file: 'offwind-ac', 'onwind', 'solar' + techs_to_keep = ["onwind", "solar"] + + ds_filtered = ds.sel(technology=techs_to_keep) + logger.info(f" Kept technologies: {techs_to_keep}") + logger.info(f" Buses remaining: {len(ds_filtered.bus)}") + + return ds_filtered + + +def extract_bus_features_single( + bus_id, ds: xr.Dataset, iso3_to_region: Dict[str, str] +) -> List[Dict]: + """Extract features for a single bus across all technologies (internal).""" + features = [] + iso3 = extract_iso3_from_bus_id(str(bus_id)) + if not iso3: + return features + region = iso3_to_region.get(iso3, None) + if not region: + return features + + try: + x_centroid = float(ds["x_centroid"].sel(bus=bus_id).values) + y_centroid = float(ds["y_centroid"].sel(bus=bus_id).values) + except Exception: + x_centroid, y_centroid = 0.0, 0.0 + + for tech in ds.technology.values: + tech = str(tech) # Convert numpy.str_ to Python str + try: + cf_ts = ds["capacity_factor"].sel(bus=bus_id, technology=tech).values + except Exception: + continue + if np.isnan(cf_ts).all() or len(cf_ts) == 0: + continue + + avg_cf = float(np.nanmean(cf_ts)) + temporal_std = float(np.nanstd(cf_ts)) + cv = temporal_std / avg_cf if avg_cf > 0 else 0.0 + + if len(cf_ts) > 24: + cf_detrended = cf_ts - np.nanmean(cf_ts) + if np.std(cf_detrended) > 1e-10 and not np.isnan(cf_detrended[:-24]).all(): + autocorr_24h = float( + np.corrcoef(cf_detrended[:-24], cf_detrended[24:])[0, 1] + ) + else: + autocorr_24h = 0.0 + else: + autocorr_24h = 0.0 + + features.append( + { + "avg_cf": avg_cf, + "temporal_std": temporal_std, + "cv": cv, + "autocorr_24h": autocorr_24h, + "lat": y_centroid, + "lon": x_centroid, + "region": region, + "technology": tech, + "bus_id": bus_id, + } + ) + return features + + +def extract_bus_features_batch( + bus_ids: List, ds: xr.Dataset, iso3_to_region: Dict[str, str] +) -> List[Dict]: + """ + Extract features for a batch of buses (parallelizable task). + + Batching reduces scheduler overhead and improves cache locality. + Returns flattened list of all feature dicts from all buses in batch. + """ + all_features = [] + for bus_id in bus_ids: + all_features.extend(extract_bus_features_single(bus_id, ds, iso3_to_region)) + return all_features + + +def extract_features( + ds: xr.Dataset, config: Dict, cache_features_path: str = None +) -> Tuple[pd.DataFrame, Dict[str, str]]: + """ + Extract 6D features per bus: avg_cf, temporal_std, cv, autocorr_24h, lat, lon. + + Parallelized by batching buses to reduce scheduler overhead. + + Returns: + DataFrame with columns: [avg_cf, temporal_std, cv, autocorr_24h, lat, lon, region, tech, bus_id] + Dict: mapping region_name -> list of buses in that region + """ + # Try to load from cache if it exists + if cache_features_path and Path(cache_features_path).exists(): + logger.info(f"Loading cached features from {cache_features_path}") + df_features = pd.read_csv(cache_features_path) + region_buses = {} + for _, row in df_features.iterrows(): + region = row["region"] + bus_id = row["bus_id"] + if region not in region_buses: + region_buses[region] = [] + if bus_id not in region_buses[region]: + region_buses[region].append(bus_id) + logger.info(f" Loaded {len(df_features)} features from cache") + return df_features, region_buses + + logger.info("Extracting temporal features (batched parallelization)...") + + iso3_to_region = build_region_map(config, {}) + + # Batch buses: 1 batch per worker for minimal overhead + n_jobs = snakemake.threads if hasattr(snakemake, "threads") else -1 + + bus_list = list(ds.bus.values) + num_batches = abs(n_jobs) if n_jobs != -1 else 1 + batch_size = (len(bus_list) + num_batches - 1) // num_batches + bus_batches = [ + bus_list[i : i + batch_size] for i in range(0, len(bus_list), batch_size) + ] + + logger.info( + f" Using {n_jobs} threads, batch size: {batch_size} buses, {len(bus_batches)} batches (1 per worker)" + ) + + with tqdm_joblib( + tqdm(total=len(bus_batches), desc="Extracting bus features", unit="batch") + ) as pbar: + batch_results = joblib.Parallel(n_jobs=n_jobs, backend="loky")( + joblib.delayed(extract_bus_features_batch)(batch, ds, iso3_to_region) + for batch in bus_batches + ) + + # Flatten batch results + features_list = [] + region_buses = {} + + for bus_features_list in batch_results: + for feat_dict in bus_features_list: + features_list.append(feat_dict) + region = feat_dict["region"] + if region not in region_buses: + region_buses[region] = [] + if feat_dict["bus_id"] not in region_buses[region]: + region_buses[region].append(feat_dict["bus_id"]) + + df_features = pd.DataFrame(features_list) + logger.info( + f" Extracted {len(df_features)} bus-technology pairs across {len(region_buses)} regions" + ) + logger.info( + f" Region distribution: {dict((k, len(set(v))) for k, v in region_buses.items())}" + ) + + # Cache features to disk for recovery if later steps fail + if cache_features_path: + df_features.to_csv(cache_features_path, index=False) + logger.info(f" ✓ Cached features to {cache_features_path}") + + return df_features, region_buses + + +def cluster_region_technology( + df_subset: pd.DataFrame, n_clusters: int, feature_weights: Dict[str, float] +) -> Tuple[np.ndarray, KMeans]: + """ + Cluster a single region-technology group using weighted K-means. + + Feature categories: + - avg_cf: 1 feature (0.5 weight) + - temporal: 3 features [temporal_std, cv, autocorr_24h] (0.3 weight) + - geospatial: 2 features [lat, lon] (0.2 weight) + """ + if len(df_subset) < n_clusters: + logger.warning( + f" Subset has {len(df_subset)} buses < {n_clusters} clusters, adjusting k" + ) + n_clusters = max(1, len(df_subset) // 2) + + # Extract feature subsets + avg_cf_vals = df_subset[["avg_cf"]].values # (n, 1) + temporal_vals = df_subset[["temporal_std", "cv", "autocorr_24h"]].values # (n, 3) + geospatial_vals = df_subset[["lat", "lon"]].values # (n, 2) + + # Normalize each category independently + scaler_cf = StandardScaler() + scaler_temporal = StandardScaler() + scaler_geo = StandardScaler() + + avg_cf_norm = scaler_cf.fit_transform(avg_cf_vals) # (n, 1) + temporal_norm = scaler_temporal.fit_transform(temporal_vals) # (n, 3) + geospatial_norm = scaler_geo.fit_transform(geospatial_vals) # (n, 2) + + # Apply weights + w_cf = feature_weights["avg_cf"] + w_temporal = feature_weights["temporal"] + w_geo = feature_weights["geospatial"] + + avg_cf_weighted = avg_cf_norm * w_cf # (n, 1) + temporal_weighted = temporal_norm * w_temporal # (n, 3) + geospatial_weighted = geospatial_norm * w_geo # (n, 2) + + # Concatenate all weighted features + X = np.hstack([avg_cf_weighted, temporal_weighted, geospatial_weighted]) # (n, 6) + + # K-means clustering + kmeans = KMeans(n_clusters=n_clusters, init="k-means++", n_init=10, random_state=42) + clusters = kmeans.fit_predict(X) + + return clusters, kmeans + + +def cluster_region_technology_pair( + region: str, + tech: str, + df_features: pd.DataFrame, + n_clusters_onwind: int, + n_clusters_pvplant: int, + feature_weights: Dict[str, float], + cache_dir: str = None, +) -> Tuple[Tuple[str, str], np.ndarray]: + """ + Cluster a single region-technology pair (parallelizable wrapper). + + Returns: + ((region, tech), clusters) tuple for dict conversion + """ + # Check cache first + if cache_dir: + cache_file = Path(cache_dir) / f"clustering_cache_{region}_{str(tech)}.json" + if cache_file.exists(): + try: + with open(cache_file, "r") as f: + cached = json.load(f) + clusters_array = np.array(cached["clusters"]) + logger.info( + f" [CACHED] {region} {str(tech)}: loaded {len(cached['clusters'])} assignments" + ) + return (region, tech), clusters_array + except Exception as e: + logger.debug(f"Could not load cache for {region} {tech}: {e}") + + df_subset = df_features[ + (df_features["region"] == region) & (df_features["technology"] == tech) + ] + + if len(df_subset) == 0: + return (region, tech), np.array([]) + + # Get n_clusters from tech type + if tech == "solar": + n_clusters = n_clusters_pvplant + tech_name = "solar" + else: + n_clusters = n_clusters_onwind + tech_name = "onwind" + + logger.info(f"Clustering {region} {tech_name} with k={n_clusters}") + + clusters, kmeans = cluster_region_technology(df_subset, n_clusters, feature_weights) + + # Cache this region-tech pair immediately + if cache_dir: + cache_file = Path(cache_dir) / f"clustering_cache_{region}_{str(tech)}.json" + try: + with open(cache_file, "w") as f: + json.dump( + { + "region": region, + "technology": str(tech), + "n_clusters": len(np.unique(clusters)), + "clusters": clusters.tolist(), + "timestamp": pd.Timestamp.now().isoformat(), + }, + f, + ) + logger.info(f" [CACHED] {region} {str(tech)}: saved clustering result") + except Exception as e: + logger.warning(f"Could not cache {region} {tech}: {e}") + + return (region, tech), clusters + + +def select_representative_bus( + df_cluster: pd.DataFrame, ds: xr.Dataset, tech: str, cf_weighted_ts: np.ndarray +) -> Tuple[str, np.ndarray, float]: + """ + Select representative bus for cluster based on highest correlation to weighted-avg timeseries. + + Returns: + representative_bus_id, scaled_cf_ts, avg_cf_cluster + """ + tech = str(tech) # Convert numpy.str_ to Python str + max_corr = -2.0 + best_idx = 0 + best_bus_id = df_cluster.iloc[0]["bus_id"] + + for idx, (_, row) in enumerate(df_cluster.iterrows()): + bus_id = row["bus_id"] + + try: + cf_ts = ds["capacity_factor"].sel(bus=bus_id, technology=tech).values + cf_ts = np.nan_to_num(cf_ts, nan=0.0) + + # Correlation to weighted-average + corr = float(np.corrcoef(cf_ts, cf_weighted_ts)[0, 1]) + if corr > max_corr: + max_corr = corr + best_idx = idx + best_bus_id = bus_id + except Exception as e: + logger.debug(f"Error computing correlation for {bus_id}: {e}") + continue + + # Get representative timeseries + cf_representative = ( + ds["capacity_factor"].sel(bus=best_bus_id, technology=tech).values + ) + cf_representative = np.nan_to_num(cf_representative, nan=0.0) + + # Scale to match weighted-average merit-order + avg_cf_cluster = float(np.mean(cf_weighted_ts)) + avg_cf_representative = float(np.mean(cf_representative)) + + if avg_cf_representative > 0: + scale_factor = avg_cf_cluster / avg_cf_representative + else: + scale_factor = 1.0 + + cf_cluster_ts = cf_representative * scale_factor + cf_cluster_ts = np.clip(cf_cluster_ts, 0, 1) + + return best_bus_id, cf_cluster_ts, avg_cf_cluster + + +def aggregate_clusters( + df_features: pd.DataFrame, + clusters_dict: Dict[Tuple[str, str], np.ndarray], + ds: xr.Dataset, + config: Dict, + cache_clusters_path: str = None, +) -> Dict: + """Aggregate clusters and generate outputs.""" + logger.info("Aggregating clusters...") + + clustered_data = {} # {(region, tech, cluster_id): {capacity, cf_ts, avg_cf}} + cluster_metadata = {} + + for (region, tech), cluster_assignments in tqdm( + clusters_dict.items(), desc="Aggregating region-tech pairs", unit="pair" + ): + tech = str(tech) # Convert numpy.str_ to Python str + df_subset = df_features[ + (df_features["region"] == region) & (df_features["technology"] == tech) + ] + + n_clusters = len(np.unique(cluster_assignments)) + logger.info( + f" {region} {tech}: {len(df_subset)} buses -> {n_clusters} clusters" + ) + + for cluster_id in tqdm( + range(n_clusters), + desc=f" {region}-{tech} clusters", + unit="cluster", + leave=False, + ): + mask = cluster_assignments == cluster_id + df_cluster = df_subset[mask].reset_index(drop=True) + + if len(df_cluster) == 0: + continue + + # Get buses in cluster + bus_ids_in_cluster = list(df_cluster["bus_id"].values) + + # Sum capacities + capacities = [] + for bus_id in bus_ids_in_cluster: + try: + cap = float( + ds["p_nom_max"].sel(bus=bus_id, technology=str(tech)).values + ) + if not np.isnan(cap): + capacities.append(cap) + except Exception: + pass + + p_nom_max_cluster = float(np.sum(capacities)) + + if p_nom_max_cluster <= 0: + logger.debug( + f" Skipping cluster {region}/{tech}/{cluster_id}: no valid capacity" + ) + continue + + # Compute weighted-average timeseries + cf_weighted_parts = [] + for bus_id in bus_ids_in_cluster: + try: + cap = float( + ds["p_nom_max"].sel(bus=bus_id, technology=str(tech)).values + ) + cf = ( + ds["capacity_factor"] + .sel(bus=bus_id, technology=str(tech)) + .values + ) + if not np.isnan(cap) and cap > 0: + cf = np.nan_to_num(cf, nan=0.0) + cf_weighted_parts.append(cap * cf) + except Exception: + pass + + if cf_weighted_parts: + cf_weighted_ts = np.sum(cf_weighted_parts, axis=0) / p_nom_max_cluster + else: + cf_weighted_ts = np.zeros(8760) + + # Select representative bus + representative_id, cf_cluster_ts, avg_cf_cluster = ( + select_representative_bus(df_cluster, ds, str(tech), cf_weighted_ts) + ) + + # Cluster name: Regionname_tech_number + tech_name = "solar" if tech == "solar" else "onwind" + cluster_name = f"{region}_{tech_name}_{cluster_id}" + + clustered_data[(region, tech, cluster_id)] = { + "capacity": p_nom_max_cluster, + "cf_ts": cf_cluster_ts, + "avg_cf": avg_cf_cluster, + "cluster_name": cluster_name, + "n_buses": len(df_cluster), + "representative_bus": representative_id, + } + + cluster_metadata[cluster_name] = { + "region": region, + "technology": tech, + "cluster_id": cluster_id, + "n_buses_consolidated": len(df_cluster), + "representative_bus_id": representative_id, + "total_capacity_mw": p_nom_max_cluster, + "avg_cf": float(avg_cf_cluster), + } + + logger.info(f" Total clusters created: {len(clustered_data)}") + + # Cache aggregated clusters to disk for recovery + if cache_clusters_path: + logger.info(f"Caching aggregated clusters to {cache_clusters_path}") + # Save metadata (can't easily pickle clustered_data due to numpy arrays) + with open(cache_clusters_path, "w") as f: + json.dump( + { + "cluster_count": len(clustered_data), + "metadata": cluster_metadata, + "timestamp": pd.Timestamp.now().isoformat(), + }, + f, + indent=2, + ) + logger.info(" ✓ Cached clustering metadata") + + return clustered_data, cluster_metadata + + +def write_clustered_netcdf( + clustered_data: Dict, output_path: str, config: Dict +) -> None: + """Write clustered data to NetCDF with (region, technology, class, time) dims.""" + logger.info(f"Writing clustered data to {output_path}") + + # Organize data by region and technology + regions = list(set(k[0] for k in clustered_data.keys())) + techs = list(set(str(k[1]) for k in clustered_data.keys())) # Convert to string + + logger.info(f" Regions: {regions}") + logger.info(f" Technologies: {techs}") + + # Build xarray dataset + time = np.arange(8760) + + # Initialize data variables + capacity_data = {} + cf_data = {} + avg_cf_data = {} + + for region in regions: + capacity_data[region] = {} + cf_data[region] = {} + avg_cf_data[region] = {} + + for tech in techs: + # Collect all clusters for this region-tech + clusters_for_rt = [ + (cluster_id, clustered_data[(region, tech, cluster_id)]) + for (r, t, cluster_id) in clustered_data.keys() + if r == region and t == tech + ] + + n_clusters = len(clusters_for_rt) + if n_clusters == 0: + continue + + capacity_data[region][tech] = np.zeros(n_clusters) + cf_data[region][tech] = np.zeros((n_clusters, 8760)) + avg_cf_data[region][tech] = np.zeros(n_clusters) + + for idx, (cluster_id, cluster_info) in enumerate(clusters_for_rt): + capacity_data[region][tech][idx] = cluster_info["capacity"] + cf_data[region][tech][idx, :] = cluster_info["cf_ts"] + avg_cf_data[region][tech][idx] = cluster_info["avg_cf"] + + # Create xarray dataset with aligned dimensions + ds_out = xr.Dataset( + data_vars={}, + coords={ + "region": regions, + "technology": techs, + "time": time, + }, + ) + + # Add data variables with heterogeneous class dimension per region-tech + # Note: xarray doesn't support ragged dimensions natively, so we'll use max_classes + max_classes = max( + len([k for k in clustered_data.keys() if k[0] == r and str(k[1]) == t]) + for r in regions + for t in techs + ) + + capacity_all = np.full( + (len(regions), len(techs), max_classes), np.nan, dtype=np.float32 + ) + cf_all = np.full( + (len(regions), len(techs), max_classes, 8760), np.nan, dtype=np.float32 + ) + avg_cf_all = np.full( + (len(regions), len(techs), max_classes), np.nan, dtype=np.float32 + ) + + for region_idx, region in enumerate(regions): + for tech_idx, tech in enumerate(techs): + clusters_for_rt = [ + (cluster_id, clustered_data[(region, tech_orig, cluster_id)]) + for (r, tech_orig, cluster_id) in clustered_data.keys() + if r == region and str(tech_orig) == tech + ] + + for class_idx, (cluster_id, cluster_info) in enumerate(clusters_for_rt): + cf_ts = cluster_info["cf_ts"] + if isinstance(cf_ts, np.ndarray): + # Ensure cf_ts is 1D + if cf_ts.ndim != 1: + logger.warning( + f" cf_ts has wrong shape {cf_ts.shape}, taking first row" + ) + cf_ts = cf_ts[0] if cf_ts.ndim > 1 else cf_ts + if len(cf_ts) == 8760: + cf_all[region_idx, tech_idx, class_idx, :] = cf_ts.astype( + np.float32 + ) + + capacity_all[region_idx, tech_idx, class_idx] = float( + cluster_info["capacity"] + ) + avg_cf_all[region_idx, tech_idx, class_idx] = float( + cluster_info["avg_cf"] + ) + + ds_out["capacity"] = (("region", "technology", "class"), capacity_all) + ds_out["capacity_factor"] = (("region", "technology", "class", "time"), cf_all) + ds_out["avg_cf"] = (("region", "technology", "class"), avg_cf_all) + + # Add metadata attributes + ds_out.attrs["clustering_algorithm"] = "weighted_kmeans" + ds_out.attrs["temporal_aware"] = "True" + ds_out.attrs["feature_weights"] = json.dumps( + {"avg_cf": 0.5, "temporal": 0.3, "geospatial": 0.2} + ) + + ds_out.to_netcdf( + output_path, + encoding={ + "capacity": {"dtype": "float32"}, + "capacity_factor": {"dtype": "float32"}, + "avg_cf": {"dtype": "float32"}, + }, + ) + + logger.info(f" ✓ Wrote {output_path}") + logger.info(f" Dimensions: {dict(ds_out.dims)}") + logger.info(f" Variables: {list(ds_out.data_vars)}") + + +def validate_clustering( + clustered_data: Dict, ds_merged: xr.Dataset, output_report: str +) -> Dict: + """Validate clustering integrity.""" + logger.info("Validating clustering...") + + validation_results = { + "total_clusters": len(clustered_data), + "capacity_preservation": {}, + "avg_cf_consistency": {}, + "temporal_quality": {}, + } + + for (region, tech), cluster_assignments in clustered_data.items(): + # Total capacity check + original_cap = [] + for bus_id in ds_merged.bus.values: + try: + cap = float( + ds_merged["p_nom_max"].sel(bus=bus_id, technology=tech).values + ) + if not np.isnan(cap): + original_cap.append(cap) + except Exception: + pass + + original_total = np.sum(original_cap) + clustered_total = sum( + clustered_data.get((region, tech, cluster_id), {}).get("capacity", 0) + for cluster_id in range(len(cluster_assignments)) + ) + + if original_total > 0: + preservation_pct = (clustered_total / original_total) * 100 + else: + preservation_pct = 100.0 + + validation_results["capacity_preservation"][f"{region}_{tech}"] = { + "original_mw": float(original_total), + "clustered_mw": float(clustered_total), + "preservation_pct": float(preservation_pct), + } + + logger.info( + f" Capacity preservation: {np.mean([v['preservation_pct'] for v in validation_results['capacity_preservation'].values()]):.1f}%" + ) + + # Write validation report + report = { + "timestamp": pd.Timestamp.now().isoformat(), + "validation_results": validation_results, + "total_clusters": len(clustered_data), + } + + with open(output_report, "w") as f: + json.dump(report, f, indent=2) + + logger.info(f" ✓ Wrote validation report to {output_report}") + + return validation_results + + +def main(): + """Main clustering pipeline.""" + logger.info("=" * 70) + logger.info("CLUSTERING RENEWABLE PROFILES FOR OPTIMIZATION") + logger.info("=" * 70) + + # Load config + config = snakemake.config + logger.info(f"Clustering config: {config.get('clustering', {})}") + + clustering_config = config.get("clustering", {}) + n_clusters_onwind = clustering_config.get("n_clusters_onwind", 40) + n_clusters_pvplant = clustering_config.get("n_clusters_pvplant", 40) + feature_weights = clustering_config.get( + "feature_weights", + { + "avg_cf": 0.5, + "temporal": 0.3, + "geospatial": 0.2, + }, + ) + + # Normalize weights + weight_sum = sum(feature_weights.values()) + feature_weights = {k: v / weight_sum for k, v in feature_weights.items()} + logger.info(f"Normalized feature weights: {feature_weights}") + + # Load and filter data + ds = load_merged_data(str(snakemake.input.merged)) + ds_filtered = filter_to_onwind_pv(ds) + + # Extract features (with caching) + cache_features = Path("resources") / "features_cache.csv" + df_features, region_buses = extract_features( + ds_filtered, config, str(cache_features) + ) + + # Cluster each region-technology group in parallel + logger.info(f"Starting parallel clustering with {snakemake.threads} threads") + + # Build list of (region, tech) pairs to cluster + region_tech_pairs = [ + (region, tech) + for region in region_buses.keys() + for tech in ds_filtered.technology.values + if len( + df_features[ + (df_features["region"] == region) & (df_features["technology"] == tech) + ] + ) + > 0 + ] + + logger.info(f" Total region-technology pairs: {len(region_tech_pairs)}") + + # Create clustering cache directory + clustering_cache_dir = Path("resources") / "clustering_cache" + clustering_cache_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Clustering cache directory: {clustering_cache_dir}") + + # Parallelize clustering across all region-tech pairs with tqdm progress + with tqdm_joblib( + tqdm( + total=len(region_tech_pairs), + desc="Clustering region-tech pairs", + unit="pair", + ) + ) as pbar: + results = joblib.Parallel(n_jobs=snakemake.threads)( + joblib.delayed(cluster_region_technology_pair)( + region, + tech, + df_features, + n_clusters_onwind, + n_clusters_pvplant, + feature_weights, + str(clustering_cache_dir), + ) + for region, tech in tqdm( + region_tech_pairs, + desc="Queuing clustering tasks", + unit="pair", + leave=False, + ) + ) + + # Convert results to dict + clusters_dict = dict(results) + + # Aggregate clusters (with caching) + cache_clusters = Path("resources") / "clusters_cache.json" + clustered_data, cluster_metadata = aggregate_clusters( + df_features, clusters_dict, ds_filtered, config, str(cache_clusters) + ) + + # Write output + write_clustered_netcdf(clustered_data, str(snakemake.output.clustered), config) + + # Validation + validate_clustering(clustered_data, ds_filtered, str(snakemake.output.report)) + + logger.info("=" * 70) + logger.info("CLUSTERING COMPLETE") + logger.info("=" * 70) + + +if __name__ == "__main__": + main() From cf88446cb3224c0e17a9c50f8ed972d710622601 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 29 May 2026 11:54:55 +0200 Subject: [PATCH 121/216] feat: finish clustering and add plotting --- config/config.yaml | 42 +- pixi.toml | 2 + ...renewables_consolidated_vs_clustered.ipynb | 1033 +++++++++++++++++ workflow/scripts/cluster_renewables.py | 99 +- 4 files changed, 1133 insertions(+), 43 deletions(-) create mode 100644 workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb diff --git a/config/config.yaml b/config/config.yaml index 00b1a18..9028035 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -168,31 +168,23 @@ regions: "East_East_Asia": ["JPN","KOR","PRK"] "Oceania": ["AUS","NZL"] -# countries: [ -# "BI","KM","DJ","ER","ET","KE","MG","MW","MU","MZ","RW","SC","SO","SS","TZ","UG","ZM","ZW", # Africa — Eastern Africa -# "AO","CM","CF","TD","CG","CD","GQ","GA","ST", # Africa — Middle Africa -# "DZ","EG","LY","MA","SD","TN", # Africa — Northern Africa -# "BW","SZ","LS","NA","ZA", # Africa — Southern Africa -# "BJ","BF","CV","CI","GM","GH","GN","GW","LR","ML","MR","NE","NG","SN","SL","TG", # Africa — Western Africa -# "CA","US", # Americas — Northern America -# "AG","BS","BB","CU","DM","DO","GD","HT","JM","KN","LC","VC","TT", # Americas — Caribbean -# "BZ","CR","SV","GT","HN","MX","NI","PA", # Americas — Central America -# "AR","BO","BR","CL","CO","EC","GY","PY","PE","SR","UY","VE", # Americas — South America -# "KZ","KG","TJ","TM","UZ", # Asia — Central Asia -# "CN","JP","KP","KR","MN","TW", # Asia — Eastern Asia -# "BN","KH","ID","LA","MY","MM","PH","SG","TH","TL","VN", # Asia — South-eastern Asia -# "AF","BD","BT","IN","IR","MV","NP","PK","LK", # Asia — Southern Asia -# "AM","AZ","BH","CY","GE","IQ","IL","JO","KW","LB","OM","QA","SA","PS","SY","TR","AE","YE", # Asia — Western Asia -# "BY","BG","CZ","HU","MD","PL","RO","RU","SK","UA", # Europe — Eastern Europe -# "DK","EE","FI","IS","IE","LV","LT","NO","SE","GB", # Europe — Northern Europe -# "AL","AD","BA","HR","GR","IT","MT","ME","MK","PT","SM","RS","SI","ES","VA", # Europe — Southern Europe -# "AT","BE","FR","DE","LI","LU","MC","NL","CH", # Europe — Western Europe -# "AU","NZ", # Oceania — Australia and New Zealand -# "FJ","PG","SB","VU", # Oceania — Melanesia -# "FM","KI","MH","NR","PW", # Oceania — Micronesia -# "TO","TV","WS" # Oceania — Polynesia -# ] - +# Renewable generator clustering configuration +# Used by cluster_renewables.py to cluster 122k+ individual renewable buses +# into manageable pseudo-buses for optimization tractability +clustering: + # Number of pseudo-buses per technology per region + n_clusters_onwind: 40 # Onshore wind clusters + n_clusters_pvplant: 40 # Solar/PV clusters + + # Feature weighting for K-means clustering + # Determines how heavily clustering emphasizes each aspect: + # - avg_cf (0.5): Merit-order / capacity factor quality (primary driver) + # - temporal (0.3): Temporal patterns (std, cv, autocorr_24h) + # - geospatial (0.2): Geographic proximity (lat, lon) + feature_weights: + avg_cf: 0.5 # High CF sites group together for optimization value + temporal: 0.3 # Similar generation patterns cluster (wind/solar separation + peakiness) + geospatial: 0.2 # Loose geographic coherence (secondary constraint) design: cost_penalty: diff --git a/pixi.toml b/pixi.toml index 5228641..72db9bc 100644 --- a/pixi.toml +++ b/pixi.toml @@ -15,6 +15,7 @@ geojson = ">=3.2.0" geohash2 = "*" geopandas = ">=1" geopy = ">=2.4.1" +joblib = ">=1.3.0" jupyter = ">=1.0" libgdal-netcdf = ">=3.10.3" linopy = ">=0.6.1" @@ -28,6 +29,7 @@ pypsa = ">=1.1.2" python = ">=3.10" pyyaml = "*" scipy = ">=1.16.3" +scikit-learn = ">=1.0" seaborn = ">=0.13.2" searoute = ">=1.5.0" shapely = ">=2.1.2,<3" diff --git a/workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb b/workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb new file mode 100644 index 0000000..ed9dee7 --- /dev/null +++ b/workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb @@ -0,0 +1,1033 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8a1e585", + "metadata": {}, + "source": [ + "# Compare Consolidated vs Clustered Renewable Datasets\n", + "\n", + "**Objective:** Compare the old consolidated renewable data structure with the new clustered approach.\n", + "\n", + "- **Consolidated:** Individual renewable sites flattened into `(region, site_id, time)` where `site_id` encodes technology + class\n", + "- **Clustered:** K-means aggregated pseudo-generators with explicit dimensions `(region, technology, class, time)`\n", + "\n", + "**Key Comparisons:**\n", + "1. Capacity preservation: total MW before/after clustering\n", + "2. Merit-order scatter plots: avg_cf vs p_nom_max\n", + "3. Timeseries preservation: representative timeseries quality\n", + "4. Regional capacity distribution by technology\n", + "5. Cluster quality metrics: size distribution, temporal patterns" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9e22644b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consolidated: ✓ ..\\..\\data\\new_renewables_consolidated.nc\n", + "Clustered: ✗ ..\\..\\resources\\renewables_clustered.nc\n", + "Metadata: ✓ ..\\..\\resources\\clusters_cache.json\n", + "\n", + "📁 Consolidated: new_renewables_consolidated.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", + "Cannot find the ecCodes library\n", + " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Dimensions: {'region': 15, 'technology': 3, 'class': 39, 'time': 8760}\n", + " Variables: ['capacity', 'capacity_factor']\n", + "\n", + "📋 Loaded cluster metadata rows: 1,160\n", + " region technology cluster_id n_buses_consolidated total_capacity_mw avg_cf\n", + "North_West_Africa onwind 0 49 6.607149e+04 0.049405\n", + "North_West_Africa onwind 1 59 4.165129e+06 0.304651\n", + "North_West_Africa onwind 2 59 3.562013e+05 0.032658\n", + "North_West_Africa onwind 3 66 1.048077e+05 0.217946\n", + "North_West_Africa onwind 4 49 8.892041e+05 0.075501\n" + ] + } + ], + "source": [ + "import xarray as xr\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "import logging\n", + "import json\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "logging.basicConfig(level=logging.INFO, format=\"%(message)s\")\n", + "\n", + "BASE_DIR = Path(\"../..\")\n", + "CONSOLIDATED_FILE = BASE_DIR / \"data/new_renewables_consolidated.nc\"\n", + "CLUSTERED_FILE = BASE_DIR / \"resources/renewables_clustered.nc\"\n", + "CLUSTER_META_FILE = BASE_DIR / \"resources/clusters_cache.json\"\n", + "\n", + "print(f\"Consolidated: {'✓' if CONSOLIDATED_FILE.exists() else '✗'} {CONSOLIDATED_FILE}\")\n", + "print(f\"Clustered: {'✓' if CLUSTERED_FILE.exists() else '✗'} {CLUSTERED_FILE}\")\n", + "print(f\"Metadata: {'✓' if CLUSTER_META_FILE.exists() else '✗'} {CLUSTER_META_FILE}\")\n", + "\n", + "ds_clustered = None\n", + "df_cluster_meta = None\n", + "clustered_source = None\n", + "\n", + "if CLUSTERED_FILE.exists():\n", + " try:\n", + " ds_clustered = xr.open_dataset(CLUSTERED_FILE)\n", + " clustered_source = \"dataset\"\n", + " except Exception as e:\n", + " print(f\"⚠️ Could not open clustered dataset: {e}\")\n", + "\n", + "if ds_clustered is None and CLUSTER_META_FILE.exists():\n", + " with open(CLUSTER_META_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " cache_payload = json.load(f)\n", + " meta_rows = []\n", + " for cluster_name, info in cache_payload.get(\"metadata\", {}).items():\n", + " row = dict(info)\n", + " row[\"cluster_name\"] = cluster_name\n", + " meta_rows.append(row)\n", + " df_cluster_meta = pd.DataFrame(meta_rows)\n", + " if not df_cluster_meta.empty:\n", + " df_cluster_meta[\"technology\"] = df_cluster_meta[\"technology\"].astype(str)\n", + " df_cluster_meta[\"region\"] = df_cluster_meta[\"region\"].astype(str)\n", + " clustered_source = \"metadata\"\n", + "elif ds_clustered is not None:\n", + " print(f\"\\n📁 Clustered: {CLUSTERED_FILE.name}\")\n", + " print(f\" Dimensions: {dict(ds_clustered.sizes)}\")\n", + " print(f\" Variables: {list(ds_clustered.data_vars)}\")\n", + "else:\n", + " print(\"\\n⚠️ No clustered dataset or metadata cache found\")\n", + "\n", + "consolidated_exists = CONSOLIDATED_FILE.exists()\n", + "if consolidated_exists:\n", + " print(f\"\\n📁 Consolidated: {CONSOLIDATED_FILE.name}\")\n", + " ds_consolidated = xr.open_dataset(CONSOLIDATED_FILE)\n", + " print(f\" Dimensions: {dict(ds_consolidated.sizes)}\")\n", + " print(f\" Variables: {list(ds_consolidated.data_vars)}\")\n", + "else:\n", + " print(\"\\n⚠️ Consolidated file not found\")\n", + " ds_consolidated = None\n", + "\n", + "if df_cluster_meta is not None:\n", + " print(f\"\\n📋 Loaded cluster metadata rows: {len(df_cluster_meta):,}\")\n", + " print(\n", + " df_cluster_meta[\n", + " [\n", + " \"region\",\n", + " \"technology\",\n", + " \"cluster_id\",\n", + " \"n_buses_consolidated\",\n", + " \"total_capacity_mw\",\n", + " \"avg_cf\",\n", + " ]\n", + " ]\n", + " .head()\n", + " .to_string(index=False)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "03a80b1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "CONSOLIDATED DATASET DIAGNOSTICS\n", + "======================================================================\n", + "Dimensions: {'region': 15, 'technology': 3, 'class': 39, 'time': 8760}\n", + "Coords: ['region', 'technology', 'class', 'time']\n", + "Region sample: ['Central_America', 'East_Asia', 'East_East_Asia', 'Eurasia', 'Europe']\n", + "Technology values: ['windoffshore', 'pvplant', 'windonshore']\n", + "onwind: selection failed -> \"not all values found in index 'technology'. Try setting the `method` keyword argument (example: method='nearest').\"\n", + "solar: selection failed -> \"not all values found in index 'technology'. Try setting the `method` keyword argument (example: method='nearest').\"\n", + "Capacity variable dims: ('region', 'technology', 'class')\n", + "Capacity variable shape: (15, 3, 39)\n" + ] + } + ], + "source": [ + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"CONSOLIDATED DATASET DIAGNOSTICS\")\n", + "print(\"=\" * 70)\n", + "\n", + "print(\"Dimensions:\", dict(ds_consolidated.sizes))\n", + "print(\"Coords:\", list(ds_consolidated.coords))\n", + "print(\"Region sample:\", [str(v) for v in ds_consolidated.region.values[:5]])\n", + "print(\"Technology values:\", [str(v) for v in ds_consolidated.technology.values])\n", + "\n", + "for tech_name in [\"onwind\", \"solar\"]:\n", + " try:\n", + " cap_values = ds_consolidated[\"capacity\"].sel(technology=tech_name).values\n", + " print(\n", + " f\"{tech_name}: selection shape {cap_values.shape}, total {float(np.nansum(cap_values)):.1f} MW\"\n", + " )\n", + " except Exception as e:\n", + " print(f\"{tech_name}: selection failed -> {e}\")\n", + "\n", + "print(\"Capacity variable dims:\", ds_consolidated[\"capacity\"].dims)\n", + "print(\"Capacity variable shape:\", ds_consolidated[\"capacity\"].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4973ebf4", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"=\" * 70)\n", + "print(\"LOADING RENEWABLE DATA\")\n", + "print(\"=\" * 70)\n", + "\n", + "print(f\"\\n📁 Clustered: {CLUSTERED_FILE.name}\")\n", + "ds_clustered = xr.open_dataset(CLUSTERED_FILE)\n", + "print(f\" Dimensions: {dict(ds_clustered.sizes)}\")\n", + "print(f\" Variables: {list(ds_clustered.data_vars)}\")\n", + "\n", + "consolidated_exists = CONSOLIDATED_FILE.exists()\n", + "if consolidated_exists:\n", + " print(f\"\\n📁 Consolidated: {CONSOLIDATED_FILE.name}\")\n", + " ds_consolidated = xr.open_dataset(CONSOLIDATED_FILE)\n", + " print(f\" Dimensions: {dict(ds_consolidated.sizes)}\")\n", + " print(f\" Variables: {list(ds_consolidated.data_vars)}\")\n", + "else:\n", + " print(\"\\n⚠️ Consolidated file not found\")\n", + " ds_consolidated = None" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13cbb7c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "CLUSTER-LEVEL COMPARISON DATA\n", + "======================================================================\n", + "Cluster-level comparison summary:\n", + "\n", + " source technology n_clusters total_capacity_mw mean_avg_cf\n", + "Consolidated solar 157 1.421861e+08 0.197379\n", + "Consolidated onwind 271 4.303580e+08 0.353087\n", + " Clustered solar 577 3.067626e+08 0.146385\n", + " Clustered onwind 583 2.633419e+08 0.169173\n", + "\n", + "Consolidated cluster rows: 428\n", + "Clustered cluster rows: 1160\n", + "\n", + "Sample consolidated rows:\n", + " source region technology cluster_id cluster_label capacity_mw avg_cf\n", + "Consolidated Central_America onwind 0 onwind 1 3.008679e+02 0.000000\n", + "Consolidated Central_America onwind 20 onwind 21 1.269267e+02 0.007015\n", + "Consolidated Central_America onwind 21 onwind 22 3.301898e+04 0.022544\n", + "Consolidated Central_America onwind 22 onwind 23 3.193277e+05 0.049610\n", + "Consolidated Central_America onwind 23 onwind 24 1.032047e+06 0.085307\n", + "\n", + "Sample clustered rows:\n", + " source region technology cluster_id cluster_label capacity_mw avg_cf\n", + "Clustered North_West_Africa onwind 0 onwind 1 6.607149e+04 0.049405\n", + "Clustered North_West_Africa onwind 1 onwind 2 4.165129e+06 0.304651\n", + "Clustered North_West_Africa onwind 2 onwind 3 3.562013e+05 0.032658\n", + "Clustered North_West_Africa onwind 3 onwind 4 1.048077e+05 0.217946\n", + "Clustered North_West_Africa onwind 4 onwind 5 8.892041e+05 0.075501\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_22260\\854233552.py:97: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " comparison_df.groupby(['source', 'technology'], as_index=False)\n" + ] + } + ], + "source": [ + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"CLUSTER-LEVEL COMPARISON DATA\")\n", + "print(\"=\" * 70)\n", + "\n", + "techs_to_compare = [\"onwind\", \"solar\"]\n", + "consolidated_tech_map = {\n", + " \"windonshore\": \"onwind\",\n", + " \"pvplant\": \"solar\",\n", + "}\n", + "\n", + "\n", + "def build_cluster_rows_from_consolidated(ds):\n", + " rows = []\n", + " for consolidated_tech_name, compare_tech_name in consolidated_tech_map.items():\n", + " try:\n", + " cap_da = ds[\"capacity\"].sel(technology=consolidated_tech_name)\n", + " cf_da = ds[\"capacity_factor\"].sel(technology=consolidated_tech_name)\n", + " avg_cf_da = cf_da.mean(dim=\"time\", skipna=True)\n", + "\n", + " for region_name in ds.region.values:\n", + " region_caps = cap_da.sel(region=region_name).values\n", + " region_avg_cf = avg_cf_da.sel(region=region_name).values\n", + " for class_idx in range(len(region_caps)):\n", + " capacity_mw = float(region_caps[class_idx])\n", + " avg_cf = float(region_avg_cf[class_idx])\n", + " if np.isnan(capacity_mw) or np.isnan(avg_cf):\n", + " continue\n", + " rows.append(\n", + " {\n", + " \"source\": \"Consolidated\",\n", + " \"region\": str(region_name),\n", + " \"technology\": str(compare_tech_name),\n", + " \"cluster_id\": int(class_idx),\n", + " \"cluster_label\": f\"{compare_tech_name} {class_idx + 1}\",\n", + " \"capacity_mw\": capacity_mw,\n", + " \"avg_cf\": avg_cf,\n", + " }\n", + " )\n", + " except Exception as e:\n", + " print(\n", + " f\" Could not read consolidated clusters for {consolidated_tech_name}: {e}\"\n", + " )\n", + " return rows\n", + "\n", + "\n", + "def build_cluster_rows_from_metadata(df_meta):\n", + " rows = []\n", + " subset = df_meta[df_meta[\"technology\"].isin(techs_to_compare)].copy()\n", + " for _, row in subset.iterrows():\n", + " rows.append(\n", + " {\n", + " \"source\": \"Clustered\",\n", + " \"region\": str(row[\"region\"]),\n", + " \"technology\": str(row[\"technology\"]),\n", + " \"cluster_id\": int(row[\"cluster_id\"]),\n", + " \"cluster_label\": f\"{row['technology']} {int(row['cluster_id']) + 1}\",\n", + " \"capacity_mw\": float(row[\"total_capacity_mw\"]),\n", + " \"avg_cf\": float(row[\"avg_cf\"]),\n", + " }\n", + " )\n", + " return rows\n", + "\n", + "\n", + "consolidated_rows = (\n", + " build_cluster_rows_from_consolidated(ds_consolidated)\n", + " if consolidated_exists and ds_consolidated is not None\n", + " else []\n", + ")\n", + "if df_cluster_meta is not None:\n", + " clustered_rows = build_cluster_rows_from_metadata(df_cluster_meta)\n", + "elif ds_clustered is not None:\n", + " clustered_rows = []\n", + " for tech_name in techs_to_compare:\n", + " try:\n", + " cap_da = ds_clustered[\"capacity\"].sel(technology=tech_name)\n", + " avg_cf_da = ds_clustered[\"avg_cf\"].sel(technology=tech_name)\n", + " for region_name in ds_clustered.region.values:\n", + " region_caps = cap_da.sel(region=region_name).values\n", + " region_avg_cf = avg_cf_da.sel(region=region_name).values\n", + " for class_idx in range(len(region_caps)):\n", + " capacity_mw = float(region_caps[class_idx])\n", + " avg_cf = float(region_avg_cf[class_idx])\n", + " if np.isnan(capacity_mw) or np.isnan(avg_cf):\n", + " continue\n", + " clustered_rows.append(\n", + " {\n", + " \"source\": \"Clustered\",\n", + " \"region\": str(region_name),\n", + " \"technology\": str(tech_name),\n", + " \"cluster_id\": int(class_idx),\n", + " \"cluster_label\": f\"{tech_name} {class_idx + 1}\",\n", + " \"capacity_mw\": capacity_mw,\n", + " \"avg_cf\": avg_cf,\n", + " }\n", + " )\n", + " except Exception as e:\n", + " print(f\" Could not read clustered dataset for {tech_name}: {e}\")\n", + "else:\n", + " clustered_rows = []\n", + "\n", + "comparison_df = pd.concat(\n", + " [pd.DataFrame(consolidated_rows), pd.DataFrame(clustered_rows)], ignore_index=True\n", + ")\n", + "\n", + "if comparison_df.empty:\n", + " raise ValueError(\"No cluster-level data found.\")\n", + "\n", + "comparison_df[\"source\"] = pd.Categorical(\n", + " comparison_df[\"source\"], categories=[\"Consolidated\", \"Clustered\"], ordered=True\n", + ")\n", + "comparison_df[\"technology\"] = pd.Categorical(\n", + " comparison_df[\"technology\"], categories=[\"solar\", \"onwind\"], ordered=True\n", + ")\n", + "comparison_df[\"region\"] = comparison_df[\"region\"].astype(str)\n", + "comparison_df[\"avg_cf\"] = comparison_df[\"avg_cf\"].astype(float)\n", + "\n", + "comparison_summary_df = (\n", + " comparison_df.groupby([\"source\", \"technology\"], as_index=False)\n", + " .agg(\n", + " n_clusters=(\"cluster_id\", \"size\"),\n", + " total_capacity_mw=(\"capacity_mw\", \"sum\"),\n", + " mean_avg_cf=(\"avg_cf\", \"mean\"),\n", + " )\n", + " .sort_values([\"source\", \"technology\"])\n", + ")\n", + "\n", + "print(\"Cluster-level comparison summary:\")\n", + "print(\"\\n\" + comparison_summary_df.to_string(index=False))\n", + "\n", + "print(\"\\nConsolidated cluster rows:\", len(consolidated_rows))\n", + "print(\"Clustered cluster rows: \", len(clustered_rows))\n", + "print(\"\\nSample consolidated rows:\")\n", + "print(pd.DataFrame(consolidated_rows).head().to_string(index=False))\n", + "print(\"\\nSample clustered rows:\")\n", + "print(pd.DataFrame(clustered_rows).head().to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "befd027b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "2. CLUSTERING STATISTICS\n", + "======================================================================\n", + "\n", + "Total clusters: 1,160\n", + "\n", + "Capacity (MW):\n", + " Min: 43.0\n", + " Max: 20293553.9\n", + " Mean: 491469.4\n", + " Median: 184945.5\n", + " Total: 570104531.2\n", + "\n", + "Average Capacity Factor:\n", + " Min: 0.0000\n", + " Max: 0.6988\n", + " Mean: 0.1578\n", + " Median: 0.1438\n" + ] + } + ], + "source": [ + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"2. CLUSTERING STATISTICS\")\n", + "print(\"=\" * 70)\n", + "\n", + "if df_cluster_meta is not None:\n", + " cap_valid = df_cluster_meta[\"total_capacity_mw\"].dropna().to_numpy()\n", + " avg_cf_valid = df_cluster_meta[\"avg_cf\"].dropna().to_numpy()\n", + " n_total = len(df_cluster_meta)\n", + "else:\n", + " cap_all = ds_clustered[\"capacity\"].values.flatten()\n", + " cap_valid = cap_all[~np.isnan(cap_all)]\n", + "\n", + " avg_cf_all = ds_clustered[\"avg_cf\"].values.flatten()\n", + " avg_cf_valid = avg_cf_all[~np.isnan(avg_cf_all)]\n", + "\n", + " n_total = len(cap_valid)\n", + "\n", + "print(f\"\\nTotal clusters: {n_total:,}\")\n", + "print(\"\\nCapacity (MW):\")\n", + "print(f\" Min: {np.min(cap_valid):>12.1f}\")\n", + "print(f\" Max: {np.max(cap_valid):>12.1f}\")\n", + "print(f\" Mean: {np.mean(cap_valid):>12.1f}\")\n", + "print(f\" Median: {np.median(cap_valid):>12.1f}\")\n", + "print(f\" Total: {np.sum(cap_valid):>12.1f}\")\n", + "\n", + "print(\"\\nAverage Capacity Factor:\")\n", + "print(f\" Min: {np.min(avg_cf_valid):>12.4f}\")\n", + "print(f\" Max: {np.max(avg_cf_valid):>12.4f}\")\n", + "print(f\" Mean: {np.mean(avg_cf_valid):>12.4f}\")\n", + "print(f\" Median: {np.median(avg_cf_valid):>12.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c26731e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Generating Europe scatter plots with Seaborn...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_22260\\4113124811.py:7: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " plot_df['cluster_order_key'] = plot_df.groupby(['source', 'technology']).cumcount()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved europe_cluster_scatter_comparison.png\n" + ] + } + ], + "source": [ + "print(\"\\nGenerating scatter plot...\")\n", + "\n", + "region = (\n", + " cluster_regions[0]\n", + " if df_cluster_meta is not None\n", + " else list(ds_clustered.region.values)[0]\n", + ")\n", + "tech = (\n", + " cluster_techs[0]\n", + " if df_cluster_meta is not None\n", + " else list(ds_clustered.technology.values)[0]\n", + ")\n", + "print(f\"Example: {region} - {tech}\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "if consolidated_exists:\n", + " try:\n", + " consol_cap = (\n", + " ds_consolidated[\"capacity\"].sel(region=region, technology=tech).values\n", + " )\n", + " if \"avg_cf\" in ds_consolidated.data_vars:\n", + " consol_avg_cf = (\n", + " ds_consolidated[\"avg_cf\"].sel(region=region, technology=tech).values\n", + " )\n", + " else:\n", + " cf_ts = (\n", + " ds_consolidated[\"capacity_factor\"]\n", + " .sel(region=region, technology=tech)\n", + " .values\n", + " )\n", + " consol_avg_cf = np.nanmean(cf_ts, axis=-1)\n", + "\n", + " valid = ~(np.isnan(consol_cap) | np.isnan(consol_avg_cf))\n", + " axes[0].scatter(\n", + " consol_avg_cf[valid], consol_cap[valid], alpha=0.4, s=30, c=\"steelblue\"\n", + " )\n", + " axes[0].text(\n", + " 0.02,\n", + " 0.98,\n", + " f\"n={sum(valid)}\",\n", + " transform=axes[0].transAxes,\n", + " va=\"top\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n", + " )\n", + " except Exception as e:\n", + " axes[0].text(\n", + " 0.5,\n", + " 0.5,\n", + " f\"Data not available: {e}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " transform=axes[0].transAxes,\n", + " )\n", + "else:\n", + " axes[0].text(\n", + " 0.5,\n", + " 0.5,\n", + " \"Consolidated file not available\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " transform=axes[0].transAxes,\n", + " )\n", + "\n", + "axes[0].set_xlabel(\"Average Capacity Factor\", fontsize=11)\n", + "axes[0].set_ylabel(\"P_nom_max (MW)\", fontsize=11)\n", + "axes[0].set_title(\n", + " f\"Consolidated: {region} - {tech}\\n(Individual Sites)\",\n", + " fontsize=12,\n", + " fontweight=\"bold\",\n", + ")\n", + "axes[0].set_yscale(\"log\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "try:\n", + " if df_cluster_meta is not None:\n", + " meta_sel = df_cluster_meta[\n", + " (df_cluster_meta[\"region\"] == region)\n", + " & (df_cluster_meta[\"technology\"] == tech)\n", + " ]\n", + " cluster_cap = meta_sel[\"total_capacity_mw\"].to_numpy()\n", + " cluster_avg_cf = meta_sel[\"avg_cf\"].to_numpy()\n", + " else:\n", + " cluster_cap = (\n", + " ds_clustered[\"capacity\"].sel(region=region, technology=tech).values\n", + " )\n", + " cluster_avg_cf = (\n", + " ds_clustered[\"avg_cf\"].sel(region=region, technology=tech).values\n", + " )\n", + " valid = ~(np.isnan(cluster_cap) | np.isnan(cluster_avg_cf))\n", + " axes[1].scatter(\n", + " cluster_avg_cf[valid],\n", + " cluster_cap[valid],\n", + " alpha=0.7,\n", + " s=120,\n", + " c=\"coral\",\n", + " edgecolor=\"darkred\",\n", + " )\n", + " axes[1].text(\n", + " 0.98,\n", + " 0.05,\n", + " f\"n_clusters={sum(valid)}\",\n", + " transform=axes[1].transAxes,\n", + " ha=\"right\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n", + " )\n", + "except Exception as e:\n", + " axes[1].text(\n", + " 0.5, 0.5, f\"Error: {e}\", ha=\"center\", va=\"center\", transform=axes[1].transAxes\n", + " )\n", + "\n", + "axes[1].set_xlabel(\"Average Capacity Factor (Representative)\", fontsize=11)\n", + "axes[1].set_ylabel(\"Cluster P_nom_max (MW)\", fontsize=11)\n", + "axes[1].set_title(\n", + " f\"Clustered: {region} - {tech}\\n(Pseudo-generators)\", fontsize=12, fontweight=\"bold\"\n", + ")\n", + "axes[1].set_yscale(\"log\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"scatter_comparison.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"✓ Saved scatter_comparison.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf86056b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Generating timeseries plot...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved timeseries_clustered.png\n" + ] + } + ], + "source": [ + "print(\"\\nGenerating timeseries plot...\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "\n", + "if df_cluster_meta is not None:\n", + " ax.text(\n", + " 0.5,\n", + " 0.5,\n", + " \"Timeseries plot unavailable from metadata-only cache\\nRe-run with clustered NetCDF to enable this plot\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " transform=ax.transAxes,\n", + " )\n", + " ax.set_axis_off()\n", + "else:\n", + " try:\n", + " cf = ds_clustered[\"capacity_factor\"].sel(region=region, technology=tech).values\n", + " cap = ds_clustered[\"capacity\"].sel(region=region, technology=tech).values\n", + " max_cap = np.nanmax(cap)\n", + " n_plotted = 0\n", + "\n", + " for cf_ts, c in zip(cf, cap):\n", + " if not np.isnan(c) and not np.all(np.isnan(cf_ts)):\n", + " cf_clean = np.nan_to_num(cf_ts, nan=0.0)\n", + " lw = 0.5 + 3.5 * (c / max_cap) if max_cap > 0 else 1\n", + " ax.plot(cf_clean, linewidth=lw, alpha=0.65)\n", + " n_plotted += 1\n", + "\n", + " ax.set_xlabel(\"Hour of Year\", fontsize=11)\n", + " ax.set_ylabel(\"Capacity Factor\", fontsize=11)\n", + " ax.set_title(\n", + " f\"Clustered Timeseries: {region} - {tech}\\n(Line width ∝ capacity, n_clusters={n_plotted})\",\n", + " fontsize=12,\n", + " fontweight=\"bold\",\n", + " )\n", + " ax.set_ylim(-0.02, 1.05)\n", + " ax.grid(True, alpha=0.3)\n", + " except Exception as e:\n", + " ax.text(\n", + " 0.5, 0.5, f\"Error: {e}\", ha=\"center\", va=\"center\", transform=ax.transAxes\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"timeseries_clustered.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"✓ Saved timeseries_clustered.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "bae711fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Generating stacked region comparison by technology...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved region_stacked_capacity_comparison.png\n" + ] + } + ], + "source": [ + "print(\"\\nGenerating stacked region comparison by technology...\")\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "tech_palette = {\"solar\": \"#D55C5C\", \"onwind\": \"#4C78A8\"}\n", + "source_offsets = {\"Consolidated\": -0.18, \"Clustered\": 0.18}\n", + "tech_width = 0.24\n", + "tech_order = [\"solar\", \"onwind\"]\n", + "source_order = [\"Consolidated\", \"Clustered\"]\n", + "\n", + "\n", + "def blend_with_white(color, intensity):\n", + " base = np.array(sns.color_palette([color])[0])\n", + " intensity = float(np.clip(intensity, 0.0, 1.0))\n", + " return tuple(base * intensity + np.array([1.0, 1.0, 1.0]) * (1.0 - intensity))\n", + "\n", + "\n", + "def draw_tech_panel(ax, df, tech_name, title):\n", + " regions = sorted(df[\"region\"].unique())\n", + " x_positions = np.arange(len(regions)) * 1.35\n", + "\n", + " for region_idx, region_name in enumerate(regions):\n", + " for source_name in source_order:\n", + " subset = df[\n", + " (df[\"region\"] == region_name)\n", + " & (df[\"technology\"] == tech_name)\n", + " & (df[\"source\"] == source_name)\n", + " ].copy()\n", + " if subset.empty:\n", + " continue\n", + "\n", + " subset = subset.sort_values(\"avg_cf\")\n", + " bottom = 0.0\n", + " cf_min = float(subset[\"avg_cf\"].min())\n", + " cf_max = float(subset[\"avg_cf\"].max())\n", + " cf_span = cf_max - cf_min if cf_max > cf_min else 1.0\n", + " border_color = \"black\" if source_name == \"Consolidated\" else \"white\"\n", + " border_width = 0.55 if source_name == \"Consolidated\" else 0.3\n", + "\n", + " for _, row in subset.iterrows():\n", + " norm_cf = (row[\"avg_cf\"] - cf_min) / cf_span\n", + " shade = 0.10 + 0.90 * (norm_cf**0.65)\n", + " color = blend_with_white(tech_palette[tech_name], shade)\n", + " ax.bar(\n", + " x_positions[region_idx] + source_offsets[source_name],\n", + " row[\"capacity_mw\"],\n", + " width=tech_width,\n", + " bottom=bottom,\n", + " color=color,\n", + " edgecolor=border_color,\n", + " linewidth=border_width,\n", + " )\n", + " bottom += row[\"capacity_mw\"]\n", + "\n", + " import matplotlib.patches as mpatches\n", + "\n", + " source_handles = [\n", + " mpatches.Patch(facecolor=\"#E0E0E0\", edgecolor=\"black\", label=\"Consolidated\"),\n", + " mpatches.Patch(facecolor=\"#A9A9A9\", edgecolor=\"white\", label=\"Clustered\"),\n", + " ]\n", + " cf_handles = [\n", + " mpatches.Patch(facecolor=\"#f2f2f2\", edgecolor=\"#cccccc\", label=\"low avg_cf\"),\n", + " mpatches.Patch(facecolor=\"#5a5a5a\", edgecolor=\"#cccccc\", label=\"high avg_cf\"),\n", + " ]\n", + " tech_handles = [mpatches.Patch(color=tech_palette[tech_name], label=tech_name)]\n", + "\n", + " source_legend = ax.legend(handles=source_handles, title=\"Source\", loc=\"upper left\")\n", + " ax.add_artist(source_legend)\n", + " cf_legend = ax.legend(handles=cf_handles, title=\"CF shade\", loc=\"upper right\")\n", + " ax.add_artist(cf_legend)\n", + " ax.legend(handles=tech_handles, title=\"Technology\", loc=\"center right\")\n", + "\n", + " ax.set_title(title, fontsize=13, fontweight=\"bold\")\n", + " ax.set_xlabel(\"Region\", fontsize=11)\n", + " ax.set_ylabel(\"Capacity (MW)\", fontsize=11)\n", + " ax.set_xticks(x_positions)\n", + " ax.set_xticklabels(regions, rotation=45, ha=\"right\")\n", + " ax.grid(True, axis=\"y\", alpha=0.3)\n", + " ax.margins(x=0.04)\n", + "\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(22, 8), sharey=True)\n", + "\n", + "draw_tech_panel(\n", + " axes[0],\n", + " comparison_df,\n", + " \"onwind\",\n", + " \"Onwind: consolidated vs clustered by region\",\n", + ")\n", + "\n", + "draw_tech_panel(\n", + " axes[1],\n", + " comparison_df,\n", + " \"solar\",\n", + " \"Solar: consolidated vs clustered by region\",\n", + ")\n", + "\n", + "fig.suptitle(\"Region-wise stacked capacity comparison\", fontsize=15, fontweight=\"bold\")\n", + "plt.tight_layout(rect=(0, 0, 1, 0.95))\n", + "plt.savefig(\"region_stacked_capacity_comparison.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"✓ Saved region_stacked_capacity_comparison.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3b7324d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Generating cluster size distributions...\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Saved cluster_distributions.png\n" + ] + } + ], + "source": [ + "print(\"\\nGenerating cluster size distributions...\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "axes[0].hist(cap_valid, bins=50, color=\"steelblue\", alpha=0.7, edgecolor=\"black\")\n", + "axes[0].set_xlabel(\"Cluster Capacity (MW)\", fontsize=11)\n", + "axes[0].set_ylabel(\"Count\", fontsize=11)\n", + "axes[0].set_title(\"Distribution of Cluster Sizes\", fontsize=12, fontweight=\"bold\")\n", + "axes[0].set_yscale(\"log\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "df_cap_by_tech = []\n", + "if df_cluster_meta is not None:\n", + " for _, row in df_cluster_meta.iterrows():\n", + " df_cap_by_tech.append(\n", + " {\"technology\": row[\"technology\"], \"capacity_mw\": row[\"total_capacity_mw\"]}\n", + " )\n", + "else:\n", + " for t in ds_clustered.technology.values:\n", + " try:\n", + " cap_t = ds_clustered[\"capacity\"].sel(technology=t).values.flatten()\n", + " cap_t_valid = cap_t[~np.isnan(cap_t)]\n", + " for c in cap_t_valid:\n", + " df_cap_by_tech.append({\"technology\": str(t), \"capacity_mw\": c})\n", + " except:\n", + " pass\n", + "\n", + "if df_cap_by_tech:\n", + " df_box = pd.DataFrame(df_cap_by_tech)\n", + " df_box.boxplot(column=\"capacity_mw\", by=\"technology\", ax=axes[1])\n", + " axes[1].set_ylabel(\"Capacity (MW)\", fontsize=11)\n", + " axes[1].set_title(\n", + " \"Capacity Distribution by Technology\", fontsize=12, fontweight=\"bold\"\n", + " )\n", + " axes[1].set_yscale(\"log\")\n", + " plt.sca(axes[1])\n", + " plt.xticks(rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"cluster_distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"✓ Saved cluster_distributions.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6a2f4a1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "SUMMARY\n", + "======================================================================\n", + "\n", + "Clustering Results:\n", + " Input: 15 regions × 2 technologies\n", + " Output: 1,160 total clusters\n", + " Total capacity: 570104531 MW\n", + "\n", + "Compression:\n", + " Consolidated: 1,755 sites\n", + " Clustered: 1,160 clusters\n", + " Compression: 1.5× reduction\n", + "\n", + "✅ Analysis complete! Generated 4 PNG plots in current directory.\n", + "\n", + "Note: timeseries plot is skipped because the JSON cache only contains metadata, not cf timeseries.\n" + ] + } + ], + "source": [ + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"SUMMARY\")\n", + "print(\"=\" * 70)\n", + "\n", + "print(\"\\nClustering Results:\")\n", + "if df_cluster_meta is not None:\n", + " print(\n", + " f\" Input: {len(df_cluster_meta['region'].unique())} regions × {len(df_cluster_meta['technology'].unique())} technologies\"\n", + " )\n", + " print(f\" Output: {len(df_cluster_meta):,} total clusters\")\n", + " print(f\" Total capacity: {df_cluster_meta['total_capacity_mw'].sum():.0f} MW\")\n", + "else:\n", + " print(\n", + " f\" Input: {len(ds_clustered.region.values)} regions × {len(ds_clustered.technology.values)} technologies\"\n", + " )\n", + " print(f\" Output: {len(cap_valid):,} total clusters\")\n", + " print(f\" Total capacity: {np.sum(cap_valid):.0f} MW\")\n", + "\n", + "if consolidated_exists:\n", + " cap_consol_all = ds_consolidated[\"capacity\"].values.flatten()\n", + " cap_consol_valid = cap_consol_all[~np.isnan(cap_consol_all)]\n", + " if df_cluster_meta is not None:\n", + " clustered_count = len(df_cluster_meta)\n", + " else:\n", + " clustered_count = len(cap_valid)\n", + " if clustered_count > 0:\n", + " compression = len(cap_consol_valid) / clustered_count\n", + " print(\"\\nCompression:\")\n", + " print(f\" Consolidated: {len(cap_consol_valid):,} sites\")\n", + " print(f\" Clustered: {clustered_count:,} clusters\")\n", + " print(f\" Compression: {compression:.1f}× reduction\")\n", + "\n", + "print(\"\\n✅ Analysis complete! Generated 4 PNG plots in current directory.\")\n", + "if df_cluster_meta is not None:\n", + " print(\n", + " \"\\nNote: timeseries plot is skipped because the JSON cache only contains metadata, not cf timeseries.\"\n", + " )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift-dev", + "language": "python", + "name": "shift-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflow/scripts/cluster_renewables.py b/workflow/scripts/cluster_renewables.py index 79de0da..9f96c84 100644 --- a/workflow/scripts/cluster_renewables.py +++ b/workflow/scripts/cluster_renewables.py @@ -443,10 +443,57 @@ def aggregate_clusters( ds: xr.Dataset, config: Dict, cache_clusters_path: str = None, + clustering_cache_dir: str = None, ) -> Dict: """Aggregate clusters and generate outputs.""" logger.info("Aggregating clusters...") + # Fast path: load the fully aggregated payload if it already exists. + if cache_clusters_path and Path(cache_clusters_path).exists(): + try: + cached_payload = joblib.load(cache_clusters_path) + if ( + "clustered_data" in cached_payload + and "cluster_metadata" in cached_payload + ): + logger.info( + f"Loading cached aggregated clusters from {cache_clusters_path}" + ) + return cached_payload["clustered_data"], cached_payload[ + "cluster_metadata" + ] + except Exception as e: + logger.info( + f"Could not load cached aggregated clusters from {cache_clusters_path}: {e}" + ) + + # Try to load pre-computed cluster results from per-region-tech cache + if clustering_cache_dir: + clustering_cache_path = Path(clustering_cache_dir) + cache_files = list(clustering_cache_path.glob("clustering_cache_*.json")) + if cache_files: + logger.info( + f"Found {len(cache_files)} cached region-tech clustering results" + ) + for cache_file in cache_files: + try: + with open(cache_file, "r") as f: + cached = json.load(f) + region = cached["region"] + tech = cached["technology"] + clusters_array = np.array(cached["clusters"]) + + # Only add to clusters_dict if not already present + if (region, tech) not in clusters_dict or len( + clusters_dict[(region, tech)] + ) == 0: + clusters_dict[(region, tech)] = clusters_array + logger.info( + f" [RECOVERED] {region} {tech}: loaded {len(clusters_array)} cluster assignments from cache" + ) + except Exception as e: + logger.debug(f"Could not load {cache_file}: {e}") + clustered_data = {} # {(region, tech, cluster_id): {capacity, cf_ts, avg_cf}} cluster_metadata = {} @@ -554,18 +601,16 @@ def aggregate_clusters( # Cache aggregated clusters to disk for recovery if cache_clusters_path: logger.info(f"Caching aggregated clusters to {cache_clusters_path}") - # Save metadata (can't easily pickle clustered_data due to numpy arrays) - with open(cache_clusters_path, "w") as f: - json.dump( - { - "cluster_count": len(clustered_data), - "metadata": cluster_metadata, - "timestamp": pd.Timestamp.now().isoformat(), - }, - f, - indent=2, - ) - logger.info(" ✓ Cached clustering metadata") + joblib.dump( + { + "clustered_data": clustered_data, + "cluster_metadata": cluster_metadata, + "timestamp": pd.Timestamp.now().isoformat(), + }, + cache_clusters_path, + compress=3, + ) + logger.info(" ✓ Cached full clustered payload") return clustered_data, cluster_metadata @@ -614,7 +659,15 @@ def write_clustered_netcdf( for idx, (cluster_id, cluster_info) in enumerate(clusters_for_rt): capacity_data[region][tech][idx] = cluster_info["capacity"] - cf_data[region][tech][idx, :] = cluster_info["cf_ts"] + cf_ts = cluster_info["cf_ts"] + if isinstance(cf_ts, np.ndarray): + if cf_ts.ndim != 1: + logger.warning( + f" cf_ts has wrong shape {cf_ts.shape}, taking first row" + ) + cf_ts = cf_ts[0] if cf_ts.ndim > 1 else cf_ts + if len(cf_ts) == 8760: + cf_data[region][tech][idx, :] = cf_ts.astype(np.float32) avg_cf_data[region][tech][idx] = cluster_info["avg_cf"] # Create xarray dataset with aligned dimensions @@ -712,7 +765,11 @@ def validate_clustering( "temporal_quality": {}, } - for (region, tech), cluster_assignments in clustered_data.items(): + region_tech_pairs = sorted( + {(region, tech) for region, tech, _ in clustered_data.keys()} + ) + + for region, tech in region_tech_pairs: # Total capacity check original_cap = [] for bus_id in ds_merged.bus.values: @@ -727,8 +784,9 @@ def validate_clustering( original_total = np.sum(original_cap) clustered_total = sum( - clustered_data.get((region, tech, cluster_id), {}).get("capacity", 0) - for cluster_id in range(len(cluster_assignments)) + cluster_info.get("capacity", 0) + for (r, t, _), cluster_info in clustered_data.items() + if r == region and str(t) == str(tech) ) if original_total > 0: @@ -851,9 +909,14 @@ def main(): clusters_dict = dict(results) # Aggregate clusters (with caching) - cache_clusters = Path("resources") / "clusters_cache.json" + cache_clusters = Path("resources") / "clusters_cache.joblib" clustered_data, cluster_metadata = aggregate_clusters( - df_features, clusters_dict, ds_filtered, config, str(cache_clusters) + df_features, + clusters_dict, + ds_filtered, + config, + str(cache_clusters), + str(clustering_cache_dir), ) # Write output From 07f8a64844f483c44df2aa8ac969b028f6bbf9ec Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 29 May 2026 17:41:58 +0200 Subject: [PATCH 122/216] feat: improve preservation of distinct cf buckets when clustering --- config/config.yaml | 25 +- workflow/scripts/cluster_renewables.py | 694 ++++++++++++++++++++----- 2 files changed, 569 insertions(+), 150 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 9028035..9e1836d 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -172,17 +172,30 @@ regions: # Used by cluster_renewables.py to cluster 122k+ individual renewable buses # into manageable pseudo-buses for optimization tractability clustering: - # Number of pseudo-buses per technology per region - n_clusters_onwind: 40 # Onshore wind clusters - n_clusters_pvplant: 40 # Solar/PV clusters - + # Cluster-count policy. The `base_clusters` values are the single source of + # truth for fixed mode and the reference points for dynamic scaling. + cluster_count_policy: + mode: dynamic + base_clusters: + onwind: 25 + solar: 25 + min_clusters: 10 + max_clusters: 50 + reference_buses: 2000 + reference_capacity_mw: 16000000 + bus_weight: 0.5 + capacity_weight: 0.5 + spread_weight: 0.15 + spread_reference: 0.20 + scale_exponent: 0.5 + # Feature weighting for K-means clustering # Determines how heavily clustering emphasizes each aspect: - # - avg_cf (0.5): Merit-order / capacity factor quality (primary driver) + # - merit_order (0.5): Merit-order / capacity factor quality (primary driver) # - temporal (0.3): Temporal patterns (std, cv, autocorr_24h) # - geospatial (0.2): Geographic proximity (lat, lon) feature_weights: - avg_cf: 0.5 # High CF sites group together for optimization value + merit_order: 0.5 # High CF / tail-shape sites group together for optimization value temporal: 0.3 # Similar generation patterns cluster (wind/solar separation + peakiness) geospatial: 0.2 # Loose geographic coherence (secondary constraint) diff --git a/workflow/scripts/cluster_renewables.py b/workflow/scripts/cluster_renewables.py index 9f96c84..1aebc21 100644 --- a/workflow/scripts/cluster_renewables.py +++ b/workflow/scripts/cluster_renewables.py @@ -5,7 +5,7 @@ 1. Loads merged renewable profiles (all 122k+ buses from merged file) 2. Filters to onwind + pvplant (excludes unreliable offshore wind) 3. Maps buses to regions using ISO3 codes and config -4. Extracts 6D temporal features: avg_cf, temporal_std, cv, autocorr_24h, lat, lon +4. Extracts a unified capacity-factor summary plus lat/lon 5. Applies weighted K-means clustering (0.5 merit-order, 0.3 temporal, 0.2 geospatial) 6. Selects representative timeseries per cluster to preserve real patterns 7. Outputs clustered NetCDF in expected format: (region, technology, class, time) @@ -26,7 +26,7 @@ import logging import json from pathlib import Path -from typing import Dict, Tuple, List +from typing import Any, Dict, Tuple, List, Optional from contextlib import contextmanager import numpy as np @@ -43,15 +43,202 @@ ) logger = logging.getLogger(__name__) +snakemake: Any = globals().get("snakemake") + + +CACHE_VERSION = "v3_cache_versioned_1d_cf_ts" +CF_QUANTILES = (0.1, 0.25, 0.5, 0.75, 0.9) +CF_THRESHOLDS = (0.2, 0.4, 0.6, 0.8) + + +def summarize_capacity_factor(cf_ts: np.ndarray) -> Dict[str, float]: + """Summarize a capacity-factor profile for clustering and validation. + + Args: + cf_ts: One-dimensional capacity-factor time series. Missing values and + out-of-range values are cleaned before summary statistics are + computed. + + Returns: + A dictionary of robust profile descriptors, including the core + temporal moments used by clustering (`avg_cf`, `temporal_std`, `cv`, + `autocorr_24h`) plus quantiles, exceedance fractions at key + thresholds, and low/high tail mass statistics. These values are used + to preserve merit-order shape when clustering and when selecting + representative buses. + """ + cf_clean = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) + if cf_clean.size == 0: + cf_clean = np.zeros(1, dtype=float) + avg_cf = float(np.nanmean(cf_clean)) + temporal_std = float(np.nanstd(cf_clean)) + cv = temporal_std / avg_cf if avg_cf > 0 else 0.0 + if len(cf_clean) > 24: + cf_detrended = cf_clean - np.nanmean(cf_clean) + if np.std(cf_detrended) > 1e-10 and not np.isnan(cf_detrended[:-24]).all(): + autocorr_24h = float( + np.corrcoef(cf_detrended[:-24], cf_detrended[24:])[0, 1] + ) + else: + autocorr_24h = 0.0 + else: + autocorr_24h = 0.0 + summary = { + f"cf_q{int(q * 100):02d}": float(np.nanquantile(cf_clean, q)) + for q in CF_QUANTILES + } + for threshold in CF_THRESHOLDS: + summary[f"cf_exceed_{int(threshold * 100):03d}"] = float( + np.mean(cf_clean >= threshold) + ) + tail = max(1, int(len(cf_clean) * 0.05)) + summary["avg_cf"] = avg_cf + summary["temporal_std"] = temporal_std + summary["cv"] = cv + summary["autocorr_24h"] = autocorr_24h + summary["cf_low_mass"] = float(np.mean(np.sort(cf_clean)[:tail])) + summary["cf_high_mass"] = float(np.mean(np.sort(cf_clean)[-tail:])) + return summary + + +def normalize_weights(weights: Dict[str, float]) -> Dict[str, float]: + """Normalize clustering weights into the three feature families. + + Args: + weights: Raw weights from config. Missing categories are treated as 0. + + Returns: + A normalized dictionary with `merit_order`, `temporal`, and + `geospatial` keys that sum to 1.0. If the provided weights are empty or + invalid, the default policy of 0.5/0.3/0.2 is returned. + """ + # Expect keys: merit_order, temporal, geospatial. + w = dict(weights or {}) + for k in ("merit_order", "temporal", "geospatial"): + w.setdefault(k, 0.0) + s = sum(w.values()) + if s <= 0: + return {"merit_order": 0.5, "temporal": 0.3, "geospatial": 0.2} + return {k: float(v / s) for k, v in w.items()} + + +def rescale_timeseries_to_mean(cf_ts: np.ndarray, target_mean: float) -> np.ndarray: + """Rescale a representative profile so its mean matches the cluster mean. + + Args: + cf_ts: Representative capacity-factor series selected from observed + buses. + target_mean: Mean capacity factor that the cluster should preserve. + + Returns: + A clipped capacity-factor series with the same shape as the input and a + mean close to `target_mean`. The result stays within the physical bounds + of [0, 1]. + """ + ts = np.clip(np.nan_to_num(cf_ts, nan=0.0).astype(float), 0.0, 1.0) + if ts.size == 0: + return ts + current = float(np.mean(ts)) + if current <= 0: + return np.full_like(ts, np.clip(target_mean, 0.0, 1.0)) + scaled = ts * (target_mean / current) + scaled = np.clip(scaled, 0.0, 1.0) + return scaled + + +def resolve_cluster_count(df_subset: pd.DataFrame, tech: str) -> int: + """Resolve the cluster count for one region-technology subset. + + Args: + df_subset: Feature rows for a single region and technology. + tech: Technology label for the subset, typically `solar` or `onwind`. + + Returns: + An integer cluster count derived from the configured policy. In dynamic + mode, the count scales with bus count and installed capacity while being + clamped to the configured min/max bounds. + """ + clustering_config = snakemake.config.get("clustering", {}) + policy = ( + clustering_config.get("cluster_count_policy", {}) + if isinstance(clustering_config, dict) + else {} + ) + base_clusters = policy.get("base_clusters", {}) if isinstance(policy, dict) else {} + mode = policy.get("mode", "dynamic") + base_key = "solar" if tech == "solar" else "onwind" + base = int(base_clusters.get(base_key, 40)) + if mode == "fixed": + return base + + n_buses = max(1, int(df_subset["bus_id"].nunique())) + total_capacity = ( + float(df_subset.get("capacity_mw", pd.Series([0])).sum()) + if "capacity_mw" in df_subset + else 0.0 + ) + ref_buses = float(policy.get("reference_buses", 1000.0)) + ref_cap = float(policy.get("reference_capacity_mw", 50000.0)) + bus_scale = (n_buses / ref_buses) ** float(policy.get("scale_exponent", 0.5)) + cap_scale = (max(1.0, total_capacity) / ref_cap) ** float( + policy.get("scale_exponent", 0.5) + ) + bus_w = float(policy.get("bus_weight", 0.5)) + cap_w = float(policy.get("capacity_weight", 0.5)) + target = int(round(base * (bus_w * bus_scale + cap_w * cap_scale))) + min_c = int(policy.get("min_clusters", 3)) + max_c = int(policy.get("max_clusters", 120)) + return max(min_c, min(max_c, max(1, min(target, len(df_subset))))) + + +def score_representative_candidate( + cf_ts: np.ndarray, + cf_target: np.ndarray, + target_summary: Dict[str, float], + candidate_summary: Dict[str, float], +) -> float: + """Score how well a candidate bus matches the cluster target shape. + + Args: + cf_ts: Candidate capacity-factor time series from an observed bus. + cf_target: Capacity-factor series representing the cluster-average + target. + target_summary: Summary statistics for `cf_target`. + candidate_summary: Summary statistics for the candidate series. + + Returns: + A similarity score where higher values indicate a better match. The + score combines correlation with a summary-statistic distance penalty so + the chosen representative preserves both shape and distributional tail + behavior. + """ + cf_ts = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) + cf_target = np.clip(np.nan_to_num(cf_target, nan=0.0), 0.0, 1.0) + corr = 0.0 + try: + if np.std(cf_ts) > 1e-12 and np.std(cf_target) > 1e-12: + corr = float(np.corrcoef(cf_ts, cf_target)[0, 1]) + except Exception: + corr = 0.0 + keys = list(target_summary.keys()) + dist = 0.0 + for k in keys: + dist += abs(candidate_summary.get(k, 0.0) - target_summary.get(k, 0.0)) + dist = dist / max(1, len(keys)) + return 0.7 * corr + 0.3 * (1.0 - dist) + @contextmanager def tqdm_joblib(tqdm_object): - """ - Context manager to integrate tqdm progress bar with joblib Parallel. + """Route joblib progress callbacks into a tqdm progress bar. + + Args: + tqdm_object: An active tqdm progress bar to update as joblib batches + complete. - Usage: - with tqdm_joblib(tqdm(total=n_tasks, desc="Processing")) as pbar: - results = Parallel(n_jobs=4)(delayed(func)(i) for i in range(n_tasks)) + Yields: + The same tqdm object, allowing the caller to use the context manager as + a transparent progress-wrapper around `joblib.Parallel` work. """ class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): @@ -68,7 +255,16 @@ def __call__(self, *args, **kwargs): def build_region_map(config: Dict, iso3_to_region: Dict[str, str]) -> Dict[str, str]: - """Build mapping from ISO3 code to region name.""" + """Build the ISO3-to-region lookup used to assign buses to regions. + + Args: + config: Global configuration containing the region definitions. + iso3_to_region: Existing lookup map. The parameter is accepted for + interface compatibility, but the mapping is rebuilt from config. + + Returns: + A dictionary mapping ISO3 country codes to region names. + """ iso3_to_region_local = {} for region_name, iso3_list in config.get("regions", {}).items(): for iso3 in iso3_list: @@ -76,8 +272,15 @@ def build_region_map(config: Dict, iso3_to_region: Dict[str, str]) -> Dict[str, return iso3_to_region_local -def extract_iso3_from_bus_id(bus_id: str) -> str: - """Extract ISO3 code from bus_id format: {ISO3}_{ON/OFF}_{geohash}.""" +def extract_iso3_from_bus_id(bus_id: str) -> Optional[str]: + """Extract the ISO3 code embedded in a bus identifier. + + Args: + bus_id: Renewable bus identifier, usually prefixed by an ISO3 code. + + Returns: + The ISO3 prefix if it can be parsed, otherwise `None`. + """ try: return bus_id.split("_")[0] except Exception: @@ -86,7 +289,15 @@ def extract_iso3_from_bus_id(bus_id: str) -> str: def load_merged_data(merged_path: str) -> xr.Dataset: - """Load merged renewable profiles.""" + """Load the merged renewable-profiles dataset from disk. + + Args: + merged_path: Path to the consolidated NetCDF input produced upstream. + + Returns: + An opened xarray dataset containing bus-level renewable profiles and + metadata needed by the clustering workflow. + """ logger.info(f"Loading merged data from {merged_path}") ds = xr.open_dataset(merged_path) logger.info(f" Merged data shape: {dict(ds.sizes)}") @@ -95,11 +306,20 @@ def load_merged_data(merged_path: str) -> xr.Dataset: def filter_to_onwind_pv(ds: xr.Dataset) -> xr.Dataset: - """Filter merged data to onwind + solar only (exclude offshore).""" + """Keep only the technologies that are clustered by this workflow. + + Args: + ds: Full merged renewable dataset with multiple technologies. + + Returns: + A reduced dataset containing only `onwind` and `solar` technology + slices. Offshore wind is intentionally excluded because this workflow + is tuned to onshore wind and solar pocket preservation. + """ logger.info("Filtering to onwind + solar (excluding offwind-ac)") - techs_present = list(ds.coords["technology"].values) - # Actual technology names in merged file: 'offwind-ac', 'onwind', 'solar' + _techs_present = list(ds.coords["technology"].values) + # The merged file includes offshore wind, which this workflow intentionally skips. techs_to_keep = ["onwind", "solar"] ds_filtered = ds.sel(technology=techs_to_keep) @@ -112,7 +332,18 @@ def filter_to_onwind_pv(ds: xr.Dataset) -> xr.Dataset: def extract_bus_features_single( bus_id, ds: xr.Dataset, iso3_to_region: Dict[str, str] ) -> List[Dict]: - """Extract features for a single bus across all technologies (internal).""" + """Extract clustering features for one bus across all technologies. + + Args: + bus_id: Bus identifier from the merged renewable dataset. + ds: Filtered xarray dataset containing capacity factors and metadata. + iso3_to_region: Lookup used to map ISO3 prefixes to analysis regions. + + Returns: + A list of feature dictionaries, one per available bus-technology pair. + Each row contains temporal moments, geospatial coordinates, capacity, + and the tail-shape descriptors used by the clustering model. + """ features = [] iso3 = extract_iso3_from_bus_id(str(bus_id)) if not iso3: @@ -136,29 +367,20 @@ def extract_bus_features_single( if np.isnan(cf_ts).all() or len(cf_ts) == 0: continue - avg_cf = float(np.nanmean(cf_ts)) - temporal_std = float(np.nanstd(cf_ts)) - cv = temporal_std / avg_cf if avg_cf > 0 else 0.0 + # Capture the tail of the capacity-factor distribution so green pockets survive. + cf_summary = summarize_capacity_factor(cf_ts) - if len(cf_ts) > 24: - cf_detrended = cf_ts - np.nanmean(cf_ts) - if np.std(cf_detrended) > 1e-10 and not np.isnan(cf_detrended[:-24]).all(): - autocorr_24h = float( - np.corrcoef(cf_detrended[:-24], cf_detrended[24:])[0, 1] - ) - else: - autocorr_24h = 0.0 - else: - autocorr_24h = 0.0 + try: + capacity_mw = float(ds["p_nom_max"].sel(bus=bus_id, technology=tech).values) + except Exception: + capacity_mw = np.nan features.append( { - "avg_cf": avg_cf, - "temporal_std": temporal_std, - "cv": cv, - "autocorr_24h": autocorr_24h, "lat": y_centroid, "lon": x_centroid, + "capacity_mw": capacity_mw, + **cf_summary, "region": region, "technology": tech, "bus_id": bus_id, @@ -170,11 +392,15 @@ def extract_bus_features_single( def extract_bus_features_batch( bus_ids: List, ds: xr.Dataset, iso3_to_region: Dict[str, str] ) -> List[Dict]: - """ - Extract features for a batch of buses (parallelizable task). + """Extract features for a batch of buses. - Batching reduces scheduler overhead and improves cache locality. - Returns flattened list of all feature dicts from all buses in batch. + Args: + bus_ids: Batch of bus identifiers assigned to one joblib worker. + ds: Filtered renewable dataset. + iso3_to_region: Lookup for region assignment. + + Returns: + Flattened feature rows for all buses in the batch. """ all_features = [] for bus_id in bus_ids: @@ -183,37 +409,68 @@ def extract_bus_features_batch( def extract_features( - ds: xr.Dataset, config: Dict, cache_features_path: str = None + ds: xr.Dataset, config: Dict, cache_features_path: Optional[str] = None ) -> Tuple[pd.DataFrame, Dict[str, str]]: - """ - Extract 6D features per bus: avg_cf, temporal_std, cv, autocorr_24h, lat, lon. + """Build the full feature table used by the clustering workflow. - Parallelized by batching buses to reduce scheduler overhead. + Args: + ds: Filtered renewable dataset containing the buses to cluster. + config: Workflow configuration used to derive the region mapping and + caching behavior. + cache_features_path: Optional CSV cache path for reusing extracted + features on subsequent runs. Returns: - DataFrame with columns: [avg_cf, temporal_std, cv, autocorr_24h, lat, lon, region, tech, bus_id] - Dict: mapping region_name -> list of buses in that region + A tuple of `(feature_table, region_buses)` where `feature_table` holds + one row per bus-technology pair and `region_buses` maps each region to + the buses assigned to it. The cache is reused when it matches the + expected feature schema. """ + required_cache_cols = { + "avg_cf", + "temporal_std", + "cv", + "autocorr_24h", + "lat", + "lon", + "capacity_mw", + "region", + "technology", + "bus_id", + "cf_q10", + "cf_q25", + "cf_q50", + "cf_q75", + "cf_q90", + "cf_exceed_020", + "cf_exceed_040", + "cf_exceed_060", + "cf_exceed_080", + "cf_low_mass", + "cf_high_mass", + } + # Try to load from cache if it exists if cache_features_path and Path(cache_features_path).exists(): logger.info(f"Loading cached features from {cache_features_path}") df_features = pd.read_csv(cache_features_path) - region_buses = {} - for _, row in df_features.iterrows(): - region = row["region"] - bus_id = row["bus_id"] - if region not in region_buses: - region_buses[region] = [] - if bus_id not in region_buses[region]: - region_buses[region].append(bus_id) - logger.info(f" Loaded {len(df_features)} features from cache") - return df_features, region_buses + if required_cache_cols.issubset(set(df_features.columns)): + region_buses = {} + for _, row in df_features.iterrows(): + region = row["region"] + bus_id = row["bus_id"] + region_buses.setdefault(region, []) + if bus_id not in region_buses[region]: + region_buses[region].append(bus_id) + logger.info(f" Loaded {len(df_features)} features from cache") + return df_features, region_buses + logger.info(" Cached feature schema is stale; recomputing") logger.info("Extracting temporal features (batched parallelization)...") iso3_to_region = build_region_map(config, {}) - # Batch buses: 1 batch per worker for minimal overhead + # Batch buses so each worker gets a non-trivial chunk of work. n_jobs = snakemake.threads if hasattr(snakemake, "threads") else -1 bus_list = list(ds.bus.values) @@ -229,7 +486,7 @@ def extract_features( with tqdm_joblib( tqdm(total=len(bus_batches), desc="Extracting bus features", unit="batch") - ) as pbar: + ): batch_results = joblib.Parallel(n_jobs=n_jobs, backend="loky")( joblib.delayed(extract_bus_features_batch)(batch, ds, iso3_to_region) for batch in bus_batches @@ -267,13 +524,19 @@ def extract_features( def cluster_region_technology( df_subset: pd.DataFrame, n_clusters: int, feature_weights: Dict[str, float] ) -> Tuple[np.ndarray, KMeans]: - """ - Cluster a single region-technology group using weighted K-means. + """Cluster one region-technology subset with weighted K-means. + + Args: + df_subset: Feature rows for a single region and technology. + n_clusters: Requested number of clusters before any size safeguards. + feature_weights: Normalized weights for merit-order, temporal, and + geospatial feature families. - Feature categories: - - avg_cf: 1 feature (0.5 weight) - - temporal: 3 features [temporal_std, cv, autocorr_24h] (0.3 weight) - - geospatial: 2 features [lat, lon] (0.2 weight) + Returns: + The fitted cluster labels for each row in `df_subset` and the trained + `KMeans` estimator. The feature matrix is built from separately + normalized feature families so the configured weights act at the family + level rather than on raw columns. """ if len(df_subset) < n_clusters: logger.warning( @@ -281,8 +544,24 @@ def cluster_region_technology( ) n_clusters = max(1, len(df_subset) // 2) + # The merit-order block keeps the upper tail visible to clustering. + merit_order_cols = [ + "avg_cf", + "cf_q10", + "cf_q25", + "cf_q50", + "cf_q75", + "cf_q90", + "cf_exceed_020", + "cf_exceed_040", + "cf_exceed_060", + "cf_exceed_080", + "cf_low_mass", + "cf_high_mass", + ] + # Extract feature subsets - avg_cf_vals = df_subset[["avg_cf"]].values # (n, 1) + merit_order_vals = df_subset[merit_order_cols].values temporal_vals = df_subset[["temporal_std", "cv", "autocorr_24h"]].values # (n, 3) geospatial_vals = df_subset[["lat", "lon"]].values # (n, 2) @@ -291,16 +570,16 @@ def cluster_region_technology( scaler_temporal = StandardScaler() scaler_geo = StandardScaler() - avg_cf_norm = scaler_cf.fit_transform(avg_cf_vals) # (n, 1) + merit_order_norm = scaler_cf.fit_transform(merit_order_vals) temporal_norm = scaler_temporal.fit_transform(temporal_vals) # (n, 3) geospatial_norm = scaler_geo.fit_transform(geospatial_vals) # (n, 2) - # Apply weights - w_cf = feature_weights["avg_cf"] + # Apply the three feature-family weights after separate normalization. + w_cf = feature_weights["merit_order"] w_temporal = feature_weights["temporal"] w_geo = feature_weights["geospatial"] - avg_cf_weighted = avg_cf_norm * w_cf # (n, 1) + avg_cf_weighted = merit_order_norm * w_cf temporal_weighted = temporal_norm * w_temporal # (n, 3) geospatial_weighted = geospatial_norm * w_geo # (n, 2) @@ -318,17 +597,27 @@ def cluster_region_technology_pair( region: str, tech: str, df_features: pd.DataFrame, - n_clusters_onwind: int, - n_clusters_pvplant: int, + clustering_config: Dict, feature_weights: Dict[str, float], - cache_dir: str = None, + cache_dir: Optional[str] = None, ) -> Tuple[Tuple[str, str], np.ndarray]: - """ - Cluster a single region-technology pair (parallelizable wrapper). + """Cluster one region-technology pair and persist the assignments. + + Args: + region: Region name selected from the configuration mapping. + tech: Technology label for the subset. + df_features: Full extracted feature table. + clustering_config: Clustering-specific configuration section. + feature_weights: Normalized family weights used by the K-means model. + cache_dir: Optional directory for storing per-pair cluster assignments. Returns: - ((region, tech), clusters) tuple for dict conversion + A `(region, tech)` key paired with the cluster-label vector for the + corresponding rows in `df_features`. Cached results are reused when + present and compatible with the current cache version. """ + tech = str(tech) + # Check cache first if cache_dir: cache_file = Path(cache_dir) / f"clustering_cache_{region}_{str(tech)}.json" @@ -336,6 +625,11 @@ def cluster_region_technology_pair( try: with open(cache_file, "r") as f: cached = json.load(f) + if cached.get("cache_version") != CACHE_VERSION: + logger.info( + f" [STALE CACHE] {region} {tech}: cache version {cached.get('cache_version')} != {CACHE_VERSION}" + ) + raise ValueError("stale cache version") clusters_array = np.array(cached["clusters"]) logger.info( f" [CACHED] {region} {str(tech)}: loaded {len(cached['clusters'])} assignments" @@ -351,13 +645,8 @@ def cluster_region_technology_pair( if len(df_subset) == 0: return (region, tech), np.array([]) - # Get n_clusters from tech type - if tech == "solar": - n_clusters = n_clusters_pvplant - tech_name = "solar" - else: - n_clusters = n_clusters_onwind - tech_name = "onwind" + n_clusters = resolve_cluster_count(df_subset, tech) + tech_name = "solar" if tech == "solar" else "onwind" logger.info(f"Clustering {region} {tech_name} with k={n_clusters}") @@ -370,6 +659,7 @@ def cluster_region_technology_pair( with open(cache_file, "w") as f: json.dump( { + "cache_version": CACHE_VERSION, "region": region, "technology": str(tech), "n_clusters": len(np.unique(clusters)), @@ -388,51 +678,55 @@ def cluster_region_technology_pair( def select_representative_bus( df_cluster: pd.DataFrame, ds: xr.Dataset, tech: str, cf_weighted_ts: np.ndarray ) -> Tuple[str, np.ndarray, float]: - """ - Select representative bus for cluster based on highest correlation to weighted-avg timeseries. + """Select the best observed bus for a cluster and re-center its mean. + + Args: + df_cluster: Rows belonging to one cluster within a region-technology + subset. + ds: Filtered renewable dataset used to retrieve candidate time series. + tech: Technology label for the cluster. + cf_weighted_ts: Capacity-weighted cluster-average profile. Returns: - representative_bus_id, scaled_cf_ts, avg_cf_cluster + A tuple containing the chosen bus identifier, the representative time + series rescaled to the exact cluster mean, and the cluster mean itself. + The representative is chosen to preserve both the central tendency and + the tail shape of the cluster. """ tech = str(tech) # Convert numpy.str_ to Python str - max_corr = -2.0 - best_idx = 0 + target_summary = summarize_capacity_factor(cf_weighted_ts) + best_score = -np.inf best_bus_id = df_cluster.iloc[0]["bus_id"] + best_cf_ts = None - for idx, (_, row) in enumerate(df_cluster.iterrows()): + for _, row in df_cluster.iterrows(): bus_id = row["bus_id"] try: cf_ts = ds["capacity_factor"].sel(bus=bus_id, technology=tech).values cf_ts = np.nan_to_num(cf_ts, nan=0.0) - - # Correlation to weighted-average - corr = float(np.corrcoef(cf_ts, cf_weighted_ts)[0, 1]) - if corr > max_corr: - max_corr = corr - best_idx = idx + candidate_summary = summarize_capacity_factor(cf_ts) + score = score_representative_candidate( + cf_ts, + cf_weighted_ts, + target_summary, + candidate_summary, + ) + if score > best_score: + best_score = score best_bus_id = bus_id + best_cf_ts = cf_ts except Exception as e: logger.debug(f"Error computing correlation for {bus_id}: {e}") continue - # Get representative timeseries - cf_representative = ( - ds["capacity_factor"].sel(bus=best_bus_id, technology=tech).values - ) - cf_representative = np.nan_to_num(cf_representative, nan=0.0) + # Prefer a real series; the rescaling step keeps the cluster mean exact. + if best_cf_ts is None: + best_cf_ts = ds["capacity_factor"].sel(bus=best_bus_id, technology=tech).values + cf_representative = np.nan_to_num(best_cf_ts, nan=0.0) - # Scale to match weighted-average merit-order avg_cf_cluster = float(np.mean(cf_weighted_ts)) - avg_cf_representative = float(np.mean(cf_representative)) - - if avg_cf_representative > 0: - scale_factor = avg_cf_cluster / avg_cf_representative - else: - scale_factor = 1.0 - - cf_cluster_ts = cf_representative * scale_factor - cf_cluster_ts = np.clip(cf_cluster_ts, 0, 1) + cf_cluster_ts = rescale_timeseries_to_mean(cf_representative, avg_cf_cluster) return best_bus_id, cf_cluster_ts, avg_cf_cluster @@ -442,10 +736,26 @@ def aggregate_clusters( clusters_dict: Dict[Tuple[str, str], np.ndarray], ds: xr.Dataset, config: Dict, - cache_clusters_path: str = None, - clustering_cache_dir: str = None, -) -> Dict: - """Aggregate clusters and generate outputs.""" + cache_clusters_path: Optional[str] = None, + clustering_cache_dir: Optional[str] = None, +) -> Tuple[Dict, Dict]: + """Aggregate cluster assignments into pseudo-bus outputs and metadata. + + Args: + df_features: Full feature table used to recover cluster membership. + clusters_dict: Mapping from `(region, tech)` to the cluster labels for + the corresponding feature rows. + ds: Filtered renewable dataset used to sum capacities and profiles. + config: Global workflow configuration. + cache_clusters_path: Optional joblib cache for the aggregated payload. + clustering_cache_dir: Optional directory containing per-pair caches of + cluster assignments. + + Returns: + A tuple of `(clustered_data, cluster_metadata)`. `clustered_data` holds + the pseudo-bus output for each cluster, while `cluster_metadata` + records the summary fields that downstream steps can inspect. + """ logger.info("Aggregating clusters...") # Fast path: load the fully aggregated payload if it already exists. @@ -453,7 +763,8 @@ def aggregate_clusters( try: cached_payload = joblib.load(cache_clusters_path) if ( - "clustered_data" in cached_payload + cached_payload.get("cache_version") == CACHE_VERSION + and "clustered_data" in cached_payload and "cluster_metadata" in cached_payload ): logger.info( @@ -462,12 +773,16 @@ def aggregate_clusters( return cached_payload["clustered_data"], cached_payload[ "cluster_metadata" ] + logger.info( + f"Discarding stale aggregated cluster cache at {cache_clusters_path}" + ) + Path(cache_clusters_path).unlink(missing_ok=True) except Exception as e: logger.info( f"Could not load cached aggregated clusters from {cache_clusters_path}: {e}" ) - # Try to load pre-computed cluster results from per-region-tech cache + # Recover per-region-tech assignments from cache if we have them. if clustering_cache_dir: clustering_cache_path = Path(clustering_cache_dir) cache_files = list(clustering_cache_path.glob("clustering_cache_*.json")) @@ -479,6 +794,12 @@ def aggregate_clusters( try: with open(cache_file, "r") as f: cached = json.load(f) + if cached.get("cache_version") != CACHE_VERSION: + logger.info( + f" [STALE] {cache_file.name}: cache version {cached.get('cache_version')} != {CACHE_VERSION}" + ) + cache_file.unlink(missing_ok=True) + continue region = cached["region"] tech = cached["technology"] clusters_array = np.array(cached["clusters"]) @@ -525,7 +846,7 @@ def aggregate_clusters( # Get buses in cluster bus_ids_in_cluster = list(df_cluster["bus_id"].values) - # Sum capacities + # Sum capacities within the cluster to preserve total installed potential. capacities = [] for bus_id in bus_ids_in_cluster: try: @@ -545,7 +866,7 @@ def aggregate_clusters( ) continue - # Compute weighted-average timeseries + # Compute the cluster-average profile before picking a representative bus. cf_weighted_parts = [] for bus_id in bus_ids_in_cluster: try: @@ -568,7 +889,7 @@ def aggregate_clusters( else: cf_weighted_ts = np.zeros(8760) - # Select representative bus + # Select representative bus and then rescale to the exact cluster mean CF. representative_id, cf_cluster_ts, avg_cf_cluster = ( select_representative_bus(df_cluster, ds, str(tech), cf_weighted_ts) ) @@ -603,6 +924,7 @@ def aggregate_clusters( logger.info(f"Caching aggregated clusters to {cache_clusters_path}") joblib.dump( { + "cache_version": CACHE_VERSION, "clustered_data": clustered_data, "cluster_metadata": cluster_metadata, "timestamp": pd.Timestamp.now().isoformat(), @@ -618,10 +940,23 @@ def aggregate_clusters( def write_clustered_netcdf( clustered_data: Dict, output_path: str, config: Dict ) -> None: - """Write clustered data to NetCDF with (region, technology, class, time) dims.""" + """Write the clustered pseudo-bus dataset to NetCDF. + + Args: + clustered_data: Aggregated cluster payload produced by + `aggregate_clusters`. + output_path: Destination NetCDF path. + config: Global configuration used to stamp metadata and preserve the + workflow context. + + Returns: + None. The clustered dataset is written to `output_path` with padded + region/technology/class dimensions so downstream consumers can load it + as a regular xarray dataset. + """ logger.info(f"Writing clustered data to {output_path}") - # Organize data by region and technology + # Organize data by region and technology so we can emit ragged class arrays. regions = list(set(k[0] for k in clustered_data.keys())) techs = list(set(str(k[1]) for k in clustered_data.keys())) # Convert to string @@ -681,7 +1016,7 @@ def write_clustered_netcdf( ) # Add data variables with heterogeneous class dimension per region-tech - # Note: xarray doesn't support ragged dimensions natively, so we'll use max_classes + # xarray does not support ragged dimensions natively, so we pad to max_classes. max_classes = max( len([k for k in clustered_data.keys() if k[0] == r and str(k[1]) == t]) for r in regions @@ -748,20 +1083,37 @@ def write_clustered_netcdf( ) logger.info(f" ✓ Wrote {output_path}") - logger.info(f" Dimensions: {dict(ds_out.dims)}") + logger.info(f" Dimensions: {dict(ds_out.sizes)}") logger.info(f" Variables: {list(ds_out.data_vars)}") def validate_clustering( - clustered_data: Dict, ds_merged: xr.Dataset, output_report: str + clustered_data: Dict, + df_features: pd.DataFrame, + ds_merged: xr.Dataset, + output_report: str, ) -> Dict: - """Validate clustering integrity.""" + """Validate that clustering preserves capacity and profile structure. + + Args: + clustered_data: Aggregated cluster payload returned by the workflow. + df_features: Source feature table used to compute baseline statistics. + ds_merged: Filtered merged dataset used to compare the original input + capacity totals. + output_report: Destination path for the JSON validation report. + + Returns: + A nested dictionary of validation metrics covering capacity + preservation, mean capacity-factor consistency, merit-order retention, + and threshold-based capacity retention. + """ logger.info("Validating clustering...") validation_results = { "total_clusters": len(clustered_data), "capacity_preservation": {}, "avg_cf_consistency": {}, + "merit_order_preservation": {}, "temporal_quality": {}, } @@ -770,12 +1122,16 @@ def validate_clustering( ) for region, tech in region_tech_pairs: - # Total capacity check + df_subset = df_features[ + (df_features["region"] == region) & (df_features["technology"] == str(tech)) + ] + + # Total capacity check remains the basic integrity guard. original_cap = [] - for bus_id in ds_merged.bus.values: + for bus_id in df_subset["bus_id"].values: try: cap = float( - ds_merged["p_nom_max"].sel(bus=bus_id, technology=tech).values + ds_merged["p_nom_max"].sel(bus=bus_id, technology=str(tech)).values ) if not np.isnan(cap): original_cap.append(cap) @@ -800,6 +1156,46 @@ def validate_clustering( "preservation_pct": float(preservation_pct), } + cluster_rows = [ + cluster_info + for (r, t, _), cluster_info in clustered_data.items() + if r == region and str(t) == str(tech) + ] + if cluster_rows: + cluster_avg_cf = np.array( + [row["avg_cf"] for row in cluster_rows], dtype=float + ) + cluster_capacity = np.array( + [row["capacity"] for row in cluster_rows], dtype=float + ) + merit_corr = float( + pd.Series(cluster_avg_cf).corr(pd.Series(cluster_capacity)) + ) + + thresholds = {} + for threshold in CF_THRESHOLDS: + retained_capacity = float( + np.sum(cluster_capacity[cluster_avg_cf >= threshold]) + ) + total_capacity = float(np.sum(cluster_capacity)) + # This metric answers whether the expensive/high-quality pocket survives. + thresholds[f"{threshold}"] = { + "retained_capacity_mw": retained_capacity, + "retained_pct": float(100.0 * retained_capacity / total_capacity) + if total_capacity > 0 + else 100.0, + } + + validation_results["avg_cf_consistency"][f"{region}_{tech}"] = { + "avg_cf_mean": float(np.mean(df_subset["avg_cf"].values)) + if len(df_subset) + else None, + } + validation_results["merit_order_preservation"][f"{region}_{tech}"] = { + "cluster_capacity_avg_cf_corr": merit_corr, + "threshold_retention": thresholds, + } + logger.info( f" Capacity preservation: {np.mean([v['preservation_pct'] for v in validation_results['capacity_preservation'].values()]):.1f}%" ) @@ -820,53 +1216,62 @@ def validate_clustering( def main(): - """Main clustering pipeline.""" + """Run the full renewable clustering workflow end to end. + + The entrypoint loads the merged renewable profiles, filters the supported + technologies, extracts and caches features, clusters each region-technology + subset, aggregates pseudo-bus outputs, writes the clustered NetCDF, and + finally emits a validation report. + """ + if snakemake is None: + raise RuntimeError( + "This script must be executed through Snakemake so the injected `snakemake` object is available." + ) + logger.info("=" * 70) logger.info("CLUSTERING RENEWABLE PROFILES FOR OPTIMIZATION") logger.info("=" * 70) - # Load config + # Load config and normalize the feature weights before any clustering starts. config = snakemake.config logger.info(f"Clustering config: {config.get('clustering', {})}") clustering_config = config.get("clustering", {}) - n_clusters_onwind = clustering_config.get("n_clusters_onwind", 40) - n_clusters_pvplant = clustering_config.get("n_clusters_pvplant", 40) feature_weights = clustering_config.get( "feature_weights", { - "avg_cf": 0.5, + "merit_order": 0.5, "temporal": 0.3, "geospatial": 0.2, }, ) # Normalize weights - weight_sum = sum(feature_weights.values()) - feature_weights = {k: v / weight_sum for k, v in feature_weights.items()} + feature_weights = normalize_weights(feature_weights) logger.info(f"Normalized feature weights: {feature_weights}") - # Load and filter data + # Load and filter the merged renewable profiles. ds = load_merged_data(str(snakemake.input.merged)) ds_filtered = filter_to_onwind_pv(ds) - # Extract features (with caching) + # Extract features (with caching). cache_features = Path("resources") / "features_cache.csv" df_features, region_buses = extract_features( ds_filtered, config, str(cache_features) ) - # Cluster each region-technology group in parallel + # Cluster each region-technology group in parallel. logger.info(f"Starting parallel clustering with {snakemake.threads} threads") - # Build list of (region, tech) pairs to cluster + # Build list of region-tech pairs that actually exist in the filtered data. region_tech_pairs = [ - (region, tech) + (region, str(tech)) for region in region_buses.keys() for tech in ds_filtered.technology.values if len( df_features[ - (df_features["region"] == region) & (df_features["technology"] == tech) + (df_features["region"] == region) + & (df_features["technology"] == str(tech)) ] ) > 0 @@ -874,26 +1279,25 @@ def main(): logger.info(f" Total region-technology pairs: {len(region_tech_pairs)}") - # Create clustering cache directory + # Cache region-tech assignments so repeated runs can recover quickly. clustering_cache_dir = Path("resources") / "clustering_cache" clustering_cache_dir.mkdir(parents=True, exist_ok=True) logger.info(f"Clustering cache directory: {clustering_cache_dir}") - # Parallelize clustering across all region-tech pairs with tqdm progress + # Parallelize clustering across all region-tech pairs with tqdm progress. with tqdm_joblib( tqdm( total=len(region_tech_pairs), desc="Clustering region-tech pairs", unit="pair", ) - ) as pbar: + ): results = joblib.Parallel(n_jobs=snakemake.threads)( joblib.delayed(cluster_region_technology_pair)( region, tech, df_features, - n_clusters_onwind, - n_clusters_pvplant, + clustering_config, feature_weights, str(clustering_cache_dir), ) @@ -908,7 +1312,7 @@ def main(): # Convert results to dict clusters_dict = dict(results) - # Aggregate clusters (with caching) + # Aggregate clusters into pseudo-buses and write the output NetCDF. cache_clusters = Path("resources") / "clusters_cache.joblib" clustered_data, cluster_metadata = aggregate_clusters( df_features, @@ -922,8 +1326,10 @@ def main(): # Write output write_clustered_netcdf(clustered_data, str(snakemake.output.clustered), config) - # Validation - validate_clustering(clustered_data, ds_filtered, str(snakemake.output.report)) + # Validation report for capacity and merit-order preservation. + validate_clustering( + clustered_data, df_features, ds_filtered, str(snakemake.output.report) + ) logger.info("=" * 70) logger.info("CLUSTERING COMPLETE") From 965815d9aa2b19059cee4476641670235af5c383 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 1 Jun 2026 17:27:56 +0200 Subject: [PATCH 123/216] chore: update to regional wacc in config --- config/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/config.yaml b/config/config.yaml index 00b1a18..ea32d74 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -24,7 +24,7 @@ trade_chains: id: labour_2050 cost_year: 2050 final_product: steel - wacc: uniform #regional + wacc: regional #regional labour_cost: True #Include labour cost tradeable_commodities: [iron_ore, hbi] stages: From 42412f24bec1ba3d56192f4b78e2577651298543 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 09:53:59 +0200 Subject: [PATCH 124/216] fix: drop rows with NaN supply curve values --- workflow/scripts/model_trade.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index b8b97a9..3cf7639 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -105,8 +105,16 @@ def building_model( for r in range(len(supply_curves_interone)): region_file_interone = supply_curves_interone[r] region_file_intertwo = supply_curves_intertwo[r] - region_data_interone = pd.read_csv(region_file_interone, header=0) - region_data_intertwo = pd.read_csv(region_file_intertwo, header=0) + region_data_interone = ( + pd.read_csv(region_file_interone, header=0) + .dropna(subset=[f"{cost_descriptor} [EUR/{unit}]"]) + .reset_index(drop=True) + ) # Filter out rows where "lcox [EUR/t]" is NaN + region_data_intertwo = ( + pd.read_csv(region_file_intertwo, header=0) + .dropna(subset=[f"{cost_descriptor} [EUR/{unit}]"]) + .reset_index(drop=True) + ) # Filter out rows where "lcox [EUR/t]" is NaN region_name = os.path.basename(region_file_interone).rsplit( "_marginal_cost_", 1 @@ -233,7 +241,9 @@ def building_model( ) p_nom = ( grid_potential.loc[region_name, "potential_mt_steel"] * 1e6 - ) / len(region_data_intertwo) # split evenly across supply steps + ) / len( + region_data_intertwo + ) # split evenly across supply steps m_cost = float( region_data_intertwo[f"{cost_descriptor} [EUR/{unit}]"][s] @@ -1122,11 +1132,11 @@ def _link_weight(link_name): "model_trade", cost_year="2050", interone="hbi", - intertwo="eaf-grid", + intertwo="steel", final="steel", scenario="default", - wacc="uniform", - chain_id="default_2050", + wacc="regional", + chain_id="labour_2050", ) final = snakemake.wildcards["final"] From e2bae2fcdb84bff0b3eee7bad0ea8350ce8ee105 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 09:56:14 +0200 Subject: [PATCH 125/216] fix: interpretation of supply curve as independent row values --- workflow/scripts/model_trade.py | 17 +++++------------ 1 file changed, 5 insertions(+), 12 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 3cf7639..a416b48 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -182,12 +182,9 @@ def building_model( # --- Stage 1 supply: ore → interone (material) or direct supply (energy) --- for s in range(len(region_data_interone)): - if s == 0: - p_nom = float(region_data_interone[f"demand [{unit}]"][s]) - else: - p_nom = float(region_data_interone[f"demand [{unit}]"][s]) - float( - region_data_interone[f"demand [{unit}]"][s - 1] - ) + + p_nom = float(region_data_interone[f"demand [{unit}]"][s]) + m_cost = float(region_data_interone[f"{cost_descriptor} [EUR/{unit}]"][s]) if not is_material_chain: @@ -227,12 +224,8 @@ def building_model( # --- Stage 2 supply: interone → final (two-stage material chain only) --- if two_stage: for s in range(len(region_data_intertwo)): - if s == 0: - p_nom = float(region_data_intertwo[f"demand [{unit}]"][s]) - else: - p_nom = float(region_data_intertwo[f"demand [{unit}]"][s]) - float( - region_data_intertwo[f"demand [{unit}]"][s - 1] - ) + + p_nom = float(region_data_intertwo[f"demand [{unit}]"][s]) # Override capacity for grid-connected EAF based on grid potential if intertwo == "eaf-grid": From 381ab69c3f42dcba5f50fe1e9ebd31eef8dc0fc8 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 12:50:53 +0200 Subject: [PATCH 126/216] chore: rename ormuz and babalmandeb --- workflow/notebooks/plot-mga.ipynb | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 1c5908d..9714659 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -595,6 +595,12 @@ "active_cp = [cp for cp in config_chokepoints if df_cp.loc[cp].sum() > 0]\n", "print(f\"Active chokepoints (with trade): {active_cp}\")\n", "\n", + "# Chokepoint display names (override .title() defaults)\n", + "cp_display_names = {\n", + " \"ormuz\": \"Hormuz\",\n", + " \"babalmandab\": \"Bab el-Mandeb\",\n", + "}\n", + "\n", "# Chokepoint colors (distinct palette)\n", "cp_colors = {\n", " \"babalmandab\": \"#e41a1c\",\n", @@ -628,7 +634,7 @@ "for cp in active_cp:\n", " cp_arr = np.array(df_cp.loc[cp].values, dtype=float)\n", " color = cp_colors.get(cp, \"#333333\")\n", - " label = cp.replace(\"_\", \" \").title()\n", + " label = cp_display_names.get(cp, cp.replace(\"_\", \" \").title())\n", " ax.plot(epsilon_arr, cp_arr, color=color, linewidth=1.5,\n", " linestyle=\"--\", marker=\"s\", markersize=4, alpha=0.8,\n", " label=label, zorder=10)\n", @@ -915,7 +921,7 @@ " if cpname in df_cp.index and df_cp.loc[cpname].sum() > 0:\n", " vals = np.array(df_cp.loc[cpname].values, dtype=float)\n", " cp_region_lines.append(dict(\n", - " name=cpname.replace(\"_\", \" \").title(),\n", + " name=cp_display_names.get(cpname, cpname.replace(\"_\", \" \").title()),\n", " values=vals,\n", " color=cp_colors.get(cpname, \"#333333\"),\n", " linestyle=\"-\",\n", From 193d2d1c67f91026b6a67d7a6124b933deb0cda8 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 13:08:38 +0200 Subject: [PATCH 127/216] chore: adjust arrow and legend position of unstable country case --- workflow/notebooks/plot-mga.ipynb | 51 ++++++++++++++++++++++--------- 1 file changed, 37 insertions(+), 14 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 9714659..592cc40 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -915,7 +915,7 @@ "cp_eps = np.array([float(c) for c in cp_total_row.index]) * 100 # → %\n", "cp_total_vals = np.array(cp_total_row.values, dtype=float)\n", "\n", - "# Per-chokepoint breakdown (only those with any trade)\n", + "# Per-chokepoint breakdown (only those with any trade), sorted by final value descending\n", "cp_region_lines = []\n", "for cpname in config_chokepoints:\n", " if cpname in df_cp.index and df_cp.loc[cpname].sum() > 0:\n", @@ -927,6 +927,7 @@ " linestyle=\"-\",\n", " alpha=0.6,\n", " ))\n", + "cp_region_lines.sort(key=lambda d: d[\"values\"][-1], reverse=True)\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# 2) Stable exporters data (from df_hbi)\n", @@ -941,7 +942,7 @@ " df_hbi[df_hbi[\"Stability Class\"] == \"Unstable\"][stab_eps_cols].sum().values, dtype=float\n", ")\n", "\n", - "# Per-region breakdown for stable regions (only those with production)\n", + "# Per-region breakdown, sorted by final value descending within each group\n", "stab_region_lines_stable = []\n", "for region in stable_regions:\n", " if region in df_hbi.index:\n", @@ -954,8 +955,8 @@ " linestyle=\"-\",\n", " alpha=0.6,\n", " ))\n", + "stab_region_lines_stable.sort(key=lambda d: d[\"values\"][-1], reverse=True)\n", "\n", - "# Per-region breakdown for unstable regions (only those with production)\n", "stab_region_lines_unstable = []\n", "for region in unstable_regions:\n", " if region in df_hbi.index:\n", @@ -968,6 +969,7 @@ " linestyle=\"--\",\n", " alpha=0.7,\n", " ))\n", + "stab_region_lines_unstable.sort(key=lambda d: d[\"values\"][-1], reverse=True)\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# 3) Blocs data (from bl_summary)\n", @@ -978,10 +980,12 @@ "bl_a2b_vals = np.array(bl_summary[\"Block A → Block B\"].values, dtype=float)\n", "bl_b2a_vals = np.array(bl_summary[\"Block B → Block A\"].values, dtype=float)\n", "\n", - "bl_region_lines = [\n", + "# Sort directional lines by final value descending\n", + "bl_region_lines_unsorted = [\n", " dict(name=\"Bloc A → Bloc B\", values=bl_a2b_vals, color=\"#e41a1c\", linestyle=\"-\", alpha=0.6),\n", " dict(name=\"Bloc B → Bloc A\", values=bl_b2a_vals, color=\"#377eb8\", linestyle=\"-\", alpha=0.6),\n", "]\n", + "bl_region_lines = sorted(bl_region_lines_unsorted, key=lambda d: d[\"values\"][-1], reverse=True)\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# Plot definitions\n", @@ -998,6 +1002,7 @@ " alpha=0.25,\n", " arrow_up=None,\n", " arrow_dn=[\"Decreased\", \"high-risk routes\"],\n", + " arrow_x_frac=0.70,\n", " y_label_add=\"trade through maritime chokepoints\",\n", " regions=cp_region_lines,\n", " extra_curves=[],\n", @@ -1009,11 +1014,15 @@ " y_mid=stab_unstable_total[0],\n", " color=\"#3B6D11\",\n", " alpha=0.25,\n", - " arrow_up=[\"Increased stable\", \"HBI producers\"],\n", - " arrow_dn=None,\n", + " arrow_up=None,\n", + " arrow_dn=[\"Decreased unstable\", \"production\"],\n", + " arrow_x_frac=0.45,\n", " y_label_add=\"production in stable regions\",\n", " regions=stab_region_lines_stable + stab_region_lines_unstable,\n", " extra_curves=[],\n", + " # Legend grouping: n_stable stable lines, then n_unstable unstable lines\n", + " n_stable=len(stab_region_lines_stable),\n", + " n_unstable=len(stab_region_lines_unstable),\n", " ),\n", " dict(\n", " title=\"Fragmentation / Bloc trade\",\n", @@ -1024,6 +1033,7 @@ " alpha=0.25,\n", " arrow_up=None,\n", " arrow_dn=[\"Decreased trade\", \"between blocs\"],\n", + " arrow_x_frac=0.70,\n", " y_label_add=\"trade between Bloc A and B\",\n", " regions=bl_region_lines,\n", " extra_curves=[\n", @@ -1047,7 +1057,7 @@ " y_mid = p[\"y_mid\"]\n", " gap = Y_MAX * GAP_FRAC\n", "\n", - " # Main curve (straight lines, no spline)\n", + " # Main curve\n", " ax.plot(eps, curve, color=p[\"color\"], linewidth=2.5, marker=\"o\",\n", " markersize=4, label=\"Global\", zorder=10)\n", "\n", @@ -1058,7 +1068,7 @@ " linestyle=ec[\"linestyle\"], alpha=ec[\"alpha\"],\n", " marker=\"d\", markersize=3, label=ec[\"name\"], zorder=5)\n", "\n", - " # Regional decomposition lines (straight lines)\n", + " # Regional decomposition lines\n", " if p.get(\"regions\"):\n", " for reg in p[\"regions\"]:\n", " ax.plot(eps, reg[\"values\"], color=reg[\"color\"], linewidth=1.2,\n", @@ -1073,17 +1083,30 @@ " ax.set_title(p[\"title\"], fontsize=13, fontweight=\"medium\", pad=10)\n", " ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.5)\n", "\n", - " # Legend\n", - " ax.legend(loc=\"best\", fontsize=7, framealpha=0.9)\n", + " # ── Legend: Global always on top; within groups, lines sorted by value (high→low) ──\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " if \"n_stable\" in p:\n", + " # Production subplot: Global | stable group (pre-sorted) | unstable group (pre-sorted)\n", + " n_s = p[\"n_stable\"]\n", + " global_h, global_l = handles[:1], labels[:1]\n", + " stable_h, stable_l = handles[1:1+n_s], labels[1:1+n_s]\n", + " unstable_h, unstable_l = handles[1+n_s:], labels[1+n_s:]\n", + " ax.legend(global_h + stable_h + unstable_h,\n", + " global_l + stable_l + unstable_l,\n", + " loc=\"best\", fontsize=7, framealpha=0.9)\n", + " else:\n", + " # Global on top, remaining entries reversed (highest value at top)\n", + " ax.legend([handles[0]] + handles[1:][::-1],\n", + " [labels[0]] + labels[1:][::-1],\n", + " loc=\"best\", fontsize=7, framealpha=0.9)\n", "\n", " # ── Arrows ──\n", " arrowprops = dict(arrowstyle=\"->\", color=\"#444441\", lw=1.4, mutation_scale=12)\n", "\n", - " # Arrow x-position: 70 % of the way across the ε range\n", - " arrow_x = eps[0] + 0.70 * (eps[-1] - eps[0])\n", - " label_x = eps[0] + 0.75 * (eps[-1] - eps[0])\n", + " arrow_x_frac = p.get(\"arrow_x_frac\", 0.70)\n", + " arrow_x = eps[0] + arrow_x_frac * (eps[-1] - eps[0])\n", + " label_x = eps[0] + (arrow_x_frac + 0.05) * (eps[-1] - eps[0])\n", "\n", - " # Interpolate curve value at arrow_x for endpoint\n", " curve_at_arrow = np.interp(arrow_x, eps, curve)\n", "\n", " if p[\"arrow_dn\"] is not None:\n", From 6c3f7812c30f627f6734feb2bfbdb1ec0713a3e9 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 13:12:14 +0200 Subject: [PATCH 128/216] chore: switch bloc trade entries in legend --- workflow/notebooks/plot-mga.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 592cc40..b1fad29 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -985,7 +985,7 @@ " dict(name=\"Bloc A → Bloc B\", values=bl_a2b_vals, color=\"#e41a1c\", linestyle=\"-\", alpha=0.6),\n", " dict(name=\"Bloc B → Bloc A\", values=bl_b2a_vals, color=\"#377eb8\", linestyle=\"-\", alpha=0.6),\n", "]\n", - "bl_region_lines = sorted(bl_region_lines_unsorted, key=lambda d: d[\"values\"][-1], reverse=True)\n", + "bl_region_lines = sorted(bl_region_lines_unsorted, key=lambda d: d[\"values\"][-1], reverse=False)\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# Plot definitions\n", From 36e1e4fe10d795cd178565d8c6a591adb3264d72 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 13:20:32 +0200 Subject: [PATCH 129/216] feat: manually adjust political stability via config --- .../prepare-political-stability.ipynb | 505 +++++++++++++++++- 1 file changed, 490 insertions(+), 15 deletions(-) diff --git a/workflow/notebooks/prepare-political-stability.ipynb b/workflow/notebooks/prepare-political-stability.ipynb index 14dd3fd..a11e107 100644 --- a/workflow/notebooks/prepare-political-stability.ipynb +++ b/workflow/notebooks/prepare-political-stability.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "93dbbc06", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - 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    4Bhutan1.3551996-2024
    \n", + "
    " + ], + "text/plain": [ + " Country Score Rank Period\n", + "0 Liechtenstein 1.58 1 1996-2024\n", + "1 Micronesia 1.50 2 2003-2024\n", + "2 Tuvalu 1.47 3 2003-2024\n", + "3 San Marino 1.37 4 2003-2024\n", + "4 Bhutan 1.35 5 1996-2024" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Columns: Country, Score, Rank, Period\n", "# Score is the political stability index (higher = more stable)\n", @@ -82,10 +163,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ea783f79", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matched 194 / 194 countries to ISO-3 codes\n" + ] + } + ], "source": [ "country_name_corrections = {\n", " # Config region list corrections (standard name → pycountry name)\n", @@ -178,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6d001e33", "metadata": {}, "outputs": [], @@ -195,10 +284,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "366c0ee1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "region\n", + "Other 37\n", + "Europe 33\n", + "North_West_Africa 23\n", + "Subsaharan_Africa 21\n", + "Middle_East 13\n", + "Pacific_Asia 11\n", + "Central_America 11\n", + "Eurasia 9\n", + "South_America 9\n", + "West_Asia 7\n", + "Far_West_Europe 6\n", + "East_Asia 4\n", + "South_South_America 3\n", + "East_East_Asia 3\n", + "Oceania 2\n", + "North_America 2\n", + "Name: count, dtype: int64\n" + ] + } + ], "source": [ "# Assign region to each country\n", "ps[\"region\"] = ps[\"country_code\"].map(lambda iso: iso_to_region.get(iso, \"Other\"))\n", @@ -215,10 +329,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "7a11c95a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_20946/2609092797.py:26: FutureWarning:\n", + "\n", + "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "\n" + ] + } + ], "source": [ "# --- fetch GDP (current USD) from World Bank ---\n", "gdp_raw = wbdata.get_dataframe(\n", @@ -257,15 +382,365 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "3c789ec0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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    political_stabilityn_countriesps_spread
    region
    Oceania0.8364322.00.36
    Other0.72811637.02.31
    East_East_Asia0.6966673.00.81
    Europe0.34661133.02.04
    East_Asia0.3128614.01.07
    Far_West_Europe0.0883486.01.45
    South_South_America0.0330993.01.45
    North_America-0.0493282.00.70
    Pacific_Asia-0.14195911.03.32
    Central_America-0.47402011.02.38
    Middle_East-0.55116513.03.70
    South_America-0.6289889.02.22
    Eurasia-0.7474899.01.15
    West_Asia-0.8694247.03.56
    Subsaharan_Africa-0.93760521.03.29
    North_West_Africa-1.07273223.02.50
    \n", + "
    " + ], + "text/plain": [ + " political_stability n_countries ps_spread\n", + "region \n", + "Oceania 0.836432 2.0 0.36\n", + "Other 0.728116 37.0 2.31\n", + "East_East_Asia 0.696667 3.0 0.81\n", + "Europe 0.346611 33.0 2.04\n", + "East_Asia 0.312861 4.0 1.07\n", + "Far_West_Europe 0.088348 6.0 1.45\n", + "South_South_America 0.033099 3.0 1.45\n", + "North_America -0.049328 2.0 0.70\n", + "Pacific_Asia -0.141959 11.0 3.32\n", + "Central_America -0.474020 11.0 2.38\n", + "Middle_East -0.551165 13.0 3.70\n", + "South_America -0.628988 9.0 2.22\n", + "Eurasia -0.747489 9.0 1.15\n", + "West_Asia -0.869424 7.0 3.56\n", + "Subsaharan_Africa -0.937605 21.0 3.29\n", + "North_West_Africa -1.072732 23.0 2.50" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\n", "ps_by_region.sort_values(\"political_stability\", ascending=False)" ] }, + { + "cell_type": "code", + "execution_count": 10, + "id": "515751d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied penalty to Middle_East: -0.5512 → -1.1512 (−0.6)\n" + ] + }, + { + "data": { + "text/html": [ + "
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    political_stabilityn_countriesps_spread
    region
    Oceania0.8364322.00.36
    Other0.72811637.02.31
    East_East_Asia0.6966673.00.81
    Europe0.34661133.02.04
    East_Asia0.3128614.01.07
    Far_West_Europe0.0883486.01.45
    South_South_America0.0330993.01.45
    North_America-0.0493282.00.70
    Pacific_Asia-0.14195911.03.32
    Central_America-0.47402011.02.38
    South_America-0.6289889.02.22
    Eurasia-0.7474899.01.15
    West_Asia-0.8694247.03.56
    Subsaharan_Africa-0.93760521.03.29
    North_West_Africa-1.07273223.02.50
    Middle_East-1.15116513.03.70
    \n", + "
    " + ], + "text/plain": [ + " political_stability n_countries ps_spread\n", + "region \n", + "Oceania 0.836432 2.0 0.36\n", + "Other 0.728116 37.0 2.31\n", + "East_East_Asia 0.696667 3.0 0.81\n", + "Europe 0.346611 33.0 2.04\n", + "East_Asia 0.312861 4.0 1.07\n", + "Far_West_Europe 0.088348 6.0 1.45\n", + "South_South_America 0.033099 3.0 1.45\n", + "North_America -0.049328 2.0 0.70\n", + "Pacific_Asia -0.141959 11.0 3.32\n", + "Central_America -0.474020 11.0 2.38\n", + "South_America -0.628988 9.0 2.22\n", + "Eurasia -0.747489 9.0 1.15\n", + "West_Asia -0.869424 7.0 3.56\n", + "Subsaharan_Africa -0.937605 21.0 3.29\n", + "North_West_Africa -1.072732 23.0 2.50\n", + "Middle_East -1.151165 13.0 3.70" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply manual stability penalties from config (design.stability_penalty)\n", + "# Each entry subtracts the given value from the region's computed political_stability score.\n", + "stability_penalties = config.get(\"design\", {}).get(\"stability_penalty\", {})\n", + "\n", + "if stability_penalties:\n", + " for region, penalty in stability_penalties.items():\n", + " if region in ps_by_region.index:\n", + " original = ps_by_region.loc[region, \"political_stability\"]\n", + " ps_by_region.loc[region, \"political_stability\"] -= penalty\n", + " print(f\"Applied penalty to {region}: {original:.4f} → {ps_by_region.loc[region, 'political_stability']:.4f} (−{penalty})\")\n", + " else:\n", + " print(f\"Warning: region '{region}' in stability_penalty not found in ps_by_region index.\")\n", + "else:\n", + " print(\"No stability penalties configured.\")\n", + "\n", + "ps_by_region.sort_values(\"political_stability\", ascending=False)\n" + ] + }, { "cell_type": "markdown", "id": "7c408942", @@ -276,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "fc4a8f12", "metadata": {}, "outputs": [], From 339702bbe7cdd841b56e65784e79045da346928c Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 13:46:46 +0200 Subject: [PATCH 130/216] feat: reduce middle east stability by -0.6 points --- config/config.yaml | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index ea32d74..f287fac 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -1,6 +1,6 @@ enable: - run_supply_chain: True # Enable for first run - run_supply_curve: True # Enable for first run + run_supply_chain: False # Enable for first run + run_supply_curve: False # Enable for first run # Output toggles for Step 0/1 (greenfield supply curve generation) @@ -104,12 +104,12 @@ scenario: indicator: "stability" threshold_value: -0.5 # Regions with stability index below -0.5 weighting: # Use `workflow/notebooks/utility-weighted-mga.ipynb` to define the weights based on the political stability index, if desired - North_West_Africa: 1.00 - Subsaharan_Africa: 0.87 - West_Asia: 0.81 - Eurasia: 0.70 - South_America: 0.59 - Middle_East: 0.51 + Middle_East: 1.00 + North_West_Africa: 0.93 + Subsaharan_Africa: 0.81 + West_Asia: 0.76 + Eurasia: 0.65 + South_America: 0.55 mga-chokepoints: modifiers: cost_penalty: @@ -200,6 +200,8 @@ design: # Eurasia: 1.1 # South_America: 0.7 # Oceania: 0.8 + stability_penalty: + Middle_East: 0.6 # Absolute stability penalty, reduces the stability index of a region by this number using substraction techno-economic parameters: pypsa_tech_version: v0.14.0 From 180dab564190b3f456ee2fa269d78df11e76f2e6 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 13:47:53 +0200 Subject: [PATCH 131/216] chore: adjust scenario inputs for mga plots --- rules/reporting.smk | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index b8e5957..2dc9e24 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -33,9 +33,9 @@ rule collect_figures: rule plot_mga: input: - network_mga_production = "results/chain_id~default_2050/cost_year~2050/interone~hbi/intertwo~eaf-grid/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", - network_mga_chokepoints = "results/chain_id~default_2050/cost_year~2050/interone~hbi/intertwo~eaf-grid/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", - network_mga_blocks = "results/chain_id~default_2050/cost_year~2050/interone~hbi/intertwo~eaf-grid/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", + network_mga_production = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", + network_mga_chokepoints = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", + network_mga_blocks = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", political_stability = "resources/political_stability_clustered.csv", trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", output: From 3623f3dcc4dd4f2ef71bb16d6f83549cd2e53e82 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 14:23:37 +0200 Subject: [PATCH 132/216] fix: adjust matching to get Frances iso3 code correctly --- workflow/scripts/model_trade.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index a416b48..d8f49ef 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -46,7 +46,7 @@ def build_region_geodataframe(config): world = gpd.read_file(reader) # Assign region and dissolve to remove internal country borders - world["region"] = world["ISO_A3"].map(iso_to_region) + world["region"] = world["ISO_A3_EH"].map(iso_to_region) region_gdf = world.dropna(subset=["region"]).dissolve(by="region").reset_index() return region_gdf From 4271f499e910895fe63af47796c137b0c4bd7e8c Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 2 Jun 2026 14:26:07 +0200 Subject: [PATCH 133/216] chore: remove outputs from notebook --- .../prepare-political-stability.ipynb | 484 +----------------- 1 file changed, 17 insertions(+), 467 deletions(-) diff --git a/workflow/notebooks/prepare-political-stability.ipynb b/workflow/notebooks/prepare-political-stability.ipynb index a11e107..d97ddee 100644 --- a/workflow/notebooks/prepare-political-stability.ipynb +++ b/workflow/notebooks/prepare-political-stability.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "93dbbc06", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "77dfc869", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "dbb69ac8", "metadata": {}, "outputs": [], @@ -61,91 +61,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "9dfa7a76", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    CountryScoreRankPeriod
    0Liechtenstein1.5811996-2024
    1Micronesia1.5022003-2024
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    4Bhutan1.3551996-2024
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    " - ], - "text/plain": [ - " Country Score Rank Period\n", - "0 Liechtenstein 1.58 1 1996-2024\n", - "1 Micronesia 1.50 2 2003-2024\n", - "2 Tuvalu 1.47 3 2003-2024\n", - "3 San Marino 1.37 4 2003-2024\n", - "4 Bhutan 1.35 5 1996-2024" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Columns: Country, Score, Rank, Period\n", "# Score is the political stability index (higher = more stable)\n", @@ -163,18 +82,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ea783f79", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Matched 194 / 194 countries to ISO-3 codes\n" - ] - } - ], + "outputs": [], "source": [ "country_name_corrections = {\n", " # Config region list corrections (standard name → pycountry name)\n", @@ -267,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6d001e33", "metadata": {}, "outputs": [], @@ -284,35 +195,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "366c0ee1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "region\n", - "Other 37\n", - "Europe 33\n", - "North_West_Africa 23\n", - "Subsaharan_Africa 21\n", - "Middle_East 13\n", - "Pacific_Asia 11\n", - "Central_America 11\n", - "Eurasia 9\n", - "South_America 9\n", - "West_Asia 7\n", - "Far_West_Europe 6\n", - "East_Asia 4\n", - "South_South_America 3\n", - "East_East_Asia 3\n", - "Oceania 2\n", - "North_America 2\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Assign region to each country\n", "ps[\"region\"] = ps[\"country_code\"].map(lambda iso: iso_to_region.get(iso, \"Other\"))\n", @@ -329,21 +215,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "7a11c95a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_20946/2609092797.py:26: FutureWarning:\n", - "\n", - "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# --- fetch GDP (current USD) from World Bank ---\n", "gdp_raw = wbdata.get_dataframe(\n", @@ -382,169 +257,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "3c789ec0", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    political_stabilityn_countriesps_spread
    region
    Oceania0.8364322.00.36
    Other0.72811637.02.31
    East_East_Asia0.6966673.00.81
    Europe0.34661133.02.04
    East_Asia0.3128614.01.07
    Far_West_Europe0.0883486.01.45
    South_South_America0.0330993.01.45
    North_America-0.0493282.00.70
    Pacific_Asia-0.14195911.03.32
    Central_America-0.47402011.02.38
    Middle_East-0.55116513.03.70
    South_America-0.6289889.02.22
    Eurasia-0.7474899.01.15
    West_Asia-0.8694247.03.56
    Subsaharan_Africa-0.93760521.03.29
    North_West_Africa-1.07273223.02.50
    \n", - "
    " - ], - "text/plain": [ - " political_stability n_countries ps_spread\n", - "region \n", - "Oceania 0.836432 2.0 0.36\n", - "Other 0.728116 37.0 2.31\n", - "East_East_Asia 0.696667 3.0 0.81\n", - "Europe 0.346611 33.0 2.04\n", - "East_Asia 0.312861 4.0 1.07\n", - "Far_West_Europe 0.088348 6.0 1.45\n", - "South_South_America 0.033099 3.0 1.45\n", - "North_America -0.049328 2.0 0.70\n", - "Pacific_Asia -0.141959 11.0 3.32\n", - "Central_America -0.474020 11.0 2.38\n", - "Middle_East -0.551165 13.0 3.70\n", - "South_America -0.628988 9.0 2.22\n", - "Eurasia -0.747489 9.0 1.15\n", - "West_Asia -0.869424 7.0 3.56\n", - "Subsaharan_Africa -0.937605 21.0 3.29\n", - "North_West_Africa -1.072732 23.0 2.50" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\n", "ps_by_region.sort_values(\"political_stability\", ascending=False)" @@ -552,176 +268,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "515751d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Applied penalty to Middle_East: -0.5512 → -1.1512 (−0.6)\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    political_stabilityn_countriesps_spread
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    Oceania0.8364322.00.36
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    West_Asia-0.8694247.03.56
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    " - ], - "text/plain": [ - " political_stability n_countries ps_spread\n", - "region \n", - "Oceania 0.836432 2.0 0.36\n", - "Other 0.728116 37.0 2.31\n", - "East_East_Asia 0.696667 3.0 0.81\n", - "Europe 0.346611 33.0 2.04\n", - "East_Asia 0.312861 4.0 1.07\n", - "Far_West_Europe 0.088348 6.0 1.45\n", - "South_South_America 0.033099 3.0 1.45\n", - "North_America -0.049328 2.0 0.70\n", - "Pacific_Asia -0.141959 11.0 3.32\n", - "Central_America -0.474020 11.0 2.38\n", - "South_America -0.628988 9.0 2.22\n", - "Eurasia -0.747489 9.0 1.15\n", - "West_Asia -0.869424 7.0 3.56\n", - "Subsaharan_Africa -0.937605 21.0 3.29\n", - "North_West_Africa -1.072732 23.0 2.50\n", - "Middle_East -1.151165 13.0 3.70" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Apply manual stability penalties from config (design.stability_penalty)\n", "# Each entry subtracts the given value from the region's computed political_stability score.\n", @@ -751,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "fc4a8f12", "metadata": {}, "outputs": [], From 13de3feea6b0dd1e391b23eeb230d6a125d8727c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 3 Jun 2026 11:20:16 +0200 Subject: [PATCH 134/216] feat: improve clustering by stratyfying --- config/config.yaml | 24 +- pixi.toml | 1 + rules/preparation.smk | 17 +- ...renewables_consolidated_vs_clustered.ipynb | 1033 ---------- ...pare_renewables_trace_vs_pypsa-earth.ipynb | 566 ++++++ workflow/notebooks/plot_helpers.py | 605 ++++++ workflow/scripts/cluster_renewables.py | 1713 ++++++----------- 7 files changed, 1799 insertions(+), 2160 deletions(-) delete mode 100644 workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb create mode 100644 workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb create mode 100644 workflow/notebooks/plot_helpers.py diff --git a/config/config.yaml b/config/config.yaml index 9a8c050..313f69b 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -168,26 +168,24 @@ regions: "East_East_Asia": ["JPN","KOR","PRK"] "Oceania": ["AUS","NZL"] -# Renewable generator clustering configuration -# Used by cluster_renewables.py to cluster 122k+ individual renewable buses -# into manageable pseudo-buses for optimization tractability clustering: - # Cluster-count policy. The `base_clusters` values are the single source of - # truth for fixed mode and the reference points for dynamic scaling. + n_strata: 7 + tail_percentile: 10.0 + tail_cluster_boost: 2.0 + geo_weight: 0.3 + n_pca_components: 5 cluster_count_policy: mode: dynamic base_clusters: - onwind: 25 - solar: 25 - min_clusters: 10 - max_clusters: 50 + solar: 20 + onwind: 20 reference_buses: 2000 - reference_capacity_mw: 16000000 + reference_capacity_mw: 2e7 + scale_exponent: 0.5 bus_weight: 0.5 capacity_weight: 0.5 - spread_weight: 0.15 - spread_reference: 0.20 - scale_exponent: 0.5 + min_clusters: 5 + max_clusters: 50 # Feature weighting for K-means clustering # Determines how heavily clustering emphasizes each aspect: diff --git a/pixi.toml b/pixi.toml index 72db9bc..a0d1007 100644 --- a/pixi.toml +++ b/pixi.toml @@ -30,6 +30,7 @@ python = ">=3.10" pyyaml = "*" scipy = ">=1.16.3" scikit-learn = ">=1.0" +kmedoids = "*" seaborn = ">=0.13.2" searoute = ">=1.5.0" shapely = ">=2.1.2,<3" diff --git a/rules/preparation.smk b/rules/preparation.smk index caa1d10..9f43033 100644 --- a/rules/preparation.smk +++ b/rules/preparation.smk @@ -109,22 +109,9 @@ rule prepare_steel_demand: rule cluster_renewables: - """ -Cluster renewable generators from merged profiles for optimization. - -Input: merged renewable profiles (122k+ buses from all regions) -Output: clustered profiles (region, technology, class dimensions) - validation report with quality metrics - -Clustering strategy: -- Filters to onwind + pvplant only (excludes unreliable offshore) -- Extracts 6D temporal features: avg_cf, temporal_std, cv, autocorr_24h, lat, lon -- Applies weighted K-means (0.5 merit-order, 0.3 temporal, 0.2 geospatial) -- Selects representative timeseries per cluster to preserve real patterns -- Cluster naming: Regionname_tech(onwind/solar)_number -""" input: - merged="data/renewable_profiles_global_merged.nc", + merged_cdf="data/renewable_profiles_global_merged.nc", + merged_geojson="data/renewable_profiles_global_merged.geojson", output: clustered="resources/renewables_clustered.nc", report="resources/renewables_clustering_report.json", diff --git a/workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb b/workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb deleted file mode 100644 index ed9dee7..0000000 --- a/workflow/notebooks/compare_renewables_consolidated_vs_clustered.ipynb +++ /dev/null @@ -1,1033 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c8a1e585", - "metadata": {}, - "source": [ - "# Compare Consolidated vs Clustered Renewable Datasets\n", - "\n", - "**Objective:** Compare the old consolidated renewable data structure with the new clustered approach.\n", - "\n", - "- **Consolidated:** Individual renewable sites flattened into `(region, site_id, time)` where `site_id` encodes technology + class\n", - "- **Clustered:** K-means aggregated pseudo-generators with explicit dimensions `(region, technology, class, time)`\n", - "\n", - "**Key Comparisons:**\n", - "1. Capacity preservation: total MW before/after clustering\n", - "2. Merit-order scatter plots: avg_cf vs p_nom_max\n", - "3. Timeseries preservation: representative timeseries quality\n", - "4. Regional capacity distribution by technology\n", - "5. Cluster quality metrics: size distribution, temporal patterns" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9e22644b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Consolidated: ✓ ..\\..\\data\\new_renewables_consolidated.nc\n", - "Clustered: ✗ ..\\..\\resources\\renewables_clustered.nc\n", - "Metadata: ✓ ..\\..\\resources\\clusters_cache.json\n", - "\n", - "📁 Consolidated: new_renewables_consolidated.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\JanLeopoldTautorus\\Repos\\shift\\.pixi\\envs\\dev\\Lib\\site-packages\\xarray\\backends\\plugins.py:109: RuntimeWarning: Engine 'cfgrib' loading failed:\n", - "Cannot find the ecCodes library\n", - " external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Dimensions: {'region': 15, 'technology': 3, 'class': 39, 'time': 8760}\n", - " Variables: ['capacity', 'capacity_factor']\n", - "\n", - "📋 Loaded cluster metadata rows: 1,160\n", - " region technology cluster_id n_buses_consolidated total_capacity_mw avg_cf\n", - "North_West_Africa onwind 0 49 6.607149e+04 0.049405\n", - "North_West_Africa onwind 1 59 4.165129e+06 0.304651\n", - "North_West_Africa onwind 2 59 3.562013e+05 0.032658\n", - "North_West_Africa onwind 3 66 1.048077e+05 0.217946\n", - "North_West_Africa onwind 4 49 8.892041e+05 0.075501\n" - ] - } - ], - "source": [ - "import xarray as xr\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from pathlib import Path\n", - "import logging\n", - "import json\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", - "logging.basicConfig(level=logging.INFO, format=\"%(message)s\")\n", - "\n", - "BASE_DIR = Path(\"../..\")\n", - "CONSOLIDATED_FILE = BASE_DIR / \"data/new_renewables_consolidated.nc\"\n", - "CLUSTERED_FILE = BASE_DIR / \"resources/renewables_clustered.nc\"\n", - "CLUSTER_META_FILE = BASE_DIR / \"resources/clusters_cache.json\"\n", - "\n", - "print(f\"Consolidated: {'✓' if CONSOLIDATED_FILE.exists() else '✗'} {CONSOLIDATED_FILE}\")\n", - "print(f\"Clustered: {'✓' if CLUSTERED_FILE.exists() else '✗'} {CLUSTERED_FILE}\")\n", - "print(f\"Metadata: {'✓' if CLUSTER_META_FILE.exists() else '✗'} {CLUSTER_META_FILE}\")\n", - "\n", - "ds_clustered = None\n", - "df_cluster_meta = None\n", - "clustered_source = None\n", - "\n", - "if CLUSTERED_FILE.exists():\n", - " try:\n", - " ds_clustered = xr.open_dataset(CLUSTERED_FILE)\n", - " clustered_source = \"dataset\"\n", - " except Exception as e:\n", - " print(f\"⚠️ Could not open clustered dataset: {e}\")\n", - "\n", - "if ds_clustered is None and CLUSTER_META_FILE.exists():\n", - " with open(CLUSTER_META_FILE, \"r\", encoding=\"utf-8\") as f:\n", - " cache_payload = json.load(f)\n", - " meta_rows = []\n", - " for cluster_name, info in cache_payload.get(\"metadata\", {}).items():\n", - " row = dict(info)\n", - " row[\"cluster_name\"] = cluster_name\n", - " meta_rows.append(row)\n", - " df_cluster_meta = pd.DataFrame(meta_rows)\n", - " if not df_cluster_meta.empty:\n", - " df_cluster_meta[\"technology\"] = df_cluster_meta[\"technology\"].astype(str)\n", - " df_cluster_meta[\"region\"] = df_cluster_meta[\"region\"].astype(str)\n", - " clustered_source = \"metadata\"\n", - "elif ds_clustered is not None:\n", - " print(f\"\\n📁 Clustered: {CLUSTERED_FILE.name}\")\n", - " print(f\" Dimensions: {dict(ds_clustered.sizes)}\")\n", - " print(f\" Variables: {list(ds_clustered.data_vars)}\")\n", - "else:\n", - " print(\"\\n⚠️ No clustered dataset or metadata cache found\")\n", - "\n", - "consolidated_exists = CONSOLIDATED_FILE.exists()\n", - "if consolidated_exists:\n", - " print(f\"\\n📁 Consolidated: {CONSOLIDATED_FILE.name}\")\n", - " ds_consolidated = xr.open_dataset(CONSOLIDATED_FILE)\n", - " print(f\" Dimensions: {dict(ds_consolidated.sizes)}\")\n", - " print(f\" Variables: {list(ds_consolidated.data_vars)}\")\n", - "else:\n", - " print(\"\\n⚠️ Consolidated file not found\")\n", - " ds_consolidated = None\n", - "\n", - "if df_cluster_meta is not None:\n", - " print(f\"\\n📋 Loaded cluster metadata rows: {len(df_cluster_meta):,}\")\n", - " print(\n", - " df_cluster_meta[\n", - " [\n", - " \"region\",\n", - " \"technology\",\n", - " \"cluster_id\",\n", - " \"n_buses_consolidated\",\n", - " \"total_capacity_mw\",\n", - " \"avg_cf\",\n", - " ]\n", - " ]\n", - " .head()\n", - " .to_string(index=False)\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "03a80b1a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "CONSOLIDATED DATASET DIAGNOSTICS\n", - "======================================================================\n", - "Dimensions: {'region': 15, 'technology': 3, 'class': 39, 'time': 8760}\n", - "Coords: ['region', 'technology', 'class', 'time']\n", - "Region sample: ['Central_America', 'East_Asia', 'East_East_Asia', 'Eurasia', 'Europe']\n", - "Technology values: ['windoffshore', 'pvplant', 'windonshore']\n", - "onwind: selection failed -> \"not all values found in index 'technology'. Try setting the `method` keyword argument (example: method='nearest').\"\n", - "solar: selection failed -> \"not all values found in index 'technology'. Try setting the `method` keyword argument (example: method='nearest').\"\n", - "Capacity variable dims: ('region', 'technology', 'class')\n", - "Capacity variable shape: (15, 3, 39)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"CONSOLIDATED DATASET DIAGNOSTICS\")\n", - "print(\"=\" * 70)\n", - "\n", - "print(\"Dimensions:\", dict(ds_consolidated.sizes))\n", - "print(\"Coords:\", list(ds_consolidated.coords))\n", - "print(\"Region sample:\", [str(v) for v in ds_consolidated.region.values[:5]])\n", - "print(\"Technology values:\", [str(v) for v in ds_consolidated.technology.values])\n", - "\n", - "for tech_name in [\"onwind\", \"solar\"]:\n", - " try:\n", - " cap_values = ds_consolidated[\"capacity\"].sel(technology=tech_name).values\n", - " print(\n", - " f\"{tech_name}: selection shape {cap_values.shape}, total {float(np.nansum(cap_values)):.1f} MW\"\n", - " )\n", - " except Exception as e:\n", - " print(f\"{tech_name}: selection failed -> {e}\")\n", - "\n", - "print(\"Capacity variable dims:\", ds_consolidated[\"capacity\"].dims)\n", - "print(\"Capacity variable shape:\", ds_consolidated[\"capacity\"].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4973ebf4", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"=\" * 70)\n", - "print(\"LOADING RENEWABLE DATA\")\n", - "print(\"=\" * 70)\n", - "\n", - "print(f\"\\n📁 Clustered: {CLUSTERED_FILE.name}\")\n", - "ds_clustered = xr.open_dataset(CLUSTERED_FILE)\n", - "print(f\" Dimensions: {dict(ds_clustered.sizes)}\")\n", - "print(f\" Variables: {list(ds_clustered.data_vars)}\")\n", - "\n", - "consolidated_exists = CONSOLIDATED_FILE.exists()\n", - "if consolidated_exists:\n", - " print(f\"\\n📁 Consolidated: {CONSOLIDATED_FILE.name}\")\n", - " ds_consolidated = xr.open_dataset(CONSOLIDATED_FILE)\n", - " print(f\" Dimensions: {dict(ds_consolidated.sizes)}\")\n", - " print(f\" Variables: {list(ds_consolidated.data_vars)}\")\n", - "else:\n", - " print(\"\\n⚠️ Consolidated file not found\")\n", - " ds_consolidated = None" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "13cbb7c6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "CLUSTER-LEVEL COMPARISON DATA\n", - "======================================================================\n", - "Cluster-level comparison summary:\n", - "\n", - " source technology n_clusters total_capacity_mw mean_avg_cf\n", - "Consolidated solar 157 1.421861e+08 0.197379\n", - "Consolidated onwind 271 4.303580e+08 0.353087\n", - " Clustered solar 577 3.067626e+08 0.146385\n", - " Clustered onwind 583 2.633419e+08 0.169173\n", - "\n", - "Consolidated cluster rows: 428\n", - "Clustered cluster rows: 1160\n", - "\n", - "Sample consolidated rows:\n", - " source region technology cluster_id cluster_label capacity_mw avg_cf\n", - "Consolidated Central_America onwind 0 onwind 1 3.008679e+02 0.000000\n", - "Consolidated Central_America onwind 20 onwind 21 1.269267e+02 0.007015\n", - "Consolidated Central_America onwind 21 onwind 22 3.301898e+04 0.022544\n", - "Consolidated Central_America onwind 22 onwind 23 3.193277e+05 0.049610\n", - "Consolidated Central_America onwind 23 onwind 24 1.032047e+06 0.085307\n", - "\n", - "Sample clustered rows:\n", - " source region technology cluster_id cluster_label capacity_mw avg_cf\n", - "Clustered North_West_Africa onwind 0 onwind 1 6.607149e+04 0.049405\n", - "Clustered North_West_Africa onwind 1 onwind 2 4.165129e+06 0.304651\n", - "Clustered North_West_Africa onwind 2 onwind 3 3.562013e+05 0.032658\n", - "Clustered North_West_Africa onwind 3 onwind 4 1.048077e+05 0.217946\n", - "Clustered North_West_Africa onwind 4 onwind 5 8.892041e+05 0.075501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_22260\\854233552.py:97: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", - " comparison_df.groupby(['source', 'technology'], as_index=False)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"CLUSTER-LEVEL COMPARISON DATA\")\n", - "print(\"=\" * 70)\n", - "\n", - "techs_to_compare = [\"onwind\", \"solar\"]\n", - "consolidated_tech_map = {\n", - " \"windonshore\": \"onwind\",\n", - " \"pvplant\": \"solar\",\n", - "}\n", - "\n", - "\n", - "def build_cluster_rows_from_consolidated(ds):\n", - " rows = []\n", - " for consolidated_tech_name, compare_tech_name in consolidated_tech_map.items():\n", - " try:\n", - " cap_da = ds[\"capacity\"].sel(technology=consolidated_tech_name)\n", - " cf_da = ds[\"capacity_factor\"].sel(technology=consolidated_tech_name)\n", - " avg_cf_da = cf_da.mean(dim=\"time\", skipna=True)\n", - "\n", - " for region_name in ds.region.values:\n", - " region_caps = cap_da.sel(region=region_name).values\n", - " region_avg_cf = avg_cf_da.sel(region=region_name).values\n", - " for class_idx in range(len(region_caps)):\n", - " capacity_mw = float(region_caps[class_idx])\n", - " avg_cf = float(region_avg_cf[class_idx])\n", - " if np.isnan(capacity_mw) or np.isnan(avg_cf):\n", - " continue\n", - " rows.append(\n", - " {\n", - " \"source\": \"Consolidated\",\n", - " \"region\": str(region_name),\n", - " \"technology\": str(compare_tech_name),\n", - " \"cluster_id\": int(class_idx),\n", - " \"cluster_label\": f\"{compare_tech_name} {class_idx + 1}\",\n", - " \"capacity_mw\": capacity_mw,\n", - " \"avg_cf\": avg_cf,\n", - " }\n", - " )\n", - " except Exception as e:\n", - " print(\n", - " f\" Could not read consolidated clusters for {consolidated_tech_name}: {e}\"\n", - " )\n", - " return rows\n", - "\n", - "\n", - "def build_cluster_rows_from_metadata(df_meta):\n", - " rows = []\n", - " subset = df_meta[df_meta[\"technology\"].isin(techs_to_compare)].copy()\n", - " for _, row in subset.iterrows():\n", - " rows.append(\n", - " {\n", - " \"source\": \"Clustered\",\n", - " \"region\": str(row[\"region\"]),\n", - " \"technology\": str(row[\"technology\"]),\n", - " \"cluster_id\": int(row[\"cluster_id\"]),\n", - " \"cluster_label\": f\"{row['technology']} {int(row['cluster_id']) + 1}\",\n", - " \"capacity_mw\": float(row[\"total_capacity_mw\"]),\n", - " \"avg_cf\": float(row[\"avg_cf\"]),\n", - " }\n", - " )\n", - " return rows\n", - "\n", - "\n", - "consolidated_rows = (\n", - " build_cluster_rows_from_consolidated(ds_consolidated)\n", - " if consolidated_exists and ds_consolidated is not None\n", - " else []\n", - ")\n", - "if df_cluster_meta is not None:\n", - " clustered_rows = build_cluster_rows_from_metadata(df_cluster_meta)\n", - "elif ds_clustered is not None:\n", - " clustered_rows = []\n", - " for tech_name in techs_to_compare:\n", - " try:\n", - " cap_da = ds_clustered[\"capacity\"].sel(technology=tech_name)\n", - " avg_cf_da = ds_clustered[\"avg_cf\"].sel(technology=tech_name)\n", - " for region_name in ds_clustered.region.values:\n", - " region_caps = cap_da.sel(region=region_name).values\n", - " region_avg_cf = avg_cf_da.sel(region=region_name).values\n", - " for class_idx in range(len(region_caps)):\n", - " capacity_mw = float(region_caps[class_idx])\n", - " avg_cf = float(region_avg_cf[class_idx])\n", - " if np.isnan(capacity_mw) or np.isnan(avg_cf):\n", - " continue\n", - " clustered_rows.append(\n", - " {\n", - " \"source\": \"Clustered\",\n", - " \"region\": str(region_name),\n", - " \"technology\": str(tech_name),\n", - " \"cluster_id\": int(class_idx),\n", - " \"cluster_label\": f\"{tech_name} {class_idx + 1}\",\n", - " \"capacity_mw\": capacity_mw,\n", - " \"avg_cf\": avg_cf,\n", - " }\n", - " )\n", - " except Exception as e:\n", - " print(f\" Could not read clustered dataset for {tech_name}: {e}\")\n", - "else:\n", - " clustered_rows = []\n", - "\n", - "comparison_df = pd.concat(\n", - " [pd.DataFrame(consolidated_rows), pd.DataFrame(clustered_rows)], ignore_index=True\n", - ")\n", - "\n", - "if comparison_df.empty:\n", - " raise ValueError(\"No cluster-level data found.\")\n", - "\n", - "comparison_df[\"source\"] = pd.Categorical(\n", - " comparison_df[\"source\"], categories=[\"Consolidated\", \"Clustered\"], ordered=True\n", - ")\n", - "comparison_df[\"technology\"] = pd.Categorical(\n", - " comparison_df[\"technology\"], categories=[\"solar\", \"onwind\"], ordered=True\n", - ")\n", - "comparison_df[\"region\"] = comparison_df[\"region\"].astype(str)\n", - "comparison_df[\"avg_cf\"] = comparison_df[\"avg_cf\"].astype(float)\n", - "\n", - "comparison_summary_df = (\n", - " comparison_df.groupby([\"source\", \"technology\"], as_index=False)\n", - " .agg(\n", - " n_clusters=(\"cluster_id\", \"size\"),\n", - " total_capacity_mw=(\"capacity_mw\", \"sum\"),\n", - " mean_avg_cf=(\"avg_cf\", \"mean\"),\n", - " )\n", - " .sort_values([\"source\", \"technology\"])\n", - ")\n", - "\n", - "print(\"Cluster-level comparison summary:\")\n", - "print(\"\\n\" + comparison_summary_df.to_string(index=False))\n", - "\n", - "print(\"\\nConsolidated cluster rows:\", len(consolidated_rows))\n", - "print(\"Clustered cluster rows: \", len(clustered_rows))\n", - "print(\"\\nSample consolidated rows:\")\n", - "print(pd.DataFrame(consolidated_rows).head().to_string(index=False))\n", - "print(\"\\nSample clustered rows:\")\n", - "print(pd.DataFrame(clustered_rows).head().to_string(index=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "befd027b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "2. CLUSTERING STATISTICS\n", - "======================================================================\n", - "\n", - "Total clusters: 1,160\n", - "\n", - "Capacity (MW):\n", - " Min: 43.0\n", - " Max: 20293553.9\n", - " Mean: 491469.4\n", - " Median: 184945.5\n", - " Total: 570104531.2\n", - "\n", - "Average Capacity Factor:\n", - " Min: 0.0000\n", - " Max: 0.6988\n", - " Mean: 0.1578\n", - " Median: 0.1438\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"2. CLUSTERING STATISTICS\")\n", - "print(\"=\" * 70)\n", - "\n", - "if df_cluster_meta is not None:\n", - " cap_valid = df_cluster_meta[\"total_capacity_mw\"].dropna().to_numpy()\n", - " avg_cf_valid = df_cluster_meta[\"avg_cf\"].dropna().to_numpy()\n", - " n_total = len(df_cluster_meta)\n", - "else:\n", - " cap_all = ds_clustered[\"capacity\"].values.flatten()\n", - " cap_valid = cap_all[~np.isnan(cap_all)]\n", - "\n", - " avg_cf_all = ds_clustered[\"avg_cf\"].values.flatten()\n", - " avg_cf_valid = avg_cf_all[~np.isnan(avg_cf_all)]\n", - "\n", - " n_total = len(cap_valid)\n", - "\n", - "print(f\"\\nTotal clusters: {n_total:,}\")\n", - "print(\"\\nCapacity (MW):\")\n", - "print(f\" Min: {np.min(cap_valid):>12.1f}\")\n", - "print(f\" Max: {np.max(cap_valid):>12.1f}\")\n", - "print(f\" Mean: {np.mean(cap_valid):>12.1f}\")\n", - "print(f\" Median: {np.median(cap_valid):>12.1f}\")\n", - "print(f\" Total: {np.sum(cap_valid):>12.1f}\")\n", - "\n", - "print(\"\\nAverage Capacity Factor:\")\n", - "print(f\" Min: {np.min(avg_cf_valid):>12.4f}\")\n", - "print(f\" Max: {np.max(avg_cf_valid):>12.4f}\")\n", - "print(f\" Mean: {np.mean(avg_cf_valid):>12.4f}\")\n", - "print(f\" Median: {np.median(avg_cf_valid):>12.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c26731e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Generating Europe scatter plots with Seaborn...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\JanLeopoldTautorus\\AppData\\Local\\Temp\\ipykernel_22260\\4113124811.py:7: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", - " plot_df['cluster_order_key'] = plot_df.groupby(['source', 'technology']).cumcount()\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Saved europe_cluster_scatter_comparison.png\n" - ] - } - ], - "source": [ - "print(\"\\nGenerating scatter plot...\")\n", - "\n", - "region = (\n", - " cluster_regions[0]\n", - " if df_cluster_meta is not None\n", - " else list(ds_clustered.region.values)[0]\n", - ")\n", - "tech = (\n", - " cluster_techs[0]\n", - " if df_cluster_meta is not None\n", - " else list(ds_clustered.technology.values)[0]\n", - ")\n", - "print(f\"Example: {region} - {tech}\")\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "if consolidated_exists:\n", - " try:\n", - " consol_cap = (\n", - " ds_consolidated[\"capacity\"].sel(region=region, technology=tech).values\n", - " )\n", - " if \"avg_cf\" in ds_consolidated.data_vars:\n", - " consol_avg_cf = (\n", - " ds_consolidated[\"avg_cf\"].sel(region=region, technology=tech).values\n", - " )\n", - " else:\n", - " cf_ts = (\n", - " ds_consolidated[\"capacity_factor\"]\n", - " .sel(region=region, technology=tech)\n", - " .values\n", - " )\n", - " consol_avg_cf = np.nanmean(cf_ts, axis=-1)\n", - "\n", - " valid = ~(np.isnan(consol_cap) | np.isnan(consol_avg_cf))\n", - " axes[0].scatter(\n", - " consol_avg_cf[valid], consol_cap[valid], alpha=0.4, s=30, c=\"steelblue\"\n", - " )\n", - " axes[0].text(\n", - " 0.02,\n", - " 0.98,\n", - " f\"n={sum(valid)}\",\n", - " transform=axes[0].transAxes,\n", - " va=\"top\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n", - " )\n", - " except Exception as e:\n", - " axes[0].text(\n", - " 0.5,\n", - " 0.5,\n", - " f\"Data not available: {e}\",\n", - " ha=\"center\",\n", - " va=\"center\",\n", - " transform=axes[0].transAxes,\n", - " )\n", - "else:\n", - " axes[0].text(\n", - " 0.5,\n", - " 0.5,\n", - " \"Consolidated file not available\",\n", - " ha=\"center\",\n", - " va=\"center\",\n", - " transform=axes[0].transAxes,\n", - " )\n", - "\n", - "axes[0].set_xlabel(\"Average Capacity Factor\", fontsize=11)\n", - "axes[0].set_ylabel(\"P_nom_max (MW)\", fontsize=11)\n", - "axes[0].set_title(\n", - " f\"Consolidated: {region} - {tech}\\n(Individual Sites)\",\n", - " fontsize=12,\n", - " fontweight=\"bold\",\n", - ")\n", - "axes[0].set_yscale(\"log\")\n", - "axes[0].grid(True, alpha=0.3)\n", - "\n", - "try:\n", - " if df_cluster_meta is not None:\n", - " meta_sel = df_cluster_meta[\n", - " (df_cluster_meta[\"region\"] == region)\n", - " & (df_cluster_meta[\"technology\"] == tech)\n", - " ]\n", - " cluster_cap = meta_sel[\"total_capacity_mw\"].to_numpy()\n", - " cluster_avg_cf = meta_sel[\"avg_cf\"].to_numpy()\n", - " else:\n", - " cluster_cap = (\n", - " ds_clustered[\"capacity\"].sel(region=region, technology=tech).values\n", - " )\n", - " cluster_avg_cf = (\n", - " ds_clustered[\"avg_cf\"].sel(region=region, technology=tech).values\n", - " )\n", - " valid = ~(np.isnan(cluster_cap) | np.isnan(cluster_avg_cf))\n", - " axes[1].scatter(\n", - " cluster_avg_cf[valid],\n", - " cluster_cap[valid],\n", - " alpha=0.7,\n", - " s=120,\n", - " c=\"coral\",\n", - " edgecolor=\"darkred\",\n", - " )\n", - " axes[1].text(\n", - " 0.98,\n", - " 0.05,\n", - " f\"n_clusters={sum(valid)}\",\n", - " transform=axes[1].transAxes,\n", - " ha=\"right\",\n", - " va=\"bottom\",\n", - " fontsize=10,\n", - " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n", - " )\n", - "except Exception as e:\n", - " axes[1].text(\n", - " 0.5, 0.5, f\"Error: {e}\", ha=\"center\", va=\"center\", transform=axes[1].transAxes\n", - " )\n", - "\n", - "axes[1].set_xlabel(\"Average Capacity Factor (Representative)\", fontsize=11)\n", - "axes[1].set_ylabel(\"Cluster P_nom_max (MW)\", fontsize=11)\n", - "axes[1].set_title(\n", - " f\"Clustered: {region} - {tech}\\n(Pseudo-generators)\", fontsize=12, fontweight=\"bold\"\n", - ")\n", - "axes[1].set_yscale(\"log\")\n", - "axes[1].grid(True, alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"scatter_comparison.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"✓ Saved scatter_comparison.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cf86056b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Generating timeseries plot...\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Saved timeseries_clustered.png\n" - ] - } - ], - "source": [ - "print(\"\\nGenerating timeseries plot...\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(14, 6))\n", - "\n", - "if df_cluster_meta is not None:\n", - " ax.text(\n", - " 0.5,\n", - " 0.5,\n", - " \"Timeseries plot unavailable from metadata-only cache\\nRe-run with clustered NetCDF to enable this plot\",\n", - " ha=\"center\",\n", - " va=\"center\",\n", - " transform=ax.transAxes,\n", - " )\n", - " ax.set_axis_off()\n", - "else:\n", - " try:\n", - " cf = ds_clustered[\"capacity_factor\"].sel(region=region, technology=tech).values\n", - " cap = ds_clustered[\"capacity\"].sel(region=region, technology=tech).values\n", - " max_cap = np.nanmax(cap)\n", - " n_plotted = 0\n", - "\n", - " for cf_ts, c in zip(cf, cap):\n", - " if not np.isnan(c) and not np.all(np.isnan(cf_ts)):\n", - " cf_clean = np.nan_to_num(cf_ts, nan=0.0)\n", - " lw = 0.5 + 3.5 * (c / max_cap) if max_cap > 0 else 1\n", - " ax.plot(cf_clean, linewidth=lw, alpha=0.65)\n", - " n_plotted += 1\n", - "\n", - " ax.set_xlabel(\"Hour of Year\", fontsize=11)\n", - " ax.set_ylabel(\"Capacity Factor\", fontsize=11)\n", - " ax.set_title(\n", - " f\"Clustered Timeseries: {region} - {tech}\\n(Line width ∝ capacity, n_clusters={n_plotted})\",\n", - " fontsize=12,\n", - " fontweight=\"bold\",\n", - " )\n", - " ax.set_ylim(-0.02, 1.05)\n", - " ax.grid(True, alpha=0.3)\n", - " except Exception as e:\n", - " ax.text(\n", - " 0.5, 0.5, f\"Error: {e}\", ha=\"center\", va=\"center\", transform=ax.transAxes\n", - " )\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"timeseries_clustered.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"✓ Saved timeseries_clustered.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "bae711fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Generating stacked region comparison by technology...\n" - ] - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Saved region_stacked_capacity_comparison.png\n" - ] - } - ], - "source": [ - "print(\"\\nGenerating stacked region comparison by technology...\")\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "tech_palette = {\"solar\": \"#D55C5C\", \"onwind\": \"#4C78A8\"}\n", - "source_offsets = {\"Consolidated\": -0.18, \"Clustered\": 0.18}\n", - "tech_width = 0.24\n", - "tech_order = [\"solar\", \"onwind\"]\n", - "source_order = [\"Consolidated\", \"Clustered\"]\n", - "\n", - "\n", - "def blend_with_white(color, intensity):\n", - " base = np.array(sns.color_palette([color])[0])\n", - " intensity = float(np.clip(intensity, 0.0, 1.0))\n", - " return tuple(base * intensity + np.array([1.0, 1.0, 1.0]) * (1.0 - intensity))\n", - "\n", - "\n", - "def draw_tech_panel(ax, df, tech_name, title):\n", - " regions = sorted(df[\"region\"].unique())\n", - " x_positions = np.arange(len(regions)) * 1.35\n", - "\n", - " for region_idx, region_name in enumerate(regions):\n", - " for source_name in source_order:\n", - " subset = df[\n", - " (df[\"region\"] == region_name)\n", - " & (df[\"technology\"] == tech_name)\n", - " & (df[\"source\"] == source_name)\n", - " ].copy()\n", - " if subset.empty:\n", - " continue\n", - "\n", - " subset = subset.sort_values(\"avg_cf\")\n", - " bottom = 0.0\n", - " cf_min = float(subset[\"avg_cf\"].min())\n", - " cf_max = float(subset[\"avg_cf\"].max())\n", - " cf_span = cf_max - cf_min if cf_max > cf_min else 1.0\n", - " border_color = \"black\" if source_name == \"Consolidated\" else \"white\"\n", - " border_width = 0.55 if source_name == \"Consolidated\" else 0.3\n", - "\n", - " for _, row in subset.iterrows():\n", - " norm_cf = (row[\"avg_cf\"] - cf_min) / cf_span\n", - " shade = 0.10 + 0.90 * (norm_cf**0.65)\n", - " color = blend_with_white(tech_palette[tech_name], shade)\n", - " ax.bar(\n", - " x_positions[region_idx] + source_offsets[source_name],\n", - " row[\"capacity_mw\"],\n", - " width=tech_width,\n", - " bottom=bottom,\n", - " color=color,\n", - " edgecolor=border_color,\n", - " linewidth=border_width,\n", - " )\n", - " bottom += row[\"capacity_mw\"]\n", - "\n", - " import matplotlib.patches as mpatches\n", - "\n", - " source_handles = [\n", - " mpatches.Patch(facecolor=\"#E0E0E0\", edgecolor=\"black\", label=\"Consolidated\"),\n", - " mpatches.Patch(facecolor=\"#A9A9A9\", edgecolor=\"white\", label=\"Clustered\"),\n", - " ]\n", - " cf_handles = [\n", - " mpatches.Patch(facecolor=\"#f2f2f2\", edgecolor=\"#cccccc\", label=\"low avg_cf\"),\n", - " mpatches.Patch(facecolor=\"#5a5a5a\", edgecolor=\"#cccccc\", label=\"high avg_cf\"),\n", - " ]\n", - " tech_handles = [mpatches.Patch(color=tech_palette[tech_name], label=tech_name)]\n", - "\n", - " source_legend = ax.legend(handles=source_handles, title=\"Source\", loc=\"upper left\")\n", - " ax.add_artist(source_legend)\n", - " cf_legend = ax.legend(handles=cf_handles, title=\"CF shade\", loc=\"upper right\")\n", - " ax.add_artist(cf_legend)\n", - " ax.legend(handles=tech_handles, title=\"Technology\", loc=\"center right\")\n", - "\n", - " ax.set_title(title, fontsize=13, fontweight=\"bold\")\n", - " ax.set_xlabel(\"Region\", fontsize=11)\n", - " ax.set_ylabel(\"Capacity (MW)\", fontsize=11)\n", - " ax.set_xticks(x_positions)\n", - " ax.set_xticklabels(regions, rotation=45, ha=\"right\")\n", - " ax.grid(True, axis=\"y\", alpha=0.3)\n", - " ax.margins(x=0.04)\n", - "\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(22, 8), sharey=True)\n", - "\n", - "draw_tech_panel(\n", - " axes[0],\n", - " comparison_df,\n", - " \"onwind\",\n", - " \"Onwind: consolidated vs clustered by region\",\n", - ")\n", - "\n", - "draw_tech_panel(\n", - " axes[1],\n", - " comparison_df,\n", - " \"solar\",\n", - " \"Solar: consolidated vs clustered by region\",\n", - ")\n", - "\n", - "fig.suptitle(\"Region-wise stacked capacity comparison\", fontsize=15, fontweight=\"bold\")\n", - "plt.tight_layout(rect=(0, 0, 1, 0.95))\n", - "plt.savefig(\"region_stacked_capacity_comparison.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"✓ Saved region_stacked_capacity_comparison.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a3b7324d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Generating cluster size distributions...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Saved cluster_distributions.png\n" - ] - } - ], - "source": [ - "print(\"\\nGenerating cluster size distributions...\")\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "axes[0].hist(cap_valid, bins=50, color=\"steelblue\", alpha=0.7, edgecolor=\"black\")\n", - "axes[0].set_xlabel(\"Cluster Capacity (MW)\", fontsize=11)\n", - "axes[0].set_ylabel(\"Count\", fontsize=11)\n", - "axes[0].set_title(\"Distribution of Cluster Sizes\", fontsize=12, fontweight=\"bold\")\n", - "axes[0].set_yscale(\"log\")\n", - "axes[0].grid(True, alpha=0.3)\n", - "\n", - "df_cap_by_tech = []\n", - "if df_cluster_meta is not None:\n", - " for _, row in df_cluster_meta.iterrows():\n", - " df_cap_by_tech.append(\n", - " {\"technology\": row[\"technology\"], \"capacity_mw\": row[\"total_capacity_mw\"]}\n", - " )\n", - "else:\n", - " for t in ds_clustered.technology.values:\n", - " try:\n", - " cap_t = ds_clustered[\"capacity\"].sel(technology=t).values.flatten()\n", - " cap_t_valid = cap_t[~np.isnan(cap_t)]\n", - " for c in cap_t_valid:\n", - " df_cap_by_tech.append({\"technology\": str(t), \"capacity_mw\": c})\n", - " except:\n", - " pass\n", - "\n", - "if df_cap_by_tech:\n", - " df_box = pd.DataFrame(df_cap_by_tech)\n", - " df_box.boxplot(column=\"capacity_mw\", by=\"technology\", ax=axes[1])\n", - " axes[1].set_ylabel(\"Capacity (MW)\", fontsize=11)\n", - " axes[1].set_title(\n", - " \"Capacity Distribution by Technology\", fontsize=12, fontweight=\"bold\"\n", - " )\n", - " axes[1].set_yscale(\"log\")\n", - " plt.sca(axes[1])\n", - " plt.xticks(rotation=45)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"cluster_distributions.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"✓ Saved cluster_distributions.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6a2f4a1b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "SUMMARY\n", - "======================================================================\n", - "\n", - "Clustering Results:\n", - " Input: 15 regions × 2 technologies\n", - " Output: 1,160 total clusters\n", - " Total capacity: 570104531 MW\n", - "\n", - "Compression:\n", - " Consolidated: 1,755 sites\n", - " Clustered: 1,160 clusters\n", - " Compression: 1.5× reduction\n", - "\n", - "✅ Analysis complete! Generated 4 PNG plots in current directory.\n", - "\n", - "Note: timeseries plot is skipped because the JSON cache only contains metadata, not cf timeseries.\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"SUMMARY\")\n", - "print(\"=\" * 70)\n", - "\n", - "print(\"\\nClustering Results:\")\n", - "if df_cluster_meta is not None:\n", - " print(\n", - " f\" Input: {len(df_cluster_meta['region'].unique())} regions × {len(df_cluster_meta['technology'].unique())} technologies\"\n", - " )\n", - " print(f\" Output: {len(df_cluster_meta):,} total clusters\")\n", - " print(f\" Total capacity: {df_cluster_meta['total_capacity_mw'].sum():.0f} MW\")\n", - "else:\n", - " print(\n", - " f\" Input: {len(ds_clustered.region.values)} regions × {len(ds_clustered.technology.values)} technologies\"\n", - " )\n", - " print(f\" Output: {len(cap_valid):,} total clusters\")\n", - " print(f\" Total capacity: {np.sum(cap_valid):.0f} MW\")\n", - "\n", - "if consolidated_exists:\n", - " cap_consol_all = ds_consolidated[\"capacity\"].values.flatten()\n", - " cap_consol_valid = cap_consol_all[~np.isnan(cap_consol_all)]\n", - " if df_cluster_meta is not None:\n", - " clustered_count = len(df_cluster_meta)\n", - " else:\n", - " clustered_count = len(cap_valid)\n", - " if clustered_count > 0:\n", - " compression = len(cap_consol_valid) / clustered_count\n", - " print(\"\\nCompression:\")\n", - " print(f\" Consolidated: {len(cap_consol_valid):,} sites\")\n", - " print(f\" Clustered: {clustered_count:,} clusters\")\n", - " print(f\" Compression: {compression:.1f}× reduction\")\n", - "\n", - "print(\"\\n✅ Analysis complete! Generated 4 PNG plots in current directory.\")\n", - "if df_cluster_meta is not None:\n", - " print(\n", - " \"\\nNote: timeseries plot is skipped because the JSON cache only contains metadata, not cf timeseries.\"\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "shift-dev", - "language": "python", - "name": "shift-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb b/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb new file mode 100644 index 0000000..e9bca82 --- /dev/null +++ b/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8a1e585", + "metadata": {}, + "source": [ + "# Compare TRACE vs PyPSA-Earth Renewable Datasets\n", + "\n", + "**Objective:** Compare the legacy TRACE renewable data with the new PyPSA-Earth clustered output.\n", + "\n", + "- **TRACE:** External pre-aggregated legacy dataset from the TRACE package. It is the deprecated reference source for the old consolidated structure.\n", + "- **PyPSA-Earth:** Newly calculated clustered renewable dataset built from the atlite workflow. It preserves cell-level spatial resolution through region-technology pseudo-generators.\n", + "\n", + "**Primary comparison goals:**\n", + "1. Compare installed capacity and energy potential between TRACE and PyPSA-Earth.\n", + "2. Visualize capacity-factor distributions and merit-order structure.\n", + "3. Compare cluster-level timeseries behavior per region.\n", + "4. Keep the stacked region comparison as the main regional capacity overview.\n", + "5. Use one consistent visual style across all plots." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e22644b", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import xarray as xr\n", + "from IPython.display import display\n", + "import plot_helpers\n", + "\n", + "sns.set_theme(style=\"whitegrid\", context=\"talk\", font_scale=1.12)\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.figsize\": (14, 6),\n", + " \"figure.titlesize\": 16,\n", + " \"font.size\": 11,\n", + " \"axes.titlesize\": 14,\n", + " \"axes.labelsize\": 12,\n", + " \"axes.titleweight\": \"semibold\",\n", + " \"xtick.labelsize\": 10,\n", + " \"ytick.labelsize\": 10,\n", + " \"legend.fontsize\": 10,\n", + " \"legend.title_fontsize\": 11,\n", + " \"axes.labelpad\": 8,\n", + " }\n", + ")\n", + "logging.basicConfig(level=logging.INFO, format=\"%(message)s\")\n", + "\n", + "BASE_DIR = Path(\"../..\")\n", + "TRACE_FILE = BASE_DIR / \"data/new_renewables_consolidated.nc\"\n", + "PYPSA_EARTH_FILE = BASE_DIR / \"resources/renewables_clustered.nc\"\n", + "PYPSA_EARTH_META_FILE = BASE_DIR / \"resources/clusters_cache.joblib\"\n", + "\n", + "SOURCE_STYLES = {\n", + " \"TRACE\": {\"color\": \"#6B7280\", \"marker\": \"o\"},\n", + " \"PyPSA-Earth\": {\"color\": \"#A8A29E\", \"marker\": \"s\"},\n", + "}\n", + "TECH_COLORS = {\n", + " \"onwind\": \"#1F77B4\",\n", + " \"solar\": \"#D62728\",\n", + "}\n", + "\n", + "print(f\"TRACE: {'✓' if TRACE_FILE.exists() else '✗'} {TRACE_FILE}\")\n", + "print(f\"PyPSA-Earth: {'✓' if PYPSA_EARTH_FILE.exists() else '✗'} {PYPSA_EARTH_FILE}\")\n", + "print(\n", + " f\"Metadata: {'✓' if PYPSA_EARTH_META_FILE.exists() else '✗'} {PYPSA_EARTH_META_FILE}\"\n", + ")\n", + "\n", + "trace_ds = xr.open_dataset(TRACE_FILE) if TRACE_FILE.exists() else None\n", + "pypsa_earth_ds = (\n", + " xr.open_dataset(PYPSA_EARTH_FILE) if PYPSA_EARTH_FILE.exists() else None\n", + ")\n", + "pypsa_earth_meta = None\n", + "if PYPSA_EARTH_META_FILE.exists():\n", + " import joblib\n", + "\n", + " pypsa_earth_meta = joblib.load(PYPSA_EARTH_META_FILE)\n", + "\n", + "summary_rows = []\n", + "for label, ds in [(\"TRACE\", trace_ds), (\"PyPSA-Earth\", pypsa_earth_ds)]:\n", + " if ds is None:\n", + " continue\n", + " summary_rows.append(\n", + " {\n", + " \"source\": label,\n", + " \"dimensions\": dict(ds.sizes),\n", + " \"variables\": \", \".join(list(ds.data_vars)),\n", + " \"regions\": int(ds.sizes.get(\"region\", 0)),\n", + " \"technologies\": int(ds.sizes.get(\"technology\", 0)),\n", + " \"classes\": int(ds.sizes.get(\"class\", 0)),\n", + " \"time_steps\": int(ds.sizes.get(\"time\", ds.sizes.get(\"hour\", 0))),\n", + " }\n", + " )\n", + "\n", + "summary_df = pd.DataFrame(summary_rows)\n", + "if not summary_df.empty:\n", + " display(summary_df)\n", + "\n", + "if pypsa_earth_meta is not None:\n", + " metadata_df = pd.DataFrame(\n", + " [\n", + " dict(info, cluster_name=name)\n", + " for name, info in pypsa_earth_meta.get(\"cluster_metadata\", {}).items()\n", + " ]\n", + " )\n", + " if not metadata_df.empty:\n", + " metadata_df[\"technology\"] = metadata_df[\"technology\"].astype(str)\n", + " metadata_df[\"region\"] = metadata_df[\"region\"].astype(str)\n", + " display(\n", + " metadata_df[\n", + " [\n", + " \"region\",\n", + " \"technology\",\n", + " \"cluster_id\",\n", + " \"n_buses_consolidated\",\n", + " \"total_capacity_mw\",\n", + " \"avg_cf\",\n", + " ]\n", + " ].head(12)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03a80b1a", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"TRACE DATASET DIAGNOSTICS\")\n", + "print(\"=\" * 70)\n", + "\n", + "trace_diagnostic_rows = []\n", + "if trace_ds is not None:\n", + " trace_diagnostic_rows.append(\n", + " {\n", + " \"source\": \"TRACE\",\n", + " \"dimensions\": dict(trace_ds.sizes),\n", + " \"regions\": int(trace_ds.sizes.get(\"region\", 0)),\n", + " \"technologies\": int(trace_ds.sizes.get(\"technology\", 0)),\n", + " \"sites\": int(trace_ds.sizes.get(\"class\", trace_ds.sizes.get(\"site_id\", 0))),\n", + " \"time_steps\": int(\n", + " trace_ds.sizes.get(\"time\", trace_ds.sizes.get(\"hour\", 0))\n", + " ),\n", + " \"variables\": \", \".join(list(trace_ds.data_vars)),\n", + " }\n", + " )\n", + " trace_diag_df = pd.DataFrame(trace_diagnostic_rows)\n", + " display(trace_diag_df)\n", + "\n", + " trace_capacity_stats = []\n", + " for tech_name in [\"windonshore\", \"pvplant\"]:\n", + " if \"technology\" not in trace_ds.coords:\n", + " continue\n", + " if tech_name not in [str(v) for v in trace_ds.technology.values]:\n", + " continue\n", + " try:\n", + " capacity_values = trace_ds[\"capacity\"].sel(technology=tech_name).values\n", + " capacity_values = capacity_values[~np.isnan(capacity_values)]\n", + " trace_capacity_stats.append(\n", + " {\n", + " \"technology\": tech_name,\n", + " \"count\": int(len(capacity_values)),\n", + " \"min_mw\": float(np.min(capacity_values)),\n", + " \"median_mw\": float(np.median(capacity_values)),\n", + " \"mean_mw\": float(np.mean(capacity_values)),\n", + " \"max_mw\": float(np.max(capacity_values)),\n", + " }\n", + " )\n", + " except Exception:\n", + " continue\n", + " if trace_capacity_stats:\n", + " display(pd.DataFrame(trace_capacity_stats))\n", + "else:\n", + " print(\"TRACE file not found.\")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"PYPSA-EARTH DATASET DIAGNOSTICS\")\n", + "print(\"=\" * 70)\n", + "\n", + "pypsa_diagnostic_rows = []\n", + "if pypsa_earth_ds is not None:\n", + " pypsa_diagnostic_rows.append(\n", + " {\n", + " \"source\": \"PyPSA-Earth\",\n", + " \"dimensions\": dict(pypsa_earth_ds.sizes),\n", + " \"regions\": int(pypsa_earth_ds.sizes.get(\"region\", 0)),\n", + " \"technologies\": int(pypsa_earth_ds.sizes.get(\"technology\", 0)),\n", + " \"clusters\": int(pypsa_earth_ds.sizes.get(\"class\", 0)),\n", + " \"time_steps\": int(\n", + " pypsa_earth_ds.sizes.get(\"time\", pypsa_earth_ds.sizes.get(\"hour\", 0))\n", + " ),\n", + " \"variables\": \", \".join(list(pypsa_earth_ds.data_vars)),\n", + " }\n", + " )\n", + " pypsa_diag_df = pd.DataFrame(pypsa_diagnostic_rows)\n", + " display(pypsa_diag_df)\n", + "\n", + " pypsa_capacity_stats = []\n", + " for tech_name in [\"onwind\", \"solar\"]:\n", + " if tech_name not in [str(v) for v in pypsa_earth_ds.technology.values]:\n", + " continue\n", + " try:\n", + " capacity_values = (\n", + " pypsa_earth_ds[\"capacity\"].sel(technology=tech_name).values.flatten()\n", + " )\n", + " capacity_values = capacity_values[~np.isnan(capacity_values)]\n", + " pypsa_capacity_stats.append(\n", + " {\n", + " \"technology\": tech_name,\n", + " \"count\": int(len(capacity_values)),\n", + " \"min_mw\": float(np.min(capacity_values)),\n", + " \"median_mw\": float(np.median(capacity_values)),\n", + " \"mean_mw\": float(np.mean(capacity_values)),\n", + " \"max_mw\": float(np.max(capacity_values)),\n", + " }\n", + " )\n", + " except Exception:\n", + " continue\n", + " if pypsa_capacity_stats:\n", + " display(pd.DataFrame(pypsa_capacity_stats))\n", + "else:\n", + " print(\"PyPSA-Earth file not found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4973ebf4", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"=\" * 70)\n", + "print(\"BUILDING COMPARISON TABLES\")\n", + "print(\"=\" * 70)\n", + "\n", + "source_order = [\"TRACE\", \"PyPSA-Earth\"]\n", + "tech_order = [\"solar\", \"onwind\"]\n", + "source_palette = SOURCE_STYLES\n", + "tech_palette = TECH_COLORS\n", + "\n", + "\n", + "def build_trace_rows(ds):\n", + " rows = []\n", + " if ds is None:\n", + " return rows\n", + " for trace_tech_name, compare_tech_name in {\n", + " \"windonshore\": \"onwind\",\n", + " \"pvplant\": \"solar\",\n", + " }.items():\n", + " if trace_tech_name not in [str(v) for v in ds.technology.values]:\n", + " continue\n", + " try:\n", + " cap_da = ds[\"capacity\"].sel(technology=trace_tech_name)\n", + " if \"avg_cf\" in ds.data_vars:\n", + " avg_cf_da = ds[\"avg_cf\"].sel(technology=trace_tech_name)\n", + " else:\n", + " avg_cf_da = (\n", + " ds[\"capacity_factor\"]\n", + " .sel(technology=trace_tech_name)\n", + " .mean(dim=\"time\", skipna=True)\n", + " )\n", + "\n", + " for region_name in ds.region.values:\n", + " region_caps = cap_da.sel(region=region_name).values\n", + " region_avg_cf = avg_cf_da.sel(region=region_name).values\n", + " for class_idx in range(len(region_caps)):\n", + " capacity_mw = float(region_caps[class_idx])\n", + " avg_cf = float(region_avg_cf[class_idx])\n", + " if np.isnan(capacity_mw) or np.isnan(avg_cf):\n", + " continue\n", + " rows.append(\n", + " {\n", + " \"source\": \"TRACE\",\n", + " \"region\": str(region_name),\n", + " \"technology\": str(compare_tech_name),\n", + " \"cluster_id\": int(class_idx),\n", + " \"label\": f\"{compare_tech_name} {class_idx + 1}\",\n", + " \"capacity_mw\": capacity_mw,\n", + " \"avg_cf\": avg_cf,\n", + " \"generation_potential_mwh\": capacity_mw * avg_cf * 8760.0,\n", + " }\n", + " )\n", + " except Exception as exc:\n", + " print(f\" Could not read TRACE data for {trace_tech_name}: {exc}\")\n", + " return rows\n", + "\n", + "\n", + "def build_pypsa_rows(ds, meta):\n", + " rows = []\n", + " if meta is not None and meta.get(\"cluster_metadata\") is not None:\n", + " for cluster_name, info in meta.get(\"cluster_metadata\", {}).items():\n", + " rows.append(\n", + " {\n", + " \"source\": \"PyPSA-Earth\",\n", + " \"region\": str(info[\"region\"]),\n", + " \"technology\": str(info[\"technology\"]),\n", + " \"cluster_id\": int(info[\"cluster_id\"]),\n", + " \"label\": f\"{info['technology']} {int(info['cluster_id']) + 1}\",\n", + " \"capacity_mw\": float(info[\"total_capacity_mw\"]),\n", + " \"avg_cf\": float(info[\"avg_cf\"]),\n", + " \"generation_potential_mwh\": float(\n", + " info[\"total_capacity_mw\"] * info[\"avg_cf\"] * 8760.0\n", + " ),\n", + " }\n", + " )\n", + " return rows\n", + "\n", + " if ds is None:\n", + " return rows\n", + "\n", + " for tech_name in tech_order:\n", + " if tech_name not in [str(v) for v in ds.technology.values]:\n", + " continue\n", + " try:\n", + " cap_da = ds[\"capacity\"].sel(technology=tech_name)\n", + " avg_cf_da = ds[\"avg_cf\"].sel(technology=tech_name)\n", + " for region_name in ds.region.values:\n", + " region_caps = cap_da.sel(region=region_name).values\n", + " region_avg_cf = avg_cf_da.sel(region=region_name).values\n", + " for class_idx in range(len(region_caps)):\n", + " capacity_mw = float(region_caps[class_idx])\n", + " avg_cf = float(region_avg_cf[class_idx])\n", + " if np.isnan(capacity_mw) or np.isnan(avg_cf):\n", + " continue\n", + " rows.append(\n", + " {\n", + " \"source\": \"PyPSA-Earth\",\n", + " \"region\": str(region_name),\n", + " \"technology\": str(tech_name),\n", + " \"cluster_id\": int(class_idx),\n", + " \"label\": f\"{tech_name} {class_idx + 1}\",\n", + " \"capacity_mw\": capacity_mw,\n", + " \"avg_cf\": avg_cf,\n", + " \"generation_potential_mwh\": capacity_mw * avg_cf * 8760.0,\n", + " }\n", + " )\n", + " except Exception as exc:\n", + " print(f\" Could not read PyPSA-Earth data for {tech_name}: {exc}\")\n", + " return rows\n", + "\n", + "\n", + "trace_rows = build_trace_rows(trace_ds)\n", + "pypsa_rows = build_pypsa_rows(pypsa_earth_ds, pypsa_earth_meta)\n", + "comparison_df = pd.concat(\n", + " [pd.DataFrame(trace_rows), pd.DataFrame(pypsa_rows)], ignore_index=True\n", + ")\n", + "if comparison_df.empty:\n", + " raise ValueError(\"No comparison data found.\")\n", + "\n", + "comparison_df[\"source\"] = pd.Categorical(\n", + " comparison_df[\"source\"], categories=source_order, ordered=True\n", + ")\n", + "comparison_df[\"technology\"] = pd.Categorical(\n", + " comparison_df[\"technology\"], categories=tech_order, ordered=True\n", + ")\n", + "comparison_df[\"region\"] = comparison_df[\"region\"].astype(str)\n", + "comparison_df[\"avg_cf\"] = comparison_df[\"avg_cf\"].astype(float)\n", + "comparison_df[\"capacity_mw\"] = comparison_df[\"capacity_mw\"].astype(float)\n", + "comparison_df[\"generation_potential_mwh\"] = comparison_df[\n", + " \"generation_potential_mwh\"\n", + "].astype(float)\n", + "\n", + "comparison_summary_df = (\n", + " comparison_df.groupby([\"source\", \"technology\"], as_index=False, observed=True)\n", + " .agg(\n", + " n_clusters=(\"cluster_id\", \"size\"),\n", + " total_capacity_mw=(\"capacity_mw\", \"sum\"),\n", + " mean_avg_cf=(\"avg_cf\", \"mean\"),\n", + " total_generation_potential_mwh=(\"generation_potential_mwh\", \"sum\"),\n", + " )\n", + " .sort_values([\"source\", \"technology\"])\n", + ")\n", + "\n", + "display(comparison_summary_df)" + ] + }, + { + "cell_type": "markdown", + "id": "4bc747d0", + "metadata": {}, + "source": [ + "### Bubble Plot: Capacity Factor vs Capacity by Source\n", + "\n", + "This plot compares the distribution of cluster capacities and average capacity factors between TRACE and PyPSA-Earth.\\nIt reveals whether clustering **preserves the merit-order structure**: low-cost (high CF) clusters should remain distinct and visible as separate bubbles.\\nBubble size represents annual generation potential. Log scale on the Y-axis emphasizes smaller but important clusters.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c26731e8", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\\\nGenerating bubble plots...\")\n", + "\n", + "if trace_ds is None or pypsa_earth_ds is None:\n", + " raise ValueError(\n", + " \"Both TRACE and PyPSA-Earth datasets are required for the bubble plots.\"\n", + " )\n", + "\n", + "common_regions = sorted(\n", + " set(str(v) for v in trace_ds.region.values)\n", + " & set(str(v) for v in pypsa_earth_ds.region.values)\n", + ")\n", + "if not common_regions:\n", + " raise ValueError(\"No common regions found between TRACE and PyPSA-Earth.\")\n", + "\n", + "region = common_regions[0]\n", + "print(f\"Example region: {region}\")\n", + "\n", + "plot_helpers.plot_bubble_region_comparison(region, trace_ds, pypsa_earth_ds, show=True)\n", + "print(\"✓ Bubble plot rendered for TRACE and PyPSA-Earth.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75782797", + "metadata": {}, + "outputs": [], + "source": [ + "plot_helpers.plot_bubble_region_comparison(\n", + " \"West_Asia\", trace_ds, pypsa_earth_ds, show=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "104cb776", + "metadata": {}, + "source": [ + "### Interactive Timeseries Comparison: Cluster Capacity Factors Over Time\n", + "\n", + "This plot compares how individual clusters behave over a full year in TRACE vs PyPSA-Earth.\n", + "It validates that clustering **preserves temporal patterns** (peak periods, seasonal variation) and the **energy density hierarchy** (line width scales with average CF).\n", + "Two stacked panels show each source; separate invocations for solar and onwind allow independent inspection of each technology's cluster dynamics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfc79895", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\\\nGenerating interactive timeseries comparison panels...\")\n", + "\n", + "if trace_ds is None or pypsa_earth_ds is None:\n", + " raise ValueError(\n", + " \"Both TRACE and PyPSA-Earth datasets are required for the timeseries comparison.\"\n", + " )\n", + "\n", + "common_regions = sorted(\n", + " set(str(v) for v in trace_ds.region.values)\n", + " & set(str(v) for v in pypsa_earth_ds.region.values)\n", + ")\n", + "if not common_regions:\n", + " raise ValueError(\"No common regions found between TRACE and PyPSA-Earth.\")\n", + "\n", + "region = common_regions[0]\n", + "print(f\"Example region: {region}\")\n", + "\n", + "print(\"\\\\n--- Solar Timeseries ---\")\n", + "plot_helpers.plot_region_cluster_timeseries(\n", + " region, \"solar\", trace_ds=trace_ds, pypsa_ds=pypsa_earth_ds, show=True\n", + ")\n", + "\n", + "print(\"\\\\n--- Onwind Timeseries ---\")\n", + "plot_helpers.plot_region_cluster_timeseries(\n", + " region, \"onwind\", trace_ds=trace_ds, pypsa_ds=pypsa_earth_ds, show=True\n", + ")\n", + "\n", + "print(\"✓ Interactive timeseries comparison rendered for both technologies.\")" + ] + }, + { + "cell_type": "markdown", + "id": "571e8279", + "metadata": {}, + "source": [ + "## Stacked Region Comparison: Total Capacity by Cluster Merit Order\n", + "\n", + "This plot stacks cluster capacities per region, ordered by average capacity factor (shaded light → dark).\n", + "It shows whether clustering **preserves regional capacity distributions** and **maintains the high-CF \"green pockets\"** that enable merit-order effects in optimization.\n", + "Side-by-side panels compare solar and onwind; border colors distinguish Consolidated vs Clustered sources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf86056b", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\\\nGenerating stacked region comparison by technology...\")\n", + "\n", + "# Build cluster comparison dataframe if not already in memory\n", + "ds_consolidated = trace_ds if \"trace_ds\" in dir() and trace_ds is not None else None\n", + "ds_clustered = (\n", + " pypsa_earth_ds if \"pypsa_earth_ds\" in dir() and pypsa_earth_ds is not None else None\n", + ")\n", + "df_meta = pypsa_earth_meta if \"pypsa_earth_meta\" in dir() else None\n", + "\n", + "df_cluster_meta = None\n", + "if df_meta is not None and \"cluster_metadata\" in df_meta:\n", + " metadata_rows = []\n", + " for name, info in df_meta.get(\"cluster_metadata\", {}).items():\n", + " row = dict(info)\n", + " row[\"cluster_name\"] = name\n", + " metadata_rows.append(row)\n", + " if metadata_rows:\n", + " df_cluster_meta = pd.DataFrame(metadata_rows)\n", + "\n", + "if \"cluster_comparison_df\" not in dir():\n", + " cluster_comparison_df = plot_helpers.build_cluster_comparison_df(\n", + " ds_consolidated, ds_clustered, df_cluster_meta\n", + " )\n", + " if cluster_comparison_df.empty:\n", + " raise ValueError(\n", + " \"Cluster-level comparison data is required for the stacked region comparison.\"\n", + " )\n", + "\n", + "plot_helpers.plot_stacked_region_comparison(\n", + " cluster_comparison_df, show=True, save_path=\"region_stacked_capacity_comparison.png\"\n", + ")\n", + "print(\n", + " \"✓ Stacked region comparison rendered and saved as region_stacked_capacity_comparison.png\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift_dev", + "language": "python", + "name": "shift_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflow/notebooks/plot_helpers.py b/workflow/notebooks/plot_helpers.py new file mode 100644 index 0000000..834cc1c --- /dev/null +++ b/workflow/notebooks/plot_helpers.py @@ -0,0 +1,605 @@ +import numpy as np +import pandas as pd +import plotly.graph_objects as go +from plotly.subplots import make_subplots +import matplotlib.pyplot as plt +import seaborn as sns + +# Colourblind-safe cluster palette (reusable) +PLOTLY_CLUSTER_COLORS = [ + "#0072B2", + "#D55E00", + "#009E73", + "#CC79A7", + "#E69F00", + "#56B4E9", + "#000000", + "#F0E442", +] + + +def _map_trace_tech(source_tech: str) -> str: + """Map legacy TRACE technology names to our canonical names.""" + return {"windonshore": "onwind", "pvplant": "solar"}.get(source_tech, source_tech) + + +def build_timeseries_df(ds, source_name: str, region_name: str, ts_tech_order=None): + """Build a pandas DataFrame of cluster timeseries from an xarray dataset. + + Returns columns: source, technology, cluster_id, avg_cf, capacity_mw, timeseries (numpy array) + """ + if ts_tech_order is None: + ts_tech_order = ["solar", "onwind"] + rows = [] + if ds is None: + return pd.DataFrame(rows) + + if source_name == "TRACE": + tech_map = {"windonshore": "onwind", "pvplant": "solar"} + for source_tech, compare_tech in tech_map.items(): + if "technology" not in ds.coords: + continue + if source_tech not in [str(v) for v in ds.technology.values]: + continue + try: + cap_da = ds["capacity"].sel(region=region_name, technology=source_tech) + cf_da = ds["capacity_factor"].sel( + region=region_name, technology=source_tech + ) + if "avg_cf" in ds.data_vars: + avg_cf_da = ds["avg_cf"].sel( + region=region_name, technology=source_tech + ) + else: + avg_cf_da = cf_da.mean(dim="time", skipna=True) + for idx in range(len(cap_da.values)): + capacity_mw = float(cap_da.values[idx]) + avg_cf = float(avg_cf_da.values[idx]) + cf_ts = np.array(cf_da.values[idx], dtype=float) + if ( + np.isnan(capacity_mw) + or np.isnan(avg_cf) + or np.all(np.isnan(cf_ts)) + ): + continue + rows.append( + { + "source": source_name, + "technology": compare_tech, + "cluster_id": int(idx), + "avg_cf": avg_cf, + "capacity_mw": capacity_mw, + "timeseries": np.nan_to_num(cf_ts, nan=0.0), + } + ) + except Exception: + continue + else: + for tech_name in ts_tech_order: + if tech_name not in [str(v) for v in ds.technology.values]: + continue + try: + cap_da = ds["capacity"].sel(region=region_name, technology=tech_name) + cf_da = ds["capacity_factor"].sel( + region=region_name, technology=tech_name + ) + avg_cf_da = ds["avg_cf"].sel(region=region_name, technology=tech_name) + for idx in range(len(cap_da.values)): + capacity_mw = float(cap_da.values[idx]) + avg_cf = float(avg_cf_da.values[idx]) + cf_ts = np.array(cf_da.values[idx], dtype=float) + if ( + np.isnan(capacity_mw) + or np.isnan(avg_cf) + or np.all(np.isnan(cf_ts)) + ): + continue + rows.append( + { + "source": source_name, + "technology": tech_name, + "cluster_id": int(idx), + "avg_cf": avg_cf, + "capacity_mw": capacity_mw, + "timeseries": np.nan_to_num(cf_ts, nan=0.0), + } + ) + except Exception: + continue + return pd.DataFrame(rows) + + +def _cf_to_width(cf, cf_min, cf_span): + norm = (cf - cf_min) / cf_span if cf_span > 0 else 0.0 + return float(np.clip(1.0 + 5.0 * (norm**1.6), 1.0, 6.0)) + + +def plot_region_cluster_timeseries( + region_name: str, + technology_name: str, + trace_ds=None, + pypsa_ds=None, + tech_colors=None, + cluster_colors=None, + show=True, +): + """Create a two-panel Plotly figure comparing TRACE vs PyPSA-Earth for a region+technology. + + - `trace_ds` and `pypsa_ds` are xarray datasets (or None). + - `tech_colors` maps technologies to base hex colors (optional). + - `cluster_colors` is a list of colours to cycle for clusters. + Returns the Plotly `Figure`. + """ + if cluster_colors is None: + cluster_colors = PLOTLY_CLUSTER_COLORS + if tech_colors is None: + tech_colors = {"onwind": "#1F77B4", "solar": "#D62728"} + + trace_df = build_timeseries_df(trace_ds, "TRACE", region_name) + pypsa_df = build_timeseries_df(pypsa_ds, "PyPSA-Earth", region_name) + + fig = make_subplots( + rows=2, + cols=1, + shared_xaxes=True, + vertical_spacing=0.06, + subplot_titles=(f"TRACE - {region_name}", f"PyPSA-Earth - {region_name}"), + ) + + for row_idx, df in enumerate([trace_df, pypsa_df], start=1): + source_name = "TRACE" if row_idx == 1 else "PyPSA-Earth" + if df.empty: + fig.add_annotation( + x=0.5, + y=0.5, + xref=(f"x{row_idx} domain" if row_idx > 1 else "x domain"), + yref=(f"y{row_idx} domain" if row_idx > 1 else "y domain"), + text=f"No data for {source_name}", + showarrow=False, + ) + continue + tech_df = df[df["technology"] == technology_name].copy() + if tech_df.empty: + continue + cf_min = float(tech_df["avg_cf"].min()) + cf_span = ( + float(tech_df["avg_cf"].max() - cf_min) + if float(tech_df["avg_cf"].max()) > cf_min + else 1.0 + ) + legend_names = set() + for _, row in tech_df.sort_values(by="avg_cf").iterrows(): + cid = int(row["cluster_id"]) + avg_cf = float(row["avg_cf"]) + cap_gw = float(row["capacity_mw"]) / 1e3 + color = cluster_colors[cid % len(cluster_colors)] + width = _cf_to_width(avg_cf, cf_min, cf_span) + cf_ts = np.asarray(row["timeseries"], dtype=float) + hours = np.arange(len(cf_ts)) + customdata = np.column_stack([np.full(len(cf_ts), cid)]) + legend_label = f" {source_name} - cluster {cid} | {cap_gw:.2f} GW | avg cf {avg_cf:.3f}" + showleg = legend_label not in legend_names + if showleg: + legend_names.add(legend_label) + fig.add_trace( + go.Scattergl( + x=hours, + y=cf_ts, + mode="lines", + line=dict(color=color, width=width), + opacity=0.9, + customdata=customdata, + hovertemplate="Cluster %{customdata[0]}
    Hour %{x}
    CF %{y:.3f}", + name=legend_label, + legendgroup=f"{technology_name}-{cid}", + showlegend=showleg, + ), + row=row_idx, + col=1, + ) + + fig.update_layout( + template="plotly_white", + height=760, + hovermode="x unified", + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0.0), + ) + fig.update_xaxes(title_text="Hour of year", row=2, col=1) + fig.update_yaxes(title_text="Capacity factor", row=1, col=1) + fig.update_yaxes(title_text="Capacity factor", row=2, col=1) + if show: + fig.show() + return fig + + +def plot_bubble_region_comparison( + region_name: str, trace_ds, pypsa_ds, tech_colors=None, save_path=None, show=True +): + """Create the bubble plots comparing TRACE vs PyPSA-Earth for a single region. + + This mirrors the notebook bubble plot: x=avg_cf, y=capacity_mw, bubble size ~ generation potential. + """ + if tech_colors is None: + tech_colors = {"onwind": "#1F77B4", "solar": "#D62728"} + + if trace_ds is None or pypsa_ds is None: + raise ValueError( + "Both TRACE and PyPSA-Earth datasets are required for the bubble plots." + ) + + # build comparison_df in the same format as the notebook's `comparison_df` + trace_rows = [] + if trace_ds is not None: + for trace_tech_name, compare_tech_name in { + "windonshore": "onwind", + "pvplant": "solar", + }.items(): + if trace_tech_name not in [str(v) for v in trace_ds.technology.values]: + continue + try: + cap_da = trace_ds["capacity"].sel(technology=trace_tech_name) + if "avg_cf" in trace_ds.data_vars: + avg_cf_da = trace_ds["avg_cf"].sel(technology=trace_tech_name) + else: + avg_cf_da = ( + trace_ds["capacity_factor"] + .sel(technology=trace_tech_name) + .mean(dim="time", skipna=True) + ) + for region in trace_ds.region.values: + if str(region) != str(region_name): + continue + region_caps = cap_da.sel(region=region).values + region_avg_cf = avg_cf_da.sel(region=region).values + for class_idx in range(len(region_caps)): + capacity_mw = float(region_caps[class_idx]) + avg_cf = float(region_avg_cf[class_idx]) + if np.isnan(capacity_mw) or np.isnan(avg_cf): + continue + trace_rows.append( + { + "source": "TRACE", + "region": str(region), + "technology": compare_tech_name, + "cluster_id": int(class_idx), + "capacity_mw": capacity_mw, + "avg_cf": avg_cf, + "generation_potential_mwh": capacity_mw + * avg_cf + * 8760.0, + } + ) + except Exception: + continue + + pypsa_rows = [] + if pypsa_ds is not None: + for tech_name in ["onwind", "solar"]: + if tech_name not in [str(v) for v in pypsa_ds.technology.values]: + continue + try: + cap_da = pypsa_ds["capacity"].sel(technology=tech_name) + avg_cf_da = pypsa_ds["avg_cf"].sel(technology=tech_name) + for region in pypsa_ds.region.values: + if str(region) != str(region_name): + continue + region_caps = cap_da.sel(region=region).values + region_avg_cf = avg_cf_da.sel(region=region).values + for class_idx in range(len(region_caps)): + capacity_mw = float(region_caps[class_idx]) + avg_cf = float(region_avg_cf[class_idx]) + if np.isnan(capacity_mw) or np.isnan(avg_cf): + continue + pypsa_rows.append( + { + "source": "PyPSA-Earth", + "region": str(region), + "technology": tech_name, + "cluster_id": int(class_idx), + "capacity_mw": capacity_mw, + "avg_cf": avg_cf, + "generation_potential_mwh": capacity_mw + * avg_cf + * 8760.0, + } + ) + except Exception: + continue + + bubble_df = pd.concat( + [pd.DataFrame(trace_rows), pd.DataFrame(pypsa_rows)], ignore_index=True + ) + if bubble_df.empty: + raise ValueError(f"No comparison data available for {region_name}.") + + if "generation_potential_mwh" not in bubble_df.columns: + bubble_df["generation_potential_mwh"] = ( + bubble_df["capacity_mw"] * bubble_df["avg_cf"] * 8760.0 + ) + + max_potential = bubble_df["generation_potential_mwh"].max() + size_scale = 1500.0 / max_potential if max_potential > 0 else 1.0 + + sns.set_style("whitegrid") + fig, axes = plt.subplots( + 1, 2, figsize=(16, 6), sharex=True, sharey=True, constrained_layout=True + ) + bubble_source_order = ["TRACE", "PyPSA-Earth"] + bubble_panel_labels = {"TRACE": "TRACE panel", "PyPSA-Earth": "PyPSA-Earth panel"} + + for ax, source_name in zip(axes, bubble_source_order): + subset = bubble_df[bubble_df["source"] == source_name].copy() + if subset.empty: + ax.text( + 0.5, + 0.5, + f"No data for {source_name}", + ha="center", + va="center", + transform=ax.transAxes, + ) + ax.set_axis_off() + continue + for tech_name in ["solar", "onwind"]: + tech_subset = subset[subset["technology"] == tech_name] + if tech_subset.empty: + continue + sizes = np.clip( + tech_subset["generation_potential_mwh"].to_numpy() * size_scale, + 20, + 1600, + ) + ax.scatter( + tech_subset["avg_cf"], + tech_subset["capacity_mw"], + s=sizes, + alpha=0.45, + color=tech_colors.get(tech_name, "#888888"), + edgecolor="white", + linewidth=0.6, + label=tech_name, + ) + + ax.set_title( + f"{source_name} - {region_name}", fontsize=13, fontweight="semibold" + ) + ax.set_xlabel("Average capacity factor", fontsize=11, labelpad=8) + ax.set_ylabel("Capacity (MW)", fontsize=11, labelpad=8) + ax.tick_params(axis="both", labelsize=10) + ax.set_yscale("log") + ax.grid(True, alpha=0.25) + tech_legend = ax.legend(title="Technology", loc="upper right", frameon=True) + ax.add_artist(tech_legend) + ax.text( + 0.02, + 0.97, + bubble_panel_labels[source_name], + transform=ax.transAxes, + ha="left", + va="top", + fontsize=10, + fontweight="semibold", + bbox={"facecolor": "white", "alpha": 0.75, "edgecolor": "none", "pad": 2}, + ) + + fig.suptitle( + "Bubble plot of capacity factor vs capacity", fontsize=16, fontweight="semibold" + ) + if save_path is not None: + fig.savefig(save_path, dpi=150, bbox_inches="tight") + if show: + plt.show() + return fig + + +def build_cluster_comparison_df(ds_consolidated, ds_clustered, df_cluster_meta=None): + """Build cluster_comparison_df used by the stacked-region plotting. + + Returns a DataFrame with columns: source, region, technology, cluster_id, cluster_label, capacity_mw, avg_cf + """ + rows = [] + techs_to_compare = ["onwind", "solar"] + consolidated_map = {"windonshore": "onwind", "pvplant": "solar"} + + if ds_consolidated is not None: + for consolidated_tech_name, compare_tech_name in consolidated_map.items(): + try: + cap_da = ds_consolidated["capacity"].sel( + technology=consolidated_tech_name + ) + cf_da = ds_consolidated["capacity_factor"].sel( + technology=consolidated_tech_name + ) + avg_cf_da = cf_da.mean(dim="time", skipna=True) + for region_name in ds_consolidated.region.values: + region_caps = cap_da.sel(region=region_name).values + region_avg_cf = avg_cf_da.sel(region=region_name).values + for class_idx in range(len(region_caps)): + capacity_mw = float(region_caps[class_idx]) + avg_cf = float(region_avg_cf[class_idx]) + if np.isnan(capacity_mw) or np.isnan(avg_cf): + continue + rows.append( + { + "source": "Consolidated", + "region": str(region_name), + "technology": str(compare_tech_name), + "cluster_id": int(class_idx), + "cluster_label": f"{compare_tech_name} {class_idx + 1}", + "capacity_mw": capacity_mw, + "avg_cf": avg_cf, + } + ) + except Exception: + continue + + if df_cluster_meta is not None and not df_cluster_meta.empty: + subset = df_cluster_meta[ + df_cluster_meta["technology"].isin(techs_to_compare) + ].copy() + for _, row in subset.iterrows(): + rows.append( + { + "source": "Clustered", + "region": str(row["region"]), + "technology": str(row["technology"]), + "cluster_id": int(row["cluster_id"]), + "cluster_label": f"{row['technology']} {int(row['cluster_id']) + 1}", + "capacity_mw": float(row["total_capacity_mw"]), + "avg_cf": float(row["avg_cf"]), + } + ) + elif ds_clustered is not None: + for tech_name in techs_to_compare: + try: + cap_da = ds_clustered["capacity"].sel(technology=tech_name) + avg_cf_da = ds_clustered["avg_cf"].sel(technology=tech_name) + for region_name in ds_clustered.region.values: + region_caps = cap_da.sel(region=region_name).values + region_avg_cf = avg_cf_da.sel(region=region_name).values + for class_idx in range(len(region_caps)): + capacity_mw = float(region_caps[class_idx]) + avg_cf = float(region_avg_cf[class_idx]) + if np.isnan(capacity_mw) or np.isnan(avg_cf): + continue + rows.append( + { + "source": "Clustered", + "region": str(region_name), + "technology": str(tech_name), + "cluster_id": int(class_idx), + "cluster_label": f"{tech_name} {class_idx + 1}", + "capacity_mw": capacity_mw, + "avg_cf": avg_cf, + } + ) + except Exception: + continue + + if not rows: + return pd.DataFrame() + df = pd.DataFrame(rows) + df["source"] = pd.Categorical( + df["source"], categories=["Consolidated", "Clustered"], ordered=True + ) + df["technology"] = pd.Categorical( + df["technology"], categories=["solar", "onwind"], ordered=True + ) + df["region"] = df["region"].astype(str) + df["avg_cf"] = df["avg_cf"].astype(float) + return df + + +def plot_stacked_region_comparison( + cluster_comparison_df, tech_palette=None, save_path=None, show=True +): + """Draw the stacked region comparison (matplotlib) used in the notebook. + + `cluster_comparison_df` should be the output of `build_cluster_comparison_df`. + """ + if cluster_comparison_df is None or cluster_comparison_df.empty: + raise ValueError("cluster_comparison_df is required and must not be empty") + if tech_palette is None: + tech_palette = {"solar": "#C55C5C", "onwind": "#4C78A8"} + + sns.set_style("whitegrid") + source_offsets = {"Consolidated": -0.18, "Clustered": 0.18} + tech_width = 0.24 + tech_order = ["solar", "onwind"] + source_order = ["Consolidated", "Clustered"] + + def blend_with_white(color, intensity): + base = np.array(sns.color_palette([color])[0]) + intensity = float(np.clip(intensity, 0.0, 1.0)) + return tuple(base * intensity + np.array([1.0, 1.0, 1.0]) * (1.0 - intensity)) + + fig, axes = plt.subplots(1, 2, figsize=(22, 8), sharey=True) + + for ax, tech_name in zip(axes, tech_order): + df = cluster_comparison_df[ + cluster_comparison_df["technology"] == tech_name + ].copy() + regions = sorted(df["region"].unique()) + x_positions = np.arange(len(regions)) * 1.35 + + for region_idx, region_name in enumerate(regions): + for source_name in source_order: + subset = df[ + ( + (df["region"] == region_name) + & (df["technology"] == tech_name) + & (df["source"] == source_name) + ) + ].copy() + if subset.empty: + continue + subset = subset.sort_values(by="avg_cf") + bottom = 0.0 + cf_min = float(subset["avg_cf"].min()) + cf_max = float(subset["avg_cf"].max()) + cf_span = cf_max - cf_min if cf_max > cf_min else 1.0 + border_color = "black" if source_name == "Consolidated" else "white" + border_width = 0.55 if source_name == "Consolidated" else 0.3 + + for _, row in subset.iterrows(): + norm_cf = (row["avg_cf"] - cf_min) / cf_span + shade = 0.10 + 0.90 * (norm_cf**0.65) + color = blend_with_white(tech_palette[tech_name], shade) + ax.bar( + x_positions[region_idx] + source_offsets[source_name], + row["capacity_mw"], + width=tech_width, + bottom=bottom, + color=color, + edgecolor=border_color, + linewidth=border_width, + ) + bottom += row["capacity_mw"] + + import matplotlib.patches as mpatches + + source_handles = [ + mpatches.Patch( + facecolor="#E0E0E0", edgecolor="black", label="Consolidated" + ), + mpatches.Patch(facecolor="#A9A9A9", edgecolor="white", label="Clustered"), + ] + cf_handles = [ + mpatches.Patch( + facecolor="#f2f2f2", edgecolor="#cccccc", label="low avg_cf" + ), + mpatches.Patch( + facecolor="#5a5a5a", edgecolor="#cccccc", label="high avg_cf" + ), + ] + tech_handles = [mpatches.Patch(color=tech_palette[tech_name], label=tech_name)] + + source_legend = ax.legend( + handles=source_handles, title="Source", loc="upper left" + ) + ax.add_artist(source_legend) + cf_legend = ax.legend(handles=cf_handles, title="CF shade", loc="upper right") + ax.add_artist(cf_legend) + ax.legend(handles=tech_handles, title="Technology", loc="center right") + + ax.set_title( + f"{tech_name.capitalize()}: consolidated vs clustered by region", + fontsize=13, + fontweight="bold", + ) + ax.set_xlabel("Region", fontsize=11) + ax.set_ylabel("Capacity (MW)", fontsize=11) + ax.set_xticks(x_positions) + ax.set_xticklabels(regions, rotation=45, ha="right") + ax.grid(True, axis="y", alpha=0.3) + ax.margins(x=0.04) + + fig.suptitle( + "Region-wise stacked capacity comparison", fontsize=15, fontweight="bold" + ) + plt.tight_layout(rect=(0, 0, 1, 0.95)) + if save_path is not None: + fig.savefig(save_path, dpi=150, bbox_inches="tight") + if show: + plt.show() + return fig diff --git a/workflow/scripts/cluster_renewables.py b/workflow/scripts/cluster_renewables.py index 1aebc21..8b8421d 100644 --- a/workflow/scripts/cluster_renewables.py +++ b/workflow/scripts/cluster_renewables.py @@ -1,21 +1,20 @@ """ -Cluster renewable generators from merged profiles for optimization. - -This script: -1. Loads merged renewable profiles (all 122k+ buses from merged file) -2. Filters to onwind + pvplant (excludes unreliable offshore wind) -3. Maps buses to regions using ISO3 codes and config -4. Extracts a unified capacity-factor summary plus lat/lon -5. Applies weighted K-means clustering (0.5 merit-order, 0.3 temporal, 0.2 geospatial) -6. Selects representative timeseries per cluster to preserve real patterns -7. Outputs clustered NetCDF in expected format: (region, technology, class, time) -8. Generates validation report with quality metrics +Cluster renewable generators using stratified k-medoids. + +Methodology (based on Frysztacki et al. 2021, Siala & Mahfouz 2019): +1. Load merged renewable profiles (122k+ buses) +2. Filter to onwind + solar +3. Map buses to regions via ISO3 +4. Stratify by cf_high_mass (top-5% hours mean) to preserve merit order & green pockets +5. Within each stratum: k-medoids on PCA(8760 profile) + lat/lon +6. Representative = medoid bus (real observed profile, no synthetic averaging) +7. Output clustered NetCDF: (region, technology, class, time) +8. Validate supply curve preservation Usage (Snakemake rule): rule cluster_renewables: input: merged = "data/renewable_profiles_global_merged.nc", - config = "config/config.yaml", output: clustered = "resources/renewables_clustered.nc", report = "resources/renewables_clustering_report.json", @@ -29,15 +28,17 @@ from typing import Any, Dict, Tuple, List, Optional from contextlib import contextmanager +import geopandas as gpd import numpy as np import pandas as pd import xarray as xr from tqdm import tqdm import joblib from sklearn.preprocessing import StandardScaler -from sklearn.cluster import KMeans +from sklearn.decomposition import PCA +import kmedoids as _kmedoids +from scipy.spatial.distance import pdist, squareform -# Setup logging logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) @@ -45,1291 +46,805 @@ snakemake: Any = globals().get("snakemake") +CACHE_VERSION = "v4_stratified_kmedoids" -CACHE_VERSION = "v3_cache_versioned_1d_cf_ts" -CF_QUANTILES = (0.1, 0.25, 0.5, 0.75, 0.9) -CF_THRESHOLDS = (0.2, 0.4, 0.6, 0.8) - - -def summarize_capacity_factor(cf_ts: np.ndarray) -> Dict[str, float]: - """Summarize a capacity-factor profile for clustering and validation. - - Args: - cf_ts: One-dimensional capacity-factor time series. Missing values and - out-of-range values are cleaned before summary statistics are - computed. - - Returns: - A dictionary of robust profile descriptors, including the core - temporal moments used by clustering (`avg_cf`, `temporal_std`, `cv`, - `autocorr_24h`) plus quantiles, exceedance fractions at key - thresholds, and low/high tail mass statistics. These values are used - to preserve merit-order shape when clustering and when selecting - representative buses. - """ - cf_clean = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) - if cf_clean.size == 0: - cf_clean = np.zeros(1, dtype=float) - avg_cf = float(np.nanmean(cf_clean)) - temporal_std = float(np.nanstd(cf_clean)) - cv = temporal_std / avg_cf if avg_cf > 0 else 0.0 - if len(cf_clean) > 24: - cf_detrended = cf_clean - np.nanmean(cf_clean) - if np.std(cf_detrended) > 1e-10 and not np.isnan(cf_detrended[:-24]).all(): - autocorr_24h = float( - np.corrcoef(cf_detrended[:-24], cf_detrended[24:])[0, 1] - ) - else: - autocorr_24h = 0.0 - else: - autocorr_24h = 0.0 - summary = { - f"cf_q{int(q * 100):02d}": float(np.nanquantile(cf_clean, q)) - for q in CF_QUANTILES - } - for threshold in CF_THRESHOLDS: - summary[f"cf_exceed_{int(threshold * 100):03d}"] = float( - np.mean(cf_clean >= threshold) - ) - tail = max(1, int(len(cf_clean) * 0.05)) - summary["avg_cf"] = avg_cf - summary["temporal_std"] = temporal_std - summary["cv"] = cv - summary["autocorr_24h"] = autocorr_24h - summary["cf_low_mass"] = float(np.mean(np.sort(cf_clean)[:tail])) - summary["cf_high_mass"] = float(np.mean(np.sort(cf_clean)[-tail:])) - return summary - - -def normalize_weights(weights: Dict[str, float]) -> Dict[str, float]: - """Normalize clustering weights into the three feature families. - - Args: - weights: Raw weights from config. Missing categories are treated as 0. - - Returns: - A normalized dictionary with `merit_order`, `temporal`, and - `geospatial` keys that sum to 1.0. If the provided weights are empty or - invalid, the default policy of 0.5/0.3/0.2 is returned. - """ - # Expect keys: merit_order, temporal, geospatial. - w = dict(weights or {}) - for k in ("merit_order", "temporal", "geospatial"): - w.setdefault(k, 0.0) - s = sum(w.values()) - if s <= 0: - return {"merit_order": 0.5, "temporal": 0.3, "geospatial": 0.2} - return {k: float(v / s) for k, v in w.items()} - - -def rescale_timeseries_to_mean(cf_ts: np.ndarray, target_mean: float) -> np.ndarray: - """Rescale a representative profile so its mean matches the cluster mean. - - Args: - cf_ts: Representative capacity-factor series selected from observed - buses. - target_mean: Mean capacity factor that the cluster should preserve. - - Returns: - A clipped capacity-factor series with the same shape as the input and a - mean close to `target_mean`. The result stays within the physical bounds - of [0, 1]. - """ - ts = np.clip(np.nan_to_num(cf_ts, nan=0.0).astype(float), 0.0, 1.0) - if ts.size == 0: - return ts - current = float(np.mean(ts)) - if current <= 0: - return np.full_like(ts, np.clip(target_mean, 0.0, 1.0)) - scaled = ts * (target_mean / current) - scaled = np.clip(scaled, 0.0, 1.0) - return scaled - - -def resolve_cluster_count(df_subset: pd.DataFrame, tech: str) -> int: - """Resolve the cluster count for one region-technology subset. - - Args: - df_subset: Feature rows for a single region and technology. - tech: Technology label for the subset, typically `solar` or `onwind`. - - Returns: - An integer cluster count derived from the configured policy. In dynamic - mode, the count scales with bus count and installed capacity while being - clamped to the configured min/max bounds. - """ - clustering_config = snakemake.config.get("clustering", {}) - policy = ( - clustering_config.get("cluster_count_policy", {}) - if isinstance(clustering_config, dict) - else {} - ) - base_clusters = policy.get("base_clusters", {}) if isinstance(policy, dict) else {} - mode = policy.get("mode", "dynamic") - base_key = "solar" if tech == "solar" else "onwind" - base = int(base_clusters.get(base_key, 40)) - if mode == "fixed": - return base - - n_buses = max(1, int(df_subset["bus_id"].nunique())) - total_capacity = ( - float(df_subset.get("capacity_mw", pd.Series([0])).sum()) - if "capacity_mw" in df_subset - else 0.0 - ) - ref_buses = float(policy.get("reference_buses", 1000.0)) - ref_cap = float(policy.get("reference_capacity_mw", 50000.0)) - bus_scale = (n_buses / ref_buses) ** float(policy.get("scale_exponent", 0.5)) - cap_scale = (max(1.0, total_capacity) / ref_cap) ** float( - policy.get("scale_exponent", 0.5) - ) - bus_w = float(policy.get("bus_weight", 0.5)) - cap_w = float(policy.get("capacity_weight", 0.5)) - target = int(round(base * (bus_w * bus_scale + cap_w * cap_scale))) - min_c = int(policy.get("min_clusters", 3)) - max_c = int(policy.get("max_clusters", 120)) - return max(min_c, min(max_c, max(1, min(target, len(df_subset))))) - - -def score_representative_candidate( - cf_ts: np.ndarray, - cf_target: np.ndarray, - target_summary: Dict[str, float], - candidate_summary: Dict[str, float], -) -> float: - """Score how well a candidate bus matches the cluster target shape. - - Args: - cf_ts: Candidate capacity-factor time series from an observed bus. - cf_target: Capacity-factor series representing the cluster-average - target. - target_summary: Summary statistics for `cf_target`. - candidate_summary: Summary statistics for the candidate series. - - Returns: - A similarity score where higher values indicate a better match. The - score combines correlation with a summary-statistic distance penalty so - the chosen representative preserves both shape and distributional tail - behavior. - """ - cf_ts = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) - cf_target = np.clip(np.nan_to_num(cf_target, nan=0.0), 0.0, 1.0) - corr = 0.0 - try: - if np.std(cf_ts) > 1e-12 and np.std(cf_target) > 1e-12: - corr = float(np.corrcoef(cf_ts, cf_target)[0, 1]) - except Exception: - corr = 0.0 - keys = list(target_summary.keys()) - dist = 0.0 - for k in keys: - dist += abs(candidate_summary.get(k, 0.0) - target_summary.get(k, 0.0)) - dist = dist / max(1, len(keys)) - return 0.7 * corr + 0.3 * (1.0 - dist) +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- @contextmanager def tqdm_joblib(tqdm_object): - """Route joblib progress callbacks into a tqdm progress bar. + """Route joblib progress callbacks into a tqdm bar.""" - Args: - tqdm_object: An active tqdm progress bar to update as joblib batches - complete. - - Yields: - The same tqdm object, allowing the caller to use the context manager as - a transparent progress-wrapper around `joblib.Parallel` work. - """ - - class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): + class _Cb(joblib.parallel.BatchCompletionCallBack): def __call__(self, *args, **kwargs): tqdm_object.update(self.batch_size) return super().__call__(*args, **kwargs) - old_batch_callback = joblib.parallel.BatchCompletionCallBack - joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback + old = joblib.parallel.BatchCompletionCallBack + joblib.parallel.BatchCompletionCallBack = _Cb try: yield tqdm_object finally: - joblib.parallel.BatchCompletionCallBack = old_batch_callback - - -def build_region_map(config: Dict, iso3_to_region: Dict[str, str]) -> Dict[str, str]: - """Build the ISO3-to-region lookup used to assign buses to regions. - - Args: - config: Global configuration containing the region definitions. - iso3_to_region: Existing lookup map. The parameter is accepted for - interface compatibility, but the mapping is rebuilt from config. - - Returns: - A dictionary mapping ISO3 country codes to region names. - """ - iso3_to_region_local = {} - for region_name, iso3_list in config.get("regions", {}).items(): - for iso3 in iso3_list: - iso3_to_region_local[iso3] = region_name - return iso3_to_region_local - - -def extract_iso3_from_bus_id(bus_id: str) -> Optional[str]: - """Extract the ISO3 code embedded in a bus identifier. + joblib.parallel.BatchCompletionCallBack = old - Args: - bus_id: Renewable bus identifier, usually prefixed by an ISO3 code. - Returns: - The ISO3 prefix if it can be parsed, otherwise `None`. - """ +def extract_iso3(bus_id: str) -> Optional[str]: try: - return bus_id.split("_")[0] + return str(bus_id).split("_")[0] except Exception: - logger.warning(f"Could not extract ISO3 from bus_id: {bus_id}") return None -def load_merged_data(merged_path: str) -> xr.Dataset: - """Load the merged renewable-profiles dataset from disk. +def build_region_map(config: Dict) -> Dict[str, str]: + """ISO3 -> region name lookup from config.""" + return { + iso3: region + for region, iso3_list in config.get("regions", {}).items() + for iso3 in iso3_list + } - Args: - merged_path: Path to the consolidated NetCDF input produced upstream. - Returns: - An opened xarray dataset containing bus-level renewable profiles and - metadata needed by the clustering workflow. - """ - logger.info(f"Loading merged data from {merged_path}") - ds = xr.open_dataset(merged_path) - logger.info(f" Merged data shape: {dict(ds.sizes)}") - logger.info(f" Variables: {list(ds.data_vars)}") - return ds +def load_bus_coordinates(geojson_path: str) -> Dict[str, Tuple[float, float]]: + """Load bus_id -> (lat, lon) from the merged geojson.""" + gdf = gpd.read_file(geojson_path) + coords = {} + for _, row in gdf.iterrows(): + bid = row["bus_id"] + coords[bid] = (float(row["y_centroid"]), float(row["x_centroid"])) + return coords -def filter_to_onwind_pv(ds: xr.Dataset) -> xr.Dataset: - """Keep only the technologies that are clustered by this workflow. +# --------------------------------------------------------------------------- +# Feature extraction — minimal: only what stratification + clustering needs +# --------------------------------------------------------------------------- - Args: - ds: Full merged renewable dataset with multiple technologies. - Returns: - A reduced dataset containing only `onwind` and `solar` technology - slices. Offshore wind is intentionally excluded because this workflow - is tuned to onshore wind and solar pocket preservation. - """ - logger.info("Filtering to onwind + solar (excluding offwind-ac)") +def compute_cf_high_mass(cf_ts: np.ndarray, tail_frac: float = 0.05) -> float: + """Mean of the top `tail_frac` hours — captures peak resource quality.""" + cf = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) + if cf.size == 0: + return 0.0 + k = max(1, int(len(cf) * tail_frac)) + return float(np.mean(np.sort(cf)[-k:])) - _techs_present = list(ds.coords["technology"].values) - # The merged file includes offshore wind, which this workflow intentionally skips. - techs_to_keep = ["onwind", "solar"] - ds_filtered = ds.sel(technology=techs_to_keep) - logger.info(f" Kept technologies: {techs_to_keep}") - logger.info(f" Buses remaining: {len(ds_filtered.bus)}") - - return ds_filtered - - -def extract_bus_features_single( - bus_id, ds: xr.Dataset, iso3_to_region: Dict[str, str] +def extract_features_batch( + bus_ids: List, + ds: xr.Dataset, + iso3_to_region: Dict[str, str], + bus_coords: Dict[str, Tuple[float, float]], ) -> List[Dict]: - """Extract clustering features for one bus across all technologies. + """Extract lightweight features for a batch of buses. - Args: - bus_id: Bus identifier from the merged renewable dataset. - ds: Filtered xarray dataset containing capacity factors and metadata. - iso3_to_region: Lookup used to map ISO3 prefixes to analysis regions. - - Returns: - A list of feature dictionaries, one per available bus-technology pair. - Each row contains temporal moments, geospatial coordinates, capacity, - and the tail-shape descriptors used by the clustering model. + Only computes what the stratified workflow actually needs: + - avg_cf, cf_high_mass (for stratification) + - lat, lon, capacity_mw (for within-stratum clustering metadata) + The raw 8760 profiles are read later during clustering, not stored here. """ - features = [] - iso3 = extract_iso3_from_bus_id(str(bus_id)) - if not iso3: - return features - region = iso3_to_region.get(iso3, None) - if not region: - return features - - try: - x_centroid = float(ds["x_centroid"].sel(bus=bus_id).values) - y_centroid = float(ds["y_centroid"].sel(bus=bus_id).values) - except Exception: - x_centroid, y_centroid = 0.0, 0.0 - - for tech in ds.technology.values: - tech = str(tech) # Convert numpy.str_ to Python str - try: - cf_ts = ds["capacity_factor"].sel(bus=bus_id, technology=tech).values - except Exception: + rows: List[Dict] = [] + for bus_id in bus_ids: + iso3 = extract_iso3(str(bus_id)) + if not iso3: continue - if np.isnan(cf_ts).all() or len(cf_ts) == 0: + region = iso3_to_region.get(iso3) + if not region: continue - # Capture the tail of the capacity-factor distribution so green pockets survive. - cf_summary = summarize_capacity_factor(cf_ts) + lat, lon = bus_coords.get(bus_id, (0.0, 0.0)) - try: - capacity_mw = float(ds["p_nom_max"].sel(bus=bus_id, technology=tech).values) - except Exception: - capacity_mw = np.nan - - features.append( - { - "lat": y_centroid, - "lon": x_centroid, - "capacity_mw": capacity_mw, - **cf_summary, - "region": region, - "technology": tech, - "bus_id": bus_id, - } - ) - return features + for tech in ds.technology.values: + tech = str(tech) + try: + cf_ts = ds["capacity_factor"].sel(bus=bus_id, technology=tech).values + except Exception: + continue + if np.isnan(cf_ts).all() or len(cf_ts) == 0: + continue + cf_clean = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) + avg_cf = float(np.mean(cf_clean)) -def extract_bus_features_batch( - bus_ids: List, ds: xr.Dataset, iso3_to_region: Dict[str, str] -) -> List[Dict]: - """Extract features for a batch of buses. - - Args: - bus_ids: Batch of bus identifiers assigned to one joblib worker. - ds: Filtered renewable dataset. - iso3_to_region: Lookup for region assignment. + try: + capacity_mw = float( + ds["p_nom_max"].sel(bus=bus_id, technology=tech).values + ) + except Exception: + capacity_mw = 0.0 + if np.isnan(capacity_mw) or capacity_mw <= 0: + continue - Returns: - Flattened feature rows for all buses in the batch. - """ - all_features = [] - for bus_id in bus_ids: - all_features.extend(extract_bus_features_single(bus_id, ds, iso3_to_region)) - return all_features + rows.append( + { + "bus_id": bus_id, + "technology": tech, + "region": region, + "lat": lat, + "lon": lon, + "capacity_mw": capacity_mw, + "avg_cf": avg_cf, + "cf_high_mass": compute_cf_high_mass(cf_clean), + } + ) + return rows def extract_features( - ds: xr.Dataset, config: Dict, cache_features_path: Optional[str] = None -) -> Tuple[pd.DataFrame, Dict[str, str]]: - """Build the full feature table used by the clustering workflow. - - Args: - ds: Filtered renewable dataset containing the buses to cluster. - config: Workflow configuration used to derive the region mapping and - caching behavior. - cache_features_path: Optional CSV cache path for reusing extracted - features on subsequent runs. + ds: xr.Dataset, + config: Dict, + bus_coords: Dict[str, Tuple[float, float]], + cache_path: Optional[str] = None, +) -> pd.DataFrame: + """Build the feature table (one row per bus-technology pair).""" - Returns: - A tuple of `(feature_table, region_buses)` where `feature_table` holds - one row per bus-technology pair and `region_buses` maps each region to - the buses assigned to it. The cache is reused when it matches the - expected feature schema. - """ - required_cache_cols = { - "avg_cf", - "temporal_std", - "cv", - "autocorr_24h", + required_cols = { + "bus_id", + "technology", + "region", "lat", "lon", "capacity_mw", - "region", - "technology", - "bus_id", - "cf_q10", - "cf_q25", - "cf_q50", - "cf_q75", - "cf_q90", - "cf_exceed_020", - "cf_exceed_040", - "cf_exceed_060", - "cf_exceed_080", - "cf_low_mass", + "avg_cf", "cf_high_mass", } - # Try to load from cache if it exists - if cache_features_path and Path(cache_features_path).exists(): - logger.info(f"Loading cached features from {cache_features_path}") - df_features = pd.read_csv(cache_features_path) - if required_cache_cols.issubset(set(df_features.columns)): - region_buses = {} - for _, row in df_features.iterrows(): - region = row["region"] - bus_id = row["bus_id"] - region_buses.setdefault(region, []) - if bus_id not in region_buses[region]: - region_buses[region].append(bus_id) - logger.info(f" Loaded {len(df_features)} features from cache") - return df_features, region_buses - logger.info(" Cached feature schema is stale; recomputing") - - logger.info("Extracting temporal features (batched parallelization)...") - - iso3_to_region = build_region_map(config, {}) - - # Batch buses so each worker gets a non-trivial chunk of work. - n_jobs = snakemake.threads if hasattr(snakemake, "threads") else -1 + if cache_path and Path(cache_path).exists(): + logger.info(f"Loading cached features from {cache_path}") + df = pd.read_csv(cache_path) + if required_cols.issubset(df.columns): + logger.info(f" Loaded {len(df)} rows from cache") + return df + logger.info(" Cache schema stale — recomputing") + iso3_to_region = build_region_map(config) bus_list = list(ds.bus.values) - num_batches = abs(n_jobs) if n_jobs != -1 else 1 - batch_size = (len(bus_list) + num_batches - 1) // num_batches - bus_batches = [ + n_jobs = getattr(snakemake, "threads", -1) + batch_size = max(1, len(bus_list) // max(1, abs(n_jobs))) + batches = [ bus_list[i : i + batch_size] for i in range(0, len(bus_list), batch_size) ] logger.info( - f" Using {n_jobs} threads, batch size: {batch_size} buses, {len(bus_batches)} batches (1 per worker)" + f"Extracting features: {len(bus_list)} buses, {len(batches)} batches, {n_jobs} workers" ) - with tqdm_joblib( - tqdm(total=len(bus_batches), desc="Extracting bus features", unit="batch") - ): - batch_results = joblib.Parallel(n_jobs=n_jobs, backend="loky")( - joblib.delayed(extract_bus_features_batch)(batch, ds, iso3_to_region) - for batch in bus_batches + with tqdm_joblib(tqdm(total=len(batches), desc="Feature extraction", unit="batch")): + results = joblib.Parallel(n_jobs=n_jobs, backend="loky")( + joblib.delayed(extract_features_batch)( + batch, ds, iso3_to_region, bus_coords + ) + for batch in batches ) - # Flatten batch results - features_list = [] - region_buses = {} + rows = [r for batch_rows in results for r in batch_rows] + df = pd.DataFrame(rows) + logger.info(f" Extracted {len(df)} bus-technology pairs") - for bus_features_list in batch_results: - for feat_dict in bus_features_list: - features_list.append(feat_dict) - region = feat_dict["region"] - if region not in region_buses: - region_buses[region] = [] - if feat_dict["bus_id"] not in region_buses[region]: - region_buses[region].append(feat_dict["bus_id"]) + if cache_path: + df.to_csv(cache_path, index=False) + logger.info(f" Cached to {cache_path}") - df_features = pd.DataFrame(features_list) - logger.info( - f" Extracted {len(df_features)} bus-technology pairs across {len(region_buses)} regions" - ) - logger.info( - f" Region distribution: {dict((k, len(set(v))) for k, v in region_buses.items())}" - ) + return df - # Cache features to disk for recovery if later steps fail - if cache_features_path: - df_features.to_csv(cache_features_path, index=False) - logger.info(f" ✓ Cached features to {cache_features_path}") - return df_features, region_buses +# --------------------------------------------------------------------------- +# Stratification + within-stratum clustering +# --------------------------------------------------------------------------- -def cluster_region_technology( - df_subset: pd.DataFrame, n_clusters: int, feature_weights: Dict[str, float] -) -> Tuple[np.ndarray, KMeans]: - """Cluster one region-technology subset with weighted K-means. +def _load_profiles_for_group( + bus_ids: List[str], tech: str, ds: xr.Dataset +) -> np.ndarray: + """Load 8760 CF profiles for a list of buses. Returns (n_buses, 8760).""" + profiles = [] + for bid in bus_ids: + try: + cf = ds["capacity_factor"].sel(bus=bid, technology=tech).values + cf = np.clip(np.nan_to_num(cf, nan=0.0), 0.0, 1.0) + profiles.append(cf) + except Exception: + profiles.append(np.zeros(8760)) + return np.stack(profiles) + + +def _cluster_within_stratum( + profiles: np.ndarray, + lats: np.ndarray, + lons: np.ndarray, + capacities: np.ndarray, + n_clusters: int, + n_pca: int = 5, + geo_weight: float = 0.3, +) -> Tuple[np.ndarray, np.ndarray]: + n = len(profiles) + if n <= n_clusters: + return np.arange(n), np.arange(n) + n_clusters = max(1, min(n_clusters, n)) + + # Build feature matrix + n_comp = min(n_pca, n - 1, profiles.shape[1]) + pca_features = PCA(n_components=max(1, n_comp)).fit_transform( + StandardScaler().fit_transform(profiles) + ) + pca_features /= np.sqrt(max(1, n_comp)) + + geo = StandardScaler().fit_transform(np.column_stack([lats, lons])) + geo /= np.sqrt(2) + + X = np.hstack([pca_features * (1 - geo_weight), geo * geo_weight]) + + # Weight rows by capacity via duplication in distance matrix + # (FasterPAM doesn't support sample_weight natively) + cap_weights = np.sqrt(capacities / capacities.mean()) # soft weighting + X_weighted = X * cap_weights[:, None] + + D = squareform(pdist(X_weighted, metric="euclidean")) + result = _kmedoids.fasterpam(D, n_clusters, random_state=42) + labels = np.array(result.labels) + medoid_indices = np.array(result.medoids) + + return labels, medoid_indices + + +def _allocate_stratum_clusters( + stratum_capacities: Dict[str, float], + total_k: int, + tail_boost: float = 2.0, + min_per_stratum: int = 1, +) -> Dict[str, int]: + """Distribute cluster budget across strata, boosting tails.""" + weights = {} + for name, cap in stratum_capacities.items(): + w = cap + if name.startswith("tail"): + w *= tail_boost + weights[name] = max(w, 1e-9) + + total_w = sum(weights.values()) + alloc = { + name: max(min_per_stratum, int(round(w / total_w * total_k))) + for name, w in weights.items() + } - Args: - df_subset: Feature rows for a single region and technology. - n_clusters: Requested number of clusters before any size safeguards. - feature_weights: Normalized weights for merit-order, temporal, and - geospatial feature families. + # Trim excess from the largest stratum + excess = sum(alloc.values()) - total_k + if excess > 0: + largest = max(alloc, key=alloc.get) + alloc[largest] = max(min_per_stratum, alloc[largest] - excess) - Returns: - The fitted cluster labels for each row in `df_subset` and the trained - `KMeans` estimator. The feature matrix is built from separately - normalized feature families so the configured weights act at the family - level rather than on raw columns. - """ - if len(df_subset) < n_clusters: - logger.warning( - f" Subset has {len(df_subset)} buses < {n_clusters} clusters, adjusting k" - ) - n_clusters = max(1, len(df_subset) // 2) + return alloc - # The merit-order block keeps the upper tail visible to clustering. - merit_order_cols = [ - "avg_cf", - "cf_q10", - "cf_q25", - "cf_q50", - "cf_q75", - "cf_q90", - "cf_exceed_020", - "cf_exceed_040", - "cf_exceed_060", - "cf_exceed_080", - "cf_low_mass", - "cf_high_mass", - ] - # Extract feature subsets - merit_order_vals = df_subset[merit_order_cols].values - temporal_vals = df_subset[["temporal_std", "cv", "autocorr_24h"]].values # (n, 3) - geospatial_vals = df_subset[["lat", "lon"]].values # (n, 2) +def _build_strata( + df_rt: pd.DataFrame, + n_strata: int = 7, + tail_pct: float = 10.0, +) -> pd.Series: + """Assign each row to a stratum based on cf_high_mass. + + Uses capacity-weighted quantile boundaries with explicit tail separation. + Returns a Series of stratum labels aligned to df_rt.index. + """ + cf_vals = df_rt["avg_cf"].values + caps = df_rt["capacity_mw"].values + + p_low = np.percentile(cf_vals, tail_pct) + p_high = np.percentile(cf_vals, 100 - tail_pct) + + labels = pd.Series("core", index=df_rt.index) + labels[cf_vals <= p_low] = "tail_low" + labels[cf_vals >= p_high] = "tail_high" + + # Subdivide core into (n_strata - 2) bins by capacity-weighted quantiles + core_mask = labels == "core" + if core_mask.sum() > (n_strata - 2): + core_cf = cf_vals[core_mask] + core_caps = caps[core_mask] + + # Capacity-weighted quantile boundaries + sort_idx = np.argsort(core_cf) + cum_cap = np.cumsum(core_caps[sort_idx]) + total_cap = cum_cap[-1] + n_core_bins = max(1, n_strata - 2) + boundaries = [] + for i in range(1, n_core_bins): + target = total_cap * i / n_core_bins + idx = np.searchsorted(cum_cap, target) + idx = min(idx, len(core_cf) - 1) + boundaries.append(core_cf[sort_idx[idx]]) + + # Assign core sub-bins + bins = [-np.inf] + sorted(set(boundaries)) + [np.inf] + core_labels = pd.cut( + cf_vals[core_mask], + bins=bins, + labels=[f"core_{i}" for i in range(len(bins) - 1)], + duplicates="drop", + ) + labels[core_mask] = core_labels.astype(str) - # Normalize each category independently - scaler_cf = StandardScaler() - scaler_temporal = StandardScaler() - scaler_geo = StandardScaler() + return labels - merit_order_norm = scaler_cf.fit_transform(merit_order_vals) - temporal_norm = scaler_temporal.fit_transform(temporal_vals) # (n, 3) - geospatial_norm = scaler_geo.fit_transform(geospatial_vals) # (n, 2) - # Apply the three feature-family weights after separate normalization. - w_cf = feature_weights["merit_order"] - w_temporal = feature_weights["temporal"] - w_geo = feature_weights["geospatial"] +def resolve_total_clusters(df_rt: pd.DataFrame, tech: str) -> int: + """Resolve total cluster count for a region-technology pair from config.""" + clustering_config = snakemake.config.get("clustering", {}) + policy = clustering_config.get("cluster_count_policy", {}) + base_clusters = policy.get("base_clusters", {}) + mode = policy.get("mode", "dynamic") + base_key = "solar" if tech == "solar" else "onwind" + base = int(base_clusters.get(base_key, 40)) - avg_cf_weighted = merit_order_norm * w_cf - temporal_weighted = temporal_norm * w_temporal # (n, 3) - geospatial_weighted = geospatial_norm * w_geo # (n, 2) + if mode == "fixed": + return base - # Concatenate all weighted features - X = np.hstack([avg_cf_weighted, temporal_weighted, geospatial_weighted]) # (n, 6) + n_buses = max(1, df_rt["bus_id"].nunique()) + total_cap = float(df_rt["capacity_mw"].sum()) + ref_buses = float(policy.get("reference_buses", 1000.0)) + ref_cap = float(policy.get("reference_capacity_mw", 50000.0)) + exp = float(policy.get("scale_exponent", 0.5)) + bus_w = float(policy.get("bus_weight", 0.5)) + cap_w = float(policy.get("capacity_weight", 0.5)) - # K-means clustering - kmeans = KMeans(n_clusters=n_clusters, init="k-means++", n_init=10, random_state=42) - clusters = kmeans.fit_predict(X) + scale = ( + bus_w * (n_buses / ref_buses) ** exp + + cap_w * (max(1.0, total_cap) / ref_cap) ** exp + ) + target = int(round(base * scale)) - return clusters, kmeans + min_c = int(policy.get("min_clusters", 3)) + max_c = int(policy.get("max_clusters", 120)) + return max(min_c, min(max_c, min(target, len(df_rt)))) -def cluster_region_technology_pair( +def cluster_region_technology( region: str, tech: str, df_features: pd.DataFrame, + ds: xr.Dataset, clustering_config: Dict, - feature_weights: Dict[str, float], cache_dir: Optional[str] = None, -) -> Tuple[Tuple[str, str], np.ndarray]: - """Cluster one region-technology pair and persist the assignments. - - Args: - region: Region name selected from the configuration mapping. - tech: Technology label for the subset. - df_features: Full extracted feature table. - clustering_config: Clustering-specific configuration section. - feature_weights: Normalized family weights used by the K-means model. - cache_dir: Optional directory for storing per-pair cluster assignments. +) -> Tuple[str, str, pd.DataFrame]: + """Stratified k-medoids clustering for one region-technology pair. Returns: - A `(region, tech)` key paired with the cluster-label vector for the - corresponding rows in `df_features`. Cached results are reused when - present and compatible with the current cache version. + (region, tech, result_df) where result_df has one row per cluster: + bus_id (medoid), capacity_mw (summed), avg_cf, cf_high_mass, stratum. """ tech = str(tech) - # Check cache first + # --- Cache check --- if cache_dir: - cache_file = Path(cache_dir) / f"clustering_cache_{region}_{str(tech)}.json" + cache_file = Path(cache_dir) / f"clusters_{region}_{tech}.parquet" if cache_file.exists(): try: - with open(cache_file, "r") as f: - cached = json.load(f) - if cached.get("cache_version") != CACHE_VERSION: - logger.info( - f" [STALE CACHE] {region} {tech}: cache version {cached.get('cache_version')} != {CACHE_VERSION}" - ) - raise ValueError("stale cache version") - clusters_array = np.array(cached["clusters"]) - logger.info( - f" [CACHED] {region} {str(tech)}: loaded {len(cached['clusters'])} assignments" - ) - return (region, tech), clusters_array - except Exception as e: - logger.debug(f"Could not load cache for {region} {tech}: {e}") - - df_subset = df_features[ + cached = pd.read_parquet(cache_file) + if cached.attrs.get("cache_version", "") == CACHE_VERSION: + logger.info(f" [CACHED] {region} {tech}: {len(cached)} clusters") + return region, tech, cached + except Exception: + pass + + df_rt = df_features[ (df_features["region"] == region) & (df_features["technology"] == tech) - ] + ].copy() - if len(df_subset) == 0: - return (region, tech), np.array([]) + if len(df_rt) == 0: + return region, tech, pd.DataFrame() - n_clusters = resolve_cluster_count(df_subset, tech) - tech_name = "solar" if tech == "solar" else "onwind" + total_k = resolve_total_clusters(df_rt, tech) + n_strata = int(clustering_config.get("n_strata", 7)) + tail_pct = float(clustering_config.get("tail_percentile", 10.0)) + tail_boost = float(clustering_config.get("tail_cluster_boost", 2.0)) + geo_weight = float(clustering_config.get("geo_weight", 0.3)) + n_pca = int(clustering_config.get("n_pca_components", 5)) - logger.info(f"Clustering {region} {tech_name} with k={n_clusters}") + logger.info( + f" {region} {tech}: {len(df_rt)} buses -> target {total_k} clusters, " + f"{n_strata} strata" + ) - clusters, kmeans = cluster_region_technology(df_subset, n_clusters, feature_weights) + # --- Stratify --- + df_rt["stratum"] = _build_strata(df_rt, n_strata=n_strata, tail_pct=tail_pct) - # Cache this region-tech pair immediately - if cache_dir: - cache_file = Path(cache_dir) / f"clustering_cache_{region}_{str(tech)}.json" - try: - with open(cache_file, "w") as f: - json.dump( - { - "cache_version": CACHE_VERSION, - "region": region, - "technology": str(tech), - "n_clusters": len(np.unique(clusters)), - "clusters": clusters.tolist(), - "timestamp": pd.Timestamp.now().isoformat(), - }, - f, - ) - logger.info(f" [CACHED] {region} {str(tech)}: saved clustering result") - except Exception as e: - logger.warning(f"Could not cache {region} {tech}: {e}") + stratum_caps = df_rt.groupby("stratum")["capacity_mw"].sum().to_dict() + stratum_k = _allocate_stratum_clusters(stratum_caps, total_k, tail_boost=tail_boost) - return (region, tech), clusters + logger.info(f" Stratum cluster allocation: {stratum_k}") + # --- Cluster within each stratum --- + cluster_rows: List[Dict] = [] + global_cluster_id = 0 -def select_representative_bus( - df_cluster: pd.DataFrame, ds: xr.Dataset, tech: str, cf_weighted_ts: np.ndarray -) -> Tuple[str, np.ndarray, float]: - """Select the best observed bus for a cluster and re-center its mean. + for stratum_name, group in df_rt.groupby("stratum"): + k = min(stratum_k.get(stratum_name, 1), len(group)) + k = max(1, k) - Args: - df_cluster: Rows belonging to one cluster within a region-technology - subset. - ds: Filtered renewable dataset used to retrieve candidate time series. - tech: Technology label for the cluster. - cf_weighted_ts: Capacity-weighted cluster-average profile. + bus_ids = group["bus_id"].values.tolist() + profiles = _load_profiles_for_group(bus_ids, tech, ds) + lats = group["lat"].values + lons = group["lon"].values + caps = group["capacity_mw"].values - Returns: - A tuple containing the chosen bus identifier, the representative time - series rescaled to the exact cluster mean, and the cluster mean itself. - The representative is chosen to preserve both the central tendency and - the tail shape of the cluster. - """ - tech = str(tech) # Convert numpy.str_ to Python str - target_summary = summarize_capacity_factor(cf_weighted_ts) - best_score = -np.inf - best_bus_id = df_cluster.iloc[0]["bus_id"] - best_cf_ts = None + labels, medoid_indices = _cluster_within_stratum( + profiles, lats, lons, caps, n_clusters=k, n_pca=n_pca, geo_weight=geo_weight + ) - for _, row in df_cluster.iterrows(): - bus_id = row["bus_id"] + for cl in range(int(labels.max()) + 1): + mask = labels == cl + if not mask.any(): + continue - try: - cf_ts = ds["capacity_factor"].sel(bus=bus_id, technology=tech).values - cf_ts = np.nan_to_num(cf_ts, nan=0.0) - candidate_summary = summarize_capacity_factor(cf_ts) - score = score_representative_candidate( - cf_ts, - cf_weighted_ts, - target_summary, - candidate_summary, + member_caps = caps[mask] + member_bus_ids = np.array(bus_ids)[mask] + total_cap = float(member_caps.sum()) + + # Find medoid for this cluster + medoid_candidates = np.where(mask)[0] + medoid_local = None + for mi in medoid_indices: + if mi in medoid_candidates: + medoid_local = mi + break + if medoid_local is None: + # Fallback: largest capacity bus in cluster + medoid_local = medoid_candidates[np.argmax(member_caps)] + + medoid_bus = bus_ids[medoid_local] + + # Capacity-weighted avg_cf for metadata + member_avg_cfs = group.iloc[np.where(mask)[0]]["avg_cf"].values + wavg_cf = float(np.average(member_avg_cfs, weights=member_caps)) + member_high_mass = group.iloc[np.where(mask)[0]]["cf_high_mass"].values + wavg_high_mass = float(np.average(member_high_mass, weights=member_caps)) + + cluster_rows.append( + { + "cluster_id": global_cluster_id, + "region": region, + "technology": tech, + "stratum": stratum_name, + "medoid_bus_id": medoid_bus, + "n_buses": int(mask.sum()), + "capacity_mw": total_cap, + "avg_cf": wavg_cf, + "cf_high_mass": wavg_high_mass, + "member_bus_ids": ",".join(str(b) for b in member_bus_ids), + } ) - if score > best_score: - best_score = score - best_bus_id = bus_id - best_cf_ts = cf_ts - except Exception as e: - logger.debug(f"Error computing correlation for {bus_id}: {e}") - continue - - # Prefer a real series; the rescaling step keeps the cluster mean exact. - if best_cf_ts is None: - best_cf_ts = ds["capacity_factor"].sel(bus=best_bus_id, technology=tech).values - cf_representative = np.nan_to_num(best_cf_ts, nan=0.0) + global_cluster_id += 1 - avg_cf_cluster = float(np.mean(cf_weighted_ts)) - cf_cluster_ts = rescale_timeseries_to_mean(cf_representative, avg_cf_cluster) + result = pd.DataFrame(cluster_rows) - return best_bus_id, cf_cluster_ts, avg_cf_cluster + # --- Cache --- + if cache_dir and len(result) > 0: + cache_file = Path(cache_dir) / f"clusters_{region}_{tech}.parquet" + result.attrs["cache_version"] = CACHE_VERSION + result.to_parquet(cache_file, index=False) + logger.info(f" Cached {len(result)} clusters to {cache_file}") + return region, tech, result -def aggregate_clusters( - df_features: pd.DataFrame, - clusters_dict: Dict[Tuple[str, str], np.ndarray], - ds: xr.Dataset, - config: Dict, - cache_clusters_path: Optional[str] = None, - clustering_cache_dir: Optional[str] = None, -) -> Tuple[Dict, Dict]: - """Aggregate cluster assignments into pseudo-bus outputs and metadata. - - Args: - df_features: Full feature table used to recover cluster membership. - clusters_dict: Mapping from `(region, tech)` to the cluster labels for - the corresponding feature rows. - ds: Filtered renewable dataset used to sum capacities and profiles. - config: Global workflow configuration. - cache_clusters_path: Optional joblib cache for the aggregated payload. - clustering_cache_dir: Optional directory containing per-pair caches of - cluster assignments. - Returns: - A tuple of `(clustered_data, cluster_metadata)`. `clustered_data` holds - the pseudo-bus output for each cluster, while `cluster_metadata` - records the summary fields that downstream steps can inspect. - """ - logger.info("Aggregating clusters...") +# --------------------------------------------------------------------------- +# Aggregation: load medoid profiles, build output +# --------------------------------------------------------------------------- - # Fast path: load the fully aggregated payload if it already exists. - if cache_clusters_path and Path(cache_clusters_path).exists(): - try: - cached_payload = joblib.load(cache_clusters_path) - if ( - cached_payload.get("cache_version") == CACHE_VERSION - and "clustered_data" in cached_payload - and "cluster_metadata" in cached_payload - ): - logger.info( - f"Loading cached aggregated clusters from {cache_clusters_path}" - ) - return cached_payload["clustered_data"], cached_payload[ - "cluster_metadata" - ] - logger.info( - f"Discarding stale aggregated cluster cache at {cache_clusters_path}" - ) - Path(cache_clusters_path).unlink(missing_ok=True) - except Exception as e: - logger.info( - f"Could not load cached aggregated clusters from {cache_clusters_path}: {e}" - ) - # Recover per-region-tech assignments from cache if we have them. - if clustering_cache_dir: - clustering_cache_path = Path(clustering_cache_dir) - cache_files = list(clustering_cache_path.glob("clustering_cache_*.json")) - if cache_files: - logger.info( - f"Found {len(cache_files)} cached region-tech clustering results" - ) - for cache_file in cache_files: - try: - with open(cache_file, "r") as f: - cached = json.load(f) - if cached.get("cache_version") != CACHE_VERSION: - logger.info( - f" [STALE] {cache_file.name}: cache version {cached.get('cache_version')} != {CACHE_VERSION}" - ) - cache_file.unlink(missing_ok=True) - continue - region = cached["region"] - tech = cached["technology"] - clusters_array = np.array(cached["clusters"]) - - # Only add to clusters_dict if not already present - if (region, tech) not in clusters_dict or len( - clusters_dict[(region, tech)] - ) == 0: - clusters_dict[(region, tech)] = clusters_array - logger.info( - f" [RECOVERED] {region} {tech}: loaded {len(clusters_array)} cluster assignments from cache" - ) - except Exception as e: - logger.debug(f"Could not load {cache_file}: {e}") - - clustered_data = {} # {(region, tech, cluster_id): {capacity, cf_ts, avg_cf}} - cluster_metadata = {} - - for (region, tech), cluster_assignments in tqdm( - clusters_dict.items(), desc="Aggregating region-tech pairs", unit="pair" - ): - tech = str(tech) # Convert numpy.str_ to Python str - df_subset = df_features[ - (df_features["region"] == region) & (df_features["technology"] == tech) - ] +def aggregate_all_clusters( + all_results: List[Tuple[str, str, pd.DataFrame]], + ds: xr.Dataset, +) -> Tuple[Dict, pd.DataFrame]: + """Build clustered_data dict and metadata from clustering results. - n_clusters = len(np.unique(cluster_assignments)) - logger.info( - f" {region} {tech}: {len(df_subset)} buses -> {n_clusters} clusters" - ) + The representative profile is the medoid's real 8760 series — no synthetic + averaging or rescaling. This preserves temporal correlations exactly. + """ + clustered_data = {} + meta_rows = [] - for cluster_id in tqdm( - range(n_clusters), - desc=f" {region}-{tech} clusters", - unit="cluster", - leave=False, - ): - mask = cluster_assignments == cluster_id - df_cluster = df_subset[mask].reset_index(drop=True) + for region, tech, df_clusters in all_results: + if df_clusters is None or len(df_clusters) == 0: + continue - if len(df_cluster) == 0: - continue + for _, row in df_clusters.iterrows(): + cid = int(row["cluster_id"]) + medoid_bus = row["medoid_bus_id"] - # Get buses in cluster - bus_ids_in_cluster = list(df_cluster["bus_id"].values) - - # Sum capacities within the cluster to preserve total installed potential. - capacities = [] - for bus_id in bus_ids_in_cluster: - try: - cap = float( - ds["p_nom_max"].sel(bus=bus_id, technology=str(tech)).values - ) - if not np.isnan(cap): - capacities.append(cap) - except Exception: - pass - - p_nom_max_cluster = float(np.sum(capacities)) - - if p_nom_max_cluster <= 0: - logger.debug( - f" Skipping cluster {region}/{tech}/{cluster_id}: no valid capacity" + # Load real medoid profile + try: + cf_ts = ( + ds["capacity_factor"].sel(bus=medoid_bus, technology=tech).values ) - continue - - # Compute the cluster-average profile before picking a representative bus. - cf_weighted_parts = [] - for bus_id in bus_ids_in_cluster: - try: - cap = float( - ds["p_nom_max"].sel(bus=bus_id, technology=str(tech)).values - ) - cf = ( - ds["capacity_factor"] - .sel(bus=bus_id, technology=str(tech)) - .values - ) - if not np.isnan(cap) and cap > 0: - cf = np.nan_to_num(cf, nan=0.0) - cf_weighted_parts.append(cap * cf) - except Exception: - pass - - if cf_weighted_parts: - cf_weighted_ts = np.sum(cf_weighted_parts, axis=0) / p_nom_max_cluster - else: - cf_weighted_ts = np.zeros(8760) - - # Select representative bus and then rescale to the exact cluster mean CF. - representative_id, cf_cluster_ts, avg_cf_cluster = ( - select_representative_bus(df_cluster, ds, str(tech), cf_weighted_ts) - ) + cf_ts = np.clip(np.nan_to_num(cf_ts, nan=0.0), 0.0, 1.0) + except Exception: + cf_ts = np.zeros(8760) - # Cluster name: Regionname_tech_number - tech_name = "solar" if tech == "solar" else "onwind" - cluster_name = f"{region}_{tech_name}_{cluster_id}" + tech_label = "solar" if tech == "solar" else "onwind" + cluster_name = f"{region}_{tech_label}_{cid}" - clustered_data[(region, tech, cluster_id)] = { - "capacity": p_nom_max_cluster, - "cf_ts": cf_cluster_ts, - "avg_cf": avg_cf_cluster, + clustered_data[(region, tech, cid)] = { + "capacity": float(row["capacity_mw"]), + "cf_ts": cf_ts, + "avg_cf": float(row["avg_cf"]), + "cf_high_mass": float(row["cf_high_mass"]), "cluster_name": cluster_name, - "n_buses": len(df_cluster), - "representative_bus": representative_id, + "n_buses": int(row["n_buses"]), + "representative_bus": medoid_bus, + "stratum": row["stratum"], } - cluster_metadata[cluster_name] = { - "region": region, - "technology": tech, - "cluster_id": cluster_id, - "n_buses_consolidated": len(df_cluster), - "representative_bus_id": representative_id, - "total_capacity_mw": p_nom_max_cluster, - "avg_cf": float(avg_cf_cluster), - } + meta_rows.append( + { + "cluster_name": cluster_name, + "region": region, + "technology": tech, + "cluster_id": cid, + "stratum": row["stratum"], + "medoid_bus_id": medoid_bus, + "n_buses": int(row["n_buses"]), + "capacity_mw": float(row["capacity_mw"]), + "avg_cf": float(row["avg_cf"]), + "cf_high_mass": float(row["cf_high_mass"]), + } + ) - logger.info(f" Total clusters created: {len(clustered_data)}") - - # Cache aggregated clusters to disk for recovery - if cache_clusters_path: - logger.info(f"Caching aggregated clusters to {cache_clusters_path}") - joblib.dump( - { - "cache_version": CACHE_VERSION, - "clustered_data": clustered_data, - "cluster_metadata": cluster_metadata, - "timestamp": pd.Timestamp.now().isoformat(), - }, - cache_clusters_path, - compress=3, - ) - logger.info(" ✓ Cached full clustered payload") + logger.info(f" Total clusters: {len(clustered_data)}") + return clustered_data, pd.DataFrame(meta_rows) - return clustered_data, cluster_metadata + +# --------------------------------------------------------------------------- +# NetCDF output +# --------------------------------------------------------------------------- def write_clustered_netcdf( clustered_data: Dict, output_path: str, config: Dict ) -> None: - """Write the clustered pseudo-bus dataset to NetCDF. - - Args: - clustered_data: Aggregated cluster payload produced by - `aggregate_clusters`. - output_path: Destination NetCDF path. - config: Global configuration used to stamp metadata and preserve the - workflow context. - - Returns: - None. The clustered dataset is written to `output_path` with padded - region/technology/class dimensions so downstream consumers can load it - as a regular xarray dataset. - """ + """Write clustered dataset to NetCDF with (region, technology, class, time).""" logger.info(f"Writing clustered data to {output_path}") - # Organize data by region and technology so we can emit ragged class arrays. - regions = list(set(k[0] for k in clustered_data.keys())) - techs = list(set(str(k[1]) for k in clustered_data.keys())) # Convert to string - - logger.info(f" Regions: {regions}") - logger.info(f" Technologies: {techs}") - - # Build xarray dataset + regions = sorted(set(k[0] for k in clustered_data)) + techs = sorted(set(str(k[1]) for k in clustered_data)) time = np.arange(8760) - # Initialize data variables - capacity_data = {} - cf_data = {} - avg_cf_data = {} - - for region in regions: - capacity_data[region] = {} - cf_data[region] = {} - avg_cf_data[region] = {} - - for tech in techs: - # Collect all clusters for this region-tech - clusters_for_rt = [ - (cluster_id, clustered_data[(region, tech, cluster_id)]) - for (r, t, cluster_id) in clustered_data.keys() - if r == region and t == tech - ] - - n_clusters = len(clusters_for_rt) - if n_clusters == 0: - continue + max_classes = max( + sum(1 for k in clustered_data if k[0] == r and str(k[1]) == t) + for r in regions + for t in techs + ) + + shape_3d = (len(regions), len(techs), max_classes) + shape_4d = (*shape_3d, 8760) + + capacity_all = np.full(shape_3d, np.nan, dtype=np.float32) + cf_all = np.full(shape_4d, np.nan, dtype=np.float32) + avg_cf_all = np.full(shape_3d, np.nan, dtype=np.float32) + cf_high_mass_all = np.full(shape_3d, np.nan, dtype=np.float32) + + for ri, region in enumerate(regions): + for ti, tech in enumerate(techs): + clusters = sorted( + [ + (cid, info) + for (r, t, cid), info in clustered_data.items() + if r == region and str(t) == tech + ], + key=lambda x: -x[1]["cf_high_mass"], # merit order: best first + ) + for ci, (cid, info) in enumerate(clusters): + capacity_all[ri, ti, ci] = info["capacity"] + avg_cf_all[ri, ti, ci] = info["avg_cf"] + cf_high_mass_all[ri, ti, ci] = info["cf_high_mass"] + ts = info["cf_ts"] + if isinstance(ts, np.ndarray) and ts.ndim == 1 and len(ts) == 8760: + cf_all[ri, ti, ci, :] = ts.astype(np.float32) - capacity_data[region][tech] = np.zeros(n_clusters) - cf_data[region][tech] = np.zeros((n_clusters, 8760)) - avg_cf_data[region][tech] = np.zeros(n_clusters) - - for idx, (cluster_id, cluster_info) in enumerate(clusters_for_rt): - capacity_data[region][tech][idx] = cluster_info["capacity"] - cf_ts = cluster_info["cf_ts"] - if isinstance(cf_ts, np.ndarray): - if cf_ts.ndim != 1: - logger.warning( - f" cf_ts has wrong shape {cf_ts.shape}, taking first row" - ) - cf_ts = cf_ts[0] if cf_ts.ndim > 1 else cf_ts - if len(cf_ts) == 8760: - cf_data[region][tech][idx, :] = cf_ts.astype(np.float32) - avg_cf_data[region][tech][idx] = cluster_info["avg_cf"] - - # Create xarray dataset with aligned dimensions ds_out = xr.Dataset( - data_vars={}, + { + "capacity": (("region", "technology", "class"), capacity_all), + "capacity_factor": (("region", "technology", "class", "time"), cf_all), + "avg_cf": (("region", "technology", "class"), avg_cf_all), + "cf_high_mass": (("region", "technology", "class"), cf_high_mass_all), + }, coords={ "region": regions, "technology": techs, "time": time, + "class": np.arange(max_classes), }, ) - # Add data variables with heterogeneous class dimension per region-tech - # xarray does not support ragged dimensions natively, so we pad to max_classes. - max_classes = max( - len([k for k in clustered_data.keys() if k[0] == r and str(k[1]) == t]) - for r in regions - for t in techs - ) + ds_out.attrs["clustering_method"] = "stratified_kmedoids" + ds_out.attrs["cache_version"] = CACHE_VERSION + ds_out.attrs["class_order"] = "descending cf_high_mass (merit order)" - capacity_all = np.full( - (len(regions), len(techs), max_classes), np.nan, dtype=np.float32 - ) - cf_all = np.full( - (len(regions), len(techs), max_classes, 8760), np.nan, dtype=np.float32 - ) - avg_cf_all = np.full( - (len(regions), len(techs), max_classes), np.nan, dtype=np.float32 - ) + enc = {v: {"dtype": "float32"} for v in ds_out.data_vars} + ds_out.to_netcdf(output_path, encoding=enc) + logger.info(f" ✓ Wrote {output_path} — dims: {dict(ds_out.sizes)}") - for region_idx, region in enumerate(regions): - for tech_idx, tech in enumerate(techs): - clusters_for_rt = [ - (cluster_id, clustered_data[(region, tech_orig, cluster_id)]) - for (r, tech_orig, cluster_id) in clustered_data.keys() - if r == region and str(tech_orig) == tech - ] - - for class_idx, (cluster_id, cluster_info) in enumerate(clusters_for_rt): - cf_ts = cluster_info["cf_ts"] - if isinstance(cf_ts, np.ndarray): - # Ensure cf_ts is 1D - if cf_ts.ndim != 1: - logger.warning( - f" cf_ts has wrong shape {cf_ts.shape}, taking first row" - ) - cf_ts = cf_ts[0] if cf_ts.ndim > 1 else cf_ts - if len(cf_ts) == 8760: - cf_all[region_idx, tech_idx, class_idx, :] = cf_ts.astype( - np.float32 - ) - - capacity_all[region_idx, tech_idx, class_idx] = float( - cluster_info["capacity"] - ) - avg_cf_all[region_idx, tech_idx, class_idx] = float( - cluster_info["avg_cf"] - ) - ds_out["capacity"] = (("region", "technology", "class"), capacity_all) - ds_out["capacity_factor"] = (("region", "technology", "class", "time"), cf_all) - ds_out["avg_cf"] = (("region", "technology", "class"), avg_cf_all) - - # Add metadata attributes - ds_out.attrs["clustering_algorithm"] = "weighted_kmeans" - ds_out.attrs["temporal_aware"] = "True" - ds_out.attrs["feature_weights"] = json.dumps( - {"avg_cf": 0.5, "temporal": 0.3, "geospatial": 0.2} - ) - - ds_out.to_netcdf( - output_path, - encoding={ - "capacity": {"dtype": "float32"}, - "capacity_factor": {"dtype": "float32"}, - "avg_cf": {"dtype": "float32"}, - }, - ) - - logger.info(f" ✓ Wrote {output_path}") - logger.info(f" Dimensions: {dict(ds_out.sizes)}") - logger.info(f" Variables: {list(ds_out.data_vars)}") +# --------------------------------------------------------------------------- +# Validation +# --------------------------------------------------------------------------- def validate_clustering( clustered_data: Dict, df_features: pd.DataFrame, - ds_merged: xr.Dataset, + ds: xr.Dataset, output_report: str, ) -> Dict: - """Validate that clustering preserves capacity and profile structure. - - Args: - clustered_data: Aggregated cluster payload returned by the workflow. - df_features: Source feature table used to compute baseline statistics. - ds_merged: Filtered merged dataset used to compare the original input - capacity totals. - output_report: Destination path for the JSON validation report. - - Returns: - A nested dictionary of validation metrics covering capacity - preservation, mean capacity-factor consistency, merit-order retention, - and threshold-based capacity retention. - """ + """Validate capacity preservation and supply curve shape.""" logger.info("Validating clustering...") - validation_results = { + report: Dict[str, Any] = { "total_clusters": len(clustered_data), "capacity_preservation": {}, - "avg_cf_consistency": {}, - "merit_order_preservation": {}, - "temporal_quality": {}, + "supply_curve_error": {}, } - region_tech_pairs = sorted( - {(region, tech) for region, tech, _ in clustered_data.keys()} - ) + region_tech_pairs = sorted({(k[0], str(k[1])) for k in clustered_data}) for region, tech in region_tech_pairs: - df_subset = df_features[ - (df_features["region"] == region) & (df_features["technology"] == str(tech)) + # --- Capacity preservation --- + df_rt = df_features[ + (df_features["region"] == region) & (df_features["technology"] == tech) ] + original_cap = float(df_rt["capacity_mw"].sum()) - # Total capacity check remains the basic integrity guard. - original_cap = [] - for bus_id in df_subset["bus_id"].values: - try: - cap = float( - ds_merged["p_nom_max"].sel(bus=bus_id, technology=str(tech)).values - ) - if not np.isnan(cap): - original_cap.append(cap) - except Exception: - pass - - original_total = np.sum(original_cap) - clustered_total = sum( - cluster_info.get("capacity", 0) - for (r, t, _), cluster_info in clustered_data.items() - if r == region and str(t) == str(tech) + clustered_cap = sum( + info["capacity"] + for (r, t, _), info in clustered_data.items() + if r == region and str(t) == tech ) - if original_total > 0: - preservation_pct = (clustered_total / original_total) * 100 - else: - preservation_pct = 100.0 - - validation_results["capacity_preservation"][f"{region}_{tech}"] = { - "original_mw": float(original_total), - "clustered_mw": float(clustered_total), - "preservation_pct": float(preservation_pct), + pres_pct = 100.0 * clustered_cap / original_cap if original_cap > 0 else 100.0 + report["capacity_preservation"][f"{region}_{tech}"] = { + "original_mw": original_cap, + "clustered_mw": clustered_cap, + "preservation_pct": pres_pct, } - cluster_rows = [ - cluster_info - for (r, t, _), cluster_info in clustered_data.items() - if r == region and str(t) == str(tech) - ] - if cluster_rows: - cluster_avg_cf = np.array( - [row["avg_cf"] for row in cluster_rows], dtype=float - ) - cluster_capacity = np.array( - [row["capacity"] for row in cluster_rows], dtype=float - ) - merit_corr = float( - pd.Series(cluster_avg_cf).corr(pd.Series(cluster_capacity)) - ) - - thresholds = {} - for threshold in CF_THRESHOLDS: - retained_capacity = float( - np.sum(cluster_capacity[cluster_avg_cf >= threshold]) + # --- Supply curve shape comparison --- + # Original: sort buses by avg_cf descending, cumulative capacity + orig_sorted = df_rt.sort_values("avg_cf", ascending=False) + orig_cum_cap = np.cumsum(orig_sorted["capacity_mw"].values) + orig_avg_cf = orig_sorted["avg_cf"].values + + # Clustered: sort clusters by avg_cf descending + clusters = sorted( + [ + info + for (r, t, _), info in clustered_data.items() + if r == region and str(t) == tech + ], + key=lambda x: -x["avg_cf"], + ) + if clusters: + clust_cum_cap = np.cumsum([c["capacity"] for c in clusters]) + clust_avg_cf = np.array([c["avg_cf"] for c in clusters]) + + # Interpolate both curves at common capacity points and compute MAE + if len(orig_cum_cap) > 1 and len(clust_cum_cap) > 1: + max_cap = min(orig_cum_cap[-1], clust_cum_cap[-1]) + eval_points = np.linspace(0, max_cap, 50) + orig_interp = np.interp(eval_points, orig_cum_cap, orig_avg_cf) + clust_interp = np.interp(eval_points, clust_cum_cap, clust_avg_cf) + mae = float(np.mean(np.abs(orig_interp - clust_interp))) + + # Error specifically in the first 10% (green pocket region) + n10 = max(1, len(eval_points) // 10) + mae_first10 = float( + np.mean(np.abs(orig_interp[:n10] - clust_interp[:n10])) ) - total_capacity = float(np.sum(cluster_capacity)) - # This metric answers whether the expensive/high-quality pocket survives. - thresholds[f"{threshold}"] = { - "retained_capacity_mw": retained_capacity, - "retained_pct": float(100.0 * retained_capacity / total_capacity) - if total_capacity > 0 - else 100.0, - } + else: + mae, mae_first10 = 0.0, 0.0 - validation_results["avg_cf_consistency"][f"{region}_{tech}"] = { - "avg_cf_mean": float(np.mean(df_subset["avg_cf"].values)) - if len(df_subset) - else None, - } - validation_results["merit_order_preservation"][f"{region}_{tech}"] = { - "cluster_capacity_avg_cf_corr": merit_corr, - "threshold_retention": thresholds, + report["supply_curve_error"][f"{region}_{tech}"] = { + "mae_avg_cf": mae, + "mae_first_10pct": mae_first10, + "n_clusters": len(clusters), } - logger.info( - f" Capacity preservation: {np.mean([v['preservation_pct'] for v in validation_results['capacity_preservation'].values()]):.1f}%" + avg_pres = np.mean( + [v["preservation_pct"] for v in report["capacity_preservation"].values()] ) + logger.info(f" Avg capacity preservation: {avg_pres:.1f}%") - # Write validation report - report = { - "timestamp": pd.Timestamp.now().isoformat(), - "validation_results": validation_results, - "total_clusters": len(clustered_data), - } + if report["supply_curve_error"]: + avg_mae = np.mean( + [v["mae_avg_cf"] for v in report["supply_curve_error"].values()] + ) + avg_mae10 = np.mean( + [v["mae_first_10pct"] for v in report["supply_curve_error"].values()] + ) + logger.info(f" Supply curve MAE (full): {avg_mae:.4f}") + logger.info(f" Supply curve MAE (first 10% / green pockets): {avg_mae10:.4f}") with open(output_report, "w") as f: - json.dump(report, f, indent=2) + json.dump( + {"timestamp": pd.Timestamp.now().isoformat(), **report}, + f, + indent=2, + ) + logger.info(f" ✓ Report: {output_report}") + return report - logger.info(f" ✓ Wrote validation report to {output_report}") - return validation_results +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- -def main(): - """Run the full renewable clustering workflow end to end. +def filter_to_onwind_pv(ds: xr.Dataset) -> xr.Dataset: + techs = [t for t in ["onwind", "solar"] if t in ds.technology.values] + ds_filtered = ds.sel(technology=techs) + logger.info(f"Filtered to {techs}: {len(ds_filtered.bus)} buses") + return ds_filtered - The entrypoint loads the merged renewable profiles, filters the supported - technologies, extracts and caches features, clusters each region-technology - subset, aggregates pseudo-bus outputs, writes the clustered NetCDF, and - finally emits a validation report. - """ + +def main(): if snakemake is None: - raise RuntimeError( - "This script must be executed through Snakemake so the injected `snakemake` object is available." - ) + raise RuntimeError("Must run via Snakemake") logger.info("=" * 70) - logger.info("CLUSTERING RENEWABLE PROFILES FOR OPTIMIZATION") + logger.info("STRATIFIED K-MEDOIDS RENEWABLE CLUSTERING") logger.info("=" * 70) - # Load config and normalize the feature weights before any clustering starts. config = snakemake.config - logger.info(f"Clustering config: {config.get('clustering', {})}") - clustering_config = config.get("clustering", {}) - feature_weights = clustering_config.get( - "feature_weights", - { - "merit_order": 0.5, - "temporal": 0.3, - "geospatial": 0.2, - }, - ) - # Normalize weights - feature_weights = normalize_weights(feature_weights) - logger.info(f"Normalized feature weights: {feature_weights}") + # Load & filter + ds = xr.open_dataset(str(snakemake.input.merged_cdf)) + ds = filter_to_onwind_pv(ds) - # Load and filter the merged renewable profiles. - ds = load_merged_data(str(snakemake.input.merged)) - ds_filtered = filter_to_onwind_pv(ds) + bus_coords = load_bus_coordinates(str(snakemake.input.merged_geojson)) + logger.info(f"Loaded coordinates for {len(bus_coords)} buses") - # Extract features (with caching). - cache_features = Path("resources") / "features_cache.csv" - df_features, region_buses = extract_features( - ds_filtered, config, str(cache_features) - ) + # Extract features + cache_features = Path("resources") / "features_cache_v4.csv" + df_features = extract_features(ds, config, bus_coords, str(cache_features)) - # Cluster each region-technology group in parallel. - logger.info(f"Starting parallel clustering with {snakemake.threads} threads") - - # Build list of region-tech pairs that actually exist in the filtered data. - region_tech_pairs = [ - (region, str(tech)) - for region in region_buses.keys() - for tech in ds_filtered.technology.values - if len( - df_features[ - (df_features["region"] == region) - & (df_features["technology"] == str(tech)) - ] - ) - > 0 - ] + # Build region-tech pairs + region_tech_pairs = ( + df_features.groupby(["region", "technology"]) + .size() + .reset_index()[["region", "technology"]] + .values.tolist() + ) + logger.info(f"Clustering {len(region_tech_pairs)} region-technology pairs") - logger.info(f" Total region-technology pairs: {len(region_tech_pairs)}") + # Cluster (parallel) + cache_dir = Path("resources") / "clustering_cache_v4" + cache_dir.mkdir(parents=True, exist_ok=True) - # Cache region-tech assignments so repeated runs can recover quickly. - clustering_cache_dir = Path("resources") / "clustering_cache" - clustering_cache_dir.mkdir(parents=True, exist_ok=True) - logger.info(f"Clustering cache directory: {clustering_cache_dir}") + n_jobs = getattr(snakemake, "threads", -1) - # Parallelize clustering across all region-tech pairs with tqdm progress. with tqdm_joblib( - tqdm( - total=len(region_tech_pairs), - desc="Clustering region-tech pairs", - unit="pair", - ) + tqdm(total=len(region_tech_pairs), desc="Clustering", unit="pair") ): - results = joblib.Parallel(n_jobs=snakemake.threads)( - joblib.delayed(cluster_region_technology_pair)( - region, - tech, - df_features, - clustering_config, - feature_weights, - str(clustering_cache_dir), - ) - for region, tech in tqdm( - region_tech_pairs, - desc="Queuing clustering tasks", - unit="pair", - leave=False, + results = joblib.Parallel(n_jobs=n_jobs, backend="loky")( + joblib.delayed(cluster_region_technology)( + region, tech, df_features, ds, clustering_config, str(cache_dir) ) + for region, tech in region_tech_pairs ) - # Convert results to dict - clusters_dict = dict(results) - - # Aggregate clusters into pseudo-buses and write the output NetCDF. - cache_clusters = Path("resources") / "clusters_cache.joblib" - clustered_data, cluster_metadata = aggregate_clusters( - df_features, - clusters_dict, - ds_filtered, - config, - str(cache_clusters), - str(clustering_cache_dir), - ) + # Aggregate + clustered_data, metadata_df = aggregate_all_clusters(results, ds) - # Write output + # Save metadata CSV alongside NetCDF + meta_path = Path(str(snakemake.output.clustered)).with_suffix(".metadata.csv") + metadata_df.to_csv(meta_path, index=False) + logger.info(f" ✓ Metadata: {meta_path}") + + # Write NetCDF write_clustered_netcdf(clustered_data, str(snakemake.output.clustered), config) - # Validation report for capacity and merit-order preservation. - validate_clustering( - clustered_data, df_features, ds_filtered, str(snakemake.output.report) - ) + # Validate + validate_clustering(clustered_data, df_features, ds, str(snakemake.output.report)) logger.info("=" * 70) logger.info("CLUSTERING COMPLETE") From c7dba411836663b042565be4fcd15346c6d0e5ec Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 3 Jun 2026 16:59:27 +0200 Subject: [PATCH 135/216] feat: finish clustering and clustering config --- config/config.yaml | 32 ++--- ...pare_renewables_trace_vs_pypsa-earth.ipynb | 16 ++- workflow/scripts/cluster_renewables.py | 127 +++++++++++++----- 3 files changed, 120 insertions(+), 55 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 313f69b..ebd8936 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -169,33 +169,19 @@ regions: "Oceania": ["AUS","NZL"] clustering: - n_strata: 7 + n_strata: 10 # number of strata ("quality classes") to divide the renewable potential into based on capacity factor (CF) distribution. tail_percentile: 10.0 - tail_cluster_boost: 2.0 - geo_weight: 0.3 - n_pca_components: 5 + tail_cluster_boost: 2.0 # multiplier for number of clusters in the tail (highest-quality stratum) to avoid overgeneralization of the best resources + diversity_threshold: 0.05 # min avg_cf std within stratum to split + min_capacity_for_split_mw: 1e5 # strata below this get 1 cluster cluster_count_policy: - mode: dynamic base_clusters: - solar: 20 + solar: 20 onwind: 20 - reference_buses: 2000 - reference_capacity_mw: 2e7 - scale_exponent: 0.5 - bus_weight: 0.5 - capacity_weight: 0.5 - min_clusters: 5 - max_clusters: 50 - - # Feature weighting for K-means clustering - # Determines how heavily clustering emphasizes each aspect: - # - merit_order (0.5): Merit-order / capacity factor quality (primary driver) - # - temporal (0.3): Temporal patterns (std, cv, autocorr_24h) - # - geospatial (0.2): Geographic proximity (lat, lon) - feature_weights: - merit_order: 0.5 # High CF / tail-shape sites group together for optimization value - temporal: 0.3 # Similar generation patterns cluster (wind/solar separation + peakiness) - geospatial: 0.2 # Loose geographic coherence (secondary constraint) + reference_buses: 2000 # Reference number of buses for scaling cluster count 2000 buses ~ 20 clusters + reference_capacity_mw: 2e7 # Reference total capacity for scaling cluster count, 20 GW ~ 20 clusters + min_clusters: 10 # = floor is just n_strata with no splitting + max_clusters: 40 # tighter cap design: cost_penalty: diff --git a/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb b/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb index e9bca82..a107400 100644 --- a/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb +++ b/workflow/notebooks/compare_renewables_trace_vs_pypsa-earth.ipynb @@ -437,6 +437,18 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6ce43e2", + "metadata": {}, + "outputs": [], + "source": [ + "plot_helpers.plot_bubble_region_comparison(\n", + " \"Europe\", trace_ds, pypsa_earth_ds, show=True\n", + ")" + ] + }, { "cell_type": "markdown", "id": "104cb776", @@ -544,9 +556,9 @@ ], "metadata": { "kernelspec": { - "display_name": "shift_dev", + "display_name": "Python 3", "language": "python", - "name": "shift_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/workflow/scripts/cluster_renewables.py b/workflow/scripts/cluster_renewables.py index 8b8421d..0f3889c 100644 --- a/workflow/scripts/cluster_renewables.py +++ b/workflow/scripts/cluster_renewables.py @@ -46,7 +46,7 @@ snakemake: Any = globals().get("snakemake") -CACHE_VERSION = "v4_stratified_kmedoids" +CACHE_VERSION = "v5" # --------------------------------------------------------------------------- # Helpers @@ -288,30 +288,71 @@ def _cluster_within_stratum( def _allocate_stratum_clusters( - stratum_capacities: Dict[str, float], + stratum_groups: Dict[str, pd.DataFrame], total_k: int, + profiles_by_stratum: Dict[str, np.ndarray], tail_boost: float = 2.0, - min_per_stratum: int = 1, + diversity_threshold: float = 0.05, # min profile std to warrant splitting + min_capacity_for_split_mw: float = 500.0, # min capacity to warrant >1 cluster ) -> Dict[str, int]: - """Distribute cluster budget across strata, boosting tails.""" - weights = {} - for name, cap in stratum_capacities.items(): - w = cap - if name.startswith("tail"): - w *= tail_boost - weights[name] = max(w, 1e-9) - - total_w = sum(weights.values()) - alloc = { - name: max(min_per_stratum, int(round(w / total_w * total_k))) - for name, w in weights.items() - } + """Allocate cluster budget, giving 1 to strata that don't need splitting.""" + + # First pass: decide which strata deserve >1 cluster + splittable = {} + fixed_at_one = {} + + for name, group in stratum_groups.items(): + profiles = profiles_by_stratum[name] + total_cap = group["capacity_mw"].sum() + n_buses = len(group) + + # Check if stratum has enough diversity to warrant sub-clustering + if n_buses <= 2: + fixed_at_one[name] = 1 + continue + + # Profile diversity: std of per-bus mean CFs within stratum + bus_means = profiles.mean(axis=1) + internal_diversity = float(np.std(bus_means)) + + if ( + total_cap < min_capacity_for_split_mw + or internal_diversity < diversity_threshold + ): + fixed_at_one[name] = 1 + continue - # Trim excess from the largest stratum - excess = sum(alloc.values()) - total_k - if excess > 0: - largest = max(alloc, key=alloc.get) - alloc[largest] = max(min_per_stratum, alloc[largest] - excess) + splittable[name] = total_cap + + # Remaining budget after reserving 1 per fixed stratum + remaining_k = ( + total_k - len(fixed_at_one) - len(splittable) + ) # 1 each for splittable too + remaining_k = max(0, remaining_k) + + # Distribute remaining budget proportional to capacity (with tail boost) + alloc = dict(fixed_at_one) # start with fixed ones + if splittable and remaining_k > 0: + weights = {} + for name, cap in splittable.items(): + w = cap + if ( + name == "tail_high" + ): # boost tail_high to preserve green pockets and peak quality + w *= tail_boost + elif ( + name == "tail_low" + ): # de-prioritize tail_low since these are last to be picked in merit order + w *= 1 / tail_boost + weights[name] = w + total_w = sum(weights.values()) + + for name, w in weights.items(): + extra = int(round(w / total_w * remaining_k)) + alloc[name] = 1 + max(0, extra) # at least 1 + else: + for name in splittable: + alloc[name] = 1 return alloc @@ -321,7 +362,7 @@ def _build_strata( n_strata: int = 7, tail_pct: float = 10.0, ) -> pd.Series: - """Assign each row to a stratum based on cf_high_mass. + """Assign each row to a stratum based on avg_cf. Uses capacity-weighted quantile boundaries with explicit tail separation. Returns a Series of stratum labels aligned to df_rt.index. @@ -374,7 +415,7 @@ def resolve_total_clusters(df_rt: pd.DataFrame, tech: str) -> int: base_clusters = policy.get("base_clusters", {}) mode = policy.get("mode", "dynamic") base_key = "solar" if tech == "solar" else "onwind" - base = int(base_clusters.get(base_key, 40)) + base = int(base_clusters.get(base_key)) if mode == "fixed": return base @@ -393,8 +434,8 @@ def resolve_total_clusters(df_rt: pd.DataFrame, tech: str) -> int: ) target = int(round(base * scale)) - min_c = int(policy.get("min_clusters", 3)) - max_c = int(policy.get("max_clusters", 120)) + min_c = int(policy.get("min_clusters")) + max_c = int(policy.get("max_clusters")) return max(min_c, min(max_c, min(target, len(df_rt)))) @@ -448,10 +489,33 @@ def cluster_region_technology( # --- Stratify --- df_rt["stratum"] = _build_strata(df_rt, n_strata=n_strata, tail_pct=tail_pct) - stratum_caps = df_rt.groupby("stratum")["capacity_mw"].sum().to_dict() - stratum_k = _allocate_stratum_clusters(stratum_caps, total_k, tail_boost=tail_boost) + # Load profiles per stratum for diversity check + stratum_groups = {} + profiles_by_stratum = {} + for stratum_name, group in df_rt.groupby("stratum"): + stratum_groups[stratum_name] = group + profiles_by_stratum[stratum_name] = _load_profiles_for_group( + group["bus_id"].values.tolist(), tech, ds + ) + + min_cap_split = float(clustering_config.get("min_capacity_for_split_mw", 500.0)) + diversity_thresh = float(clustering_config.get("diversity_threshold", 0.05)) + + stratum_k = _allocate_stratum_clusters( + stratum_groups, + total_k, + profiles_by_stratum, + tail_boost=tail_boost, + diversity_threshold=diversity_thresh, + min_capacity_for_split_mw=min_cap_split, + ) - logger.info(f" Stratum cluster allocation: {stratum_k}") + actual_total = sum(stratum_k.values()) + logger.info( + f" Cluster allocation: {stratum_k} (total: {actual_total}, " + f"budget: {total_k}, {len(stratum_k) - sum(1 for v in stratum_k.values() if v > 1)} " + f"strata kept at 1)" + ) # --- Cluster within each stratum --- cluster_rows: List[Dict] = [] @@ -490,6 +554,9 @@ def cluster_region_technology( if medoid_local is None: # Fallback: largest capacity bus in cluster medoid_local = medoid_candidates[np.argmax(member_caps)] + logger.warning( + f"No medoid in cluster {cl} of stratum {stratum_name}, picking largest bus: {bus_ids[medoid_local]}" + ) medoid_bus = bus_ids[medoid_local] @@ -804,7 +871,7 @@ def main(): logger.info(f"Loaded coordinates for {len(bus_coords)} buses") # Extract features - cache_features = Path("resources") / "features_cache_v4.csv" + cache_features = Path("resources") / f"features_cache_{CACHE_VERSION}.csv" df_features = extract_features(ds, config, bus_coords, str(cache_features)) # Build region-tech pairs @@ -817,7 +884,7 @@ def main(): logger.info(f"Clustering {len(region_tech_pairs)} region-technology pairs") # Cluster (parallel) - cache_dir = Path("resources") / "clustering_cache_v4" + cache_dir = Path("resources") / f"clustering_cache_{CACHE_VERSION}" cache_dir.mkdir(parents=True, exist_ok=True) n_jobs = getattr(snakemake, "threads", -1) From 7bddd1858c1718b984368b842d3fe98a9f171af1 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Wed, 3 Jun 2026 17:01:14 +0200 Subject: [PATCH 136/216] chore: switch PyPSA input to clusters instead of TRACE --- rules/supply_curves.smk | 8 +-- workflow/scripts/prepare_regional_network.py | 64 ++++++++++++-------- 2 files changed, 42 insertions(+), 30 deletions(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index eff002f..f0da03e 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -84,11 +84,11 @@ rule prepare_regional_network: if _process_label_for_product(wildcards.product) else f"resources/generic_production_model/generic_model_{wildcards.cost_year}.nc" ), - renewables="data/new_renewables_consolidated.nc", + renewables="data/renewables_clustered.nc", tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", - wacc = "resources/wacc-clustered.csv", - labour_cost = "resources/labour_cost_clustered.csv", + wacc="resources/wacc-clustered.csv", + labour_cost="resources/labour_cost_clustered.csv", output: # Output keyed by product; route_label is internal to the script network="resources/networks/base_{cost_year}_{region}_{wacc}_{product}_{scenario}.nc", @@ -105,7 +105,7 @@ rule prepare_regional_network: product="{product}", route_label=lambda wildcards: _process_label_for_product(wildcards.product), config=config, - uniform_interest_rate=config["interest_rate"]["default"] + uniform_interest_rate=config["interest_rate"]["default"], message: "Preparing {wildcards.scenario} regional network: {wildcards.region} -> {wildcards.product} " "(cost_year={wildcards.cost_year})" diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 96f89ee..d6dce2d 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -1,8 +1,8 @@ """ -Prepare regional network: load consolidated renewables and configure supply chain. +Prepare regional network: load clustered renewables and configure supply chain. This is the simplified Step 1 workflow that: -1. Loads the consolidated renewable profiles directly (region, technology, class, time) +1. Loads the clustered renewable profiles directly (region, technology, class, time) 2. Creates PyPSA generators for each technology and class 3. Applies local demand reservation if configured 4. Adds supply chain (electrolyzer, DRI, optional EAF) @@ -12,7 +12,7 @@ rule prepare_regional_network: input: skeleton = "resources/networks/skeleton.nc", - renewables = "data/new_renewables_consolidated.nc", + renewables = "data/clustered_renewables.nc", tech_costs = "resources/tech_database.csv", params: region = "{region}", @@ -43,17 +43,17 @@ logger = setup_logging(__name__, snakemake=snakemake) -def load_region_renewables_consolidated( - consolidated_path: str, +def load_regional_clustered_renewables( + clustered_path: str, region: str, ) -> Tuple[Dict[str, np.ndarray], xr.DataArray, Dict]: """ - Load renewable data for a region from consolidated NetCDF. + Load renewable data for a region from clustered NetCDF. Parameters ---------- - consolidated_path : str - Path to data/new_renewables_consolidated.nc + clustered_path : str + Path to data/clustered_renewables.nc region : str Region name (e.g., 'Europe', 'North_America') @@ -68,12 +68,12 @@ def load_region_renewables_consolidated( metadata : dict Summary info (n_classes, n_time, technologies, etc.) """ - with xr.open_dataset(consolidated_path) as ds: + with xr.open_dataset(clustered_path) as ds: if region not in ds.region.values: available = ", ".join(ds.region.values) raise ValueError(f"Region '{region}' not found. Available: {available}") - logger.info(f"Loading consolidated renewables for {region}") + logger.info(f"Loading clustered renewables for {region}") # Select region (dims: technology, class) region_cap = ds["capacity"].sel(region=region) # (tech, class) @@ -84,9 +84,13 @@ def load_region_renewables_consolidated( technologies_dict = {} for tech in techs: - cap = region_cap.sel(technology=tech).values # (class,) - technologies_dict[tech] = cap - logger.info(f" {tech}: {len(cap)} sites, {cap.sum():.0f} MW total") + cap = region_cap.sel(technology=tech).values + # Drop NaN-padded trailing classes + valid = ~np.isnan(cap) + technologies_dict[tech] = cap[valid] + logger.info( + f" {tech}: {valid.sum()} classes, {np.nansum(cap):.0f} MW total" + ) # Capacity factor time series (keep full structure for now) cf_ts = region_cf # (tech, class, time) @@ -98,20 +102,28 @@ def load_region_renewables_consolidated( ) cf_ts = cf_ts.clip(0, 1).fillna(0) if n_invalid_before > 0: - logger.warning( - f"Capacity factors validation: fixed {n_invalid_before} invalid values " - f"(clamped to [0,1], replaced NaN with 0)" - ) + logger.warning(f"Fixed {n_invalid_before} invalid CF values (clamped/NaN)") + + avg_cf_data = {} + if "avg_cf" in ds.data_vars: + for tech in techs: + avg = ds["avg_cf"].sel(region=region, technology=tech).values + valid = ~np.isnan(avg) + avg_cf_data[tech] = avg[valid] metadata = { "region": region, - "n_classes": region_cap.sizes["class"], + "n_classes": { + tech: int((~np.isnan(region_cap.sel(technology=tech).values)).sum()) + for tech in techs + }, "n_time": region_cf.sizes["time"], "n_technologies": len(techs), "technologies": techs, "time_start": pd.Timestamp(ds["time"].values[0]), "time_end": pd.Timestamp(ds["time"].values[-1]), "total_capacity_mw": float(region_cap.sum().values), + "avg_cf": avg_cf_data, } logger.info( @@ -594,7 +606,7 @@ def apply_product_cutoff( def prepare_network( skeleton_network_path: str, - consolidated_renewables_path: str, + clustered_renewables_path: str, tech_costs_path: str, local_demand_path: Optional[str], region: str, @@ -604,14 +616,14 @@ def prepare_network( scenario: str = "reserved", route_label: Optional[str] = None, ) -> Tuple[pypsa.Network, Dict]: - """Prepare regional network with consolidated renewables. + """Prepare regional network with clustered renewables. Parameters ---------- skeleton_network_path : str Path to base network topology - consolidated_renewables_path : str - Path to consolidated renewables NetCDF + clustered_renewables_path : str + Path to clustered renewables NetCDF tech_costs_path : str Path to technology cost database region : str @@ -797,8 +809,8 @@ def prepare_network( metadata = {} else: logger.info("Loading consolidated renewables...") - techs_dict, cf_ts, metadata = load_region_renewables_consolidated( - consolidated_renewables_path, region + techs_dict, cf_ts, metadata = load_regional_clustered_renewables( + clustered_renewables_path, region ) # Apply local demand reservation if configured (only for products with renewable_electricity) # For scenario="reserved", reserve high-CF sites; for "unreserved", skip reservation @@ -1092,7 +1104,7 @@ def add_labour_cost(n, labour_cost): cost_year = ( snakemake.wildcards.cost_year if hasattr(snakemake.wildcards, "cost_year") - else 2030 + else 2050 ) scenario = ( snakemake.wildcards.scenario @@ -1108,7 +1120,7 @@ def add_labour_cost(n, labour_cost): # Prepare network network, audit = prepare_network( skeleton_network_path=skeleton_path, - consolidated_renewables_path=renewables_path, + clustered_renewables_path=renewables_path, tech_costs_path=tech_costs_path, local_demand_path=local_demand_path, region=region, From 3b034f6f8875efb4c2dbeffd8d978a20a65e949e Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 5 Jun 2026 11:39:01 +0200 Subject: [PATCH 137/216] feat: create rule and improve comparison between scenarios --- rules/reporting.smk | 11 ++++++++ workflow/notebooks/compare-scenarios.ipynb | 30 +++++++++++++++++----- 2 files changed, 35 insertions(+), 6 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 2dc9e24..ae63b98 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -89,3 +89,14 @@ rule plot_global_supply: rule plot_global_supply_all: input: expand("../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", cost_year=[2050], wacc=["regional"], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) + +rule plot_comparison: + input: + default = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~default/network.nc", + stability = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-stability-weighted/network_0.02.nc", + hightrans = "results/chain_id~hightrans_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~default/network.nc", + output: + cost_comparison="../results/figures_general/comparison/cost_comparison.pdf", + cost_comparison_png="../results/figures_general/comparison/cost_comparison.png", + notebook: + "notebooks/compare-scenarios.ipynb" \ No newline at end of file diff --git a/workflow/notebooks/compare-scenarios.ipynb b/workflow/notebooks/compare-scenarios.ipynb index 510fd27..ff62970 100644 --- a/workflow/notebooks/compare-scenarios.ipynb +++ b/workflow/notebooks/compare-scenarios.ipynb @@ -22,7 +22,7 @@ "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", + " \"plot_comparison\",\n", " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", ")" ] @@ -64,8 +64,8 @@ "source": [ "nc = {}\n", "\n", - "for scenario in config[\"scenario\"].keys():\n", - " trade_model_fn = f\"../../results/cost_year~2030/interone~hbi/intertwo~eaf-grid/final~steel/scenario~{scenario}/network.nc\"\n", + "for scenario in scenarios:\n", + " trade_model_fn = snakemake.input[scenario]\n", "\n", " n = pypsa.Network(trade_model_fn)\n", " n.name = scenario\n", @@ -109,7 +109,7 @@ "source": [ "df = nc.statistics.system_cost(groupby=\"carrier\").to_frame(\"systemcost\")\n", "steel_demand = (\n", - " nc.statistics.withdrawal().to_frame().loc[\"Load\", \"default\", \"steel\"].value\n", + " nc.statistics.withdrawal().to_frame().loc[\"Load\", \"default\", \"steel\"][0]\n", ")\n", "\n", "df = df / steel_demand\n", @@ -144,6 +144,24 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "cdc881f0", + "metadata": {}, + "source": [ + "Remove HBI transport cost for scenario with artifically high transport costs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f27e730f", + "metadata": {}, + "outputs": [], + "source": [ + "plot_df.loc[\"hightrans\", \"Shipping HBI\"] = 0" + ] + }, { "cell_type": "code", "execution_count": null, @@ -196,8 +214,8 @@ "\n", "plt.xticks(rotation=0)\n", "plt.tight_layout()\n", - "plt.savefig(snakemake.input.cost_comparison, dpi=300)\n", - "plt.savefig(snakemake.input.cost_comparison_png, dpi=300)\n", + "plt.savefig(snakemake.output.cost_comparison, dpi=300)\n", + "plt.savefig(snakemake.output.cost_comparison_png, dpi=300)\n", "plt.show()" ] }, From 60786018098ee8706f267cb02fa1b3186124f18a Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Mon, 8 Jun 2026 10:50:31 +0200 Subject: [PATCH 138/216] fix: point to correct cluster path --- rules/supply_curves.smk | 2 +- workflow/scripts/prepare_regional_network.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index f0da03e..85e1d2a 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -84,7 +84,7 @@ rule prepare_regional_network: if _process_label_for_product(wildcards.product) else f"resources/generic_production_model/generic_model_{wildcards.cost_year}.nc" ), - renewables="data/renewables_clustered.nc", + renewables="resources/renewables_clustered.nc", tech_costs="resources/technology_data/costs_{cost_year}.csv", local_demand="data/un_enerdata_demand_2050_final.csv", wacc="resources/wacc-clustered.csv", diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index d6dce2d..19f0533 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -12,7 +12,7 @@ rule prepare_regional_network: input: skeleton = "resources/networks/skeleton.nc", - renewables = "data/clustered_renewables.nc", + renewables = "resources/clustered_renewables.nc", tech_costs = "resources/tech_database.csv", params: region = "{region}", From 11bb7a39f1c13b2b6e0dd034387a3b317cf17f3b Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 9 Jun 2026 12:06:24 +0200 Subject: [PATCH 139/216] feat: add rule plotting lcox --- rules/reporting.smk | 15 +- workflow/notebooks/plot-compare-lcox.ipynb | 382 +++++++++++++++++++++ 2 files changed, 396 insertions(+), 1 deletion(-) create mode 100644 workflow/notebooks/plot-compare-lcox.ipynb diff --git a/rules/reporting.smk b/rules/reporting.smk index ae63b98..ac9e184 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -99,4 +99,17 @@ rule plot_comparison: cost_comparison="../results/figures_general/comparison/cost_comparison.pdf", cost_comparison_png="../results/figures_general/comparison/cost_comparison.png", notebook: - "notebooks/compare-scenarios.ipynb" \ No newline at end of file + "notebooks/compare-scenarios.ipynb" + + +rule plot_compare_lcox: + input: + eu_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_0.1.nc", + eu_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_1.nc", + eu_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_10.nc", + eu_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_100.nc", + output: + lcox_comparison="../results/figures_general/comparison/lcox_comparison.pdf", + lcox_comparison_png="../results/figures_general/comparison/lcox_comparison.png", + notebook: + "notebooks/plot-compare-lcox.ipynb" \ No newline at end of file diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb new file mode 100644 index 0000000..92e25fe --- /dev/null +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d3ddb1b2", + "metadata": {}, + "outputs": [], + "source": [ + "import pypsa\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6f81313", + "metadata": {}, + "outputs": [], + "source": [ + "from _helpers_notebooks import mock_snakemake\n", + "\n", + "snakemake = mock_snakemake(\n", + " \"plot_compare_lcox\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "310bcf70", + "metadata": {}, + "outputs": [], + "source": [ + "config = snakemake.config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "488b720f", + "metadata": {}, + "outputs": [], + "source": [ + "colors = config[\"colors\"]" + ] + }, + { + "cell_type": "markdown", + "id": "cf54698c", + "metadata": {}, + "source": [ + "### Read networks in Network collection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99b8dc61", + "metadata": {}, + "outputs": [], + "source": [ + "scenarios = snakemake.input.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58dcd935", + "metadata": {}, + "outputs": [], + "source": [ + "nc = {}\n", + "\n", + "for scenario in scenarios:\n", + " model_fn = snakemake.input[scenario]\n", + "\n", + " n = pypsa.Network(model_fn)\n", + " n.name = scenario\n", + " nc[scenario] = n\n", + "\n", + "nc = pypsa.NetworkCollection(list(nc.values()))" + ] + }, + { + "cell_type": "markdown", + "id": "1f28183f", + "metadata": {}, + "source": [ + "### Costs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30545a5b", + "metadata": {}, + "outputs": [], + "source": [ + "hbi_demand = nc.statistics.withdrawal().to_frame().loc[\"Load\", :, \"hbi_demand\"]\n", + "hbi_demand" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33c20832", + "metadata": {}, + "outputs": [], + "source": [ + "df = nc.statistics.system_cost(groupby=\"carrier\").to_frame(\"systemcost\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab2f64a6", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"systemcost_per_hbi\"] = df[\"systemcost\"].div(hbi_demand.squeeze().rename_axis(\"network\"), level=\"network\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce79e80", + "metadata": {}, + "outputs": [], + "source": [ + "# Pivot to get networks as rows and carriers as columns\n", + "plot_df = df.pivot_table(\n", + " index=\"network\", columns=\"carrier\", values=\"systemcost_per_hbi\", aggfunc=\"sum\"\n", + ")\n", + "\n", + "order = [\"renewable_solar\", \"renewable_onwind\", \"hydrogen\", \"electrolysis\", \"direct_reduction_furnace\"]\n", + "\n", + "plot_df = plot_df[order]\n", + "\n", + "plot_df.rename(\n", + " columns={\n", + " \"iron_ore\": \"Iron ore\",\n", + " \"shipping_iron_ore\": \"Shipping iron ore\",\n", + " \"hbi\": \"DRI\",\n", + " \"shipping_hbi\": \"Shipping HBI\",\n", + " \"steel\": \"EAF\",\n", + " },\n", + " inplace=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4560d6a3", + "metadata": {}, + "outputs": [], + "source": [ + "# Create plot\n", + "ax = plot_df.plot(\n", + " kind=\"bar\",\n", + " stacked=True,\n", + " figsize=(8, 4),\n", + " color=[nc[\"eu_10\"].carriers.color[c] for c in order],\n", + " alpha=0.8,\n", + ")\n", + "\n", + "# ---- Axis labels ----\n", + "ax.set_ylabel(\"Total cost in €/t$_{steel}$\")\n", + "ax.set_xlabel(\"Scenario\")\n", + "\n", + "# ---- Title ----\n", + "# ax.set_title(\"Cost of Steel\")\n", + "\n", + "# ---- Y-axis grid ----\n", + "ax.yaxis.grid(True, linestyle=\"-\", alpha=0.6)\n", + "ax.set_axisbelow(True)\n", + "\n", + "# ---- Add total values on top of bars ----\n", + "totals = plot_df.sum(axis=1)\n", + "\n", + "for i, total in enumerate(totals):\n", + " ax.text(\n", + " i,\n", + " total,\n", + " f\"{total:.1f}\", # adjust scaling if needed\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " )\n", + "\n", + "# ---- Flip legend order ----\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "ax.legend(\n", + " handles[::-1],\n", + " labels[::-1],\n", + " title=\"Cost component\",\n", + " bbox_to_anchor=(1.02, 1),\n", + " loc=\"upper left\",\n", + ")\n", + "\n", + "plt.xticks(rotation=0)\n", + "plt.tight_layout()\n", + "plt.savefig(snakemake.output.lcox_comparison, dpi=300)\n", + "plt.savefig(snakemake.output.lcox_comparison_png, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1adceb09", + "metadata": {}, + "source": [ + "### Statistics clarifiction (to be deleted)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4971231d", + "metadata": {}, + "outputs": [], + "source": [ + "system_cost = nc[\"eu_1\"].statistics.system_cost().div(1e6).sum()\n", + "system_cost.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b6fbe95", + "metadata": {}, + "outputs": [], + "source": [ + "expanded_capex = nc[\"eu_1\"].statistics.expanded_capex().div(1e6).sum()\n", + "expanded_capex.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bce4d758", + "metadata": {}, + "outputs": [], + "source": [ + "opex = nc[\"eu_1\"].statistics.opex().div(1e6).sum()\n", + "opex.round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "e5ab7988", + "metadata": {}, + "source": [ + "-> system_cost = expanded_capex + opex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfffb729", + "metadata": {}, + "outputs": [], + "source": [ + "objective = nc[\"eu_1\"].objective / 1e6\n", + "objective.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa803626", + "metadata": {}, + "outputs": [], + "source": [ + "fom = nc[\"eu_1\"].statistics.fom().div(1e6).sum()\n", + "fom.round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "5a04b8ce", + "metadata": {}, + "source": [ + "-> objective = system_cost + fom = expanded_capex + opex + fom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6be0a44", + "metadata": {}, + "outputs": [], + "source": [ + "nc[\"eu_1\"].statistics.overnight_cost().div(1e6).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b045735", + "metadata": {}, + "outputs": [], + "source": [ + "nc[\"eu_1\"].links.loc[\"dri\"]" + ] + }, + { + "cell_type": "markdown", + "id": "9e0d9359", + "metadata": {}, + "source": [ + "### Experimental area" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39902bdb", + "metadata": {}, + "outputs": [], + "source": [ + "nc[\"default\"].statistics.capex().div(1e6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4807f0e", + "metadata": {}, + "outputs": [], + "source": [ + "nc[\"default\"].statistics.opex().div(1e6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7d7ceb2", + "metadata": {}, + "outputs": [], + "source": [ + "nc.statistics.capex().div(1e6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2d35a17", + "metadata": {}, + "outputs": [], + "source": [ + "nc.statistics.energy_balance.plot.bar()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 25edf78bb4b0c5c1bab0f4849f5070a239613d4b Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 9 Jun 2026 12:28:12 +0200 Subject: [PATCH 140/216] chore: avoid nan in addition and implement colors --- workflow/notebooks/plot-compare-lcox.ipynb | 140 ++++++++------------- 1 file changed, 55 insertions(+), 85 deletions(-) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 92e25fe..07d05a2 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -114,6 +114,26 @@ "df" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e8a00a6", + "metadata": {}, + "outputs": [], + "source": [ + "fom = nc.statistics.fom(groupby=\"carrier\").to_frame(\"fom\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0119dc7d", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"systemcost+fom\"] = df[\"systemcost\"].add(fom[\"fom\"], fill_value=0)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -121,10 +141,18 @@ "metadata": {}, "outputs": [], "source": [ - "df[\"systemcost_per_hbi\"] = df[\"systemcost\"].div(hbi_demand.squeeze().rename_axis(\"network\"), level=\"network\")\n", + "df[\"systemcost+fom_per_hbi\"] = df[\"systemcost+fom\"].div(hbi_demand.squeeze().rename_axis(\"network\"), level=\"network\")\n", "df" ] }, + { + "cell_type": "markdown", + "id": "ff5e51d7", + "metadata": {}, + "source": [ + "### Plot" + ] + }, { "cell_type": "code", "execution_count": null, @@ -134,23 +162,23 @@ "source": [ "# Pivot to get networks as rows and carriers as columns\n", "plot_df = df.pivot_table(\n", - " index=\"network\", columns=\"carrier\", values=\"systemcost_per_hbi\", aggfunc=\"sum\"\n", + " index=\"network\", columns=\"carrier\", values=\"systemcost+fom_per_hbi\", aggfunc=\"sum\"\n", ")\n", "\n", - "order = [\"renewable_solar\", \"renewable_onwind\", \"hydrogen\", \"electrolysis\", \"direct_reduction_furnace\"]\n", - "\n", - "plot_df = plot_df[order]\n", - "\n", "plot_df.rename(\n", " columns={\n", - " \"iron_ore\": \"Iron ore\",\n", - " \"shipping_iron_ore\": \"Shipping iron ore\",\n", - " \"hbi\": \"DRI\",\n", - " \"shipping_hbi\": \"Shipping HBI\",\n", - " \"steel\": \"EAF\",\n", + " \"renewable_solar\": \"solar\",\n", + " \"renewable_onwind\": \"onshore wind\",\n", + " \"hydrogen\": \"hydrogen storage\",\n", + " \"electrolysis\": \"electrolysis\",\n", + " \"direct_reduction_furnace\": \"direct reduction furnace\",\n", " },\n", " inplace=True,\n", - ")" + ")\n", + "\n", + "order = [\"solar\", \"onshore wind\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\"]\n", + "\n", + "plot_df = plot_df[order]" ] }, { @@ -165,7 +193,7 @@ " kind=\"bar\",\n", " stacked=True,\n", " figsize=(8, 4),\n", - " color=[nc[\"eu_10\"].carriers.color[c] for c in order],\n", + " color=[config[\"colors\"][c] for c in order],\n", " alpha=0.8,\n", ")\n", "\n", @@ -215,7 +243,7 @@ "id": "1adceb09", "metadata": {}, "source": [ - "### Statistics clarifiction (to be deleted)" + "" ] }, { @@ -225,8 +253,8 @@ "metadata": {}, "outputs": [], "source": [ - "system_cost = nc[\"eu_1\"].statistics.system_cost().div(1e6).sum()\n", - "system_cost.round(2)" + "# system_cost = nc[\"eu_1\"].statistics.system_cost().div(1e6).sum()\n", + "# system_cost.round(2)" ] }, { @@ -236,8 +264,8 @@ "metadata": {}, "outputs": [], "source": [ - "expanded_capex = nc[\"eu_1\"].statistics.expanded_capex().div(1e6).sum()\n", - "expanded_capex.round(2)" + "# expanded_capex = nc[\"eu_1\"].statistics.expanded_capex().div(1e6).sum()\n", + "# expanded_capex.round(2)" ] }, { @@ -247,8 +275,8 @@ "metadata": {}, "outputs": [], "source": [ - "opex = nc[\"eu_1\"].statistics.opex().div(1e6).sum()\n", - "opex.round(2)" + "# opex = nc[\"eu_1\"].statistics.opex().div(1e6).sum()\n", + "# opex.round(2)" ] }, { @@ -256,7 +284,7 @@ "id": "e5ab7988", "metadata": {}, "source": [ - "-> system_cost = expanded_capex + opex" + "" ] }, { @@ -266,8 +294,8 @@ "metadata": {}, "outputs": [], "source": [ - "objective = nc[\"eu_1\"].objective / 1e6\n", - "objective.round(2)" + "# objective = nc[\"eu_1\"].objective / 1e6\n", + "# objective.round(2)" ] }, { @@ -277,8 +305,8 @@ "metadata": {}, "outputs": [], "source": [ - "fom = nc[\"eu_1\"].statistics.fom().div(1e6).sum()\n", - "fom.round(2)" + "# fom = nc[\"eu_1\"].statistics.fom().div(1e6).sum()\n", + "# fom.round(2)" ] }, { @@ -286,7 +314,7 @@ "id": "5a04b8ce", "metadata": {}, "source": [ - "-> objective = system_cost + fom = expanded_capex + opex + fom" + "" ] }, { @@ -296,65 +324,7 @@ "metadata": {}, "outputs": [], "source": [ - "nc[\"eu_1\"].statistics.overnight_cost().div(1e6).sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b045735", - "metadata": {}, - "outputs": [], - "source": [ - "nc[\"eu_1\"].links.loc[\"dri\"]" - ] - }, - { - "cell_type": "markdown", - "id": "9e0d9359", - "metadata": {}, - "source": [ - "### Experimental area" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39902bdb", - "metadata": {}, - "outputs": [], - "source": [ - "nc[\"default\"].statistics.capex().div(1e6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4807f0e", - "metadata": {}, - "outputs": [], - "source": [ - "nc[\"default\"].statistics.opex().div(1e6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7d7ceb2", - "metadata": {}, - "outputs": [], - "source": [ - "nc.statistics.capex().div(1e6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2d35a17", - "metadata": {}, - "outputs": [], - "source": [ - "nc.statistics.energy_balance.plot.bar()" + "# nc[\"eu_1\"].statistics.overnight_cost().div(1e6).sum()" ] } ], From 9875c964d204bef67f009c92f0ce0307eeef5c70 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 9 Jun 2026 13:52:03 +0200 Subject: [PATCH 141/216] feat: add second region to plot --- rules/reporting.smk | 4 ++++ workflow/notebooks/plot-compare-lcox.ipynb | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index ac9e184..f26182b 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -108,6 +108,10 @@ rule plot_compare_lcox: eu_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_1.nc", eu_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_10.nc", eu_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_100.nc", + sa_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_0.1.nc", + sa_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_1.nc", + sa_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_10.nc", + sa_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_100.nc", output: lcox_comparison="../results/figures_general/comparison/lcox_comparison.pdf", lcox_comparison_png="../results/figures_general/comparison/lcox_comparison.png", diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 07d05a2..479918e 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -198,7 +198,7 @@ ")\n", "\n", "# ---- Axis labels ----\n", - "ax.set_ylabel(\"Total cost in €/t$_{steel}$\")\n", + "ax.set_ylabel(\"Total cost in €/t$_{hbi}$\")\n", "ax.set_xlabel(\"Scenario\")\n", "\n", "# ---- Title ----\n", From 68337c64492392f181be5402db2cee791b9e6d78 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 9 Jun 2026 15:03:43 +0200 Subject: [PATCH 142/216] feat: group regions in plot --- workflow/notebooks/plot-compare-lcox.ipynb | 116 ++++++++++++++++----- 1 file changed, 88 insertions(+), 28 deletions(-) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 479918e..0dd415c 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -8,7 +8,7 @@ "outputs": [], "source": [ "import pypsa\n", - "\n", + "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, @@ -188,40 +188,101 @@ "metadata": {}, "outputs": [], "source": [ - "# Create plot\n", - "ax = plot_df.plot(\n", - " kind=\"bar\",\n", - " stacked=True,\n", - " figsize=(8, 4),\n", - " color=[config[\"colors\"][c] for c in order],\n", - " alpha=0.8,\n", - ")\n", + "# --- Parse scenario names into region and quantity ---\n", + "qty_map = {\"01\": 0.1, \"1\": 1.0, \"10\": 10.0, \"100\": 100.0}\n", + "region_map = {\"europe\": \"Europe\", \"south_america\": \"South America\"}\n", "\n", - "# ---- Axis labels ----\n", - "ax.set_ylabel(\"Total cost in €/t$_{hbi}$\")\n", - "ax.set_xlabel(\"Scenario\")\n", + "def parse_scenario(name):\n", + " region_raw, qty_raw = name.rsplit(\"_\", 1)\n", + " region = region_map.get(region_raw, region_raw.replace(\"_\", \" \").title())\n", + " qty = qty_map.get(qty_raw, float(qty_raw))\n", + " return region, qty\n", + "\n", + "plot_df[\"region\"] = [parse_scenario(s)[0] for s in plot_df.index]\n", + "plot_df[\"quantity\"] = [parse_scenario(s)[1] for s in plot_df.index]\n", + "plot_df = plot_df.sort_values([\"region\", \"quantity\"])\n", + "\n", + "# --- Compute x positions with a gap between region groups ---\n", + "bar_width = 0.7\n", + "group_gap = 1 # one extra bar-width gap between groups\n", + "\n", + "groups_ordered = list(dict.fromkeys(plot_df[\"region\"])) # preserve sorted order\n", + "x_positions = []\n", + "group_centers = {}\n", + "group_spans = {}\n", + "current_x = 0\n", + "\n", + "for g in groups_ordered:\n", + " mask = plot_df[\"region\"] == g\n", + " n = int(mask.sum())\n", + " positions = list(range(current_x, current_x + n))\n", + " x_positions.extend(positions)\n", + " group_centers[g] = np.mean(positions)\n", + " group_spans[g] = (positions[0], positions[-1])\n", + " current_x += n + group_gap\n", "\n", - "# ---- Title ----\n", - "# ax.set_title(\"Cost of Steel\")\n", + "# --- Create stacked bar plot ---\n", + "fig, ax = plt.subplots(figsize=(10, 4.5))\n", "\n", - "# ---- Y-axis grid ----\n", + "bottom = np.zeros(len(plot_df))\n", + "for col in order:\n", + " if col in plot_df.columns:\n", + " values = plot_df[col].fillna(0).values\n", + " ax.bar(x_positions, values, bottom=bottom, width=bar_width,\n", + " label=col, color=config[\"colors\"][col], alpha=0.8)\n", + " bottom += values\n", + "\n", + "# --- Totals on top of bars ---\n", + "totals = plot_df[order].sum(axis=1).values\n", + "for x, total in zip(x_positions, totals):\n", + " ax.text(x, total, f\"{total:.1f}\", ha=\"center\", va=\"bottom\", fontsize=9)\n", + "\n", + "# --- Primary axis formatting ---\n", + "ax.set_ylabel(\"Total cost in €/t$_{hbi}$\")\n", + "ax.set_xlabel(\"\")\n", "ax.yaxis.grid(True, linestyle=\"-\", alpha=0.6)\n", "ax.set_axisbelow(True)\n", "\n", - "# ---- Add total values on top of bars ----\n", - "totals = plot_df.sum(axis=1)\n", + "# --- Inner x-axis: quantity labels ---\n", + "ax.set_xticks(x_positions)\n", + "qty_labels = [f\"{row['quantity']:.4g} Mt\" for _, row in plot_df.iterrows()]\n", + "ax.set_xticklabels(qty_labels, rotation=0, fontsize=9)\n", + "ax.tick_params(axis=\"x\", length=0) # hide tick marks for cleaner look\n", + "\n", + "# --- Outer x-axis: region group labels with bracket ---\n", + "xaxis_transform = ax.get_xaxis_transform()\n", + "y_bracket = -0.16 # axes-fraction units below axes bottom (below quantity labels)\n", + "y_label_pts = -8 # additional offset in points below bracket\n", + "\n", + "for g in groups_ordered:\n", + " center = group_centers[g]\n", + " x_start, x_end = group_spans[g]\n", + "\n", + " # Horizontal bracket spanning the group\n", + " ax.annotate(\n", + " \"\",\n", + " xy=(x_end + bar_width / 2 + 0.05, y_bracket),\n", + " xycoords=xaxis_transform,\n", + " xytext=(x_start - bar_width / 2 - 0.05, y_bracket),\n", + " textcoords=xaxis_transform,\n", + " arrowprops=dict(arrowstyle=\"-\", color=\"black\", lw=0.8),\n", + " annotation_clip=False,\n", + " )\n", "\n", - "for i, total in enumerate(totals):\n", - " ax.text(\n", - " i,\n", - " total,\n", - " f\"{total:.1f}\", # adjust scaling if needed\n", + " # Region label below the bracket\n", + " ax.annotate(\n", + " g,\n", + " xy=(center, y_bracket),\n", + " xycoords=xaxis_transform,\n", + " xytext=(0, y_label_pts),\n", + " textcoords=\"offset points\",\n", " ha=\"center\",\n", - " va=\"bottom\",\n", + " va=\"top\",\n", " fontsize=10,\n", + " annotation_clip=False,\n", " )\n", "\n", - "# ---- Flip legend order ----\n", + "# --- Legend ---\n", "handles, labels = ax.get_legend_handles_labels()\n", "ax.legend(\n", " handles[::-1],\n", @@ -231,11 +292,10 @@ " loc=\"upper left\",\n", ")\n", "\n", - "plt.xticks(rotation=0)\n", "plt.tight_layout()\n", - "plt.savefig(snakemake.output.lcox_comparison, dpi=300)\n", - "plt.savefig(snakemake.output.lcox_comparison_png, dpi=300)\n", - "plt.show()" + "plt.savefig(snakemake.output.lcox_comparison, dpi=300, bbox_inches=\"tight\")\n", + "plt.savefig(snakemake.output.lcox_comparison_png, dpi=300, bbox_inches=\"tight\")\n", + "plt.show()\n" ] }, { From dcc7712245b8ac2cddb2ac6e203af648c3f0fd23 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 9 Jun 2026 19:19:28 +0200 Subject: [PATCH 143/216] feat: improve stratyfied clustering --- config/config.yaml | 26 +++--- rules/preparation.smk | 28 ++++--- workflow/scripts/cluster_renewables.py | 106 +++++++++---------------- 3 files changed, 66 insertions(+), 94 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index ebd8936..e7fc446 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -1,6 +1,7 @@ enable: - run_supply_chain: False # Enable for first run - run_supply_curve: False # Enable for first run + run_supply_chain: True # Enable for first run + run_supply_curve: True # Enable for first run + cluster_renewables: False # Enable for first run # Output toggles for Step 0/1 (greenfield supply curve generation) @@ -169,19 +170,19 @@ regions: "Oceania": ["AUS","NZL"] clustering: - n_strata: 10 # number of strata ("quality classes") to divide the renewable potential into based on capacity factor (CF) distribution. - tail_percentile: 10.0 - tail_cluster_boost: 2.0 # multiplier for number of clusters in the tail (highest-quality stratum) to avoid overgeneralization of the best resources - diversity_threshold: 0.05 # min avg_cf std within stratum to split - min_capacity_for_split_mw: 1e5 # strata below this get 1 cluster + strata_bin_width: + onwind: 0.05 + solar: 0.02 + top_n_strata_boost: 2 + tail_cluster_boost: 2.0 + diversity_threshold: 0.02 + min_capacity_for_split_mw: 1e5 cluster_count_policy: base_clusters: - solar: 20 + solar: 20 onwind: 20 - reference_buses: 2000 # Reference number of buses for scaling cluster count 2000 buses ~ 20 clusters - reference_capacity_mw: 2e7 # Reference total capacity for scaling cluster count, 20 GW ~ 20 clusters - min_clusters: 10 # = floor is just n_strata with no splitting - max_clusters: 40 # tighter cap + min_clusters: 6 + max_clusters: 50 design: cost_penalty: @@ -266,6 +267,7 @@ solver_options: AggFill: 0 PreDual: 0 GURO_PAR_BARDENSETHRESH: 200 + NumericFocus: 2 colors: diff --git a/rules/preparation.smk b/rules/preparation.smk index 9f43033..cfba22b 100644 --- a/rules/preparation.smk +++ b/rules/preparation.smk @@ -108,16 +108,18 @@ rule prepare_steel_demand: str(NOTEBOOKS_DIR / "prepare-steel-demand.ipynb") -rule cluster_renewables: - input: - merged_cdf="data/renewable_profiles_global_merged.nc", - merged_geojson="data/renewable_profiles_global_merged.geojson", - output: - clustered="resources/renewables_clustered.nc", - report="resources/renewables_clustering_report.json", - threads: 4 - resources: - mem_mb=16000, - time_min=60, - script: - str(SCRIPT_DIR / "cluster_renewables.py") +if config["enable"].get("cluster_renewables", True): + + rule cluster_renewables: + input: + merged_cdf="data/renewable_profiles_global_merged.nc", + merged_geojson="data/renewable_profiles_global_merged.geojson", + output: + clustered="resources/renewables_clustered.nc", + report="resources/renewables_clustering_report.json", + threads: 4 + resources: + mem_mb=16000, + time_min=60, + script: + str(SCRIPT_DIR / "cluster_renewables.py") diff --git a/workflow/scripts/cluster_renewables.py b/workflow/scripts/cluster_renewables.py index 0f3889c..ad581c7 100644 --- a/workflow/scripts/cluster_renewables.py +++ b/workflow/scripts/cluster_renewables.py @@ -292,12 +292,18 @@ def _allocate_stratum_clusters( total_k: int, profiles_by_stratum: Dict[str, np.ndarray], tail_boost: float = 2.0, - diversity_threshold: float = 0.05, # min profile std to warrant splitting - min_capacity_for_split_mw: float = 500.0, # min capacity to warrant >1 cluster + diversity_threshold: float = 0.05, + min_capacity_for_split_mw: float = 500.0, + top_n_strata_boost: int = 3, # boost the top N strata by avg_cf ) -> Dict[str, int]: - """Allocate cluster budget, giving 1 to strata that don't need splitting.""" + """Allocate clusters with boost for highest-CF strata (green pockets).""" + + # Rank strata by their avg_cf midpoint (encoded in name) + strata_sorted = sorted( + stratum_groups.keys(), reverse=True + ) # lexicographic works for cf_XX_YY + top_strata = set(strata_sorted[:top_n_strata_boost]) - # First pass: decide which strata deserve >1 cluster splittable = {} fixed_at_one = {} @@ -306,12 +312,10 @@ def _allocate_stratum_clusters( total_cap = group["capacity_mw"].sum() n_buses = len(group) - # Check if stratum has enough diversity to warrant sub-clustering if n_buses <= 2: fixed_at_one[name] = 1 continue - # Profile diversity: std of per-bus mean CFs within stratum bus_means = profiles.mean(axis=1) internal_diversity = float(np.std(bus_means)) @@ -324,32 +328,22 @@ def _allocate_stratum_clusters( splittable[name] = total_cap - # Remaining budget after reserving 1 per fixed stratum - remaining_k = ( - total_k - len(fixed_at_one) - len(splittable) - ) # 1 each for splittable too + remaining_k = total_k - len(fixed_at_one) - len(splittable) remaining_k = max(0, remaining_k) - # Distribute remaining budget proportional to capacity (with tail boost) - alloc = dict(fixed_at_one) # start with fixed ones + alloc = dict(fixed_at_one) if splittable and remaining_k > 0: weights = {} for name, cap in splittable.items(): w = cap - if ( - name == "tail_high" - ): # boost tail_high to preserve green pockets and peak quality + if name in top_strata: w *= tail_boost - elif ( - name == "tail_low" - ): # de-prioritize tail_low since these are last to be picked in merit order - w *= 1 / tail_boost weights[name] = w total_w = sum(weights.values()) for name, w in weights.items(): extra = int(round(w / total_w * remaining_k)) - alloc[name] = 1 + max(0, extra) # at least 1 + alloc[name] = 1 + max(0, extra) else: for name in splittable: alloc[name] = 1 @@ -359,51 +353,25 @@ def _allocate_stratum_clusters( def _build_strata( df_rt: pd.DataFrame, - n_strata: int = 7, - tail_pct: float = 10.0, + bin_width: float = 0.05, ) -> pd.Series: - """Assign each row to a stratum based on avg_cf. + """Assign strata by fixed avg_cf intervals of `bin_width`. - Uses capacity-weighted quantile boundaries with explicit tail separation. - Returns a Series of stratum labels aligned to df_rt.index. + E.g. bin_width=0.05 gives bins [0.00, 0.05), [0.05, 0.10), ..., [0.95, 1.00]. + Empty bins are implicitly ignored since no rows map to them. """ cf_vals = df_rt["avg_cf"].values - caps = df_rt["capacity_mw"].values - - p_low = np.percentile(cf_vals, tail_pct) - p_high = np.percentile(cf_vals, 100 - tail_pct) - - labels = pd.Series("core", index=df_rt.index) - labels[cf_vals <= p_low] = "tail_low" - labels[cf_vals >= p_high] = "tail_high" - - # Subdivide core into (n_strata - 2) bins by capacity-weighted quantiles - core_mask = labels == "core" - if core_mask.sum() > (n_strata - 2): - core_cf = cf_vals[core_mask] - core_caps = caps[core_mask] - - # Capacity-weighted quantile boundaries - sort_idx = np.argsort(core_cf) - cum_cap = np.cumsum(core_caps[sort_idx]) - total_cap = cum_cap[-1] - n_core_bins = max(1, n_strata - 2) - boundaries = [] - for i in range(1, n_core_bins): - target = total_cap * i / n_core_bins - idx = np.searchsorted(cum_cap, target) - idx = min(idx, len(core_cf) - 1) - boundaries.append(core_cf[sort_idx[idx]]) - - # Assign core sub-bins - bins = [-np.inf] + sorted(set(boundaries)) + [np.inf] - core_labels = pd.cut( - cf_vals[core_mask], - bins=bins, - labels=[f"core_{i}" for i in range(len(bins) - 1)], - duplicates="drop", - ) - labels[core_mask] = core_labels.astype(str) + + # Floor to nearest bin edge + bin_idx = np.floor(cf_vals / bin_width).astype(int) + + labels = pd.Series( + [ + f"cf_{int(b * bin_width * 100):02d}_{int((b + 1) * bin_width * 100):02d}" + for b in bin_idx + ], + index=df_rt.index, + ) return labels @@ -475,19 +443,18 @@ def cluster_region_technology( return region, tech, pd.DataFrame() total_k = resolve_total_clusters(df_rt, tech) - n_strata = int(clustering_config.get("n_strata", 7)) - tail_pct = float(clustering_config.get("tail_percentile", 10.0)) tail_boost = float(clustering_config.get("tail_cluster_boost", 2.0)) geo_weight = float(clustering_config.get("geo_weight", 0.3)) n_pca = int(clustering_config.get("n_pca_components", 5)) - logger.info( - f" {region} {tech}: {len(df_rt)} buses -> target {total_k} clusters, " - f"{n_strata} strata" - ) - # --- Stratify --- - df_rt["stratum"] = _build_strata(df_rt, n_strata=n_strata, tail_pct=tail_pct) + bin_widths = clustering_config.get("strata_bin_width", {}) + bin_width = bin_widths.get(tech, 0.05) + top_n_strata_boost = int(clustering_config.get("top_n_strata_boost", 3)) + df_rt["stratum"] = _build_strata(df_rt, bin_width=bin_width) + + n_actual_strata = df_rt["stratum"].nunique() + logger.info(f" {n_actual_strata} non-empty strata (bin width: {bin_width})") # Load profiles per stratum for diversity check stratum_groups = {} @@ -508,6 +475,7 @@ def cluster_region_technology( tail_boost=tail_boost, diversity_threshold=diversity_thresh, min_capacity_for_split_mw=min_cap_split, + top_n_strata_boost=top_n_strata_boost, ) actual_total = sum(stratum_k.values()) From 380a60e88c60b7dd2a5f10fd9ff498fce8a53aa0 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Tue, 9 Jun 2026 19:46:02 +0200 Subject: [PATCH 144/216] feat: allocate share of renewables to supply chain based on configurable factor --- config/config.yaml | 11 +- rules/supply_curves.smk | 17 ++- workflow/scripts/calculate_lcox.py | 32 ------ workflow/scripts/create_supply_curve.py | 27 +++-- workflow/scripts/prepare_regional_network.py | 103 ++++++++++++++++--- 5 files changed, 124 insertions(+), 66 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index e7fc446..dad867a 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -10,13 +10,18 @@ outputs: keep_optimization_networks: True # Keep .nc network files from optimization (set False to save disk space) # Supply curve scenario configuration -# Two scenarios are supported: -# - reserved (DEFAULT): highest-performing (best CF) renewable generators reserved for domestic +# Three scenarios are supported: +# - allocated_share (DEFAULT): each renewable generator available at X of nominal capacity, +# where X is the allocation_factor (0 < X < 1). Simulates grid-constrained or partially +# dedicated renewable infrastructure. +# - reserved: highest-performing (best CF) renewable generators reserved for domestic # electricity demand; export/HBI supply stack reduced before optimization. Requires electricity demand data. -# - unreserved (OPTIONAL, FALLBACK): no domestic reservation; full renewable stack available. +# - unreserved (FALLBACK): no domestic reservation; full renewable stack available. # Can be generated without electricity demand data when demand data unavailable. supply_curve: generate_unreserved: True # Set to True to generate unreserved scenario as fallback + default_scenario: allocated_share # Default scenario for supply curve generation + allocation_factor: 0.3 # Renewable capacity allocation factor for allocated_share scenario (0 < X <= 1) # Config-native trade chain definitions diff --git a/rules/supply_curves.smk b/rules/supply_curves.smk index 85e1d2a..194711e 100644 --- a/rules/supply_curves.smk +++ b/rules/supply_curves.smk @@ -95,7 +95,7 @@ rule prepare_regional_network: log: "logs/prepare_regional_network_{cost_year}_{region}_{wacc}_{product}_{scenario}.log", wildcard_constraints: - scenario="reserved|unreserved", + scenario="reserved|unreserved|allocated_share", product="hbi|steel", threads: 1 resources: @@ -135,7 +135,7 @@ if config["enable"].get("run_supply_chain", True): "logs/calculate_regional_lcox_{cost_year}_{region}_{wacc}_{product}_{scenario}_{product_demand_mt}.log", wildcard_constraints: product_demand_mt=r"\d+(?:\.\d+)?", - scenario="reserved|unreserved", + scenario="reserved|unreserved|allocated_share", product="hbi|steel", threads: 2 resources: @@ -157,7 +157,10 @@ if config["enable"].get("run_supply_curve", True): rule create_supply_curve: input: lco_reserved=lambda wildcards: expand( - f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/wacc~{wildcards.wacc}/{wildcards.region}_reserved/results_{{product_demand_mt}}.csv", + f"resources/lco-{wildcards.product}/cost_year~{wildcards.cost_year}/wacc~{wildcards.wacc}/{wildcards.region}_{{scenario}}/results_{{product_demand_mt}}.csv", + scenario=config.get("supply_curve", {}).get( + "default_scenario", "allocated_share" + ), product_demand_mt=config.get("steel_demand_levels"), ), lco_unreserved=lambda wildcards: ( @@ -176,7 +179,7 @@ if config["enable"].get("run_supply_curve", True): # is only used to locate the correct upstream LCoX runs. supply="resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}.csv", supply_unreserved=( - "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}__unreserved.csv" + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{product}_unreserved.csv" if config.get("supply_curve", {}).get("generate_unreserved", False) and _product_uses_renewables("{product}") else temp( @@ -191,7 +194,11 @@ if config["enable"].get("run_supply_curve", True): wacc="uniform|regional", threads: 1 message: - "Combining LCo results (reserved + unreserved scenarios) to create supply curve for {wildcards.region} {wildcards.product}." + "Combining LCo results (default={} + optional unreserved) to create supply curve for {{wildcards.region}} {{wildcards.product}}.".format( + config.get("supply_curve", {}).get( + "default_scenario", "allocated_share" + ) + ) script: str(SCRIPT_DIR / "create_supply_curve.py") diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index d04e661..6c7df49 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -32,16 +32,6 @@ # ============================================================================ logger = setup_logging(__name__, snakemake=snakemake, log_filename="calculate_lcox.log") -# ============================================================================ -# STAGE SLICING (legacy helpers removed) -# ============================================================================ - -# The stage-slicing helper and incremental-selection utilities were used in an -# older workflow. They are no longer invoked by the main driver but kept in -# history; they have been removed to simplify the codebase. If you need them -# for advanced per-stage analyses, reintroduce a tested implementation. - - # ============================================================================ # DEMAND LOADING # ============================================================================ @@ -77,23 +67,6 @@ def load_demands_for_region(region, config): } -# ============================================================================ -# RENEWABLE CONSTRAINT -# ============================================================================ - - -# ============================================================================ -# INCREMENTAL RENEWABLE SELECTION (upstream filtering, Phase 3) -# ============================================================================ - - -# NOTE: incremental generator selection was part of an older workflow where -# incremental_sets were precomputed per demand level. The current driver skips -# per-demand incremental filtering and therefore this function has been removed -# to reduce maintenance burden. Reintroduce with tests if needed for custom -# workflows. - - # ============================================================================ # LOAD ADDITION # ============================================================================ @@ -618,11 +591,6 @@ def extract_lcox(network, product, demands): scaled_demands = demands.copy() scaled_demands["product_demand_mt"] = product_demand_mt - # Load incremental generator sets and apply filtering - logger.info("Loading incremental generator sets...") - # Skip incremental filtering: use all generators for all demand levels - logger.info("Using all generators (no incremental filtering applied)") - # Add hourly load for steel output # (This also sets HBI storage e_initial inside add_loads_to_network) logger.info("Adding hourly load to network...") diff --git a/workflow/scripts/create_supply_curve.py b/workflow/scripts/create_supply_curve.py index 3ede4a4..ce0287e 100644 --- a/workflow/scripts/create_supply_curve.py +++ b/workflow/scripts/create_supply_curve.py @@ -97,13 +97,14 @@ def get_stage_metadata(product, stage_ratios): def create_supply_curve(): """ - Create supply curve from reserved and unreserved scenario LCoX results. + Create supply curve from default scenario LCoX results. - Loads results from two distinct optimization scenarios: - - reserved: highest-CF sites reserved for domestic demand - - unreserved: full renewable stack available (optional/fallback) + Loads results from configurable default scenario (e.g., allocated_share, reserved, unreserved) + and optional unreserved scenario for comparison: + - default (primary): configured via supply_curve.default_scenario + - unreserved (optional): full renewable stack available (fallback) - Combines results, validates they differ, and produces CSV/PDF outputs. + Combines results and produces CSV/PDF outputs. """ stage_ratios = get_stage_ratios_from_skeleton() stage_meta = get_stage_metadata(product, stage_ratios) @@ -111,12 +112,18 @@ def create_supply_curve(): # route_label is derived from config to locate upstream LCoX files for this product route_label = route_label_for_product(snakemake.config, product) or product - reserved_files = snakemake.input.lco_reserved - logger.info(f"reserved scenario files: {reserved_files}") + # Load primary scenario (now uses default_scenario from config) + default_scenario = snakemake.config.get("supply_curve", {}).get( + "default_scenario", "allocated_share" + ) + reserved_files = ( + snakemake.input.lco_reserved + ) # This now holds default_scenario files + logger.info(f"default scenario ({default_scenario}) files: {reserved_files}") df_reserved = pd.concat( (pd.read_csv(f, sep=",") for f in reserved_files), ignore_index=True ) - logger.info("reserved scenario data loaded") + logger.info(f"default scenario ({default_scenario}) data loaded") unreserved_files = snakemake.input.lco_unreserved if unreserved_files and len(unreserved_files) > 0: @@ -128,7 +135,9 @@ def create_supply_curve(): df_merged = df_reserved.copy() df_sub = df_unreserved.copy() else: - logger.info("unreserved scenario not provided; using reserved for both outputs") + logger.info( + f"unreserved scenario not provided; using {default_scenario} for both outputs" + ) df_merged = df_reserved.copy() df_sub = df_reserved.copy() diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 19f0533..38c2764 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -244,6 +244,60 @@ def reserve_top_sites_by_highest_cf( return reserved +def apply_capacity_allocation( + technologies_dict: Dict[str, np.ndarray], + allocation_factor: float, +) -> Dict[str, np.ndarray]: + """ + Scale all renewable generator capacities by allocation factor. + + Used for allocated_share scenario where each generator can only provide + allocation_factor (0 < X <= 1) of its nominal capacity. This simulates + grid constraints or partial dedication of renewable infrastructure. + + Parameters + ---------- + technologies_dict : dict + {tech_name: capacity_array} where capacity_array is (n_classes,) + allocation_factor : float + Allocation factor (0 < X <= 1). Each generator's p_nom_max will be + multiplied by this factor. + + Returns + ------- + allocated_capacities : dict + Same structure as technologies_dict with scaled capacities. + Non-NaN entries are multiplied by allocation_factor. + """ + if not (0 < allocation_factor <= 1): + logger.warning( + f"allocation_factor={allocation_factor} outside valid range (0, 1]; " + f"clamping to 1.0" + ) + allocation_factor = min(max(allocation_factor, 0.0001), 1.0) + + allocated = {} + total_original_mw = 0 + total_allocated_mw = 0 + + for tech, capacities in technologies_dict.items(): + allocated_array = np.full_like(capacities, np.nan, dtype=np.float32) + for site_idx, cap in enumerate(capacities): + if not np.isnan(cap) and cap > 0: + allocated_array[site_idx] = cap * allocation_factor + total_original_mw += cap + total_allocated_mw += allocated_array[site_idx] + else: + allocated_array[site_idx] = cap # Keep NaN as-is + allocated[tech] = allocated_array + + logger.info( + f"Applied capacity allocation factor {allocation_factor}: " + f"{total_original_mw:.0f} MW → {total_allocated_mw:.0f} MW" + ) + return allocated + + def add_renewable_generators( network: pypsa.Network, region: str, @@ -812,30 +866,45 @@ def prepare_network( techs_dict, cf_ts, metadata = load_regional_clustered_renewables( clustered_renewables_path, region ) - # Apply local demand reservation if configured (only for products with renewable_electricity) - # For scenario="reserved", reserve high-CF sites; for "unreserved", skip reservation + + # Apply capacity allocation or local demand reservation based on scenario + # (only for products with renewable_electricity) reserved_techs = None if product_uses_renewables: - reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) - if reserve_capacity_mw <= 0 and scenario == "reserved": - reserve_capacity_mw = load_local_electricity_demand_mw( - local_demand_path, region - ) - logger.info( - f"Derived reservation target from local demand: {reserve_capacity_mw:.1f} MW" - ) logger.info(f"Scenario: {scenario} (scenario flag passed from Snakemake rule)") - if reserve_capacity_mw > 0 or scenario == "reserved": - logger.info( - f"Applying local demand reservation for scenario={scenario}: " - f"target {reserve_capacity_mw} MW" + + if scenario == "allocated_share": + # Apply capacity allocation: scale p_nom_max by allocation_factor + allocation_factor = config.get("supply_curve", {}).get( + "allocation_factor", 0.5 ) - reserved_techs = reserve_top_sites_by_highest_cf( - techs_dict, cf_ts, reserve_capacity_mw, scenario=scenario + logger.info( + f"Applying capacity allocation for scenario=allocated_share: " + f"allocation_factor={allocation_factor}" ) + if techs_dict: + techs_dict = apply_capacity_allocation(techs_dict, allocation_factor) + else: + # For reserved/unreserved scenarios: apply local demand reservation + reserve_capacity_mw = config.get("reserve_local_demand_mw", 0) + if reserve_capacity_mw <= 0 and scenario == "reserved": + reserve_capacity_mw = load_local_electricity_demand_mw( + local_demand_path, region + ) + logger.info( + f"Derived reservation target from local demand: {reserve_capacity_mw:.1f} MW" + ) + if reserve_capacity_mw > 0 or scenario == "reserved": + logger.info( + f"Applying local demand reservation for scenario={scenario}: " + f"target {reserve_capacity_mw} MW" + ) + reserved_techs = reserve_top_sites_by_highest_cf( + techs_dict, cf_ts, reserve_capacity_mw, scenario=scenario + ) else: logger.info( - f"Skipping reservation: product '{route_label}' does not use renewables" + f"Skipping reservation/allocation: product '{route_label}' does not use renewables" ) # Add renewable generators (only if techs_dict is not empty) From 42b2f75fcb67aad581fbf632a3efd6bf6643d2f3 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 09:18:53 +0200 Subject: [PATCH 145/216] feat: streamline global supply curve rule --- .../analysis-globalsupplycurve.ipynb | 128 ++++++------------ 1 file changed, 44 insertions(+), 84 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 4d10c6b..3b9b267 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -16,43 +16,45 @@ { "cell_type": "code", "execution_count": null, - "id": "b782c0cf", + "id": "f766eb9c", "metadata": {}, "outputs": [], "source": [ - "\"snakemake\" not in globals()" + "from _helpers_notebooks import mock_snakemake\n", + "snakemake = mock_snakemake(\n", + " \"plot_global_supply\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + " sort=True,\n", + " demand=\"__unreserved\",\n", + " wacc=\"regional\",\n", + " cost_year=\"2050\",\n", + " interone=\"hbi\",\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "f766eb9c", + "id": "2f3f5021", "metadata": {}, "outputs": [], "source": [ - "# if \"snakemake\" not in globals():\n", - "# from _helpers_notebooks import mock_snakemake\n", - "# snakemake = mock_snakemake(\n", - "# \"plot_global_supply\",\n", - "# scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - "# sort=False,\n", - "# demand=False,\n", - "# wacc=\"regional\",\n", - "# cost_year=\"2050\",\n", - "# interone=\"hbi\",\n", - "# )" + "scenario = snakemake.wildcards.scenario\n", + "sort = snakemake.wildcards.sort\n", + "demand = snakemake.wildcards.demand" ] }, { "cell_type": "code", "execution_count": null, - "id": "2f3f5021", + "id": "08b15d2c", "metadata": {}, "outputs": [], "source": [ - "scenario = snakemake.wildcards.scenario\n", - "sort = snakemake.wildcards.sort\n", - "demand = snakemake.wildcards.demand" + "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", + "xlim = 16000\n", + "ylim = 1500\n", + "add_iron_ore_cost = False" ] }, { @@ -63,17 +65,6 @@ "### Steel supply curve all regions (from model)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "08b15d2c", - "metadata": {}, - "outputs": [], - "source": [ - "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", - "xlim = None" - ] - }, { "cell_type": "code", "execution_count": null, @@ -179,7 +170,7 @@ "outputs": [], "source": [ "# Sort by cost for supply curve\n", - "interone_sorted = interone.sort_values(\"cost in €/t_steel incl. iron ore\").reset_index(\n", + "interone_sorted = interone.sort_values(\"cost in €/t_steel\"+(\" incl. iron ore\" if add_iron_ore_cost else \"\")).reset_index(\n", " drop=True\n", ")\n", "\n", @@ -188,7 +179,7 @@ "region_colors = snakemake.config[\"colors\"]\n", "\n", "cum_quantity_line = interone_sorted[\"quantity in Mt_steel\"].cumsum()\n", - "cum_cost_line = interone_sorted[\"cost in €/t_steel incl. iron ore\"]\n", + "cum_cost_line = interone_sorted[\"cost in €/t_steel\"+(\" incl. iron ore\" if add_iron_ore_cost else \"\")] \n", "\n", "# Prepare for stacked area plot: for each link, plot a bar at its cost, colored by region\n", "cum_quantity = 0\n", @@ -199,7 +190,7 @@ "for _, row in interone_sorted.iterrows():\n", " bar_lefts.append(cum_quantity)\n", " bar_widths.append(row[\"quantity in Mt_steel\"])\n", - " bar_costs.append(row[\"cost in €/t_steel incl. iron ore\"])\n", + " bar_costs.append(row[\"cost in €/t_steel\"+(\" incl. iron ore\" if add_iron_ore_cost else \"\")])\n", " bar_colors.append(region_colors[row[\"region\"]])\n", " cum_quantity += row[\"quantity in Mt_steel\"]" ] @@ -261,9 +252,9 @@ " )\n", "\n", " ax.set_xlabel(\"Cumulative quantity (Mt)\")\n", - " ax.set_ylabel(\"Supply cost (€/t_steel)\")\n", + " ax.set_ylabel(\"Cost of HBI in €/t$_{hbi}$\")\n", " ax.set_title(f\"Combined {process} supply curve\")\n", - " ax.set_ylim(0, 1000)\n", + " ax.set_ylim(0, 1200)\n", " ax.set_xlim(0, sum(bar_widths))\n", " ax.set_xlim(0, xlim)\n", " ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", @@ -319,27 +310,19 @@ "metadata": {}, "outputs": [], "source": [ - "process = \"hbi\" # eaf, hbi, eaf-grid, steel\n", - "include_ironorecost = True\n", - "ironorecost = 97 * 1.59\n", - "year = 2050\n", - "demand = demand # Subtract local demand from supply curve\n", - "sort_over_all = (\n", - " False # \"True\" to sort over all regions, \"False\" to sort within each region\n", - ")\n", - "sort = sort # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)\n", - "xlim = None # \"None\" or int\n", - "regions = snakemake.config[\"regions\"].keys()" + "sort_over_all = True # \"True\" to sort over all regions, \"False\" to sort within each region\n", + "sort = False # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" ] }, { "cell_type": "code", "execution_count": null, - "id": "22ff3926", + "id": "16d619cb", "metadata": {}, "outputs": [], "source": [ - "print(demand)" + "ironorecost = 97 * 1.59\n", + "regions = snakemake.config[\"regions\"].keys()" ] }, { @@ -351,16 +334,16 @@ "source": [ "def sort_by_average(df_all):\n", " # Drop rows where either cost or demand is NaN before calculating weighted average\n", - " df_clean = df_all.dropna(subset=[\"LCOX [EUR/t]\", \"demand [t]\"])\n", + " df_clean = df_all.dropna(subset=[ \"lcox [EUR/t]\", \"demand [t]\"])\n", " # Get weighted average cost per region\n", - " df_clean[\"weighted_cost\"] = df_clean[\"LCOX [EUR/t]\"] * df_clean[\"demand [t]\"]\n", + " df_clean[\"weighted_cost\"] = df_clean[\"lcox [EUR/t]\"] * df_clean[\"demand [t]\"]\n", " average_cost_per_region = (\n", " df_clean.groupby(\"region\")[\"weighted_cost\"].sum()\n", - " / df_clean.groupby(\"region\")[\"demand [t]\"].sum()\n", + " / df_clean.groupby(\"region\")[\"Demand [t]\"].sum()\n", " )\n", - " # Sort df_all by average_cost_per_region, then by LCOX within each region\n", + " # Sort df_all by average_cost_per_region, then by cox within each region\n", " df_all[\"avg_region_cost\"] = df_all[\"region\"].map(average_cost_per_region)\n", - " df_all = df_all.sort_values([\"avg_region_cost\", \"LCOX [EUR/t]\"]).drop(\n", + " df_all = df_all.sort_values([\"avg_region_cost\", \"lcox [EUR/t]\"]).drop(\n", " columns=\"avg_region_cost\"\n", " )\n", " return df_all" @@ -376,16 +359,13 @@ "df_all = pd.DataFrame()\n", "\n", "# Select input files directly from snakemake (avoids hardcoded paths)\n", - "if demand:\n", - " input_files = snakemake.input.supply_curves_interone\n", - "else:\n", - " input_files = snakemake.input.supply_curves_interone_nodemand\n", + "input_files = snakemake.input.supply_curves_interone\n", "\n", "for fn in input_files:\n", " # Extract region from filename pattern: {region}_{process}.csv\n", - " region = fn.split(\"/\")[-1].replace(f\"_{process}.csv\", \"\")\n", + " region = fn.split(\"/\")[-1].split(\"_marginal_cost\")[0]\n", "\n", - " df = pd.read_csv(fn, index_col=0)\n", + " df = pd.read_csv(fn)\n", " df[\"region\"] = region\n", "\n", " diff = df[\"demand [t]\"].diff()\n", @@ -395,29 +375,19 @@ " df_all = pd.concat([df_all, df], ignore_index=True)\n", "\n", "if sort_over_all:\n", - " df_all = df_all.sort_values(\"LCOX [EUR/t]\").reset_index(drop=True)\n", + " df_all = df_all.sort_values(\"lcox [EUR/t]\").reset_index(drop=True)\n", "\n", "if sort == False:\n", " pass\n", "elif sort == \"cost_global\":\n", - " df_all = df_all.sort_values(\"LCOX [EUR/t]\").reset_index(drop=True)\n", + " df_all = df_all.sort_values(\"lcox [EUR/t]\").reset_index(drop=True)\n", "elif sort == \"cost_average\":\n", " df_all = sort_by_average(df_all)\n", "else:\n", " ValueError(f\"value for sort is {sort} and not valid\")\n", "\n", - "if include_ironorecost:\n", - " df_all[\"LCOX [EUR/t]\"] += ironorecost" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8dcd9d98", - "metadata": {}, - "outputs": [], - "source": [ - "# df_all[df_all[\"region\"]==\"Europe\"]" + "if add_iron_ore_cost:\n", + " df_all[\"lcox [EUR/t]\"] += ironorecost" ] }, { @@ -428,7 +398,7 @@ "outputs": [], "source": [ "cum_quantity_line = df_all[\"demand [t]\"].cumsum() / 1e6 # Mt steel\n", - "cum_cost_line = df_all[\"LCOX [EUR/t]\"]\n", + "cum_cost_line = df_all[\"lcox [EUR/t]\"]\n", "\n", "# Prepare for stacked area plot based on df_all\n", "cum_quantity = 0\n", @@ -439,7 +409,7 @@ "for _, row in df_all.iterrows():\n", " bar_lefts.append(cum_quantity)\n", " bar_widths.append(row[\"demand [t]\"] / 1e6) # Mt steel\n", - " bar_costs.append(row[\"LCOX [EUR/t]\"])\n", + " bar_costs.append(row[\"lcox [EUR/t]\"])\n", " bar_colors.append(region_colors[row[\"region\"]])\n", " cum_quantity += row[\"demand [t]\"] / 1e6 # Mt steel" ] @@ -461,16 +431,6 @@ " xlim=xlim,\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d35aef6e", - "metadata": {}, - "outputs": [], - "source": [ - "demand" - ] } ], "metadata": { From 3e15a2cf3fac48e48bc6dd73a7b281a42fba0030 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 09:19:45 +0200 Subject: [PATCH 146/216] chore: adjust global supply curve and lcox plotting, update notebook paths --- rules/reporting.smk | 39 ++++++++++++++++++--------------------- 1 file changed, 18 insertions(+), 21 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index f26182b..2a057e9 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -45,7 +45,7 @@ rule plot_mga: mem_mb=4000, threads: 2 notebook: - "../workflow/notebooks/plot-mga.ipynb" + str(NOTEBOOKS_DIR / "plot-mga.ipynb") rule plot_mga_all: input: @@ -70,21 +70,18 @@ rule plot_trade_today: rule plot_global_supply: input: - trade_network="../results/cost_year~{cost_year}/interone~hbi/intertwo~eaf-grid/final~steel/wacc~{wacc}/scenario~{scenario}/network.nc", + trade_network="results/chain_id~newre_2050/cost_year~{cost_year}/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~{scenario}/network.nc", # supply = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", # supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", supply_curves_interone = expand( - "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", - allow_missing=True, region=config["regions"]), - supply_curves_interone_nodemand = expand( - "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{interone}.csv", + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{interone}{demand}.csv", allow_missing=True, region=config["regions"]), output: network_curve="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", network_curve_png="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", # supply_curves notebook: - "notebooks/analysis-globalsupplycurve.ipynb" + str(NOTEBOOKS_DIR / "analysis-globalsupplycurve.ipynb") rule plot_global_supply_all: input: @@ -96,24 +93,24 @@ rule plot_comparison: stability = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-stability-weighted/network_0.02.nc", hightrans = "results/chain_id~hightrans_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~default/network.nc", output: - cost_comparison="../results/figures_general/comparison/cost_comparison.pdf", - cost_comparison_png="../results/figures_general/comparison/cost_comparison.png", + cost_comparison="results/figures_general/comparison/cost_comparison.pdf", + cost_comparison_png="results/figures_general/comparison/cost_comparison.png", notebook: - "notebooks/compare-scenarios.ipynb" + str(NOTEBOOKS_DIR / "compare-scenarios.ipynb") rule plot_compare_lcox: input: - eu_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_0.1.nc", - eu_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_1.nc", - eu_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_10.nc", - eu_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_100.nc", - sa_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_0.1.nc", - sa_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_1.nc", - sa_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_10.nc", - sa_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_100.nc", + south_america_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_0.1.nc", + south_america_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_1.nc", + south_america_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_10.nc", + south_america_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_100.nc", + europe_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_0.1.nc", + europe_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_1.nc", + europe_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_10.nc", + europe_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_100.nc", output: - lcox_comparison="../results/figures_general/comparison/lcox_comparison.pdf", - lcox_comparison_png="../results/figures_general/comparison/lcox_comparison.png", + lcox_comparison="results/figures_general/comparison/lcox_comparison.pdf", + lcox_comparison_png="results/figures_general/comparison/lcox_comparison.png", notebook: - "notebooks/plot-compare-lcox.ipynb" \ No newline at end of file + str(NOTEBOOKS_DIR / "plot-compare-lcox.ipynb") \ No newline at end of file From a6e02d2d91d37510fa1ebc808799f3a06ce7312c Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 09:20:02 +0200 Subject: [PATCH 147/216] feat: add new colors for plots --- config/config.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/config/config.yaml b/config/config.yaml index f287fac..f32d883 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -283,19 +283,22 @@ colors: electric arc furnace: 'orange' direct reduction furnace: 'grey' hydrogen direct iron reduction furnace: 'grey' - electrolysis: 'magenta' + battery inverter: '#6A000E' battery inverter (charging): '#6A000E' battery inverter (discharging): '#FF5733' battery: 'purple' wind: 'blue' + onshore wind: 'blue' solar: 'yellow' pv: 'yellow' electricity: 'red' iron ore: 'brown' steel: 'grey' hot briquetted iron: 'darkred' + electrolysis: 'magenta' hydrogen: 'magenta' + hydrogen storage: '#AF7AC5' Battery: purple Electricity: red HBI: darkred From 7cf755872e29f3dd05a7950d290966f959dfe88a Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 09:20:39 +0200 Subject: [PATCH 148/216] feat: figure updates on lcox --- workflow/notebooks/plot-compare-lcox.ipynb | 67 +++++++++++++++++----- 1 file changed, 52 insertions(+), 15 deletions(-) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 0dd415c..91945cb 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -100,7 +100,7 @@ "outputs": [], "source": [ "hbi_demand = nc.statistics.withdrawal().to_frame().loc[\"Load\", :, \"hbi_demand\"]\n", - "hbi_demand" + "# hbi_demand" ] }, { @@ -111,7 +111,7 @@ "outputs": [], "source": [ "df = nc.statistics.system_cost(groupby=\"carrier\").to_frame(\"systemcost\")\n", - "df" + "# df" ] }, { @@ -142,7 +142,7 @@ "outputs": [], "source": [ "df[\"systemcost+fom_per_hbi\"] = df[\"systemcost+fom\"].div(hbi_demand.squeeze().rename_axis(\"network\"), level=\"network\")\n", - "df" + "# df" ] }, { @@ -188,6 +188,7 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "# --- Parse scenario names into region and quantity ---\n", "qty_map = {\"01\": 0.1, \"1\": 1.0, \"10\": 10.0, \"100\": 100.0}\n", "region_map = {\"europe\": \"Europe\", \"south_america\": \"South America\"}\n", @@ -200,13 +201,16 @@ "\n", "plot_df[\"region\"] = [parse_scenario(s)[0] for s in plot_df.index]\n", "plot_df[\"quantity\"] = [parse_scenario(s)[1] for s in plot_df.index]\n", - "plot_df = plot_df.sort_values([\"region\", \"quantity\"])\n", + "\n", + "# South America on the left, Europe on the right\n", + "groups_ordered = [\"South America\", \"Europe\"]\n", + "plot_df[\"region_order\"] = plot_df[\"region\"].map({g: i for i, g in enumerate(groups_ordered)})\n", + "plot_df = plot_df.sort_values([\"region_order\", \"quantity\"]).drop(columns=\"region_order\")\n", "\n", "# --- Compute x positions with a gap between region groups ---\n", - "bar_width = 0.7\n", - "group_gap = 1 # one extra bar-width gap between groups\n", + "bar_width = 0.5\n", + "group_gap = 1\n", "\n", - "groups_ordered = list(dict.fromkeys(plot_df[\"region\"])) # preserve sorted order\n", "x_positions = []\n", "group_centers = {}\n", "group_spans = {}\n", @@ -238,7 +242,7 @@ " ax.text(x, total, f\"{total:.1f}\", ha=\"center\", va=\"bottom\", fontsize=9)\n", "\n", "# --- Primary axis formatting ---\n", - "ax.set_ylabel(\"Total cost in €/t$_{hbi}$\")\n", + "ax.set_ylabel(\"Cost of HBI in €/t$_{hbi}$\")\n", "ax.set_xlabel(\"\")\n", "ax.yaxis.grid(True, linestyle=\"-\", alpha=0.6)\n", "ax.set_axisbelow(True)\n", @@ -247,18 +251,17 @@ "ax.set_xticks(x_positions)\n", "qty_labels = [f\"{row['quantity']:.4g} Mt\" for _, row in plot_df.iterrows()]\n", "ax.set_xticklabels(qty_labels, rotation=0, fontsize=9)\n", - "ax.tick_params(axis=\"x\", length=0) # hide tick marks for cleaner look\n", + "ax.tick_params(axis=\"x\", length=0)\n", "\n", - "# --- Outer x-axis: region group labels with bracket ---\n", + "# --- Outer x-axis: region group labels with bracket (moved up) ---\n", "xaxis_transform = ax.get_xaxis_transform()\n", - "y_bracket = -0.16 # axes-fraction units below axes bottom (below quantity labels)\n", - "y_label_pts = -8 # additional offset in points below bracket\n", + "y_bracket = -0.11\n", + "y_label_pts = -7\n", "\n", "for g in groups_ordered:\n", " center = group_centers[g]\n", " x_start, x_end = group_spans[g]\n", "\n", - " # Horizontal bracket spanning the group\n", " ax.annotate(\n", " \"\",\n", " xy=(x_end + bar_width / 2 + 0.05, y_bracket),\n", @@ -268,8 +271,6 @@ " arrowprops=dict(arrowstyle=\"-\", color=\"black\", lw=0.8),\n", " annotation_clip=False,\n", " )\n", - "\n", - " # Region label below the bracket\n", " ax.annotate(\n", " g,\n", " xy=(center, y_bracket),\n", @@ -282,6 +283,42 @@ " annotation_clip=False,\n", " )\n", "\n", + "# --- % difference annotation: leftmost Europe vs leftmost South America ---\n", + "plot_df_idx_list = list(plot_df.index)\n", + "eu_first = plot_df[plot_df[\"region\"] == \"Europe\"].index[0]\n", + "sa_first = plot_df[plot_df[\"region\"] == \"South America\"].index[0]\n", + "\n", + "eu_first_xpos = x_positions[plot_df_idx_list.index(eu_first)]\n", + "sa_first_xpos = x_positions[plot_df_idx_list.index(sa_first)]\n", + "\n", + "totals_series = plot_df[order].sum(axis=1)\n", + "y_eu_top = float(totals_series[eu_first])\n", + "y_sa_top = float(totals_series[sa_first])\n", + "\n", + "# Europe is the base: negative means SA is cheaper\n", + "pct_diff = (y_sa_top - y_eu_top) / y_eu_top * 100\n", + "\n", + "# Red horizontal line from Europe's leftmost bar top across to directly above SA's leftmost bar\n", + "ax.plot([sa_first_xpos, eu_first_xpos], [y_eu_top, y_eu_top],\n", + " color=\"red\", linewidth=0.8, clip_on=False, zorder=5)\n", + "\n", + "# Downward arrow and percentage annotation\n", + "ax.annotate(\"\", xy=(sa_first_xpos, y_sa_top+20),\n", + " xytext=(sa_first_xpos, y_eu_top),\n", + " arrowprops=dict(arrowstyle=\"-|>\", color=\"red\", lw=0.8),\n", + " annotation_clip=False,\n", + ")\n", + "\n", + "ax.text(\n", + " sa_first_xpos - 0.1,\n", + " (y_eu_top + y_sa_top) / 2,\n", + " f\"{pct_diff:.1f}%\",\n", + " ha=\"right\",\n", + " va=\"center\",\n", + " fontsize=8,\n", + " color=\"red\",\n", + ")\n", + "\n", "# --- Legend ---\n", "handles, labels = ax.get_legend_handles_labels()\n", "ax.legend(\n", From be1b4034abad014891c6253cb944ed35a7a56b97 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 09:33:35 +0200 Subject: [PATCH 149/216] chore: adjust results path for plot --- rules/reporting.smk | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 2a057e9..dab4b0c 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -77,8 +77,8 @@ rule plot_global_supply: "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{interone}{demand}.csv", allow_missing=True, region=config["regions"]), output: - network_curve="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", - network_curve_png="../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", + network_curve="results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", + network_curve_png="results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", # supply_curves notebook: str(NOTEBOOKS_DIR / "analysis-globalsupplycurve.ipynb") From fb7366368cf204ed4b282aac081d1992f3511597 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 09:34:55 +0200 Subject: [PATCH 150/216] chore: minor notebook cleaning --- workflow/notebooks/analysis-globalsupplycurve.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 3b9b267..87b484b 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -25,7 +25,7 @@ " \"plot_global_supply\",\n", " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", " sort=True,\n", - " demand=\"__unreserved\",\n", + " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", " wacc=\"regional\",\n", " cost_year=\"2050\",\n", " interone=\"hbi\",\n", @@ -253,7 +253,7 @@ "\n", " ax.set_xlabel(\"Cumulative quantity (Mt)\")\n", " ax.set_ylabel(\"Cost of HBI in €/t$_{hbi}$\")\n", - " ax.set_title(f\"Combined {process} supply curve\")\n", + " # ax.set_title(f\"Combined {process} supply curve\")\n", " ax.set_ylim(0, 1200)\n", " ax.set_xlim(0, sum(bar_widths))\n", " ax.set_xlim(0, xlim)\n", @@ -310,7 +310,7 @@ "metadata": {}, "outputs": [], "source": [ - "sort_over_all = True # \"True\" to sort over all regions, \"False\" to sort within each region\n", + "sort_over_all = False # \"True\" to sort over all regions, \"False\" to sort within each region\n", "sort = False # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" ] }, From 4dab7be61dfa6f968fbec6eef829aabf9aa2d293 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 11:19:29 +0200 Subject: [PATCH 151/216] fix: add scenarios to plot --- workflow/notebooks/compare-scenarios.ipynb | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/workflow/notebooks/compare-scenarios.ipynb b/workflow/notebooks/compare-scenarios.ipynb index ff62970..cbf005f 100644 --- a/workflow/notebooks/compare-scenarios.ipynb +++ b/workflow/notebooks/compare-scenarios.ipynb @@ -55,6 +55,16 @@ "### Read networks in Network collection" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "99b8dc61", + "metadata": {}, + "outputs": [], + "source": [ + "scenarios = snakemake.input.keys()" + ] + }, { "cell_type": "code", "execution_count": null, From f27ba493971254facd8c9d9204371312e77cceb3 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 12:02:52 +0200 Subject: [PATCH 152/216] feat: include new assumptions in config --- config/config.yaml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/config/config.yaml b/config/config.yaml index f287fac..461fc64 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -218,6 +218,13 @@ trade: ports: "predefined" # "closest": route from region centroid (searoute snaps to nearest sea node); "predefined": route from hand-curated port city coordinates parsed from the notes column in trade_opt.csv diversity_factor: False # "False" or float as max constraint for one single hbi supply route in the trade model. 1: no limit, 0.5: max 50% of demand can be supplied by one single supply route, etc. # Note: The diversity factor constraints the trade route, not the producer itself. (Import from exporter route R <= diversity_factor * demand of importer I.). As a result, if the trade passes through one region, it is limited based on the demand of this particular region * diversity_factor. Therefore the trade routes in a scenario with a diversity_factor of 100% may differ to a scenario where diversity_factor=False. + shipping: + port_loading: 7 # USD/t, for each loading, see 10.1038/s41467-023-38123-2 supplementary S12 + nh3_cost: 460 # USD/t_NH3, see 10.1038/s41467-023-38123-2 supplementary S12 + nh3_consumption: 0.2 # t_NH3/nm, see 10.1038/s41467-023-38123-2 supplementary S12 + charter_rate: 0.0008 # USD/(t*km), see 10.1038/s41467-023-38123-2 supplementary S12 + panamax_load: 80000 # t + nm_to_km: 1.852 # 1 nautical mile equals 1.852 kilometers grid_electricity: marginal_cost: 80 # EUR/MWh, guesstimate for average grid electricity cost in 2030 From 4b61f47192d4846149fef336f25f91c24ce6f230 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 12:03:04 +0200 Subject: [PATCH 153/216] chore: remove depreciated dataset --- rules/trade_model.smk | 1 - 1 file changed, 1 deletion(-) diff --git a/rules/trade_model.smk b/rules/trade_model.smk index ac71ce2..4bf4f3f 100644 --- a/rules/trade_model.smk +++ b/rules/trade_model.smk @@ -21,7 +21,6 @@ rule model_trade: region=config["regions"], intertwo=["steel"], ), - transport_costs="data/transport_costs/steel_r_iron_r.csv", trade_options="resources/trade_opt_chokepoints.csv", bus_locations="data/bus_locations.csv", demand="data/un_enerdata_demand_2050_final.csv", From d5ed536cc2b0eb831951e670527bd2844130b7e6 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 12:03:14 +0200 Subject: [PATCH 154/216] feat: revise transport methodology --- workflow/scripts/model_trade.py | 99 ++++++++++----------------------- 1 file changed, 28 insertions(+), 71 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index d8f49ef..84a9276 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -260,47 +260,29 @@ def building_model( return n -def create_links(transport_costs, trade_options): +def create_links(trade_options): # for in range of length of input csv with all the different links, region_from = column , region_to = column 2 # create links with the correct corresponding costs - # marginal and fixed cost for the different type of transport - ship_mc = float( - transport_costs.loc[transport_costs["transport_type"] == "shipping"] - .loc[:, "marginal_cost"] - .values[0] - ) - pipe_mc = float( - transport_costs.loc[transport_costs["transport_type"] == "pipeline"] - .loc[:, "marginal_cost"] - .values[0] - ) - ship_c = float( - transport_costs.loc[transport_costs["transport_type"] == "shipping"] - .loc[:, "fixed_cost"] - .values[0] - ) + shipping = snakemake.config["trade"]["shipping"] - ship_iron_ore_mc = ( - transport_costs.loc[transport_costs["transport_type"] == "shipping_iron_ore"] - .loc[:, "marginal_cost"] - .values[0] - ) + port_loading = shipping["port_loading"] + nh3_cost = shipping["nh3_cost"] # USD/t_NH3 + nh3_consumption = shipping["nh3_consumption"] # t_NH3/nm + charter_rate = shipping["charter_rate"] # USD/(t*km) + panamax_load = shipping["panamax_load"] # t + nm_to_km = shipping["nm_to_km"] + eur_usd = snakemake.config["techno-economic parameters"]["eur_usd"] - ship_interone_mc = ( - transport_costs.loc[transport_costs["transport_type"] == f"shipping_{interone}"] - .loc[:, "marginal_cost"] - .values[0] - ) - input_demand = 0.42 # MWh/km for LH2, IEA future of hydrogen 2019 - boat_capacity = 363000 # MWh for LH2, IEA future of hydrogen 2019 - speed = 30 # km/h, IEA future of hydrogen 2019 - BOG = 0.2 / 100 # %/day, IEA future of hydrogen 2019 + variable_cost = ( + (nh3_cost * nh3_consumption / nm_to_km) / panamax_load + charter_rate + ) / eur_usd # EUR/(t*km) + fixed_cost = port_loading * 2 / eur_usd # EUR/t - logger.info(f"ship + pipe cost {ship_mc} {ship_c} {pipe_mc}") - logger.info(f"shipping cost {interone} {ship_interone_mc} EUR/(t*km)") - logger.info(f"shipping cost iron ore {ship_iron_ore_mc} EUR/(t*km)") + logger.info( + f"variable shipping cost {variable_cost:.6f} EUR/(t*km) and fixed cost {fixed_cost:.6f} EUR/t applied to shipping links" + ) # if there should be a link, create a link # do this for both shipping and pipeline @@ -310,27 +292,10 @@ def create_links(transport_costs, trade_options): r_from = trade_options["region_from"][r] r_to = trade_options["region_to"][r] - # If shipping costs are made up from marginal and capital - # total_cost = ship_c + int( - # float(trade_options["shipping_distance [km]"][r]) * ship_mc - # ) - # If shipping costs are made up from marginal only - total_cost_interone = ship_interone_mc * float( - trade_options["shipping_distance [km]"][r] - ) - total_cost = total_cost_interone - - # calculating efficiency - days_at_sea = ( - float(trade_options["shipping_distance [km]"][r]) / speed - ) / 24 - tot_BOG = 1 - (1 - BOG) ** days_at_sea - tot_fuel_demand = ( - (2 * float(trade_options["shipping_distance [km]"][r])) - * input_demand - / boat_capacity - ) - eff = 1 - max(tot_BOG, tot_fuel_demand) + shipping_cost = ( + variable_cost * float(trade_options["shipping_distance [km]"][r]) + + fixed_cost + ) # EUR/t n.add( "Link", @@ -338,24 +303,18 @@ def create_links(transport_costs, trade_options): carrier="shipping_" + interone, bus0=r_from + "_" + interone, bus1=r_to + "_" + interone, - efficiency=eff, # %, calculated above - marginal_cost=total_cost, # EUR/MWh or EUR/t + efficiency=1, + marginal_cost=shipping_cost, capital_cost=1 / 1000, # to prevent optimisation shenenigans p_nom_extendable=True, ) logger.info( - "shipping %s link made from %s to %s - eff %s", + "shipping %s link made from %s to %s", interone, r_from, r_to, - eff, ) - # Add iron ore shipping link - total_cost_iron_ore = ship_iron_ore_mc * float( - trade_options["shipping_distance [km]"][r] - ) # TODO Capital cost are not separate but included - n.add( "Link", "shipping iron ore {}-{}".format(r_from, r_to), @@ -363,15 +322,14 @@ def create_links(transport_costs, trade_options): bus0=r_from + "_ore", bus1=r_to + "_ore", efficiency=1, - marginal_cost=total_cost_iron_ore, # EUR/t_ironore + marginal_cost=shipping_cost, # EUR/t_ironore capital_cost=1 / 1000, # to prevent optimisation shenenigans p_nom_extendable=True, ) logger.info( - "iron ore shipping link made from %s_ore to %s_ore - eff %s", + "iron ore shipping link made from %s_ore to %s_ore", r_from, r_to, - eff, ) # checking if the row connects with pipeline @@ -1125,11 +1083,11 @@ def _link_weight(link_name): "model_trade", cost_year="2050", interone="hbi", - intertwo="steel", + intertwo="eaf", final="steel", scenario="default", wacc="regional", - chain_id="labour_2050", + chain_id="newre_2050", ) final = snakemake.wildcards["final"] @@ -1160,7 +1118,6 @@ def _link_weight(link_name): logger.info("starting up with all regions--- ") # making dataframes - transport_costs = pd.read_csv(snakemake.input.transport_costs, header=0) trade_options = pd.read_csv(snakemake.input.trade_options, header=0) supply_curves_interone = snakemake.input.supply_curves_interone supply_curves_intertwo = snakemake.input.supply_curves_intertwo @@ -1222,7 +1179,7 @@ def _link_weight(link_name): # building transport network connecting the individual buses logger.info("building transportation links") - create_links(transport_costs, trade_options) + create_links(trade_options) # Cost penalty if snakemake.config["scenario"][scenario]["modifiers"]["cost_penalty"] is None: From 97f59a51748eeb7dcfdc9bfb15010f15fad7d4ac Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 10 Jun 2026 12:04:45 +0200 Subject: [PATCH 155/216] feat: add notebook to analyse transport cost --- .../notebooks/analysis-transport-cost.ipynb | 102 ++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 workflow/notebooks/analysis-transport-cost.ipynb diff --git a/workflow/notebooks/analysis-transport-cost.ipynb b/workflow/notebooks/analysis-transport-cost.ipynb new file mode 100644 index 0000000..ac2aa97 --- /dev/null +++ b/workflow/notebooks/analysis-transport-cost.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "b0311363", + "metadata": {}, + "outputs": [], + "source": [ + "import pypsa\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98efb58", + "metadata": {}, + "outputs": [], + "source": [ + "n = pypsa.Network(\"../../results/chain_id~newre_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~default/network.nc\")" + ] + }, + { + "cell_type": "markdown", + "id": "5a18fba6", + "metadata": {}, + "source": [ + "### Transport efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6edd6dee", + "metadata": {}, + "outputs": [], + "source": [ + "n.links[n.links.carrier == \"shipping_iron_ore\"].efficiency.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a3a5201", + "metadata": {}, + "outputs": [], + "source": [ + "n.links[n.links.carrier == \"shipping_hbi\"].efficiency.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "b69794c5", + "metadata": {}, + "source": [ + "### Transport cost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6b82f5f", + "metadata": {}, + "outputs": [], + "source": [ + "n.links[n.links.carrier == \"shipping_iron_ore\"].marginal_cost.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5942c78", + "metadata": {}, + "outputs": [], + "source": [ + "n.links[n.links.carrier == \"shipping_hbi\"].marginal_cost.describe()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f37f5de537995bc3a324a84c35137fb28c13c12c Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 12 Jun 2026 10:50:13 +0200 Subject: [PATCH 156/216] feat: add notebook to analyze lcox networks --- workflow/notebooks/analze-solving-re.ipynb | 419 +++++++++++++++++++++ 1 file changed, 419 insertions(+) create mode 100644 workflow/notebooks/analze-solving-re.ipynb diff --git a/workflow/notebooks/analze-solving-re.ipynb b/workflow/notebooks/analze-solving-re.ipynb new file mode 100644 index 0000000..46789b1 --- /dev/null +++ b/workflow/notebooks/analze-solving-re.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "08f8ed54", + "metadata": {}, + "outputs": [], + "source": [ + "import pypsa\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3845da48", + "metadata": {}, + "outputs": [], + "source": [ + "from _helpers_notebooks import mock_snakemake\n", + "snakemake = mock_snakemake(\n", + " \"plot_global_supply\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + " sort=True,\n", + " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", + " wacc=\"regional\",\n", + " cost_year=\"2050\",\n", + " interone=\"hbi\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e090967a", + "metadata": {}, + "outputs": [], + "source": [ + "n = pypsa.Network(\"../../resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_20-newre.nc\")\n", + "n_old = pypsa.Network(\"../../resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_20-oldre.nc\")\n", + "n_ea = pypsa.Network(\"../../resources/lco-hbi/cost_year~2050/wacc~regional/East_Asia_allocated_share/network_10.nc\")" + ] + }, + { + "cell_type": "markdown", + "id": "d2e7c58a", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e5be371", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators.p_nom_max.div(1e3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "311e081b", + "metadata": {}, + "outputs": [], + "source": [ + "n_old.generators.p_nom_max.div(1e3).sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00f72e6b", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators_t.p_max_pu.mean().sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74f2da82", + "metadata": {}, + "outputs": [], + "source": [ + "n_old.generators_t.p_max_pu.mean().sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe28a52e", + "metadata": {}, + "outputs": [], + "source": [ + "n_ea.links" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf57d350", + "metadata": {}, + "outputs": [], + "source": [ + "n.stores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e11a2ac", + "metadata": {}, + "outputs": [], + "source": [ + "n.buses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84e9b093", + "metadata": {}, + "outputs": [], + "source": [ + "n.links" + ] + }, + { + "cell_type": "markdown", + "id": "e86f1d82", + "metadata": {}, + "source": [ + "### Network adjustments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec2a6fc4", + "metadata": {}, + "outputs": [], + "source": [ + "n.add(\"Link\", \"link_bat_to_re\", bus0=\"battery\", bus1=\"renewable_electricity\", p_nom_extendable=True, efficiency=1, capital_cost=1)\n", + "\n", + "n.add(\"Link\", \"link_bat_to_bat\", bus0=\"renewable_electricity\", bus1=\"battery\", p_nom_extendable=True, efficiency=1, capital_cost=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b59ea02", + "metadata": {}, + "outputs": [], + "source": [ + "# # Multiply p nom max by 10 in the network itself\n", + "\n", + "n.generators.p_nom_max *= 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4f56475", + "metadata": {}, + "outputs": [], + "source": [ + "n.stores.loc[\"hbi_storage\", \"e_cyclic\"] = True" + ] + }, + { + "cell_type": "markdown", + "id": "5581579c", + "metadata": {}, + "source": [ + "### Solving of network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac0bbf16", + "metadata": {}, + "outputs": [], + "source": [ + "config = snakemake.config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a91bc21", + "metadata": {}, + "outputs": [], + "source": [ + "solver_cfg = config.get(\"solver\", {})\n", + "solver_name = os.getenv(\"SHIFT_SOLVER\", solver_cfg.get(\"name\", \"glpk\"))\n", + "solver_options_key = os.getenv(\n", + " \"SHIFT_SOLVER_OPTIONS\", solver_cfg.get(\"options\", \"default\")\n", + ")\n", + "solver_options = config.get(\"solver_options\", {}).get(solver_options_key, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b572043e", + "metadata": {}, + "outputs": [], + "source": [ + "status = n.optimize(\n", + " n.snapshots,\n", + " solver_name=solver_name,\n", + " solver_options=solver_options,\n", + " multi_investment_periods=False,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7761d9d", + "metadata": {}, + "outputs": [], + "source": [ + "status" + ] + }, + { + "cell_type": "markdown", + "id": "a62e9b62", + "metadata": {}, + "source": [ + "### Post solving analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "364b1b18", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators.p_nom_opt.div(1e3).round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "032e4a59", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators.p_nom_opt.div(1e3).round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3f8e986", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_6\"][4000:4100].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "732896be", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_1\"][4000:4100].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35ecd1d9", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_1\"].sort_values(ascending=False).reset_index(drop=True).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d8f41fb", + "metadata": {}, + "outputs": [], + "source": [ + "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_6\"].sort_values(ascending=False).reset_index(drop=True).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dafbf1d2", + "metadata": {}, + "outputs": [], + "source": [ + "hbi_demand = n.statistics.withdrawal().to_frame().loc[\"Load\", \"hbi_demand\"][0]\n", + "hbi_demand" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49fc9467", + "metadata": {}, + "outputs": [], + "source": [ + "(n.statistics.system_cost() / hbi_demand).round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6311cc04", + "metadata": {}, + "outputs": [], + "source": [ + "n.stores.e_nom_opt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be359fbb", + "metadata": {}, + "outputs": [], + "source": [ + "n.links" + ] + }, + { + "cell_type": "markdown", + "id": "c682bdc1", + "metadata": {}, + "source": [ + "#### Stores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91fa1cbf", + "metadata": {}, + "outputs": [], + "source": [ + "n.stores_t.e[\"h2_storage\"].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40df0f4d", + "metadata": {}, + "outputs": [], + "source": [ + "n.stores.e_initial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a84b1f03", + "metadata": {}, + "outputs": [], + "source": [ + "n.stores.e_cyclic" + ] + }, + { + "cell_type": "markdown", + "id": "161b6b6e", + "metadata": {}, + "source": [ + "### Bottom line and improvements" + ] + }, + { + "cell_type": "markdown", + "id": "79f24da5", + "metadata": {}, + "source": [ + "- Reestablish links from and to the battery\n", + "- Set hbi store `n.stores.loc[\"hbi_storage\", \"e_cyclic\"] = True`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From de539a20be1994b8899b979b4ef4af0f5c0b1b1c Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 12 Jun 2026 12:59:52 +0200 Subject: [PATCH 157/216] fix: make iron and steel storage cyclic --- workflow/scripts/build_x_supply_chain.py | 2 ++ workflow/scripts/calculate_lcox.py | 21 --------------------- 2 files changed, 2 insertions(+), 21 deletions(-) diff --git a/workflow/scripts/build_x_supply_chain.py b/workflow/scripts/build_x_supply_chain.py index 9fbaab4..cf24b34 100644 --- a/workflow/scripts/build_x_supply_chain.py +++ b/workflow/scripts/build_x_supply_chain.py @@ -389,6 +389,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> fom_cost=0.0, # No maintenance cost discount_rate=0.0, # No cost, discount rate doesn't matter but required by PyPSA standing_loss=0.0, # HBI storage doesn't lose energy + e_cyclic=True, ) # Steel Storage: flexible intermediate inventory between EAF and demand @@ -402,6 +403,7 @@ def _add_storage(network: pypsa.Network, tech_costs: pd.Series, config: dict) -> fom_cost=0.0, # No maintenance cost discount_rate=0.0, # No cost, discount rate doesn't matter but required by PyPSA standing_loss=0.0, # Steel storage doesn't lose energy + e_cyclic=True, # End state must equal start state ) diff --git a/workflow/scripts/calculate_lcox.py b/workflow/scripts/calculate_lcox.py index 6c7df49..47f4eb2 100644 --- a/workflow/scripts/calculate_lcox.py +++ b/workflow/scripts/calculate_lcox.py @@ -76,12 +76,9 @@ def add_loads_to_network(network, product, demands): """Add hourly Load components and set storage boundary conditions. Converts annual demand to hourly load: hourly_load = annual_demand / HOURS_PER_YEAR. - Sets product storage e_initial and e_final to annual_demand / 52 (approx. 2-week buffer). - This provides flexibility while ensuring bounded stock levels. """ if product == "hydrogen": bus_name = "hydrogen" - storage_name = "h2_storage" # Hydrogen is measured in kg/year, convert to kg/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR @@ -90,7 +87,6 @@ def add_loads_to_network(network, product, demands): elif product == "hbi": bus_name = "hbi" - storage_name = "hbi_storage" # HBI is measured in t/year, convert to t/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR @@ -99,7 +95,6 @@ def add_loads_to_network(network, product, demands): elif product == "steel": bus_name = "steel" - storage_name = "steel_storage" # Steel is measured in t/year, convert to t/h (hourly) hourly_demand_t = ( demands["product_demand_mt"] * 1e6 / HOURS_PER_YEAR @@ -129,22 +124,6 @@ def add_loads_to_network(network, product, demands): p_set=p_set, # Constant hourly demand ) - # Set product storage boundary conditions: initial and final stock at annual_demand/52 - annual_demand_t = demands["product_demand_mt"] * 1e6 # Mt → t - storage_buffer = annual_demand_t / 52 # Approx. 1 week of annual demand - - if storage_name in network.stores.index: - network.stores.at[storage_name, "e_initial"] = storage_buffer - network.stores.at[storage_name, "e_final"] = storage_buffer - logger.info( - f"Set {storage_name} e_initial and e_final to {storage_buffer:.2f} t " - f"(annual_demand/52 for {hourly_demand_t:.4f} t/h demand)" - ) - else: - logger.warning( - f"Storage '{storage_name}' not found in network; skipping boundary condition setup" - ) - logger.info( f"Added hourly load for {product}: {load_name} = {p_set:.4f} {unit_str} (constant all hours)" ) From 0708d28ee80d9b47968e61b21df277b0596dd222 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 12 Jun 2026 14:07:12 +0200 Subject: [PATCH 158/216] fix: prevent battery buses from being removed --- workflow/scripts/prepare_regional_network.py | 10 ++-------- workflow/scripts/trade_chain_utils.py | 1 + 2 files changed, 3 insertions(+), 8 deletions(-) diff --git a/workflow/scripts/prepare_regional_network.py b/workflow/scripts/prepare_regional_network.py index 38c2764..ede3d2e 100644 --- a/workflow/scripts/prepare_regional_network.py +++ b/workflow/scripts/prepare_regional_network.py @@ -470,12 +470,7 @@ def _product_has_renewables(config: dict, product: str) -> bool: Returns True if the product should have renewable generators and reservation logic applied. Returns False if the product uses grid electricity only. """ - try: - return bool( - build_product_components(config, product).get("has_renewables", False) - ) - except Exception: - return False + return bool(build_product_components(config, product).get("has_renewables", False)) def sanitize_and_fix( @@ -849,7 +844,6 @@ def prepare_network( logger.info("Loading technology costs...") tech_costs = td.load_tech_costs(tech_costs_path) - # Load consolidated renewables for region (only if this stage needs renewables) # Check product_components config to see if product uses renewable_electricity product_uses_renewables = _product_has_renewables(config, route_label or product) @@ -862,7 +856,7 @@ def prepare_network( cf_ts = None metadata = {} else: - logger.info("Loading consolidated renewables...") + logger.info("Loading clustered renewables...") techs_dict, cf_ts, metadata = load_regional_clustered_renewables( clustered_renewables_path, region ) diff --git a/workflow/scripts/trade_chain_utils.py b/workflow/scripts/trade_chain_utils.py index 76e1334..72112b8 100644 --- a/workflow/scripts/trade_chain_utils.py +++ b/workflow/scripts/trade_chain_utils.py @@ -340,6 +340,7 @@ def build_product_components(config: Dict, product: str) -> Dict[str, object]: if has_renewables: stores.add("battery") buses.add("battery") + links.update({"batt_charge", "batt_discharge"}) return { "links": links, "stores": stores, From 1eeb6d80e02d80fc63deff6c8429a7ab41391112 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 12 Jun 2026 14:08:40 +0200 Subject: [PATCH 159/216] chore: change NumericFocus back to default --- config/config.yaml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index dad867a..babf0f1 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -19,7 +19,7 @@ outputs: # - unreserved (FALLBACK): no domestic reservation; full renewable stack available. # Can be generated without electricity demand data when demand data unavailable. supply_curve: - generate_unreserved: True # Set to True to generate unreserved scenario as fallback + generate_unreserved: False # Set to True to generate unreserved scenario as fallback default_scenario: allocated_share # Default scenario for supply curve generation allocation_factor: 0.3 # Renewable capacity allocation factor for allocated_share scenario (0 < X <= 1) @@ -142,7 +142,7 @@ scenario: # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.01, 0.1, 1, 10, 100, 1000] # Mt/year +steel_demand_levels: [0.1, 0.5, 1, 5, 10, 50, 100] # Mt/year hydrogen_storage_cost: False electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' @@ -272,7 +272,6 @@ solver_options: AggFill: 0 PreDual: 0 GURO_PAR_BARDENSETHRESH: 200 - NumericFocus: 2 colors: From 1ed8f85b35f5bb59e9f59b3c259f7b7ff6b79c85 Mon Sep 17 00:00:00 2001 From: Jan Tautorus Date: Fri, 12 Jun 2026 14:09:09 +0200 Subject: [PATCH 160/216] chore: add up-to-date lock file --- pixi.lock | 467 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 467 insertions(+) diff --git a/pixi.lock b/pixi.lock index 7600099..0a1ec51 100644 --- a/pixi.lock +++ b/pixi.lock @@ -4,6 +4,7 @@ environments: channels: - url: https://conda.anaconda.org/conda-forge/ - url: https://conda.anaconda.org/bioconda/ + - url: https://conda.anaconda.org/gurobi/ indexes: - https://pypi.org/simple options: @@ -18,8 +19,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.4.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/amply-0.1.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.12.1-pyhcf101f3_0.conda - 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appdirs>=1.4,<2 + - backoff>=2.2.1,<3 + - cachetools>=5.3.2,<6 + - dateparser>=1.2.0,<2 + - decorator>=5.1.1,<6 + - requests>=2.0,<3 + - shelved-cache>=0.3.1,<0.4 + - tabulate>=0.8.5,<1 + - mkdocs>=1.5.3,<2 ; extra == 'docs' + - mkdocstrings[python]>=0.30,<1 ; extra == 'docs' + - pandas>=1,<3 ; extra == 'pandas' + requires_python: '>=3.10,<4' - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.6.0-pyhd8ed1ab_0.conda sha256: e298b508b2473c4227206800dfb14c39e4b14fd79d4636132e9e1e4244cdf4aa md5: c3197f8c0d5b955c904616b716aca093 From f904593950703576d1b026bbc1dc7961a42a937a Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 12 Jun 2026 17:53:59 +0200 Subject: [PATCH 161/216] feat: improve comparison by including new components --- workflow/notebooks/plot-compare-lcox.ipynb | 63 +++++++++++++++++----- 1 file changed, 50 insertions(+), 13 deletions(-) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 91945cb..1b58c06 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -84,6 +84,29 @@ "nc = pypsa.NetworkCollection(list(nc.values()))" ] }, + { + "cell_type": "markdown", + "id": "2675e821", + "metadata": {}, + "source": [ + "### Adjust carrier name before statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23a05566", + "metadata": {}, + "outputs": [], + "source": [ + "# Rename for all scenarios\n", + "for scenario in scenarios:\n", + " nc[scenario].links[\"carrier\"] = nc[scenario].links[\"carrier\"].replace({\n", + " \"battery_elec\": \"battery discharge\",\n", + " \"renewable_electricity\": \"battery charge\"\n", + " })" + ] + }, { "cell_type": "markdown", "id": "1f28183f", @@ -169,14 +192,18 @@ " columns={\n", " \"renewable_solar\": \"solar\",\n", " \"renewable_onwind\": \"onshore wind\",\n", + " \"battery charge\": \"battery inverter (charging)\",\n", + " \"battery discharge\": \"battery inverter (discharging)\",\n", + " \"battery_elec\": \"battery\",\n", " \"hydrogen\": \"hydrogen storage\",\n", " \"electrolysis\": \"electrolysis\",\n", " \"direct_reduction_furnace\": \"direct reduction furnace\",\n", + "\n", " },\n", " inplace=True,\n", ")\n", "\n", - "order = [\"solar\", \"onshore wind\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\"]\n", + "order = [\"solar\", \"onshore wind\", \"battery inverter (charging)\", \"battery inverter (discharging)\", \"battery\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\"]\n", "\n", "plot_df = plot_df[order]" ] @@ -343,6 +370,16 @@ "" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "58dbb0c1", + "metadata": {}, + "outputs": [], + "source": [ + "# nc[\"europe_100\"].objective / 1e6" + ] + }, { "cell_type": "code", "execution_count": null, @@ -350,10 +387,21 @@ "metadata": {}, "outputs": [], "source": [ - "# system_cost = nc[\"eu_1\"].statistics.system_cost().div(1e6).sum()\n", + "# system_cost = nc[\"europe_100\"].statistics.system_cost().div(1e6).sum()\n", "# system_cost.round(2)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa803626", + "metadata": {}, + "outputs": [], + "source": [ + "# fom = nc[\"europe_100\"].statistics.fom().div(1e6).sum()\n", + "# fom.round(2)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -395,17 +443,6 @@ "# objective.round(2)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa803626", - "metadata": {}, - "outputs": [], - "source": [ - "# fom = nc[\"eu_1\"].statistics.fom().div(1e6).sum()\n", - "# fom.round(2)" - ] - }, { "cell_type": "markdown", "id": "5a04b8ce", From dc3e4ed62671360004a11fa513f0e6f6fbaacfd9 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 12 Jun 2026 17:54:26 +0200 Subject: [PATCH 162/216] chore: use allocated share data for comparison --- rules/reporting.smk | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index dab4b0c..5119d40 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -101,14 +101,14 @@ rule plot_comparison: rule plot_compare_lcox: input: - south_america_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_0.1.nc", - south_america_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_1.nc", - south_america_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_10.nc", - south_america_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_unreserved/network_100.nc", - europe_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_0.1.nc", - europe_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_1.nc", - europe_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_10.nc", - europe_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_unreserved/network_100.nc", + south_america_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_0.1.nc", + south_america_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_1.nc", + south_america_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_10.nc", + south_america_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_100.nc", + europe_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_0.1.nc", + europe_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_1.nc", + europe_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_10.nc", + europe_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_100.nc", output: lcox_comparison="results/figures_general/comparison/lcox_comparison.pdf", lcox_comparison_png="results/figures_general/comparison/lcox_comparison.png", From bf6fea7a2fa31731a0de0be2d31d050ed6ab51ae Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 13 Jun 2026 12:32:55 +0200 Subject: [PATCH 163/216] feat: adjust input and output of global supply curve rule --- rules/reporting.smk | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 5119d40..44a846e 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -70,7 +70,7 @@ rule plot_trade_today: rule plot_global_supply: input: - trade_network="results/chain_id~newre_2050/cost_year~{cost_year}/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~{scenario}/network.nc", + trade_network="results/chain_id~{trade_chain}/cost_year~{cost_year}/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~{scenario}/network.nc", # supply = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", # supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", supply_curves_interone = expand( @@ -85,7 +85,7 @@ rule plot_global_supply: rule plot_global_supply_all: input: - expand("../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", cost_year=[2050], wacc=["regional"], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) + expand("../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/{trade_chain}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", trade_chain=[config["trade_chains"]["id"]], cost_year=[2050], wacc=["regional"], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) rule plot_comparison: input: From e81068b593bb19ff4ced4de479a6c72cd52d9f20 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 13 Jun 2026 12:33:51 +0200 Subject: [PATCH 164/216] fix: supply curve plot improvements --- .../notebooks/analysis-globalsupplycurve.ipynb | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 87b484b..77aea95 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -29,6 +29,7 @@ " wacc=\"regional\",\n", " cost_year=\"2050\",\n", " interone=\"hbi\",\n", + " trade_chain=\"newre1206_2050\"\n", ")" ] }, @@ -52,7 +53,7 @@ "outputs": [], "source": [ "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", - "xlim = 16000\n", + "xlim = None\n", "ylim = 1500\n", "add_iron_ore_cost = False" ] @@ -311,7 +312,7 @@ "outputs": [], "source": [ "sort_over_all = False # \"True\" to sort over all regions, \"False\" to sort within each region\n", - "sort = False # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" + "sort = \"cost_average\" # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" ] }, { @@ -339,7 +340,7 @@ " df_clean[\"weighted_cost\"] = df_clean[\"lcox [EUR/t]\"] * df_clean[\"demand [t]\"]\n", " average_cost_per_region = (\n", " df_clean.groupby(\"region\")[\"weighted_cost\"].sum()\n", - " / df_clean.groupby(\"region\")[\"Demand [t]\"].sum()\n", + " / df_clean.groupby(\"region\")[\"demand [t]\"].sum()\n", " )\n", " # Sort df_all by average_cost_per_region, then by cox within each region\n", " df_all[\"avg_region_cost\"] = df_all[\"region\"].map(average_cost_per_region)\n", @@ -368,12 +369,17 @@ " df = pd.read_csv(fn)\n", " df[\"region\"] = region\n", "\n", - " diff = df[\"demand [t]\"].diff()\n", - " diff[0] = diff[1] # set first value to second to avoid NaN\n", - " df[\"demand [t]\"] = diff\n", + " # Activate if values should not be interpreted individually but as difference to previous value (e.g. for cumulative demand)\n", + " # diff = df[\"demand [t]\"].diff()\n", + " # diff[0] = diff[1] # set first value to second to avoid NaN\n", + " # df[\"demand [t]\"] = diff\n", "\n", " df_all = pd.concat([df_all, df], ignore_index=True)\n", "\n", + "# Remove rows where the lcox [EUR/t] value is NaN (e.g. due to missing data for some regions)\n", + "df_all = df_all.dropna(subset=[\"lcox [EUR/t]\"])\n", + "\n", + "\n", "if sort_over_all:\n", " df_all = df_all.sort_values(\"lcox [EUR/t]\").reset_index(drop=True)\n", "\n", From 05801d75f566144c23bede895e4fe131625edc95 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 11:13:57 +0200 Subject: [PATCH 165/216] feat: adjust supply curve interpretation as diff and not cumulative in trade model --- workflow/scripts/model_trade.py | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 84a9276..5ad000d 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -183,7 +183,12 @@ def building_model( # --- Stage 1 supply: ore → interone (material) or direct supply (energy) --- for s in range(len(region_data_interone)): - p_nom = float(region_data_interone[f"demand [{unit}]"][s]) + if s == 0: + p_nom_interone = float(region_data_interone[f"demand [{unit}]"][s]) + else: + p_nom_interone = float( + region_data_interone[f"demand [{unit}]"][s] + ) - float(region_data_interone[f"demand [{unit}]"][s - 1]) m_cost = float(region_data_interone[f"{cost_descriptor} [EUR/{unit}]"][s]) @@ -197,7 +202,7 @@ def building_model( bus=region_name, carrier=final, p_nom_extendable=True, - p_nom_max=p_nom, # MWh, demand = potential supply + p_nom_max=p_nom_interone, # MWh, demand = potential supply marginal_cost=m_cost, # EUR/MWh capital_cost=1 / 1000, # to prevent optimisation shenanigans ) @@ -214,7 +219,8 @@ def building_model( bus0=region_name + "_ore", bus1=region_name + "_" + interone, carrier=interone, - p_nom_max=p_nom * ore_ratio, # t, demand = potential supply + p_nom_max=p_nom_interone + * ore_ratio, # t, demand = potential supply p_nom_extendable=True, efficiency=1 / ore_ratio, marginal_cost=m_cost / ore_ratio, # referred to bus0 @@ -225,14 +231,19 @@ def building_model( if two_stage: for s in range(len(region_data_intertwo)): - p_nom = float(region_data_intertwo[f"demand [{unit}]"][s]) + if s == 0: + p_nom_intertwo = float(region_data_intertwo[f"demand [{unit}]"][s]) + else: + p_nom_intertwo = float( + region_data_intertwo[f"demand [{unit}]"][s] + ) - float(region_data_intertwo[f"demand [{unit}]"][s - 1]) # Override capacity for grid-connected EAF based on grid potential if intertwo == "eaf-grid": grid_potential = pd.read_csv( snakemake.input.grid_potential, header=0, index_col=0 ) - p_nom = ( + p_nom_intertwo = ( grid_potential.loc[region_name, "potential_mt_steel"] * 1e6 ) / len( region_data_intertwo @@ -250,7 +261,7 @@ def building_model( bus0=region_name + "_" + interone, bus1=region_name + "_" + final, carrier=final, - p_nom_max=p_nom, # t, demand = potential supply + p_nom_max=p_nom_intertwo, # t, demand = potential supply p_nom_extendable=True, efficiency=1, # direct conversion, no ratio needed marginal_cost=m_cost, # EUR/t @@ -1087,7 +1098,7 @@ def _link_weight(link_name): final="steel", scenario="default", wacc="regional", - chain_id="newre_2050", + chain_id="newre1206_2050", ) final = snakemake.wildcards["final"] From c30dbdc7897b79123d5265d48d15c44d1f711cd3 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 11:14:29 +0200 Subject: [PATCH 166/216] feat: adjust supply curve interpretation as diff and not cumulative in global plot --- .../notebooks/analysis-globalsupplycurve.ipynb | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 77aea95..d173b2f 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -54,7 +54,7 @@ "source": [ "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", "xlim = None\n", - "ylim = 1500\n", + "ylim = 1000\n", "add_iron_ore_cost = False" ] }, @@ -204,7 +204,7 @@ "outputs": [], "source": [ "def plot_supply_curve(\n", - " bar_lefts, bar_widths, bar_costs, bar_colors, region_colors, region_list, xlim=2500\n", + " bar_lefts, bar_widths, bar_costs, bar_colors, region_colors, region_list, xlim=2500, ylim=1500\n", "):\n", "\n", " fig, ax = plt.subplots(figsize=(10, 5))\n", @@ -255,7 +255,7 @@ " ax.set_xlabel(\"Cumulative quantity (Mt)\")\n", " ax.set_ylabel(\"Cost of HBI in €/t$_{hbi}$\")\n", " # ax.set_title(f\"Combined {process} supply curve\")\n", - " ax.set_ylim(0, 1200)\n", + " ax.set_ylim(0, ylim)\n", " ax.set_xlim(0, sum(bar_widths))\n", " ax.set_xlim(0, xlim)\n", " ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", @@ -293,6 +293,7 @@ " region_colors=region_colors,\n", " region_list=region_list,\n", " xlim=xlim,\n", + " ylim=ylim,\n", ")" ] }, @@ -312,7 +313,7 @@ "outputs": [], "source": [ "sort_over_all = False # \"True\" to sort over all regions, \"False\" to sort within each region\n", - "sort = \"cost_average\" # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" + "sort = \"cost_global\" # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" ] }, { @@ -369,10 +370,10 @@ " df = pd.read_csv(fn)\n", " df[\"region\"] = region\n", "\n", - " # Activate if values should not be interpreted individually but as difference to previous value (e.g. for cumulative demand)\n", - " # diff = df[\"demand [t]\"].diff()\n", - " # diff[0] = diff[1] # set first value to second to avoid NaN\n", - " # df[\"demand [t]\"] = diff\n", + " # Calculate the difference in demand to get the quantity supplied at each cost step\n", + " diff = df[\"demand [t]\"].diff()\n", + " diff[0] = diff[1] # set first value to second to avoid NaN\n", + " df[\"demand [t]\"] = diff\n", "\n", " df_all = pd.concat([df_all, df], ignore_index=True)\n", "\n", @@ -435,6 +436,7 @@ " region_colors=region_colors,\n", " region_list=region_list,\n", " xlim=xlim,\n", + " ylim=ylim,\n", ")" ] } From 2261be01c64e0e04e250c002484d646b8cb9b14d Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 13:48:25 +0200 Subject: [PATCH 167/216] feat: global supply curve: get remind steel demand and optionally plot optimal supply curve --- .../analysis-globalsupplycurve.ipynb | 36 +++++++++++++++---- 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index d173b2f..a66bc26 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -53,9 +53,28 @@ "outputs": [], "source": [ "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", - "xlim = None\n", + "xlim = 1200\n", "ylim = 1000\n", - "add_iron_ore_cost = False" + "add_iron_ore_cost = False\n", + "plot_optimal_curve = True # Uses the optimal supply curve from the trade model, which may differ from the single curves due to the trade-offs between different producers. If False, the single curves are plotted, which may not reflect the optimal supply curve from the trade model." + ] + }, + { + "cell_type": "markdown", + "id": "df7e4a41", + "metadata": {}, + "source": [ + "### Get steel demand" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca9505fe", + "metadata": {}, + "outputs": [], + "source": [ + "steel_demand = pd.read_csv(snakemake.input.steel_demand, index_col=\"region\")" ] }, { @@ -158,9 +177,14 @@ "outputs": [], "source": [ "# Add quantity\n", - "interone.loc[:, \"quantity in Mt_steel\"] = (\n", - " interone.p_nom_max.values / iron_ore_to_steel / 1e6\n", - ")" + "if plot_optimal_curve:\n", + " interone.loc[:, \"quantity in Mt_steel\"] = (\n", + " interone.p_nom_opt.values / iron_ore_to_steel / 1e6\n", + " )\n", + "else:\n", + " interone.loc[:, \"quantity in Mt_steel\"] = (\n", + " interone.p_nom_max.values / iron_ore_to_steel / 1e6\n", + " )" ] }, { @@ -240,7 +264,7 @@ " rotation=90,\n", " color=\"red\",\n", " )\n", - " remind_demand = 725 # Mt steel, REMIND global steel long-term hydrogen: 700-750 Mt\n", + " remind_demand = steel_demand[\"SteelDemand_DRI_Mt\"].sum() # Mt steel, REMIND global steel long-term hydrogen: 700-750 Mt\n", " ax.axvline(\n", " remind_demand, color=\"orange\", linestyle=\"--\", label=\"REMIND 2050 demand\"\n", " )\n", From f93544ccb2286ffa9c94564ae198c1d970599b8b Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 13:48:45 +0200 Subject: [PATCH 168/216] feat: add trade options to params for trade model --- rules/trade_model.smk | 1 + 1 file changed, 1 insertion(+) diff --git a/rules/trade_model.smk b/rules/trade_model.smk index 4bf4f3f..65516e8 100644 --- a/rules/trade_model.smk +++ b/rules/trade_model.smk @@ -42,6 +42,7 @@ rule model_trade: iron_ore_potential=config["iron_ore"]["potential_allowance"], cost_penalty=config["design"]["cost_penalty"], scenarios=config["scenario"], + trade=config["trade"], script: str(SCRIPT_DIR / "model_trade.py") From 31a755feba19e59c14038bd659663e79588aac99 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 13:49:12 +0200 Subject: [PATCH 169/216] feat: add remind steel demand to global supply curve worklfow --- rules/reporting.smk | 1 + 1 file changed, 1 insertion(+) diff --git a/rules/reporting.smk b/rules/reporting.smk index 44a846e..b14d276 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -76,6 +76,7 @@ rule plot_global_supply: supply_curves_interone = expand( "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{interone}{demand}.csv", allow_missing=True, region=config["regions"]), + steel_demand="resources/steel_demand_clustered_{cost_year}.csv", output: network_curve="results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", network_curve_png="results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", From c5dbabefc7ade0381e21da8d5e2e6c2dabbb9f56 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 14:06:20 +0200 Subject: [PATCH 170/216] chore: scenario updates in config and new blocs definition --- config/config.yaml | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 2752546..7ae91c0 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -1,6 +1,6 @@ enable: - run_supply_chain: True # Enable for first run - run_supply_curve: True # Enable for first run + run_supply_chain: False # Enable for first run + run_supply_curve: False # Enable for first run cluster_renewables: False # Enable for first run @@ -27,7 +27,7 @@ supply_curve: # Config-native trade chain definitions # Defines the commodity transformation chain with ordered stages and process labels trade_chains: - id: labour_2050 + id: newre1206_2050 cost_year: 2050 final_product: steel wacc: regional #regional @@ -52,6 +52,8 @@ trade_chains: trade_scenarios: - default - mga-stability-weighted + - mga-chokepoints + - mga-blocs scenario: @@ -134,15 +136,16 @@ scenario: carrier: "hbi" indicator: "blocks" threshold_value: - block_a: ["Europe","Far_West_Europe","North_America","East_East_Asia","South_South_America", "Oceania"] - block_b: ["Middle_East","Subsaharan_Africa","North_West_Africa", "Eurasia","South_America","Central_America","West_Asia","East_Asia","Pacific_Asia"] - + # Used "definition_C" from config/blocs_traceregions.yaml + block_1: ["Europe, Far_West_Europe, North_America"] + block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, South_America, South_South_America, Central_America, West_Asia, East_Asia, Pacific_Asia"] + block_3: ["East_East_Asia, Oceania"] # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.1, 0.5, 1, 5, 10, 50, 100] # Mt/year +steel_demand_levels: [0.1, 0.5, 1, 5, 10, 25, 50, 75, 100, 200, 300, 500, 1000] # Mt/year hydrogen_storage_cost: False electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' From 9dc7a1f00680f50965b600d0e77d82e703379842 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 14:06:49 +0200 Subject: [PATCH 171/216] feat: add new blocs definition suitable for config drop-in --- config/blocs_traceregions.yaml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/config/blocs_traceregions.yaml b/config/blocs_traceregions.yaml index 89badaa..6b36345 100644 --- a/config/blocs_traceregions.yaml +++ b/config/blocs_traceregions.yaml @@ -62,3 +62,26 @@ regions: Oceania: country_members: [AU, NZ] bloc_alignment: {A: 1, B: 1, C: 3, D: 3} + + +# Config-compatible bloc definitions based on the above region definitions. + +definition_A: + block_1: ["Europe, Far_West_Europe, North_America, South_America, South_South_America, East_East_Asia, Oceania"] + block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, Central_America, West_Asia, East_Asia, Pacific_Asia"] + block_3: [""] + +definition_B: + block_1: ["Europe, Far_West_Europe, North_America, East_East_Asia, Oceania"] + block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, South_America, South_South_America, Central_America, West_Asia, East_Asia, Pacific_Asia"] + block_3: [""] + +definition_C: + block_1: ["Europe, Far_West_Europe, North_America"] + block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, South_America, South_South_America, Central_America, West_Asia, East_Asia, Pacific_Asia"] + block_3: ["East_East_Asia, Oceania"] + +definition_D: + block_1: ["Europe, Far_West_Europe, North_America"] + block_2: ["South_America, South_South_America, East_Asia, Pacific_Asia"] + block_3: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, Central_America, West_Asia, East_East_Asia, Oceania"] \ No newline at end of file From c2ca4d43a1e470347c2015555c2cbc38f0502f24 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 15 Jun 2026 14:07:13 +0200 Subject: [PATCH 172/216] chore: scenario-flexible input/output of mga plotting rule --- rules/reporting.smk | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index b14d276..92441f7 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -33,14 +33,14 @@ rule collect_figures: rule plot_mga: input: - network_mga_production = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", - network_mga_chokepoints = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", - network_mga_blocks = "results/chain_id~labour_2050/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", + network_mga_production = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", + network_mga_chokepoints = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", + network_mga_blocks = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", political_stability = "resources/political_stability_clustered.csv", trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", output: - mga_plot = "results/figures_general/mga/wacc~{wacc}/mga_analysis.pdf", - mga_plot_png = "results/figures_general/mga/wacc~{wacc}/mga_analysis.png", + mga_plot = "results/figures_general/mga/chain_id~{trade_chain}/wacc~{wacc}/mga_analysis.pdf", + mga_plot_png = "results/figures_general/mga/chain_id~{trade_chain}/wacc~{wacc}/mga_analysis.png", resources: mem_mb=4000, threads: 2 @@ -49,7 +49,7 @@ rule plot_mga: rule plot_mga_all: input: - expand("results/figures_general/mga/wacc~{wacc}/mga_analysis.pdf", wacc=["regional"], allow_missing=True) #wacc=["uniform", "regional"] + expand("results/figures_general/mga/chain_id~{trade_chain}/wacc~{wacc}/mga_analysis.pdf", trade_chain=[config["trade_chains"]["id"]], wacc=[config["trade_chains"]["wacc"]], allow_missing=True) #wacc=["uniform", "regional"] rule plot_trade_today: From 331fa911b17fd6a4a3003c71ab21e459b8764550 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 16 Jun 2026 09:25:16 +0200 Subject: [PATCH 173/216] fix: adjust quotation marks for mga blocs --- config/blocs_traceregions.yaml | 24 ++++++++++++------------ config/config.yaml | 6 +++--- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/config/blocs_traceregions.yaml b/config/blocs_traceregions.yaml index 6b36345..8d121d9 100644 --- a/config/blocs_traceregions.yaml +++ b/config/blocs_traceregions.yaml @@ -67,21 +67,21 @@ regions: # Config-compatible bloc definitions based on the above region definitions. definition_A: - block_1: ["Europe, Far_West_Europe, North_America, South_America, South_South_America, East_East_Asia, Oceania"] - block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, Central_America, West_Asia, East_Asia, Pacific_Asia"] - block_3: [""] + block_1: ["Europe", "Far_West_Europe", "North_America", "South_America", "South_South_America", "East_East_Asia", "Oceania"] + block_2: ["Middle_East", "North_West_Africa", "Subsaharan_Africa", "Eurasia", "Central_America", "West_Asia", "East_Asia", "Pacific_Asia"] + block_3: [] definition_B: - block_1: ["Europe, Far_West_Europe, North_America, East_East_Asia, Oceania"] - block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, South_America, South_South_America, Central_America, West_Asia, East_Asia, Pacific_Asia"] - block_3: [""] + block_1: ["Europe", "Far_West_Europe", "North_America", "East_East_Asia", "Oceania"] + block_2: ["Middle_East", "North_West_Africa", "Subsaharan_Africa", "Eurasia", "South_America", "South_South_America", "Central_America", "West_Asia", "East_Asia", "Pacific_Asia"] + block_3: [] definition_C: - block_1: ["Europe, Far_West_Europe, North_America"] - block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, South_America, South_South_America, Central_America, West_Asia, East_Asia, Pacific_Asia"] - block_3: ["East_East_Asia, Oceania"] + block_1: ["Europe", "Far_West_Europe", "North_America"] + block_2: ["Middle_East", "North_West_Africa", "Subsaharan_Africa", "Eurasia", "South_America", "South_South_America", "Central_America", "West_Asia", "East_Asia", "Pacific_Asia"] + block_3: ["East_East_Asia", "Oceania"] definition_D: - block_1: ["Europe, Far_West_Europe, North_America"] - block_2: ["South_America, South_South_America, East_Asia, Pacific_Asia"] - block_3: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, Central_America, West_Asia, East_East_Asia, Oceania"] \ No newline at end of file + block_1: ["Europe", "Far_West_Europe", "North_America"] + block_2: ["South_America", "South_South_America", "East_Asia", "Pacific_Asia"] + block_3: ["Middle_East", "North_West_Africa", "Subsaharan_Africa", "Eurasia", "Central_America", "West_Asia", "East_East_Asia", "Oceania"] \ No newline at end of file diff --git a/config/config.yaml b/config/config.yaml index 7ae91c0..3b5a940 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -137,9 +137,9 @@ scenario: indicator: "blocks" threshold_value: # Used "definition_C" from config/blocs_traceregions.yaml - block_1: ["Europe, Far_West_Europe, North_America"] - block_2: ["Middle_East, North_West_Africa, Subsaharan_Africa, Eurasia, South_America, South_South_America, Central_America, West_Asia, East_Asia, Pacific_Asia"] - block_3: ["East_East_Asia, Oceania"] + block_1: ["Europe", "Far_West_Europe", "North_America"] + block_2: ["Middle_East", "North_West_Africa", "Subsaharan_Africa", "Eurasia", "South_America", "South_South_America", "Central_America", "West_Asia", "East_Asia", "Pacific_Asia"] + block_3: ["East_East_Asia", "Oceania"] # Absolute steel demand levels (Mt/year) for supply curve sweep From 41fc6ca222e13a7c9d6b20275584def786d55207 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 16 Jun 2026 09:29:14 +0200 Subject: [PATCH 174/216] feat: adjust model_trade and mga plot to 3 blocs configuration --- workflow/notebooks/plot-mga.ipynb | 145 +++++++++++++++++++----------- workflow/scripts/model_trade.py | 19 ++-- 2 files changed, 105 insertions(+), 59 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index b1fad29..1eda4b5 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -20,7 +20,8 @@ " from _helpers_notebooks import mock_snakemake\n", " snakemake = mock_snakemake(\n", " \"plot_mga\",\n", - " wacc=\"uniform\"\n", + " wacc=\"regional\",\n", + " trade_chain=\"newre1206_2050\",\n", " )" ] }, @@ -693,22 +694,37 @@ "bl_mga_slack_values = bl_mga_modifiers.get(\"mga\", {}).get(\"slack\", [])\n", "bl_carrier = bl_mga_modifiers.get(\"mga\", {}).get(\"carrier\", \"hbi\")\n", "\n", - "# Block definitions from config\n", + "# Block definitions from config.\n", + "# Each entry in the YAML list may be a comma-separated string, so split defensively.\n", + "def _parse_block_regions(region_list):\n", + " \"\"\"Flatten potentially comma-separated strings within a YAML list.\"\"\"\n", + " result = []\n", + " for entry in (region_list or []):\n", + " for r in str(entry).split(\",\"):\n", + " r = r.strip()\n", + " if r:\n", + " result.append(r)\n", + " return result\n", + "\n", "bl_threshold = bl_mga_modifiers.get(\"mga\", {}).get(\"threshold_value\", {})\n", - "block_a_regions = bl_threshold.get(\"block_a\", [])\n", - "block_b_regions = bl_threshold.get(\"block_b\", [])\n", + "block_1_regions = _parse_block_regions(bl_threshold.get(\"block_1\", []))\n", + "block_2_regions = _parse_block_regions(bl_threshold.get(\"block_2\", []))\n", + "block_3_regions = _parse_block_regions(bl_threshold.get(\"block_3\", []))\n", "\n", "print(f\"Blocks MGA scenario: {bl_scenario}\")\n", "print(f\"Slack values: {bl_mga_slack_values}\")\n", - "print(f\"Block A ({len(block_a_regions)} regions): {block_a_regions}\")\n", - "print(f\"Block B ({len(block_b_regions)} regions): {block_b_regions}\")\n", + "print(f\"Block 1 ({len(block_1_regions)} regions): {block_1_regions}\")\n", + "print(f\"Block 2 ({len(block_2_regions)} regions): {block_2_regions}\")\n", + "print(f\"Block 3 ({len(block_3_regions)} regions): {block_3_regions}\")\n", "\n", "# Build region → block mapping\n", "region_to_block = {}\n", - "for r in block_a_regions:\n", - " region_to_block[r] = \"block_a\"\n", - "for r in block_b_regions:\n", - " region_to_block[r] = \"block_b\"\n", + "for r in block_1_regions:\n", + " region_to_block[r] = \"block_1\"\n", + "for r in block_2_regions:\n", + " region_to_block[r] = \"block_2\"\n", + "for r in block_3_regions:\n", + " region_to_block[r] = \"block_3\"\n", "\n", "# ── Load PyPSA networks from mga-blocks results ──\n", "bl_results_dir = os.path.abspath(os.path.join(\n", @@ -764,25 +780,24 @@ "# or intra-block and sum trade volumes.\n", "\n", "carrier = bl_carrier\n", + "BLOCKS = [\"block_1\", \"block_2\", \"block_3\"]\n", "\n", - "# Track: total inter-block, a→b, b→a\n", + "# All 6 directional inter-block pairs\n", "bl_interblock_total = []\n", - "bl_a_to_b = []\n", - "bl_b_to_a = []\n", "bl_intrablock_total = []\n", + "# keyed as \"block_X → block_Y\"\n", + "pair_series = {f\"{src} → {dst}\": [] for src in BLOCKS for dst in BLOCKS if src != dst}\n", "\n", "for net_key in bl_sorted_keys:\n", " n_bl = bl_networks_dict[net_key]\n", "\n", " inter_total = 0.0\n", - " a2b = 0.0\n", - " b2a = 0.0\n", " intra_total = 0.0\n", + " pair_totals = {k: 0.0 for k in pair_series}\n", "\n", " shipping_links = n_bl.links[n_bl.links.carrier == f\"shipping_{carrier}\"]\n", "\n", " for link_name, link_row in shipping_links.iterrows():\n", - " # Extract region_from, region_to from bus names\n", " bus0 = link_row[\"bus0\"]\n", " bus1 = link_row[\"bus1\"]\n", " r_from = bus0.rsplit(f\"_{carrier}\", 1)[0]\n", @@ -797,29 +812,29 @@ "\n", " if block_from and block_to and block_from != block_to:\n", " inter_total += trade_vol\n", - " if block_from == \"block_a\":\n", - " a2b += trade_vol\n", - " else:\n", - " b2a += trade_vol\n", + " pair_key = f\"{block_from} → {block_to}\"\n", + " if pair_key in pair_totals:\n", + " pair_totals[pair_key] += trade_vol\n", " else:\n", " intra_total += trade_vol\n", "\n", " bl_interblock_total.append(inter_total)\n", - " bl_a_to_b.append(a2b)\n", - " bl_b_to_a.append(b2a)\n", " bl_intrablock_total.append(intra_total)\n", + " for k in pair_series:\n", + " pair_series[k].append(pair_totals[k])\n", "\n", "# Build summary DataFrame\n", - "bl_summary = pd.DataFrame({\n", - " \"Inter-block total\": bl_interblock_total,\n", - " \"Block A → Block B\": bl_a_to_b,\n", - " \"Block B → Block A\": bl_b_to_a,\n", - " \"Intra-block total\": bl_intrablock_total,\n", - "}, index=bl_epsilon_values) / 1e6 # Mt\n", - "\n", + "summary_dict = {\"Inter-block total\": bl_interblock_total}\n", + "for k, vals in pair_series.items():\n", + " # humanise key: \"block_1 → block_2\" → \"Block 1 → Block 2\"\n", + " label = k.replace(\"block_\", \"Block \").replace(\"_\", \" \")\n", + " summary_dict[label] = vals\n", + "summary_dict[\"Intra-block total\"] = bl_intrablock_total\n", + "\n", + "bl_summary = pd.DataFrame(summary_dict, index=bl_epsilon_values) / 1e6 # Mt\n", "bl_summary.index.name = \"MGA Slack (ε)\"\n", "print(\"HBI shipping trade by block classification (Mt):\")\n", - "bl_summary.round(1)" + "bl_summary.round(1)\n" ] }, { @@ -839,10 +854,18 @@ "source": [ "bl_epsilon_arr = np.array(bl_epsilon_values, dtype=float)\n", "\n", - "inter_arr = np.array(bl_interblock_total, dtype=float) / 1e6\n", - "a2b_arr = np.array(bl_a_to_b, dtype=float) / 1e6\n", - "b2a_arr = np.array(bl_b_to_a, dtype=float) / 1e6\n", - "intra_arr = np.array(bl_intrablock_total, dtype=float) / 1e6\n", + "inter_arr = np.array(bl_summary[\"Inter-block total\"].values, dtype=float)\n", + "intra_arr = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", + "\n", + "# All 6 directional pairs with distinct colours / styles\n", + "pair_plot_cfg = {\n", + " \"Block 1 → Block 2\": dict(color=\"#e41a1c\", linestyle=\"--\", marker=\"s\"),\n", + " \"Block 1 → Block 3\": dict(color=\"#e41a1c\", linestyle=\":\", marker=\"^\"),\n", + " \"Block 2 → Block 1\": dict(color=\"#377eb8\", linestyle=\"--\", marker=\"s\"),\n", + " \"Block 2 → Block 3\": dict(color=\"#377eb8\", linestyle=\":\", marker=\"^\"),\n", + " \"Block 3 → Block 1\": dict(color=\"#4daf4a\", linestyle=\"--\", marker=\"s\"),\n", + " \"Block 3 → Block 2\": dict(color=\"#4daf4a\", linestyle=\":\", marker=\"^\"),\n", + "}\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n", "\n", @@ -850,13 +873,14 @@ "ax.plot(bl_epsilon_arr, inter_arr, color=\"black\", linewidth=3, marker=\"o\",\n", " markersize=8, label=\"Inter-block total\", zorder=20)\n", "\n", - "# ── Directional breakdown (dashed) ──\n", - "ax.plot(bl_epsilon_arr, a2b_arr, color=\"#e41a1c\", linewidth=1.5,\n", - " linestyle=\"--\", marker=\"s\", markersize=5, alpha=0.8,\n", - " label=\"Block A → Block B\", zorder=10)\n", - "ax.plot(bl_epsilon_arr, b2a_arr, color=\"#377eb8\", linewidth=1.5,\n", - " linestyle=\"--\", marker=\"^\", markersize=5, alpha=0.8,\n", - " label=\"Block B → Block A\", zorder=10)\n", + "# ── All 6 directional pairs ──\n", + "for label, cfg in pair_plot_cfg.items():\n", + " if label in bl_summary.columns:\n", + " vals = np.array(bl_summary[label].values, dtype=float)\n", + " ax.plot(bl_epsilon_arr, vals,\n", + " color=cfg[\"color\"], linewidth=1.5, linestyle=cfg[\"linestyle\"],\n", + " marker=cfg[\"marker\"], markersize=5, alpha=0.8,\n", + " label=label, zorder=10)\n", "\n", "# ── Intra-block trade (dotted grey) ──\n", "ax.plot(bl_epsilon_arr, intra_arr, color=\"#999999\", linewidth=1.5,\n", @@ -865,13 +889,13 @@ "\n", "# ── Axes formatting ──\n", "ax.set_xlim(min(bl_epsilon_arr) - 0.002, max(bl_epsilon_arr) + 0.01)\n", - "y_max = max(max(inter_arr), max(intra_arr)) * 1.15\n", + "y_max = max(inter_arr.max(), intra_arr.max()) * 1.15\n", "ax.set_ylim(0, y_max)\n", "ax.set_xlabel(\"MGA slack (fraction, ε)\", fontsize=12)\n", "ax.set_ylabel(\"HBI shipping trade (Mt)\", fontsize=12)\n", - "ax.set_title(\"Inter-Block vs Intra-Block HBI Trade vs MGA Slack (mga-blocks)\", fontsize=13)\n", + "ax.set_title(\"Inter-Block vs Intra-Block HBI Trade vs MGA Slack (mga-blocs)\", fontsize=13)\n", "ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.5)\n", - "ax.legend(loc=\"best\", fontsize=10, framealpha=0.95, title=\"Trade classification\")\n", + "ax.legend(loc=\"best\", fontsize=9, framealpha=0.95, title=\"Trade classification\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", @@ -883,8 +907,9 @@ "print(f\"Intra-block trade: {intra_arr[0]:.1f} Mt (optimal) → {intra_arr[-1]:.1f} Mt (ε={bl_epsilon_arr[-1]})\")\n", "\n", "# Block definitions legend\n", - "print(f\"\\nBlock A: {', '.join(block_a_regions)}\")\n", - "print(f\"Block B: {', '.join(block_b_regions)}\")" + "print(f\"\\nBlock 1: {', '.join(block_1_regions)}\")\n", + "print(f\"Block 2: {', '.join(block_2_regions)}\")\n", + "print(f\"Block 3: {', '.join(block_3_regions)}\")\n" ] }, { @@ -977,14 +1002,26 @@ "bl_eps = np.array([float(c) for c in bl_summary.index]) * 100 # → %\n", "bl_inter_vals = np.array(bl_summary[\"Inter-block total\"].values, dtype=float)\n", "bl_intra_vals = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", - "bl_a2b_vals = np.array(bl_summary[\"Block A → Block B\"].values, dtype=float)\n", - "bl_b2a_vals = np.array(bl_summary[\"Block B → Block A\"].values, dtype=float)\n", "\n", - "# Sort directional lines by final value descending\n", - "bl_region_lines_unsorted = [\n", - " dict(name=\"Bloc A → Bloc B\", values=bl_a2b_vals, color=\"#e41a1c\", linestyle=\"-\", alpha=0.6),\n", - " dict(name=\"Bloc B → Bloc A\", values=bl_b2a_vals, color=\"#377eb8\", linestyle=\"-\", alpha=0.6),\n", - "]\n", + "# All 6 directional pairs with consistent colours per source bloc\n", + "_bl_pair_cfg = {\n", + " \"Block 1 → Block 2\": dict(color=\"#e41a1c\", linestyle=\"--\"),\n", + " \"Block 1 → Block 3\": dict(color=\"#e41a1c\", linestyle=\":\"),\n", + " \"Block 2 → Block 1\": dict(color=\"#377eb8\", linestyle=\"--\"),\n", + " \"Block 2 → Block 3\": dict(color=\"#377eb8\", linestyle=\":\"),\n", + " \"Block 3 → Block 1\": dict(color=\"#4daf4a\", linestyle=\"--\"),\n", + " \"Block 3 → Block 2\": dict(color=\"#4daf4a\", linestyle=\":\"),\n", + "}\n", + "bl_region_lines_unsorted = []\n", + "for label, cfg in _bl_pair_cfg.items():\n", + " if label in bl_summary.columns:\n", + " bl_region_lines_unsorted.append(dict(\n", + " name=label,\n", + " values=np.array(bl_summary[label].values, dtype=float),\n", + " color=cfg[\"color\"],\n", + " linestyle=cfg[\"linestyle\"],\n", + " alpha=0.7,\n", + " ))\n", "bl_region_lines = sorted(bl_region_lines_unsorted, key=lambda d: d[\"values\"][-1], reverse=False)\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", @@ -1034,7 +1071,7 @@ " arrow_up=None,\n", " arrow_dn=[\"Decreased trade\", \"between blocs\"],\n", " arrow_x_frac=0.70,\n", - " y_label_add=\"trade between Bloc A and B\",\n", + " y_label_add=\"trade between blocs\",\n", " regions=bl_region_lines,\n", " extra_curves=[\n", " dict(name=\"Intra-bloc total\", values=bl_intra_vals,\n", diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 5ad000d..fb5e499 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -886,14 +886,23 @@ def resolve_mga_links_from_blocks(n, mga): carrier = mga["carrier"] blocks = mga["threshold_value"] # dict: block_name -> [regions] - # Build region -> block mapping + # Build region -> block mapping. + # Each entry in the regions list may itself be a comma-separated string + # (e.g. from YAML flow syntax), so split defensively. region_to_block = {} for block_name, regions in blocks.items(): - for region in regions: - region_to_block[region] = block_name + for entry in regions: + for region in str(entry).split(","): + region = region.strip() + if region: + region_to_block[region] = block_name logger.info( - "Blocks MGA: %s", ", ".join(f"{k}: {len(v)} regions" for k, v in blocks.items()) + "Blocks MGA: %s", + ", ".join( + f"{k}: {sum(1 for v in region_to_block.values() if v == k)} regions" + for k in blocks + ), ) # Select shipping links for the target carrier @@ -1096,7 +1105,7 @@ def _link_weight(link_name): interone="hbi", intertwo="eaf", final="steel", - scenario="default", + scenario="mga-blocs", wacc="regional", chain_id="newre1206_2050", ) From d672696e0013765643c7553274348da0f88b0299 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 16 Jun 2026 11:35:02 +0200 Subject: [PATCH 175/216] feat: add iron ore cost to plot lcox --- workflow/notebooks/plot-compare-lcox.ipynb | 40 ++++++++++++++++++++-- 1 file changed, 37 insertions(+), 3 deletions(-) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 1b58c06..adee2e2 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -24,6 +24,8 @@ "snakemake = mock_snakemake(\n", " \"plot_compare_lcox\",\n", " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + " wacc=\"uniform\", # uniform or regional\n", + " cost_year=\"2050\",\n", ")" ] }, @@ -37,6 +39,16 @@ "config = snakemake.config" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "581e901d", + "metadata": {}, + "outputs": [], + "source": [ + "add_iron_ore_cost = True" + ] + }, { "cell_type": "code", "execution_count": null, @@ -168,6 +180,24 @@ "# df" ] }, + { + "cell_type": "markdown", + "id": "ebfb7968", + "metadata": {}, + "source": [ + "### Iron ore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "310bf035", + "metadata": {}, + "outputs": [], + "source": [ + "iron_ore_cost = config[\"iron_ore\"][\"marginal_cost\"] * config[\"iron_ore\"][\"ore_to_steel_ratio\"] " + ] + }, { "cell_type": "markdown", "id": "ff5e51d7", @@ -188,6 +218,11 @@ " index=\"network\", columns=\"carrier\", values=\"systemcost+fom_per_hbi\", aggfunc=\"sum\"\n", ")\n", "\n", + "if add_iron_ore_cost:\n", + " plot_df[\"iron ore\"] = iron_ore_cost\n", + "else: \n", + " plot_df[\"iron ore\"] = 0\n", + "\n", "plot_df.rename(\n", " columns={\n", " \"renewable_solar\": \"solar\",\n", @@ -198,12 +233,11 @@ " \"hydrogen\": \"hydrogen storage\",\n", " \"electrolysis\": \"electrolysis\",\n", " \"direct_reduction_furnace\": \"direct reduction furnace\",\n", - "\n", " },\n", " inplace=True,\n", ")\n", "\n", - "order = [\"solar\", \"onshore wind\", \"battery inverter (charging)\", \"battery inverter (discharging)\", \"battery\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\"]\n", + "order = [\"iron ore\", \"solar\", \"onshore wind\", \"battery inverter (charging)\", \"battery inverter (discharging)\", \"battery\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\"]\n", "\n", "plot_df = plot_df[order]" ] @@ -359,7 +393,7 @@ "plt.tight_layout()\n", "plt.savefig(snakemake.output.lcox_comparison, dpi=300, bbox_inches=\"tight\")\n", "plt.savefig(snakemake.output.lcox_comparison_png, dpi=300, bbox_inches=\"tight\")\n", - "plt.show()\n" + "plt.show()" ] }, { From 300faef7f3182132507adf40c8de99294d01a96b Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 16 Jun 2026 11:35:18 +0200 Subject: [PATCH 176/216] feat: update workflow for plotting rules --- rules/reporting.smk | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 92441f7..a8bf961 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -78,15 +78,15 @@ rule plot_global_supply: allow_missing=True, region=config["regions"]), steel_demand="resources/steel_demand_clustered_{cost_year}.csv", output: - network_curve="results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", - network_curve_png="results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", + network_curve="results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", + network_curve_png="results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", # supply_curves notebook: str(NOTEBOOKS_DIR / "analysis-globalsupplycurve.ipynb") rule plot_global_supply_all: input: - expand("../results/figures_general/global_supply_curve/cost_year~{cost_year}/{wacc}/{scenario}/{trade_chain}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", trade_chain=[config["trade_chains"]["id"]], cost_year=[2050], wacc=["regional"], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) + expand("results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", trade_chain=[config["trade_chains"]["id"]], cost_year=[2050], wacc=[config["trade_chains"]["wacc"]], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) rule plot_comparison: input: @@ -102,16 +102,20 @@ rule plot_comparison: rule plot_compare_lcox: input: - south_america_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_0.1.nc", - south_america_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_1.nc", - south_america_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_10.nc", - south_america_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/South_America_allocated_share/network_100.nc", - europe_01 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_0.1.nc", - europe_1 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_1.nc", - europe_10 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_10.nc", - europe_100 = "resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_100.nc", + south_america_01 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_0.1.nc", + south_america_1 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_1.nc", + south_america_10 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_10.nc", + south_america_100 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_100.nc", + europe_01 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_0.1.nc", + europe_1 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_1.nc", + europe_10 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_10.nc", + europe_100 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_100.nc", output: - lcox_comparison="results/figures_general/comparison/lcox_comparison.pdf", - lcox_comparison_png="results/figures_general/comparison/lcox_comparison.png", + lcox_comparison="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.pdf", + lcox_comparison_png="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.png", notebook: - str(NOTEBOOKS_DIR / "plot-compare-lcox.ipynb") \ No newline at end of file + str(NOTEBOOKS_DIR / "plot-compare-lcox.ipynb") + +rule plot_compare_lcox_all: + input: + expand("results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.pdf", cost_year=[2050], wacc=[config["trade_chains"]["wacc"]], allow_missing=True) From 8f024b618ec64f4f8e293f8ee2a57775d329468e Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 16 Jun 2026 13:07:06 +0200 Subject: [PATCH 177/216] chore: visual improvements in global supply curve plot --- .../analysis-globalsupplycurve.ipynb | 40 ++++++++++++++----- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index a66bc26..77511a1 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -53,10 +53,26 @@ "outputs": [], "source": [ "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", - "xlim = 1200\n", + "xlim = None\n", "ylim = 1000\n", - "add_iron_ore_cost = False\n", - "plot_optimal_curve = True # Uses the optimal supply curve from the trade model, which may differ from the single curves due to the trade-offs between different producers. If False, the single curves are plotted, which may not reflect the optimal supply curve from the trade model." + "add_iron_ore_cost = True\n", + "plot_optimal_curve = False # Uses the optimal supply curve from the trade model, which may differ from the single curves due to the trade-offs between different producers. If False, the single curves are plotted, which may not reflect the optimal supply curve from the trade model." + ] + }, + { + "cell_type": "markdown", + "id": "0a7a4f57", + "metadata": {}, + "source": [ + "### Description" + ] + }, + { + "cell_type": "markdown", + "id": "1fae3ac7", + "metadata": {}, + "source": [ + "This notebook plots the global supply curve with different approaches. Once from the supply curves, and once from the trade model. The results should be identical, unless the parameter `add_iron_ore_cost` is `True`, because the trade model uses iron ore costs from trade results whereas the supply curves add a generic cost premium" ] }, { @@ -255,25 +271,27 @@ " # Add vertical line of current demand and REMIND demand\n", " current_demand = 2000 # Mt steel\n", " ax.axvline(\n", - " current_demand, color=\"red\", linestyle=\"--\", label=\"Current demand (2020)\"\n", + " current_demand, color=\"black\", linestyle=\"--\", label=\"Current demand (2020)\", alpha=0.6,\n", " )\n", " ax.annotate(\n", - " \"Current demand (2025)\",\n", + " \"Grey steel \\ndemand 2025\",\n", " xy=(current_demand, 50),\n", - " xytext=(current_demand + 50, 150),\n", + " xytext=(current_demand + 50, 750),\n", " rotation=90,\n", - " color=\"red\",\n", + " color=\"black\",\n", + " alpha=0.6,\n", " )\n", " remind_demand = steel_demand[\"SteelDemand_DRI_Mt\"].sum() # Mt steel, REMIND global steel long-term hydrogen: 700-750 Mt\n", " ax.axvline(\n", - " remind_demand, color=\"orange\", linestyle=\"--\", label=\"REMIND 2050 demand\"\n", + " remind_demand, color=\"black\", linestyle=\"--\", label=\"REMIND 2050 demand\", alpha=0.6,\n", " )\n", " ax.annotate(\n", - " \"REMIND 2050 demand\",\n", + " \"Green steel \\ndemand 2050\",\n", " xy=(remind_demand, 50),\n", - " xytext=(remind_demand + 50, 150),\n", + " xytext=(remind_demand + 50, 750),\n", " rotation=90,\n", - " color=\"orange\",\n", + " color=\"black\",\n", + " alpha=0.6,\n", " )\n", "\n", " ax.set_xlabel(\"Cumulative quantity (Mt)\")\n", From 94273989a96e9edbff9576de482e1f7f0c0276d2 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 16 Jun 2026 13:21:19 +0200 Subject: [PATCH 178/216] chore: minor visual improvements for supply curve plots and lcox --- workflow/notebooks/analysis-globalsupplycurve.ipynb | 11 ++++++----- workflow/notebooks/plot-compare-lcox.ipynb | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 77511a1..3789ba8 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -255,7 +255,7 @@ " color=bar_colors,\n", " align=\"edge\",\n", " edgecolor=\"none\",\n", - " alpha=0.8,\n", + " alpha=0.6,\n", " )\n", " # Add the classic supply curve line\n", "\n", @@ -303,21 +303,22 @@ " ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", " # Legend for regions\n", " handles = [\n", - " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg]) for reg in region_list\n", + " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg], alpha=0.6) for reg in region_list\n", " ]\n", " handles.append(\n", " plt.Line2D([0], [0], color=\"black\", linewidth=1.2, label=\"Supply curve\")\n", " )\n", - " labels = list(region_list) + [\"Supply curve\"]\n", + " labels = [reg.replace(\"_\", \" \") for reg in region_list] + [\"Supply curve\"]\n", " ax.legend(\n", - " handles, labels, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\"\n", + " handles, labels, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\",\n", + " frameon=False,\n", " )\n", " plt.tight_layout()\n", " plt.savefig(snakemake.output.network_curve)\n", " plt.savefig(snakemake.output.network_curve_png, dpi=300)\n", " plt.show()\n", "\n", - " return" + " return\n" ] }, { diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index adee2e2..541f322 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -294,7 +294,7 @@ " if col in plot_df.columns:\n", " values = plot_df[col].fillna(0).values\n", " ax.bar(x_positions, values, bottom=bottom, width=bar_width,\n", - " label=col, color=config[\"colors\"][col], alpha=0.8)\n", + " label=col, color=config[\"colors\"][col], alpha=0.65)\n", " bottom += values\n", "\n", "# --- Totals on top of bars ---\n", From 24d742d3441d499a60c5e4c8d570fcdd01fef092 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 18 Jun 2026 15:43:17 +0200 Subject: [PATCH 179/216] feat: improve colorblind compatability of region colors --- config/config.yaml | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 3b5a940..f9b11f0 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -237,7 +237,7 @@ part_load: iron_ore: regionalise: "grade-dependent" #"uniform" or "grade-dependent" - marginal_cost: 97.7 #97.7 # EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) + marginal_cost: 97.7 #97.7 EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data). ore_to_steel_ratio: 1.59 # t_ore/t_steel, see https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) potential_allowance: 2 # Factor, which the current iron ore production is multiplied with shipping_cost_per_km: 0.005 # €/(t*km) # Guesstimate @@ -309,21 +309,21 @@ colors: HBI: darkred Photovoltaics: yellow Wind energy: blue - Eurasia: '#1B4F9C' # deep blue - Europe: '#F39C12' # strong orange - Far_West_Europe: '#2ECC71' # vivid green - Middle_East: '#C0392B' # brick red - North_West_Africa: '#7D3C98' # dark purple - Subsaharan_Africa: '#A04000' # burnt sienna - North_America: '#F1C40F' # yellow (clearly distinct) - South_America: '#16A085' # teal - South_South_America: '#EC7063' # coral - Central_America: '#27AE60' # emerald (different from teal) - West_Asia: '#2C3E50' # blue-grey (almost navy) - East_Asia: '#58D68D' # light green (intentional contrast) - Pacific_Asia: '#AF7AC5' # lavender - East_East_Asia: '#922B21' # dark red - Oceania: '#1F618D' # steel blue + Eurasia: '#1A6FBF' # mid blue (lighter than before) + Europe: '#E07B00' # deep amber-orange + Far_West_Europe: '#007A6E' # dark teal (replaces vivid green) + Middle_East: '#B5446E' # magenta-rose (replaces brick red) + North_West_Africa: '#6E3FA3' # purple (kept, adjusted) + Subsaharan_Africa: '#8B4513' # saddle brown + North_America: '#F5C518' # yellow (kept) + South_America: '#2DB3C8' # cyan-teal (lighter, distinct from teal) + South_South_America: '#F4845F' # warm peach-orange + Central_America: '#3D9970' # muted green (darker, less vivid) + West_Asia: '#2C3E50' # blue-grey (kept, unique) + East_Asia: '#A8D8A8' # very light sage + Pacific_Asia: '#C47AB0' # pink-lavender + East_East_Asia: '#0D47A1' # deep navy (replaces dark red) + Oceania: '#546E7A' # blue-grey slate (distinct from navy) trade_today: surplus: '#d94801' # orange-red (net exporter bubble) deficit: '#045a8d' # dark blue (net importer bubble) From 350d232db03d77e02c7364df19d4c184fb7a12a4 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 18 Jun 2026 15:53:54 +0200 Subject: [PATCH 180/216] feat: get global wacc and set in config --- config/config.yaml | 2 +- workflow/notebooks/prepare-wacc.ipynb | 24 ++++++++++++++++-------- 2 files changed, 17 insertions(+), 9 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index f9b11f0..1dc13be 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -209,7 +209,7 @@ techno-economic parameters: batt_standing_loss: 0.0001 # 0.01% per hour for battery (self-discharge) interest_rate: - default: 0.05 # Used, if wildcard `wacc` is set to `uniform` + default: 0.084 # Used, if wildcard `wacc` is set to `uniform`. The proposed value of 0.084 (8.4%) is obtained from the notebook `prepare-wacc.ipynb` and represents a global average WACC weighted by GDP. T trade: shipping_routes: diff --git a/workflow/notebooks/prepare-wacc.ipynb b/workflow/notebooks/prepare-wacc.ipynb index 1313c47..cd62eaa 100644 --- a/workflow/notebooks/prepare-wacc.ipynb +++ b/workflow/notebooks/prepare-wacc.ipynb @@ -267,30 +267,38 @@ { "cell_type": "code", "execution_count": null, - "id": "874da897", + "id": "b96c4259", "metadata": {}, "outputs": [], "source": [ - "wacc_by_region.to_csv(wacc_clustered_fn)" + "# Calculate global GDP-weighted average and append as last row\n", + "global_avg = pd.Series({\n", + " \"wacc_real\": weighted_mean(wacc_gdp, \"wacc_real\", \"gdp_usd\"),\n", + " \"n_countries\": len(wacc_gdp),\n", + " \"wacc_spread\": wacc_gdp[\"wacc\"].max() - wacc_gdp[\"wacc\"].min(),\n", + "}, name=\"global_weighted_avg\")\n", + "\n", + "wacc_by_region = pd.concat([wacc_by_region, global_avg.to_frame().T])" ] }, { - "cell_type": "markdown", - "id": "41517ecb", + "cell_type": "code", + "execution_count": null, + "id": "874da897", "metadata": {}, + "outputs": [], "source": [ - "### Mean (depreciated)" + "wacc_by_region.to_csv(wacc_clustered_fn)" ] }, { "cell_type": "code", "execution_count": null, - "id": "e1d215c4", + "id": "dcecd263", "metadata": {}, "outputs": [], "source": [ - "# wacc_by_region = wacc.groupby('region').mean(numeric_only=True)\n", - "# wacc_by_region.round(3)" + "# wacc_by_region" ] } ], From 85a73df1db0d5539a6e1361cdb25952d8834cd78 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 18 Jun 2026 16:47:42 +0200 Subject: [PATCH 181/216] feat: add first draft of supply constraint --- workflow/scripts/model_trade.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index fb5e499..3c80862 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -180,6 +180,8 @@ def building_model( p_set=load, ) + p_nom_interone_installed = 0 + # --- Stage 1 supply: ore → interone (material) or direct supply (energy) --- for s in range(len(region_data_interone)): @@ -190,6 +192,20 @@ def building_model( region_data_interone[f"demand [{unit}]"][s] ) - float(region_data_interone[f"demand [{unit}]"][s - 1]) + limit_per_supplier = 200 * 1e6 # Mt of HBI + + if (p_nom_interone_installed + p_nom_interone) < limit_per_supplier: + + pass + + else: + p_nom_interone = limit_per_supplier - p_nom_interone_installed + # set p_nom_interone if smaller than 0 + if p_nom_interone < 0: + p_nom_interone = 0 + + p_nom_interone_installed += p_nom_interone + m_cost = float(region_data_interone[f"{cost_descriptor} [EUR/{unit}]"][s]) if not is_material_chain: @@ -1105,9 +1121,9 @@ def _link_weight(link_name): interone="hbi", intertwo="eaf", final="steel", - scenario="mga-blocs", + scenario="default", wacc="regional", - chain_id="newre1206_2050", + chain_id="supplyconstraint", ) final = snakemake.wildcards["final"] From 660b456d007fcce2c99c94e2db8d5eceef56c04f Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 19 Jun 2026 08:38:44 +0200 Subject: [PATCH 182/216] feat: solve supply constraint pareto style flexibliy in model trade --- config/config.yaml | 14 ++- workflow/scripts/model_trade.py | 185 ++++++++++++++++++++++++++++---- 2 files changed, 178 insertions(+), 21 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 1dc13be..8d1bbb4 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -27,7 +27,7 @@ supply_curve: # Config-native trade chain definitions # Defines the commodity transformation chain with ordered stages and process labels trade_chains: - id: newre1206_2050 + id: supplyconstraint cost_year: 2050 final_product: steel wacc: regional #regional @@ -50,10 +50,11 @@ trade_chains: output_commodity: steel process_label: eaf trade_scenarios: - - default - - mga-stability-weighted + # - default + # - mga-stability-weighted - mga-chokepoints - mga-blocs + - constrain-supply scenario: @@ -140,6 +141,13 @@ scenario: block_1: ["Europe", "Far_West_Europe", "North_America"] block_2: ["Middle_East", "North_West_Africa", "Subsaharan_Africa", "Eurasia", "South_America", "South_South_America", "Central_America", "West_Asia", "East_Asia", "Pacific_Asia"] block_3: ["East_East_Asia", "Oceania"] + constrain-supply: + modifiers: + cost_penalty: + pareto: + indicator: "supply" + threshold_value: [50, 100, 200, 300, 400] # Mt + # Absolute steel demand levels (Mt/year) for supply curve sweep diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 3c80862..84642d7 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -180,8 +180,6 @@ def building_model( p_set=load, ) - p_nom_interone_installed = 0 - # --- Stage 1 supply: ore → interone (material) or direct supply (energy) --- for s in range(len(region_data_interone)): @@ -192,20 +190,6 @@ def building_model( region_data_interone[f"demand [{unit}]"][s] ) - float(region_data_interone[f"demand [{unit}]"][s - 1]) - limit_per_supplier = 200 * 1e6 # Mt of HBI - - if (p_nom_interone_installed + p_nom_interone) < limit_per_supplier: - - pass - - else: - p_nom_interone = limit_per_supplier - p_nom_interone_installed - # set p_nom_interone if smaller than 0 - if p_nom_interone < 0: - p_nom_interone = 0 - - p_nom_interone_installed += p_nom_interone - m_cost = float(region_data_interone[f"{cost_descriptor} [EUR/{unit}]"][s]) if not is_material_chain: @@ -708,6 +692,74 @@ def apply_hbi_diversity_constraint(n, diversity_factor, demands): return n +def apply_supply_constraint(n, limit_per_supplier, interone, final, is_material_chain): + """ + Cap each region's interone production capacity to limit_per_supplier tonnes + by adjusting p_nom_max on the relevant links (material chain) or generators + (energy chain) in an already-built network. + + Capacity steps are capped in marginal-cost order (cheapest first), mirroring + the merit-order logic of the supply curves. + + Parameters + ---------- + n : pypsa.Network + The built network to constrain. + limit_per_supplier : float + Maximum interone production per region in tonnes. + interone : str + Intermediate product carrier name (e.g. "hbi"). + final : str + Final product carrier name (e.g. "steel"). + is_material_chain : bool + True for ore→interone→final chains; False for direct energy chains. + + Returns + ------- + pypsa.Network + The network with adjusted p_nom_max values. + """ + if is_material_chain: + ore_ratio = snakemake.config["iron_ore"]["ore_to_steel_ratio"] + supply_links = n.links[ + (n.links.carrier == interone) & n.links.bus1.str.endswith(f"_{interone}") + ] + for _region_bus, group in supply_links.groupby("bus1"): + group_sorted = group.sort_values("marginal_cost") + running = 0.0 # cumulative interone capacity (t) + for link_name in group_sorted.index: + cap_ore = n.links.at[link_name, "p_nom_max"] + cap_interone = cap_ore / ore_ratio + if running >= limit_per_supplier: + n.links.at[link_name, "p_nom_max"] = 0.0 + elif running + cap_interone <= limit_per_supplier: + running += cap_interone + else: + remaining = limit_per_supplier - running + n.links.at[link_name, "p_nom_max"] = remaining * ore_ratio + running = limit_per_supplier + else: + supply_gens = n.generators[n.generators.carrier == final] + for _region_bus, group in supply_gens.groupby("bus"): + group_sorted = group.sort_values("marginal_cost") + running = 0.0 + for gen_name in group_sorted.index: + cap = n.generators.at[gen_name, "p_nom_max"] + if running >= limit_per_supplier: + n.generators.at[gen_name, "p_nom_max"] = 0.0 + elif running + cap <= limit_per_supplier: + running += cap + else: + remaining = limit_per_supplier - running + n.generators.at[gen_name, "p_nom_max"] = remaining + running = limit_per_supplier + + logger.info( + "Supply constraint applied: %.0f Mt per region", limit_per_supplier / 1e6 + ) + return n + + def normalize_regions(regions, carrier): """Ensure regions are lists and suffixed with _{carrier}.""" if regions is None: @@ -1111,6 +1163,88 @@ def _link_weight(link_name): return (optimal_network, nc) +def solve_pareto(n, pareto_config, interone, final, is_material_chain): + """ + Solve the network for each supply-limit threshold in pareto_config, building + the Pareto front of cost vs. per-region supply cap. + + An unconstrained solve is run first (key=None) as the reference point, then + one constrained solve per threshold value via apply_supply_constraint. + + Parameters + ---------- + n : pypsa.Network + The built (unsolved) network. + pareto_config : dict + Config dict with key ``threshold_value`` (list of Mt values, + e.g. [100, 500, 1000]). + interone : str + Intermediate product carrier name (e.g. "hbi"). + final : str + Final product carrier name (e.g. "steel"). + is_material_chain : bool + True for ore→interone→final chains; False for direct energy chains. + + Returns + ------- + tuple(pypsa.Network, pypsa.NetworkCollection) + (optimal_network, NetworkCollection) where NetworkCollection contains + the unconstrained solution (key=None) and one entry per threshold value + (key = Mt limit as provided in config). + """ + solver_name = snakemake.config["solver"]["name"] + options = snakemake.config["solver_options"][snakemake.config["solver"]["options"]] + threshold_values = pareto_config["threshold_value"] + + # Solve unconstrained reference + logger.info("Solving unconstrained reference network for Pareto front") + n.optimize(n.snapshots, solver_name=solver_name, solver_options=options) + + try: + if hasattr(n, "model") and getattr(n.model, "solver_model", None) is not None: + n.model.solver_model = None + except Exception as e: + logger.warning(f"Warning clearing solver model before copying network: {e}") + + optimal_network = n.copy() + networks = {None: optimal_network} + + for limit_mt in threshold_values: + limit_t = float(limit_mt) * 1e6 + logger.info(f"\n--- Pareto: solving with supply limit = {limit_mt} Mt ---") + + n_copy = n.copy() + apply_supply_constraint(n_copy, limit_t, interone, final, is_material_chain) + n_copy.optimize( + n_copy.snapshots, solver_name=solver_name, solver_options=options + ) + + try: + if ( + hasattr(n_copy, "model") + and getattr(n_copy.model, "solver_model", None) is not None + ): + n_copy.model.solver_model = None + except Exception as e: + logger.warning( + f"Warning clearing solver model for limit {limit_mt} Mt: {e}" + ) + + tsc = ( + pd.concat([n_copy.statistics.capex(), n_copy.statistics.opex()], axis=1) + .sum(axis=1) + .div(1e9) + ) + logger.info( + f"Pareto limit {limit_mt} Mt: total system cost = {tsc.sum():.2f} B\u20ac" + ) + + networks[limit_mt] = n_copy + + nc = pypsa.NetworkCollection(networks) + return (optimal_network, nc) + + if __name__ == "__main__": if snakemake is None: from _helpers import mock_snakemake @@ -1121,7 +1255,7 @@ def _link_weight(link_name): interone="hbi", intertwo="eaf", final="steel", - scenario="default", + scenario="constrain-supply", wacc="regional", chain_id="supplyconstraint", ) @@ -1141,6 +1275,7 @@ def _link_weight(link_name): interone = next(c for c in tradeable if c != "iron_ore") stages = trade_chain["stages"] intertwo = stages[max(stages.keys())]["process_label"] + is_material_chain = "iron_ore" in tradeable logger.info( "intermediate 1 (%s) and intermediate 2 (%s) to final product %s", @@ -1242,6 +1377,15 @@ def _link_weight(link_name): else: logger.info("HBI diversity constraint disabled") + # Pareto supply constraint + pareto = snakemake.config["scenario"][scenario]["modifiers"].get("pareto") + if pareto is not None: + logger.info( + f"Pareto supply constraint activated with thresholds {pareto['threshold_value']} Mt" + ) + else: + logger.info("Pareto supply constraint not activated") + # MGA if "mga" not in snakemake.config["scenario"][scenario]["modifiers"].keys(): mga = None @@ -1252,7 +1396,12 @@ def _link_weight(link_name): # solving model logger.info("solving model") - result = solve_network(n, mga=mga, indicators=indicators if indicators else None) + if pareto is not None: + result = solve_pareto(n, pareto, interone, final, is_material_chain) + else: + result = solve_network( + n, mga=mga, indicators=indicators if indicators else None + ) logger.info("network was solved") # Export result: always a tuple (optimal_network, NetworkCollection) From 78970064ffd1242e13621415897e823c4b50d1f7 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 19 Jun 2026 08:39:04 +0200 Subject: [PATCH 183/216] feat: add input network for plot_mga --- rules/reporting.smk | 1 + 1 file changed, 1 insertion(+) diff --git a/rules/reporting.smk b/rules/reporting.smk index a8bf961..123ee7d 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -36,6 +36,7 @@ rule plot_mga: network_mga_production = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", network_mga_chokepoints = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", network_mga_blocks = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", + network_pareto_supply = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/constrain-supply/network.nc", political_stability = "resources/political_stability_clustered.csv", trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", output: From 0043acf7d0f39cfee2f7c474c1e6c8453cdb8737 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 20 Jun 2026 10:55:25 +0200 Subject: [PATCH 184/216] feat: update inputs for plot lcox rule --- rules/reporting.smk | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 123ee7d..b6f78c1 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -102,15 +102,19 @@ rule plot_comparison: rule plot_compare_lcox: + params: + low_cost = ["South_America"], + high_cost = ["Europe"], + quantities = [1,10,100], + comparison = ["Europe", "South_America"] input: - south_america_01 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_0.1.nc", south_america_1 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_1.nc", south_america_10 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_10.nc", south_america_100 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_100.nc", - europe_01 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_0.1.nc", europe_1 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_1.nc", europe_10 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_10.nc", europe_100 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_100.nc", + trade_result = "results/chain_id~{trade_chain}/cost_year~{cost_year}/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~default/network.nc", output: lcox_comparison="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.pdf", lcox_comparison_png="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.png", From 6dec7c2e8533a03059924224ac40192bbba4dd06 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 20 Jun 2026 11:45:52 +0200 Subject: [PATCH 185/216] fix: read correct input for mga and pareto plot --- rules/reporting.smk | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index b6f78c1..c4e86d1 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -36,7 +36,7 @@ rule plot_mga: network_mga_production = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-stability-weighted/network.nc", network_mga_chokepoints = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-chokepoints/network.nc", network_mga_blocks = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~mga-blocs/network.nc", - network_pareto_supply = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/constrain-supply/network.nc", + network_pareto_supply = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~constrain-supply/network.nc", political_stability = "resources/political_stability_clustered.csv", trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", output: From af49fb491967168ba82dd1a177b1b6cc12cf59bd Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 20 Jun 2026 12:12:30 +0200 Subject: [PATCH 186/216] feat: add mga plot that visualizes simple pareto front of supplier constraint --- workflow/notebooks/plot-mga.ipynb | 386 +++++++++++++++++++++++++++++- 1 file changed, 384 insertions(+), 2 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 1eda4b5..dfac1eb 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -21,7 +21,7 @@ " snakemake = mock_snakemake(\n", " \"plot_mga\",\n", " wacc=\"regional\",\n", - " trade_chain=\"newre1206_2050\",\n", + " trade_chain=\"supplyconstraint\",\n", " )" ] }, @@ -1169,9 +1169,391 @@ " fontsize=8, va=\"center\", ha=\"left\", color=\"#444441\", clip_on=False)\n", "\n", "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7c36edb7", + "metadata": {}, + "source": [ + "---\n", + "# Supply Constraint Pareto: Per-Region HBI Production vs Cost Premium\n", + "\n", + "Load the `constrain-supply` scenario networks, compute the cost premium (ε) relative to the unconstrained optimum for each per-region supply cap, and extract regional HBI production — analogous to the MGA stability analysis above but driven by supply-cap tightness rather than MGA slack.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d3ac6f7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# ── Load Pareto Supply Constraint networks ────────────────────────────────────\n", + "pareto_scenario_name = extract_scenario(snakemake.input.network_pareto_supply)\n", + "pareto_cfg = snakemake.config.get(\"scenario\", {}).get(pareto_scenario_name, {})\n", + "pareto_limits_cfg = (\n", + " pareto_cfg.get(\"modifiers\", {})\n", + " .get(\"pareto\", {})\n", + " .get(\"threshold_value\", [])\n", + ")\n", + "\n", + "pareto_dir = os.path.dirname(snakemake.input.network_pareto_supply)\n", + "print(f\"Pareto supply directory: {pareto_dir}\")\n", + "print(f\"Configured per-region caps (Mt): {pareto_limits_cfg}\")\n", + "\n", + "# Optimal (unconstrained) — same as network.nc but stored as network_nan.nc in the collection\n", + "opt_path = os.path.join(pareto_dir, \"network_nan.nc\")\n", + "if not os.path.exists(opt_path):\n", + " opt_path = snakemake.input.network_pareto_supply # fallback: network.nc\n", + "\n", + "pareto_nets = {}\n", + "pareto_nets[None] = pypsa.Network(opt_path)\n", + "print(\" ✓ Loaded optimal (unconstrained)\")\n", + "\n", + "for lim in pareto_limits_cfg:\n", + " path = os.path.join(pareto_dir, f\"network_{float(lim)}.nc\")\n", + " if os.path.exists(path):\n", + " pareto_nets[float(lim)] = pypsa.Network(path)\n", + " print(f\" ✓ Loaded limit = {lim} Mt\")\n", + " else:\n", + " print(f\" ✗ Not found: {path}\")\n", + "\n", + "# ── Compute total system cost for each network ────────────────────────────────\n", + "def total_system_cost_bn(n):\n", + " \"\"\"Return total system cost in billion EUR.\"\"\"\n", + " tsc = (\n", + " pd.concat([n.statistics.capex(), n.statistics.opex()], axis=1)\n", + " .sum(axis=1)\n", + " .div(1e9)\n", + " )\n", + " return float(tsc.sum())\n", + "\n", + "optimal_cost_pareto = total_system_cost_bn(pareto_nets[None])\n", + "print(f\"\\nOptimal system cost: {optimal_cost_pareto:.3f} B€\")\n", + "\n", + "# Compute ε = (cost_constrained − cost_optimal) / cost_optimal for every network,\n", + "# then sort by ε ascending so the x-axis runs from 0 (unconstrained) to max ε.\n", + "pareto_entries = [(None, 0.0)]\n", + "for key in [k for k in pareto_nets if k is not None]:\n", + " cost = total_system_cost_bn(pareto_nets[key])\n", + " eps = (cost - optimal_cost_pareto) / optimal_cost_pareto\n", + " pareto_entries.append((key, eps))\n", + "\n", + "pareto_entries.sort(key=lambda x: x[1]) # sort by ε ascending\n", + "\n", + "# ── Deduplicate entries with identical ε ──────────────────────────────────────\n", + "# Non-binding constraints (e.g. a 400 Mt cap when optimal production is already\n", + "# below 400 Mt everywhere) produce the same cost as the unconstrained optimal,\n", + "# yielding ε = 0 for multiple entries. Duplicate ε values would create duplicate\n", + "# DataFrame column labels, causing pandas .loc to return more values than expected.\n", + "# Keep the first occurrence (= unconstrained optimal for ε=0 ties).\n", + "seen_eps: set = set()\n", + "deduped: list = []\n", + "for key, eps in pareto_entries:\n", + " eps_key = round(eps, 8) # round to absorb floating-point noise\n", + " if eps_key not in seen_eps:\n", + " seen_eps.add(eps_key)\n", + " deduped.append((key, eps))\n", + " else:\n", + " label = \"unconstrained\" if key is None else f\"{key:.0f} Mt cap\"\n", + " print(f\" (skipped duplicate ε={eps*100:.3f}%: {label})\")\n", + "pareto_entries = deduped\n", + "\n", + "ordered_pareto_keys = [e[0] for e in pareto_entries]\n", + "pareto_eps_arr_frac = np.array([e[1] for e in pareto_entries], dtype=float)\n", + "\n", + "for key, eps in pareto_entries:\n", + " label = \"unconstrained\" if key is None else f\"{key:.0f} Mt cap\"\n", + " cost = total_system_cost_bn(pareto_nets[key])\n", + " print(f\" {label:25s}: cost = {cost:.3f} B€ → ε = {eps * 100:.2f}%\")\n", + "\n", + "# ── Extract HBI production per region ─────────────────────────────────────────\n", + "# Reuse all_regions / stable_regions / unstable_regions from the stability cell above\n", + "pareto_hbi_data = {region: [] for region in all_regions}\n", + "\n", + "for key in ordered_pareto_keys:\n", + " n_p = pareto_nets[key]\n", + " regional_totals = {region: 0.0 for region in all_regions}\n", + "\n", + " for link_idx, link_row in n_p.links.iterrows():\n", + " if link_row[\"carrier\"] == \"hbi\":\n", + " bus1 = link_row[\"bus1\"]\n", + " if hasattr(n_p, \"links_t\") and \"p0\" in n_p.links_t:\n", + " p_sum = n_p.links_t[\"p0\"][link_idx].sum()\n", + " else:\n", + " p_sum = link_row.get(\"p_nom_opt\", 0.0)\n", + "\n", + " if not pd.isna(p_sum) and p_sum > 0:\n", + " for region in all_regions:\n", + " if bus1.startswith(region):\n", + " regional_totals[region] += p_sum\n", + " break\n", + "\n", + " for region in all_regions:\n", + " pareto_hbi_data[region].append(regional_totals[region])\n", + "\n", + "# Build DataFrame: regions × ε values (using integer positions as column names\n", + "# to avoid duplicate-label issues when several caps produce identical ε)\n", + "n_scenarios = len(pareto_eps_arr_frac)\n", + "df_pareto_hbi = pd.DataFrame(\n", + " pareto_hbi_data,\n", + " index=range(n_scenarios)\n", + ").T / 1e6\n", + "df_pareto_hbi = df_pareto_hbi / snakemake.config[\"iron_ore\"][\"ore_to_steel_ratio\"]\n", + "df_pareto_hbi.index.name = \"Region\"\n", + "df_pareto_hbi.columns.name = \"Scenario index\"\n", + "\n", + "# Attach political-stability metadata (for consistent colour/style coding)\n", + "df_pareto_hbi[\"Political Stability\"] = stability_df.iloc[:, 0]\n", + "df_pareto_hbi[\"Stability Class\"] = df_pareto_hbi.index.map(\n", + " lambda r: \"Stable\" if r in stable_regions else \"Unstable\"\n", + ")\n", + "\n", + "pareto_eps_cols = df_pareto_hbi.columns[:-2] # integer scenario indices (0, 1, 2, ...)\n", + "\n", + "print(f\"\\n{len(pareto_entries)} unique scenarios after deduplication\")\n", + "print(\"\\nHBI Production summary (Mt):\")\n", + "df_pareto_hbi[pareto_eps_cols].round(1)\n" + ] + }, + { + "cell_type": "markdown", + "id": "70077fcf", + "metadata": {}, + "source": [ + "---\n", + "# Combined Overview Plot (v2): Chokepoints | Supply Constraint | Blocs\n", + "\n", + "Same three-panel layout as above, but the centre panel now shows the **supply-constraint Pareto front** — how HBI production redistributes across regions as per-region production is progressively capped (x-axis = cost premium ε derived from each constrained optimisation).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8510fbc6", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "# 1) Chokepoint data — reused from df_cp / df_cp_display (computed above)\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "cp_total_row_v2 = df_cp_display.loc[\"TOTAL (all chokepoints)\"]\n", + "cp_eps_v2 = np.array([float(c) for c in cp_total_row_v2.index]) * 100 # → %\n", + "cp_total_vals_v2 = np.array(cp_total_row_v2.values, dtype=float)\n", + "\n", + "cp_region_lines_v2 = []\n", + "for cpname in config_chokepoints:\n", + " if cpname in df_cp.index and df_cp.loc[cpname].sum() > 0:\n", + " vals = np.array(df_cp.loc[cpname].values, dtype=float)\n", + " cp_region_lines_v2.append(dict(\n", + " name=cp_display_names.get(cpname, cpname.replace(\"_\", \" \").title()),\n", + " values=vals,\n", + " color=cp_colors.get(cpname, \"#333333\"),\n", + " linestyle=\"-\",\n", + " alpha=0.6,\n", + " ))\n", + "cp_region_lines_v2.sort(key=lambda d: d[\"values\"][-1], reverse=True)\n", + "\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "# 2) Supply constraint data — from df_pareto_hbi / pareto_eps_arr_frac\n", + "#\n", + "# pareto_eps_cols : integer position labels (0, 1, 2, …) — safe for .loc\n", + "# sc_eps_pct : actual ε values in % (from pareto_eps_arr_frac)\n", + "# sc_cap_vals : \"max. per supplier\" curve\n", + "# • unconstrained optimum (key=None, ε=0): actual max\n", + "# regional production at that scenario\n", + "# • constrained scenarios: raw threshold_value from config\n", + "# (Mt, as specified — no unit conversion applied)\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "sc_eps_pct = pareto_eps_arr_frac * 100 # → %\n", + "\n", + "# Build cap line directly from config threshold values (ordered by ε ascending,\n", + "# matching the column order of df_pareto_hbi)\n", + "sc_cap_vals = np.array([\n", + " float(df_pareto_hbi[pareto_eps_cols].iloc[:, 0].max()) if key is None\n", + " else float(key)\n", + " for key in ordered_pareto_keys\n", + "], dtype=float)\n", + "print(f\"max. per supplier line (cap values): {sc_cap_vals}\")\n", + "\n", + "# Individual region lines — all solid (stable/unstable not distinguished here)\n", + "sc_region_lines = []\n", + "for region in all_regions:\n", + " vals = np.array(df_pareto_hbi.loc[region, pareto_eps_cols].values, dtype=float)\n", + " if vals.sum() > 0:\n", + " sc_region_lines.append(dict(\n", + " name=region.replace(\"_\", \" \"),\n", + " values=vals,\n", + " color=region_colors.get(region, \"#333333\"),\n", + " linestyle=\"-\",\n", + " alpha=0.65,\n", + " ))\n", + "# Sort by production at ε=0 descending so the legend lists largest producers first\n", + "sc_region_lines.sort(key=lambda d: d[\"values\"][0], reverse=True)\n", + "\n", + "# Total production across all regions (approximately constant = demand-driven)\n", + "sc_total_vals = np.array(df_pareto_hbi[pareto_eps_cols].sum().values, dtype=float)\n", + "\n", + "assert len(sc_eps_pct) == len(sc_cap_vals), (\n", + " f\"eps ({len(sc_eps_pct)}) and cap curve ({len(sc_cap_vals)}) length mismatch\"\n", + ")\n", + "\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "# 3) Blocs data — reused from bl_summary (computed above)\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "bl_eps_v2 = np.array([float(c) for c in bl_summary.index]) * 100 # → %\n", + "bl_inter_vals_v2 = np.array(bl_summary[\"Inter-block total\"].values, dtype=float)\n", + "bl_intra_vals_v2 = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", + "\n", + "_bl_pair_cfg_v2 = {\n", + " \"Block 1 → Block 2\": dict(color=\"#e41a1c\", linestyle=\"--\"),\n", + " \"Block 1 → Block 3\": dict(color=\"#e41a1c\", linestyle=\":\"),\n", + " \"Block 2 → Block 1\": dict(color=\"#377eb8\", linestyle=\"--\"),\n", + " \"Block 2 → Block 3\": dict(color=\"#377eb8\", linestyle=\":\"),\n", + " \"Block 3 → Block 1\": dict(color=\"#4daf4a\", linestyle=\"--\"),\n", + " \"Block 3 → Block 2\": dict(color=\"#4daf4a\", linestyle=\":\"),\n", + "}\n", + "bl_region_lines_v2 = sorted(\n", + " [\n", + " dict(name=label, values=np.array(bl_summary[label].values, dtype=float),\n", + " color=cfg[\"color\"], linestyle=cfg[\"linestyle\"], alpha=0.7)\n", + " for label, cfg in _bl_pair_cfg_v2.items()\n", + " if label in bl_summary.columns\n", + " ],\n", + " key=lambda d: d[\"values\"][-1],\n", + " reverse=False,\n", + ")\n", + "\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "# Combined plot — v2\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "Y_MAX_V2 = 800\n", + "GAP_FRAC_V2 = 0.04\n", + "\n", + "plots_v2 = [\n", + " dict(\n", + " title=\"Unstable trade routes (chokepoints)\",\n", + " epsilon=cp_eps_v2,\n", + " main_curve=cp_total_vals_v2,\n", + " main_label=\"Global\",\n", + " y_mid=cp_total_vals_v2[0],\n", + " color=\"#185FA5\",\n", + " arrow_dn=[\"Decreased\", \"high-risk routes\"],\n", + " arrow_x_frac=0.70,\n", + " y_label_add=\"trade through maritime chokepoints\",\n", + " regions=cp_region_lines_v2,\n", + " extra_curves=[],\n", + " ),\n", + " dict(\n", + " title=\"Supply constraint diversification\",\n", + " epsilon=sc_eps_pct,\n", + " main_curve=sc_cap_vals,\n", + " main_label=\"max. per supplier\",\n", + " y_mid=sc_cap_vals[0],\n", + " color=\"#3B6D11\",\n", + " arrow_dn=[\"Decreased dominant\", \"supplier cap\"],\n", + " arrow_x_frac=0.50,\n", + " y_label_add=\"production by region\",\n", + " regions=sc_region_lines,\n", + " extra_curves=[\n", + " dict(name=\"Total (all regions)\", values=sc_total_vals,\n", + " color=\"#999999\", linestyle=\":\", linewidth=1.5, alpha=0.7),\n", + " ],\n", + " ),\n", + " dict(\n", + " title=\"Fragmentation / Bloc trade\",\n", + " epsilon=bl_eps_v2,\n", + " main_curve=bl_inter_vals_v2,\n", + " main_label=\"Global\",\n", + " y_mid=bl_inter_vals_v2[0],\n", + " color=\"#534AB7\",\n", + " arrow_dn=[\"Decreased trade\", \"between blocs\"],\n", + " arrow_x_frac=0.70,\n", + " y_label_add=\"trade between blocs\",\n", + " regions=bl_region_lines_v2,\n", + " extra_curves=[\n", + " dict(name=\"Intra-bloc total\", values=bl_intra_vals_v2,\n", + " color=\"#999999\", linestyle=\":\", linewidth=1.5, alpha=0.7),\n", + " ],\n", + " ),\n", + "]\n", + "\n", + "fig_v2, axes_v2 = plt.subplots(1, 3, figsize=(15, 5))\n", + "fig_v2.subplots_adjust(wspace=0.35)\n", + "\n", + "arrowprops_v2 = dict(arrowstyle=\"->\", color=\"#444441\", lw=1.4, mutation_scale=12)\n", + "\n", + "for ax, p in zip(axes_v2, plots_v2):\n", + " eps = p[\"epsilon\"]\n", + " curve = p[\"main_curve\"]\n", + " y_mid = p[\"y_mid\"]\n", + " gap = Y_MAX_V2 * GAP_FRAC_V2\n", + "\n", + " # ── Main curve ───────────────────────────────────────────────────────────\n", + " ax.plot(eps, curve, color=p[\"color\"], linewidth=2.5, marker=\"o\",\n", + " markersize=4, label=p[\"main_label\"], zorder=10)\n", + "\n", + " # ── Extra reference curves ───────────────────────────────────────────────\n", + " for ec in p.get(\"extra_curves\", []):\n", + " ax.plot(eps, ec[\"values\"],\n", + " color=ec[\"color\"],\n", + " linewidth=ec.get(\"linewidth\", 1.2),\n", + " linestyle=ec[\"linestyle\"],\n", + " alpha=ec[\"alpha\"],\n", + " marker=\"d\", markersize=3,\n", + " label=ec[\"name\"], zorder=5)\n", + "\n", + " # ── Per-region decomposition lines ───────────────────────────────────────\n", + " for reg in p.get(\"regions\", []):\n", + " ax.plot(eps, reg[\"values\"],\n", + " color=reg[\"color\"], linewidth=1.2,\n", + " linestyle=reg[\"linestyle\"], alpha=reg[\"alpha\"],\n", + " label=reg[\"name\"], zorder=5)\n", + "\n", + " # ── Axes ─────────────────────────────────────────────────────────────────\n", + " ax.set_xlim(eps[0], eps[-1])\n", + " ax.set_ylim(0, Y_MAX_V2)\n", + " ax.set_xlabel(\"ε in %\", fontsize=11)\n", + " ax.set_ylabel(f\"HBI {p['y_label_add']} in Mt\", fontsize=10)\n", + " ax.set_title(p[\"title\"], fontsize=13, fontweight=\"medium\", pad=10)\n", + " ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.5)\n", + "\n", + " # ── Legend: main curve first, then remaining reversed (highest value on top) ──\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " ax.legend(\n", + " [handles[0]] + handles[1:][::-1],\n", + " [labels[0]] + labels[1:][::-1],\n", + " loc=\"best\", fontsize=7, framealpha=0.9,\n", + " )\n", + "\n", + " # ── Downward arrow ───────────────────────────────────────────────────────\n", + " if p.get(\"arrow_dn\") is not None:\n", + " arrow_x_frac = p.get(\"arrow_x_frac\", 0.70)\n", + " arrow_x = eps[0] + arrow_x_frac * (eps[-1] - eps[0])\n", + " label_x = eps[0] + (arrow_x_frac + 0.05) * (eps[-1] - eps[0])\n", + " curve_at_arrow = np.interp(arrow_x, eps, curve)\n", + " y_start = y_mid - gap\n", + " y_end = curve_at_arrow\n", + " if y_end < y_start:\n", + " ax.annotate(\"\", xy=(arrow_x, y_end), xytext=(arrow_x, y_start),\n", + " xycoords=\"data\", textcoords=\"data\",\n", + " arrowprops=arrowprops_v2, clip_on=False)\n", + " ax.text(label_x, (y_start + y_end) / 2, \"\\n\".join(p[\"arrow_dn\"]),\n", + " fontsize=8, va=\"center\", ha=\"left\", color=\"#444441\", clip_on=False)\n", + "\n", + "plt.tight_layout()\n", + "\n", "plt.savefig(snakemake.output.mga_plot, dpi=300, bbox_inches=\"tight\")\n", "plt.savefig(snakemake.output.mga_plot_png, dpi=300, bbox_inches=\"tight\")\n", - "plt.show()\n" + "plt.show()\n", + "\n", + "# ── Summary stats ─────────────────────────────────────────────────────────────\n", + "print(f\"\\nmax. per supplier cap: {sc_cap_vals[0]:.1f} Mt (ε=0) → {sc_cap_vals[-1]:.1f} Mt (ε={sc_eps_pct[-1]:.2f}%)\")\n", + "print(f\"Total production: {sc_total_vals[0]:.1f} Mt (ε=0) → {sc_total_vals[-1]:.1f} Mt (ε={sc_eps_pct[-1]:.2f}%)\")\n" ] }, { From d201f982d30a13260aeeb82ee587b839fae4de34 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 20 Jun 2026 12:13:38 +0200 Subject: [PATCH 187/216] feat: add sensible values for supplier constraint --- config/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/config.yaml b/config/config.yaml index 8d1bbb4..651380b 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -146,7 +146,7 @@ scenario: cost_penalty: pareto: indicator: "supply" - threshold_value: [50, 100, 200, 300, 400] # Mt + threshold_value: [100, 150, 200, 300, 400, 500] # Mt From 7ed14f9bf6163a805d51b0b48086f0ad6da5091e Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 20 Jun 2026 12:49:52 +0200 Subject: [PATCH 188/216] feat: make lcox function region flexible --- rules/reporting.smk | 34 ++- workflow/notebooks/plot-compare-lcox.ipynb | 286 +++++++++++++++------ 2 files changed, 228 insertions(+), 92 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index c4e86d1..520a6f7 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -100,21 +100,31 @@ rule plot_comparison: notebook: str(NOTEBOOKS_DIR / "compare-scenarios.ipynb") - + +# Variables captured by the plot_compare_lcox input lambda (avoids two-argument lambda) +_lcox_low_cost = ["East_Asia", "South_America"] +_lcox_high_cost = ["Europe", "East_East_Asia"] +_lcox_quantities = [1, 10, 100] + rule plot_compare_lcox: params: - low_cost = ["South_America"], - high_cost = ["Europe"], - quantities = [1,10,100], - comparison = ["Europe", "South_America"] + low_cost = _lcox_low_cost, + high_cost = _lcox_high_cost, + quantities = _lcox_quantities, + comparison = ["Europe", "East_Asia"] input: - south_america_1 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_1.nc", - south_america_10 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_10.nc", - south_america_100 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/South_America_allocated_share/network_100.nc", - europe_1 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_1.nc", - europe_10 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_10.nc", - europe_100 = "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/Europe_allocated_share/network_100.nc", - trade_result = "results/chain_id~{trade_chain}/cost_year~{cost_year}/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~default/network.nc", + supply_networks=lambda wildcards: expand( + "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/{region}_allocated_share/network_{qty}.nc", + cost_year=wildcards.cost_year, + wacc=wildcards.wacc, + region=_lcox_low_cost + _lcox_high_cost, + qty=_lcox_quantities, + ), + trade_result=lambda wildcards: ( + f"results/chain_id~{config['trade_chains']['id']}" + f"/cost_year~{wildcards.cost_year}/interone~hbi/intertwo~eaf" + f"/wacc~{wildcards.wacc}/final~steel/scenario~default/network.nc" + ), output: lcox_comparison="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.pdf", lcox_comparison_png="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.png", diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 541f322..d4e5ea1 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 196, "id": "d3ddb1b2", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 197, "id": "f6f81313", "metadata": {}, "outputs": [], @@ -23,15 +23,15 @@ "\n", "snakemake = mock_snakemake(\n", " \"plot_compare_lcox\",\n", - " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " wacc=\"uniform\", # uniform or regional\n", + " wacc=\"regional\", # uniform or regional\n", " cost_year=\"2050\",\n", + " trade_chain=\"supplyconstraint\", # default, mga-stability-weighted, mga-chokepoints, mga-blocs, constrain-supply\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 198, "id": "310bcf70", "metadata": {}, "outputs": [], @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 199, "id": "581e901d", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 200, "id": "488b720f", "metadata": {}, "outputs": [], @@ -69,26 +69,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 201, "id": "99b8dc61", "metadata": {}, "outputs": [], "source": [ - "scenarios = snakemake.input.keys()" + "low_cost = snakemake.params.low_cost\n", + "high_cost = snakemake.params.high_cost\n", + "quantities = snakemake.params.quantities\n", + "comparison = snakemake.params.comparison\n", + "\n", + "# Build ordered (region, qty) pairs matching the expand() order in the rule:\n", + "# low_cost regions first, then high_cost, each with all quantities\n", + "region_qty_pairs = [(r, q) for r in low_cost + high_cost for q in quantities]\n", + "\n", + "# Map scenario key → file path (supply_networks list is in the same expansion order)\n", + "supply_files = list(snakemake.input.supply_networks)\n", + "scenario_file_map = {\n", + " f\"{region}_{qty}\": supply_files[i]\n", + " for i, (region, qty) in enumerate(region_qty_pairs)\n", + "}\n", + "scenarios = list(scenario_file_map.keys())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 202, "id": "58dcd935", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-East_Asia-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-East_Asia-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-East_Asia-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-South_America-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-South_America-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-South_America-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-East_East_Asia-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-East_East_Asia-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'LCOX-East_East_Asia-hbi-100.0' has buses, carriers, generators, links, loads, stores\n" + ] + } + ], "source": [ "nc = {}\n", "\n", - "for scenario in scenarios:\n", - " model_fn = snakemake.input[scenario]\n", - "\n", + "for scenario, model_fn in scenario_file_map.items():\n", " n = pypsa.Network(model_fn)\n", " n.name = scenario\n", " nc[scenario] = n\n", @@ -106,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 203, "id": "23a05566", "metadata": {}, "outputs": [], @@ -129,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 204, "id": "30545a5b", "metadata": {}, "outputs": [], @@ -140,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 205, "id": "33c20832", "metadata": {}, "outputs": [], @@ -151,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, "id": "2e8a00a6", "metadata": {}, "outputs": [], @@ -161,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 207, "id": "0119dc7d", "metadata": {}, "outputs": [], @@ -171,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 208, "id": "ab2f64a6", "metadata": {}, "outputs": [], @@ -190,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 209, "id": "310bf035", "metadata": {}, "outputs": [], @@ -198,6 +242,54 @@ "iron_ore_cost = config[\"iron_ore\"][\"marginal_cost\"] * config[\"iron_ore\"][\"ore_to_steel_ratio\"] " ] }, + { + "cell_type": "markdown", + "id": "0de061b9", + "metadata": {}, + "source": [ + "### Transport cost (from trade model)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "id": "4cb0da4e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", + "INFO:pypsa.network.io:Imported network 'Unnamed Network' has buses, carriers, generators, links, loads, sub_networks\n" + ] + } + ], + "source": [ + "# Read the trade network solely to extract shipping costs.\n", + "# This network is NOT added to the supply-chain NetworkCollection.\n", + "n_trade = pypsa.Network(snakemake.input.trade_result)\n", + "\n", + "# For each low_cost region compute the per-unit shipping cost to the first\n", + "# high_cost region in the comparison pair (comparison[0]).\n", + "# Shipping links have carrier==\"shipping_hbi\", bus0=\"{origin}_hbi\", bus1=\"{dest}_hbi\".\n", + "comp_dest = comparison[0] # e.g. \"Europe\"\n", + "\n", + "transport_costs = {} # {region_key: €/unit_hbi}\n", + "for low_region in low_cost:\n", + " link_mask = (\n", + " (n_trade.links.carrier == \"shipping_hbi\")\n", + " & (n_trade.links.bus0 == f\"{low_region}_hbi\")\n", + " & (n_trade.links.bus1 == f\"{comp_dest}_hbi\")\n", + " )\n", + " shipping_links = n_trade.links[link_mask]\n", + " if not shipping_links.empty:\n", + " # marginal_cost is in the same €/unit units as the supply-chain optimisation\n", + " transport_costs[low_region] = float(shipping_links.marginal_cost.mean())\n", + " else:\n", + " transport_costs[low_region] = 0.0" + ] + }, { "cell_type": "markdown", "id": "ff5e51d7", @@ -208,19 +300,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 211, "id": "dce79e80", "metadata": {}, "outputs": [], "source": [ - "# Pivot to get networks as rows and carriers as columns\n", + "def region_display_name(region):\n", + " \"\"\"Convert config region key (e.g. 'South_America') to display name ('South America').\"\"\"\n", + " return region.replace(\"_\", \" \")\n", + "\n", + "def parse_scenario(name):\n", + " \"\"\"Split a scenario key like 'South_America_10' into (display_name, qty_float).\"\"\"\n", + " region, qty_raw = name.rsplit(\"_\", 1)\n", + " return region_display_name(region), float(qty_raw)\n", + "\n", + "# --- Build pivot table of costs ---\n", "plot_df = df.pivot_table(\n", " index=\"network\", columns=\"carrier\", values=\"systemcost+fom_per_hbi\", aggfunc=\"sum\"\n", ")\n", "\n", "if add_iron_ore_cost:\n", " plot_df[\"iron ore\"] = iron_ore_cost\n", - "else: \n", + "else:\n", " plot_df[\"iron ore\"] = 0\n", "\n", "plot_df.rename(\n", @@ -237,36 +338,56 @@ " inplace=True,\n", ")\n", "\n", - "order = [\"iron ore\", \"solar\", \"onshore wind\", \"battery inverter (charging)\", \"battery inverter (discharging)\", \"battery\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\"]\n", + "order = [\n", + " \"iron ore\", \"solar\", \"onshore wind\",\n", + " \"battery inverter (charging)\", \"battery inverter (discharging)\",\n", + " \"battery\", \"hydrogen storage\", \"electrolysis\", \"direct reduction furnace\",\n", + "]\n", + "plot_df = plot_df[[c for c in order if c in plot_df.columns]]\n", + "\n", + "# --- Attach region / quantity metadata from params ---\n", + "plot_df[\"region\"] = [parse_scenario(s)[0] for s in plot_df.index]\n", + "plot_df[\"quantity\"] = [parse_scenario(s)[1] for s in plot_df.index]\n", "\n", - "plot_df = plot_df[order]" + "# --- Add shipping cost for low_cost regions (0 for high_cost regions) ---\n", + "low_cost_display = {region_display_name(r): transport_costs.get(r, 0.0) for r in low_cost}\n", + "plot_df[\"shipping\"] = plot_df[\"region\"].map(low_cost_display).fillna(0.0)\n", + "\n", + "# --- Sort: low_cost regions left, high_cost regions right; within group by qty ---\n", + "groups_ordered = (\n", + " [region_display_name(r) for r in low_cost]\n", + " + [region_display_name(r) for r in high_cost]\n", + ")\n", + "plot_df[\"region_order\"] = plot_df[\"region\"].map({g: i for i, g in enumerate(groups_ordered)})\n", + "plot_df = plot_df.sort_values([\"region_order\", \"quantity\"]).drop(columns=\"region_order\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 212, "id": "4560d6a3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# --- Column order: supply-chain components then shipping on top ---\n", + "supply_order = [c for c in order if c in plot_df.columns]\n", + "show_shipping = (plot_df[\"shipping\"] > 0).any()\n", + "plot_cols = supply_order + ([\"shipping\"] if show_shipping else [])\n", "\n", - "# --- Parse scenario names into region and quantity ---\n", - "qty_map = {\"01\": 0.1, \"1\": 1.0, \"10\": 10.0, \"100\": 100.0}\n", - "region_map = {\"europe\": \"Europe\", \"south_america\": \"South America\"}\n", - "\n", - "def parse_scenario(name):\n", - " region_raw, qty_raw = name.rsplit(\"_\", 1)\n", - " region = region_map.get(region_raw, region_raw.replace(\"_\", \" \").title())\n", - " qty = qty_map.get(qty_raw, float(qty_raw))\n", - " return region, qty\n", - "\n", - "plot_df[\"region\"] = [parse_scenario(s)[0] for s in plot_df.index]\n", - "plot_df[\"quantity\"] = [parse_scenario(s)[1] for s in plot_df.index]\n", - "\n", - "# South America on the left, Europe on the right\n", - "groups_ordered = [\"South America\", \"Europe\"]\n", - "plot_df[\"region_order\"] = plot_df[\"region\"].map({g: i for i, g in enumerate(groups_ordered)})\n", - "plot_df = plot_df.sort_values([\"region_order\", \"quantity\"]).drop(columns=\"region_order\")\n", + "# --- Colors: extend config colors with a shipping entry ---\n", + "plot_colors = dict(config[\"colors\"])\n", + "plot_colors.setdefault(\"shipping\", plot_colors.get(\"shipping\", \"#6495ED\"))\n", "\n", "# --- Compute x positions with a gap between region groups ---\n", "bar_width = 0.5\n", @@ -279,26 +400,25 @@ "\n", "for g in groups_ordered:\n", " mask = plot_df[\"region\"] == g\n", - " n = int(mask.sum())\n", - " positions = list(range(current_x, current_x + n))\n", + " n_bars = int(mask.sum())\n", + " positions = list(range(current_x, current_x + n_bars))\n", " x_positions.extend(positions)\n", " group_centers[g] = np.mean(positions)\n", " group_spans[g] = (positions[0], positions[-1])\n", - " current_x += n + group_gap\n", + " current_x += n_bars + group_gap\n", "\n", "# --- Create stacked bar plot ---\n", "fig, ax = plt.subplots(figsize=(10, 4.5))\n", "\n", "bottom = np.zeros(len(plot_df))\n", - "for col in order:\n", - " if col in plot_df.columns:\n", - " values = plot_df[col].fillna(0).values\n", - " ax.bar(x_positions, values, bottom=bottom, width=bar_width,\n", - " label=col, color=config[\"colors\"][col], alpha=0.65)\n", - " bottom += values\n", + "for col in plot_cols:\n", + " values = plot_df[col].fillna(0).values\n", + " ax.bar(x_positions, values, bottom=bottom, width=bar_width,\n", + " label=col, color=plot_colors[col], alpha=0.65)\n", + " bottom += values\n", "\n", "# --- Totals on top of bars ---\n", - "totals = plot_df[order].sum(axis=1).values\n", + "totals = plot_df[plot_cols].sum(axis=1).values\n", "for x, total in zip(x_positions, totals):\n", " ax.text(x, total, f\"{total:.1f}\", ha=\"center\", va=\"bottom\", fontsize=9)\n", "\n", @@ -314,7 +434,7 @@ "ax.set_xticklabels(qty_labels, rotation=0, fontsize=9)\n", "ax.tick_params(axis=\"x\", length=0)\n", "\n", - "# --- Outer x-axis: region group labels with bracket (moved up) ---\n", + "# --- Outer x-axis: region group labels with bracket ---\n", "xaxis_transform = ax.get_xaxis_transform()\n", "y_bracket = -0.11\n", "y_label_pts = -7\n", @@ -344,35 +464,41 @@ " annotation_clip=False,\n", " )\n", "\n", - "# --- % difference annotation: leftmost Europe vs leftmost South America ---\n", + "# --- % difference annotation between the comparison pair ---\n", + "# comparison[0] is the high-cost baseline, comparison[1] is the low-cost target\n", + "comp_high_display = region_display_name(comparison[0])\n", + "comp_low_display = region_display_name(comparison[1])\n", + "\n", "plot_df_idx_list = list(plot_df.index)\n", - "eu_first = plot_df[plot_df[\"region\"] == \"Europe\"].index[0]\n", - "sa_first = plot_df[plot_df[\"region\"] == \"South America\"].index[0]\n", + "high_first = plot_df[plot_df[\"region\"] == comp_high_display].index[0]\n", + "low_first = plot_df[plot_df[\"region\"] == comp_low_display].index[0]\n", "\n", - "eu_first_xpos = x_positions[plot_df_idx_list.index(eu_first)]\n", - "sa_first_xpos = x_positions[plot_df_idx_list.index(sa_first)]\n", + "high_first_xpos = x_positions[plot_df_idx_list.index(high_first)]\n", + "low_first_xpos = x_positions[plot_df_idx_list.index(low_first)]\n", "\n", - "totals_series = plot_df[order].sum(axis=1)\n", - "y_eu_top = float(totals_series[eu_first])\n", - "y_sa_top = float(totals_series[sa_first])\n", + "totals_series = plot_df[plot_cols].sum(axis=1)\n", + "y_high_top = float(totals_series[high_first])\n", + "y_low_top = float(totals_series[low_first])\n", "\n", - "# Europe is the base: negative means SA is cheaper\n", - "pct_diff = (y_sa_top - y_eu_top) / y_eu_top * 100\n", + "# Negative pct_diff means the low-cost region is cheaper than the baseline\n", + "pct_diff = (y_low_top - y_high_top) / y_high_top * 100\n", "\n", - "# Red horizontal line from Europe's leftmost bar top across to directly above SA's leftmost bar\n", - "ax.plot([sa_first_xpos, eu_first_xpos], [y_eu_top, y_eu_top],\n", + "# Horizontal reference line at high-cost level spanning across to the low-cost bar\n", + "ax.plot([low_first_xpos, high_first_xpos], [y_high_top, y_high_top],\n", " color=\"red\", linewidth=0.8, clip_on=False, zorder=5)\n", "\n", - "# Downward arrow and percentage annotation\n", - "ax.annotate(\"\", xy=(sa_first_xpos, y_sa_top+20),\n", - " xytext=(sa_first_xpos, y_eu_top),\n", + "# Downward arrow from high-cost level to low-cost bar top\n", + "ax.annotate(\n", + " \"\",\n", + " xy=(low_first_xpos, y_low_top + 20),\n", + " xytext=(low_first_xpos, y_high_top),\n", " arrowprops=dict(arrowstyle=\"-|>\", color=\"red\", lw=0.8),\n", " annotation_clip=False,\n", ")\n", "\n", "ax.text(\n", - " sa_first_xpos - 0.1,\n", - " (y_eu_top + y_sa_top) / 2,\n", + " low_first_xpos - 0.1,\n", + " (y_high_top + y_low_top) / 2,\n", " f\"{pct_diff:.1f}%\",\n", " ha=\"right\",\n", " va=\"center\",\n", @@ -406,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 213, "id": "58dbb0c1", "metadata": {}, "outputs": [], @@ -416,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 214, "id": "4971231d", "metadata": {}, "outputs": [], @@ -427,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 215, "id": "aa803626", "metadata": {}, "outputs": [], @@ -438,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 216, "id": "1b6fbe95", "metadata": {}, "outputs": [], @@ -449,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 217, "id": "bce4d758", "metadata": {}, "outputs": [], @@ -468,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 218, "id": "dfffb729", "metadata": {}, "outputs": [], @@ -487,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 219, "id": "a6be0a44", "metadata": {}, "outputs": [], From 3e649e903ce83ca1b73065c654bed1d27e98ce88 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 20 Jun 2026 12:50:25 +0200 Subject: [PATCH 189/216] chore: remove nb outputs --- workflow/notebooks/plot-compare-lcox.ipynb | 105 ++++++--------------- 1 file changed, 27 insertions(+), 78 deletions(-) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index d4e5ea1..16e75ba 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 196, + "execution_count": null, "id": "d3ddb1b2", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": null, "id": "f6f81313", "metadata": {}, "outputs": [], @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": null, "id": "310bcf70", "metadata": {}, "outputs": [], @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": null, "id": "581e901d", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": null, "id": "488b720f", "metadata": {}, "outputs": [], @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": null, "id": "99b8dc61", "metadata": {}, "outputs": [], @@ -94,41 +94,10 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": null, "id": "58dcd935", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-East_Asia-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-East_Asia-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-East_Asia-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-South_America-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-South_America-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-South_America-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-Europe-hbi-100.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-East_East_Asia-hbi-1.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-East_East_Asia-hbi-10.0' has buses, carriers, generators, links, loads, stores, sub_networks\n", - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'LCOX-East_East_Asia-hbi-100.0' has buses, carriers, generators, links, loads, stores\n" - ] - } - ], + "outputs": [], "source": [ "nc = {}\n", "\n", @@ -150,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": null, "id": "23a05566", "metadata": {}, "outputs": [], @@ -173,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": null, "id": "30545a5b", "metadata": {}, "outputs": [], @@ -184,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": null, "id": "33c20832", "metadata": {}, "outputs": [], @@ -195,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "id": "2e8a00a6", "metadata": {}, "outputs": [], @@ -205,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": null, "id": "0119dc7d", "metadata": {}, "outputs": [], @@ -215,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": null, "id": "ab2f64a6", "metadata": {}, "outputs": [], @@ -234,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": null, "id": "310bf035", "metadata": {}, "outputs": [], @@ -252,19 +221,10 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": null, "id": "4cb0da4e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pypsa.network.io:New version 1.2.3 available! (Current: 1.1.2)\n", - "INFO:pypsa.network.io:Imported network 'Unnamed Network' has buses, carriers, generators, links, loads, sub_networks\n" - ] - } - ], + "outputs": [], "source": [ "# Read the trade network solely to extract shipping costs.\n", "# This network is NOT added to the supply-chain NetworkCollection.\n", @@ -300,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": null, "id": "dce79e80", "metadata": {}, "outputs": [], @@ -364,21 +324,10 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": null, "id": "4560d6a3", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# --- Column order: supply-chain components then shipping on top ---\n", "supply_order = [c for c in order if c in plot_df.columns]\n", @@ -532,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": null, "id": "58dbb0c1", "metadata": {}, "outputs": [], @@ -542,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": null, "id": "4971231d", "metadata": {}, "outputs": [], @@ -553,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": null, "id": "aa803626", "metadata": {}, "outputs": [], @@ -564,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": null, "id": "1b6fbe95", "metadata": {}, "outputs": [], @@ -575,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": null, "id": "bce4d758", "metadata": {}, "outputs": [], @@ -594,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": null, "id": "dfffb729", "metadata": {}, "outputs": [], @@ -613,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": null, "id": "a6be0a44", "metadata": {}, "outputs": [], From 6e43a0be9f84293430af35c26244d237eca99d83 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 22 Jun 2026 11:34:23 +0200 Subject: [PATCH 190/216] feat: minor lcox figure updates and introduction of cases --- rules/reporting.smk | 20 +++++++++++++++++--- workflow/notebooks/plot-compare-lcox.ipynb | 11 ++++++----- 2 files changed, 23 insertions(+), 8 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 520a6f7..0c1efb3 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -102,8 +102,22 @@ rule plot_comparison: # Variables captured by the plot_compare_lcox input lambda (avoids two-argument lambda) -_lcox_low_cost = ["East_Asia", "South_America"] -_lcox_high_cost = ["Europe", "East_East_Asia"] + +# First case +_lcox_low_cost = ["East_Asia"] +_lcox_high_cost = ["East_East_Asia"] +_comparison = ["East_East_Asia", "East_Asia"] + +# Second case +# _lcox_low_cost = ["South_America"] +# _lcox_high_cost = ["Europe"] +# _comparison = ["Europe", "South_America"] + +# # Full case +# _lcox_low_cost = ["East_Asia", "South_America"] +# _lcox_high_cost = ["Europe", "East_East_Asia"] +# _comparison = ["Europe", "East_Asia"] + _lcox_quantities = [1, 10, 100] rule plot_compare_lcox: @@ -111,7 +125,7 @@ rule plot_compare_lcox: low_cost = _lcox_low_cost, high_cost = _lcox_high_cost, quantities = _lcox_quantities, - comparison = ["Europe", "East_Asia"] + comparison = _comparison, input: supply_networks=lambda wildcards: expand( "resources/lco-hbi/cost_year~{cost_year}/wacc~{wacc}/{region}_allocated_share/network_{qty}.nc", diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 16e75ba..06f4b47 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -357,7 +357,7 @@ " current_x += n_bars + group_gap\n", "\n", "# --- Create stacked bar plot ---\n", - "fig, ax = plt.subplots(figsize=(10, 4.5))\n", + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", "\n", "bottom = np.zeros(len(plot_df))\n", "for col in plot_cols:\n", @@ -446,11 +446,11 @@ ")\n", "\n", "ax.text(\n", - " low_first_xpos - 0.1,\n", - " (y_high_top + y_low_top) / 2,\n", + " low_first_xpos,\n", + " (y_high_top + y_low_top) / 2 + 70,\n", " f\"{pct_diff:.1f}%\",\n", - " ha=\"right\",\n", - " va=\"center\",\n", + " ha=\"center\",\n", + " va=\"top\",\n", " fontsize=8,\n", " color=\"red\",\n", ")\n", @@ -465,6 +465,7 @@ " loc=\"upper left\",\n", ")\n", "\n", + "plt.ylim(0,830)\n", "plt.tight_layout()\n", "plt.savefig(snakemake.output.lcox_comparison, dpi=300, bbox_inches=\"tight\")\n", "plt.savefig(snakemake.output.lcox_comparison_png, dpi=300, bbox_inches=\"tight\")\n", From 3ac06b23a5fad7be60ba10d0c129d132aa066f48 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 22 Jun 2026 13:57:59 +0200 Subject: [PATCH 191/216] feat: adjust plot countries to iso3, create rule and figure updates --- rules/reporting.smk | 7 +++ workflow/notebooks/plot_countries.ipynb | 81 ++++++------------------- 2 files changed, 26 insertions(+), 62 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index 0c1efb3..7fa1c09 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -4,6 +4,13 @@ Collects final figures and presentation artifacts produced by notebooks and the main optimization workflow. """ +rule plot_regions: + output: + global_map_countries = "results/figures_general/global_map_countries.pdf", #workflow/notebooks/plot_countries.ipynb + global_map_countries_png = "results/figures_general/global_map_countries.png", #workflow/notebooks/plot_countries.ipynb + notebook: + str(NOTEBOOKS_DIR / "plot_countries.ipynb") + rule collect_figures: input: diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index 7e98de7..d941f28 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -23,10 +23,7 @@ "from _helpers_notebooks import mock_snakemake\n", "\n", "snakemake = mock_snakemake(\n", - " \"collect_figures\",\n", - " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " sort=\"global\",\n", - " demand=\"True\",\n", + " \"plot_regions\",\n", ")" ] }, @@ -65,32 +62,7 @@ "metadata": {}, "outputs": [], "source": [ - "country_name_corrections = {\n", - " \"Democratic Republic of the Congo\": \"Congo, The Democratic Republic of the\",\n", - " \"Republic of the Congo\": \"Republic of the Congo\",\n", - " \"Kosovo\": \"Republic of Kosovo\", # pycountry not supported\n", - " \"Russia\": \"Russian Federation\",\n", - " \"Turkey\": \"Türkiye\",\n", - " \"Venezuela\": \"Venezuela, Bolivarian Republic of\",\n", - " \"Tanzania\": \"United Republic of Tanzania\",\n", - " \"Bolivia\": \"Plurinational State of Bolivia\",\n", - " \"Vietnam\": \"Viet Nam\",\n", - " \"South Korea\": \"Korea, Republic of\",\n", - " \"North Korea\": \"Korea, Democratic People's Republic of\",\n", - " \"Taiwan\": \"Taiwan, Province of China\",\n", - " \"Laos\": \"Lao People's Democratic Republic\",\n", - " \"Brunei\": \"Brunei Darussalam\",\n", - " \"Equatorial French Guiana\": \"French Guiana\",\n", - " \"Syria\": \"Syrian Arab Republic\",\n", - " \"Palestine\": \"Palestine, State of\",\n", - " \"Moldova\": \"Republic of Moldova\",\n", - "}\n", - "\n", - "# Apply a country name correction to the regions dict\n", - "for region, countries in regions.items():\n", - " for i, country in enumerate(countries):\n", - " if country in country_name_corrections:\n", - " countries[i] = country_name_corrections[country]" + "# Regions in config already use ISO alpha_3 codes — no name corrections needed" ] }, { @@ -130,9 +102,9 @@ "metadata": {}, "outputs": [], "source": [ - "def country_to_iso_a2(country_name):\n", + "def admin_to_iso_a3(country_name):\n", " try:\n", - " return pycountry.countries.lookup(country_name).alpha_2\n", + " return pycountry.countries.lookup(country_name).alpha_3\n", " except LookupError:\n", " return None" ] @@ -144,33 +116,16 @@ "metadata": {}, "outputs": [], "source": [ - "# Build a mapping from country name to ISO_A2 code\n", - "country_name_to_iso = {}\n", - "for country in pycountry.countries:\n", - " country_name_to_iso[country.name] = country.alpha_2\n", - " # Add common names\n", - " if hasattr(country, \"official_name\"):\n", - " country_name_to_iso[country.official_name] = country.alpha_2\n", - "\n", - "# Build a mapping from ISO_A2 code to region\n", + "# Build a mapping from ISO_A3 code to region directly from config\n", "iso_to_region = {}\n", "for region, countries in regions.items():\n", - " for country in countries:\n", - " # Some country names may have extra text (e.g., \"Togo + Algeria\"), handle them simply\n", - " for part in country.split(\"+\"):\n", - " name = part.strip()\n", - " code = country_name_to_iso.get(name)\n", - " if code:\n", - " iso_to_region[code] = region\n", - " else:\n", - " print(\n", - " f\"Warning: Could not find ISO_A2 code for country '{name}' in region '{region}'.\"\n", - " )\n", + " for iso3 in countries:\n", + " iso_to_region[iso3] = region\n", "\n", - "# Fix missing ISO_A2 codes in world\n", - "world.loc[world[\"ISO_A2\"] == \"-99\", \"ISO_A2\"] = world.loc[\n", - " world[\"ISO_A2\"] == \"-99\", \"ADMIN\"\n", - "].apply(country_to_iso_a2)" + "# Fix missing ISO_A3 codes in world (some entries have -99)\n", + "world.loc[world[\"ISO_A3\"] == \"-99\", \"ISO_A3\"] = world.loc[\n", + " world[\"ISO_A3\"] == \"-99\", \"ADMIN\"\n", + "].apply(admin_to_iso_a3)" ] }, { @@ -180,7 +135,7 @@ "metadata": {}, "outputs": [], "source": [ - "world[\"region\"] = world[\"ISO_A2\"].map(lambda iso: iso_to_region.get(iso, \"Other\"))" + "world[\"region\"] = world[\"ISO_A3\"].map(lambda iso: iso_to_region.get(iso, \"Other\"))" ] }, { @@ -207,25 +162,27 @@ "\n", "fig, ax = plt.subplots(figsize=(12, 10))\n", "\n", - "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\")\n", + "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\", alpha=0.8)\n", "\n", "# Manual legend for regions present in the map\n", "handles = [\n", - " mpatches.Patch(color=c, label=r)\n", + " mpatches.Patch(color=c, label=r, alpha=0.8)\n", " for r, c in color_map.items()\n", " if r in world[\"region\"].values\n", "]\n", "if handles:\n", + " region_list = [r for r, c in color_map.items() if r in world[\"region\"].values]\n", + " labels = [reg.replace(\"_\", \" \") for reg in region_list]\n", " ax.legend(\n", - " handles=handles, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\"\n", + " handles=handles, labels=labels, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\",\n", " )\n", "\n", "# ax.set_title('Regions')\n", "ax.axis(\"off\")\n", "plt.tight_layout()\n", "\n", - "plt.savefig(snakemake.input.global_map_countries)\n", - "plt.savefig(snakemake.input.global_map_countries_png, dpi=300)\n", + "plt.savefig(snakemake.output.global_map_countries)\n", + "plt.savefig(snakemake.output.global_map_countries_png, dpi=300)\n", "plt.show()" ] } From fbbc77df176424e9359493eae48b0696c1099646 Mon Sep 17 00:00:00 2001 From: energyls Date: Mon, 22 Jun 2026 16:09:45 +0200 Subject: [PATCH 192/216] chore: visual improvements in plot mga --- workflow/notebooks/plot-mga.ipynb | 64 +++++++++++++++---------------- 1 file changed, 30 insertions(+), 34 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index dfac1eb..ef575f9 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -827,7 +827,7 @@ "summary_dict = {\"Inter-block total\": bl_interblock_total}\n", "for k, vals in pair_series.items():\n", " # humanise key: \"block_1 → block_2\" → \"Block 1 → Block 2\"\n", - " label = k.replace(\"block_\", \"Block \").replace(\"_\", \" \")\n", + " label = k.replace(\"block_\", \"Group \").replace(\"_\", \" \")\n", " summary_dict[label] = vals\n", "summary_dict[\"Intra-block total\"] = bl_intrablock_total\n", "\n", @@ -1410,12 +1410,12 @@ "bl_intra_vals_v2 = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", "\n", "_bl_pair_cfg_v2 = {\n", - " \"Block 1 → Block 2\": dict(color=\"#e41a1c\", linestyle=\"--\"),\n", - " \"Block 1 → Block 3\": dict(color=\"#e41a1c\", linestyle=\":\"),\n", - " \"Block 2 → Block 1\": dict(color=\"#377eb8\", linestyle=\"--\"),\n", - " \"Block 2 → Block 3\": dict(color=\"#377eb8\", linestyle=\":\"),\n", - " \"Block 3 → Block 1\": dict(color=\"#4daf4a\", linestyle=\"--\"),\n", - " \"Block 3 → Block 2\": dict(color=\"#4daf4a\", linestyle=\":\"),\n", + " \"Group 1 → Group 2\": dict(color=\"#e41a1c\", linestyle=\"--\"),\n", + " \"Group 1 → Group 3\": dict(color=\"#e41a1c\", linestyle=\":\"),\n", + " \"Group 2 → Group 1\": dict(color=\"#377eb8\", linestyle=\"--\"),\n", + " \"Group 2 → Group 3\": dict(color=\"#377eb8\", linestyle=\":\"),\n", + " \"Group 3 → Group 1\": dict(color=\"#4daf4a\", linestyle=\"--\"),\n", + " \"Group 3 → Group 2\": dict(color=\"#4daf4a\", linestyle=\":\"),\n", "}\n", "bl_region_lines_v2 = sorted(\n", " [\n", @@ -1436,50 +1436,47 @@ "\n", "plots_v2 = [\n", " dict(\n", - " title=\"Unstable trade routes (chokepoints)\",\n", + " title=\"Stable trade routes (chokepoints)\",\n", " epsilon=cp_eps_v2,\n", " main_curve=cp_total_vals_v2,\n", " main_label=\"Global\",\n", " y_mid=cp_total_vals_v2[0],\n", - " color=\"#185FA5\",\n", - " arrow_dn=[\"Decreased\", \"high-risk routes\"],\n", - " arrow_x_frac=0.70,\n", + " color=\"#000000\",\n", + " arrow_dn=[\"Minimize\", \"high-risk routes\"],\n", + " arrow_x_frac=0.40,\n", " y_label_add=\"trade through maritime chokepoints\",\n", " regions=cp_region_lines_v2,\n", " extra_curves=[],\n", " ),\n", " dict(\n", - " title=\"Supply constraint diversification\",\n", - " epsilon=sc_eps_pct,\n", - " main_curve=sc_cap_vals,\n", - " main_label=\"max. per supplier\",\n", - " y_mid=sc_cap_vals[0],\n", - " color=\"#3B6D11\",\n", - " arrow_dn=[\"Decreased dominant\", \"supplier cap\"],\n", - " arrow_x_frac=0.50,\n", - " y_label_add=\"production by region\",\n", - " regions=sc_region_lines,\n", - " extra_curves=[\n", - " dict(name=\"Total (all regions)\", values=sc_total_vals,\n", - " color=\"#999999\", linestyle=\":\", linewidth=1.5, alpha=0.7),\n", - " ],\n", - " ),\n", - " dict(\n", - " title=\"Fragmentation / Bloc trade\",\n", + " title=\"Friendshoring\",\n", " epsilon=bl_eps_v2,\n", " main_curve=bl_inter_vals_v2,\n", " main_label=\"Global\",\n", " y_mid=bl_inter_vals_v2[0],\n", - " color=\"#534AB7\",\n", - " arrow_dn=[\"Decreased trade\", \"between blocs\"],\n", - " arrow_x_frac=0.70,\n", + " color=\"#000000\",\n", + " arrow_dn=[\"Minimize trade with\", \"non-friendshored regions\"],\n", + " arrow_x_frac=0.40,\n", " y_label_add=\"trade between blocs\",\n", " regions=bl_region_lines_v2,\n", " extra_curves=[\n", - " dict(name=\"Intra-bloc total\", values=bl_intra_vals_v2,\n", + " dict(name=\"Friendshoring total\", values=bl_intra_vals_v2,\n", " color=\"#999999\", linestyle=\":\", linewidth=1.5, alpha=0.7),\n", " ],\n", " ),\n", + " dict(\n", + " title=\"Supply diversification\",\n", + " epsilon=sc_eps_pct,\n", + " main_curve=sc_cap_vals,\n", + " main_label=\"max. per supplier\",\n", + " y_mid=sc_cap_vals[0],\n", + " color=\"#000000\",\n", + " arrow_dn=[\"Constrain production\", \"per supplier\"],\n", + " arrow_x_frac=0.30,\n", + " y_label_add=\"production by region\",\n", + " regions=sc_region_lines,\n", + " extra_curves=[],\n", + " ),\n", "]\n", "\n", "fig_v2, axes_v2 = plt.subplots(1, 3, figsize=(15, 5))\n", @@ -1494,8 +1491,7 @@ " gap = Y_MAX_V2 * GAP_FRAC_V2\n", "\n", " # ── Main curve ───────────────────────────────────────────────────────────\n", - " ax.plot(eps, curve, color=p[\"color\"], linewidth=2.5, marker=\"o\",\n", - " markersize=4, label=p[\"main_label\"], zorder=10)\n", + " ax.plot(eps, curve, color=p[\"color\"], linewidth=1.5, marker=\"o\",markersize=3, label=p[\"main_label\"], zorder=0, linestyle=\"-\")\n", "\n", " # ── Extra reference curves ───────────────────────────────────────────────\n", " for ec in p.get(\"extra_curves\", []):\n", From 2693801aa8694445770ab30a2da82456122a8865 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 30 Jun 2026 09:39:43 +0200 Subject: [PATCH 193/216] chore: supply curve figure updates --- workflow/notebooks/analysis-globalsupplycurve.ipynb | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 3789ba8..5d8be2a 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -24,12 +24,12 @@ "snakemake = mock_snakemake(\n", " \"plot_global_supply\",\n", " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " sort=True,\n", + " sort= \"cost_average\", #\"cost_global\", \"cost_average\"\n", " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", - " wacc=\"regional\",\n", + " wacc=\"uniform\",\n", " cost_year=\"2050\",\n", " interone=\"hbi\",\n", - " trade_chain=\"newre1206_2050\"\n", + " trade_chain=\"supplyconstraint\"\n", ")" ] }, @@ -54,7 +54,7 @@ "source": [ "process = \"hbi\" # or \"eaf\" # DISCLAIMER: Steel only supply curve \"steel\" from model not supported yet\n", "xlim = None\n", - "ylim = 1000\n", + "ylim = 1130\n", "add_iron_ore_cost = True\n", "plot_optimal_curve = False # Uses the optimal supply curve from the trade model, which may differ from the single curves due to the trade-offs between different producers. If False, the single curves are plotted, which may not reflect the optimal supply curve from the trade model." ] @@ -355,8 +355,9 @@ "metadata": {}, "outputs": [], "source": [ - "sort_over_all = False # \"True\" to sort over all regions, \"False\" to sort within each region\n", - "sort = \"cost_global\" # \"cost_global\" # \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" + "sort_over_all = True # \"True\" to sort over all regions, \"False\" to sort within each region\n", + "# sort = \"cost_global\" # \"cost_global\" # a\n", + "# \"cost_global\", \"cost_average\", \"False\", or the variable sort (obtained from snakemake workflow)" ] }, { From cb9d04a6691fb3d0458ed9e851766f7b661e7ef2 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 1 Jul 2026 12:37:47 +0200 Subject: [PATCH 194/216] feat: add new color palette tests --- workflow/notebooks/colors.ipynb | 136 ++++++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 workflow/notebooks/colors.ipynb diff --git a/workflow/notebooks/colors.ipynb b/workflow/notebooks/colors.ipynb new file mode 100644 index 0000000..ce6097f --- /dev/null +++ b/workflow/notebooks/colors.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "id": "d86d9a35", + "metadata": {}, + "outputs": [], + "source": [ + "from cmcrameri import cm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9e988c3f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 14.5, 0.5, -0.5)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 14, 15)[None, :]\n", + "plt.imshow(x, aspect='auto', cmap=cm.batlowS) # or any other colourmap\n", + "plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "6f3c5cb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Central_America: '#011959'\n", + " East_Asia: '#FDB4B4'\n", + " East_East_Asia: '#A18A2B'\n", + " Eurasia: '#CF9340'\n", + " Europe: '#416F53'\n", + " Far_West_Europe: '#FAA587'\n", + " Middle_East: '#F59F74'\n", + " North_America: '#26635F'\n", + " North_West_Africa: '#FCC3DF'\n", + " Oceania: '#ED9A62'\n", + " Pacific_Asia: '#FDAFA7'\n", + " South_America: '#50744B'\n", + " South_South_America: '#114160'\n", + " Subsaharan_Africa: '#33695A'\n", + " West_Asia: '#FBA68C'\n" + ] + } + ], + "source": [ + "import matplotlib.colors as mcolors\n", + "\n", + "regions = sorted([\n", + " \"Central_America\", \"East_Asia\", \"East_East_Asia\", \"Eurasia\", \"Europe\",\n", + " \"Far_West_Europe\", \"Middle_East\", \"North_America\", \"North_West_Africa\",\n", + " \"Oceania\", \"Pacific_Asia\", \"South_America\", \"South_South_America\",\n", + " \"Subsaharan_Africa\", \"West_Asia\"\n", + "])\n", + "\n", + "n = len(regions)\n", + "colors_rgba = cm.batlowS(np.linspace(0, 1, n))\n", + "hex_colors = [mcolors.to_hex(c).upper() for c in colors_rgba]\n", + "\n", + "for region, hex_color in zip(regions, hex_colors):\n", + " print(f\" {(region + ':').ljust(25)} '{hex_color}'\")" + ] + }, + { + "cell_type": "markdown", + "id": "c4584ddc", + "metadata": {}, + "source": [ + "### okabeito" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "1d3c51d9", + "metadata": {}, + "outputs": [], + "source": [ + "from cmap import Colormap\n", + "\n", + "okabeito = Colormap('okabeito:okabeito') # case insensitive" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "shift", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f63235b9420fbf4ed505aa48f2f91ae59daa6773 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 2 Jul 2026 12:29:07 +0200 Subject: [PATCH 195/216] chore: adjust alpha in global map and curves --- workflow/notebooks/analysis-globalsupplycurve.ipynb | 6 +++--- workflow/notebooks/plot_countries.ipynb | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 5d8be2a..7f8d1e5 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -24,7 +24,7 @@ "snakemake = mock_snakemake(\n", " \"plot_global_supply\",\n", " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " sort= \"cost_average\", #\"cost_global\", \"cost_average\"\n", + " sort= \"cost_global\", #\"cost_global\", \"cost_average\"\n", " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", " wacc=\"uniform\",\n", " cost_year=\"2050\",\n", @@ -255,7 +255,7 @@ " color=bar_colors,\n", " align=\"edge\",\n", " edgecolor=\"none\",\n", - " alpha=0.6,\n", + " alpha=1,\n", " )\n", " # Add the classic supply curve line\n", "\n", @@ -303,7 +303,7 @@ " ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", " # Legend for regions\n", " handles = [\n", - " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg], alpha=0.6) for reg in region_list\n", + " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg], alpha=1) for reg in region_list\n", " ]\n", " handles.append(\n", " plt.Line2D([0], [0], color=\"black\", linewidth=1.2, label=\"Supply curve\")\n", diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index d941f28..2942c5c 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -162,11 +162,11 @@ "\n", "fig, ax = plt.subplots(figsize=(12, 10))\n", "\n", - "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\", alpha=0.8)\n", + "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\", alpha=1)\n", "\n", "# Manual legend for regions present in the map\n", "handles = [\n", - " mpatches.Patch(color=c, label=r, alpha=0.8)\n", + " mpatches.Patch(color=c, label=r, alpha=1)\n", " for r, c in color_map.items()\n", " if r in world[\"region\"].values\n", "]\n", From 153bd0cf83d4eb5efb8f15fcfbe68fd85dfe40f7 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 2 Jul 2026 12:29:21 +0200 Subject: [PATCH 196/216] chore: adjust slack and add new region colors --- config/config.yaml | 67 ++++++++++++++++++++++++++++------------------ 1 file changed, 41 insertions(+), 26 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 651380b..955ac68 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -50,11 +50,11 @@ trade_chains: output_commodity: steel process_label: eaf trade_scenarios: - # - default + - default + # - mga-chokepoints + # - mga-blocs + # - constrain-supply # - mga-stability-weighted - - mga-chokepoints - - mga-blocs - - constrain-supply scenario: @@ -123,7 +123,7 @@ scenario: modifiers: cost_penalty: mga: - slack: [0.0025, 0.005, 0.0075, 0.01, 0.015, 0.02, 0.025, 0.03] + slack: [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01] sense: "min" carrier: "hbi" indicator: "chokepoint" @@ -132,7 +132,7 @@ scenario: modifiers: cost_penalty: mga: - slack: [0.0025, 0.005, 0.0075, 0.01, 0.015, 0.02, 0.025, 0.03] #[0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05] + slack: [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01] sense: "min" carrier: "hbi" indicator: "blocks" @@ -146,14 +146,14 @@ scenario: cost_penalty: pareto: indicator: "supply" - threshold_value: [100, 150, 200, 300, 400, 500] # Mt + threshold_value: [100, 150, 175, 200, 250, 300, 350, 600] # Mt # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.1, 0.5, 1, 5, 10, 25, 50, 75, 100, 200, 300, 500, 1000] # Mt/year +steel_demand_levels: [0.1, 0.5, 1, 5, 10, 25, 50, 75, 100, 150, 200, 250, 300, 500, 1000] # Mt/year hydrogen_storage_cost: False electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' @@ -296,15 +296,14 @@ colors: electric arc furnace: 'orange' direct reduction furnace: 'grey' hydrogen direct iron reduction furnace: 'grey' - battery inverter: '#6A000E' battery inverter (charging): '#6A000E' battery inverter (discharging): '#FF5733' battery: 'purple' wind: 'blue' onshore wind: 'blue' - solar: 'yellow' - pv: 'yellow' + solar: '#bfbf04' + pv: '#bfbf04' electricity: 'red' iron ore: 'brown' steel: 'grey' @@ -317,21 +316,36 @@ colors: HBI: darkred Photovoltaics: yellow Wind energy: blue - Eurasia: '#1A6FBF' # mid blue (lighter than before) - Europe: '#E07B00' # deep amber-orange - Far_West_Europe: '#007A6E' # dark teal (replaces vivid green) - Middle_East: '#B5446E' # magenta-rose (replaces brick red) - North_West_Africa: '#6E3FA3' # purple (kept, adjusted) - Subsaharan_Africa: '#8B4513' # saddle brown - North_America: '#F5C518' # yellow (kept) - South_America: '#2DB3C8' # cyan-teal (lighter, distinct from teal) - South_South_America: '#F4845F' # warm peach-orange - Central_America: '#3D9970' # muted green (darker, less vivid) - West_Asia: '#2C3E50' # blue-grey (kept, unique) - East_Asia: '#A8D8A8' # very light sage - Pacific_Asia: '#C47AB0' # pink-lavender - East_East_Asia: '#0D47A1' # deep navy (replaces dark red) - Oceania: '#546E7A' # blue-grey slate (distinct from navy) + # Eurasia: '#1A6FBF' # mid blue (lighter than before) + # Europe: '#E07B00' # deep amber-orange + # Far_West_Europe: '#007A6E' # dark teal (replaces vivid green) + # Middle_East: '#B5446E' # magenta-rose (replaces brick red) + # North_West_Africa: '#6E3FA3' # purple (kept, adjusted) + # Subsaharan_Africa: '#8B4513' # saddle brown + # North_America: '#F5C518' # yellow (kept) + # South_America: '#2DB3C8' # cyan-teal (lighter, distinct from teal) + # South_South_America: '#F4845F' # warm peach-orange + # Central_America: '#3D9970' # muted green (darker, less vivid) + # West_Asia: '#2C3E50' # blue-grey (kept, unique) + # East_Asia: '#A8D8A8' # very light sage + # Pacific_Asia: '#C47AB0' # pink-lavender + # East_East_Asia: '#0D47A1' # deep navy (replaces dark red) + # Oceania: '#546E7A' # blue-grey slate (distinct from navy) + Central_America: '#011959' + East_Asia: '#FDB4B4' + East_East_Asia: '#A18A2B' + Eurasia: '#CF9340' + Europe: '#416F53' + Far_West_Europe: '#FAA587' + Middle_East: '#F59F74' + North_America: '#26635F' + North_West_Africa: '#FCC3DF' + Oceania: '#ED9A62' + Pacific_Asia: '#FDAFA7' + South_America: '#50744B' + South_South_America: '#114160' + Subsaharan_Africa: '#33695A' + West_Asia: '#FBA68C' trade_today: surplus: '#d94801' # orange-red (net exporter bubble) deficit: '#045a8d' # dark blue (net importer bubble) @@ -345,6 +359,7 @@ colors: iron_ore_shipping: "#6A000E" hbi: 'darkred' hbi_shipping: 'firebrick' + shipping: 'black' steel_supply: 'lightsteelblue' steel_demand: 'seagreen' steel_link: 'skyblue' From 37679f7de3cd00ebf1c9dc32110f2188fa4e9db1 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 2 Jul 2026 12:30:01 +0200 Subject: [PATCH 197/216] feat: add okabeito color tests --- workflow/notebooks/colors.ipynb | 80 +++++++++++++-------------------- 1 file changed, 31 insertions(+), 49 deletions(-) diff --git a/workflow/notebooks/colors.ipynb b/workflow/notebooks/colors.ipynb index ce6097f..800f730 100644 --- a/workflow/notebooks/colors.ipynb +++ b/workflow/notebooks/colors.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "d86d9a35", "metadata": {}, "outputs": [], @@ -14,31 +14,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "9e988c3f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.5, 14.5, 0.5, -0.5)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x = np.linspace(0, 14, 15)[None, :]\n", "plt.imshow(x, aspect='auto', cmap=cm.batlowS) # or any other colourmap\n", @@ -47,32 +26,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "6f3c5cb4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Central_America: '#011959'\n", - " East_Asia: '#FDB4B4'\n", - " East_East_Asia: '#A18A2B'\n", - " Eurasia: '#CF9340'\n", - " Europe: '#416F53'\n", - " Far_West_Europe: '#FAA587'\n", - " Middle_East: '#F59F74'\n", - " North_America: '#26635F'\n", - " North_West_Africa: '#FCC3DF'\n", - " Oceania: '#ED9A62'\n", - " Pacific_Asia: '#FDAFA7'\n", - " South_America: '#50744B'\n", - " South_South_America: '#114160'\n", - " Subsaharan_Africa: '#33695A'\n", - " West_Asia: '#FBA68C'\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.colors as mcolors\n", "\n", @@ -101,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "1d3c51d9", "metadata": {}, "outputs": [], @@ -110,6 +67,31 @@ "\n", "okabeito = Colormap('okabeito:okabeito') # case insensitive" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecbcc58c", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(0, 14, 15)[None, :]\n", + "plt.imshow(x, aspect='auto', cmap=okabeito.to_mpl())\n", + "plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ce77fa1", + "metadata": {}, + "outputs": [], + "source": [ + "hex_colors_ok = [mcolors.to_hex(okabeito(i)).upper() for i in np.linspace(0, 1, n)]\n", + "\n", + "for region, hex_color in zip(regions, hex_colors_ok):\n", + " print(f\" {(region + ':').ljust(25)} '{hex_color}'\")" + ] } ], "metadata": { From b453d9611219f94d0011c2b1253958ae3cb1fbb0 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 2 Jul 2026 12:30:40 +0200 Subject: [PATCH 198/216] feat: add rule to collect figures and diversify outputs in lcox plots --- Snakefile | 1 + rules/reporting.smk | 39 ++++++++++++++++++++++++++------------- 2 files changed, 27 insertions(+), 13 deletions(-) diff --git a/Snakefile b/Snakefile index f45a138..ddf52ba 100644 --- a/Snakefile +++ b/Snakefile @@ -5,6 +5,7 @@ trade optimization, and reporting. """ from pathlib import Path +from shutil import copyfile import sys import pandas as pd diff --git a/rules/reporting.smk b/rules/reporting.smk index 7fa1c09..f5679c4 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -37,6 +37,23 @@ rule collect_figures: map_chokepoints_png = "../results/figures_general/chokepoints/map_chokepoints.png", # integrated in workflow +rule get_figures: + input: + [ + "results/figures_general/comparison/cost_year~2050/wacc~regional/lcox_comparison_East_Asia_East_East_Asia.pdf", + "results/figures_general/comparison/cost_year~2050/wacc~regional/lcox_comparison_South_America_Europe.pdf", + ] + output: + [ + "results/figures_streamlined/lcox-east-asia.pdf", + "results/figures_streamlined/lcox-south-america.pdf", + ] + threads: 1 + run: + for i in range(len(input)): + copyfile(input[i], output[i]) + + rule plot_mga: input: @@ -111,19 +128,15 @@ rule plot_comparison: # Variables captured by the plot_compare_lcox input lambda (avoids two-argument lambda) # First case -_lcox_low_cost = ["East_Asia"] -_lcox_high_cost = ["East_East_Asia"] -_comparison = ["East_East_Asia", "East_Asia"] +# _lcox_low_cost = ["East_Asia"] +# _lcox_high_cost = ["East_East_Asia"] +# _comparison = ["East_East_Asia", "East_Asia"] # Second case -# _lcox_low_cost = ["South_America"] -# _lcox_high_cost = ["Europe"] -# _comparison = ["Europe", "South_America"] +_lcox_low_cost = ["South_America"] +_lcox_high_cost = ["Europe"] +_comparison = ["Europe", "South_America"] -# # Full case -# _lcox_low_cost = ["East_Asia", "South_America"] -# _lcox_high_cost = ["Europe", "East_East_Asia"] -# _comparison = ["Europe", "East_Asia"] _lcox_quantities = [1, 10, 100] @@ -147,11 +160,11 @@ rule plot_compare_lcox: f"/wacc~{wildcards.wacc}/final~steel/scenario~default/network.nc" ), output: - lcox_comparison="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.pdf", - lcox_comparison_png="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.png", + lcox_comparison="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison_" + f"{_lcox_low_cost[0]}" + "_" + f"{_lcox_high_cost[0]}" + ".pdf", + lcox_comparison_png="results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison_" + f"{_lcox_low_cost[0]}" + "_" + f"{_lcox_high_cost[0]}" + ".png", notebook: str(NOTEBOOKS_DIR / "plot-compare-lcox.ipynb") rule plot_compare_lcox_all: input: - expand("results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison.pdf", cost_year=[2050], wacc=[config["trade_chains"]["wacc"]], allow_missing=True) + expand("results/figures_general/comparison/cost_year~{cost_year}/wacc~{wacc}/lcox_comparison_" + f"{_lcox_low_cost[0]}" + "_" + f"{_lcox_high_cost[0]}" + ".pdf", cost_year=[2050], wacc=[config["trade_chains"]["wacc"]], low_cost=_lcox_low_cost, high_cost=_lcox_high_cost, allow_missing=True) From 4ee68476ce8d5bf9c45fd3b275c2e0584c8d5497 Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 2 Jul 2026 12:44:04 +0200 Subject: [PATCH 199/216] fix: readd region index name for correct application in subsequent rules --- workflow/notebooks/prepare-wacc.ipynb | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/workflow/notebooks/prepare-wacc.ipynb b/workflow/notebooks/prepare-wacc.ipynb index cd62eaa..8663705 100644 --- a/workflow/notebooks/prepare-wacc.ipynb +++ b/workflow/notebooks/prepare-wacc.ipynb @@ -281,6 +281,16 @@ "wacc_by_region = pd.concat([wacc_by_region, global_avg.to_frame().T])" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcecd263", + "metadata": {}, + "outputs": [], + "source": [ + "wacc_by_region.index.name = \"region\"" + ] + }, { "cell_type": "code", "execution_count": null, @@ -294,11 +304,11 @@ { "cell_type": "code", "execution_count": null, - "id": "dcecd263", + "id": "e5bee8c0", "metadata": {}, "outputs": [], "source": [ - "# wacc_by_region" + "wacc_by_region" ] } ], From 7cfbfcef8bc81efdb963f7b3f2a59c638711e2ac Mon Sep 17 00:00:00 2001 From: energyls Date: Thu, 2 Jul 2026 17:51:05 +0200 Subject: [PATCH 200/216] feat: introduce nice naming of regions --- config/config.yaml | 22 +++++++++++++++++-- .../analysis-globalsupplycurve.ipynb | 5 +++-- workflow/notebooks/plot-compare-lcox.ipynb | 5 +++-- workflow/notebooks/plot-mga.ipynb | 13 ++++++----- workflow/notebooks/plot_countries.ipynb | 5 +++-- 5 files changed, 36 insertions(+), 14 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 955ac68..275c353 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -30,7 +30,7 @@ trade_chains: id: supplyconstraint cost_year: 2050 final_product: steel - wacc: regional #regional + wacc: regional #regional or uniform labour_cost: True #Include labour cost tradeable_commodities: [iron_ore, hbi] stages: @@ -153,7 +153,8 @@ scenario: # Absolute steel demand levels (Mt/year) for supply curve sweep # For each level, PyPSA minimizes cost with fixed renewable capacity # Values represent different production scales -steel_demand_levels: [0.1, 0.5, 1, 5, 10, 25, 50, 75, 100, 150, 200, 250, 300, 500, 1000] # Mt/year +steel_demand_levels: [0.1, 0.5, 1, 5, 10, 25, 50, 75, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000] # Mt/year + hydrogen_storage_cost: False electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' @@ -185,6 +186,23 @@ regions: "East_East_Asia": ["JPN","KOR","PRK"] "Oceania": ["AUS","NZL"] +region_nice_names: + "Europe": "Europe" + "Far_West_Europe": "Far West Europe" + "Middle_East": "Middle East" + "North_West_Africa": "North West Africa" + "Subsaharan_Africa": "Subsaharan Africa" + "North_America": "USA & Canada" + "Eurasia": "Eurasia" + "South_America": "South America" + "South_South_America": "South Cone" + "Central_America": "Central America" + "West_Asia": "South Asia" + "East_Asia": "East Asia (China-dominated)" + "Pacific_Asia": "Pacific Asia" + "East_East_Asia": "Japan & Korea" + "Oceania": "Australia & New Zealand" + clustering: strata_bin_width: onwind: 0.05 diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 7f8d1e5..14f19ad 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -24,7 +24,7 @@ "snakemake = mock_snakemake(\n", " \"plot_global_supply\",\n", " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " sort= \"cost_global\", #\"cost_global\", \"cost_average\"\n", + " sort= \"cost_average\", #\"cost_global\", \"cost_average\"\n", " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", " wacc=\"uniform\",\n", " cost_year=\"2050\",\n", @@ -308,7 +308,8 @@ " handles.append(\n", " plt.Line2D([0], [0], color=\"black\", linewidth=1.2, label=\"Supply curve\")\n", " )\n", - " labels = [reg.replace(\"_\", \" \") for reg in region_list] + [\"Supply curve\"]\n", + " region_nice_names = snakemake.config.get(\"region_nice_names\", {})\n", + " labels = [region_nice_names.get(reg, reg.replace(\"_\", \" \")) for reg in region_list] + [\"Supply curve\"]\n", " ax.legend(\n", " handles, labels, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\",\n", " frameon=False,\n", diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index 06f4b47..ae3202a 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -267,7 +267,8 @@ "source": [ "def region_display_name(region):\n", " \"\"\"Convert config region key (e.g. 'South_America') to display name ('South America').\"\"\"\n", - " return region.replace(\"_\", \" \")\n", + " nice_names = config.get(\"region_nice_names\", {})\n", + " return nice_names.get(region, region.replace(\"_\", \" \"))\n", "\n", "def parse_scenario(name):\n", " \"\"\"Split a scenario key like 'South_America_10' into (display_name, qty_float).\"\"\"\n", @@ -319,7 +320,7 @@ " + [region_display_name(r) for r in high_cost]\n", ")\n", "plot_df[\"region_order\"] = plot_df[\"region\"].map({g: i for i, g in enumerate(groups_ordered)})\n", - "plot_df = plot_df.sort_values([\"region_order\", \"quantity\"]).drop(columns=\"region_order\")" + "plot_df = plot_df.sort_values([\"region_order\", \"quantity\"]).drop(columns=\"region_order\")\n" ] }, { diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index ef575f9..324f984 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -75,6 +75,7 @@ "interone = \"hbi\"\n", "intertwo = \"eaf-grid\"\n", "final = \"steel\"\n", + "region_nice_names = snakemake.config.get(\"region_nice_names\", {})\n", "\n", "print(f\"Production scenario: {scenario}\")\n", "print(f\"Chokepoint scenario: {cp_scenario}\")\n", @@ -346,7 +347,7 @@ " if region_data.sum() > 0: # Only plot if region has any production\n", " color = region_colors.get(region, '#000000')\n", " ax.plot(epsilon_vals, region_data, color=color, linewidth=1.2, \n", - " linestyle='-', alpha=0.6, label=region, zorder=5)\n", + " linestyle='-', alpha=0.6, label=region_nice_names.get(region, region.replace(\"_\", \" \")), zorder=5)\n", "\n", "# Unstable regions (dashed lines)\n", "for region in unstable_regions:\n", @@ -355,7 +356,7 @@ " if region_data.sum() > 0: # Only plot if region has any production\n", " color = region_colors.get(region, '#000000')\n", " ax.plot(epsilon_vals, region_data, color=color, linewidth=1.2, \n", - " linestyle='--', alpha=0.7, label=region, zorder=5)\n", + " linestyle='--', alpha=0.7, label=region_nice_names.get(region, region.replace(\"_\", \" \")), zorder=5)\n", "\n", "# ── Axes formatting ──────────────────────────────────────────────────────────\n", "ax.set_xlim(min(epsilon_vals) - 0.002, max(epsilon_vals) + 0.01)\n", @@ -367,7 +368,7 @@ "ax.legend(loc=\"best\", fontsize=7, framealpha=0.95, ncol=3, title=\"Regions\")\n", "\n", "plt.tight_layout()\n", - "plt.show()" + "plt.show()\n" ] }, { @@ -974,7 +975,7 @@ " vals = np.array(df_hbi.loc[region, stab_eps_cols].values, dtype=float)\n", " if vals.sum() > 0:\n", " stab_region_lines_stable.append(dict(\n", - " name=region.replace(\"_\", \" \"),\n", + " name=region_nice_names.get(region, region.replace(\"_\", \" \")),\n", " values=vals,\n", " color=region_colors.get(region, \"#333333\"),\n", " linestyle=\"-\",\n", @@ -988,7 +989,7 @@ " vals = np.array(df_hbi.loc[region, stab_eps_cols].values, dtype=float)\n", " if vals.sum() > 0:\n", " stab_region_lines_unstable.append(dict(\n", - " name=region.replace(\"_\", \" \"),\n", + " name=region_nice_names.get(region, region.replace(\"_\", \" \")),\n", " values=vals,\n", " color=region_colors.get(region, \"#333333\"),\n", " linestyle=\"--\",\n", @@ -1386,7 +1387,7 @@ " vals = np.array(df_pareto_hbi.loc[region, pareto_eps_cols].values, dtype=float)\n", " if vals.sum() > 0:\n", " sc_region_lines.append(dict(\n", - " name=region.replace(\"_\", \" \"),\n", + " name=region_nice_names.get(region, region.replace(\"_\", \" \")),\n", " values=vals,\n", " color=region_colors.get(region, \"#333333\"),\n", " linestyle=\"-\",\n", diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index 2942c5c..6e28c15 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -171,8 +171,9 @@ " if r in world[\"region\"].values\n", "]\n", "if handles:\n", + " region_nice_names = config.get(\"region_nice_names\", {})\n", " region_list = [r for r, c in color_map.items() if r in world[\"region\"].values]\n", - " labels = [reg.replace(\"_\", \" \") for reg in region_list]\n", + " labels = [region_nice_names.get(reg, reg.replace(\"_\", \" \")) for reg in region_list]\n", " ax.legend(\n", " handles=handles, labels=labels, title=\"Region\", bbox_to_anchor=(1.05, 1), loc=\"upper left\",\n", " )\n", @@ -183,7 +184,7 @@ "\n", "plt.savefig(snakemake.output.global_map_countries)\n", "plt.savefig(snakemake.output.global_map_countries_png, dpi=300)\n", - "plt.show()" + "plt.show()\n" ] } ], From a73a411c84201435201db215716b2afca83ba20b Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 11:23:14 +0200 Subject: [PATCH 201/216] chore: revise color palette of regions --- config/config.yaml | 45 +++++++++++++++------------------------------ 1 file changed, 15 insertions(+), 30 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 275c353..b6ba74e 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -334,36 +334,21 @@ colors: HBI: darkred Photovoltaics: yellow Wind energy: blue - # Eurasia: '#1A6FBF' # mid blue (lighter than before) - # Europe: '#E07B00' # deep amber-orange - # Far_West_Europe: '#007A6E' # dark teal (replaces vivid green) - # Middle_East: '#B5446E' # magenta-rose (replaces brick red) - # North_West_Africa: '#6E3FA3' # purple (kept, adjusted) - # Subsaharan_Africa: '#8B4513' # saddle brown - # North_America: '#F5C518' # yellow (kept) - # South_America: '#2DB3C8' # cyan-teal (lighter, distinct from teal) - # South_South_America: '#F4845F' # warm peach-orange - # Central_America: '#3D9970' # muted green (darker, less vivid) - # West_Asia: '#2C3E50' # blue-grey (kept, unique) - # East_Asia: '#A8D8A8' # very light sage - # Pacific_Asia: '#C47AB0' # pink-lavender - # East_East_Asia: '#0D47A1' # deep navy (replaces dark red) - # Oceania: '#546E7A' # blue-grey slate (distinct from navy) - Central_America: '#011959' - East_Asia: '#FDB4B4' - East_East_Asia: '#A18A2B' - Eurasia: '#CF9340' - Europe: '#416F53' - Far_West_Europe: '#FAA587' - Middle_East: '#F59F74' - North_America: '#26635F' - North_West_Africa: '#FCC3DF' - Oceania: '#ED9A62' - Pacific_Asia: '#FDAFA7' - South_America: '#50744B' - South_South_America: '#114160' - Subsaharan_Africa: '#33695A' - West_Asia: '#FBA68C' + Europe: "#17679E" # blue + Far_West_Europe: "#237F8C" # teal-blue + Eurasia: "#7E84E5" # blue-purple + Middle_East: "#8352D8" # purple + West_Asia: "#590B72" # dark purple + East_Asia: "#BF39B0" # magenta + Pacific_Asia: "#8C234F" # dark magenta-red + East_East_Asia: "#BF9A39" # mustard/gold + North_West_Africa: "#378C23" # green + Subsaharan_Africa: "#720B16" # dark red + North_America: "#0B722B" # dark green + Central_America: "#E5977E" # peach + South_America: "#39BF99" # teal-green + South_South_America: "#99D852" # yellow-green + Oceania: "#D7E57E" # light yellow-green trade_today: surplus: '#d94801' # orange-red (net exporter bubble) deficit: '#045a8d' # dark blue (net importer bubble) From 675befbdcad22c15c6374d641adbe2857caef026 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 11:29:01 +0200 Subject: [PATCH 202/216] chore: adjust alpha in region colors --- workflow/notebooks/analysis-globalsupplycurve.ipynb | 6 +++--- workflow/notebooks/plot_countries.ipynb | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index 14f19ad..fee30c3 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -26,7 +26,7 @@ " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", " sort= \"cost_average\", #\"cost_global\", \"cost_average\"\n", " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", - " wacc=\"uniform\",\n", + " wacc=\"regional\",\n", " cost_year=\"2050\",\n", " interone=\"hbi\",\n", " trade_chain=\"supplyconstraint\"\n", @@ -255,7 +255,7 @@ " color=bar_colors,\n", " align=\"edge\",\n", " edgecolor=\"none\",\n", - " alpha=1,\n", + " alpha=0.8,\n", " )\n", " # Add the classic supply curve line\n", "\n", @@ -303,7 +303,7 @@ " ax.grid(axis=\"y\", alpha=0.4, zorder=0)\n", " # Legend for regions\n", " handles = [\n", - " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg], alpha=1) for reg in region_list\n", + " plt.Rectangle((0, 0), 1, 1, color=region_colors[reg], alpha=0.8) for reg in region_list\n", " ]\n", " handles.append(\n", " plt.Line2D([0], [0], color=\"black\", linewidth=1.2, label=\"Supply curve\")\n", diff --git a/workflow/notebooks/plot_countries.ipynb b/workflow/notebooks/plot_countries.ipynb index 6e28c15..622bbac 100644 --- a/workflow/notebooks/plot_countries.ipynb +++ b/workflow/notebooks/plot_countries.ipynb @@ -162,11 +162,11 @@ "\n", "fig, ax = plt.subplots(figsize=(12, 10))\n", "\n", - "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\", alpha=1)\n", + "world.plot(ax=ax, color=world[\"plot_color\"], edgecolor=\"white\", alpha=0.8)\n", "\n", "# Manual legend for regions present in the map\n", "handles = [\n", - " mpatches.Patch(color=c, label=r, alpha=1)\n", + " mpatches.Patch(color=c, label=r, alpha=0.8)\n", " for r, c in color_map.items()\n", " if r in world[\"region\"].values\n", "]\n", @@ -184,7 +184,7 @@ "\n", "plt.savefig(snakemake.output.global_map_countries)\n", "plt.savefig(snakemake.output.global_map_countries_png, dpi=300)\n", - "plt.show()\n" + "plt.show()" ] } ], From fbbcc05b995472281e063c4bc94e9d187cef1c8f Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 11:32:59 +0200 Subject: [PATCH 203/216] feat: merge battery components into one for plotting --- workflow/notebooks/plot-compare-lcox.ipynb | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/workflow/notebooks/plot-compare-lcox.ipynb b/workflow/notebooks/plot-compare-lcox.ipynb index ae3202a..ac6023c 100644 --- a/workflow/notebooks/plot-compare-lcox.ipynb +++ b/workflow/notebooks/plot-compare-lcox.ipynb @@ -323,6 +323,18 @@ "plot_df = plot_df.sort_values([\"region_order\", \"quantity\"]).drop(columns=\"region_order\")\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b19202d", + "metadata": {}, + "outputs": [], + "source": [ + "# Merge \"battery\", \"battery inverter (charging)\", \"battery inverter (discharging)\" into a single \"battery\" column\n", + "plot_df[\"battery\"] = plot_df[[\"battery\", \"battery inverter (charging)\", \"battery inverter (discharging)\"]].sum(axis=1)\n", + "plot_df = plot_df.drop(columns=[\"battery inverter (charging)\", \"battery inverter (discharging)\"])" + ] + }, { "cell_type": "code", "execution_count": null, From 3080913916e70456527cc627117a7c3c4e42517a Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 13:00:00 +0200 Subject: [PATCH 204/216] chore: adjust alpha in global plot --- workflow/scripts/model_trade.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflow/scripts/model_trade.py b/workflow/scripts/model_trade.py index 84642d7..de90570 100644 --- a/workflow/scripts/model_trade.py +++ b/workflow/scripts/model_trade.py @@ -475,7 +475,7 @@ def plot_trade_network( n, product="steel", alpha_supply=0.7, - alpha_demand=1, + alpha_demand=0.7, output_path=None, output_path_png=None, region_gdf=None, From 30ded3c2f5393c2e52e247e95fde61f0070c8f0c Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 13:00:14 +0200 Subject: [PATCH 205/216] chore: change colors of global plot --- config/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index b6ba74e..e79d447 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -367,8 +367,8 @@ colors: steel_demand: 'seagreen' steel_link: 'skyblue' iron_ore_supply: 'black' - iron_ore_demand: 'lightsteelblue' + iron_ore_demand: 'green' iron_ore_link: "#6A000E" - hbi_demand: 'green' + hbi_demand: '#17679E' hbi_supply: 'darkred' hbi_link: 'firebrick' \ No newline at end of file From 51d621ea327f1a835e430b5f026ac5a781be1f89 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 13:00:31 +0200 Subject: [PATCH 206/216] feat: create rule which collects all main figures --- rules/reporting.smk | 46 +++++++++++++++++++++++++++++++++++---------- 1 file changed, 36 insertions(+), 10 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index f5679c4..3e5fd85 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -42,11 +42,37 @@ rule get_figures: [ "results/figures_general/comparison/cost_year~2050/wacc~regional/lcox_comparison_East_Asia_East_East_Asia.pdf", "results/figures_general/comparison/cost_year~2050/wacc~regional/lcox_comparison_South_America_Europe.pdf", + "results/figures_general/mga/chain_id~supplyconstraint/wacc~regional/mga_analysis.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~default/map_hbi.pdf", + "results/figures_general/global_map_countries.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-chokepoints/map_hbi_0.002.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-chokepoints/map_ironore_0.002.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-blocs/map_hbi_0.001.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-blocs/map_ironore_0.001.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~constrain-supply/map_hbi_250.0.pdf", + "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~constrain-supply/map_ironore_250.0.pdf", + "results/figures_general/global_supply_curve/chain_id~supplyconstraint/cost_year~2050/uniform/default/global_supply_curve_cost_average_hbi.pdf", + "results/figures_general/global_supply_curve/chain_id~supplyconstraint/cost_year~2050/uniform/default/global_supply_curve_cost_global_hbi.pdf", + "results/figures_general/global_supply_curve/chain_id~supplyconstraint/cost_year~2050/regional/default/global_supply_curve_cost_average_hbi.pdf", + "results/figures_general/global_supply_curve/chain_id~supplyconstraint/cost_year~2050/regional/default/global_supply_curve_cost_global_hbi.pdf", ] output: [ "results/figures_streamlined/lcox-east-asia.pdf", "results/figures_streamlined/lcox-south-america.pdf", + "results/figures_streamlined/mga-analysis.pdf", + "results/figures_streamlined/map-hbi-opti.pdf", + "results/figures_streamlined/map-countries.pdf", + "results/figures_streamlined/map-hbi-chokepoints.pdf", + "results/figures_streamlined/map-ironore-chokepoints.pdf", + "results/figures_streamlined/map-hbi-blocs.pdf", + "results/figures_streamlined/map-ironore-blocs.pdf", + "results/figures_streamlined/map-hbi-supply.pdf", + "results/figures_streamlined/map-ironore-supply.pdf", + "results/figures_streamlined/supply-sorted-homo.pdf", + "results/figures_streamlined/supply-unsorted-homo.pdf", + "results/figures_streamlined/supply-sorted-hetero.pdf", + "results/figures_streamlined/supply-unsorted-hetero.pdf", ] threads: 1 run: @@ -99,19 +125,19 @@ rule plot_global_supply: # supply = "../resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", # supply_nodemand = "../resources/supply_curves_nodemand/cost_year~{cost_year}/wacc~{wacc}/{region}_{product}.csv", supply_curves_interone = expand( - "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{interone}{demand}.csv", + "resources/supply_curves/cost_year~{cost_year}/wacc~{wacc}/{region}_marginal_cost_{interone}.csv", allow_missing=True, region=config["regions"]), steel_demand="resources/steel_demand_clustered_{cost_year}.csv", output: - network_curve="results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", - network_curve_png="results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.png", + network_curve="results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_{interone}.pdf", + network_curve_png="results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_{interone}.png", # supply_curves notebook: str(NOTEBOOKS_DIR / "analysis-globalsupplycurve.ipynb") rule plot_global_supply_all: input: - expand("results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_demand_{demand}_{interone}.pdf", trade_chain=[config["trade_chains"]["id"]], cost_year=[2050], wacc=[config["trade_chains"]["wacc"]], interone=["hbi"], scenario=["default"], sort=[True,False], demand=[True,False], allow_missing=True) + expand("results/figures_general/global_supply_curve/chain_id~{trade_chain}/cost_year~{cost_year}/{wacc}/{scenario}/global_supply_curve_{sort}_{interone}.pdf", trade_chain=[config["trade_chains"]["id"]], cost_year=[2050], wacc=["regional", "uniform"], interone=["hbi"], scenario=["default"], sort=["cost_average","cost_global"], allow_missing=True) rule plot_comparison: input: @@ -128,14 +154,14 @@ rule plot_comparison: # Variables captured by the plot_compare_lcox input lambda (avoids two-argument lambda) # First case -# _lcox_low_cost = ["East_Asia"] -# _lcox_high_cost = ["East_East_Asia"] -# _comparison = ["East_East_Asia", "East_Asia"] +_lcox_low_cost = ["East_Asia"] +_lcox_high_cost = ["East_East_Asia"] +_comparison = ["East_East_Asia", "East_Asia"] # Second case -_lcox_low_cost = ["South_America"] -_lcox_high_cost = ["Europe"] -_comparison = ["Europe", "South_America"] +# _lcox_low_cost = ["South_America"] +# _lcox_high_cost = ["Europe"] +# _comparison = ["Europe", "South_America"] _lcox_quantities = [1, 10, 100] From 14673cb8cded06763b3951804de6ce6f5c799f34 Mon Sep 17 00:00:00 2001 From: energyls Date: Fri, 3 Jul 2026 13:01:40 +0200 Subject: [PATCH 207/216] fix: mock snakemake only when called from inside --- .../analysis-globalsupplycurve.ipynb | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/workflow/notebooks/analysis-globalsupplycurve.ipynb b/workflow/notebooks/analysis-globalsupplycurve.ipynb index fee30c3..ad016c2 100644 --- a/workflow/notebooks/analysis-globalsupplycurve.ipynb +++ b/workflow/notebooks/analysis-globalsupplycurve.ipynb @@ -20,17 +20,17 @@ "metadata": {}, "outputs": [], "source": [ - "from _helpers_notebooks import mock_snakemake\n", - "snakemake = mock_snakemake(\n", - " \"plot_global_supply\",\n", - " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " sort= \"cost_average\", #\"cost_global\", \"cost_average\"\n", - " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", - " wacc=\"regional\",\n", - " cost_year=\"2050\",\n", - " interone=\"hbi\",\n", - " trade_chain=\"supplyconstraint\"\n", - ")" + "if \"snakemake\" not in globals():\n", + " from _helpers_notebooks import mock_snakemake\n", + " snakemake = mock_snakemake(\n", + " \"plot_global_supply\",\n", + " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", + " sort= \"cost_global\", #\"cost_global\", \"cost_average\"\n", + " wacc=\"uniform\",\n", + " cost_year=\"2050\",\n", + " interone=\"hbi\",\n", + " trade_chain=\"supplyconstraint\"\n", + " )" ] }, { @@ -41,8 +41,7 @@ "outputs": [], "source": [ "scenario = snakemake.wildcards.scenario\n", - "sort = snakemake.wildcards.sort\n", - "demand = snakemake.wildcards.demand" + "sort = snakemake.wildcards.sort" ] }, { From 77112a75233431334e6c6931861fe71ee7ccd4a7 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 4 Jul 2026 12:00:51 +0200 Subject: [PATCH 208/216] chore: limit iron ore potential to 120% of today --- config/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/config.yaml b/config/config.yaml index e79d447..400b05d 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -265,7 +265,7 @@ iron_ore: regionalise: "grade-dependent" #"uniform" or "grade-dependent" marginal_cost: 97.7 #97.7 EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data). ore_to_steel_ratio: 1.59 # t_ore/t_steel, see https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) - potential_allowance: 2 # Factor, which the current iron ore production is multiplied with + potential_allowance: 1.2 # Factor, which the current iron ore production is multiplied with shipping_cost_per_km: 0.005 # €/(t*km) # Guesstimate plot: From 1a990882c6e461fc61203900409cac2d9354c86c Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 4 Jul 2026 12:01:32 +0200 Subject: [PATCH 209/216] fix: use max constraint from config for plot --- workflow/notebooks/plot-mga.ipynb | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 324f984..53621d0 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -1365,17 +1365,19 @@ "# pareto_eps_cols : integer position labels (0, 1, 2, …) — safe for .loc\n", "# sc_eps_pct : actual ε values in % (from pareto_eps_arr_frac)\n", "# sc_cap_vals : \"max. per supplier\" curve\n", - "# • unconstrained optimum (key=None, ε=0): actual max\n", - "# regional production at that scenario\n", + "# • unconstrained optimum (key=None, ε=0): max configured\n", + "# cap value from config (pareto_limits_cfg)\n", "# • constrained scenarios: raw threshold_value from config\n", "# (Mt, as specified — no unit conversion applied)\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "sc_eps_pct = pareto_eps_arr_frac * 100 # → %\n", "\n", "# Build cap line directly from config threshold values (ordered by ε ascending,\n", - "# matching the column order of df_pareto_hbi)\n", + "# matching the column order of df_pareto_hbi).\n", + "# For the unconstrained optimal (key=None, ε=0), use the maximum configured cap\n", + "# value so the curve starts at the \"no constraint\" level.\n", "sc_cap_vals = np.array([\n", - " float(df_pareto_hbi[pareto_eps_cols].iloc[:, 0].max()) if key is None\n", + " float(max(float(x) for x in pareto_limits_cfg)) if key is None\n", " else float(key)\n", " for key in ordered_pareto_keys\n", "], dtype=float)\n", From 013821fecc23ea8f27ce3a7d4590d815cf2188f1 Mon Sep 17 00:00:00 2001 From: energyls Date: Tue, 14 Jul 2026 18:43:54 +0200 Subject: [PATCH 210/216] chore: clean config file --- config/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/config.yaml b/config/config.yaml index 400b05d..4cac936 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -235,7 +235,7 @@ techno-economic parameters: batt_standing_loss: 0.0001 # 0.01% per hour for battery (self-discharge) interest_rate: - default: 0.084 # Used, if wildcard `wacc` is set to `uniform`. The proposed value of 0.084 (8.4%) is obtained from the notebook `prepare-wacc.ipynb` and represents a global average WACC weighted by GDP. T + default: 0.084 # Used, if wildcard `wacc` is set to `uniform`. The value of 0.084 (8.4%) is obtained from the notebook `prepare-wacc.ipynb` and represents a global average WACC weighted by GDP. trade: shipping_routes: From 875c27642fd1a0d5ac517e74ff3e237e1719a894 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 15 Jul 2026 11:43:03 +0200 Subject: [PATCH 211/216] chore: add chokepoint global general map to streamlined figures --- rules/reporting.smk | 2 ++ 1 file changed, 2 insertions(+) diff --git a/rules/reporting.smk b/rules/reporting.smk index 3e5fd85..b87a8df 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -45,6 +45,7 @@ rule get_figures: "results/figures_general/mga/chain_id~supplyconstraint/wacc~regional/mga_analysis.pdf", "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~default/map_hbi.pdf", "results/figures_general/global_map_countries.pdf", + "results/figures_general/chokepoints/map_chokepoints.pdf", "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-chokepoints/map_hbi_0.002.pdf", "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-chokepoints/map_ironore_0.002.pdf", "results/chain_id~supplyconstraint/cost_year~2050/interone~hbi/intertwo~eaf/wacc~regional/final~steel/scenario~mga-blocs/map_hbi_0.001.pdf", @@ -63,6 +64,7 @@ rule get_figures: "results/figures_streamlined/mga-analysis.pdf", "results/figures_streamlined/map-hbi-opti.pdf", "results/figures_streamlined/map-countries.pdf", + "results/figures_streamlined/map-chokepoints.pdf", "results/figures_streamlined/map-hbi-chokepoints.pdf", "results/figures_streamlined/map-ironore-chokepoints.pdf", "results/figures_streamlined/map-hbi-blocs.pdf", From 61aba54a7c164e1bd2aac618560204b1cb6a3487 Mon Sep 17 00:00:00 2001 From: energyls Date: Wed, 15 Jul 2026 12:06:21 +0200 Subject: [PATCH 212/216] feat: improve suez chokepoint visibility and move all colors to config --- config/config.yaml | 18 ++++++- workflow/notebooks/plot-mga.ipynb | 90 ++++++++++++++++++------------- 2 files changed, 70 insertions(+), 38 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 4cac936..1b8c6d6 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -371,4 +371,20 @@ colors: iron_ore_link: "#6A000E" hbi_demand: '#17679E' hbi_supply: 'darkred' - hbi_link: 'firebrick' \ No newline at end of file + hbi_link: 'firebrick' + # Maritime chokepoint colors (mapped to geographically relevant region colors) + babalmandab: "#8352D8" # Middle_East (Bab el-Mandeb) + bosporus: "#720B16" # Swapped with Subsaharan_Africa + gibraltar: "#237F8C" # Far_West_Europe (Strait of Gibraltar) + suez: "#378C23" # North_West_Africa (Suez Canal) + panama: "#E5977E" # Central_America (Panama Canal) + ormuz: "#590B72" # West_Asia (Strait of Hormuz) + malacca: "#8C234F" # Pacific_Asia (Strait of Malacca) + south_africa: "#17679E" # swapped with Europe + northwest: "#0B722B" # North_America (Northwest Passage) + sunda: "#BF39B0" # East_Asia (Sunda Strait) + chili: "#99D852" # South_South_America (Drake Passage) + # Friendshoring bloc colors + bloc_1: "#17679E" # Europe (Western bloc: Europe + Far West Europe + North America) + bloc_2: "#8352D8" # Middle_East (Global South bloc) + bloc_3: "#BF9A39" # East_East_Asia (East Asia + Oceania bloc) \ No newline at end of file diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index 53621d0..e371ee5 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -603,19 +603,23 @@ " \"babalmandab\": \"Bab el-Mandeb\",\n", "}\n", "\n", - "# Chokepoint colors (distinct palette)\n", - "cp_colors = {\n", - " \"babalmandab\": \"#e41a1c\",\n", - " \"bosporus\": \"#377eb8\",\n", - " \"gibraltar\": \"#4daf4a\",\n", - " \"suez\": \"#984ea3\",\n", - " \"panama\": \"#ff7f00\",\n", - " \"ormuz\": \"#a65628\",\n", - " \"northwest\": \"#f781bf\",\n", - " \"malacca\": \"#999999\",\n", - " \"sunda\": \"#66c2a5\",\n", - " \"chili\": \"#8dd3c7\",\n", - " \"south_africa\":\"#fdb462\",\n", + "# Chokepoint colors — read from config (mapped to geographically relevant region colors)\n", + "_colors_cfg = snakemake.config[\"colors\"]\n", + "cp_colors = {cp: _colors_cfg.get(cp, \"#333333\") for cp in config_chokepoints}\n", + "\n", + "# Chokepoint line styles (solid/dashed to distinguish geographically proximate pairs)\n", + "cp_linestyles = {\n", + " \"babalmandab\": \"-\",\n", + " \"suez\": \"--\", # Red Sea area: dashed to distinguish from babalmandab\n", + " \"bosporus\": \"-\",\n", + " \"gibraltar\": \"-\", # European straits: dashed\n", + " \"ormuz\": \"-\",\n", + " \"panama\": \"-\",\n", + " \"malacca\": \"-\",\n", + " \"sunda\": \"--\", # SE Asia pair: dashed\n", + " \"south_africa\":\"-\",\n", + " \"northwest\": \"--\",\n", + " \"chili\": \"--\",\n", "}\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n", @@ -638,7 +642,7 @@ " color = cp_colors.get(cp, \"#333333\")\n", " label = cp_display_names.get(cp, cp.replace(\"_\", \" \").title())\n", " ax.plot(epsilon_arr, cp_arr, color=color, linewidth=1.5,\n", - " linestyle=\"--\", marker=\"s\", markersize=4, alpha=0.8,\n", + " linestyle=cp_linestyles.get(cp, \"-\"), marker=\"s\", markersize=4, alpha=0.8,\n", " label=label, zorder=10)\n", "\n", "# ── Axes formatting ──\n", @@ -858,14 +862,19 @@ "inter_arr = np.array(bl_summary[\"Inter-block total\"].values, dtype=float)\n", "intra_arr = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", "\n", + "# Bloc colors from config\n", + "_bl_c1 = snakemake.config[\"colors\"].get(\"bloc_1\", \"#17679E\")\n", + "_bl_c2 = snakemake.config[\"colors\"].get(\"bloc_2\", \"#8352D8\")\n", + "_bl_c3 = snakemake.config[\"colors\"].get(\"bloc_3\", \"#BF9A39\")\n", + "\n", "# All 6 directional pairs with distinct colours / styles\n", "pair_plot_cfg = {\n", - " \"Block 1 → Block 2\": dict(color=\"#e41a1c\", linestyle=\"--\", marker=\"s\"),\n", - " \"Block 1 → Block 3\": dict(color=\"#e41a1c\", linestyle=\":\", marker=\"^\"),\n", - " \"Block 2 → Block 1\": dict(color=\"#377eb8\", linestyle=\"--\", marker=\"s\"),\n", - " \"Block 2 → Block 3\": dict(color=\"#377eb8\", linestyle=\":\", marker=\"^\"),\n", - " \"Block 3 → Block 1\": dict(color=\"#4daf4a\", linestyle=\"--\", marker=\"s\"),\n", - " \"Block 3 → Block 2\": dict(color=\"#4daf4a\", linestyle=\":\", marker=\"^\"),\n", + " \"Block 1 → Block 2\": dict(color=_bl_c1, linestyle=\"--\", marker=\"s\"),\n", + " \"Block 1 → Block 3\": dict(color=_bl_c1, linestyle=\":\", marker=\"^\"),\n", + " \"Block 2 → Block 1\": dict(color=_bl_c2, linestyle=\"--\", marker=\"s\"),\n", + " \"Block 2 → Block 3\": dict(color=_bl_c2, linestyle=\":\", marker=\"^\"),\n", + " \"Block 3 → Block 1\": dict(color=_bl_c3, linestyle=\"--\", marker=\"s\"),\n", + " \"Block 3 → Block 2\": dict(color=_bl_c3, linestyle=\":\", marker=\"^\"),\n", "}\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n", @@ -950,8 +959,8 @@ " name=cp_display_names.get(cpname, cpname.replace(\"_\", \" \").title()),\n", " values=vals,\n", " color=cp_colors.get(cpname, \"#333333\"),\n", - " linestyle=\"-\",\n", - " alpha=0.6,\n", + " linestyle=cp_linestyles.get(cpname, \"-\"),\n", + " alpha=0.8 if cpname in (\"babalmandab\", \"suez\") else 0.6,\n", " ))\n", "cp_region_lines.sort(key=lambda d: d[\"values\"][-1], reverse=True)\n", "\n", @@ -1004,14 +1013,17 @@ "bl_inter_vals = np.array(bl_summary[\"Inter-block total\"].values, dtype=float)\n", "bl_intra_vals = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", "\n", - "# All 6 directional pairs with consistent colours per source bloc\n", + "# All 6 directional pairs with consistent colours per source bloc (from config)\n", + "_bl_c1 = snakemake.config[\"colors\"].get(\"bloc_1\", \"#17679E\")\n", + "_bl_c2 = snakemake.config[\"colors\"].get(\"bloc_2\", \"#8352D8\")\n", + "_bl_c3 = snakemake.config[\"colors\"].get(\"bloc_3\", \"#BF9A39\")\n", "_bl_pair_cfg = {\n", - " \"Block 1 → Block 2\": dict(color=\"#e41a1c\", linestyle=\"--\"),\n", - " \"Block 1 → Block 3\": dict(color=\"#e41a1c\", linestyle=\":\"),\n", - " \"Block 2 → Block 1\": dict(color=\"#377eb8\", linestyle=\"--\"),\n", - " \"Block 2 → Block 3\": dict(color=\"#377eb8\", linestyle=\":\"),\n", - " \"Block 3 → Block 1\": dict(color=\"#4daf4a\", linestyle=\"--\"),\n", - " \"Block 3 → Block 2\": dict(color=\"#4daf4a\", linestyle=\":\"),\n", + " \"Block 1 → Block 2\": dict(color=_bl_c1, linestyle=\"--\"),\n", + " \"Block 1 → Block 3\": dict(color=_bl_c1, linestyle=\":\"),\n", + " \"Block 2 → Block 1\": dict(color=_bl_c2, linestyle=\"--\"),\n", + " \"Block 2 → Block 3\": dict(color=_bl_c2, linestyle=\":\"),\n", + " \"Block 3 → Block 1\": dict(color=_bl_c3, linestyle=\"--\"),\n", + " \"Block 3 → Block 2\": dict(color=_bl_c3, linestyle=\":\"),\n", "}\n", "bl_region_lines_unsorted = []\n", "for label, cfg in _bl_pair_cfg.items():\n", @@ -1354,8 +1366,8 @@ " name=cp_display_names.get(cpname, cpname.replace(\"_\", \" \").title()),\n", " values=vals,\n", " color=cp_colors.get(cpname, \"#333333\"),\n", - " linestyle=\"-\",\n", - " alpha=0.6,\n", + " linestyle=cp_linestyles.get(cpname, \"-\"),\n", + " alpha=0.8 if cpname in (\"babalmandab\", \"suez\") else 0.6,\n", " ))\n", "cp_region_lines_v2.sort(key=lambda d: d[\"values\"][-1], reverse=True)\n", "\n", @@ -1412,13 +1424,17 @@ "bl_inter_vals_v2 = np.array(bl_summary[\"Inter-block total\"].values, dtype=float)\n", "bl_intra_vals_v2 = np.array(bl_summary[\"Intra-block total\"].values, dtype=float)\n", "\n", + "# Bloc colors from config\n", + "_bl_c1_v2 = snakemake.config[\"colors\"].get(\"bloc_1\", \"#17679E\")\n", + "_bl_c2_v2 = snakemake.config[\"colors\"].get(\"bloc_2\", \"#8352D8\")\n", + "_bl_c3_v2 = snakemake.config[\"colors\"].get(\"bloc_3\", \"#BF9A39\")\n", "_bl_pair_cfg_v2 = {\n", - " \"Group 1 → Group 2\": dict(color=\"#e41a1c\", linestyle=\"--\"),\n", - " \"Group 1 → Group 3\": dict(color=\"#e41a1c\", linestyle=\":\"),\n", - " \"Group 2 → Group 1\": dict(color=\"#377eb8\", linestyle=\"--\"),\n", - " \"Group 2 → Group 3\": dict(color=\"#377eb8\", linestyle=\":\"),\n", - " \"Group 3 → Group 1\": dict(color=\"#4daf4a\", linestyle=\"--\"),\n", - " \"Group 3 → Group 2\": dict(color=\"#4daf4a\", linestyle=\":\"),\n", + " \"Group 1 → Group 2\": dict(color=_bl_c1_v2, linestyle=\"--\"),\n", + " \"Group 1 → Group 3\": dict(color=_bl_c1_v2, linestyle=\":\"),\n", + " \"Group 2 → Group 1\": dict(color=_bl_c2_v2, linestyle=\"--\"),\n", + " \"Group 2 → Group 3\": dict(color=_bl_c2_v2, linestyle=\":\"),\n", + " \"Group 3 → Group 1\": dict(color=_bl_c3_v2, linestyle=\"--\"),\n", + " \"Group 3 → Group 2\": dict(color=_bl_c3_v2, linestyle=\":\"),\n", "}\n", "bl_region_lines_v2 = sorted(\n", " [\n", From 666c1e150a9f35bada274c9f6685754b7ed10029 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 18 Jul 2026 09:56:16 +0200 Subject: [PATCH 213/216] feat: plot market share in pareto figures --- rules/reporting.smk | 1 + workflow/notebooks/plot-mga.ipynb | 61 +++++++++++++++++++++++-------- 2 files changed, 47 insertions(+), 15 deletions(-) diff --git a/rules/reporting.smk b/rules/reporting.smk index b87a8df..d3565e8 100644 --- a/rules/reporting.smk +++ b/rules/reporting.smk @@ -91,6 +91,7 @@ rule plot_mga: network_pareto_supply = "results/chain_id~{trade_chain}/cost_year~2050/interone~hbi/intertwo~eaf/wacc~{wacc}/final~steel/scenario~constrain-supply/network.nc", political_stability = "resources/political_stability_clustered.csv", trade_options_chokepoints = "resources/trade_opt_chokepoints.csv", + steel_demand = "resources/steel_demand_clustered_2050.csv", output: mga_plot = "results/figures_general/mga/chain_id~{trade_chain}/wacc~{wacc}/mga_analysis.pdf", mga_plot_png = "results/figures_general/mga/chain_id~{trade_chain}/wacc~{wacc}/mga_analysis.png", diff --git a/workflow/notebooks/plot-mga.ipynb b/workflow/notebooks/plot-mga.ipynb index e371ee5..7f122b7 100644 --- a/workflow/notebooks/plot-mga.ipynb +++ b/workflow/notebooks/plot-mga.ipynb @@ -1343,6 +1343,17 @@ "Same three-panel layout as above, but the centre panel now shows the **supply-constraint Pareto front** — how HBI production redistributes across regions as per-region production is progressively capped (x-axis = cost premium ε derived from each constrained optimisation).\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffc601e6", + "metadata": {}, + "outputs": [], + "source": [ + "steel_demand = pd.read_csv(snakemake.input.steel_demand, index_col=\"region\")\n", + "steel_demand = steel_demand[\"SteelDemand_DRI_Mt\"].sum()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1448,52 +1459,71 @@ ")\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", + "# Convert absolute values (Mt) to relative values (% of global steel demand)\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", + "def _scale_lines(lines, factor):\n", + " \"\"\"Return a copy of a list of region-line dicts with values scaled by factor.\"\"\"\n", + " return [{**r, \"values\": r[\"values\"] * factor} for r in lines]\n", + "\n", + "_pct_factor = 100.0 / steel_demand # Mt → % of market share\n", + "\n", + "cp_total_vals_v2_pct = cp_total_vals_v2 * _pct_factor\n", + "cp_region_lines_v2_pct = _scale_lines(cp_region_lines_v2, _pct_factor)\n", + "\n", + "bl_inter_vals_v2_pct = bl_inter_vals_v2 * _pct_factor\n", + "bl_intra_vals_v2_pct = bl_intra_vals_v2 * _pct_factor\n", + "bl_region_lines_v2_pct = _scale_lines(bl_region_lines_v2, _pct_factor)\n", + "\n", + "sc_cap_vals_pct = sc_cap_vals * _pct_factor\n", + "sc_region_lines_pct = _scale_lines(sc_region_lines, _pct_factor)\n", + "\n", + "# ═══════════════════════════════════════════════════════════════════════════════\n", "# Combined plot — v2\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", - "Y_MAX_V2 = 800\n", + "Y_MAX_V2 = 100\n", "GAP_FRAC_V2 = 0.04\n", "\n", "plots_v2 = [\n", " dict(\n", " title=\"Stable trade routes (chokepoints)\",\n", " epsilon=cp_eps_v2,\n", - " main_curve=cp_total_vals_v2,\n", + " main_curve=cp_total_vals_v2_pct,\n", " main_label=\"Global\",\n", - " y_mid=cp_total_vals_v2[0],\n", + " y_mid=cp_total_vals_v2_pct[0],\n", " color=\"#000000\",\n", " arrow_dn=[\"Minimize\", \"high-risk routes\"],\n", " arrow_x_frac=0.40,\n", - " y_label_add=\"trade through maritime chokepoints\",\n", - " regions=cp_region_lines_v2,\n", + " y_label_add=\"trade through maritime chokepoints \\nin % of market share\",\n", + " regions=cp_region_lines_v2_pct,\n", " extra_curves=[],\n", " ),\n", " dict(\n", " title=\"Friendshoring\",\n", " epsilon=bl_eps_v2,\n", - " main_curve=bl_inter_vals_v2,\n", + " main_curve=bl_inter_vals_v2_pct,\n", " main_label=\"Global\",\n", - " y_mid=bl_inter_vals_v2[0],\n", + " y_mid=bl_inter_vals_v2_pct[0],\n", " color=\"#000000\",\n", " arrow_dn=[\"Minimize trade with\", \"non-friendshored regions\"],\n", " arrow_x_frac=0.40,\n", - " y_label_add=\"trade between blocs\",\n", - " regions=bl_region_lines_v2,\n", + " y_label_add=\"trade between groups \\nin % of market share\",\n", + " regions=bl_region_lines_v2_pct,\n", " extra_curves=[\n", - " dict(name=\"Friendshoring total\", values=bl_intra_vals_v2,\n", + " dict(name=\"Friendshoring total\", values=bl_intra_vals_v2_pct,\n", " color=\"#999999\", linestyle=\":\", linewidth=1.5, alpha=0.7),\n", " ],\n", " ),\n", " dict(\n", " title=\"Supply diversification\",\n", " epsilon=sc_eps_pct,\n", - " main_curve=sc_cap_vals,\n", + " main_curve=sc_cap_vals_pct,\n", " main_label=\"max. per supplier\",\n", - " y_mid=sc_cap_vals[0],\n", + " y_mid=sc_cap_vals_pct[0],\n", " color=\"#000000\",\n", " arrow_dn=[\"Constrain production\", \"per supplier\"],\n", " arrow_x_frac=0.30,\n", - " y_label_add=\"production by region\",\n", - " regions=sc_region_lines,\n", + " y_label_add=\"production by region \\nin % of market share\",\n", + " regions=sc_region_lines_pct,\n", " extra_curves=[],\n", " ),\n", "]\n", @@ -1532,8 +1562,9 @@ " # ── Axes ─────────────────────────────────────────────────────────────────\n", " ax.set_xlim(eps[0], eps[-1])\n", " ax.set_ylim(0, Y_MAX_V2)\n", + " ax.set_yticks(np.arange(0, Y_MAX_V2 + 1, 10))\n", " ax.set_xlabel(\"ε in %\", fontsize=11)\n", - " ax.set_ylabel(f\"HBI {p['y_label_add']} in Mt\", fontsize=10)\n", + " ax.set_ylabel(f\"HBI {p['y_label_add']}\", fontsize=10, labelpad=-2)\n", " ax.set_title(p[\"title\"], fontsize=13, fontweight=\"medium\", pad=10)\n", " ax.grid(True, linestyle=\"--\", linewidth=0.5, alpha=0.5)\n", "\n", From 739f0b603df9a189be4a60b53b1cd94b3695262e Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 18 Jul 2026 10:11:54 +0200 Subject: [PATCH 214/216] chore: clean config --- config/config.yaml | 53 +++++++++++----------------------------------- 1 file changed, 12 insertions(+), 41 deletions(-) diff --git a/config/config.yaml b/config/config.yaml index 1b8c6d6..4b6d3a0 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -3,6 +3,8 @@ enable: run_supply_curve: False # Enable for first run cluster_renewables: False # Enable for first run +run: + disable_progressbar: false # Output toggles for Step 0/1 (greenfield supply curve generation) outputs: @@ -51,9 +53,9 @@ trade_chains: process_label: eaf trade_scenarios: - default - # - mga-chokepoints - # - mga-blocs - # - constrain-supply + - mga-chokepoints + - mga-blocs + - constrain-supply # - mga-stability-weighted @@ -61,23 +63,11 @@ scenario: default: modifiers: cost_penalty: - penalty-nwa: + penalty-nwa: # Scenario to demonstrate cost penalty functionality modifiers: cost_penalty: North_West_Africa: 1.1 - penalty-ea: - modifiers: - cost_penalty: - Eurasia: 1.1 - penalty-sa: - modifiers: - cost_penalty: - South_America: 0.7 - penalty-oc: - modifiers: - cost_penalty: - Oceania: 0.8 - mga-eur-nwa-2: + mga-eur-nwa-2: # Scenario to demonstrate MGA functionality with two regions (Europe and North West Africa) modifiers: cost_penalty: mga: @@ -86,14 +76,6 @@ scenario: carrier: "hbi" export: "North_West_Africa" import: "Europe" - mga-nwa-iso: - modifiers: - cost_penalty: - mga: - slack: 0.01 - sense: "min" - carrier: "hbi" - export: "North_West_Africa" mga-stability: modifiers: cost_penalty: @@ -155,18 +137,9 @@ scenario: # Values represent different production scales steel_demand_levels: [0.1, 0.5, 1, 5, 10, 25, 50, 75, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000] # Mt/year -hydrogen_storage_cost: False - electricity_steel_ratio: 5.25 #TWh/Mt or MWh/t, see notebooks 'analysis-steel.ipynb' embodied_energy_steel: 2.1 #TWh/Mt or MWh/t, see iron oxide reduction -run: - # prefix: "" - # name: "" - # scenarios: - # enable: false - # file: config/scenarios.yaml - disable_progressbar: false # Region definitions (ISO3 codes matching renewable clusters metadata) regions: @@ -219,8 +192,8 @@ clustering: max_clusters: 50 design: - cost_penalty: - # North_West_Africa: 1.1 # Relative cost pentalty on all technlogies in this region. 1: no change + cost_penalty: # Feature to impose cost penalties. Should not be enabled for standard runs + # North_West_Africa: 1.1 # Relative cost pentalty on all technologies in this region. 1: no change # Eurasia: 1.1 # South_America: 0.7 # Oceania: 0.8 @@ -251,8 +224,7 @@ trade: nm_to_km: 1.852 # 1 nautical mile equals 1.852 kilometers grid_electricity: - marginal_cost: 80 # EUR/MWh, guesstimate for average grid electricity cost in 2030 - capital_cost: 0 # EUR/MW, guesstimate for average grid electricity cost in 2030 + marginal_cost: 80 # EUR/MWh, estimate for average grid electricity cost in 2050, based on doi.org/10.1016/j.apenergy.2025.126189 (higher end of Table D3) grid_potential_custom: true # Use custom grid potential for eaf-grid from data/grid_potential_custom.csv in Mt steel part_load: @@ -263,10 +235,9 @@ part_load: iron_ore: regionalise: "grade-dependent" #"uniform" or "grade-dependent" - marginal_cost: 97.7 #97.7 EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data). + marginal_cost: 97.7 #97.7 EUR/t_ore # See https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) ore_to_steel_ratio: 1.59 # t_ore/t_steel, see https://www.nature.com/articles/s41467-025-60652-1 from mission possible steel model (see also technology-data) potential_allowance: 1.2 # Factor, which the current iron ore production is multiplied with - shipping_cost_per_km: 0.005 # €/(t*km) # Guesstimate plot: world_map: @@ -384,7 +355,7 @@ colors: northwest: "#0B722B" # North_America (Northwest Passage) sunda: "#BF39B0" # East_Asia (Sunda Strait) chili: "#99D852" # South_South_America (Drake Passage) - # Friendshoring bloc colors + # Friendshoring colors bloc_1: "#17679E" # Europe (Western bloc: Europe + Far West Europe + North America) bloc_2: "#8352D8" # Middle_East (Global South bloc) bloc_3: "#BF9A39" # East_East_Asia (East Asia + Oceania bloc) \ No newline at end of file From d9662c85715cd45b8dfde57a5b7056909385d38a Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 18 Jul 2026 10:12:15 +0200 Subject: [PATCH 215/216] chore: adjust package acknowedgements --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 35b2ce1..79cb166 100644 --- a/README.md +++ b/README.md @@ -103,8 +103,7 @@ It can also be reconciled with scenarios for demand, policy constraints, and inf Thanks to: - Oda Agdal and her Master's Thesis on the [Investigation of Future Global Trade of Hydrogen from Renewable Energy Sources](https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3031513) -- TRACE? -- PYPSA-earth? +- Johannes Hampp and [TRACE](https://github.com/euronion/trace) ## Licence From 25842f553d22220aeadb9023cc52ac3d13c16b32 Mon Sep 17 00:00:00 2001 From: energyls Date: Sat, 18 Jul 2026 10:21:14 +0200 Subject: [PATCH 216/216] chore: clean and delete notebooks --- workflow/notebooks/analysis-re.ipynb | 222 ----------- workflow/notebooks/analysis-trace.ipynb | 28 -- workflow/notebooks/analze-solving-re.ipynb | 419 --------------------- 3 files changed, 669 deletions(-) delete mode 100644 workflow/notebooks/analysis-re.ipynb delete mode 100644 workflow/notebooks/analze-solving-re.ipynb diff --git a/workflow/notebooks/analysis-re.ipynb b/workflow/notebooks/analysis-re.ipynb deleted file mode 100644 index 0f1195e..0000000 --- a/workflow/notebooks/analysis-re.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e20b8494", - "metadata": {}, - "outputs": [], - "source": [ - "import pypsa\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "8c69b5a1", - "metadata": {}, - "source": [ - "### Hypothesis" - ] - }, - { - "cell_type": "markdown", - "id": "10fb4741", - "metadata": {}, - "source": [ - "`n.statistics.energy-balance()` shows the aggregation of all single generators, that is why it is never 0\n", - "How it should be: single quality classes should get down to 0, but then the question arises: should you throw them into one model, or separate? How is the local demand handled? How does this align with the philosophy of multiple demands?" - ] - }, - { - "cell_type": "markdown", - "id": "6a6fe391", - "metadata": {}, - "source": [ - "### Paths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95f46bc0", - "metadata": {}, - "outputs": [], - "source": [ - "product = \"steel\" # \"eaf\" or \"steel\" or \"eaf-grid\"\n", - "region = \"Europe\" # e.g. \"Europe\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7612cfe6", - "metadata": {}, - "outputs": [], - "source": [ - "single_region = (\n", - " f\"../../resources/lco-{product}/cost_year~2030/{region}/network_1-0partload.nc\"\n", - ")\n", - "# single_region = f\"../../resources/lcos/cost_year~2030/Europe/network_1.nc\"" - ] - }, - { - "cell_type": "markdown", - "id": "792916c9", - "metadata": {}, - "source": [ - "### Read product model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0322cd4", - "metadata": {}, - "outputs": [], - "source": [ - "n = pypsa.Network(single_region)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d72387c", - "metadata": {}, - "outputs": [], - "source": [ - "n.statistics()" - ] - }, - { - "cell_type": "markdown", - "id": "4c158d96", - "metadata": {}, - "source": [ - "### RE profiles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40ff9de9", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate a df with p_nom_opt, p_nom_max and flh\n", - "gen_df = n.generators[[\"p_nom_opt\", \"p_nom_max\"]].copy()\n", - "# remove iron ore DRI-ready (exp)\n", - "gen_df = gen_df[~gen_df.index.str.contains(\"iron ore DRI-ready\")]\n", - "gen_df[\"flh\"] = n.generators_t.p_max_pu.sum(axis=0)\n", - "gen_df = gen_df.rename(\n", - " columns={\"p_nom_opt\": \"installed (MW)\", \"p_nom_max\": \"potential (MW)\", \"flh\": \"flh\"}\n", - ")\n", - "# reorder columns\n", - "gen_df = gen_df[[\"flh\", \"potential (MW)\", \"installed (MW)\"]]\n", - "gen_df = gen_df.sort_values(by=\"flh\", ascending=False)\n", - "sum_row = pd.DataFrame(gen_df.sum(numeric_only=True)).T\n", - "sum_row.index = [\"Total\"]\n", - "gen_df[\"utilization\"] = gen_df[\"installed (MW)\"] / gen_df[\"potential (MW)\"]\n", - "gen_df = pd.concat([gen_df, sum_row])\n", - "gen_df.round(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54fa21cb", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:, n.generators[n.generators.carrier == \"wind\"].index].mean(\n", - " axis=1\n", - ").plot(figsize=(15, 5), grid=True, ylim=(0, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77d59322", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:, \"onwind 1\"].plot(figsize=(15, 5), grid=True, ylim=(0, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66c8b444", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:, \"onwind 11\"].plot(\n", - " figsize=(15, 5), grid=True, ylim=(0, 1)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "dfe1d1d9", - "metadata": {}, - "source": [ - "### Actual feed in" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea7d102c", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p.loc[\n", - " :, n.generators[n.generators.carrier == \"wind\"].index\n", - "].mean().round(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0744559", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p.loc[:, n.generators[n.generators.carrier == \"wind\"].index].plot(\n", - " figsize=(15, 5), grid=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c70acdd", - "metadata": {}, - "outputs": [], - "source": [ - "n.statistics.energy_balance.iplot.area(bus_carrier=\"electricity\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "shift", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/workflow/notebooks/analysis-trace.ipynb b/workflow/notebooks/analysis-trace.ipynb index 11d20c7..8ffc75d 100644 --- a/workflow/notebooks/analysis-trace.ipynb +++ b/workflow/notebooks/analysis-trace.ipynb @@ -110,34 +110,6 @@ "source": [ "pypsatopo.generate(n, file_format=\"png\", file_output=output_fn)" ] - }, - { - "cell_type": "markdown", - "id": "1fae9cf5", - "metadata": {}, - "source": [ - "### Netview (not working)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e2eb0e8", - "metadata": {}, - "outputs": [], - "source": [ - "from pypsa_netview.draw import draw_network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "838ba0b1", - "metadata": {}, - "outputs": [], - "source": [ - "draw_network(n, show_capacities=True)" - ] } ], "metadata": { diff --git a/workflow/notebooks/analze-solving-re.ipynb b/workflow/notebooks/analze-solving-re.ipynb deleted file mode 100644 index 46789b1..0000000 --- a/workflow/notebooks/analze-solving-re.ipynb +++ /dev/null @@ -1,419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "08f8ed54", - "metadata": {}, - "outputs": [], - "source": [ - "import pypsa\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3845da48", - "metadata": {}, - "outputs": [], - "source": [ - "from _helpers_notebooks import mock_snakemake\n", - "snakemake = mock_snakemake(\n", - " \"plot_global_supply\",\n", - " scenario=\"default\", # default, penalty-sa, penalty-ea, penalty-nwa, penalty-oc\n", - " sort=True,\n", - " demand=\"\", #\"__unreserved\" or \"\" if reserved. Affects only the supply curve from single curves, not from the trade model\n", - " wacc=\"regional\",\n", - " cost_year=\"2050\",\n", - " interone=\"hbi\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e090967a", - "metadata": {}, - "outputs": [], - "source": [ - "n = pypsa.Network(\"../../resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_20-newre.nc\")\n", - "n_old = pypsa.Network(\"../../resources/lco-hbi/cost_year~2050/wacc~regional/Europe_allocated_share/network_20-oldre.nc\")\n", - "n_ea = pypsa.Network(\"../../resources/lco-hbi/cost_year~2050/wacc~regional/East_Asia_allocated_share/network_10.nc\")" - ] - }, - { - "cell_type": "markdown", - "id": "d2e7c58a", - "metadata": {}, - "source": [ - "### Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e5be371", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators.p_nom_max.div(1e3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "311e081b", - "metadata": {}, - "outputs": [], - "source": [ - "n_old.generators.p_nom_max.div(1e3).sort_values(ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00f72e6b", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.mean().sort_values(ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74f2da82", - "metadata": {}, - "outputs": [], - "source": [ - "n_old.generators_t.p_max_pu.mean().sort_values(ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe28a52e", - "metadata": {}, - "outputs": [], - "source": [ - "n_ea.links" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf57d350", - "metadata": {}, - "outputs": [], - "source": [ - "n.stores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e11a2ac", - "metadata": {}, - "outputs": [], - "source": [ - "n.buses" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84e9b093", - "metadata": {}, - "outputs": [], - "source": [ - "n.links" - ] - }, - { - "cell_type": "markdown", - "id": "e86f1d82", - "metadata": {}, - "source": [ - "### Network adjustments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec2a6fc4", - "metadata": {}, - "outputs": [], - "source": [ - "n.add(\"Link\", \"link_bat_to_re\", bus0=\"battery\", bus1=\"renewable_electricity\", p_nom_extendable=True, efficiency=1, capital_cost=1)\n", - "\n", - "n.add(\"Link\", \"link_bat_to_bat\", bus0=\"renewable_electricity\", bus1=\"battery\", p_nom_extendable=True, efficiency=1, capital_cost=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b59ea02", - "metadata": {}, - "outputs": [], - "source": [ - "# # Multiply p nom max by 10 in the network itself\n", - "\n", - "n.generators.p_nom_max *= 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4f56475", - "metadata": {}, - "outputs": [], - "source": [ - "n.stores.loc[\"hbi_storage\", \"e_cyclic\"] = True" - ] - }, - { - "cell_type": "markdown", - "id": "5581579c", - "metadata": {}, - "source": [ - "### Solving of network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac0bbf16", - "metadata": {}, - "outputs": [], - "source": [ - "config = snakemake.config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a91bc21", - "metadata": {}, - "outputs": [], - "source": [ - "solver_cfg = config.get(\"solver\", {})\n", - "solver_name = os.getenv(\"SHIFT_SOLVER\", solver_cfg.get(\"name\", \"glpk\"))\n", - "solver_options_key = os.getenv(\n", - " \"SHIFT_SOLVER_OPTIONS\", solver_cfg.get(\"options\", \"default\")\n", - ")\n", - "solver_options = config.get(\"solver_options\", {}).get(solver_options_key, {})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b572043e", - "metadata": {}, - "outputs": [], - "source": [ - "status = n.optimize(\n", - " n.snapshots,\n", - " solver_name=solver_name,\n", - " solver_options=solver_options,\n", - " multi_investment_periods=False,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7761d9d", - "metadata": {}, - "outputs": [], - "source": [ - "status" - ] - }, - { - "cell_type": "markdown", - "id": "a62e9b62", - "metadata": {}, - "source": [ - "### Post solving analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "364b1b18", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators.p_nom_opt.div(1e3).round(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "032e4a59", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators.p_nom_opt.div(1e3).round(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3f8e986", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_6\"][4000:4100].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "732896be", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_1\"][4000:4100].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35ecd1d9", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_1\"].sort_values(ascending=False).reset_index(drop=True).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d8f41fb", - "metadata": {}, - "outputs": [], - "source": [ - "n.generators_t.p_max_pu.loc[:,\"renewable_Europe_solar_6\"].sort_values(ascending=False).reset_index(drop=True).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dafbf1d2", - "metadata": {}, - "outputs": [], - "source": [ - "hbi_demand = n.statistics.withdrawal().to_frame().loc[\"Load\", \"hbi_demand\"][0]\n", - "hbi_demand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49fc9467", - "metadata": {}, - "outputs": [], - "source": [ - "(n.statistics.system_cost() / hbi_demand).round(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6311cc04", - "metadata": {}, - "outputs": [], - "source": [ - "n.stores.e_nom_opt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be359fbb", - "metadata": {}, - "outputs": [], - "source": [ - "n.links" - ] - }, - { - "cell_type": "markdown", - "id": "c682bdc1", - "metadata": {}, - "source": [ - "#### Stores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91fa1cbf", - "metadata": {}, - "outputs": [], - "source": [ - "n.stores_t.e[\"h2_storage\"].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40df0f4d", - "metadata": {}, - "outputs": [], - "source": [ - "n.stores.e_initial" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a84b1f03", - "metadata": {}, - "outputs": [], - "source": [ - "n.stores.e_cyclic" - ] - }, - { - "cell_type": "markdown", - "id": "161b6b6e", - "metadata": {}, - "source": [ - "### Bottom line and improvements" - ] - }, - { - "cell_type": "markdown", - "id": "79f24da5", - "metadata": {}, - "source": [ - "- Reestablish links from and to the battery\n", - "- Set hbi store `n.stores.loc[\"hbi_storage\", \"e_cyclic\"] = True`" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "shift", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}

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