Merge branch 'master' into scenario-management

2024-02-10 17:22:01 +01:00 · 2024-02-10 17:22:01 +01:00 · 6de08bd523
commit 6de08bd523
parent f96c2d0589 cbb3ab36a6
134 changed files with 8105 additions and 3284 deletions
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@ -6,3 +6,4 @@
 5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0
 92080b1cd2ca5f123158571481722767b99c2b27
 13769f90af4500948b0376d57df4cceaa13e78b5
+9865a970893d9e515786f33c629b14f71645bf1e
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@ -32,7 +32,14 @@ jobs:
        - ubuntu-latest
        - macos-latest
        - windows-latest
-
+        inhouse:
+        - stable
+        - master
+        exclude:
+        - os: macos-latest
+          inhouse: master
+        - os: windows-latest
+          inhouse: master
    runs-on: ${{ matrix.os }}

    defaults:
@ -46,16 +53,6 @@ jobs:
      run: |
        echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc

-    - name: Add solver to environment
-      run: |
-        echo -e "- glpk\n- ipopt<3.13.3" >> envs/environment.yaml
-      if: ${{ matrix.os }} == 'windows-latest'
-
-    - name: Add solver to environment
-      run: |
-        echo -e "- glpk\n- ipopt" >> envs/environment.yaml
-      if: ${{ matrix.os }} != 'windows-latest'
-
    - name: Setup micromamba
      uses: mamba-org/setup-micromamba@v1
      with:
@ -66,6 +63,11 @@ jobs:
        cache-environment: true
        cache-downloads: true

+    - name: Install inhouse packages
+      run: |
+        pip install git+https://github.com/PyPSA/atlite.git@master git+https://github.com/PyPSA/powerplantmatching.git@master git+https://github.com/PyPSA/linopy.git@master
+      if: ${{ matrix.inhouse }} == 'master'
+
    - name: Set cache dates
      run: |
        echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
@ -79,14 +81,10 @@ jobs:
        key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}

    - name: Test snakemake workflow
-      run: |
-        snakemake -call solve_elec_networks --configfile config/test/config.electricity.yaml --rerun-triggers=mtime
-        snakemake -call all --configfile config/test/config.overnight.yaml --rerun-triggers=mtime
-        snakemake -call all --configfile config/test/config.myopic.yaml --rerun-triggers=mtime
-        snakemake -call solve_elec_networks --configfile config/test/config.scenarios.electricity.yaml
+      run: ./test.sh

    - name: Upload artifacts
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v4.3.0
      with:
        name: resources-results
        path: |
@ -94,3 +92,4 @@ jobs:
          results
        if-no-files-found: warn
        retention-days: 1
+      if: matrix.os == 'ubuntu' && matrix.inhouse == 'stable'
--- a/.gitignore
+++ b/.gitignore
@ -8,6 +8,7 @@ __pycache__
 *dconf
 gurobi.log
 .vscode
+*.orig

 /bak
 /resources
@ -33,23 +34,24 @@ dconf
 /data/links_p_nom.csv
 /data/*totals.csv
 /data/biomass*
-/data/emobility/
-/data/eea*
-/data/jrc*
+/data/bundle-sector/emobility/
+/data/bundle-sector/eea*
+/data/bundle-sector/jrc*
 /data/heating/
-/data/eurostat*
+/data/bundle-sector/eurostat*
 /data/odyssee/
 /data/transport_data.csv
-/data/switzerland*
+/data/bundle-sector/switzerland*
 /data/.nfs*
-/data/Industrial_Database.csv
+/data/bundle-sector/Industrial_Database.csv
 /data/retro/tabula-calculator-calcsetbuilding.csv
-/data/nuts*
+/data/bundle-sector/nuts*
 data/gas_network/scigrid-gas/
 data/costs_*.csv

 dask-worker-space/
 publications.jrc.ec.europa.eu/
+d1gam3xoknrgr2.cloudfront.net/

 *.org

--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -5,7 +5,7 @@ exclude: "^LICENSES"

 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
+  rev: v4.5.0
  hooks:
  - id: check-merge-conflict
  - id: end-of-file-fixer
@ -17,7 +17,7 @@ repos:

  # Sort package imports alphabetically
 - repo: https://github.com/PyCQA/isort
-  rev: 5.12.0
+  rev: 5.13.2
  hooks:
  - id: isort
    args: ["--profile", "black", "--filter-files"]
@ -30,10 +30,10 @@ repos:

  # Find common spelling mistakes in comments and docstrings
 - repo: https://github.com/codespell-project/codespell
-  rev: v2.2.5
+  rev: v2.2.6
  hooks:
  - id: codespell
-    args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom,appartment,bage,ore,setis,tabacco,berfore'] # Ignore capital case words, e.g. country codes
+    args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom,appartment,bage,ore,setis,tabacco,berfore,vor'] # Ignore capital case words, e.g. country codes
    types_or: [python, rst, markdown]
    files: ^(scripts|doc)/

@ -45,13 +45,13 @@ repos:
    args: ["--in-place", "--make-summary-multi-line", "--pre-summary-newline"]

 - repo: https://github.com/keewis/blackdoc
-  rev: v0.3.8
+  rev: v0.3.9
  hooks:
  - id: blackdoc

  # Formatting with "black" coding style
- repo: https://github.com/psf/black
-  rev: 23.7.0
+- repo: https://github.com/psf/black-pre-commit-mirror
+  rev: 24.1.1
  hooks:
      # Format Python files
  - id: black
@ -67,14 +67,14 @@ repos:

  # Do YAML formatting (before the linter checks it for misses)
 - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
-  rev: v2.10.0
+  rev: v2.12.0
  hooks:
  - id: pretty-format-yaml
    args: [--autofix, --indent, "2", --preserve-quotes]

  # Format Snakemake rule / workflow files
 - repo: https://github.com/snakemake/snakefmt
-  rev: v0.8.4
+  rev: v0.10.0
  hooks:
  - id: snakefmt

@ -87,6 +87,6 @@ repos:

  # Check for FSFE REUSE compliance (licensing)
 - repo: https://github.com/fsfe/reuse-tool
-  rev: v2.1.0
+  rev: v3.0.1
  hooks:
  - id: reuse
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@ -14,4 +14,3 @@ build:
 python:
  install:
  - requirements: doc/requirements.txt
-  system_packages: false
--- a/CITATION.cff
+++ b/CITATION.cff
@ -6,7 +6,7 @@ cff-version: 1.1.0
 message: "If you use this package, please cite it in the following way."
 title: "PyPSA-Eur: An open sector-coupled optimisation model of the European energy system"
 repository: https://github.com/pypsa/pypsa-eur
-version: 0.8.1
+version: 0.9.0
 license: MIT
 authors:
  - family-names: Brown
--- a/README.md
+++ b/README.md
@ -61,9 +61,9 @@ The dataset consists of:

 - A grid model based on a modified [GridKit](https://github.com/bdw/GridKit)
  extraction of the [ENTSO-E Transmission System
-  Map](https://www.entsoe.eu/data/map/). The grid model contains 6763 lines
+  Map](https://www.entsoe.eu/data/map/). The grid model contains 7072 lines
  (alternating current lines at and above 220kV voltage level and all high
-  voltage direct current lines) and 3642 substations.
+  voltage direct current lines) and 3803 substations.
 - The open power plant database
  [powerplantmatching](https://github.com/FRESNA/powerplantmatching).
 - Electrical demand time series from the
@ -103,6 +103,6 @@ We strongly welcome anyone interested in contributing to this project. If you ha
 # Licence

 The code in PyPSA-Eur is released as free software under the
-[MIT License](https://opensource.org/licenses/MIT), see `LICENSE.txt`.
+[MIT License](https://opensource.org/licenses/MIT), see [`doc/licenses.rst`](doc/licenses.rst).
 However, different licenses and terms of use may apply to the various
 input data.
--- a/42
+++ b/42
@ -13,9 +13,10 @@ from scripts._helpers import path_provider
 min_version("7.7")
 HTTP = HTTPRemoteProvider()

-
-if not exists("config/config.yaml"):
-    copyfile("config/config.default.yaml", "config/config.yaml")
+conf_file = os.path.join(workflow.current_basedir, "config/config.yaml")
+conf_default_file = os.path.join(workflow.current_basedir, "config/config.default.yaml")
+if not exists(conf_file) and exists(conf_default_file):
+    copyfile(conf_default_file, conf_file)


 configfile: "config/config.yaml"
@ -42,6 +43,12 @@ resources = path_provider("resources/", RDIR, run["shared_resources"])
 CDIR = "" if run["shared_cutouts"] else RDIR
 LOGS = "logs/" + RDIR
 BENCHMARKS = "benchmarks/" + RDIR
+if not (shared_resources := run.get("shared_resources")):
+    RESOURCES = "resources/" + RDIR
+elif isinstance(shared_resources, str):
+    RESOURCES = "resources/" + shared_resources + "/"
+else:
+    RESOURCES = "resources/"
 RESULTS = "results/" + RDIR


@ -77,13 +84,31 @@ if config["foresight"] == "myopic":
    include: "rules/solve_myopic.smk"


+if config["foresight"] == "perfect":
+
+    include: "rules/solve_perfect.smk"
+
+
+rule all:
+    input:
+        RESULTS + "graphs/costs.pdf",
+    default_target: True
+
+
 rule purge:
-    message:
-        "Purging generated resources, results and docs. Downloads are kept."
    run:
-        rmtree("resources/", ignore_errors=True)
-        rmtree("results/", ignore_errors=True)
-        rmtree("doc/_build", ignore_errors=True)
+        import builtins
+
+        do_purge = builtins.input(
+            "Do you really want to delete all generated resources, \nresults and docs (downloads are kept)? [y/N] "
+        )
+        if do_purge == "y":
+            rmtree("resources/", ignore_errors=True)
+            rmtree("results/", ignore_errors=True)
+            rmtree("doc/_build", ignore_errors=True)
+            print("Purging generated resources, results and docs. Downloads are kept.")
+        else:
+            raise Exception(f"Input {do_purge}. Aborting purge.")


 rule dag:
@ -118,6 +143,7 @@ rule sync:
    shell:
        """
        rsync -uvarh --ignore-missing-args --files-from=.sync-send . {params.cluster}
+        rsync -uvarh --no-g {params.cluster}/resources . || echo "No resources directory, skipping rsync"
        rsync -uvarh --no-g {params.cluster}/results . || echo "No results directory, skipping rsync"
        rsync -uvarh --no-g {params.cluster}/logs . || echo "No logs directory, skipping rsync"
        """
--- a/config/config.default.yaml
+++ b/config/config.default.yaml
@ -3,7 +3,7 @@
 # SPDX-License-Identifier: CC0-1.0

 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#top-level-configuration
-version: 0.8.1
+version: 0.9.0
 tutorial: false

 logging:
@ -47,7 +47,7 @@ scenario:
  opts:
  - ''
  sector_opts:
-  - Co2L0-3H-T-H-B-I-A-solar+p3-dist1
+  - Co2L0-3H-T-H-B-I-A-dist1
  planning_horizons:
  # - 2020
  # - 2030
@ -62,6 +62,9 @@ snapshots:
  start: "2013-01-01"
  end: "2014-01-01"
  inclusive: 'left'
+  resolution: false
+  segmentation: false
+  #representative: false

 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#enable
 enable:
@ -71,11 +74,13 @@ enable:
  retrieve_sector_databundle: true
  retrieve_cost_data: true
  build_cutout: false
+  retrieve_irena: false
  retrieve_cutout: true
  build_natura_raster: false
  retrieve_natura_raster: true
  custom_busmap: false

+
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#co2-budget
 co2_budget:
  2020: 0.701
@ -88,8 +93,10 @@ co2_budget:

 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#electricity
 electricity:
-  voltages: [220., 300., 380.]
+  voltages: [220., 300., 380., 500., 750.]
+  gaslimit_enable: false
  gaslimit: false
+  co2limit_enable: false
  co2limit: 7.75e+7
  co2base: 1.487e+9
  agg_p_nom_limits: data/agg_p_nom_minmax.csv
@ -110,8 +117,9 @@ electricity:
    Store: [battery, H2]
    Link: [] # H2 pipeline

-  powerplants_filter: (DateOut >= 2022 or DateOut != DateOut)
+  powerplants_filter: (DateOut >= 2023 or DateOut != DateOut) and not (Country == 'Germany' and Fueltype == 'Nuclear')
  custom_powerplants: false
+  everywhere_powerplants: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]

  conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
  renewable_carriers: [solar, onwind, offwind-ac, offwind-dc, hydro]
@ -126,6 +134,10 @@ electricity:
      Onshore: [onwind]
      PV: [solar]

+  autarky:
+    enable: false
+    by_country: false
+
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#atlite
 atlite:
  default_cutout: europe-2013-era5
@ -137,14 +149,14 @@ atlite:
      # module: era5
    europe-2013-era5:
      module: era5 # in priority order
-      x: [-12., 35.]
+      x: [-12., 42.]
      y: [33., 72]
      dx: 0.3
      dy: 0.3
      time: ['2013', '2013']
    europe-2013-sarah:
      module: [sarah, era5] # in priority order
-      x: [-12., 45.]
+      x: [-12., 42.]
      y: [33., 65]
      dx: 0.2
      dy: 0.2
@ -160,45 +172,51 @@ renewable:
    resource:
      method: wind
      turbine: Vestas_V112_3MW
+      add_cutout_windspeed: true
    capacity_per_sqkm: 3
    # correction_factor: 0.93
    corine:
      grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32]
      distance: 1000
      distance_grid_codes: [1, 2, 3, 4, 5, 6]
+    luisa: false
+      # grid_codes: [1111, 1121, 1122, 1123, 1130, 1210, 1221, 1222, 1230, 1241, 1242]
+      # distance: 1000
+      # distance_grid_codes: [1111, 1121, 1122, 1123, 1130, 1210, 1221, 1222, 1230, 1241, 1242]
    natura: true
    excluder_resolution: 100
-    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  offwind-ac:
    cutout: europe-2013-era5
    resource:
      method: wind
-      turbine: NREL_ReferenceTurbine_5MW_offshore
+      turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
+      add_cutout_windspeed: true
    capacity_per_sqkm: 2
    correction_factor: 0.8855
    corine: [44, 255]
+    luisa: false # [0, 5230]
    natura: true
    ship_threshold: 400
    max_depth: 50
    max_shore_distance: 30000
    excluder_resolution: 200
-    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  offwind-dc:
    cutout: europe-2013-era5
    resource:
      method: wind
-      turbine: NREL_ReferenceTurbine_5MW_offshore
+      turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
+      add_cutout_windspeed: true
    capacity_per_sqkm: 2
    correction_factor: 0.8855
    corine: [44, 255]
+    luisa: false # [0, 5230]
    natura: true
    ship_threshold: 400
    max_depth: 50
    min_shore_distance: 30000
    excluder_resolution: 200
-    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  solar:
    cutout: europe-2013-sarah
@ -208,12 +226,12 @@ renewable:
      orientation:
        slope: 35.
        azimuth: 180.
-    capacity_per_sqkm: 1.7
+    capacity_per_sqkm: 5.1
    # correction_factor: 0.854337
    corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
+    luisa: false # [1111, 1121, 1122, 1123, 1130, 1210, 1221, 1222, 1230, 1241, 1242, 1310, 1320, 1330, 1410, 1421, 1422, 2110, 2120, 2130, 2210, 2220, 2230, 2310, 2410, 2420, 3210, 3320, 3330]
    natura: true
    excluder_resolution: 100
-    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  hydro:
    cutout: europe-2013-era5
@ -237,10 +255,13 @@ lines:
    220.: "Al/St 240/40 2-bundle 220.0"
    300.: "Al/St 240/40 3-bundle 300.0"
    380.: "Al/St 240/40 4-bundle 380.0"
+    500.: "Al/St 240/40 4-bundle 380.0"
+    750.: "Al/St 560/50 4-bundle 750.0"
  s_max_pu: 0.7
  s_nom_max: .inf
-  max_extension: .inf
+  max_extension: 20000 #MW
  length_factor: 1.25
+  reconnect_crimea: true
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
  dynamic_line_rating:
    activate: false
@ -253,7 +274,7 @@ lines:
 links:
  p_max_pu: 1.0
  p_nom_max: .inf
-  max_extension: .inf
+  max_extension: 30000 #MW
  include_tyndp: true
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity

@ -263,7 +284,7 @@ transformers:
  s_nom: 2000.
  type: ''

-# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#load
+# docs-load in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#load
 load:
  power_statistics: true
  interpolate_limit: 3
@ -288,6 +309,7 @@ pypsa_eur:
  - offwind-dc
  - solar
  - ror
+  - nuclear
  StorageUnit:
  - PHS
  - hydro
@ -338,6 +360,7 @@ existing_capacities:
  grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
  grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
  threshold_capacity: 10
+  default_heating_lifetime: 20
  conventional_carriers:
  - lignite
  - coal
@ -350,11 +373,14 @@ sector:
    potential: 0.6
    progress:
      2020: 0.0
+      2025: 0.15
      2030: 0.3
+      2035: 0.45
      2040: 0.6
+      2045: 0.8
      2050: 1.0
    district_heating_loss: 0.15
-  cluster_heat_buses: false
+  cluster_heat_buses: true
  bev_dsm_restriction_value: 0.75
  bev_dsm_restriction_time: 7
  transport_heating_deadband_upper: 20.
@ -374,18 +400,27 @@ sector:
  v2g: true
  land_transport_fuel_cell_share:
    2020: 0
-    2030: 0.05
-    2040: 0.1
-    2050: 0.15
+    2025: 0
+    2030: 0
+    2035: 0
+    2040: 0
+    2045: 0
+    2050: 0
  land_transport_electric_share:
    2020: 0
-    2030: 0.25
-    2040: 0.6
-    2050: 0.85
+    2025: 0.15
+    2030: 0.3
+    2035: 0.45
+    2040: 0.7
+    2045: 0.85
+    2050: 1
  land_transport_ice_share:
    2020: 1
+    2025: 0.85
    2030: 0.7
+    2035: 0.55
    2040: 0.3
+    2045: 0.15
    2050: 0
  transport_fuel_cell_efficiency: 0.5
  transport_internal_combustion_efficiency: 0.3
@ -399,18 +434,27 @@ sector:
  shipping_hydrogen_liquefaction: false
  shipping_hydrogen_share:
    2020: 0
+    2025: 0
    2030: 0
+    2035: 0
    2040: 0
+    2045: 0
    2050: 0
  shipping_methanol_share:
    2020: 0
+    2025: 0.15
    2030: 0.3
+    2035: 0.5
    2040: 0.7
+    2045: 0.85
    2050: 1
  shipping_oil_share:
    2020: 1
+    2025: 0.85
    2030: 0.7
+    2035: 0.5
    2040: 0.3
+    2045: 0.15
    2050: 0
  shipping_methanol_efficiency: 0.46
  shipping_oil_efficiency: 0.40
@ -439,22 +483,27 @@ sector:
    decentral: 3
    central: 180
  boilers: true
+  resistive_heaters: true
  oil_boilers: false
  biomass_boiler: true
+  overdimension_individual_heating: 1.1  #to cover demand peaks bigger than data
  chp: true
  micro_chp: false
  solar_thermal: true
  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
  marginal_cost_storage: 0. #1e-4
  methanation: true
-  helmeth: false
  coal_cc: false
  dac: true
  co2_vent: false
+  central_heat_vent: false
  allam_cycle: false
  hydrogen_fuel_cell: true
  hydrogen_turbine: false
  SMR: true
+  SMR_cc: true
+  regional_methanol_demand: false
+  regional_oil_demand: false
  regional_co2_sequestration_potential:
    enable: false
    attribute: 'conservative estimate Mt'
@ -464,8 +513,10 @@ sector:
    years_of_storage: 25
  co2_sequestration_potential: 200
  co2_sequestration_cost: 10
+  co2_sequestration_lifetime: 50
  co2_spatial: false
  co2network: false
+  co2_network_cost_factor: 1
  cc_fraction: 0.9
  hydrogen_underground_storage: true
  hydrogen_underground_storage_locations:
@ -473,14 +524,28 @@ sector:
  - nearshore    # within 50 km of sea
    # - offshore
  ammonia: false
-  min_part_load_fischer_tropsch: 0.9
-  min_part_load_methanolisation: 0.5
+  min_part_load_fischer_tropsch: 0.7
+  min_part_load_methanolisation: 0.3
+  min_part_load_methanation: 0.3
  use_fischer_tropsch_waste_heat: true
+  use_haber_bosch_waste_heat: true
+  use_methanolisation_waste_heat: true
+  use_methanation_waste_heat: true
  use_fuel_cell_waste_heat: true
-  use_electrolysis_waste_heat: false
+  use_electrolysis_waste_heat: true
  electricity_distribution_grid: true
  electricity_distribution_grid_cost_factor: 1.0
  electricity_grid_connection: true
+  transmission_efficiency:
+    DC:
+      efficiency_static: 0.98
+      efficiency_per_1000km: 0.977
+    H2 pipeline:
+      efficiency_per_1000km: 1 # 0.979
+      compression_per_1000km: 0.019
+    gas pipeline:
+      efficiency_per_1000km: 1 #0.977
+      compression_per_1000km: 0.01
  H2_network: true
  gas_network: false
  H2_retrofit: false
@ -490,10 +555,25 @@ sector:
  gas_distribution_grid_cost_factor: 1.0
  biomass_spatial: false
  biomass_transport: false
+  biogas_upgrading_cc: false
  conventional_generation:
    OCGT: gas
  biomass_to_liquid: false
  biosng: false
+  limit_max_growth:
+    enable: false
+    # allowing 30% larger than max historic growth
+    factor: 1.3
+    max_growth:  # unit GW
+      onwind: 16 # onshore max grow so far 16 GW in Europe https://www.iea.org/reports/renewables-2020/wind
+      solar: 28 # solar max grow so far 28 GW in Europe https://www.iea.org/reports/renewables-2020/solar-pv
+      offwind-ac: 35 # offshore max grow so far 3.5 GW in Europe https://windeurope.org/about-wind/statistics/offshore/european-offshore-wind-industry-key-trends-statistics-2019/
+      offwind-dc: 35
+    max_relative_growth:
+      onwind: 3
+      solar: 3
+      offwind-ac: 3
+      offwind-dc: 3

 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#industry
 industry:
@ -526,14 +606,39 @@ industry:
  MWh_NH3_per_tNH3: 5.166
  MWh_CH4_per_tNH3_SMR: 10.8
  MWh_elec_per_tNH3_SMR: 0.7
-  MWh_H2_per_tNH3_electrolysis: 6.5
-  MWh_elec_per_tNH3_electrolysis: 1.17
+  MWh_H2_per_tNH3_electrolysis: 5.93
+  MWh_elec_per_tNH3_electrolysis: 0.2473
  MWh_NH3_per_MWh_H2_cracker: 1.46 # https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
  NH3_process_emissions: 24.5
  petrochemical_process_emissions: 25.5
-  HVC_primary_fraction: 1.
-  HVC_mechanical_recycling_fraction: 0.
-  HVC_chemical_recycling_fraction: 0.
+  #HVC primary/recycling based on values used in Neumann et al https://doi.org/10.1016/j.joule.2023.06.016, linearly interpolated between 2020 and 2050
+  #2020 recycling rates based on Agora https://static.agora-energiewende.de/fileadmin/Projekte/2021/2021_02_EU_CEAP/A-EW_254_Mobilising-circular-economy_study_WEB.pdf
+  #fractions refer to the total primary HVC production in 2020
+  #assumes 6.7 Mtplastics produced from recycling in 2020
+  HVC_primary_fraction:
+    2020: 1.0
+    2025: 0.9
+    2030: 0.8
+    2035: 0.7
+    2040: 0.6
+    2045: 0.5
+    2050: 0.4
+  HVC_mechanical_recycling_fraction:
+    2020: 0.12
+    2025: 0.15
+    2030: 0.18
+    2035: 0.21
+    2040: 0.24
+    2045: 0.27
+    2050: 0.30
+  HVC_chemical_recycling_fraction:
+    2020: 0.0
+    2025: 0.0
+    2030: 0.04
+    2035: 0.08
+    2040: 0.12
+    2045: 0.16
+    2050: 0.20
  HVC_production_today: 52.
  MWh_elec_per_tHVC_mechanical_recycling: 0.547
  MWh_elec_per_tHVC_chemical_recycling: 6.9
@ -546,11 +651,13 @@ industry:
  hotmaps_locate_missing: false
  reference_year: 2015

+
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#costs
 costs:
  year: 2030
-  version: v0.6.0
+  version: v0.7.0
  rooftop_share: 0.14  # based on the potentials, assuming  (0.1 kW/m2 and 10 m2/person)
+  social_discountrate: 0.02
  fill_values:
    FOM: 0
    VOM: 0
@ -574,10 +681,13 @@ costs:
    battery: 0.
    battery inverter: 0.
  emission_prices:
+    enable: false
    co2: 0.
+    co2_monthly_prices: false

 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#clustering
 clustering:
+  focus_weights: false
  simplify_network:
    to_substations: false
    algorithm: kmeans # choose from: [hac, kmeans]
@ -606,14 +716,22 @@ solving:
    skip_iterations: true
    rolling_horizon: false
    seed: 123
+    custom_extra_functionality: "../data/custom_extra_functionality.py"
+    # io_api: "direct"  # Increases performance but only supported for the highs and gurobi solvers
    # options that go into the optimize function
    track_iterations: false
    min_iterations: 4
    max_iterations: 6
-    transmission_losses: 0
+    transmission_losses: 2
    linearized_unit_commitment: true
    horizon: 365

+  constraints:
+    CCL: false
+    EQ: false
+    BAU: false
+    SAFE: false
+
  solver:
    name: gurobi
    options: gurobi-default
@ -668,6 +786,10 @@ solving:
      solutiontype: 2 # non basic solution, ie no crossover
      barrier.convergetol: 1.e-5
      feasopt.tolerance: 1.e-6
+    copt-default:
+      Threads: 8
+      LpMethod: 2
+      Crossover: 0
    cbc-default: {} # Used in CI
    glpk-default: {} # Used in CI

@ -681,6 +803,13 @@ plotting:
    color_geomap:
      ocean: white
      land: white
+  projection:
+    name: "EqualEarth"
+    # See https://scitools.org.uk/cartopy/docs/latest/reference/projections.html for alternatives, for example:
+    # name: "LambertConformal"
+    # central_longitude: 10.
+    # central_latitude: 50.
+    # standard_parallels: [35, 65]
  eu_node_location:
    x: -5.5
    y: 46.
@ -703,6 +832,7 @@ plotting:
    H2: "Hydrogen Storage"
    lines: "Transmission Lines"
    ror: "Run of River"
+    load: "Load Shedding"
    ac: "AC"
    dc: "DC"

@ -726,7 +856,6 @@ plotting:
    hydroelectricity: '#298c81'
    PHS: '#51dbcc'
    hydro+PHS: "#08ad97"
-    wave: '#a7d4cf'
    # solar
    solar: "#f9d002"
    solar PV: "#f9d002"
@ -753,6 +882,7 @@ plotting:
    fossil gas: '#e05b09'
    natural gas: '#e05b09'
    biogas to gas: '#e36311'
+    biogas to gas CC: '#e51245'
    CCGT: '#a85522'
    CCGT marginal: '#a85522'
    allam: '#B98F76'
@ -764,6 +894,7 @@ plotting:
    gas pipeline new: '#a87c62'
    # oil
    oil: '#c9c9c9'
+    imported oil: '#a3a3a3'
    oil boiler: '#adadad'
    residential rural oil boiler: '#a9a9a9'
    services rural oil boiler: '#a5a5a5'
@ -782,6 +913,7 @@ plotting:
    Coal: '#545454'
    coal: '#545454'
    Coal marginal: '#545454'
+    coal for industry: '#343434'
    solid: '#545454'
    Lignite: '#826837'
    lignite: '#826837'
@ -852,12 +984,14 @@ plotting:
    # heat demand
    Heat load: '#cc1f1f'
    heat: '#cc1f1f'
+    heat vent: '#aa3344'
    heat demand: '#cc1f1f'
    rural heat: '#ff5c5c'
    residential rural heat: '#ff7c7c'
    services rural heat: '#ff9c9c'
    central heat: '#cc1f1f'
    urban central heat: '#d15959'
+    urban central heat vent: '#a74747'
    decentral heat: '#750606'
    residential urban decentral heat: '#a33c3c'
    services urban decentral heat: '#cc1f1f'
@ -870,9 +1004,11 @@ plotting:
    air heat pump: '#36eb41'
    residential urban decentral air heat pump: '#48f74f'
    services urban decentral air heat pump: '#5af95d'
+    services rural air heat pump: '#5af95d'
    urban central air heat pump: '#6cfb6b'
    ground heat pump: '#2fb537'
    residential rural ground heat pump: '#48f74f'
+    residential rural air heat pump: '#48f74f'
    services rural ground heat pump: '#5af95d'
    Ambient: '#98eb9d'
    CHP: '#8a5751'
@ -895,6 +1031,7 @@ plotting:
    H2 for shipping: "#ebaee0"
    H2: '#bf13a0'
    hydrogen: '#bf13a0'
+    retrofitted H2 boiler: '#e5a0d9'
    SMR: '#870c71'
    SMR CC: '#4f1745'
    H2 liquefaction: '#d647bd'
@ -919,7 +1056,6 @@ plotting:
    Sabatier: '#9850ad'
    methanation: '#c44ce6'
    methane: '#c44ce6'
-    helmeth: '#e899ff'
    # synfuels
    Fischer-Tropsch: '#25c49a'
    liquid: '#25c49a'
@ -934,6 +1070,7 @@ plotting:
    CO2 sequestration: '#f29dae'
    DAC: '#ff5270'
    co2 stored: '#f2385a'
+    co2 sequestered: '#f2682f'
    co2: '#f29dae'
    co2 vent: '#ffd4dc'
    CO2 pipeline: '#f5627f'
@ -965,3 +1102,4 @@ plotting:
    DC: "#8a1caf"
    DC-DC: "#8a1caf"
    DC link: "#8a1caf"
+    load: "#dd2e23"
--- a/config/config.entsoe-all.yaml
+++ b/config/config.entsoe-all.yaml
@ -0,0 +1,43 @@
+# SPDX-FileCopyrightText: 2017-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: CC0-1.0
+
+run:
+  name: "entsoe-all"
+  disable_progressbar: true
+  shared_resources: false
+  shared_cutouts: true
+
+scenario:
+  simpl:
+  - ''
+  ll:
+  - vopt
+  clusters:
+  - 39
+  - 128
+  - 256
+  opts:
+  - ''
+  sector_opts:
+  - ''
+  planning_horizons:
+  - ''
+
+# TODO add Turkey (TR)
+countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MD', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK', 'UA']
+
+electricity:
+  custom_powerplants: true
+  co2limit: 9.59e+7
+  co2base: 1.918e+9
+
+lines:
+  reconnect_crimea: true
+
+enable:
+  retrieve: true
+  retrieve_databundle: true
+  retrieve_sector_databundle: false
+  retrieve_cost_data: true
+  retrieve_cutout: true
--- a/config/config.perfect.yaml
+++ b/config/config.perfect.yaml
@ -0,0 +1,46 @@
+# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: CC0-1.0
+run:
+  name: "perfect"
+
+# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#foresight
+foresight: perfect
+
+# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#scenario
+# Wildcard docs in https://pypsa-eur.readthedocs.io/en/latest/wildcards.html
+scenario:
+  simpl:
+  - ''
+  ll:
+  - v1.0
+  clusters:
+  - 37
+  opts:
+  - ''
+  sector_opts:
+  - 1p5-4380H-T-H-B-I-A-dist1
+  - 1p7-4380H-T-H-B-I-A-dist1
+  - 2p0-4380H-T-H-B-I-A-dist1
+  planning_horizons:
+  - 2020
+  - 2025
+  - 2030
+  - 2035
+  - 2040
+  - 2045
+  - 2050
+
+
+# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#co2-budget
+co2_budget:
+  # update of IPCC 6th AR compared to the 1.5SR. (discussed here: https://twitter.com/JoeriRogelj/status/1424743828339167233)
+  1p5: 34.2  # 25.7 # Budget in Gt CO2 for 1.5 for Europe, global 420 Gt, assuming per capita share
+  1p6: 43.259666  # 35  # Budget in Gt CO2 for 1.6 for Europe, global 580 Gt
+  1p7: 51.4  # 45  # Budget in Gt CO2 for 1.7 for Europe, global 800 Gt
+  2p0: 69.778  # 73.9 # Budget in Gt CO2 for 2 for Europe, global 1170 Gt
+
+
+sector:
+  min_part_load_fischer_tropsch: 0
+  min_part_load_methanolisation: 0
--- a/config/test/config.electricity.yaml
+++ b/config/test/config.electricity.yaml
@ -8,14 +8,14 @@ tutorial: true
 run:
  name: "test-elec" # use this to keep track of runs with different settings
  disable_progressbar: true
-  shared_resources: true
+  shared_resources: "test"
  shared_cutouts: true

 scenario:
  clusters:
  - 5
  opts:
-  - Co2L-24H
+  - Co2L-24h

 countries: ['BE']

--- a/config/test/config.myopic.yaml
+++ b/config/test/config.myopic.yaml
@ -7,7 +7,7 @@ tutorial: true
 run:
  name: "test-sector-myopic"
  disable_progressbar: true
-  shared_resources: true
+  shared_resources: "test"
  shared_cutouts: true

 foresight: myopic
@ -18,7 +18,7 @@ scenario:
  clusters:
  - 5
  sector_opts:
-  - 24H-T-H-B-I-A-solar+p3-dist1
+  - 24h-T-H-B-I-A-dist1
  planning_horizons:
  - 2030
  - 2040
@ -30,6 +30,9 @@ snapshots:
  start: "2013-03-01"
  end: "2013-03-08"

+sector:
+  central_heat_vent: true
+
 electricity:
  co2limit: 100.e+6

--- a/config/test/config.overnight.yaml
+++ b/config/test/config.overnight.yaml
@ -7,7 +7,7 @@ tutorial: true
 run:
  name: "test-sector-overnight"
  disable_progressbar: true
-  shared_resources: true
+  shared_resources: "test"
  shared_cutouts: true


@ -17,7 +17,7 @@ scenario:
  clusters:
  - 5
  sector_opts:
-  - CO2L0-24H-T-H-B-I-A-solar+p3-dist1
+  - CO2L0-24h-T-H-B-I-A-dist1
  planning_horizons:
  - 2030

--- a/config/test/config.perfect.yaml
+++ b/config/test/config.perfect.yaml
@ -0,0 +1,92 @@
+# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: CC0-1.0
+
+tutorial: true
+
+run:
+  name: "test-sector-perfect"
+  disable_progressbar: true
+  shared_resources: "test"
+  shared_cutouts: true
+
+foresight: perfect
+
+scenario:
+  ll:
+  - v1.0
+  clusters:
+  - 5
+  sector_opts:
+  - 8760h-T-H-B-I-A-dist1
+  planning_horizons:
+  - 2030
+  - 2040
+  - 2050
+
+countries: ['BE']
+
+snapshots:
+  start: "2013-03-01"
+  end: "2013-03-08"
+
+electricity:
+  co2limit: 100.e+6
+
+  extendable_carriers:
+    Generator: [OCGT]
+    StorageUnit: [battery]
+    Store: [H2]
+    Link: [H2 pipeline]
+
+  renewable_carriers: [solar, onwind, offwind-ac, offwind-dc]
+
+sector:
+  min_part_load_fischer_tropsch: 0
+  min_part_load_methanolisation: 0
+
+atlite:
+  default_cutout: be-03-2013-era5
+  cutouts:
+    be-03-2013-era5:
+      module: era5
+      x: [4., 15.]
+      y: [46., 56.]
+      time: ["2013-03-01", "2013-03-08"]
+
+renewable:
+  onwind:
+    cutout: be-03-2013-era5
+  offwind-ac:
+    cutout: be-03-2013-era5
+    max_depth: false
+  offwind-dc:
+    cutout: be-03-2013-era5
+    max_depth: false
+  solar:
+    cutout: be-03-2013-era5
+
+industry:
+  St_primary_fraction:
+    2020: 0.8
+    2030: 0.6
+    2040: 0.5
+    2050: 0.4
+
+solving:
+  solver:
+    name: glpk
+    options: glpk-default
+  mem: 4000
+
+plotting:
+  map:
+    boundaries:
+  eu_node_location:
+    x: -5.5
+    y: 46.
+  costs_max: 1000
+  costs_threshold: 0.0000001
+  energy_max:
+  energy_min:
+  energy_threshold: 0.000001
--- a/data/GDP_PPP_30arcsec_v3_mapped_default.csv
+++ b/data/GDP_PPP_30arcsec_v3_mapped_default.csv
@ -0,0 +1,151 @@
+name,GDP_PPP,country
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+4408,150782.51691456966,UA
+4409,112776.7399582134,UA
+4410,153076.56860965435,UA
+4412,192629.31238456024,UA
+4413,181295.3120834606,UA
+4414,995694.9413199169,UA
+4416,157640.7868989174,UA
+4418,77580.20674809469,UA
+4420,122320.99275223716,UA
+4424,184891.10924920067,UA
+4425,84486.75974340564,UA
+4431,50485.84380961137,UA
+4435,231040.45446464577,UA
+4436,81222.18707585508,UA
+4438,114819.76472988473,UA
+4439,76839.1052178896,UA
+4440,135337.0313562152,UA
+4441,49159.485269198034,UA
+7031,42001.73757065917,UA
+7059,159790.48382874,UA
+7063,39599.10564971086,UA
--- a/data/custom_extra_functionality.py
+++ b/data/custom_extra_functionality.py
@ -0,0 +1,11 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2023- The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+
+
+def custom_extra_functionality(n, snapshots, snakemake):
+    """
+    Add custom extra functionality constraints.
+    """
+    pass
--- a/data/custom_powerplants.csv
+++ b/data/custom_powerplants.csv
@ -1 +1,37 @@
-Name,Fueltype,Technology,Set,Country,Capacity,Efficiency,Duration,Volume_Mm3,DamHeight_m,YearCommissioned,Retrofit,lat,lon,projectID,YearDecommissioning
+,Name,Fueltype,Technology,Set,Country,Capacity,Efficiency,Duration,Volume_Mm3,DamHeight_m,StorageCapacity_MWh,DateIn,DateRetrofit,DateMothball,DateOut,lat,lon,EIC,projectID
+1266,Khmelnitskiy,Nuclear,,PP,UA,1901.8916595755832,,0.0,0.0,0.0,0.0,1988.0,2005.0,,,50.3023,26.6466,[nan],"{'GEO': ['GEO3842'], 'GPD': ['WRI1005111'], 'CARMA': ['CARMA22000']}"
+1268,Kaniv,Hydro,Reservoir,PP,UA,452.1656050955414,,0.0,0.0,0.0,0.0,1972.0,2003.0,,,49.76653,31.47165,[nan],"{'GEO': ['GEO43017'], 'GPD': ['WRI1005122'], 'CARMA': ['CARMA21140']}"
+1269,Kahovska kakhovka,Hydro,Reservoir,PP,UA,352.45222929936307,,0.0,0.0,0.0,0.0,1955.0,1956.0,,,46.77858,33.36965,[nan],"{'GEO': ['GEO43018'], 'GPD': ['WRI1005118'], 'CARMA': ['CARMA20855']}"
+1347,Kharkiv,Natural Gas,Steam Turbine,CHP,UA,494.94274967602314,,0.0,0.0,0.0,0.0,1979.0,1980.0,,,49.9719,36107,[nan],"{'GEO': ['GEO43027'], 'GPD': ['WRI1005126'], 'CARMA': ['CARMA21972']}"
+1348,Kremenchuk,Hydro,Reservoir,PP,UA,617.0382165605096,,0.0,0.0,0.0,0.0,1959.0,1960.0,,,49.07759,33.2505,[nan],"{'GEO': ['GEO43019'], 'GPD': ['WRI1005121'], 'CARMA': ['CARMA23072']}"
+1377,Krivorozhskaya,Hard Coal,Steam Turbine,PP,UA,2600.0164509342876,,0.0,0.0,0.0,0.0,1965.0,1992.0,,,47.5432,33.6583,[nan],"{'GEO': ['GEO42989'], 'GPD': ['WRI1005100'], 'CARMA': ['CARMA23176']}"
+1407,Zmiyevskaya zmiivskaya,Hard Coal,Steam Turbine,PP,UA,2028.3816283884514,,0.0,0.0,0.0,0.0,1960.0,2005.0,,,49.5852,36.5231,[nan],"{'GEO': ['GEO42999'], 'GPD': ['WRI1005103'], 'CARMA': ['CARMA51042']}"
+1408,Pridneprovskaya,Hard Coal,Steam Turbine,CHP,UA,1627.3152609570984,,0.0,0.0,0.0,0.0,1959.0,1966.0,,,48.4051,35.1131,[nan],"{'GEO': ['GEO42990'], 'GPD': ['WRI1005102'], 'CARMA': ['CARMA35874']}"
+1409,Kurakhovskaya,Hard Coal,Steam Turbine,PP,UA,1371.0015824607397,,0.0,0.0,0.0,0.0,1972.0,2003.0,,,47.9944,37.24022,[nan],"{'GEO': ['GEO42994'], 'GPD': ['WRI1005104'], 'CARMA': ['CARMA23339']}"
+1410,Dobrotvorsky,Hard Coal,Steam Turbine,PP,UA,553.1949895604868,,0.0,0.0,0.0,0.0,1960.0,1964.0,,,50.2133,24375,[nan],"{'GEO': ['GEO42992'], 'GPD': ['WRI1005096'], 'CARMA': ['CARMA10971']}"
+1422,Zuyevskaya,Hard Coal,Steam Turbine,PP,UA,1147.87960333801,,0.0,0.0,0.0,0.0,1982.0,2007.0,,,48.0331,38.28615,[nan],"{'GEO': ['GEO42995'], 'GPD': ['WRI1005106'], 'CARMA': ['CARMA51083']}"
+1423,Zaporozhye,Nuclear,,PP,UA,5705.67497872675,,0.0,0.0,0.0,0.0,1985.0,1996.0,,,47.5119,34.5863,[nan],"{'GEO': ['GEO6207'], 'GPD': ['WRI1005114'], 'CARMA': ['CARMA50875']}"
+1424,Trypilska,Hard Coal,Steam Turbine,PP,UA,1659.5849686814602,,0.0,0.0,0.0,0.0,1969.0,1972.0,,,50.1344,30.7468,[nan],"{'GEO': ['GEO43000'], 'GPD': ['WRI1005099'], 'CARMA': ['CARMA46410']}"
+1425,Tashlyk,Hydro,Pumped Storage,Store,UA,285.55968954109585,,0.0,0.0,0.0,0.0,2006.0,2007.0,,,47.7968,31.1811,[nan],"{'GEO': ['GEO43025'], 'GPD': ['WRI1005117'], 'CARMA': ['CARMA44696']}"
+1426,Starobeshivska,Hard Coal,Steam Turbine,PP,UA,1636.5351774497733,,0.0,0.0,0.0,0.0,1961.0,1967.0,,,47.7997,38.00612,[nan],"{'GEO': ['GEO43003'], 'GPD': ['WRI1005105'], 'CARMA': ['CARMA43083']}"
+1427,South,Nuclear,,PP,UA,2852.837489363375,,0.0,0.0,0.0,0.0,1983.0,1989.0,,,47812,31.22,[nan],"{'GEO': ['GEO5475'], 'GPD': ['WRI1005113'], 'CARMA': ['CARMA42555']}"
+1428,Rovno rivne,Nuclear,,PP,UA,2695.931427448389,,0.0,0.0,0.0,0.0,1981.0,2006.0,,,51.3245,25.89744,[nan],"{'GEO': ['GEO5174'], 'GPD': ['WRI1005112'], 'CARMA': ['CARMA38114']}"
+1429,Ladyzhinska,Hard Coal,Steam Turbine,PP,UA,1659.5849686814602,,0.0,0.0,0.0,0.0,1970.0,1971.0,,,48706,29.2202,[nan],"{'GEO': ['GEO42993'], 'GPD': ['WRI1005098'], 'CARMA': ['CARMA24024']}"
+1430,Kiev,Hydro,Pumped Storage,PP,UA,635.8694635681177,,0.0,0.0,0.0,0.0,1964.0,1972.0,,,50.5998,30501,"[nan, nan]","{'GEO': ['GEO43024', 'GEO43023'], 'GPD': ['WRI1005123', 'WRI1005124'], 'CARMA': ['CARMA23516', 'CARMA23517']}"
+2450,Cet chisinau,Natural Gas,,PP,MD,306.0,,0.0,0.0,0.0,0.0,,,,,47.027550000000005,28.8801,"[nan, nan]","{'GPD': ['WRI1002985', 'WRI1002984'], 'CARMA': ['CARMA8450', 'CARMA8451']}"
+2460,Hydropower che costesti,Hydro,,PP,MD,16.0,,0.0,0.0,0.0,0.0,1978.0,,,,47.8381,27.2246,[nan],"{'GPD': ['WRI1002987'], 'CARMA': ['CARMA9496']}"
+2465,Moldavskaya gres,Hard Coal,,PP,MD,2520.0,,0.0,0.0,0.0,0.0,,,,,46.6292,29.9407,[nan],"{'GPD': ['WRI1002989'], 'CARMA': ['CARMA28979']}"
+2466,Hydropower dubasari,Hydro,,PP,MD,48.0,,0.0,0.0,0.0,0.0,,,,,47.2778,29123,[nan],"{'GPD': ['WRI1002988'], 'CARMA': ['CARMA11384']}"
+2676,Cet nord balti,Natural Gas,,PP,MD,24.0,,0.0,0.0,0.0,0.0,,,,,47.7492,27.8938,[nan],"{'GPD': ['WRI1002986'], 'CARMA': ['CARMA3071']}"
+2699,Dniprodzerzhynsk,Hydro,Reservoir,PP,UA,360.3503184713376,,0.0,0.0,0.0,0.0,1963.0,1964.0,,,48.5485,34.541015,[nan],"{'GEO': ['GEO43020'], 'GPD': ['WRI1005119']}"
+2707,Burshtynska tes,Hard Coal,Steam Turbine,PP,UA,2212.779958241947,,0.0,0.0,0.0,0.0,1965.0,1984.0,,,49.21038,24.66654,[nan],"{'GEO': ['GEO42991'], 'GPD': ['WRI1005097']}"
+2708,Danipro dnieper,Hydro,Reservoir,PP,UA,1484.8407643312103,,0.0,0.0,0.0,0.0,1932.0,1947.0,,,47.86944,35.08611,[nan],"{'GEO': ['GEO43016'], 'GPD': ['WRI1005120']}"
+2709,Dniester,Hydro,Pumped Storage,Store,UA,612.7241020616891,,0.0,0.0,0.0,0.0,2009.0,2011.0,,,48.51361,27.47333,[nan],"{'GEO': ['GEO43022'], 'GPD': ['WRI1005116', 'WRI1005115']}"
+2710,Kiev,Natural Gas,Steam Turbine,CHP,UA,458.2803237740955,,0.0,0.0,0.0,0.0,1982.0,1984.0,,,50532,30.6625,[nan],"{'GEO': ['GEO42998'], 'GPD': ['WRI1005125']}"
+2712,Luganskaya,Hard Coal,Steam Turbine,PP,UA,1060.2903966575996,,0.0,0.0,0.0,0.0,1962.0,1969.0,,,48.74781,39.2624,[nan],"{'GEO': ['GEO42996'], 'GPD': ['WRI1005110']}"
+2713,Slavyanskaya,Hard Coal,Steam Turbine,PP,UA,737.5933194139823,,0.0,0.0,0.0,0.0,1971.0,1971.0,,,48872,37.76567,[nan],"{'GEO': ['GEO43002'], 'GPD': ['WRI1005109']}"
+2714,Vuhlehirska uglegorskaya,Hard Coal,Steam Turbine,PP,UA,3319.1699373629203,,0.0,0.0,0.0,0.0,1972.0,1977.0,,,48.4633,38.20328,[nan],"{'GEO': ['GEO43001'], 'GPD': ['WRI1005107']}"
+2715,Zaporiska,Hard Coal,Steam Turbine,PP,UA,3319.1699373629203,,0.0,0.0,0.0,0.0,1972.0,1977.0,,,47.5089,34.6253,[nan],"{'GEO': ['GEO42988'], 'GPD': ['WRI1005101']}"
+3678,Mironovskaya,Hard Coal,,PP,UA,815.0,,0.0,0.0,0.0,0.0,,,,,48.3407,38.4049,[nan],"{'GPD': ['WRI1005108'], 'CARMA': ['CARMA28679']}"
+3679,Kramatorskaya,Hard Coal,,PP,UA,120.0,,0.0,0.0,0.0,0.0,1974.0,,,,48.7477,37.5723,[nan],"{'GPD': ['WRI1075856'], 'CARMA': ['CARMA54560']}"
+3680,Chernihiv,Hard Coal,,PP,UA,200.0,,0.0,0.0,0.0,0.0,1968.0,,,,51455,31.2602,[nan],"{'GPD': ['WRI1075853'], 'CARMA': ['CARMA8190']}"
--- a/data/eia_hydro_annual_generation.csv
+++ b/data/eia_hydro_annual_generation.csv
@ -1,50 +1,53 @@
-https://www.eia.gov/international/data/world/electricity/electricity-generation?pd=2&p=000000000000000000000000000000g&u=1&f=A&v=mapbubble&a=-&i=none&vo=value&t=R&g=000000000000002&l=73-1028i008017kg6368g80a4k000e0ag00gg0004g8g0ho00g000400008&s=315532800000&e=1577836800000&ev=false&
-Report generated on: 03-28-2022 11:20:48
-"API","","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020"
-"","hydroelectricity net generation (billion kWh)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
-"INTL.33-12-EURO-BKWH.A","    Europe","458.018","464.155","459.881","473.685","481.241","476.739","459.535","491.085","534.517","465.365","474.466","475.47","509.041","526.448","531.815","543.743","529.114164","543.845616","562.441501","569.308453","591.206662","587.371195","541.542535","506.19703","544.536443","545.176179","537.335934","540.934407","567.557921","564.244482","619.96477","543.05273","600.46622","631.86431","619.59229","615.53013","629.98906","562.59258","619.31106","610.62616","670.925"
-"INTL.33-12-ALB-BKWH.A","        Albania","2.919","3.018","3.093","3.167","3.241","3.315","3.365","3.979","3.713","3.846","2.82","3.483","3.187","3.281","3.733","4.162","5.669","4.978","4.872","5.231","4.548","3.519","3.477","5.117","5.411","5.319","4.951","2.76","3.759","5.201","7.49133","4.09068","4.67775","6.88941","4.67676","5.83605","7.70418","4.47975","8.46648","5.15394","5.281"
-"INTL.33-12-AUT-BKWH.A","        Austria","28.501","30.008","29.893","29.577","28.384","30.288","30.496","25.401","35.151","34.641","31.179","31.112","34.483","36.336","35.349","36.696","33.874","35.744","36.792","40.292","41.418","40.05","39.825","32.883","36.394","36.31","35.48","36.732","37.969","40.487","36.466","32.511","41.862","40.138","39.001","35.255","37.954","36.462","35.73","40.43655","45.344"
-"INTL.33-12-BEL-BKWH.A","        Belgium","0.274","0.377","0.325","0.331","0.348","0.282","0.339","0.425","0.354","0.3","0.263","0.226","0.338","0.252","0.342","0.335","0.237","0.30195","0.38511","0.338","0.455","0.437","0.356","0.245","0.314","0.285","0.355","0.385","0.406","0.325","0.298","0.193","0.353","0.376","0.289","0.314","0.367","0.268","0.311","0.108","1.29"
-"INTL.33-12-BIH-BKWH.A","        Bosnia and Herzegovina","--","--","--","--","--","--","--","--","--","--","--","--","3.374","2.343","3.424","3.607","5.104","4.608","4.511","5.477","5.043","5.129","5.215","4.456","5.919","5.938","5.798","3.961","4.818","6.177","7.946","4.343","4.173","7.164","5.876","5.495","5.585","3.7521","6.35382","6.02019","6.1"
-"INTL.33-12-BGR-BKWH.A","        Bulgaria","3.674","3.58","3.018","3.318","3.226","2.214","2.302","2.512","2.569","2.662","1.859","2.417","2.042","1.923","1.453","2.291","2.89","2.726","3.066","2.725","2.646","1.72","2.172","2.999","3.136","4.294","4.196","2.845","2.796","3.435","4.98168","2.84328","3.14622","3.99564","4.55598","5.59845","3.8412","2.79972","5.09553","3.34917","3.37"
-"INTL.33-12-HRV-BKWH.A","        Croatia","--","--","--","--","--","--","--","--","--","--","--","--","4.298","4.302","4.881","5.212","7.156","5.234","5.403","6.524","5.794","6.482","5.311","4.827","6.888","6.27","5.94","4.194","5.164","6.663","9.035","4.983","4.789","8.536","8.917","6.327","6.784","5.255","7.62399","5.87268","3.4"
-"INTL.33-12-CYP-BKWH.A","        Cyprus","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"
-"INTL.33-12-CZE-BKWH.A","        Czech Republic","--","--","--","--","--","--","--","--","--","--","--","--","--","1.355","1.445","1.982","1.949","1.68201","1.382","1.664","1.7404","2.033","2.467","1.369","1.999","2.356","2.525","2.068","2.004","2.405","2.775","1.95","2.107","2.704","1.909","1.779","1.983","1.852","1.615","1.98792","3.4"
-"INTL.33-12-DNK-BKWH.A","        Denmark","0.03","0.031","0.028","0.036","0.028","0.027","0.029","0.029","0.032","0.027","0.027","0.026","0.028","0.027","0.033","0.03","0.019","0.019","0.02673","0.031","0.03","0.028","0.032","0.021","0.027","0.023","0.023","0.028","0.026","0.019","0.021","0.017","0.017","0.013","0.015","0.018","0.019","0.018","0.015","0.01584","0.02"
-"INTL.33-12-EST-BKWH.A","        Estonia","--","--","--","--","--","--","--","--","--","--","--","--","0.001","0.001","0.003","0.002","0.002","0.003","0.004","0.004","0.005","0.007","0.006","0.013","0.022","0.022","0.014","0.021","0.028","0.032","0.027","0.03","0.042","0.026","0.027","0.027","0.035","0.026","0.015","0.01881","0.04"
-"INTL.33-12-FRO-BKWH.A","        Faroe Islands","0.049","0.049","0.049","0.049","0.049","0.049","0.049","0.049","0.062","0.071","0.074","0.074","0.083","0.073","0.075","0.075","0.069564","0.075066","0.076501","0.069453","0.075262","0.075195","0.095535","0.08483","0.093443","0.097986","0.099934","0.103407","0.094921","0.091482","0.06676","0.092","0.099","0.091","0.121","0.132","0.105","0.11","0.107","0.102","0.11"
-"INTL.33-12-FIN-BKWH.A","        Finland","10.115","13.518","12.958","13.445","13.115","12.211","12.266","13.658","13.229","12.9","10.75","13.065","14.956","13.341","11.669","12.796","11.742","12.11958","14.9","12.652","14.513","13.073","10.668","9.495","14.919","13.646","11.379","14.035","16.941","12.559","12.743","12.278","16.667","12.672","13.24","16.584","15.634","14.61","13.137","12.31461","15.56"
-"INTL.33-12-CSK-BKWH.A","        Former Czechoslovakia","4.8","4.2","3.7","3.9","3.2","4.3","4","4.853","4.355","4.229","3.919","3.119","3.602","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
-"INTL.33-12-SCG-BKWH.A","        Former Serbia and Montenegro","--","--","--","--","--","--","--","--","--","--","--","--","11.23","10.395","11.016","12.071","14.266","12.636","12.763","13.243","11.88","12.326","11.633","9.752","11.01","11.912","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
-"INTL.33-12-YUG-BKWH.A","        Former Yugoslavia","27.868","25.044","23.295","21.623","25.645","24.363","27.474","25.98","25.612","23.256","19.601","18.929","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
-"INTL.33-12-FRA-BKWH.A","        France","68.253","70.358","68.6","67.515","64.01","60.248","60.953","68.623","73.952","45.744","52.796","56.277","68.313","64.3","78.057","72.196","64.43","63.151","61.479","71.832","66.466","73.888","59.992","58.567","59.276","50.965","55.741","57.029","63.017","56.428","61.945","45.184","59.099","71.042","62.993","54.876","60.094","49.389","64.485","56.98242","64.84"
-"INTL.33-12-DEU-BKWH.A","        Germany","--","--","--","--","--","--","--","--","--","--","--","14.742","17.223","17.699","19.731","21.562","21.737","17.18343","17.044","19.451","21.515","22.506","22.893","19.071","20.866","19.442","19.808","20.957","20.239","18.841","20.678","17.323","21.331","22.66","19.31","18.664","20.214","19.985","17.815","19.86039","24.75"
-"INTL.33-12-DDR-BKWH.A","        Germany, East","1.658","1.718","1.748","1.683","1.748","1.758","1.767","1.726","1.719","1.551","1.389","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
-"INTL.33-12-DEUW-BKWH.A","        Germany, West","17.125","17.889","17.694","16.713","16.434","15.354","16.526","18.36","18.128","16.482","15.769","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
-"INTL.33-12-GIB-BKWH.A","        Gibraltar","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"
-"INTL.33-12-GRC-BKWH.A","        Greece","3.396","3.398","3.551","2.331","2.852","2.792","3.222","2.768","2.354","1.888","1.751","3.068","2.181","2.26","2.573","3.494","4.305","3.84318","3.68","4.546","3.656","2.076","2.772","4.718","4.625","4.967","5.806","2.565","3.279","5.32","7.431","3.998","4.387","6.337","4.464","5.782","5.543","3.962","5.035","3.9798","3.43"
-"INTL.33-12-HUN-BKWH.A","        Hungary","0.111","0.166","0.158","0.153","0.179","0.153","0.152","0.167","0.167","0.156","0.176","0.192","0.156","0.164","0.159","0.161","0.205","0.21384","0.15345","0.179","0.176","0.184","0.192","0.169","0.203","0.2","0.184","0.208","0.211","0.226","0.184","0.216","0.206","0.208","0.294","0.227","0.253","0.214","0.216","0.21681","0.24"
-"INTL.33-12-ISL-BKWH.A","        Iceland","3.053","3.085","3.407","3.588","3.738","3.667","3.846","3.918","4.169","4.217","4.162","4.162","4.267","4.421","4.47","4.635","4.724","5.15493","5.565","5.987","6.292","6.512","6.907","7.017","7.063","6.949","7.22","8.31","12.303","12.156","12.51","12.382","12.214","12.747","12.554","13.541","13.092","13.892","13.679","13.32441","12.46"
-"INTL.33-12-IRL-BKWH.A","        Ireland","0.833","0.855","0.792","0.776","0.68","0.824","0.91","0.673","0.862","0.684","0.69","0.738","0.809","0.757","0.911","0.706","0.715","0.67122","0.907","0.838","0.838","0.59","0.903","0.592","0.624","0.625","0.717","0.66","0.959","0.893","0.593","0.699","0.795","0.593","0.701","0.798","0.674","0.685","0.687","0.87813","1.21"
-"INTL.33-12-ITA-BKWH.A","        Italy","44.997","42.782","41.216","40.96","41.923","40.616","40.626","39.05","40.205","33.647","31.31","41.817","41.778","41.011","44.212","37.404","41.617","41.18697","40.808","44.911","43.763","46.343","39.125","33.303","41.915","35.706","36.624","32.488","41.207","48.647","50.506","45.36477","41.45625","52.24626","57.95955","45.08163","42.00768","35.83701","48.29913","45.31824","47.72"
-"INTL.33-12-XKS-BKWH.A","        Kosovo","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","0.075","0.119","0.154","0.104","0.095","0.142","0.149","0.139","0.243","0.177","0.27027","0.2079","0.26"
-"INTL.33-12-LVA-BKWH.A","        Latvia","--","--","--","--","--","--","--","--","--","--","--","--","2.498","2.846","3.272","2.908","1.841","2.922","2.99","2.729","2.791","2.805","2.438","2.243","3.078","3.293","2.671","2.706","3.078","3.422","3.488","2.857","3.677","2.838","1.953","1.841","2.523","4.356","2.417","2.08692","2.59"
-"INTL.33-12-LTU-BKWH.A","        Lithuania","--","--","--","--","--","--","--","--","--","--","--","--","0.308","0.389","0.447","0.369","0.323","0.291","0.413","0.409","0.336","0.322","0.35","0.323","0.417","0.446193","0.393","0.417","0.398","0.42","0.535","0.475","0.419","0.516","0.395","0.346","0.45","0.597","0.427","0.34254","1.06"
-"INTL.33-12-LUX-BKWH.A","        Luxembourg","0.086","0.095","0.084","0.083","0.088","0.071","0.084","0.101","0.097","0.072","0.07","0.083","0.069","0.066","0.117","0.087","0.059","0.082","0.114","0.084","0.119","0.117","0.098","0.078","0.103","0.093","0.11","0.116","0.131","0.105","0.104","0.061","0.095","0.114","0.104","0.095","0.111","0.082","0.089","0.10593","1.09"
-"INTL.33-12-MLT-BKWH.A","        Malta","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"
-"INTL.33-12-MNE-BKWH.A","        Montenegro","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","1.733","1.271","1.524","2.05","2.723","1.192","1.462","2.479","1.734","1.476","1.825","1.014","2.09187","1.78","1.8"
-"INTL.33-12-NLD-BKWH.A","        Netherlands","0","0","0","0","0","0.003","0.003","0.001","0.002","0.037","0.119","0.079","0.119","0.091","0.1","0.087","0.079","0.09108","0.111","0.089","0.141","0.116","0.109","0.071","0.094","0.087","0.105","0.106","0.101","0.097","0.105","0.057","0.104","0.114","0.112","0.093","0.1","0.061","0.072","0.07326","0.05"
-"INTL.33-12-MKD-BKWH.A","        North Macedonia","--","--","--","--","--","--","--","--","--","--","--","--","0.817","0.517","0.696","0.793","0.842","0.891","1.072","1.375","1.158","0.62","0.749","1.36","1.467","1.477","1.634","1","0.832","1.257","2.407","1.419","1.031","1.568","1.195","1.846","1.878","1.099","1.773","1.15236","1.24"
-"INTL.33-12-NOR-BKWH.A","        Norway","82.717","91.876","91.507","104.704","104.895","101.464","95.321","102.341","107.919","117.369","119.933","109.032","115.505","118.024","110.398","120.315","102.823","108.677","114.546","120.237","140.4","119.258","128.078","104.425","107.693","134.331","118.175","132.319","137.654","124.03","116.257","119.78","141.189","127.551","134.844","136.662","142.244","141.651","138.202","123.66288","141.69"
-"INTL.33-12-POL-BKWH.A","        Poland","2.326","2.116","1.528","1.658","1.394","1.833","1.534","1.644","1.775","1.593","1.403","1.411","1.492","1.473","1.716","1.868","1.912","1.941","2.286","2.133","2.085","2.302","2.256","1.654","2.06","2.179","2.022","2.328","2.13","2.351","2.9","2.313","2.02","2.421","2.165","1.814","2.117","2.552","1.949","1.93842","2.93"
-"INTL.33-12-PRT-BKWH.A","        Portugal","7.873","4.934","6.82","7.897","9.609","10.512","8.364","9.005","12.037","5.72","9.065","8.952","4.599","8.453","10.551","8.26","14.613","12.97395","12.853","7.213","11.21","13.894","7.722","15.566","9.77","4.684","10.892","9.991","6.73","8.201","15.954","11.423","5.589","13.652","15.471","8.615","15.608","5.79","12.316","8.6526","13.96"
-"INTL.33-12-ROU-BKWH.A","        Romania","12.506","12.605","11.731","9.934","11.208","11.772","10.688","11.084","13.479","12.497","10.87","14.107","11.583","12.64","12.916","16.526","15.597","17.334","18.69","18.107","14.63","14.774","15.886","13.126","16.348","20.005","18.172","15.806","17.023","15.379","19.684","14.581","11.945","14.807","18.618","16.467","17.848","14.349","17.48736","15.65289","15.53"
-"INTL.33-12-SRB-BKWH.A","        Serbia","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","10.855","9.937","9.468","10.436","11.772","8.58","9.193","10.101","10.893","9.979","10.684","9.061","10.53261","10.07028","9.66"
-"INTL.33-12-SVK-BKWH.A","        Slovakia","--","--","--","--","--","--","--","--","--","--","--","--","--","3.432","4.311","4.831","4.185","4.023","4.224","4.429","4.569","4.878","5.215","3.4452","4.059","4.592","4.355","4.406","4","4.324","5.184","3.211","3.687","4.329","3.762","3.701","4.302","4.321","3.506","4.27383","4.67"
-"INTL.33-12-SVN-BKWH.A","        Slovenia","--","--","--","--","--","--","--","--","--","--","--","--","3.379","2.974","3.348","3.187","3.616","3.046","3.4","3.684","3.771","3.741","3.265","2.916","4.033","3.426","3.555","3.233","3.978","4.666","4.452","3.506","3.841","4.562","6.011","3.75","4.443","3.814","4.643","4.43421","5.24"
-"INTL.33-12-ESP-BKWH.A","        Spain","29.16","21.64","25.99","26.696","31.088","30.895","26.105","27.016","34.76","19.046","25.16","27.01","18.731","24.133","27.898","22.881","39.404","34.43","33.665","22.634","29.274","40.617","22.691","40.643","31.359","18.209","25.699","27.036","23.13","26.147","41.576","30.07","20.192","36.45","38.815","27.656","35.77","18.007","33.743","24.23025","33.34"
-"INTL.33-12-SWE-BKWH.A","        Sweden","58.133","59.006","54.369","62.801","67.106","70.095","60.134","70.95","69.016","70.911","71.778","62.603","73.588","73.905","58.508","67.421","51.2226","68.365","74.25","70.974","77.798","78.269","65.696","53.005","59.522","72.075","61.106","65.497","68.378","65.193","66.279","66.047","78.333","60.81","63.227","74.734","61.645","64.651","61.79","64.46583","71.6"
-"INTL.33-12-CHE-BKWH.A","        Switzerland","32.481","35.13","35.974","35.069","29.871","31.731","32.576","34.328","35.437","29.477","29.497","31.756","32.373","35.416","38.678","34.817","28.458","33.70257","33.136","39.604","36.466","40.895","34.862","34.471","33.411","30.914","30.649","34.898","35.676","35.366","35.704","32.069","38.218","38.08","37.659","37.879","34.281","33.754","34.637","37.6596","40.62"
-"INTL.33-12-TUR-BKWH.A","        Turkey","11.159","12.308","13.81","11.13","13.19","11.822","11.637","18.314","28.447","17.61","22.917","22.456","26.302","33.611","30.28","35.186","40.07","39.41784","41.80671","34.33","30.57","23.77","33.346","34.977","45.623","39.165","43.802","35.492","32.937","35.598","51.423","51.155","56.669","58.225","39.75","65.856","66.686","57.824","59.49","87.99714","77.39"
-"INTL.33-12-GBR-BKWH.A","        United Kingdom","3.921","4.369","4.543","4.548","3.992","4.08","4.767","4.13","4.915","4.732","5.119","4.534","5.329","4.237","5.043","4.79","3.359","4.127","5.067","5.283","5.035","4.015","4.74","3.195","4.795","4.873","4.547","5.026","5.094","5.178","3.566","5.655","5.286","4.667","5.832","6.246","5.342","5.836","5.189","5.89941","7.64"
+https://www.eia.gov/international/data/world/electricity/electricity-generation?pd=2&p=000000000000000000000000000000g&u=1&f=A&v=mapbubble&a=-&i=none&vo=value&t=R&g=000000000000002&l=73-1028i008017kg6368g80a4k000e0ag00gg0004g8g0ho00g000400008&l=72-00000000000000000000000000080000000000000000000g&s=315532800000&e=1609459200000&ev=false&,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Report generated on: 01-06-2023 21:17:46,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+API,,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021
+,hydroelectricity net generation (billion kWh),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+INTL.33-12-EURO-BKWH.A,    Europe,"458,018","464,155","459,881","473,685","481,241","476,739","459,535","491,085","534,517","465,365","474,466","475,47","509,041","526,448","531,815","543,743","529,114164","543,845616","562,491501","566,861453","588,644662","584,806195","539,051405","503,7067","542,112443","542,974669","535,006084","538,449707","565,143111","561,761402","617,547148","540,926277","598,055253","629,44709","617,111295","613,079848","627,720566217","560,362524","616,5081462","606,5997419","644,1106599","628,1390143"
+INTL.33-12-ALB-BKWH.A,        Albania,"2,919","3,018","3,093","3,167","3,241","3,315","3,365","3,979","3,713","3,846","2,82","3,483","3,187","3,281","3,733","4,162","5,669","4,978","4,872","5,231","4,548","3,519","3,477","5,117","5,411","5,319","4,951","2,76","3,759","5,201","7,49133","4,09068","4,67775","6,88941","4,67676","5,83605","7,70418","4,47975","8,46648","5,15394","5,281","8,891943"
+INTL.33-12-AUT-BKWH.A,        Austria,"28,501","30,008","29,893","29,577","28,384","30,288","30,496","25,401","35,151","34,641","31,179","31,112","34,483","36,336","35,349","36,696","33,874","35,744","36,792","40,292","41,418","40,05","39,825","32,883","36,394","36,31","35,48","36,732","37,969","40,487","36,466","32,511","41,862","40,138","39,001","35,255","37,954","36,462","35,73","40,43655","41,9356096","38,75133"
+INTL.33-12-BEL-BKWH.A,        Belgium,"0,274","0,377","0,325","0,331","0,348","0,282","0,339","0,425","0,354","0,3","0,263","0,226","0,338","0,252","0,342","0,335","0,237","0,30195","0,38511","0,338","0,455","0,437","0,356","0,245","0,314","0,285","0,355","0,385","0,406","0,325","0,298","0,193","0,353","0,376","0,289","0,314","0,367","0,268","0,3135","0,302","0,2669","0,3933"
+INTL.33-12-BIH-BKWH.A,        Bosnia and Herzegovina,--,--,--,--,--,--,--,--,--,--,--,--,"3,374","2,343","3,424","3,607","5,104","4,608","4,511","5,477","5,043","5,129","5,215","4,456","5,919","5,938","5,798","3,961","4,818","6,177","7,946","4,343","4,173","7,164","5,876","5,495","5,585","3,7521","6,35382","6,02019","4,58","6,722"
+INTL.33-12-BGR-BKWH.A,        Bulgaria,"3,674","3,58","3,018","3,318","3,226","2,214","2,302","2,512","2,569","2,662","1,859","2,417","2,042","1,923","1,453","2,291","2,89","2,726","3,066","2,725","2,646","1,72","2,172","2,999","3,136","4,294","4,196","2,845","2,796","3,435","4,98168","2,84328","3,14622","3,99564","4,55598","5,59845","3,8412","2,79972","5,09553","2,929499","2,820398","4,819205"
+INTL.33-12-HRV-BKWH.A,        Croatia,--,--,--,--,--,--,--,--,--,--,--,--,"4,298","4,302","4,881","5,212","7,156","5,234","5,403","6,524","5,794","6,482","5,311","4,827","6,888","6,27","5,94","4,194","5,164","6,663","9,035","4,983","4,789","8,536","8,917","6,327","6,784","5,255","7,62399","5,87268","5,6624","7,1277"
+INTL.33-12-CYP-BKWH.A,        Cyprus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+INTL.33-12-CZE-BKWH.A,        Czechia,--,--,--,--,--,--,--,--,--,--,--,--,--,"1,355","1,445","1,982","1,949","1,68201","1,382","1,664","1,7404","2,033","2,467","1,369","1,999","2,356","2,525","2,068","2,004","2,405","2,775","1,95","2,107","2,704","1,909","1,779","1,983","1,852","1,615","1,98792","2,143884","2,40852"
+INTL.33-12-DNK-BKWH.A,        Denmark,"0,03","0,031","0,028","0,036","0,028","0,027","0,029","0,029","0,032","0,027","0,027","0,026","0,028","0,027","0,033","0,03","0,019","0,019","0,02673","0,031","0,03","0,028","0,032","0,021","0,027","0,023","0,023","0,028","0,026","0,019","0,021","0,017","0,017","0,013","0,015","0,01803","0,01927","0,017871","0,0148621","0,0172171","0,017064","0,016295"
+INTL.33-12-EST-BKWH.A,        Estonia,--,--,--,--,--,--,--,--,--,--,--,--,"0,001","0,001","0,003","0,002","0,002","0,003","0,004","0,004","0,005","0,007","0,006","0,013","0,022","0,022","0,014","0,021","0,028","0,032","0,027","0,029999","0,042","0,026","0,027","0,027","0,035","0,025999","0,0150003","0,0189999","0,03","0,0248"
+INTL.33-12-FRO-BKWH.A,        Faroe Islands,"0,049","0,049","0,049","0,049","0,049","0,049","0,049","0,049","0,062","0,071","0,074","0,074","0,083","0,073","0,075","0,075","0,069564","0,075066","0,076501","0,069453","0,075262","0,075195","0,095535","0,08483","0,093443","0,097986","0,099934","0,103407","0,094921","0,091482","0,06676","0,092","0,099","0,091","0,121","0,132","0,105","0,11","0,107","0,102","0,11","0,11"
+INTL.33-12-FIN-BKWH.A,        Finland,"10,115","13,518","12,958","13,445","13,115","12,211","12,266","13,658","13,229","12,9","10,75","13,065","14,956","13,341","11,669","12,796","11,742","12,11958","14,9","12,652","14,513","13,073","10,668","9,495","14,919","13,646","11,379","14,035","16,941","12,559","12,743","12,278001","16,666998","12,672","13,240001","16,583999","15,634127","14,609473","13,1369998","12,2454823","15,883","15,766"
+INTL.33-12-CSK-BKWH.A,        Former Czechoslovakia,"4,8","4,2","3,7","3,9","3,2","4,3",4,"4,853","4,355","4,229","3,919","3,119","3,602",--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--
+INTL.33-12-SCG-BKWH.A,        Former Serbia and Montenegro,--,--,--,--,--,--,--,--,--,--,--,--,"11,23","10,395","11,016","12,071","14,266","12,636","12,763","13,243","11,88","12,326","11,633","9,752","11,01","11,912",--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--
+INTL.33-12-YUG-BKWH.A,        Former Yugoslavia,"27,868","25,044","23,295","21,623","25,645","24,363","27,474","25,98","25,612","23,256","19,601","18,929",--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--
+INTL.33-12-FRA-BKWH.A,        France,"68,253","70,358","68,6","67,515","64,01","60,248","60,953","68,623","73,952","45,744","52,796","56,277","68,313","64,3","78,057","72,196","64,43","63,151","61,479","71,832","66,466","73,888","59,992","58,567","59,276","50,965","55,741","57,029","63,017","56,428","61,945","45,184","59,099","71,042","62,993","54,876","60,094","49,389","64,485","56,913891","62,06191","58,856657"
+INTL.33-12-DEU-BKWH.A,        Germany,--,--,--,--,--,--,--,--,--,--,--,"14,742","17,223","17,699","19,731","21,562","21,737","17,18343","17,044","19,451","21,515","22,506","22,893","19,071","20,866","19,442","19,808","20,957","20,239","18,841","20,678","17,323","21,331","22,66","19,31","18,664","20,214","19,985","17,694","19,731","18,322","19,252"
+INTL.33-12-DDR-BKWH.A,"        Germany, East","1,658","1,718","1,748","1,683","1,748","1,758","1,767","1,726","1,719","1,551","1,389",--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--
+INTL.33-12-DEUW-BKWH.A,"        Germany, West","17,125","17,889","17,694","16,713","16,434","15,354","16,526","18,36","18,128","16,482","15,769",--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--
+INTL.33-12-GIB-BKWH.A,        Gibraltar,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+INTL.33-12-GRC-BKWH.A,        Greece,"3,396","3,398","3,551","2,331","2,852","2,792","3,222","2,768","2,354","1,888","1,751","3,068","2,181","2,26","2,573","3,494","4,305","3,84318","3,68","4,546","3,656","2,076","2,772","4,718","4,625","4,967","5,806","2,565","3,279","5,32","7,431","3,998","4,387","6,337","4,464","5,782","5,543","3,962","5,035","3,9798","3,343687","5,909225"
+INTL.33-12-HUN-BKWH.A,        Hungary,"0,111","0,166","0,158","0,153","0,179","0,153","0,152","0,167","0,167","0,156","0,176","0,192","0,156","0,164","0,159","0,161","0,205","0,21384","0,15345","0,179","0,176","0,184","0,192","0,169","0,203","0,2","0,184","0,208","0,211","0,226","0,184","0,215999","0,205999","0,207999","0,294001","0,226719","0,253308","0,213999","0,216","0,2129999","0,238","0,202379"
+INTL.33-12-ISL-BKWH.A,        Iceland,"3,053","3,085","3,407","3,588","3,738","3,667","3,846","3,918","4,169","4,217","4,162","4,162","4,267","4,421","4,47","4,635","4,724","5,15493","5,565","5,987","6,292","6,512","6,907","7,017","7,063","6,949","7,22","8,31","12,303","12,156","12,509999","12,381999","12,213999","12,747001","12,554","13,541","13,091609","13,891929","13,679377","13,32911","12,9196201","13,5746171"
+INTL.33-12-IRL-BKWH.A,        Ireland,"0,833","0,855","0,792","0,776","0,68","0,824","0,91","0,673","0,862","0,684","0,69","0,738","0,809","0,757","0,911","0,706","0,715","0,67122","0,907","0,838","0,838","0,59","0,903","0,592","0,624","0,625","0,717","0,66","0,959","0,893","0,593","0,699","0,795","0,593","0,701","0,798","0,674","0,685","0,687","0,87813","0,932656","0,750122"
+INTL.33-12-ITA-BKWH.A,        Italy,"44,997","42,782","41,216","40,96","41,923","40,616","40,626","39,05","40,205","33,647","31,31","41,817","41,778","41,011","44,212","37,404","41,617","41,18697","40,808","44,911","43,763","46,343","39,125","33,303","41,915","35,706","36,624","32,488","41,207","48,647","50,506","45,36477","41,45625","52,24626","57,95955","45,08163","42,00768","35,83701","48,29913","45,31824","47,551784","44,739"
+INTL.33-12-XKS-BKWH.A,        Kosovo,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,"0,075","0,119","0,154","0,104","0,095","0,142","0,149","0,139","0,243","0,177","0,27027","0,2079","0,262826","0,300635"
+INTL.33-12-LVA-BKWH.A,        Latvia,--,--,--,--,--,--,--,--,--,--,--,--,"2,498","2,846","3,272","2,908","1,841","2,922","2,99","2,729","2,791","2,805","2,438","2,243","3,078","3,293","2,671","2,706","3,078","3,422","3,487998","2,8568","3,677","2,838","1,953","1,841","2,522819","4,355513","2,4170639","2,0958919","2,5840101","2,6889293"
+INTL.33-12-LTU-BKWH.A,        Lithuania,--,--,--,--,--,--,--,--,--,--,--,--,"0,308","0,389","0,447","0,369","0,323","0,291","0,413","0,409","0,336","0,322","0,35","0,323","0,417","0,446193","0,393","0,417","0,398","0,42","0,535","0,475","0,419","0,516","0,395","0,346","0,45","0,597","0,427","0,34254","0,3006","0,3837"
+INTL.33-12-LUX-BKWH.A,        Luxembourg,"0,086","0,095","0,084","0,083","0,088","0,071","0,084","0,101","0,097","0,072","0,07","0,083","0,069","0,066","0,117","0,087","0,059","0,082","0,114","0,084","0,119","0,117","0,098","0,078","0,103","0,093","0,11","0,116","0,131","0,105","0,104","0,061","0,095","0,114","0,104","0,095","0,111","0,082","0,089","0,10593","0,091602","0,1068"
+INTL.33-12-MLT-BKWH.A,        Malta,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+INTL.33-12-MNE-BKWH.A,        Montenegro,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,"1,733","1,271","1,524","2,05","2,723","1,192","1,462","2,479","1,734","1,476","1,825","1,014","1,693443","1,262781","0,867637","1,212652"
+INTL.33-12-NLD-BKWH.A,        Netherlands,0,0,0,0,0,"0,003","0,003","0,001","0,002","0,037","0,119","0,079","0,119","0,091","0,1","0,087","0,079","0,09108","0,111","0,089","0,141","0,116","0,109","0,071","0,094","0,087","0,105","0,106","0,101","0,097","0,105","0,057","0,104389","0,11431","0,112202","0,0927","0,100078","0,060759","0,0723481","0,074182","0,0462851","0,0838927"
+INTL.33-12-MKD-BKWH.A,        North Macedonia,--,--,--,--,--,--,--,--,--,--,--,--,"0,817","0,517","0,696","0,793","0,842","0,891","1,072","1,375","1,158","0,62","0,749","1,36","1,467","1,477","1,634",1,"0,832","1,257","2,407","1,419","1,031","1,568","1,195","1,846","1,878","1,099","1,773","1,15236","1,277144","1,451623"
+INTL.33-12-NOR-BKWH.A,        Norway,"82,717","91,876","91,507","104,704","104,895","101,464","95,321","102,341","107,919","117,369","119,933","109,032","115,505","118,024","110,398","120,315","102,823","108,677","114,546","120,237","140,4","119,258","128,078","104,425","107,693","134,331","118,175","132,319","137,654","124,03","116,257","119,78","141,189","127,551","134,844","136,662","142,244","141,651","138,202","123,66288","141,69",144
+INTL.33-12-POL-BKWH.A,        Poland,"2,326","2,116","1,528","1,658","1,394","1,833","1,534","1,644","1,775","1,593","1,403","1,411","1,492","1,473","1,716","1,868","1,912","1,941","2,286","2,133","2,085","2,302","2,256","1,654","2,06","2,179","2,022","2,328","2,13","2,351","2,9","2,313","2,02","2,421","2,165","1,814","2,117","2,552","1,949","1,93842","2,118337","2,339192"
+INTL.33-12-PRT-BKWH.A,        Portugal,"7,873","4,934","6,82","7,897","9,609","10,512","8,364","9,005","12,037","5,72","9,065","8,952","4,599","8,453","10,551","8,26","14,613","12,97395","12,853","7,213","11,21","13,894","7,722","15,566","9,77","4,684","10,892","9,991","6,73","8,201","15,954","11,423","5,589","13,652","15,471","8,615","15,608","5,79","12,316","8,6526","12,082581","11,846464"
+INTL.33-12-ROU-BKWH.A,        Romania,"12,506","12,605","11,731","9,934","11,208","11,772","10,688","11,084","13,479","12,497","10,87","14,107","11,583","12,64","12,916","16,526","15,597","17,334","18,69","18,107","14,63","14,774","15,886","13,126","16,348","20,005","18,172","15,806","17,023","15,379","19,684","14,581","11,945","14,807","18,618","16,467","17,848","14,349","17,48736","15,580622","15,381243","17,376933"
+INTL.33-12-SRB-BKWH.A,        Serbia,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,--,"10,855","9,937","9,468","10,436","11,772","8,58","9,193","10,101","10,893","9,979","10,684","9,061","10,53261","9,457175","9,034496","11,284232"
+INTL.33-12-SVK-BKWH.A,        Slovakia,--,--,--,--,--,--,--,--,--,--,--,--,--,"3,432","4,311","4,831","4,185","4,023","4,224","4,429","4,569","4,878","5,215","3,4452","4,059","4,592","4,355","4,406",4,"4,324","5,184","3,211","3,687","4,329","3,762","3,701","4,302","4,321","3,506","4,27383","4,517","4,17"
+INTL.33-12-SVN-BKWH.A,        Slovenia,--,--,--,--,--,--,--,--,--,--,--,--,"3,379","2,974","3,348","3,187","3,616","3,046","3,4","3,684","3,771","3,741","3,265","2,916","4,033","3,426","3,555","3,233","3,978","4,666","4,452","3,506","3,841","4,562","6,011","3,75","4,443","3,814","4,643","4,43421","4,93406","4,711944"
+INTL.33-12-ESP-BKWH.A,        Spain,"29,16","21,64","25,99","26,696","31,088","30,895","26,105","27,016","34,76","19,046","25,16","27,01","18,731","24,133","27,898","22,881","39,404","34,43","33,665","22,634","29,274","40,617","22,691","40,643","31,359","18,209","25,699","27,036","23,13","26,147","41,576","30,07","20,192","36,45","38,815","27,656","35,77","18,007","33,743","24,23025","30,507","29,626"
+INTL.33-12-SWE-BKWH.A,        Sweden,"58,133","59,006","54,369","62,801","67,106","70,095","60,134","70,95","69,016","70,911","71,778","62,603","73,588","73,905","58,508","67,421","51,2226","68,365","74,25","70,974","77,798","78,269","65,696","53,005","59,522","72,075","61,106","65,497","68,378","65,193","66,279","66,047","78,333","60,81","63,227","74,734","61,645","64,651","61,79","64,46583","71,6","71,086"
+INTL.33-12-CHE-BKWH.A,        Switzerland,"32,481","35,13","35,974","35,069","29,871","31,731","32,576","34,328","35,437","29,477","29,497","31,756","32,373","35,416","38,678","34,817","28,458","33,70257","33,136","37,104","33,854","38,29","32,323","31,948","30,938","28,664","28,273","32,362","33,214","32,833","33,261","29,906","35,783","35,628","35,122","35,378","31,984","31,47968","32,095881","35,156989","37,867647","36,964485"
+INTL.33-12-TUR-BKWH.A,        Turkey,"11,159","12,308","13,81","11,13","13,19","11,822","11,637","18,314","28,447","17,61","22,917","22,456","26,302","33,611","30,28","35,186","40,07","39,41784","41,80671","34,33","30,57","23,77","33,346","34,977","45,623","39,165","43,802","35,492","32,937","35,598","51,423001","51,154999","56,668998","58,225","39,750001","65,856","66,685883","57,823851","59,490211","88,2094218","78,094369","55,1755392"
+INTL.33-12-GBR-BKWH.A,        United Kingdom,"3,921","4,369","4,543","4,548","3,992","4,08","4,767","4,13","4,915","4,732","5,119","4,534","5,329","4,237","5,043","4,79","3,359","4,127","5,117","5,336","5,085","4,055","4,78787","3,22767","4,844","4,92149","4,59315","5,0773","5,14119","5,22792","3,59138","5,69175","5,30965","4,70147","5,8878","6,29727","5,370412217","5,88187","5,44327","5,84628","6,75391","5,0149"
+,    Eurasia,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+INTL.33-12-MDA-BKWH.A,        Moldova,--,--,--,--,--,--,--,--,--,--,--,--,"0,255","0,371","0,275","0,321","0,362","0,378","0,387","0,363","0,392","0,359","0,348","0,358","0,35","0,359","0,365","0,354","0,385","0,354","0,403","0,348","0,266","0,311","0,317","0,265","0,228","0,282","0,27324","0,29799","0,276","0,316"
+INTL.33-12-UKR-BKWH.A,        Ukraine,--,--,--,--,--,--,--,--,--,--,--,--,"7,725","10,929","11,997","9,853","8,546","9,757","15,756","14,177","11,161","11,912","9,531","9,146","11,635","12,239","12,757","10,042","11,397","11,817","13,02","10,837","10,374","13,663","8,393","5,343","7,594","8,856","10,32372","6,5083","7,5638","10,3326"
--- a/data/existing_infrastructure/offwind_capacity_IRENA.csv
+++ b/data/existing_infrastructure/offwind_capacity_IRENA.csv
@ -1,34 +1,34 @@
-Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
-Albania,,,,,,,,,,,,,,,,,,,
-Austria,,,,,,,,,,,,,,,,,,,
-Belgium,,,,,,,,,,31.5,196.5,196.5,381,707.7,707.7,712,712.2,877.2,1185.9
-Bosnia Herzg,,,,,,,,,,,,,,,,,,,
-Bulgaria,,,,,,,,,,,,,,,,,,,
-Croatia,,,,,,,,,,,,,,,,,,,
-Czechia,,,,,,,,,,,,,,,,,,,
-Denmark,50,50,214,423.4,423.4,423.4,423.4,423.4,423.4,660.9,867.9,871.5,921.9,1271.1,1271.1,1271.1,1271.1,1263.8,1700.8
-Estonia,,,,,,,,,,,,,,,,,,,
-Finland,,,,,,,,,24,24,26.3,26.3,26.3,26.3,26.3,32,32,72.7,72.7
-France,,,,,,,,,,,,,,,,,,2,2
-Germany,,,,,,,,,,35,80,188,268,508,994,3283,4132,5406,6396
-Greece,,,,,,,,,,,,,,,,,,,
-Hungary,,,,,,,,,,,,,,,,,,,
-Ireland,,,,,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2
-Italy,,,,,,,,,,,,,,,,,,,
-Latvia,,,,,,,,,,,,,,,,,,,
-Lithuania,,,,,,,,,,,,,,,,,,,
-Luxembourg,,,,,,,,,,,,,,,,,,,
-Montenegro,,,,,,,,,,,,,,,,,,,
-Netherlands,,,,,,,108,108,228,228,228,228,228,228,228,357,957,957,957
-North Macedonia,,,,,,,,,,,,,,,,,,,
-Norway,,,,,,,,,,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3
-Poland,,,,,,,,,,,,,,,,,,,
-Portugal,,,,,,,,,,,,1.9,2,2,2,2,,,
-Romania,,,,,,,,,,,,,,,,,,,
-Serbia,,,,,,,,,,,,,,,,,,,
-Slovakia,,,,,,,,,,,,,,,,,,,
-Slovenia,,,,,,,,,,,,,,,,,,,
-Spain,,,,,,,,,,,,,,5,5,5,5,5,5
-Sweden,13,22,22,22,22,22,22,131,133,163,163,163,163,212,213,213,203,203,203
-Switzerland,,,,,,,,,,,,,,,,,,,
-UK,3.8,3.8,3.8,63.8,123.8,213.8,303.8,393.8,596.2,951.2,1341.5,1838.3,2995.5,3696,4501.3,5093.4,5293.4,6987.9,8216.5
+Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022
+Albania,,,,,,,,,,,,,,,,,,,,,,,
+Austria,,,,,,,,,,,,,,,,,,,,,,,
+Belgium,,,,,,,,,,31.5,196.5,196.5,381.0,707.7,707.7,712.0,712.2,877.2,1185.9,1555.5,2261.8,2261.8,2261.8
+Bosnia Herzg,,,,,,,,,,,,,,,,,,,,,,,
+Bulgaria,,,,,,,,,,,,,,,,,,,,,,,
+Croatia,,,,,,,,,,,,,,,,,,,,,,,
+Czechia,,,,,,,,,,,,,,,,,,,,,,,
+Denmark,49.95,49.95,213.95,423.35,423.35,423.35,423.35,423.35,423.35,660.85,867.85,871.45,921.85,1271.05,1271.05,1271.05,1271.05,1263.8,1700.8,1700.8,1700.8,2305.6,2305.6
+Estonia,,,,,,,,,,,,,,,,,,,,,,,
+Finland,,,,,,,,,24.0,24.0,26.3,26.3,26.3,26.3,26.3,32.0,32.0,72.7,72.7,73.0,73.0,73.0,73.0
+France,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,2.0,482.0
+Germany,,,,,,,,,,35.0,80.0,188.0,268.0,508.0,994.0,3283.0,4132.0,5406.0,6393.0,7555.0,7787.0,7787.0,8129.0
+Greece,,,,,,,,,,,,,,,,,,,,,,,
+Hungary,,,,,,,,,,,,,,,,,,,,,,,
+Ireland,,,,,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2
+Italy,,,,,,,,,,,,,,,,,,,,,,,30.0
+Latvia,,,,,,,,,,,,,,,,,,,,,,,
+Lithuania,,,,,,,,,,,,,,,,,,,,,,,
+Luxembourg,,,,,,,,,,,,,,,,,,,,,,,
+Montenegro,,,,,,,,,,,,,,,,,,,,,,,
+Netherlands,,,,,,,108.0,108.0,228.0,228.0,228.0,228.0,228.0,228.0,228.0,357.0,957.0,957.0,957.0,957.0,2459.5,2459.5,2571.0
+North Macedonia,,,,,,,,,,,,,,,,,,,,,,,
+Norway,,,,,,,,,,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,6.3,66.3
+Poland,,,,,,,,,,,,,,,,,,,,,,,
+Portugal,,,,,,,,,,,,1.86,2.0,2.0,2.0,2.0,,,,,25.0,25.0,25.0
+Romania,,,,,,,,,,,,,,,,,,,,,,,
+Serbia,,,,,,,,,,,,,,,,,,,,,,,
+Slovakia,,,,,,,,,,,,,,,,,,,,,,,
+Slovenia,,,,,,,,,,,,,,,,,,,,,,,
+Spain,,,,,,,,,,,,,,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0
+Sweden,13.0,22.0,22.0,22.0,22.0,22.0,22.0,131.0,133.0,163.0,163.0,163.0,163.0,212.0,213.0,213.0,203.0,203.0,203.0,203.0,203.0,193.0,193.0
+Switzerland,,,,,,,,,,,,,,,,,,,,,,,
+UK,4.0,4.0,4.0,64.0,124.0,214.0,304.0,394.0,596.2,951.0,1341.0,1838.0,2995.0,3696.0,4501.0,5093.0,5293.0,6988.0,8181.0,9888.0,10383.0,11255.0,13928.0
--- a/data/existing_infrastructure/onwind_capacity_IRENA.csv
+++ b/data/existing_infrastructure/onwind_capacity_IRENA.csv
@ -1,34 +1,34 @@
-Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
-Albania,,,,,,,,,,,,,,,,,,,
-Austria,50,67,109,322,581,825.2,968.3,991.2,992,1001,1015.8,1106,1337.2,1674.5,2110.3,2488.7,2730,2886.7,3132.7
-Belgium,14,26,31,67,96,167,212,276,324,576.5,715.5,872.5,989,1072.3,1236.3,1464,1657.8,1919.3,2074.8
-Bosnia Herzg,,,,,,,,,,,,0.3,0.3,0.3,0.3,0.3,0.3,0.3,50.9
-Bulgaria,,,,,1,8,27,30,114,333,488,541,677,683,699,699,699,698.4,698.9
-Croatia,,,,,6,6,17,17,17,70,79,130,180,254,339,418,483,576.1,586.3
-Czechia,2,,6.4,10.6,16.5,22,43.5,113.8,150,193,213,213,258,262,278,281,282,308.2,316.2
-Denmark,2340.1,2447.2,2680.6,2696.6,2700.4,2704.5,2712.3,2700.9,2739.5,2821.2,2934,3080.5,3240.1,3547.9,3615.4,3805.9,3974.5,4225.8,4419.8
-Estonia,,,1,3,7,31,31,50,77,104,108,180,266,248,275,300,310,311.8,310
-Finland,38,39,43,52,82,82,86,110,119,123,170.7,172.7,230.7,420.7,600.7,973,1533,1971.3,1968.3
-France,38,66,138,218,358,690,1412,2223,3403,4582,5912,6758,7607.5,8156,9201.4,10298.2,11566.6,13497.4,14898.1
-Germany,6095,8754,12001,14381,16419,18248,20474,22116,22794,25697,26823,28524,30711,32969,37620,41297,45303,50174,52447
-Greece,226,270,287,371,470,491,749,846,1022,1171,1298,1640,1753,1809,1978,2091,2370,2624,2877.5
-Hungary,,1,1,3,3,17,33,61,134,203,293,331,325,329,329,329,329,329,329
-Ireland,116.5,122.9,134.8,210.3,311.2,468.1,651.3,715.3,917.1,1226.1,1365.2,1559.4,1679.2,1983,2258.1,2426,2760.8,3292.8,3650.9
-Italy,363,664,780,874,1127,1635,1902,2702,3525,4879,5794,6918,8102,8542,8683,9137,9384,9736.6,10230.2
-Latvia,2,2,22,26,26,26,26,26,28,29,30,36,59,65.9,68.9,68.2,69.9,77.1,78.2
-Lithuania,,,,,1,1,31,47,54,98,133,202,275,279,288,436,509,518,533
-Luxembourg,14,13.9,13.9,20.5,34.9,34.9,34.9,34.9,42.9,42.9,43.7,44.5,58.3,58.3,58.3,63.8,119.7,119.7,122.9
-Montenegro,,,,,,,,,,,,,,,,,,72,118
-Netherlands,447,486,672,905,1075,1224,1453,1641,1921,1994,2009,2088,2205,2485,2637,3034,3300,3245,3436
-North Macedonia,,,,,,,,,,,,,,,37,37,37,37,37
-Norway,13,13,97,97,152,265,284,348,395,420.7,422.7,509.7,702.7,815.7,856.7,864.7,880.7,1204.7,1708
-Poland,4,19,32,35,40,121,172,306,526,709,1108,1800,2564,3429,3836,4886,5747,5759.4,5766.1
-Portugal,83,125,190,268,553,1064,1681,2201,2857,3326,3796,4254.4,4409.6,4607.9,4854.6,4934.8,5124.1,5124.1,5172.4
-Romania,,,,,,1,1,3,5,15,389,988,1822,2773,3244,3130,3025,3029.8,3032.3
-Serbia,,,,,,,,,,,,,0.5,0.5,0.5,10.4,17,25,25
-Slovakia,,,,3,3,5,5,5,5,3,3,3,3,5,3,3,3,4,3
-Slovenia,,,,,,,,,,,,,,4,4,5,5,5,5.2
-Spain,2206,3397,4891,5945,8317,9918,11722,14820,16555,19176,20693,21529,22789,22953,22920,22938,22985,23119.5,23400.1
-Sweden,196,273,335,395,453,500,563,692,956,1312,1854,2601,3443,3982,4875,5606,6232,6408,7097
-Switzerland,3,5,5,5,9,12,12,12,14,18,42,46,49,60,60,60,75,75,75
-UK,408.2,489.2,530.2,678.2,809.2,1351.2,1651.2,2083.2,2849.8,3470.8,4079.8,4758,6035,7586.3,8572.7,9212.2,10832.3,12596.9,13553.9
+Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022
+Albania,,,,,,,,,,,,,,,,,,,,,,,
+Austria,50.0,67.0,109.0,322.0,581.0,825.22,968.27,991.16,991.97,1000.99,1015.83,1105.97,1337.15,1674.54,2110.28,2488.73,2730.0,2886.7,3132.71,3224.12,3225.98,3407.81,3735.81
+Belgium,14.0,26.0,31.0,67.0,96.0,167.0,212.0,276.0,324.0,576.5,715.5,872.5,985.9,1061.3,1225.0,1469.3,1621.6,1902.2,2119.0,2308.0,2410.9,2686.6,2989.6
+Bosnia Herzg,,,,,,,,,,,,0.3,0.3,0.3,0.3,0.3,0.3,0.3,51.0,87.0,87.0,135.0,135.0
+Bulgaria,,,,,1.0,8.0,27.0,30.0,114.0,333.0,488.0,541.0,677.0,683.0,699.0,699.0,699.0,698.39,698.92,703.12,702.8,704.38,704.38
+Croatia,,,,,6.0,6.0,17.0,17.0,17.0,70.0,79.0,130.0,180.0,254.0,339.0,418.0,483.0,576.1,586.3,646.3,801.3,986.9,1042.9
+Czechia,2.0,,6.4,10.6,16.5,22.0,43.5,113.8,150.0,193.0,213.0,213.0,258.0,262.0,278.0,281.0,282.0,308.21,316.2,339.41,339.42,339.41,339.41
+Denmark,2340.07,2447.2,2680.58,2696.57,2700.36,2704.49,2712.35,2700.86,2739.52,2821.24,2933.98,3080.53,3240.09,3547.87,3615.35,3805.92,3974.09,4225.15,4421.86,4409.74,4566.23,4715.24,4782.24
+Estonia,,,1.0,3.0,7.0,31.0,31.0,50.0,77.0,104.0,108.0,180.0,266.0,248.0,275.0,300.0,310.0,311.8,310.0,316.0,317.0,315.0,315.0
+Finland,38.0,39.0,43.0,52.0,82.0,82.0,86.0,110.0,119.0,123.0,170.7,172.7,230.7,420.7,600.7,973.0,1533.0,1971.3,1968.3,2211.0,2513.0,3184.0,5541.0
+France,38.0,66.0,138.0,218.0,358.0,690.0,1412.0,2223.0,3403.0,4582.0,5912.0,6758.02,7607.5,8155.96,9201.42,10298.18,11566.56,13497.35,14898.14,16424.85,17512.0,18737.98,20637.98
+Germany,6095.0,8754.0,12001.0,14381.0,16419.0,18248.0,20474.0,22116.0,22794.0,25697.0,26823.0,28524.0,30711.0,32969.0,37620.0,41297.0,45303.0,50174.0,52328.0,53187.0,54414.0,56046.0,58165.0
+Greece,226.0,270.0,287.0,371.0,470.0,491.0,749.0,846.0,1022.0,1171.0,1298.0,1640.0,1753.0,1809.0,1978.0,2091.0,2370.0,2624.0,2877.5,3589.0,4119.25,4649.13,4879.13
+Hungary,,1.0,1.0,3.0,3.0,17.0,33.0,61.0,134.0,203.0,293.0,331.0,325.0,329.0,329.0,329.0,329.0,329.0,329.0,323.0,323.0,324.0,324.0
+Ireland,116.5,122.9,134.8,210.3,311.2,468.1,651.3,715.3,917.1,1226.1,1365.2,1559.4,1679.15,1898.1,2258.05,2425.95,2776.45,3293.95,3648.65,4101.25,4281.5,4313.84,4593.84
+Italy,363.0,664.0,780.0,874.0,1127.0,1635.0,1902.0,2702.0,3525.0,4879.0,5794.0,6918.0,8102.0,8542.0,8683.0,9137.0,9384.0,9736.58,10230.25,10679.46,10870.62,11253.73,11749.73
+Latvia,2.0,2.0,22.0,26.0,26.0,26.0,26.0,26.0,28.0,29.0,30.0,36.0,59.0,65.89,68.92,68.17,69.91,77.11,78.17,78.07,78.07,77.13,136.13
+Lithuania,,,,,1.0,1.0,31.0,47.0,54.0,98.0,133.0,202.0,275.0,279.0,288.0,436.0,509.0,518.0,533.0,534.0,540.0,671.0,814.0
+Luxembourg,14.0,13.9,13.9,20.5,34.9,34.9,34.9,34.9,42.92,42.93,43.73,44.53,58.33,58.33,58.34,63.79,119.69,119.69,122.89,135.79,152.74,136.44,165.44
+Montenegro,,,,,,,,,,,,,,,,,,72.0,72.0,118.0,118.0,118.0,118.0
+Netherlands,447.0,486.0,672.0,905.0,1075.0,1224.0,1453.0,1641.0,1921.0,1994.0,2009.0,2088.0,2205.0,2485.0,2637.0,3033.84,3300.12,3245.0,3436.11,3527.16,4188.38,5309.87,6176.0
+North Macedonia,,,,,,,,,,,,,,,37.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0
+Norway,13.0,13.0,97.0,97.0,152.0,265.0,284.0,348.0,395.0,420.7,422.7,509.7,702.7,815.7,856.7,864.7,880.7,1204.7,1707.7,2911.7,4027.7,5042.7,5067.7
+Poland,4.0,19.0,32.0,35.0,40.0,121.0,172.0,306.0,526.0,709.0,1108.0,1800.0,2564.0,3429.0,3836.0,4886.0,5747.0,5759.36,5766.08,5837.76,6298.25,6967.34,7987.34
+Portugal,83.0,125.0,190.0,268.0,553.0,1064.0,1681.0,2201.0,2857.0,3326.0,3796.0,4254.35,4409.55,4607.95,4854.56,4934.84,5124.1,5124.1,5172.36,5222.75,5097.26,5402.33,5430.33
+Romania,,,,,,1.0,1.0,3.0,5.0,15.0,389.0,988.0,1822.0,2773.0,3244.0,3130.0,3025.0,3029.8,3032.26,3037.52,3012.53,3014.96,3014.96
+Serbia,,,,,,,,,,,,,0.5,0.5,0.5,10.4,17.0,25.0,227.0,398.0,398.0,398.0,398.0
+Slovakia,,,,3.0,3.0,5.0,5.0,5.0,5.0,3.0,3.0,3.0,3.0,5.0,3.0,3.0,3.0,4.0,3.0,4.0,4.0,4.0,4.0
+Slovenia,,,,,,,,,,,,,2.0,2.0,3.0,3.0,3.0,3.3,3.3,3.3,3.3,3.33,3.33
+Spain,2206.0,3397.0,4891.0,5945.0,8317.0,9918.0,11722.0,14820.0,16555.0,19176.0,20693.0,21529.0,22789.0,22953.0,22920.0,22938.0,22985.0,23119.48,23400.06,25585.08,26814.19,27902.65,29302.84
+Sweden,196.0,273.0,335.0,395.0,453.0,500.0,563.0,692.0,956.0,1312.0,1854.0,2601.0,3443.0,3982.0,4875.0,5606.0,6232.0,6408.0,7097.0,8478.0,9773.0,11923.0,14364.0
+Switzerland,3.0,5.0,5.0,5.0,9.0,12.0,12.0,12.0,14.0,18.0,42.0,46.0,49.0,60.0,60.0,60.0,75.0,75.0,75.0,75.0,87.0,87.0,87.0
+UK,431.0,490.0,531.0,678.0,809.0,1351.0,1651.0,2083.0,2849.8,3468.0,4080.0,4758.0,6035.0,7586.0,8573.0,9212.0,10833.0,12597.0,13425.0,13999.0,14075.0,14492.0,14832.0
--- a/data/existing_infrastructure/solar_capacity_IRENA.csv
+++ b/data/existing_infrastructure/solar_capacity_IRENA.csv
@ -1,34 +1,34 @@
-Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
-Albania,,0.1,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.3,0.4,0.6,0.7,0.8,0.9,1.1,1,1,1
-Austria,5,7,9,23,27,21,22.4,24.2,30.1,48.9,88.8,174.1,337.5,626,785.2,937.1,1096,1269,1437.6
-Belgium,,,1,1,1,2,2,20,62,386,1007,1979,2647,2902,3015.2,3131.7,3327,3616.2,3986.5
-Bosnia Herzg,,,,0.1,0.2,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,1.3,7.2,8.2,14.1,16,18.2
-Bulgaria,,,,,,,,0,0.1,2,25,154,1013,1020,1026,1029,1028,1035.6,1032.7
-Croatia,,,,,,,,,,0.3,0.3,0.3,4,19,33,47.8,55.8,60,67.7
-Czechia,0.1,0.1,0.2,0.3,0.4,0.6,0.8,4,39.5,464.6,1727,1913,2022,2063.5,2067.4,2074.9,2067.9,2069.5,2075.1
-Denmark,1,1,2,2,2,3,3,3,3,5,7,17,402,571,607,782.1,851,906.4,998
-Estonia,,,,,,,,,,0.1,0.1,0.2,0.4,1.5,3.3,6.5,10,15,31.9
-Finland,2,3,3,3,4,4,5,5,6,6,7,7,8,9,11,17,39,82,140
-France,7,7,8,9,11,13,15,26,80,277,1044,3003.6,4358.8,5277.3,6034.4,7137.5,7702.1,8610.4,9617
-Germany,114,195,260,435,1105,2056,2899,4170,6120,10564,18004,25914,34075,36708,37898,39222,40677,42291,45179
-Greece,,1,1,1,1,1,5,9,12,46,202,612,1536,2579,2596,2604,2604,2605.5,2651.6
-Hungary,,,,,,,,0.4,1,1,2,4,12,35,89,172,235,344,726
-Ireland,,,,,,,,,,0.6,0.7,0.8,0.9,1,1.6,2.4,5.9,15.7,24.2
-Italy,19,20,22,26,31,34,45,110,483,1264,3592,13131,16785,18185,18594,18901,19283,19682.3,20107.6
-Latvia,,,,,,,,,,,,,0.2,0.2,0.2,0.2,0.7,0.7,2
-Lithuania,,,,,,,,,0.1,0.1,0.1,0.3,7,68,69,69,70,73.8,82
-Luxembourg,,0.2,1.6,14.2,23.6,23.6,23.7,23.9,24.6,26.4,29.5,40.7,74.7,95,109.9,116.3,121.9,128.1,130.6
-Montenegro,,,,,,,0,0.2,0.4,0.4,0.6,0.8,0.9,1.1,2.1,2.7,3.1,3.4,3.4
-Netherlands,13,21,26,46,50,51,53,54,59,69,90,149,369,746,1048,1515,2049,2903,4522
-North Macedonia,,,,,,,,,,,0,2,4,7,15,17,16.7,16.7,20.6
-Norway,6,6,6,7,7,7,8,8,8.3,8.7,9.1,9.5,10,11,13,15,26.7,44.9,68.4
-Poland,,,,,,,,,,,,1.1,1.3,2.4,27.2,107.8,187.2,287.1,562
-Portugal,1,1,1,2,2,2,3,24,59,115,134,172,238,296,415,447,512.8,579.2,667.4
-Romania,,,,,,,,,0.1,0.1,0.1,1,41,761,1293,1326,1372,1374.1,1385.8
-Serbia,,,,,,0.1,0.2,0.4,0.9,1.2,1.3,1.5,3.1,4.7,6,9,11,10,10
-Slovakia,,,,,,,,,,,19,496,513,533,533,533,533,528,472
-Slovenia,,,0,0,0,0,0.2,0.6,1,4,12,57,142,187,223,238,233,246.8,221.3
-Spain,10,13,17,22,33,52,130,494,3384,3423,3873,4283,4569,4690,4697,4704,4713,4723,4763.5
-Sweden,3,3,3,4,4,4,5,6,8,9,11,12,24,43,60,104,153,402,492
-Switzerland,16,18,20,22,24,28,30,37,49,79,125,223,437,756,1061,1394,1664,1906,2171
-UK,2,3,4,6,8,11,14,18,23,27,95,1000,1753,2937,5528,9601.2,11930.5,12781.8,13118.3
+Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022
+Albania,,0.1,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.3,0.4,0.56,0.68,0.76,0.87,1.05,1.0,1.0,1.0,14.0,21.0,23.0,28.6
+Austria,5.0,7.0,9.0,23.0,27.0,18.49,19.61,21.42,27.0,45.56,85.27,169.88,333.09,620.78,779.76,931.56,1089.53,1262.01,1447.94,1694.4,2034.74,2773.91,3538.91
+Belgium,,,1.0,1.0,1.0,2.0,2.0,20.0,62.0,386.0,1006.6,1978.6,2646.6,2901.6,3015.0,3131.6,3328.8,3620.6,4000.0,4636.6,5572.8,6012.4,6898.4
+Bosnia Herzg,,,,0.1,0.2,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.35,1.34,7.17,8.17,14.12,16.0,18.15,22.35,34.89,56.51,107.47
+Bulgaria,,,,,,,,0.03,0.1,2.0,25.0,154.0,921.99,1038.54,1028.92,1027.89,1029.89,1030.7,1033.06,1044.39,1100.21,1274.71,1948.36
+Croatia,,,,,,,,,,0.3,0.3,0.3,4.0,19.0,33.0,47.8,55.8,60.0,67.7,84.8,108.5,138.3,182.3
+Czechia,0.1,0.1,0.2,0.3,0.4,0.59,0.84,3.96,39.5,464.6,1727.0,1913.0,2022.0,2063.5,2067.4,2074.9,2067.9,2075.44,2081.05,2110.67,2171.96,2246.09,2627.09
+Denmark,1.0,1.0,2.0,2.0,2.0,3.0,3.0,3.0,3.0,5.0,7.0,17.0,402.0,571.0,607.0,782.11,850.95,906.35,998.0,1080.0,1304.29,1704.04,3122.04
+Estonia,,,,,,,,,,0.1,0.1,0.2,0.38,1.5,3.34,6.5,10.0,15.0,31.9,120.6,207.67,394.77,534.77
+Finland,2.0,3.0,3.0,3.0,4.0,4.0,5.0,5.0,6.0,6.0,7.0,7.0,8.0,9.0,11.0,17.0,39.0,82.0,140.0,222.0,318.0,425.0,590.6
+France,7.0,7.0,8.0,9.0,11.0,13.0,15.0,26.0,80.0,277.0,1044.0,3003.57,4358.75,5277.29,6034.42,7137.52,7702.08,8610.44,9638.88,10738.39,11812.2,14436.97,17036.97
+Germany,114.0,195.0,260.0,435.0,1105.0,2056.0,2899.0,4170.0,6120.0,10564.0,18004.0,25914.0,34075.0,36708.0,37898.0,39222.0,40677.0,42291.0,45156.0,48912.0,53669.0,59371.0,66662.0
+Greece,,1.0,1.0,1.0,1.0,1.0,5.0,9.0,12.0,46.0,202.0,612.0,1536.0,2579.0,2596.0,2604.0,2604.0,2605.53,2651.57,2833.79,3287.72,4277.42,5557.42
+Hungary,,,,,,,,0.4,1.0,1.0,2.0,4.0,12.0,35.0,89.0,172.0,235.0,344.0,728.0,1400.0,2131.0,2968.0,2988.0
+Ireland,,,,,,,,,,,,,,,,,,,,,,,
+Italy,19.0,20.0,22.0,26.0,31.0,34.0,45.0,110.0,483.0,1264.0,3592.0,13131.0,16785.0,18185.0,18594.0,18901.0,19283.0,19682.29,20107.59,20865.28,21650.04,22594.26,25076.56
+Latvia,,,,,,,,,,,,,,,,,0.69,0.69,1.96,3.3,5.1,7.16,56.16
+Lithuania,,,,,,,,,0.1,0.1,0.1,0.3,7.0,68.0,69.0,69.0,70.0,70.08,72.0,73.0,80.0,84.0,397.0
+Luxembourg,,0.16,1.59,14.17,23.56,23.58,23.7,23.93,24.56,26.36,29.45,40.67,74.65,95.02,109.93,116.27,121.9,128.1,130.62,159.74,186.64,277.16,319.16
+Montenegro,,,,,,,,,,,,,,,,,,,,,2.57,2.57,22.2
+Netherlands,13.0,21.0,26.0,46.0,50.0,51.0,53.0,54.0,59.0,69.0,90.0,149.0,287.0,650.0,1007.0,1526.26,2135.02,2910.89,4608.0,7226.0,11108.43,14910.69,18848.69
+North Macedonia,,,,,,,,,,,,2.0,4.0,7.0,15.0,17.0,16.7,16.7,16.7,16.71,84.93,84.93,84.93
+Norway,6.0,6.0,6.0,7.0,7.0,7.0,8.0,8.0,8.3,8.7,9.1,9.5,10.0,11.0,13.0,15.0,26.7,44.9,53.11,102.53,141.53,186.53,302.53
+Poland,,,,,,,,,,,,1.11,1.3,2.39,27.15,107.78,187.25,287.09,561.98,1539.26,3954.96,7415.52,11166.52
+Portugal,1.0,1.0,1.0,2.0,2.0,2.0,3.0,24.0,59.0,115.0,134.0,169.6,235.6,293.6,412.6,441.75,493.05,539.42,617.85,832.74,1010.07,1474.78,2364.78
+Romania,,,,,,,,,0.1,0.1,0.1,1.0,41.0,761.0,1293.0,1326.0,1372.0,1374.13,1385.82,1397.71,1382.54,1393.92,1413.92
+Serbia,,,,,,0.1,0.2,0.4,0.9,1.2,1.3,1.5,3.1,4.7,6.0,9.0,11.0,10.0,11.0,11.0,11.5,11.94,11.94
+Slovakia,,,,,,,,,,,19.0,496.0,513.0,533.0,533.0,533.0,533.0,528.0,472.0,590.0,535.0,537.0,537.0
+Slovenia,1.0,1.0,,,,0.05,0.19,0.59,1.0,4.0,12.0,57.0,142.0,187.0,223.0,238.0,233.0,246.8,246.8,277.88,369.78,461.16,632.16
+Spain,1.0,3.0,6.0,10.0,19.0,37.0,113.0,476.0,3365.0,3403.0,3851.0,4260.0,4545.0,4665.0,4672.0,4677.0,4687.0,4696.0,4730.7,8772.02,10100.42,13678.4,18176.73
+Sweden,3.0,3.0,3.0,4.0,4.0,4.0,5.0,6.0,8.0,9.0,11.0,12.0,24.0,43.0,60.0,104.0,153.0,231.0,411.0,698.0,1090.0,1587.0,2587.0
+Switzerland,16.0,18.0,20.0,22.0,24.0,28.0,30.0,37.0,49.0,79.0,125.0,223.0,437.0,756.0,1061.0,1394.0,1664.0,1906.0,2173.0,2498.0,2973.0,3655.0,4339.92
+UK,2.0,3.0,4.0,6.0,8.0,11.0,14.0,18.0,23.0,27.0,95.0,1000.0,1753.0,2937.0,5528.0,9601.0,11914.0,12760.0,13059.0,13345.0,13579.0,13965.0,14660.0
--- a/doc/conf.py
+++ b/doc/conf.py
@ -80,9 +80,9 @@ author = "Tom Brown (KIT, TUB, FIAS), Jonas Hoersch (KIT, FIAS), Fabian Hofmann
 # built documents.
 #
 # The short X.Y version.
-version = "0.8"
+version = "0.9"
 # The full version, including alpha/beta/rc tags.
-release = "0.8.1"
+release = "0.9.0"

 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
--- a/doc/configtables/clustering.csv
+++ b/doc/configtables/clustering.csv
@ -1,4 +1,5 @@
 ,Unit,Values,Description
+focus_weights,,,Optionally specify the focus weights for the clustering of countries. For instance: `DE: 0.8` will distribute 80% of all nodes to Germany and 20% to the rest of the countries.
 simplify_network,,,
 -- to_substations,bool,"{'true','false'}","Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones"
 -- algorithm,str,"One of {‘kmeans’, ‘hac’, ‘modularity‘}",
--- a/doc/configtables/costs.csv
+++ b/doc/configtables/costs.csv
@ -1,9 +1,12 @@
-,Unit,Values,Description
-year,--,"YYYY; e.g. '2030'","Year for which to retrieve cost assumptions of ``resources/costs.csv``."
-version,--,"vX.X.X; e.g. 'v0.5.0'","Version of ``technology-data`` repository to use."
-rooftop_share,--,float,"Share of rooftop PV when calculating capital cost of solar (joint rooftop and utility-scale PV)."
-fill_values,--,float,"Default values if not specified for a technology in ``resources/costs.csv``."
-capital_cost,EUR/MW,"Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float.","For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``."
-marginal_cost,EUR/MWh,"Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float.","For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``."
-emission_prices,,,"Specify exogenous prices for emission types listed in ``network.carriers`` to marginal costs."
-- co2,EUR/t,float,"Exogenous price of carbon-dioxide added to the marginal costs of fossil-fuelled generators according to their carbon intensity. Added through the keyword ``Ep`` in the ``{opts}`` wildcard only in the rule :mod:`prepare_network``."
+,Unit,Values,Description
+year,--,YYYY; e.g. '2030',Year for which to retrieve cost assumptions of ``resources/costs.csv``.
+version,--,vX.X.X; e.g. 'v0.5.0',Version of ``technology-data`` repository to use.
+rooftop_share,--,float,Share of rooftop PV when calculating capital cost of solar (joint rooftop and utility-scale PV).
+social_discountrate,p.u.,float,Social discount rate to compare costs in different investment periods. 0.02 corresponds to a social discount rate of 2%.
+fill_values,--,float,Default values if not specified for a technology in ``resources/costs.csv``.
+capital_cost,EUR/MW,Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float.,"For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``."
+marginal_cost,EUR/MWh,Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float.,"For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``."
+emission_prices,,,Specify exogenous prices for emission types listed in ``network.carriers`` to marginal costs.
+-- enable,bool,true or false,Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well)
+-- co2,EUR/t,float,Exogenous price of carbon-dioxide added to the marginal costs of fossil-fuelled generators according to their carbon intensity. Added through the keyword ``Ep`` in the ``{opts}`` wildcard only in the rule :mod:`prepare_network``.
+-- co2_monthly_price,bool,true or false,Add monthly cost for a carbon-dioxide price based on historical values built by the rule ``build_monthly_prices``
--- a/doc/configtables/electricity.csv
+++ b/doc/configtables/electricity.csv
@ -1,6 +1,8 @@
 ,Unit,Values,Description
 voltages,kV,"Any subset of {220., 300., 380.}",Voltage levels to consider
+gaslimit_enable,bool,true or false,Add an overall absolute gas limit configured in ``electricity: gaslimit``.
 gaslimit,MWhth,float or false,Global gas usage limit
+co2limit_enable,bool,true or false,Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``.
 co2limit,:math:`t_{CO_2-eq}/a`,float,Cap on total annual system carbon dioxide emissions
 co2base,:math:`t_{CO_2-eq}/a`,float,Reference value of total annual system carbon dioxide emissions if relative emission reduction target is specified in ``{opts}`` wildcard.
 agg_p_nom_limits,file,path,Reference to ``.csv`` file specifying per carrier generator nominal capacity constraints for individual countries if ``'CCL'`` is in ``{opts}`` wildcard. Defaults to ``data/agg_p_nom_minmax.csv``.
@ -22,6 +24,8 @@ powerplants_filter,--,"use `pandas.query <https://pandas.pydata.org/pandas-docs/
 ,,,
 custom_powerplants,--,"use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. ``Country in ['Germany']``",Filter query for the custom powerplant database.
 ,,,
+everywhere_powerplants,--,"Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass}","List of conventional power plants to add to every node in the model with zero initial capacity. To be used in combination with ``extendable_carriers`` to allow for building conventional powerplants irrespective of existing locations."
+,,,
 conventional_carriers,--,"Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass}","List of conventional power plants to include in the model from ``resources/powerplants.csv``. If an included carrier is also listed in ``extendable_carriers``, the capacity is taken as a lower bound."
 ,,,
 renewable_carriers,--,"Any subset of {solar, onwind, offwind-ac, offwind-dc, hydro}",List of renewable generators to include in the model.
@ -34,3 +38,6 @@ estimate_renewable_capacities,,,
 -- -- Offshore,--,"Any subset of {offwind-ac, offwind-dc}","List of PyPSA-Eur carriers that is considered as (IRENA, OPSD) onshore technology."
 -- -- Offshore,--,{onwind},"List of PyPSA-Eur carriers that is considered as (IRENA, OPSD) offshore technology."
 -- -- PV,--,{solar},"List of PyPSA-Eur carriers that is considered as (IRENA, OPSD) PV technology."
+autarky,,,
+-- enable,bool,true or false,Require each node to be autarkic by removing all lines and links.
+-- by_country,bool,true or false,Require each country to be autarkic by removing all cross-border lines and links. ``electricity: autarky`` must be enabled.
--- a/doc/configtables/enable.csv
+++ b/doc/configtables/enable.csv
@ -5,6 +5,7 @@ retrieve_databundle,bool,"{true, false}","Switch to retrieve databundle from zen
 retrieve_sector_databundle,bool,"{true, false}","Switch to retrieve sector databundle from zenodo via the rule :mod:`retrieve_sector_databundle` or whether to keep a custom databundle located in the corresponding folder."
 retrieve_cost_data,bool,"{true, false}","Switch to retrieve technology cost data from `technology-data repository <https://github.com/PyPSA/technology-data>`_."
 build_cutout,bool,"{true, false}","Switch to enable the building of cutouts via the rule :mod:`build_cutout`."
+retrieve_irena,bool,"{true, false}",Switch to enable the retrieval of ``existing_capacities`` from IRENASTAT with :mod:`retrieve_irena`.
 retrieve_cutout,bool,"{true, false}","Switch to enable the retrieval of cutouts from zenodo with :mod:`retrieve_cutout`."
 build_natura_raster,bool,"{true, false}","Switch to enable the creation of the raster ``natura.tiff`` via the rule :mod:`build_natura_raster`."
 retrieve_natura_raster,bool,"{true, false}","Switch to enable the retrieval of ``natura.tiff`` from zenodo with :mod:`retrieve_natura_raster`."
--- a/doc/configtables/existing_capacities.csv
+++ b/doc/configtables/existing_capacities.csv
@ -3,4 +3,5 @@ grouping_years_power ,--,A list of years,Intervals to group existing capacities
 grouping_years_heat ,--,A list of years below 2020,Intervals to group existing capacities for heat

 threshold_capacity ,MW,float,Capacities generators and links of below threshold are removed during add_existing_capacities
+default_heating_lifetime ,years,int,Default lifetime for heating technologies
 conventional_carriers ,--,"Any subset of {uranium, coal, lignite, oil} ",List of conventional power plants to include in the sectoral network
--- a/doc/configtables/licenses-sector.csv
+++ b/doc/configtables/licenses-sector.csv
@ -9,9 +9,8 @@ Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,un
 BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Stundenwerte.html?nn=626916
 BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
 heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
-George Lavidas wind/wave costs,WindWaveWEC_GLTB.xlsx,unknown,George Lavidas
 co2 budgets,co2_budget.csv,CC BY 4.0,https://arxiv.org/abs/2004.11009
-existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
+existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
 IRENA existing VRE capacities,existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv,unknown,https://www.irena.org/Statistics/Download-Data
 USGS ammonia production,myb1-2017-nitro.xls,unknown,https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information
 hydrogen salt cavern potentials,h2_salt_caverns_GWh_per_sqkm.geojson,CC BY 4.0,https://doi.org/10.1016/j.ijhydene.2019.12.161 https://doi.org/10.20944/preprints201910.0187.v1
--- a/doc/configtables/licenses.csv
+++ b/doc/configtables/licenses.csv
@ -5,7 +5,7 @@
 "naturalearth/*",,,,,http://www.naturalearthdata.com/about/terms-of-use/
 "NUTS_2013 _60M_SH/*","x","x",,"x",https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
 "cantons.csv","x",,"x",,https://en.wikipedia.org/wiki/Data_codes_for_Switzerland
-"EIA_hydro_generation _2000_2014.csv","x",,,,https://www.eia.gov/about/copyrights_reuse.php
+"eia_hydro_annual_generation.csv","x",,,,https://www.eia.gov/about/copyrights_reuse.php
 "GEBCO_2014_2D.nc","x",,,,https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf
 "hydro_capacities.csv","x",,,,
 "je-e-21.03.02.xls","x","x",,,https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html
--- a/doc/configtables/lines.csv
+++ b/doc/configtables/lines.csv
@ -5,6 +5,7 @@ s_nom_max,MW,"float","Global upper limit for the maximum capacity of each extend
 max_extension,MW,"float","Upper limit for the extended capacity of each extendable line."
 length_factor,--,float,"Correction factor to account for the fact that buses are *not* connected by lines through air-line distance."
 under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity}","Specifies how to handle lines which are currently under construction."
+reconnect_crimea,--,"true or false","Whether to reconnect Crimea to the Ukrainian grid"
 dynamic_line_rating,,,
 -- activate,bool,"true or false","Whether to take dynamic line rating into account"
 -- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
--- a/doc/configtables/offwind-ac.csv
+++ b/doc/configtables/offwind-ac.csv
@ -2,15 +2,15 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_","Specifies the turbine type and its characteristic power curve."
+-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_.  Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."
 corine,--,"Any *realistic* subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement."
+luisa,--,"Any subset of the `LUISA Base Map codes in Annex 1 <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_","Specifies areas according to the LUISA Base Map codes which are generally eligible for AC-connected offshore wind turbine placement."
 natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
 ship_threshold,--,float,"Ship density threshold from which areas are excluded."
 max_depth,m,float,"Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential."
 min_shore_distance,m,float,"Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential."
 max_shore_distance,m,float,"Maximum distance to the shore above which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential."
-potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
 clip_p_max_pu,p.u.,float,"To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero."
--- a/doc/configtables/offwind-dc.csv
+++ b/doc/configtables/offwind-dc.csv
@ -2,15 +2,15 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__","Specifies the turbine type and its characteristic power curve."
+-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."
 corine,--,"Any *realistic* subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement."
+luisa,--,"Any subset of the `LUISA Base Map codes in Annex 1 <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_","Specifies areas according to the LUISA Base Map codes which are generally eligible for DC-connected offshore wind turbine placement."
 natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
 ship_threshold,--,float,"Ship density threshold from which areas are excluded."
 max_depth,m,float,"Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential."
 min_shore_distance,m,float,"Minimum distance to the shore below which wind turbines cannot be build."
 max_shore_distance,m,float,"Maximum distance to the shore above which wind turbines cannot be build."
-potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
 clip_p_max_pu,p.u.,float,"To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero."
--- a/doc/configtables/onwind.csv
+++ b/doc/configtables/onwind.csv
@ -2,14 +2,17 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__","Specifies the turbine type and its characteristic power curve."
+-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 corine,,,
 -- grid_codes,--,"Any subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for wind turbine placement."
 -- distance,m,float,"Distance to keep from areas specified in ``distance_grid_codes``"
 -- distance_grid_codes,--,"Any subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes to which wind turbines must maintain a distance specified in the setting ``distance``."
+luisa,,,
+-- grid_codes,--,"Any subset of the `LUISA Base Map codes in Annex 1 <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_","Specifies areas according to the LUISA Base Map codes which are generally eligible for wind turbine placement."
+-- distance,m,float,"Distance to keep from areas specified in ``distance_grid_codes``"
+-- distance_grid_codes,--,"Any subset of the `LUISA Base Map codes in Annex 1 <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_","Specifies areas according to the LUISA Base Map codes to which wind turbines must maintain a distance specified in the setting ``distance``."
 natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
-potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
 clip_p_max_pu,p.u.,float,"To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."
--- a/doc/configtables/opts.csv
+++ b/doc/configtables/opts.csv
@ -1,13 +1,13 @@
-Trigger, Description, Definition, Status
-``nH``; i.e. ``2H``-``6H``, Resample the time-resolution by averaging over every ``n`` snapshots, ``prepare_network``: `average_every_nhours() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L110>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L146>`__), In active use
-``nSEG``; e.g. ``4380SEG``, "Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load.", ``prepare_network``: apply_time_segmentation(), In active use
-``Co2L``, Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``), ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`__, In active use
-``Ep``, Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well), ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`__, In active use
-``Ept``, Add monthly cost for a carbon-dioxide price based on historical values built by the rule ``build_monthly_prices``, In active use
-``CCL``, Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``., ``solve_network``, In active use
-``EQ``, "Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption.", ``solve_network``, In active use
-``ATK``, "Require each node to be autarkic. Example: ``ATK`` removes all lines and links. ``ATKc`` removes all cross-border lines and links.", ``prepare_network``, In active use
-``BAU``, Add a per-``carrier`` minimal overall capacity; i.e. at least ``40GW`` of ``OCGT`` in Europe; configured in ``electricity: BAU_mincapacities``, ``solve_network``: `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L66>`__, Untested
-``SAFE``, Add a capacity reserve margin of a certain fraction above the peak demand to which renewable generators and storage do *not* contribute. Ignores network., ``solve_network`` `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L73>`__, Untested
-``carrier+{c|p|m}factor``,"Alter the capital cost (``c``), installable potential (``p``) or marginal costs (``m``) of a carrier by a factor. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values.", ``prepare_network``, In active use
-``CH4L``,"Add an overall absolute gas limit. If configured in ``electricity: gaslimit`` it is given in MWh thermal, if a float is appended, the overall gaslimit is assumed to be given in TWh thermal (e.g. ``CH4L200`` limits gas dispatch to 200 TWh termal)", ``prepare_network``: ``add_gaslimit()``, In active use
+Trigger, Description, Definition, Status
+``nH``; i.e. ``2H``-``6H``, Resample the time-resolution by averaging over every ``n`` snapshots, ``prepare_network``: `average_every_nhours() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L110>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L146>`__), In active use
+``nSEG``; e.g. ``4380SEG``,"Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load.", ``prepare_network``: apply_time_segmentation(), In active use
+``Co2L``,Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``), ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`__, In active use
+``Ep``,Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well), ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`__, In active use
+``Ept``,Add monthly cost for a carbon-dioxide price based on historical values built by the rule ``build_monthly_prices``, In active use,
+``CCL``,Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``., ``solve_network``, In active use
+``EQ``,Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption., ``solve_network``, In active use
+``ATK``,Require each node to be autarkic. Example: ``ATK`` removes all lines and links. ``ATKc`` removes all cross-border lines and links., ``prepare_network``, In active use
+``BAU``,Add a per-``carrier`` minimal overall capacity; i.e. at least ``40GW`` of ``OCGT`` in Europe; configured in ``electricity: BAU_mincapacities``, ``solve_network``: `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L66>`__, Untested
+``SAFE``,Add a capacity reserve margin of a certain fraction above the peak demand to which renewable generators and storage do *not* contribute. Ignores network., ``solve_network`` `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L73>`__, Untested
+``carrier+{c|p|m}factor``,"Alter the capital cost (``c``), installable potential (``p``) or marginal costs (``m``) of a carrier by a factor. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values.", ``prepare_network``, In active use
+``CH4L``,"Add an overall absolute gas limit. If configured in ``electricity: gaslimit`` it is given in MWh thermal, if a float is appended, the overall gaslimit is assumed to be given in TWh thermal (e.g. ``CH4L200`` limits gas dispatch to 200 TWh termal)", ``prepare_network``: ``add_gaslimit()``, In active use
--- a/doc/configtables/plotting.csv
+++ b/doc/configtables/plotting.csv
@ -1,6 +1,9 @@
 ,Unit,Values,Description
 map,,,
 -- boundaries,°,"[x1,x2,y1,y2]",Boundaries of the map plots in degrees latitude (y) and longitude (x)
+projection,,,,
+-- name,--,"Valid Cartopy projection name","See https://scitools.org.uk/cartopy/docs/latest/reference/projections.html for list of available projections."
+-- args,--,--,"Other entries under 'projection' are passed as keyword arguments to the projection constructor, e.g. ``central_longitude: 10.``."
 costs_max,bn Euro,float,Upper y-axis limit in cost bar plots.
 costs_threshold,bn Euro,float,Threshold below which technologies will not be shown in cost bar plots.
 energy_max,TWh,float,Upper y-axis limit in energy bar plots.
--- a/doc/configtables/sector.csv
+++ b/doc/configtables/sector.csv
@ -62,16 +62,17 @@ tes,--,"{true, false}",Add option for storing thermal energy in large water pits
 tes_tau,,,The time constant used to calculate the decay of thermal energy in thermal energy storage (TES): 1- :math:`e^{-1/24τ}`.
 -- decentral,days,float,The time constant in decentralized thermal energy storage (TES)
 -- central,days,float,The time constant in centralized thermal energy storage (TES)
-boilers,--,"{true, false}",Add option for transforming electricity into heat using resistive heater
+boilers,--,"{true, false}",Add option for transforming gas into heat using gas boilers
+resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
 oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers
 biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers
+overdimension_individual_heating,--,"float",Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
 chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP)
 micro_chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) for decentral areas.
 solar_thermal,--,"{true, false}",Add option for using solar thermal to generate heat.
 solar_cf_correction,--,float,The correction factor for the value provided by the solar thermal profile calculations
 marginal_cost_storage,currency/MWh ,float,The marginal cost of discharging batteries in distributed grids
 methanation,--,"{true, false}",Add option for transforming hydrogen and CO2 into methane using methanation.
-helmeth,--,"{true, false}",Add option for transforming power into gas using HELMETH (Integrated High-Temperature ELectrolysis and METHanation for Effective Power to Gas Conversion)
 coal_cc,--,"{true, false}",Add option for coal CHPs with carbon capture
 dac,--,"{true, false}",Add option for Direct Air Capture (DAC)
 co2_vent,--,"{true, false}",Add option for vent out CO2 from storages to the atmosphere.
@ -79,6 +80,9 @@ allam_cycle,--,"{true, false}",Add option to include `Allam cycle gas power plan
 hydrogen_fuel_cell,--,"{true, false}",Add option to include hydrogen fuel cell for re-electrification. Assuming OCGT technology costs
 hydrogen_turbine,--,"{true, false}",Add option to include hydrogen turbine for re-electrification. Assuming OCGT technology costs
 SMR,--,"{true, false}",Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR)
+SMR CC,--,"{true, false}",Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR) and Carbon Capture (CC)
+regional_methanol_demand,--,"{true, false}",Spatially resolve methanol demand. Set to true if regional CO2 constraints needed.
+regional_oil_demand,--,"{true, false}",Spatially resolve oil demand. Set to true if regional CO2 constraints needed.
 regional_co2 _sequestration_potential,,,
 -- enable,--,"{true, false}",Add option for regionally-resolved geological carbon dioxide sequestration potentials based on `CO2StoP <https://setis.ec.europa.eu/european-co2-storage-database_en>`_.
 -- attribute,--,string,Name of the attribute for the sequestration potential
@ -88,9 +92,11 @@ regional_co2 _sequestration_potential,,,
 -- years_of_storage,years,float,The years until potential exhausted at optimised annual rate
 co2_sequestration_potential,MtCO2/a,float,The potential of sequestering CO2 in Europe per year
 co2_sequestration_cost,currency/tCO2,float,The cost of sequestering a ton of CO2
+co2_sequestration_lifetime,years,int,The lifetime of a CO2 sequestration site
 co2_spatial,--,"{true, false}","Add option to spatially resolve carrier representing stored carbon dioxide. This allows for more detailed modelling of CCUTS, e.g. regarding the capturing of industrial process emissions, usage as feedstock for electrofuels, transport of carbon dioxide, and geological sequestration sites."
 ,,,
 co2network,--,"{true, false}",Add option for planning a new carbon dioxide transmission network
+co2_network_cost_factor,p.u.,float,The cost factor for the capital cost of the carbon dioxide transmission network
 ,,,
 cc_fraction,--,float,The default fraction of CO2 captured with post-combustion capture
 hydrogen_underground _storage,--,"{true, false}",Add options for storing hydrogen underground. Storage potential depends regionally.
@ -107,6 +113,11 @@ electricity_distribution _grid,--,"{true, false}",Add a simplified representatio
 electricity_distribution _grid_cost_factor,,,Multiplies the investment cost of the electricity distribution grid
 ,,,
 electricity_grid _connection,--,"{true, false}",Add the cost of electricity grid connection for onshore wind and solar
+transmission_efficiency,,,Section to specify transmission losses or compression energy demands of bidirectional links. Splits them into two capacity-linked unidirectional links.
+-- {carrier},--,str,The carrier of the link.
+-- -- efficiency_static,p.u.,float,Length-independent transmission efficiency.
+-- -- efficiency_per_1000km,p.u. per 1000 km,float,Length-dependent transmission efficiency ($\eta^{\text{length}}$)
+-- -- compression_per_1000km,p.u. per 1000 km,float,Length-dependent electricity demand for compression ($\eta \cdot \text{length}$) implemented as multi-link to local electricity bus.
 H2_network,--,"{true, false}",Add option for new hydrogen pipelines
 gas_network,--,"{true, false}","Add existing natural gas infrastructure, incl. LNG terminals, production and entry-points. The existing gas network is added with a lossless transport model. A length-weighted `k-edge augmentation algorithm   <https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation>`_   can be run to add new candidate gas pipelines such that all regions of the model can be connected to the gas network. When activated, all the gas demands are regionally disaggregated as well."
 H2_retrofit,--,"{true, false}",Add option for retrofiting existing pipelines to transport hydrogen.
@ -117,6 +128,14 @@ gas_distribution_grid _cost_factor,,,Multiplier for the investment cost of the g
 ,,,
 biomass_spatial,--,"{true, false}",Add option for resolving biomass demand regionally
 biomass_transport,--,"{true, false}",Add option for transporting solid biomass between nodes
+biogas_upgrading_cc,--,"{true, false}",Add option to capture CO2 from biomass upgrading
 conventional_generation,,,Add a more detailed description of conventional carriers. Any power generation requires the consumption of fuel from nodes representing that fuel.
 biomass_to_liquid,--,"{true, false}",Add option for transforming solid biomass into liquid fuel with the same properties as oil
 biosng,--,"{true, false}",Add option for transforming solid biomass into synthesis gas with the same properties as natural gas
+limit_max_growth,,,
+-- enable,--,"{true, false}",Add option to limit the maximum growth of a carrier
+-- factor,p.u.,float,The maximum growth factor of a carrier (e.g. 1.3 allows  30% larger than max historic growth)
+-- max_growth,,,
+-- -- {carrier},GW,float,The historic maximum growth of a carrier
+-- max_relative_growth,
+-- -- {carrier},p.u.,float,The historic maximum relative growth of a carrier
--- a/doc/configtables/snapshots.csv
+++ b/doc/configtables/snapshots.csv
@ -1,4 +1,6 @@
-,Unit,Values,Description
-start,--,"str or datetime-like; e.g. YYYY-MM-DD","Left bound of date range"
-end,--,"str or datetime-like; e.g. YYYY-MM-DD","Right bound of date range"
-inclusive,--,"One of {'neither', 'both', ‘left’, ‘right’}","Make the time interval closed to the ``left``, ``right``, or both sides ``both`` or neither side ``None``."
+,Unit,Values,Description
+start,--,str or datetime-like; e.g. YYYY-MM-DD,Left bound of date range
+end,--,str or datetime-like; e.g. YYYY-MM-DD,Right bound of date range
+inclusive,--,"One of {'neither', 'both', ‘left’, ‘right’}","Make the time interval closed to the ``left``, ``right``, or both sides ``both`` or neither side ``None``."
+resolution ,--,"{false,``nH``; i.e. ``2H``-``6H``}","Resample the time-resolution by averaging over every ``n`` snapshots in :mod:`prepare_network`. **Warning:** This option should currently only be used with electricity-only networks, not for sector-coupled networks."
+segmentation,--,"{false,``n``; e.g. ``4380``}","Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load in :mod:`prepare_network`. **Warning:** This option should currently only be used with electricity-only networks, not for sector-coupled networks."
--- a/doc/configtables/solar.csv
+++ b/doc/configtables/solar.csv
@ -2,14 +2,14 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module can be ERA5 or SARAH-2.","Specifies the directory where the relevant weather data ist stored that is specified at ``atlite/cutouts`` configuration. Both ``sarah`` and ``era5`` work."
 resource,,,
 -- method,--,"Must be 'pv'","A superordinate technology type."
-- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`__","Specifies the solar panel technology and its characteristic attributes."
+-- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`_ . Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the solar panel technology and its characteristic attributes."
 -- orientation,,,
 -- -- slope,°,"Realistically any angle in [0., 90.]","Specifies the tilt angle (or slope) of the solar panel. A slope of zero corresponds to the face of the panel aiming directly overhead. A positive tilt angle steers the panel towards the equator."
 -- -- azimuth,°,"Any angle in [0., 360.]","Specifies the `azimuth <https://en.wikipedia.org/wiki/Azimuth>`_ orientation of the solar panel. South corresponds to 180.°."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of solar panel placement."
 correction_factor,--,float,"A correction factor for the capacity factor (availability) time series."
 corine,--,"Any subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for solar panel placement."
+luisa,--,"Any subset of the `LUISA Base Map codes in Annex 1 <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_","Specifies areas according to the LUISA Base Map codes which are generally eligible for solar panel placement."
 natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
-potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
 clip_p_max_pu,p.u.,float,"To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."
--- a/doc/configtables/solving.csv
+++ b/doc/configtables/solving.csv
@ -6,12 +6,19 @@ options,,,
 -- skip_iterations,bool,"{'true','false'}","Skip iterating, do not update impedances of branches. Defaults to true."
 -- rolling_horizon,bool,"{'true','false'}","Whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively."
 -- seed,--,int,Random seed for increased deterministic behaviour.
+-- custom_extra_functionality,--,str,Path to a Python file with custom extra functionality code to be injected into the solving rules of the workflow relative to ``rules`` directory.
+-- io_api,string,"{'lp','mps','direct'}",Passed to linopy and determines the API used to communicate with the solver. With the ``'lp'`` and ``'mps'`` options linopy passes a file to the solver; with the ``'direct'`` option (only supported for HIGHS and Gurobi) linopy uses an in-memory python API resulting in better performance.
 -- track_iterations,bool,"{'true','false'}",Flag whether to store the intermediate branch capacities and objective function values are recorded for each iteration in ``network.lines['s_nom_opt_X']`` (where ``X`` labels the iteration)
 -- min_iterations,--,int,Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
 -- max_iterations,--,int,Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
 -- transmission_losses,int,[0-9],"Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored."
 -- linearized_unit_commitment,bool,"{'true','false'}",Whether to optimise using the linearized unit commitment formulation.
 -- horizon,--,int,Number of snapshots to consider in each iteration. Defaults to 100.
+constraints ,,,
+-- CCL,bool,"{'true','false'}",Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``.
+-- EQ,bool/string,"{'false',`n(c| )``; i.e. ``0.5``-``0.7c``}",Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption.
+-- BAU,bool,"{'true','false'}",Add a per-``carrier`` minimal overall capacity; i.e. at least ``40GW`` of ``OCGT`` in Europe; configured in ``electricity: BAU_mincapacities``
+-- SAFE,bool,"{'true','false'}",Add a capacity reserve margin of a certain fraction above the peak demand to which renewable generators and storage do *not* contribute. Ignores network.
 solver,,,
 -- name,--,"One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible",Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow.
 -- options,--,Key listed under ``solver_options``.,Link to specific parameter settings.
--- a/doc/configuration.rst
+++ b/doc/configuration.rst
@ -383,7 +383,7 @@ overwrite the existing values.

 .. literalinclude:: ../config/config.default.yaml
   :language: yaml
-   :start-after:   type:
+   :start-after: # docs-load
   :end-before: # docs

 .. csv-table::
--- a/doc/foresight.rst
+++ b/doc/foresight.rst
@ -41,10 +41,10 @@ Perfect foresight scenarios

 .. warning::

-  Perfect foresight is currently under development and not yet implemented.
+  Perfect foresight is currently implemented as an experimental test version.

-For running perfect foresight scenarios, in future versions you will be able to
-set in the ``config/config.yaml``:
+For running perfect foresight scenarios, you can adjust the
+ ``config/config.perfect.yaml``:

 .. code:: yaml

--- a/doc/img/base.png
+++ b/doc/img/base.png
--- a/doc/img/elec_s_X.png
+++ b/doc/img/elec_s_X.png
--- a/doc/img/intro-workflow.png
+++ b/doc/img/intro-workflow.png
--- a/doc/index.rst
+++ b/doc/index.rst
@ -35,6 +35,8 @@ PyPSA-Eur: A Sector-Coupled Open Optimisation Model of the European Energy Syste
   :target: https://stackoverflow.com/questions/tagged/pypsa
   :alt: Stackoverflow

+|
+
 PyPSA-Eur is an open model dataset of the European energy system at the
 transmission network level that covers the full ENTSO-E area. It covers demand
 and supply for all energy sectors. From version v0.8.0, PyPSA-Eur includes all
@ -116,7 +118,7 @@ of the individual parts.
    topics we are working on. Please feel free to help or make suggestions.

 This project is currently maintained by the `Department of Digital
-Transformation in Energy Systems <https:/www.ensys.tu-berlin.de>`_ at the
+Transformation in Energy Systems <https://www.tu.berlin/en/ensys>`_ at the
 `Technische Universität Berlin <https://www.tu.berlin>`_. Previous versions were
 developed within the `IAI <http://www.iai.kit.edu>`_ at the `Karlsruhe Institute
 of Technology (KIT) <http://www.kit.edu/english/index.php>`_ which was funded by
@ -185,7 +187,7 @@ For sector-coupling studies: ::
        pages = "1--25"
        year = "2023",
        eprint = "2207.05816",
-        doi = "10.1016/j.joule.2022.04.016",
+        doi = "10.1016/j.joule.2023.06.016",
    }

 For sector-coupling studies with pathway optimisation: ::
@ -209,24 +211,6 @@ If you want to cite a specific PyPSA-Eur version, each release of PyPSA-Eur is s
   :target: https://doi.org/10.5281/zenodo.3520874


-Pre-Built Networks as a Dataset
-===============================
-
-There are pre-built networks available as a dataset on Zenodo as well for every release of PyPSA-Eur.
-
-.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3601881.svg
-   :target: https://doi.org/10.5281/zenodo.3601881
-
-The included ``.nc`` files are PyPSA network files which can be imported with PyPSA via:
-
-.. code:: python
-
-    import pypsa
-
-    filename = "elec_s_1024_ec.nc"  # example
-    n = pypsa.Network(filename)
-
-
 Operating Systems
 =================

--- a/doc/introduction.rst
+++ b/doc/introduction.rst
@ -89,8 +89,8 @@ Folder Structure
 - ``results``: Stores the solved PyPSA network data, summary files and plots.
 - ``logs``: Stores log files.
 - ``benchmarks``: Stores ``snakemake`` benchmarks.
- ``test``: Includes the test configuration files used for continuous integration.
 - ``doc``: Includes the documentation of PyPSA-Eur.
+- ``graphics``: Includes some graphics for the documentation of PyPSA-Eur.

 System Requirements
 ===================
--- a/doc/plotting.rst
+++ b/doc/plotting.rst
@ -22,7 +22,22 @@ Rule ``plot_summary``

 .. _map_plot:

-Rule ``plot_network``
-========================
+Rule ``plot_power_network``
+===========================

-.. automodule:: plot_network
+.. automodule:: plot_power_network
+
+Rule ``plot_power_network_perfect``
+===================================
+
+.. automodule:: plot_power_network_perfect
+
+Rule ``plot_hydrogen_network``
+==============================
+
+.. automodule:: plot_hydrogen_network
+
+Rule ``plot_gas_network``
+=========================
+
+.. automodule:: plot_gas_network
--- a/doc/preparation.rst
+++ b/doc/preparation.rst
@ -94,6 +94,13 @@ Rule ``build_electricity_demand``

 .. automodule:: build_electricity_demand

+.. _monthlyprices:
+
+Rule ``build_monthly_prices``
+=============================
+
+.. automodule:: build_monthly_prices
+
 .. _ship:

 Rule ``build_ship_raster``
@ -102,6 +109,12 @@ Rule ``build_ship_raster``

 .. automodule:: build_ship_raster

+.. _availabilitymatrixmdua:
+
+Rule ``determine_availability_matrix_MD_UA``
+============================================
+
+.. automodule:: determine_availability_matrix_MD_UA

 .. _renewableprofiles:

--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@ -10,13 +10,388 @@ Release Notes
 Upcoming Release
 ================

-* Updated Global Energy Monitor LNG terminal data to March 2023 version.
-* PyPSA-EUR now supports the simultaneous execution of multiple scenarios. For this purpose, a scenarios.yaml file has been introduced which contains customizable scenario names with corresponding configuration overrides. To enable it, set the ``run: scenarios:`` key to ``True`` and define the scenario names to run under ``run: name:`` in the configuration file. The latter must be a subset of toplevel keys in the scenario file.
+* PyPSA-EUR now supports the simultaneous execution of multiple scenarios. For
+  this purpose, a scenarios.yaml file has been introduced which contains
+  customizable scenario names with corresponding configuration overrides. To
+  enable it, set the ``run: scenarios:`` key to ``True`` and define the scenario
+  names to run under ``run: name:`` in the configuration file. The latter must
+  be a subset of toplevel keys in the scenario file.
+
+* Add new default to overdimension heating in individual buildings. This allows
+  them to cover heat demand peaks e.g. 10% higher than those in the data. The
+  disadvantage of manipulating the costs is that the capacity is then not quite
+  right. This way at least the costs are right.
+
+* Add option to specify to set a default heating lifetime for existing heating
+  (``existing_capacities: default_heating_lifetime:``).
+
+* Correctly source the existing heating technologies for buildings since the
+  source URL has changed. It represents the year 2012 and is only for
+  buildings, not district heating. So the capacities for urban central are now
+  set to zero from this source.
+
+* Remove long-deprecated function ``attach_extendable_generators`` in :mod:`add_electricity`.
+
+* The filtering of power plants in the ``config.default.yaml`` has been updated regarding phased-out power plants in 2023.
+
+* Upgrade techno-economic assumptions to ``technology-data`` v0.7.0.
+
+* Bugfix: Correct technology keys for the electricity production plotting to work out the box.
+
+* New configuration option ``everywhere_powerplants`` to build conventional powerplants everywhere, irrespective of existing powerplants locations, in the network (https://github.com/PyPSA/pypsa-eur/pull/850).
+
+* Remove option for wave energy as technology data is not maintained.
+
+* Define global constraint for CO2 emissions on the final state of charge of the
+  CO2 atmosphere store. This gives a more sparse constraint that should improve
+  the performance of the solving process.
+
+* Bugfix: Assure entering of code block which corrects Norwegian heat demand.
+
+* Add warning when BEV availability weekly profile has negative values in `build_transport_demand`.
+
+* Stacktrace of uncaught exceptions should now be correctly included inside log files (via `configure_logging(..)`).
+
+* Cluster residential and services heat buses by default. Can be disabled with ``cluster_heat_buses: false``.
+
+* Bugfix: Do not reduce district heat share when building population-weighted
+  energy statistics. Previously the district heating share was being multiplied
+  by the population weighting, reducing the DH share with multiple nodes.
+
+* Move building of daily heat profile to its own rule
+  :mod:`build_hourly_heat_demand` from :mod:`prepare_sector_network`.
+
+* In :mod:`build_energy_totals`, district heating shares are now reported in a
+  separate file.
+
+* Move calculation of district heating share to its own rule
+  :mod:`build_district_heat_share`.
+
+* Move building of distribution of existing heating to own rule
+  :mod:`build_existing_heating_distribution`. This makes the distribution of
+  existing heating to urban/rural, residential/services and spatially more
+  transparent.
+
+* Bugfix: Correctly read out number of solver threads from configuration file.
+
+* Air-sourced heat pumps can now also be built in rural areas. Previously, only
+  ground-sourced heat pumps were considered for this category.
+
+* Bugfix: Correctly read out number of solver threads from configuration file.
+
+* Add support for the linopy ``io_api`` option; set to ``"direct"`` to increase model reading and writing performance for the highs and gurobi solvers.
+
+* Add the option to customise map projection in plotting config.
+
+* The order of buses (bus0, bus1, ...) for DAC components has changed to meet the convention of the other components. Therefore, `bus0` refers to the electricity bus (input), `bus1` to the heat bus (input), 'bus2' to the CO2 atmosphere bus (input), and `bus3` to the CO2 storage bus (output).
+
+* The rule ``plot_network`` has been split into separate rules for plotting
+  electricity, hydrogen and gas networks.
+
+* To determine the optimal topology to meet the number of clusters, the workflow used pyomo in combination with ``ipopt`` or ``gurobi``. This dependency has been replaced by using ``linopy`` in combination with ``scipopt`` or ``gurobi``. The environment file has been updated accordingly.
+
+* The ``highs`` solver was added to the default environment file.
+
+* Various minor bugfixes to the perfect foresight workflow, though perfect foresight must still be considered experimental.
+
+* It is now possible to determine the directory for shared resources by setting `shared_resources` to a string.
+
+* A ``test.sh`` script was added to the repository to run the tests locally.
+
+* Default settings for recycling rates and primary product shares of high-value
+  chemicals have been set in accordance with the values used in `Neumann et al.
+  (2023) <https://doi.org/10.1016/j.joule.2023.06.016>`_ linearly interpolated
+  between 2020 and 2050. The recycling rates are based on data from `Agora
+  Energiewende (2021)
+  <https://static.agora-energiewende.de/fileadmin/Projekte/2021/2021_02_EU_CEAP/A-EW_254_Mobilising-circular-economy_study_WEB.pdf>`_.
+
+* Added option to specify turbine and solar panel models for specific years as a
+  dictionary (e.g. ``renewable: onwind: resource: turbine:``). The years will be
+  interpreted as years from when the the corresponding turbine model substitutes
+  the previous model for new installations. This will only have an effect on
+  workflows with foresight "myopic" and still needs to be added foresight option
+  "perfect".


-* For industry distribution, use EPRTR as fallback if ETS data is not available.
+PyPSA-Eur 0.9.0 (5th January 2024)
+==================================
+
+**New Features**
+
+* Add option to specify losses for bidirectional links, e.g. pipelines or HVDC
+  links, in configuration file under ``sector: transmission_efficiency:``. Users
+  can specify static or length-dependent values as well as a length-dependent
+  electricity demand for compression, which is implemented as a multi-link to
+  the local electricity buses. The bidirectional links will then be split into
+  two unidirectional links with linked capacities (https://github.com/PyPSA/pypsa-eur/pull/739).
+
+* Merged option to extend geographical scope to Ukraine and Moldova. These
+  countries are excluded by default and is currently constrained to power-sector
+  only parts of the workflow. A special config file
+  `config/config.entsoe-all.yaml` was added as an example to run the workflow
+  with all ENTSO-E member countries (including observer members like Ukraine and
+  Moldova). Moldova can currently only be included in conjunction with Ukraine
+  due to the absence of demand data. The Crimean power system is manually
+  reconnected to the main Ukrainian grid with the configuration option
+  `reconnect_crimea` (https://github.com/PyPSA/pypsa-eur/pull/321).
+
+* New experimental support for multi-decade optimisation with perfect foresight
+  (``foresight: perfect``). Maximum growth rates for carriers, global carbon
+  budget constraints and emission constraints for particular investment periods.
+
+* Add option to reference an additional source file where users can specify
+  custom ``extra_functionality`` constraints in the configuration file. The
+  default setting points to an empty hull at
+  ``data/custom_extra_functionality.py`` (https://github.com/PyPSA/pypsa-eur/pull/824).
+
+* Add locations, capacities and costs of existing gas storage using Global
+  Energy Monitor's `Europe Gas Tracker
+  <https://globalenergymonitor.org/projects/europe-gas-tracker>`_
+  (https://github.com/PyPSA/pypsa-eur/pull/835).
+
+* Add option to use `LUISA Base Map
+  <https://publications.jrc.ec.europa.eu/repository/handle/JRC124621>`_ 50m land
+  coverage dataset for land eligibility analysis in
+  :mod:`build_renewable_profiles`. Settings are analogous to the CORINE dataset
+  but with the key ``luisa:`` in the configuration file. To leverage the
+  dataset's full advantages, set the excluder resolution to 50m
+  (``excluder_resolution: 50``). For land category codes, see `Annex 1 of the
+  technical documentation
+  <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_
+  (https://github.com/PyPSA/pypsa-eur/pull/842).
+
+* Add option to capture CO2 contained in biogas when upgrading (``sector:
+  biogas_to_gas_cc``) (https://github.com/PyPSA/pypsa-eur/pull/615).
+
+* If load shedding is activated, it is now applied to all carriers, not only
+  electricity (https://github.com/PyPSA/pypsa-eur/pull/784).
+
+* Add option for heat vents in district heating (``sector:
+  central_heat_vent:``). The combination of must-run conditions for some
+  power-to-X processes, waste heat usage enabled and decreasing heating demand,
+  can lead to infeasibilities in pathway optimisation for some investment
+  periods since larger Fischer-Tropsch capacities are needed in early years but
+  the waste heat exceeds the heat demand in later investment periods.
+  (https://github.com/PyPSA/pypsa-eur/pull/791).
+
+* Allow possibility to go from copperplated to regionally resolved methanol and
+  oil demand with switches ``sector: regional_methanol_demand: true`` and
+  ``sector: regional_oil_demand: true``. This allows nodal/regional CO2
+  constraints to be applied (https://github.com/PyPSA/pypsa-eur/pull/827).
+
+* Allow retrofitting of existing gas boilers to hydrogen boilers in pathway
+  optimisation.
+
+* Add option to add time-varying CO2 emission prices (electricity-only, ``costs:
+  emission_prices: co2_monthly_prices: true``). This is linked to the new
+  ``{opts}`` wildcard option ``Ept``.
+
+* Network clustering can now consider efficiency classes when aggregating
+  carriers. The option ``clustering: consider_efficiency_classes:`` aggregates
+  each carriers into the top 10-quantile (high), the bottom 90-quantile (low),
+  and everything in between (medium).
+
+* Added option ``conventional: dynamic_fuel_price:`` to consider the monthly
+  fluctuating fuel prices for conventional generators. Refer to the CSV file
+  ``data/validation/monthly_fuel_price.csv``.
+
+* For hydro-electricity, add switches ``flatten_dispatch`` to consider an upper
+  limit for the hydro dispatch. The limit is given by the average capacity
+  factor plus the buffer given in  ``flatten_dispatch_buffer``.
+
+* Extend options for waste heat usage from Haber-Bosch, methanolisation and
+  methanation (https://github.com/PyPSA/pypsa-eur/pull/834).
+
+* Add new ``sector_opts`` wildcard option "nowasteheat" to disable all waste
+  heat usage (https://github.com/PyPSA/pypsa-eur/pull/834).
+
+* Add new rule ``retrieve_irena`` to automatically retrieve up-to-date values
+  for existing renewables capacities (https://github.com/PyPSA/pypsa-eur/pull/756).
+
+* Print Irreducible Infeasible Subset (IIS) if model is infeasible. Only for
+  solvers with IIS support (https://github.com/PyPSA/pypsa-eur/pull/841).
+
+* More wildcard options now have a corresponding config entry. If the wildcard
+  is given, then its value is used. If the wildcard is not given but the options
+  in config are enabled, then the value from config is used. If neither is
+  given, the options are skipped (https://github.com/PyPSA/pypsa-eur/pull/827).
+
+* Validate downloads from Zenodo using MD5 checksums. This identifies corrupted
+  or incomplete downloads (https://github.com/PyPSA/pypsa-eur/pull/821).
+
+* Add rule ``sync`` to synchronise with a remote machine using the ``rsync``
+  library. Configuration settings are found under ``remote:``.
+
+**Breaking Changes**
+
+* Remove all negative loads on the ``co2 atmosphere`` bus representing emissions
+  for e.g. fixed fossil demands for transport oil. Instead these are handled
+  more transparently with a fixed transport oil demand and a link taking care of
+  the emissions to the ``co2 atmosphere`` bus. This is also a preparation for
+  endogenous transport optimisation, where demand will be subject to
+  optimisation (e.g. fuel switching in the transport sector)
+  (https://github.com/PyPSA/pypsa-eur/pull/827).
+
+* Process emissions from steam crackers (i.e. naphtha processing for HVC) are
+  now piped from the consumption link to the process emissions bus where the
+  model can decide about carbon capture. Previously the process emissions for
+  naphtha were a fixed load (https://github.com/PyPSA/pypsa-eur/pull/827).
+
+* Distinguish between stored and sequestered CO2. Stored CO2 is stored
+  overground in tanks and can be used for CCU (e.g. methanolisation).
+  Sequestered CO2 is stored underground and can no longer be used for CCU. This
+  distinction is made because storage in tanks is more expensive than
+  underground storage. The link that connects stored and sequestered CO2 is
+  unidirectional (https://github.com/PyPSA/pypsa-eur/pull/844).
+
+* Files extracted from sector-coupled data bundle have been moved from ``data/``
+  to ``data/sector-bundle``.
+
+* Split configuration to enable SMR and SMR CC (``sector: smr:`` and ``sector:
+  smr_cc:``) (https://github.com/PyPSA/pypsa-eur/pull/757).
+
+* Add separate option to add resistive heaters to the technology choices
+  (``sector: resistive_heaters:``). Previously they were always added when
+  boilers were added (https://github.com/PyPSA/pypsa-eur/pull/808).
+
+* Remove HELMETH option (``sector: helmeth:``).
+
+* Remove "conservative" renewable potentials estimation option
+  (https://github.com/PyPSA/pypsa-eur/pull/838).
+
+* With this release we stop posting updates to the network pre-builts.
+
+**Changes**
+
+* Updated Global Energy Monitor LNG terminal data to March 2023 version
+  (https://github.com/PyPSA/pypsa-eur/pull/707).
+
+* For industry distribution, use EPRTR as fallback if ETS data is not available
+  (https://github.com/PyPSA/pypsa-eur/pull/721).
+
+* It is now possible to specify years for biomass potentials which do not exist
+  in the JRC-ENSPRESO database, e.g. 2037. These are linearly interpolated
+  (https://github.com/PyPSA/pypsa-eur/pull/744).
+
+* In pathway mode, the biomass potential is linked to the investment year
+  (https://github.com/PyPSA/pypsa-eur/pull/744).
+
+* Increase allowed deployment density of solar to 5.1 MW/sqkm by default.
+
+* Default to full electrification of land transport by 2050.
+
+* Provide exogenous transition settings in 5-year steps.
+
+* Default to approximating transmission losses in HVAC lines
+  (``transmission_losses: 2``).
+
+* Use electrolysis waste heat by default.
+
+* Set minimum part loads for PtX processes to 30% for methanolisation and
+  methanation, and to 70% for Fischer-Tropsch synthesis.
+
+* Add VOM as marginal cost to PtX processes
+  (https://github.com/PyPSA/pypsa-eur/pull/830).
+
+* Add pelletizing costs for biomass boilers (https://github.com/PyPSA/pypsa-eur/pull/833).
+
+* Update default offshore wind turbine model to "NREL Reference 2020 ATB 5.5 MW"
+  (https://github.com/PyPSA/pypsa-eur/pull/832).
+
+* Switch to using hydrogen and electricity inputs for Haber-Bosch from
+  https://github.com/PyPSA/technology-data (https://github.com/PyPSA/pypsa-eur/pull/831).
+
+* The configuration setting for country focus weights when clustering the
+  network has been moved from ``focus_weights:`` to ``clustering:
+  focus_weights:``. Backwards compatibility to old config files is maintained
+  (https://github.com/PyPSA/pypsa-eur/pull/794).
+
+* The ``mock_snakemake`` function can now be used with a Snakefile from a
+  different directory using the new ``root_dir`` argument
+  (https://github.com/PyPSA/pypsa-eur/pull/771).
+
+* Rule ``purge`` now initiates a dialog to confirm if purge is desired
+  (https://github.com/PyPSA/pypsa-eur/pull/745).
+
+* Files downloaded from zenodo are now write-protected to prevent accidental
+  re-download (https://github.com/PyPSA/pypsa-eur/pull/730).
+
+* Performance improvements for rule ``build_ship_raster``
+  (https://github.com/PyPSA/pypsa-eur/pull/845).
+
+* Improve time logging in :mod:`build_renewable_profiles`
+  (https://github.com/PyPSA/pypsa-eur/pull/837).
+
+* In myopic pathway optimisation, disable power grid expansion if line volume
+  already hit (https://github.com/PyPSA/pypsa-eur/pull/840).
+
+* JRC-ENSPRESO data is now downloaded from a Zenodo mirror because the link was
+  unreliable (https://github.com/PyPSA/pypsa-eur/pull/801).
+
+* Add focus weights option for clustering to documentation
+  (https://github.com/PyPSA/pypsa-eur/pull/781).
+
+* Add proxy for biomass transport costs if no explicit biomass transport network
+  is considered (https://github.com/PyPSA/pypsa-eur/pull/711).
+
+**Bugs and Compatibility**
+
+* The minimum PyPSA version is now 0.26.1.
+
+* Update to ``tsam>=0.2.3`` for performance improvents in temporal clustering.
+
+* Pin ``snakemake`` version to below 8.0.0, as the new version is not yet
+  supported. The next release will switch to the requirement ``snakemake>=8``.
+
+* Bugfix: Add coke and coal demand for integrated steelworks
+  (https://github.com/PyPSA/pypsa-eur/pull/718).
+
+* Bugfix: Make :mod:`build_renewable_profiles` consider subsets of cutout time
+  scope (https://github.com/PyPSA/pypsa-eur/pull/709).
+
+* Bugfix: In :mod:`simplify network`, remove 'underground' column to avoid
+  consense error (https://github.com/PyPSA/pypsa-eur/pull/714).
+
+* Bugfix: Fix in :mod:`add_existing_baseyear` to account for the case when there
+  is no rural heating demand for some nodes in network
+  (https://github.com/PyPSA/pypsa-eur/pull/706).
+
+* Bugfix: The unit of the capital cost of Haber-Bosch plants was corrected
+  (https://github.com/PyPSA/pypsa-eur/pull/829).
+
+* The minimum capacity for renewable generators when using the myopic option has
+  been fixed (https://github.com/PyPSA/pypsa-eur/pull/728).
+
+* Compatibility for running with single node and single country
+  (https://github.com/PyPSA/pypsa-eur/pull/839).
+
+* A bug preventing the addition of custom powerplants specified in
+  ``data/custom_powerplants.csv`` was fixed.
+  (https://github.com/PyPSA/pypsa-eur/pull/732)
+
+* Fix nodal fraction in :mod:`add_existing_year` when using distributed
+  generators (https://github.com/PyPSA/pypsa-eur/pull/798).
+
+* Bugfix: District heating without progress caused division by zero
+  (https://github.com/PyPSA/pypsa-eur/pull/796).
+
+* Bugfix: Drop duplicates in :mod:`build_industrial_distribution_keys`, which
+  can occur through the geopandas ``.sjoin()`` function if a point is located on
+  a border (https://github.com/PyPSA/pypsa-eur/pull/726).
+
+* For network clustering fall back to ``ipopt`` when ``highs`` is designated
+  solver (https://github.com/PyPSA/pypsa-eur/pull/795).
+
+* Fix typo in buses definition for oil boilers in ``add_industry`` in
+  :mod:`prepare_sector_network` (https://github.com/PyPSA/pypsa-eur/pull/812).
+
+* Resolve code issues for endogenous building retrofitting. Select correct
+  sector names, address deprecations, distinguish between district heating,
+  decentral heating in urban areas or rural areas for floor area calculations
+  (https://github.com/PyPSA/pypsa-eur/pull/808).
+
+* Addressed various deprecations.

-* The minimum capacity for renewable generators when using the myopic option has been fixed.

 PyPSA-Eur 0.8.1 (27th July 2023)
 ================================
@ -141,6 +516,8 @@ PyPSA-Eur 0.8.1 (27th July 2023)
  (https://github.com/PyPSA/pypsa-eur/pull/672)


+* Addressed deprecation warnings for ``pandas=2.0``. ``pandas=2.0`` is now minimum requirement.
+
 PyPSA-Eur 0.8.0 (18th March 2023)
 =================================

@ -1402,8 +1779,4 @@ Release Process

 * Make a `GitHub release <https://github.com/PyPSA/pypsa-eur-sec/releases>`_, which automatically triggers archiving to the `zenodo code repository <https://doi.org/10.5281/zenodo.3520874>`_ with `MIT license <https://opensource.org/licenses/MIT>`_.

-* Create pre-built networks for ``config.default.yaml`` by running ``snakemake -call prepare_sector_networks``.
-
-* Upload pre-built networks to `zenodo data repository <https://doi.org/10.5281/zenodo.3601881>`_ with `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_ license.
-
 * Send announcement on the `PyPSA mailing list <https://groups.google.com/forum/#!forum/pypsa>`_.
--- a/doc/retrieve.rst
+++ b/doc/retrieve.rst
@ -22,11 +22,11 @@ Rule ``retrieve_databundle``
 Rule ``retrieve_cutout``
 ============================

-.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517949.svg
-   :target: https://doi.org/10.5281/zenodo.3517949
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.6382570.svg
+   :target: https://doi.org/10.5281/zenodo.6382570

 Cutouts are spatio-temporal subsets of the European weather data from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ reanalysis dataset and the `CMSAF SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_ solar surface radiation dataset for the year 2013.
-They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or retrieve them directly from `zenodo <https://doi.org/10.5281/zenodo.3517949>`__ through the rule ``retrieve_cutout``.
+They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or retrieve them directly from `zenodo <https://doi.org/10.5281/zenodo.6382570>`__ through the rule ``retrieve_cutout``.
 The :ref:`tutorial` uses a smaller cutout than required for the full model (30 MB), which is also automatically downloaded.

 .. note::
@ -118,6 +118,11 @@ This rule downloads techno-economic assumptions from the `technology-data reposi

 - ``resources/costs.csv``

+Rule ``retrieve_irena``
+================================
+
+.. automodule:: retrieve_irena
+
 Rule ``retrieve_ship_raster``
 ================================

--- a/doc/sector.rst
+++ b/doc/sector.rst
@ -20,6 +20,12 @@ Rule ``add_existing_baseyear``

 .. automodule:: add_existing_baseyear

+Rule ``build_existing_heating_distribution``
+==============================================================================
+
+.. automodule:: build_existing_heating_distribution
+
+
 Rule ``build_ammonia_production``
 ==============================================================================

@ -60,10 +66,20 @@ Rule ``build_gas_network``

 .. automodule:: build_gas_network

-Rule ``build_heat_demand``
+Rule ``build_daily_heat_demand``
 ==============================================================================

-.. automodule:: build_heat_demand
+.. automodule:: build_daily_heat_demand
+
+Rule ``build_hourly_heat_demand``
+==============================================================================
+
+.. automodule:: build_hourly_heat_demand
+
+Rule ``build_district_heat_share``
+==============================================================================
+
+.. automodule:: build_district_heat_share

 Rule ``build_industrial_distribution_key``
 ==============================================================================
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@ -45,7 +45,7 @@ Here are some examples of how spatial resolution is set for different sectors in

 •	CO2: It can be modeled as a single node for Europe or it can be nodally resolved with CO2 transport pipelines if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. It should mentioned that in single node mode a transport and storage cost is added for sequestered CO2, the cost of which can be adjusted in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.

-•	Liquid hydrocarbons: Modeled as a single node for Europe, since transport costs for liquids are low and no bottlenecks are expected.
+•	Carbonaceous fuels: Modeled as a single node for Europe by default, since transport costs for liquids are low and no bottlenecks are expected. Can be regionally resolved in configuration.

 **Electricity distribution network**

--- a/doc/tutorial.rst
+++ b/doc/tutorial.rst
@ -25,7 +25,7 @@ full model, which allows the user to explore most of its functionalities on a
 local machine. The tutorial will cover examples on how to configure and
 customise the PyPSA-Eur model and run the ``snakemake`` workflow step by step
 from network creation to the solved network. The configuration for the tutorial
-is located at ``test/config.electricity.yaml``. It includes parts deviating from
+is located at ``config/test/config.electricity.yaml``. It includes parts deviating from
 the default config file ``config/config.default.yaml``. To run the tutorial with this
 configuration, execute

@ -96,7 +96,7 @@ open-source solver GLPK.
   :start-at: solver:
   :end-before: plotting:

-Note, that ``test/config.electricity.yaml`` only includes changes relative to
+Note, that ``config/test/config.electricity.yaml`` only includes changes relative to
 the default configuration. There are many more configuration options, which are
 documented at :ref:`config`.

@ -133,89 +133,82 @@ This triggers a workflow of multiple preceding jobs that depend on each rule's i
        graph[bgcolor=white, margin=0];
        node[shape=box, style=rounded, fontname=sans,                 fontsize=10, penwidth=2];
        edge[penwidth=2, color=grey];
-        0[label = "solve_network", color = "0.21 0.6 0.85", style="rounded"];
-        1[label = "prepare_network\nll: copt\nopts: Co2L-24H", color = "0.02 0.6 0.85", style="rounded"];
-        2[label = "add_extra_components", color = "0.37 0.6 0.85", style="rounded"];
-        3[label = "cluster_network\nclusters: 6", color = "0.39 0.6 0.85", style="rounded"];
-        4[label = "simplify_network\nsimpl: ", color = "0.11 0.6 0.85", style="rounded"];
-        5[label = "add_electricity", color = "0.23 0.6 0.85", style="rounded"];
-        6[label = "build_renewable_profiles\ntechnology: onwind", color = "0.57 0.6 0.85", style="rounded"];
-        7[label = "base_network", color = "0.09 0.6 0.85", style="rounded"];
-        8[label = "build_shapes", color = "0.41 0.6 0.85", style="rounded"];
-        9[label = "retrieve_databundle", color = "0.28 0.6 0.85", style="rounded"];
-        10[label = "retrieve_natura_raster", color = "0.62 0.6 0.85", style="rounded"];
-        11[label = "build_bus_regions", color = "0.53 0.6 0.85", style="rounded"];
-        12[label = "retrieve_cutout\ncutout: europe-2013-era5", color = "0.05 0.6 0.85", style="rounded,dashed"];
-        13[label = "build_renewable_profiles\ntechnology: offwind-ac", color = "0.57 0.6 0.85", style="rounded"];
-        14[label = "build_ship_raster", color = "0.64 0.6 0.85", style="rounded"];
-        15[label = "retrieve_ship_raster", color = "0.07 0.6 0.85", style="rounded,dashed"];
-        16[label = "retrieve_cutout\ncutout: europe-2013-sarah", color = "0.05 0.6 0.85", style="rounded,dashed"];
-        17[label = "build_renewable_profiles\ntechnology: offwind-dc", color = "0.57 0.6 0.85", style="rounded"];
-        18[label = "build_renewable_profiles\ntechnology: solar", color = "0.57 0.6 0.85", style="rounded"];
-        19[label = "build_hydro_profile", color = "0.44 0.6 0.85", style="rounded"];
-        20[label = "retrieve_cost_data", color = "0.30 0.6 0.85", style="rounded"];
-        21[label = "build_powerplants", color = "0.16 0.6 0.85", style="rounded"];
-        22[label = "build_electricity_demand", color = "0.00 0.6 0.85", style="rounded"];
-        23[label = "retrieve_electricity_demand", color = "0.34 0.6 0.85", style="rounded,dashed"];
-        1 -> 0
-        2 -> 1
-        20 -> 1
-        3 -> 2
-        20 -> 2
-        4 -> 3
-        20 -> 3
-        5 -> 4
-        20 -> 4
-        11 -> 4
-        6 -> 5
-        13 -> 5
-        17 -> 5
-        18 -> 5
-        19 -> 5
-        7 -> 5
-        20 -> 5
-        11 -> 5
-        21 -> 5
-        9 -> 5
-        22 -> 5
-        8 -> 5
-        7 -> 6
-        9 -> 6
-        10 -> 6
-        8 -> 6
-        11 -> 6
-        12 -> 6
-        8 -> 7
-        9 -> 8
-        8 -> 11
-        7 -> 11
-        7 -> 13
-        9 -> 13
-        10 -> 13
-        14 -> 13
-        8 -> 13
-        11 -> 13
-        12 -> 13
-        15 -> 14
-        12 -> 14
-        16 -> 14
-        7 -> 17
-        9 -> 17
-        10 -> 17
-        14 -> 17
-        8 -> 17
-        11 -> 17
-        12 -> 17
-        7 -> 18
-        9 -> 18
-        10 -> 18
-        8 -> 18
-        11 -> 18
-        16 -> 18
-        8 -> 19
-        12 -> 19
-        7 -> 21
-        23 -> 22
+            0[label = "solve_network", color = "0.33 0.6 0.85", style="rounded"];
+            1[label = "prepare_network\nll: copt\nopts: Co2L-24H", color = "0.03 0.6 0.85", style="rounded"];
+            2[label = "add_extra_components", color = "0.45 0.6 0.85", style="rounded"];
+            3[label = "cluster_network\nclusters: 6", color = "0.46 0.6 0.85", style="rounded"];
+            4[label = "simplify_network\nsimpl: ", color = "0.52 0.6 0.85", style="rounded"];
+            5[label = "add_electricity", color = "0.55 0.6 0.85", style="rounded"];
+            6[label = "build_renewable_profiles\ntechnology: solar", color = "0.15 0.6 0.85", style="rounded"];
+            7[label = "base_network", color = "0.37 0.6 0.85", style="rounded,dashed"];
+            8[label = "build_shapes", color = "0.07 0.6 0.85", style="rounded,dashed"];
+            9[label = "retrieve_databundle", color = "0.60 0.6 0.85", style="rounded"];
+            10[label = "retrieve_natura_raster", color = "0.42 0.6 0.85", style="rounded"];
+            11[label = "build_bus_regions", color = "0.09 0.6 0.85", style="rounded,dashed"];
+            12[label = "build_renewable_profiles\ntechnology: onwind", color = "0.15 0.6 0.85", style="rounded"];
+            13[label = "build_renewable_profiles\ntechnology: offwind-ac", color = "0.15 0.6 0.85", style="rounded"];
+            14[label = "build_ship_raster", color = "0.02 0.6 0.85", style="rounded"];
+            15[label = "retrieve_ship_raster", color = "0.40 0.6 0.85", style="rounded"];
+            16[label = "build_renewable_profiles\ntechnology: offwind-dc", color = "0.15 0.6 0.85", style="rounded"];
+            17[label = "build_line_rating", color = "0.32 0.6 0.85", style="rounded"];
+            18[label = "retrieve_cost_data\nyear: 2030", color = "0.50 0.6 0.85", style="rounded"];
+            19[label = "build_powerplants", color = "0.64 0.6 0.85", style="rounded,dashed"];
+            20[label = "build_electricity_demand", color = "0.13 0.6 0.85", style="rounded,dashed"];
+            21[label = "retrieve_electricity_demand", color = "0.31 0.6 0.85", style="rounded"];
+            22[label = "copy_config", color = "0.23 0.6 0.85", style="rounded"];
+            1 -> 0
+            22 -> 0
+            2 -> 1
+            18 -> 1
+            3 -> 2
+            18 -> 2
+            4 -> 3
+            18 -> 3
+            5 -> 4
+            18 -> 4
+            11 -> 4
+            6 -> 5
+            12 -> 5
+            13 -> 5
+            16 -> 5
+            7 -> 5
+            17 -> 5
+            18 -> 5
+            11 -> 5
+            19 -> 5
+            9 -> 5
+            20 -> 5
+            8 -> 5
+            7 -> 6
+            9 -> 6
+            10 -> 6
+            8 -> 6
+            11 -> 6
+            8 -> 7
+            9 -> 8
+            8 -> 11
+            7 -> 11
+            7 -> 12
+            9 -> 12
+            10 -> 12
+            8 -> 12
+            11 -> 12
+            7 -> 13
+            9 -> 13
+            10 -> 13
+            14 -> 13
+            8 -> 13
+            11 -> 13
+            15 -> 14
+            7 -> 16
+            9 -> 16
+            10 -> 16
+            14 -> 16
+            8 -> 16
+            11 -> 16
+            7 -> 17
+            7 -> 19
+            21 -> 20
    }

 |
--- a/doc/tutorial_sector.rst
+++ b/doc/tutorial_sector.rst
@ -59,7 +59,7 @@ To run an overnight / greenfiled scenario with the specifications above, run

 .. code:: bash

-    snakemake -call --configfile config/test/config.overnight.yaml all
+    snakemake -call all --configfile config/test/config.overnight.yaml

 which will result in the following *additional* jobs ``snakemake`` wants to run
 on top of those already included in the electricity-only tutorial:
@ -318,7 +318,7 @@ To run a myopic foresight scenario with the specifications above, run

 .. code:: bash

-    snakemake -call --configfile config/test/config.myopic.yaml all
+    snakemake -call all --configfile config/test/config.myopic.yaml

 which will result in the following *additional* jobs ``snakemake`` wants to run:

--- a/envs/environment.fixed.yaml
+++ b/envs/environment.fixed.yaml
@ -1,10 +1,11 @@
-# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
+# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
 #
 # SPDX-License-Identifier: CC0-1.0

 name: pypsa-eur
 channels:
 - bioconda
+- gurobi
 - http://conda.anaconda.org/gurobi
 - conda-forge
 - defaults
@ -12,94 +13,96 @@ dependencies:
 - _libgcc_mutex=0.1
 - _openmp_mutex=4.5
 - affine=2.4.0
- alsa-lib=1.2.9
+- alsa-lib=1.2.10
 - ampl-mp=3.1.0
 - amply=0.1.6
- anyio=3.7.1
+- anyio=4.2.0
 - appdirs=1.4.4
- argon2-cffi=21.3.0
+- argon2-cffi=23.1.0
 - argon2-cffi-bindings=21.2.0
- asttokens=2.2.1
- async-lru=2.0.3
+- arrow=1.3.0
+- asttokens=2.4.1
+- async-lru=2.0.4
 - atk-1.0=2.38.0
- atlite=0.2.11
+- atlite=0.2.12
 - attr=2.5.1
- attrs=23.1.0
- aws-c-auth=0.7.0
- aws-c-cal=0.6.0
- aws-c-common=0.8.23
+- attrs=23.2.0
+- aws-c-auth=0.7.8
+- aws-c-cal=0.6.9
+- aws-c-common=0.9.10
 - aws-c-compression=0.2.17
- aws-c-event-stream=0.3.1
- aws-c-http=0.7.11
- aws-c-io=0.13.28
- aws-c-mqtt=0.8.14
- aws-c-s3=0.3.13
- aws-c-sdkutils=0.1.11
- aws-checksums=0.1.16
- aws-crt-cpp=0.20.3
- aws-sdk-cpp=1.10.57
- babel=2.12.1
- backcall=0.2.0
- backports=1.0
- backports.functools_lru_cache=1.6.5
+- aws-c-event-stream=0.3.2
+- aws-c-http=0.7.15
+- aws-c-io=0.13.36
+- aws-c-mqtt=0.10.0
+- aws-c-s3=0.4.7
+- aws-c-sdkutils=0.1.13
+- aws-checksums=0.1.17
+- aws-crt-cpp=0.25.1
+- aws-sdk-cpp=1.11.210
+- babel=2.14.0
 - beautifulsoup4=4.12.2
- bleach=6.0.0
- blosc=1.21.4
- bokeh=3.2.1
- boost-cpp=1.78.0
+- bleach=6.1.0
+- blosc=1.21.5
+- bokeh=3.3.2
 - bottleneck=1.3.7
- branca=0.6.0
- brotli=1.0.9
- brotli-bin=1.0.9
- brotli-python=1.0.9
+- branca=0.7.0
+- brotli=1.1.0
+- brotli-bin=1.1.0
+- brotli-python=1.1.0
 - bzip2=1.0.8
- c-ares=1.19.1
- c-blosc2=2.10.0
- ca-certificates=2023.7.22
- cairo=1.16.0
- cartopy=0.21.1
+- c-ares=1.24.0
+- c-blosc2=2.12.0
+- ca-certificates=2023.11.17
+- cached-property=1.5.2
+- cached_property=1.5.2
+- cairo=1.18.0
+- cartopy=0.22.0
 - cdsapi=0.6.1
- certifi=2023.7.22
- cffi=1.15.1
- cfitsio=4.2.0
- cftime=1.6.2
- charset-normalizer=3.2.0
- click=8.1.6
+- certifi=2023.11.17
+- cffi=1.16.0
+- cfitsio=4.3.1
+- cftime=1.6.3
+- charset-normalizer=3.3.2
+- click=8.1.7
 - click-plugins=1.1.1
 - cligj=0.7.2
- cloudpickle=2.2.1
+- cloudpickle=3.0.0
+- coin-or-cbc=2.10.10
+- coin-or-cgl=0.60.7
+- coin-or-clp=1.17.8
+- coin-or-osi=0.108.8
+- coin-or-utils=2.11.9
+- coincbc=2.10.10
 - colorama=0.4.6
- comm=0.1.3
+- comm=0.1.4
 - configargparse=1.7
 - connection_pool=0.0.3
- contourpy=1.1.0
- country_converter=1.0.0
- curl=8.2.0
- cycler=0.11.0
+- contourpy=1.2.0
+- country_converter=1.2
+- cycler=0.12.1
 - cytoolz=0.12.2
- dask=2023.7.1
- dask-core=2023.7.1
+- dask=2023.12.1
+- dask-core=2023.12.1
 - datrie=0.8.2
 - dbus=1.13.6
- debugpy=1.6.7
+- debugpy=1.8.0
 - decorator=5.1.1
 - defusedxml=0.7.1
 - deprecation=2.1.0
 - descartes=1.1.0
- distributed=2023.7.1
+- distributed=2023.12.1
 - distro=1.8.0
 - docutils=0.20.1
 - dpath=2.1.6
 - entrypoints=0.4
- entsoe-py=0.5.10
+- entsoe-py=0.6.1
 - et_xmlfile=1.1.0
- exceptiongroup=1.1.2
- executing=1.2.0
+- exceptiongroup=1.2.0
+- executing=2.0.1
 - expat=2.5.0
- filelock=3.12.2
- fiona=1.9.4
- flit-core=3.9.0
- folium=0.14.0
+- fiona=1.9.5
+- folium=0.15.1
 - font-ttf-dejavu-sans-mono=2.37
 - font-ttf-inconsolata=3.000
 - font-ttf-source-code-pro=2.038
@ -107,165 +110,184 @@ dependencies:
 - fontconfig=2.14.2
 - fonts-conda-ecosystem=1
 - fonts-conda-forge=1
- fonttools=4.41.1
+- fonttools=4.47.0
+- fqdn=1.5.1
 - freetype=2.12.1
- freexl=1.0.6
+- freexl=2.0.0
 - fribidi=1.0.10
- fsspec=2023.6.0
- gdal=3.7.0
+- fsspec=2023.12.2
+- gdal=3.7.3
 - gdk-pixbuf=2.42.10
 - geographiclib=1.52
- geojson-rewind=1.0.2
- geopandas=0.13.2
- geopandas-base=0.13.2
- geopy=2.3.0
- geos=3.11.2
+- geojson-rewind=1.1.0
+- geopandas=0.14.1
+- geopandas-base=0.14.1
+- geopy=2.4.1
+- geos=3.12.1
 - geotiff=1.7.1
 - gettext=0.21.1
 - gflags=2.2.2
 - giflib=5.2.1
- gitdb=4.0.10
- gitpython=3.1.32
- glib=2.76.4
- glib-tools=2.76.4
+- gitdb=4.0.11
+- gitpython=3.1.40
+- glib=2.78.3
+- glib-tools=2.78.3
 - glog=0.6.0
- gmp=6.2.1
+- glpk=5.0
+- gmp=6.3.0
 - graphite2=1.3.13
- graphviz=8.1.0
- gst-plugins-base=1.22.5
- gstreamer=1.22.5
+- graphviz=9.0.0
+- gst-plugins-base=1.22.8
+- gstreamer=1.22.8
 - gtk2=2.24.33
 - gts=0.7.6
- harfbuzz=7.3.0
+- gurobi=11.0.0
+- harfbuzz=8.3.0
 - hdf4=4.2.15
- hdf5=1.14.1
+- hdf5=1.14.3
 - humanfriendly=10.0
- icu=72.1
- idna=3.4
- importlib-metadata=6.8.0
- importlib_metadata=6.8.0
- importlib_resources=6.0.0
+- icu=73.2
+- idna=3.6
+- importlib-metadata=7.0.1
+- importlib_metadata=7.0.1
+- importlib_resources=6.1.1
 - iniconfig=2.0.0
- ipopt=3.14.12
- ipykernel=6.24.0
- ipython=8.14.0
- ipython_genutils=0.2.0
- ipywidgets=8.0.7
- jedi=0.18.2
+- ipopt=3.14.13
+- ipykernel=6.28.0
+- ipython=8.19.0
+- ipywidgets=8.1.1
+- isoduration=20.11.0
+- jedi=0.19.1
 - jinja2=3.1.2
- joblib=1.3.0
- json-c=0.16
+- joblib=1.3.2
+- json-c=0.17
 - json5=0.9.14
- jsonschema=4.18.4
- jsonschema-specifications=2023.7.1
+- jsonpointer=2.4
+- jsonschema=4.20.0
+- jsonschema-specifications=2023.12.1
+- jsonschema-with-format-nongpl=4.20.0
 - jupyter=1.0.0
- jupyter-lsp=2.2.0
- jupyter_client=8.3.0
+- jupyter-lsp=2.2.1
+- jupyter_client=8.6.0
 - jupyter_console=6.6.3
- jupyter_core=5.3.1
- jupyter_events=0.6.3
- jupyter_server=2.7.0
- jupyter_server_terminals=0.4.4
- jupyterlab=4.0.3
- jupyterlab_pygments=0.2.2
- jupyterlab_server=2.24.0
- jupyterlab_widgets=3.0.8
- kealib=1.5.1
+- jupyter_core=5.6.1
+- jupyter_events=0.9.0
+- jupyter_server=2.12.1
+- jupyter_server_terminals=0.5.1
+- jupyterlab=4.0.10
+- jupyterlab_pygments=0.3.0
+- jupyterlab_server=2.25.2
+- jupyterlab_widgets=3.0.9
+- kealib=1.5.3
 - keyutils=1.6.1
- kiwisolver=1.4.4
- krb5=1.21.1
+- kiwisolver=1.4.5
+- krb5=1.21.2
 - lame=3.100
- lcms2=2.15
+- lcms2=2.16
 - ld_impl_linux-64=2.40
 - lerc=4.0.0
- libabseil=20230125.3
- libaec=1.0.6
- libarchive=3.6.2
- libarrow=12.0.1
+- libabseil=20230802.1
+- libaec=1.1.2
+- libarchive=3.7.2
+- libarrow=14.0.2
+- libarrow-acero=14.0.2
+- libarrow-dataset=14.0.2
+- libarrow-flight=14.0.2
+- libarrow-flight-sql=14.0.2
+- libarrow-gandiva=14.0.2
+- libarrow-substrait=14.0.2
 - libblas=3.9.0
- libbrotlicommon=1.0.9
- libbrotlidec=1.0.9
- libbrotlienc=1.0.9
- libcap=2.67
+- libboost-headers=1.84.0
+- libbrotlicommon=1.1.0
+- libbrotlidec=1.1.0
+- libbrotlienc=1.1.0
+- libcap=2.69
 - libcblas=3.9.0
 - libclang=15.0.7
 - libclang13=15.0.7
 - libcrc32c=1.1.2
 - libcups=2.3.3
- libcurl=8.2.0
- libdeflate=1.18
+- libcurl=8.5.0
+- libdeflate=1.19
 - libedit=3.1.20191231
 - libev=4.33
 - libevent=2.1.12
 - libexpat=2.5.0
 - libffi=3.4.2
 - libflac=1.4.3
- libgcc-ng=13.1.0
- libgcrypt=1.10.1
+- libgcc-ng=13.2.0
+- libgcrypt=1.10.3
 - libgd=2.3.3
- libgdal=3.7.0
- libgfortran-ng=13.1.0
- libgfortran5=13.1.0
- libglib=2.76.4
- libgomp=13.1.0
+- libgdal=3.7.3
+- libgfortran-ng=13.2.0
+- libgfortran5=13.2.0
+- libglib=2.78.3
+- libgomp=13.2.0
 - libgoogle-cloud=2.12.0
 - libgpg-error=1.47
- libgrpc=1.56.2
+- libgrpc=1.59.3
+- libhwloc=2.9.1
 - libiconv=1.17
- libjpeg-turbo=2.1.5.1
+- libjpeg-turbo=3.0.0
 - libkml=1.3.0
 - liblapack=3.9.0
 - liblapacke=3.9.0
 - libllvm15=15.0.7
 - libnetcdf=4.9.2
- libnghttp2=1.52.0
- libnsl=2.0.0
+- libnghttp2=1.58.0
+- libnl=3.9.0
+- libnsl=2.0.1
 - libnuma=2.0.16
 - libogg=1.3.4
- libopenblas=0.3.23
+- libopenblas=0.3.25
 - libopus=1.3.1
+- libparquet=14.0.2
 - libpng=1.6.39
- libpq=15.3
- libprotobuf=4.23.3
- librsvg=2.56.1
+- libpq=16.1
+- libprotobuf=4.24.4
+- libre2-11=2023.06.02
+- librsvg=2.56.3
 - librttopo=1.1.0
- libsndfile=1.2.0
+- libsndfile=1.2.2
 - libsodium=1.0.18
 - libspatialindex=1.9.3
- libspatialite=5.0.1
- libsqlite=3.42.0
+- libspatialite=5.1.0
+- libspral=2023.08.02
+- libsqlite=3.44.2
 - libssh2=1.11.0
- libstdcxx-ng=13.1.0
- libsystemd0=253
- libthrift=0.18.1
- libtiff=4.5.1
- libtool=2.4.7
+- libstdcxx-ng=13.2.0
+- libsystemd0=255
+- libthrift=0.19.0
+- libtiff=4.6.0
 - libutf8proc=2.8.0
 - libuuid=2.38.1
 - libvorbis=1.3.7
- libwebp=1.3.1
- libwebp-base=1.3.1
+- libwebp=1.3.2
+- libwebp-base=1.3.2
 - libxcb=1.15
- libxkbcommon=1.5.0
- libxml2=2.11.4
+- libxcrypt=4.4.36
+- libxkbcommon=1.6.0
+- libxml2=2.11.6
 - libxslt=1.1.37
- libzip=1.9.2
+- libzip=1.10.1
 - libzlib=1.2.13
+- linopy=0.3.2
 - locket=1.0.0
 - lxml=4.9.3
 - lz4=4.3.2
 - lz4-c=1.9.4
 - lzo=2.10
- mapclassify=2.5.0
+- mapclassify=2.6.1
 - markupsafe=2.1.3
- matplotlib=3.5.3
- matplotlib-base=3.5.3
+- matplotlib=3.8.2
+- matplotlib-base=3.8.2
 - matplotlib-inline=0.1.6
 - memory_profiler=0.61.0
- metis=5.1.1
- mistune=3.0.0
- mpg123=1.31.3
- msgpack-python=1.0.5
+- metis=5.1.0
+- minizip=4.0.4
+- mistune=3.0.2
+- mpg123=1.32.3
+- msgpack-python=1.0.7
 - mumps-include=5.2.1
 - mumps-seq=5.2.1
 - munch=4.0.0
@ -273,200 +295,202 @@ dependencies:
 - mysql-common=8.0.33
 - mysql-libs=8.0.33
 - nbclient=0.8.0
- nbconvert=7.7.2
- nbconvert-core=7.7.2
- nbconvert-pandoc=7.7.2
- nbformat=5.9.1
+- nbconvert=7.14.0
+- nbconvert-core=7.14.0
+- nbconvert-pandoc=7.14.0
+- nbformat=5.9.2
 - ncurses=6.4
- nest-asyncio=1.5.6
- netcdf4=1.6.4
- networkx=3.1
+- nest-asyncio=1.5.8
+- netcdf4=1.6.5
+- networkx=3.2.1
 - nomkl=1.0
- notebook=7.0.0
+- notebook=7.0.6
 - notebook-shim=0.2.3
 - nspr=4.35
- nss=3.89
- numexpr=2.8.4
- numpy=1.25.1
- openjdk=17.0.3
+- nss=3.96
+- numexpr=2.8.8
+- numpy=1.26.2
+- openjdk=21.0.1
 - openjpeg=2.5.0
 - openpyxl=3.1.2
- openssl=3.1.1
- orc=1.9.0
- overrides=7.3.1
- packaging=23.1
- pandas=2.0.3
+- openssl=3.2.0
+- orc=1.9.2
+- overrides=7.4.0
+- packaging=23.2
+- pandas=2.1.4
 - pandoc=3.1.3
 - pandocfilters=1.5.0
 - pango=1.50.14
 - parso=0.8.3
- partd=1.4.0
- patsy=0.5.3
- pcre2=10.40
+- partd=1.4.1
+- patsy=0.5.5
+- pcre2=10.42
 - pexpect=4.8.0
 - pickleshare=0.7.5
- pillow=10.0.0
- pip=23.2.1
- pixman=0.40.0
+- pillow=10.2.0
+- pip=23.3.2
+- pixman=0.42.2
 - pkgutil-resolve-name=1.3.10
- plac=1.3.5
- platformdirs=3.9.1
- pluggy=1.2.0
+- plac=1.4.2
+- platformdirs=4.1.0
+- pluggy=1.3.0
 - ply=3.11
- pooch=1.7.0
- poppler=23.05.0
+- poppler=23.12.0
 - poppler-data=0.4.12
- postgresql=15.3
- powerplantmatching=0.5.7
- progressbar2=4.2.0
- proj=9.2.1
- prometheus_client=0.17.1
- prompt-toolkit=3.0.39
- prompt_toolkit=3.0.39
- psutil=5.9.5
+- postgresql=16.1
+- powerplantmatching=0.5.8
+- progressbar2=4.3.2
+- proj=9.3.0
+- prometheus_client=0.19.0
+- prompt-toolkit=3.0.42
+- prompt_toolkit=3.0.42
+- psutil=5.9.7
 - pthread-stubs=0.4
 - ptyprocess=0.7.0
 - pulp=2.7.0
 - pulseaudio-client=16.1
 - pure_eval=0.2.2
 - py-cpuinfo=9.0.0
- pyarrow=12.0.1
+- pyarrow=14.0.2
+- pyarrow-hotfix=0.6
 - pycountry=22.3.5
 - pycparser=2.21
- pygments=2.15.1
+- pygments=2.17.2
 - pyomo=6.6.1
- pyparsing=3.1.0
- pyproj=3.6.0
- pyqt=5.15.7
- pyqt5-sip=12.11.0
+- pyparsing=3.1.1
+- pyproj=3.6.1
+- pypsa=0.26.2
+- pyqt=5.15.9
+- pyqt5-sip=12.12.2
 - pyshp=2.3.1
 - pysocks=1.7.1
- pytables=3.8.0
- pytest=7.4.0
- python=3.10.12
+- pytables=3.9.2
+- pytest=7.4.4
+- python=3.11.7
 - python-dateutil=2.8.2
- python-fastjsonschema=2.18.0
+- python-fastjsonschema=2.19.1
 - python-json-logger=2.0.7
- python-tzdata=2023.3
- python-utils=3.7.0
- python_abi=3.10
- pytz=2023.3
+- python-tzdata=2023.4
+- python-utils=3.8.1
+- python_abi=3.11
+- pytz=2023.3.post1
 - pyxlsb=1.0.10
- pyyaml=6.0
- pyzmq=25.1.0
+- pyyaml=6.0.1
+- pyzmq=25.1.2
 - qt-main=5.15.8
- qtconsole=5.4.3
- qtconsole-base=5.4.3
- qtpy=2.3.1
- rasterio=1.3.8
- rdma-core=28.9
- re2=2023.03.02
+- qtconsole-base=5.5.1
+- qtpy=2.4.1
+- rasterio=1.3.9
+- rdma-core=49.0
+- re2=2023.06.02
 - readline=8.2
- referencing=0.30.0
+- referencing=0.32.0
 - requests=2.31.0
 - reretry=0.11.8
 - rfc3339-validator=0.1.4
 - rfc3986-validator=0.1.1
- rioxarray=0.14.1
- rpds-py=0.9.2
- rtree=1.0.1
- s2n=1.3.46
- scikit-learn=1.3.0
- scipy=1.11.1
+- rioxarray=0.15.0
+- rpds-py=0.16.2
+- rtree=1.1.0
+- s2n=1.4.1
+- scikit-learn=1.3.2
+- scipy=1.11.4
 - scotch=6.0.9
- seaborn=0.12.2
- seaborn-base=0.12.2
+- seaborn=0.13.0
+- seaborn-base=0.13.0
 - send2trash=1.8.2
- setuptools=68.0.0
- setuptools-scm=7.1.0
- setuptools_scm=7.1.0
- shapely=2.0.1
- sip=6.7.10
+- setuptools=69.0.3
+- setuptools-scm=8.0.4
+- setuptools_scm=8.0.4
+- shapely=2.0.2
+- sip=6.7.12
 - six=1.16.0
- smart_open=6.3.0
- smmap=3.0.5
- snakemake-minimal=7.30.2
+- smart_open=6.4.0
+- smmap=5.0.0
+- snakemake-minimal=7.32.4
 - snappy=1.1.10
 - sniffio=1.3.0
 - snuggs=1.4.7
 - sortedcontainers=2.4.0
- soupsieve=2.3.2.post1
- sqlite=3.42.0
+- soupsieve=2.5
+- sqlite=3.44.2
 - stack_data=0.6.2
- statsmodels=0.14.0
+- statsmodels=0.14.1
 - stopit=1.1.2
- tabula-py=2.6.0
+- tabula-py=2.7.0
 - tabulate=0.9.0
- tblib=1.7.0
- terminado=0.17.1
+- tblib=3.0.0
+- terminado=0.18.0
 - threadpoolctl=3.2.0
- throttler=1.2.1
- tiledb=2.13.2
+- throttler=1.2.2
+- tiledb=2.18.2
 - tinycss2=1.2.1
- tk=8.6.12
+- tk=8.6.13
 - toml=0.10.2
 - tomli=2.0.1
 - toolz=0.12.0
 - toposort=1.10
- tornado=6.3.2
- tqdm=4.65.0
- traitlets=5.9.0
- typing-extensions=4.7.1
- typing_extensions=4.7.1
+- tornado=6.3.3
+- tqdm=4.66.1
+- traitlets=5.14.1
+- types-python-dateutil=2.8.19.14
+- typing-extensions=4.9.0
+- typing_extensions=4.9.0
 - typing_utils=0.1.0
- tzcode=2023c
- tzdata=2023c
- ucx=1.14.1
- unicodedata2=15.0.0
- unidecode=1.3.6
- unixodbc=2.3.10
- urllib3=2.0.4
- wcwidth=0.2.6
+- tzcode=2023d
+- tzdata=2023d
+- ucx=1.15.0
+- unidecode=1.3.7
+- unixodbc=2.3.12
+- uri-template=1.3.0
+- uriparser=0.9.7
+- urllib3=2.1.0
+- validators=0.22.0
+- wcwidth=0.2.12
+- webcolors=1.13
 - webencodings=0.5.1
- websocket-client=1.6.1
- wheel=0.41.0
- widgetsnbextension=4.0.8
- wrapt=1.15.0
- xarray=2023.7.0
+- websocket-client=1.7.0
+- wheel=0.42.0
+- widgetsnbextension=4.0.9
+- wrapt=1.16.0
+- xarray=2023.12.0
 - xcb-util=0.4.0
 - xcb-util-image=0.4.0
 - xcb-util-keysyms=0.4.0
 - xcb-util-renderutil=0.3.9
 - xcb-util-wm=0.4.1
- xerces-c=3.2.4
- xkeyboard-config=2.39
+- xerces-c=3.2.5
+- xkeyboard-config=2.40
 - xlrd=2.0.1
 - xorg-fixesproto=5.0
 - xorg-inputproto=2.3.2
 - xorg-kbproto=1.0.7
 - xorg-libice=1.1.1
 - xorg-libsm=1.2.4
- xorg-libx11=1.8.6
+- xorg-libx11=1.8.7
 - xorg-libxau=1.0.11
 - xorg-libxdmcp=1.1.3
 - xorg-libxext=1.3.4
 - xorg-libxfixes=5.0.3
 - xorg-libxi=1.7.10
 - xorg-libxrender=0.9.11
+- xorg-libxt=1.3.0
 - xorg-libxtst=1.2.3
 - xorg-recordproto=1.14.2
 - xorg-renderproto=0.11.1
 - xorg-xextproto=7.3.0
 - xorg-xf86vidmodeproto=2.3.1
 - xorg-xproto=7.0.31
- xyzservices=2023.7.0
+- xyzservices=2023.10.1
 - xz=5.2.6
 - yaml=0.2.5
- yte=1.5.1
- zeromq=4.3.4
+- yte=1.5.4
+- zeromq=4.3.5
 - zict=3.0.0
- zipp=3.16.2
+- zipp=3.17.0
 - zlib=1.2.13
 - zlib-ng=2.0.7
- zstd=1.5.2
+- zstd=1.5.5
 - pip:
-  - gurobipy==10.0.2
-  - linopy==0.2.2
-  - pypsa==0.25.1
-  - tsam==2.3.0
-  - validators==0.20.0
+  - highspy==1.5.3
+  - tsam==2.3.1
--- a/envs/environment.yaml
+++ b/envs/environment.yaml
@ -11,6 +11,8 @@ dependencies:
 - pip

 - atlite>=0.2.9
+- pypsa>=0.26.1
+- linopy
 - dask

  # Dependencies of the workflow itself
@ -18,23 +20,25 @@ dependencies:
 - openpyxl!=3.1.1
 - pycountry
 - seaborn
- snakemake-minimal>=7.7.0
+  # snakemake 8 introduced a number of breaking changes which the workflow has yet to be made compatible with
+- snakemake-minimal>=7.7.0,<8.0.0
 - memory_profiler
 - yaml
 - pytables
 - lxml
- powerplantmatching>=0.5.5
+- powerplantmatching>=0.5.5,!=0.5.9
 - numpy
- pandas>=1.4
+- pandas>=2.1
 - geopandas>=0.11.0
- xarray
+- xarray>=2023.11.0
 - rioxarray
 - netcdf4
 - networkx
 - scipy
+- glpk
 - shapely>=2.0
- pyomo
- matplotlib<3.6
+- pyscipopt
+- matplotlib
 - proj
 - fiona
 - country_converter
@ -44,6 +48,7 @@ dependencies:
 - tabula-py
 - pyxlsb
 - graphviz
+- pre-commit

  # Keep in conda environment when calling ipython
 - ipython
@ -55,5 +60,5 @@ dependencies:


 - pip:
-  - tsam>=1.1.0
-  - pypsa>=0.25.1
+  - tsam>=2.3.1
+  - highspy
--- a/envs/retrieve.yaml
+++ b/envs/retrieve.yaml
@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+
+name: pypsa-eur-retrieve
+channels:
+- conda-forge
+- bioconda
+dependencies:
+- python>=3.8
+- snakemake-minimal>=7.7.0,<8.0.0
+- pandas>=2.1
+- tqdm
--- a/graphics/workflow.png
+++ b/graphics/workflow.png
--- a/rules/build_electricity.smk
+++ b/rules/build_electricity.smk
@ -20,7 +20,7 @@ if config["enable"].get("prepare_links_p_nom", False):

 rule build_electricity_demand:
    params:
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config provider
        countries=config_provider("countries"),
        load=config_provider("load"),
    input:
@ -41,6 +41,7 @@ rule build_powerplants:
    params:
        powerplants_filter=config_provider("electricity", "powerplants_filter"),
        custom_powerplants=config_provider("electricity", "custom_powerplants"),
+        everywhere_powerplants=config_provider("electricity", "everywhere_powerplants"),
        countries=config_provider("countries"),
    input:
        base_network=resources("networks/base.nc"),
@ -61,7 +62,7 @@ rule build_powerplants:
 rule base_network:
    params:
        countries=config_provider("countries"),
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config provider
        lines=config_provider("lines"),
        links=config_provider("links"),
        transformers=config_provider("transformers"),
@ -144,7 +145,7 @@ if config["enable"].get("build_cutout", False):

    rule build_cutout:
        params:
-            snapshots=config_provider("snapshots"),
+            snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config provider
            cutouts=config_provider("atlite", "cutouts"),
        input:
            regions_onshore=resources("regions_onshore.geojson"),
@ -206,10 +207,62 @@ rule build_ship_raster:
        "../scripts/build_ship_raster.py"


+rule determine_availability_matrix_MD_UA:
+    input:
+        copernicus="data/Copernicus_LC100_global_v3.0.1_2019-nrt_Discrete-Classification-map_EPSG-4326.tif",
+        wdpa="data/WDPA.gpkg",
+        wdpa_marine="data/WDPA_WDOECM_marine.gpkg",
+        gebco=lambda w: (
+            "data/bundle/GEBCO_2014_2D.nc"
+            if "max_depth" in config["renewable"][w.technology].keys()
+            else []
+        ),
+        ship_density=lambda w: (
+            RESOURCES + "shipdensity_raster.tif"
+            if "ship_threshold" in config["renewable"][w.technology].keys()
+            else []
+        ),
+        country_shapes=RESOURCES + "country_shapes.geojson",
+        offshore_shapes=RESOURCES + "offshore_shapes.geojson",
+        regions=lambda w: (
+            RESOURCES + "regions_onshore.geojson"
+            if w.technology in ("onwind", "solar")
+            else RESOURCES + "regions_offshore.geojson"
+        ),
+        cutout=lambda w: "cutouts/"
+        + CDIR
+        + config["renewable"][w.technology]["cutout"]
+        + ".nc",
+    output:
+        availability_matrix=RESOURCES + "availability_matrix_MD-UA_{technology}.nc",
+        availability_map=RESOURCES + "availability_matrix_MD-UA_{technology}.png",
+    log:
+        LOGS + "determine_availability_matrix_MD_UA_{technology}.log",
+    threads: ATLITE_NPROCESSES
+    resources:
+        mem_mb=ATLITE_NPROCESSES * 5000,
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/determine_availability_matrix_MD_UA.py"
+
+
+# Optional input when having Ukraine (UA) or Moldova (MD) in the countries list
+if {"UA", "MD"}.intersection(set(config["countries"])):
+    opt = {
+        "availability_matrix_MD_UA": RESOURCES
+        + "availability_matrix_MD-UA_{technology}.nc"
+    }
+else:
+    opt = {}
+
+
 rule build_renewable_profiles:
    params:
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config provider
        renewable=config_provider("renewable"),
    input:
+        **opt,
        base_network=resources("networks/base.nc"),
        corine=ancient("data/bundle/corine/g250_clc06_V18_5.tif"),
        natura=lambda w: (
@ -217,6 +270,11 @@ rule build_renewable_profiles:
            if config_provider("renewable", w.technology, "natura")(w)
            else []
        ),
+        luisa=lambda w: (
+            "data/LUISA_basemap_020321_50m.tif"
+            if config["renewable"][w.technology].get("luisa")
+            else []
+        ),
        gebco=ancient(
            lambda w: (
                "data/bundle/GEBCO_2014_2D.nc"
@ -298,6 +356,8 @@ rule build_hydro_profile:
 if config["lines"]["dynamic_line_rating"]["activate"]:

    rule build_line_rating:
+        params:
+            snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
        input:
            base_network=resources("networks/base.nc"),
            cutout="cutouts/"
@ -355,6 +415,7 @@ rule add_electricity:
        else [],
        load=resources("load.csv"),
        nuts3_shapes=resources("nuts3_shapes.geojson"),
+        ua_md_gdp="data/GDP_PPP_30arcsec_v3_mapped_default.csv",
    output:
        resources("networks/elec.nc"),
    log:
@ -376,7 +437,7 @@ rule simplify_network:
        aggregation_strategies=config_provider(
            "clustering", "aggregation_strategies", default={}
        ),
-        focus_weights=config_provider("focus_weights", default=None),
+        focus_weights=config_provider("clustering", "focus_weights", default=None),
        renewable_carriers=config_provider("electricity", "renewable_carriers"),
        max_hours=config_provider("electricity", "max_hours"),
        length_factor=config_provider("lines", "length_factor"),
@ -413,7 +474,7 @@ rule cluster_network:
            "clustering", "aggregation_strategies", default={}
        ),
        custom_busmap=config_provider("enable", "custom_busmap", default=False),
-        focus_weights=config_provider("focus_weights", default=None),
+        focus_weights=config_provider("clustering", "focus_weights", default=None),
        renewable_carriers=config_provider("electricity", "renewable_carriers"),
        conventional_carriers=config_provider(
            "electricity", "conventional_carriers", default=[]
@ -476,17 +537,24 @@ rule add_extra_components:

 rule prepare_network:
    params:
+        snapshots={
+            "resolution": config["snapshots"].get("resolution", False),
+            "segmentation": config["snapshots"].get("segmentation", False),
+        }, # TODO: use config provider
        links=config_provider("links"),
        lines=config_provider("lines"),
        co2base=config_provider("electricity", "co2base"),
+        co2limit_enable=config_provider("electricity", "co2limit_enable", default=False),
        co2limit=config_provider("electricity", "co2limit"),
+        gaslimit_enable=config_provider("electricity", "gaslimit_enable", default=False),
        gaslimit=config_provider("electricity", "gaslimit"),
        max_hours=config_provider("electricity", "max_hours"),
        costs=config_provider("costs"),
+        autarky=config_provider("electricity", "autarky", default={}),
    input:
        resources("networks/elec_s{simpl}_{clusters}_ec.nc"),
        tech_costs=COSTS,
-        co2_price=resources("co2_price.csv"),
+        co2_price=lambda w: resources("co2_price.csv") if "Ept" in w.opts else [],
    output:
        resources("networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"),
    log:
--- a/rules/build_sector.smk
+++ b/rules/build_sector.smk
@ -67,107 +67,107 @@ rule build_simplified_population_layouts:
        "../scripts/build_clustered_population_layouts.py"


-if config["sector"]["gas_network"] or config["sector"]["H2_retrofit"]:
-
-    rule build_gas_network:
-        input:
-            gas_network="data/gas_network/scigrid-gas/data/IGGIELGN_PipeSegments.geojson",
-        output:
-            cleaned_gas_network=resources("gas_network.csv"),
-        resources:
-            mem_mb=4000,
-        log:
-            logs("build_gas_network.log"),
-        conda:
-            "../envs/environment.yaml"
-        script:
-            "../scripts/build_gas_network.py"
-
-    rule build_gas_input_locations:
-        input:
-            lng=HTTP.remote(
-                "https://globalenergymonitor.org/wp-content/uploads/2023/07/Europe-Gas-Tracker-2023-03-v3.xlsx",
-                keep_local=True,
-            ),
-            entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
-            production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson",
-            regions_onshore=resources(
-                "regions_onshore_elec_s{simpl}_{clusters}.geojson"
-            ),
-            regions_offshore=resources(
-                "regions_offshore_elec_s{simpl}_{clusters}.geojson"
-            ),
-        output:
-            gas_input_nodes=resources("gas_input_locations_s{simpl}_{clusters}.geojson"),
-            gas_input_nodes_simplified=resources(
-                "gas_input_locations_s{simpl}_{clusters}_simplified.csv"
-            ),
-        resources:
-            mem_mb=2000,
-        log:
-            logs("build_gas_input_locations_s{simpl}_{clusters}.log"),
-        conda:
-            "../envs/environment.yaml"
-        script:
-            "../scripts/build_gas_input_locations.py"
-
-    rule cluster_gas_network:
-        input:
-            cleaned_gas_network=resources("gas_network.csv"),
-            regions_onshore=resources(
-                "regions_onshore_elec_s{simpl}_{clusters}.geojson"
-            ),
-            regions_offshore=resources(
-                "regions_offshore_elec_s{simpl}_{clusters}.geojson"
-            ),
-        output:
-            clustered_gas_network=resources("gas_network_elec_s{simpl}_{clusters}.csv"),
-        resources:
-            mem_mb=4000,
-        log:
-            logs("cluster_gas_network_s{simpl}_{clusters}.log"),
-        conda:
-            "../envs/environment.yaml"
-        script:
-            "../scripts/cluster_gas_network.py"
-
-    gas_infrastructure = {
-        **rules.cluster_gas_network.output,
-        **rules.build_gas_input_locations.output,
-    }
+rule build_gas_network:
+    input:
+        gas_network="data/gas_network/scigrid-gas/data/IGGIELGN_PipeSegments.geojson",
+    output:
+        cleaned_gas_network=resources("gas_network.csv"),
+    resources:
+        mem_mb=4000,
+    log:
+        logs("build_gas_network.log"),
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_gas_network.py"


-if not (config["sector"]["gas_network"] or config["sector"]["H2_retrofit"]):
-    # this is effecively an `else` statement which is however not liked by snakefmt
-
-    gas_infrastructure = {}
+rule build_gas_input_locations:
+    input:
+        gem=HTTP.remote(
+            "https://globalenergymonitor.org/wp-content/uploads/2023/07/Europe-Gas-Tracker-2023-03-v3.xlsx",
+            keep_local=True,
+        ),
+        entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
+        storage="data/gas_network/scigrid-gas/data/IGGIELGN_Storages.geojson",
+        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
+        regions_offshore=resources("regions_offshore_elec_s{simpl}_{clusters}.geojson"),
+    output:
+        gas_input_nodes=resources("gas_input_locations_s{simpl}_{clusters}.geojson"),
+        gas_input_nodes_simplified=resources("gas_input_locations_s{simpl}_{clusters}_simplified.csv"),
+    resources:
+        mem_mb=2000,
+    log:
+        logs("build_gas_input_locations_s{simpl}_{clusters}.log"),
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_gas_input_locations.py"


-rule build_heat_demands:
+rule cluster_gas_network:
+    input:
+        cleaned_gas_network=resources("gas_network.csv"),
+        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
+        regions_offshore=resources("regions_offshore_elec_s{simpl}_{clusters}.geojson"),
+    output:
+        clustered_gas_network=resources("gas_network_elec_s{simpl}_{clusters}.csv"),
+    resources:
+        mem_mb=4000,
+    log:
+        logs("cluster_gas_network_s{simpl}_{clusters}.log"),
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/cluster_gas_network.py"
+
+
+rule build_daily_heat_demand:
    params:
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config_provider
    input:
        pop_layout=resources("pop_layout_{scope}.nc"),
        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
        cutout="cutouts/" + CDIR + config["atlite"]["default_cutout"] + ".nc",
    output:
-        heat_demand=resources("heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"),
+        heat_demand=resources("daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"),
    resources:
        mem_mb=20000,
    threads: 8
    log:
-        logs("build_heat_demands_{scope}_{simpl}_{clusters}.loc"),
+        logs("build_daily_heat_demand_{scope}_{simpl}_{clusters}.loc"),
    benchmark:
-        benchmarks("build_heat_demands/{scope}_s{simpl}_{clusters}")
+        benchmarks("build_daily_heat_demand/{scope}_s{simpl}_{clusters}")
    conda:
        "../envs/environment.yaml"
    script:
-        "../scripts/build_heat_demand.py"
+        "../scripts/build_daily_heat_demand.py"
+
+
+rule build_hourly_heat_demand:
+    params:
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
+    input:
+        heat_profile="data/heat_load_profile_BDEW.csv",
+        heat_demand=RESOURCES + "daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
+    output:
+        heat_demand=RESOURCES + "hourly_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
+    resources:
+        mem_mb=2000,
+    threads: 8
+    log:
+        LOGS + "build_hourly_heat_demand_{scope}_{simpl}_{clusters}.loc",
+    benchmark:
+        BENCHMARKS + "build_hourly_heat_demand/{scope}_s{simpl}_{clusters}"
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_hourly_heat_demand.py"


 rule build_temperature_profiles:
    params:
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config_provider
    input:
        pop_layout=resources("pop_layout_{scope}.nc"),
        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
@ -219,7 +219,7 @@ rule build_cop_profiles:

 rule build_solar_thermal_profiles:
    params:
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO use config_provider
        solar_thermal=config_provider("solar_thermal"),
    input:
        pop_layout=resources("pop_layout_{scope}.nc"),
@ -246,15 +246,16 @@ rule build_energy_totals:
        energy=config_provider("energy"),
    input:
        nuts3_shapes=resources("nuts3_shapes.geojson"),
-        co2="data/eea/UNFCCC_v23.csv",
-        swiss="data/switzerland-sfoe/switzerland-new_format.csv",
-        idees="data/jrc-idees-2015",
+        co2="data/bundle-sector/eea/UNFCCC_v23.csv",
+        swiss="data/bundle-sector/switzerland-sfoe/switzerland-new_format.csv",
+        idees="data/bundle-sector/jrc-idees-2015",
        district_heat_share="data/district_heat_share.csv",
        eurostat=input_eurostat,
    output:
        energy_name=resources("energy_totals.csv"),
        co2_name=resources("co2_totals.csv"),
        transport_name=resources("transport_data.csv"),
+        district_heat_share=resources("district_heat_share.csv"),
    threads: 16
    resources:
        mem_mb=10000,
@ -273,10 +274,10 @@ rule build_biomass_potentials:
        biomass=config_provider("biomass"),
    input:
        enspreso_biomass=HTTP.remote(
-            "https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx",
+            "https://zenodo.org/records/10356004/files/ENSPRESO_BIOMASS.xlsx",
            keep_local=True,
        ),
-        nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",  # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
+        nuts2="data/bundle-sector/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",  # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
        nuts3_population=ancient("data/bundle/nama_10r_3popgdp.tsv.gz"),
        swiss_cantons=ancient("data/bundle/ch_cantons.csv"),
@ -284,16 +285,16 @@ rule build_biomass_potentials:
        country_shapes=resources("country_shapes.geojson"),
    output:
        biomass_potentials_all=resources(
-            "biomass_potentials_all_s{simpl}_{clusters}.csv"
+            "biomass_potentials_all_s{simpl}_{clusters}_{planning_horizons}.csv"
        ),
-        biomass_potentials=resources("biomass_potentials_s{simpl}_{clusters}.csv"),
+        biomass_potentials=resources("biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv"),
    threads: 1
    resources:
        mem_mb=1000,
    log:
-        logs("build_biomass_potentials_s{simpl}_{clusters}.log"),
+        logs("build_biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.log"),
    benchmark:
-        benchmarks("build_biomass_potentials_s{simpl}_{clusters}")
+        benchmarks("build_biomass_potentials_s{simpl}_{clusters}_{planning_horizons}")
    conda:
        "../envs/environment.yaml"
    script:
@ -374,7 +375,7 @@ if not config["sector"]["regional_co2_sequestration_potential"]["enable"]:

 rule build_salt_cavern_potentials:
    input:
-        salt_caverns="data/h2_salt_caverns_GWh_per_sqkm.geojson",
+        salt_caverns="data/bundle-sector/h2_salt_caverns_GWh_per_sqkm.geojson",
        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
        regions_offshore=resources("regions_offshore_elec_s{simpl}_{clusters}.geojson"),
    output:
@ -396,7 +397,7 @@ rule build_ammonia_production:
    params:
        countries=config_provider("countries"),
    input:
-        usgs="data/myb1-2017-nitro.xls",
+        usgs="data/bundle-sector/myb1-2017-nitro.xls",
    output:
        ammonia_production=resources("ammonia_production.csv"),
    threads: 1
@ -418,7 +419,7 @@ rule build_industry_sector_ratios:
        ammonia=config_provider("sector", "ammonia", default=False),
    input:
        ammonia_production=resources("ammonia_production.csv"),
-        idees="data/jrc-idees-2015",
+        idees="data/bundle-sector/jrc-idees-2015",
    output:
        industry_sector_ratios=resources("industry_sector_ratios.csv"),
    threads: 1
@ -440,8 +441,8 @@ rule build_industrial_production_per_country:
        countries=config_provider("countries"),
    input:
        ammonia_production=resources("ammonia_production.csv"),
-        jrc="data/jrc-idees-2015",
-        eurostat="data/eurostat-energy_balances-may_2018_edition",
+        jrc="data/bundle-sector/jrc-idees-2015",
+        eurostat="data/bundle-sector/eurostat-energy_balances-may_2018_edition",
    output:
        industrial_production_per_country=resources(
            "industrial_production_per_country.csv"
@ -496,7 +497,7 @@ rule build_industrial_distribution_key:
    input:
        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
        clustered_pop_layout=resources("pop_layout_elec_s{simpl}_{clusters}.csv"),
-        hotmaps_industrial_database="data/Industrial_Database.csv",
+        hotmaps_industrial_database="data/bundle-sector/Industrial_Database.csv",
    output:
        industrial_distribution_key=resources(
            "industrial_distribution_key_elec_s{simpl}_{clusters}.csv"
@ -582,7 +583,7 @@ rule build_industrial_energy_demand_per_country_today:
        countries=config_provider("countries"),
        industry=config_provider("industry"),
    input:
-        jrc="data/jrc-idees-2015",
+        jrc="data/bundle-sector/jrc-idees-2015",
        ammonia_production=resources("ammonia_production.csv"),
        industrial_production_per_country=resources(
            "industrial_production_per_country.csv"
@ -637,7 +638,7 @@ if config["sector"]["retrofitting"]["retro_endogen"]:
            countries=config_provider("countries"),
        input:
            building_stock="data/retro/data_building_stock.csv",
-            data_tabula="data/retro/tabula-calculator-calcsetbuilding.csv",
+            data_tabula="data/bundle-sector/retro/tabula-calculator-calcsetbuilding.csv",
            air_temperature=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
            u_values_PL="data/retro/u_values_poland.csv",
            tax_w="data/retro/electricity_taxes_eu.csv",
@ -706,7 +707,7 @@ rule build_shipping_demand:

 rule build_transport_demand:
    params:
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config_provider
        sector=config_provider("sector"),
    input:
        clustered_pop_layout=resources("pop_layout_elec_s{simpl}_{clusters}.csv"),
@ -714,8 +715,8 @@ rule build_transport_demand:
            "pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
        ),
        transport_data=resources("transport_data.csv"),
-        traffic_data_KFZ="data/emobility/KFZ__count",
-        traffic_data_Pkw="data/emobility/Pkw__count",
+        traffic_data_KFZ="data/bundle-sector/emobility/KFZ__count",
+        traffic_data_Pkw="data/bundle-sector/emobility/Pkw__count",
        temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
    output:
        transport_demand=resources("transport_demand_s{simpl}_{clusters}.csv"),
@ -733,6 +734,60 @@ rule build_transport_demand:
        "../scripts/build_transport_demand.py"


+rule build_district_heat_share:
+    params:
+        sector=config["sector"],
+    input:
+        district_heat_share=RESOURCES + "district_heat_share.csv",
+        clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
+    output:
+        district_heat_share=RESOURCES
+        + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+    threads: 1
+    resources:
+        mem_mb=1000,
+    log:
+        LOGS + "build_district_heat_share_s{simpl}_{clusters}_{planning_horizons}.log",
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_district_heat_share.py"
+
+
+rule build_existing_heating_distribution:
+    params:
+        baseyear=config["scenario"]["planning_horizons"][0],
+        sector=config["sector"],
+        existing_capacities=config["existing_capacities"],
+    input:
+        existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
+        clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
+        clustered_pop_energy_layout=RESOURCES
+        + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
+        district_heat_share=RESOURCES
+        + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+    output:
+        existing_heating_distribution=RESOURCES
+        + "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+    wildcard_constraints:
+        planning_horizons=config["scenario"]["planning_horizons"][0],  #only applies to baseyear
+    threads: 1
+    resources:
+        mem_mb=2000,
+    log:
+        LOGS
+        + "build_existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.log",
+    benchmark:
+        (
+            BENCHMARKS
+            + "build_existing_heating_distribution/elec_s{simpl}_{clusters}_{planning_horizons}"
+        )
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_existing_heating_distribution.py"
+
+
 rule prepare_sector_network:
    params:
        co2_budget=config_provider("co2_budget"),
@ -753,26 +808,31 @@ rule prepare_sector_network:
    input:
        **build_retro_cost_output,
        **build_biomass_transport_costs_output,
-        **gas_infrastructure,
+        **rules.cluster_gas_network.output,
+        **rules.build_gas_input_locations.output,
        **build_sequestration_potentials_output,
        network=resources("networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"),
        energy_totals_name=resources("energy_totals.csv"),
        eurostat=input_eurostat,
-        pop_weighted_energy_totals=resources(
-            "pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
-        ),
+        pop_weighted_energy_totals=resources("pop_weighted_energy_totals_s{simpl}_{clusters}.csv"),
        shipping_demand=resources("shipping_demand_s{simpl}_{clusters}.csv"),
        transport_demand=resources("transport_demand_s{simpl}_{clusters}.csv"),
        transport_data=resources("transport_data_s{simpl}_{clusters}.csv"),
        avail_profile=resources("avail_profile_s{simpl}_{clusters}.csv"),
        dsm_profile=resources("dsm_profile_s{simpl}_{clusters}.csv"),
        co2_totals_name=resources("co2_totals.csv"),
-        co2="data/eea/UNFCCC_v23.csv",
-        biomass_potentials=resources("biomass_potentials_s{simpl}_{clusters}.csv"),
-        heat_profile="data/heat_load_profile_BDEW.csv",
-        costs="data/costs_{}.csv".format(config["costs"]["year"])
-        if config["foresight"] == "overnight"
-        else "data/costs_{planning_horizons}.csv",
+        co2="data/bundle-sector/eea/UNFCCC_v23.csv",
+        biomass_potentials=(
+            resources("biomass_potentials_s{simpl}_{clusters}_"
+            + "{}.csv".format(config["biomass"]["year"]))
+            if config["foresight"] == "overnight"
+            else resources("biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv")
+        ),
+        costs=(
+            "data/costs_{}.csv".format(config["costs"]["year"])
+            if config["foresight"] == "overnight"
+            else "data/costs_{planning_horizons}.csv"
+        ),
        profile_offwind_ac=resources("profile_offwind-ac.nc"),
        profile_offwind_dc=resources("profile_offwind-dc.nc"),
        h2_cavern=resources("salt_cavern_potentials_s{simpl}_{clusters}.csv"),
@ -780,12 +840,9 @@ rule prepare_sector_network:
        busmap=resources("busmap_elec_s{simpl}_{clusters}.csv"),
        clustered_pop_layout=resources("pop_layout_elec_s{simpl}_{clusters}.csv"),
        simplified_pop_layout=resources("pop_layout_elec_s{simpl}.csv"),
-        industrial_demand=resources(
-            "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
-        ),
-        heat_demand_urban=resources("heat_demand_urban_elec_s{simpl}_{clusters}.nc"),
-        heat_demand_rural=resources("heat_demand_rural_elec_s{simpl}_{clusters}.nc"),
-        heat_demand_total=resources("heat_demand_total_elec_s{simpl}_{clusters}.nc"),
+        industrial_demand=resources("industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv"),
+        hourly_heat_demand_total=resources("hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
+        district_heat_share=resources("district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv"),
        temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
        temp_soil_rural=resources("temp_soil_rural_elec_s{simpl}_{clusters}.nc"),
        temp_soil_urban=resources("temp_soil_urban_elec_s{simpl}_{clusters}.nc"),
@ -798,21 +855,21 @@ rule prepare_sector_network:
        cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
        cop_air_rural=resources("cop_air_rural_elec_s{simpl}_{clusters}.nc"),
        cop_air_urban=resources("cop_air_urban_elec_s{simpl}_{clusters}.nc"),
-        solar_thermal_total=resources(
-            "solar_thermal_total_elec_s{simpl}_{clusters}.nc"
-        )
-        if config["sector"]["solar_thermal"]
-        else [],
-        solar_thermal_urban=resources(
-            "solar_thermal_urban_elec_s{simpl}_{clusters}.nc"
-        )
-        if config["sector"]["solar_thermal"]
-        else [],
-        solar_thermal_rural=resources(
-            "solar_thermal_rural_elec_s{simpl}_{clusters}.nc"
-        )
-        if config["sector"]["solar_thermal"]
-        else [],
+        solar_thermal_total=(
+            resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc")
+            if config["sector"]["solar_thermal"]
+            else []
+        ),
+        solar_thermal_urban=(
+            resources("solar_thermal_urban_elec_s{simpl}_{clusters}.nc")
+            if config["sector"]["solar_thermal"]
+            else []
+        ),
+        solar_thermal_rural=(
+            resources("solar_thermal_rural_elec_s{simpl}_{clusters}.nc")
+            if config["sector"]["solar_thermal"]
+            else []
+        ),
    output:
        RESULTS
        + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
--- a/rules/collect.smk
+++ b/rules/collect.smk
@ -11,7 +11,6 @@ localrules:
    prepare_sector_networks,
    solve_elec_networks,
    solve_sector_networks,
-    plot_networks,


 rule all:
@ -76,17 +75,7 @@ rule solve_sector_networks:
        ),


-rule plot_elec_networks:
-    input:
-        expand(
-            RESULTS
-            + "figures/.statistics_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
-            **config["scenario"],
-            run=config["run"]["name"]
-        ),
-
-
-rule plot_networks:
+rule solve_sector_networks_perfect:
    input:
        expand(
            RESULTS
--- a/rules/common.smk
+++ b/rules/common.smk
@ -5,6 +5,16 @@
 import copy
 from functools import partial, lru_cache

+import os, sys, glob
+
+helper_source_path = [match for match in glob.glob("**/_helpers.py", recursive=True)]
+
+for path in helper_source_path:
+    path = os.path.dirname(os.path.abspath(path))
+    sys.path.insert(0, os.path.abspath(path))
+
+from _helpers import validate_checksum
+

 def get_config(config, keys, default=None):
    """Retrieve a nested value from a dictionary using a tuple of keys."""
@ -67,6 +77,13 @@ def config_provider(*keys, default=None):
        return partial(static_getter, keys=keys, default=default)


+def solver_threads(w):
+    solver_options = config["solving"]["solver_options"]
+    option_set = config["solving"]["solver"]["options"]
+    threads = solver_options[option_set].get("threads", 4)
+    return threads
+
+
 def memory(w):
    factor = 3.0
    for o in w.opts.split("-"):
@ -87,6 +104,13 @@ def memory(w):
        return int(factor * (10000 + 195 * int(w.clusters)))


+def input_custom_extra_functionality(w):
+    path = config["solving"]["options"].get("custom_extra_functionality", False)
+    if path:
+        return os.path.join(os.path.dirname(workflow.snakefile), path)
+    return []
+
+
 # Check if the workflow has access to the internet by trying to access the HEAD of specified url
 def has_internet_access(url="www.zenodo.org") -> bool:
    import http.client as http_client
@ -106,7 +130,7 @@ def has_internet_access(url="www.zenodo.org") -> bool:
 def input_eurostat(w):
    # 2016 includes BA, 2017 does not
    report_year = config["energy"]["eurostat_report_year"]
-    return f"data/eurostat-energy_balances-june_{report_year}_edition"
+    return f"data/bundle-sector/eurostat-energy_balances-june_{report_year}_edition"


 def solved_previous_horizon(wildcards):
--- a/rules/postprocess.smk
+++ b/rules/postprocess.smk
@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
+# SPDX-FileCopyrightText: : 2023-2024 The PyPSA-Eur Authors
 #
 # SPDX-License-Identifier: MIT

@ -7,31 +7,139 @@ localrules:
    copy_config,


-rule plot_network:
-    params:
-        foresight=config_provider("foresight"),
-        plotting=config_provider("plotting"),
-    input:
-        network=RESULTS
-        + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
-        regions=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
-    output:
-        map=RESULTS
-        + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
-        today=RESULTS
-        + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}-today.pdf",
-    threads: 2
-    resources:
-        mem_mb=10000,
-    benchmark:
-        (
+if config_provider("foresight") != "perfect":
+
+    rule plot_power_network_clustered:
+        params:
+            plotting=config_provider("plotting"),
+        input:
+            network=RESOURCES + "networks/elec_s{simpl}_{clusters}.nc",
+            regions_onshore=RESOURCES
+            + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        output:
+            map=RESULTS + "maps/power-network-s{simpl}-{clusters}.pdf",
+        threads: 1
+        resources:
+            mem_mb=4000,
+        benchmark:
+            BENCHMARKS + "plot_power_network_clustered/elec_s{simpl}_{clusters}"
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/plot_power_network_clustered.py"
+
+    rule plot_power_network:
+        params:
+            plotting=config_provider("plotting"),
+        input:
+            network=RESULTS
+            + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
+            regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        output:
+            map=RESULTS
+            + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
+        threads: 2
+        resources:
+            mem_mb=10000,
+        log:
+            (
+                LOGS
+                + "plot_power_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.log"
+            ),
+        benchmark:
+            (
+                BENCHMARKS
+                + "plot_power_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
+            )
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/plot_power_network.py"
+
+    rule plot_hydrogen_network:
+        params:
+            plotting=config_provider("plotting"),
+            foresight=config_provider("foresight"),
+        input:
+            network=RESULTS
+            + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
+            regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        output:
+            map=RESULTS
+            + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-h2_network_{planning_horizons}.pdf",
+        threads: 2
+        resources:
+            mem_mb=10000,
+        log:
+            (
+                LOGS
+                + "plot_hydrogen_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.log"
+            ),
+        benchmark:
+            (
+                BENCHMARKS
+                + "plot_hydrogen_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
+            )
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/plot_hydrogen_network.py"
+
+    rule plot_gas_network:
+        params:
+            plotting=config_provider("plotting"),
+        input:
+            network=RESULTS
+            + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
+            regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        output:
+            map=RESULTS
+            + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-ch4_network_{planning_horizons}.pdf",
+        threads: 2
+        resources:
+            mem_mb=10000,
+        log:
+            (
+                LOGS
+                + "plot_gas_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.log"
+            ),
+        benchmark:
+            (
+                BENCHMARKS
+                + "plot_gas_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
+            )
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/plot_gas_network.py"
+
+
+if config_provider("foresight") == "perfect":
+
+    rule plot_power_network_perfect:
+        params:
+            plotting=config_provider("plotting"),
+        input:
+            network=RESULTS
+            + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
+            regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
+        output:
+            **{
+                f"map_{year}": RESULTS
+                + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_"
+                + f"{year}.pdf"
+                for year in config_provider("scenario", "planning_horizons")
+            },
+        threads: 2
+        resources:
+            mem_mb=10000,
+        benchmark:
            BENCHMARKS
-            + "plot_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
-        )
-    conda:
-        "../envs/environment.yaml"
-    script:
-        "../scripts/plot_network.py"
+            +"postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_benchmark"
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/plot_power_network_perfect.py"


 rule copy_config:
@ -54,25 +162,55 @@ rule make_summary:
    params:
        foresight=config_provider("foresight"),
        costs=config_provider("costs"),
-        snapshots=config_provider("snapshots"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config_provider
        scenario=config_provider("scenario"),
        RDIR=RDIR,
    input:
+        expand(
+            RESULTS + "maps/power-network-s{simpl}-{clusters}.pdf",
+            **config["scenario"],
+        ),
        networks=expand(
            RESULTS
            + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
            **config["scenario"],
            run=config["run"]["name"]
        ),
-        costs="data/costs_{}.csv".format(config["costs"]["year"])
-        if config["foresight"] == "overnight"
-        else "data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
-        plots=expand(
+        costs=(
+            "data/costs_{}.csv".format(config["costs"]["year"])
+            if config_provider("foresight") == "overnight"
+            else "data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0])
+        ),
+        ac_plot=expand(
+            RESULTS + "maps/power-network-s{simpl}-{clusters}.pdf",
+            **config["scenario"],
+        ),
+        costs_plot=expand(
            RESULTS
            + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
            **config["scenario"],
            run=config["run"]["name"]
        ),
+        h2_plot=expand(
+            (
+                RESULTS
+                + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-h2_network_{planning_horizons}.pdf"
+                if config["sector"]["H2_network"]
+                else []
+            ),
+            **config["scenario"],
+            run=config["run"]["name"]
+        ),
+        ch4_plot=expand(
+            (
+                RESULTS
+                + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-ch4_network_{planning_horizons}.pdf"
+                if config["sector"]["gas_network"]
+                else []
+            ),
+            **config["scenario"],
+            run=config["run"]["name"]
+        ),
    output:
        nodal_costs=RESULTS + "csvs/nodal_costs.csv",
        nodal_capacities=RESULTS + "csvs/nodal_capacities.csv",
@ -116,7 +254,7 @@ rule plot_summary:
        energy=RESULTS + "csvs/energy.csv",
        balances=RESULTS + "csvs/supply_energy.csv",
        eurostat=input_eurostat,
-        co2="data/eea/UNFCCC_v23.csv",
+        co2="data/bundle-sector/eea/UNFCCC_v23.csv",
    output:
        costs=RESULTS + "graphs/costs.pdf",
        energy=RESULTS + "graphs/energy.pdf",
--- a/rules/retrieve.smk
+++ b/rules/retrieve.smk
@ -2,6 +2,9 @@
 #
 # SPDX-License-Identifier: MIT

+import requests
+from datetime import datetime, timedelta
+
 if config["enable"].get("retrieve", "auto") == "auto":
    config["enable"]["retrieve"] = has_internet_access()

@ -27,18 +30,36 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",

    rule retrieve_databundle:
        output:
-            expand("data/bundle/{file}", file=datafiles),
+            protected(expand("data/bundle/{file}", file=datafiles)),
        log:
            "logs/retrieve_databundle.log",
        resources:
            mem_mb=1000,
        retries: 2
        conda:
-            "../envs/environment.yaml"
+            "../envs/retrieve.yaml"
        script:
            "../scripts/retrieve_databundle.py"


+if config["enable"].get("retrieve_irena"):
+
+    rule retrieve_irena:
+        output:
+            offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv",
+            onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
+            solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
+        log:
+            LOGS + "retrieve_irena.log",
+        resources:
+            mem_mb=1000,
+        retries: 2
+        conda:
+            "../envs/retrieve.yaml"
+        script:
+            "../scripts/retrieve_irena.py"
+
+
 if config["enable"]["retrieve"] and config["enable"].get("retrieve_cutout", True):

    rule retrieve_cutout:
@ -56,6 +77,7 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_cutout", True
        retries: 2
        run:
            move(input[0], output[0])
+            validate_checksum(output[0], input[0])


 if config["enable"]["retrieve"] and config["enable"].get("retrieve_cost_data", True):
@ -100,55 +122,65 @@ if config["enable"]["retrieve"] and config["enable"].get(
        retries: 2
        run:
            move(input[0], output[0])
+            validate_checksum(output[0], input[0])


 if config["enable"]["retrieve"] and config["enable"].get(
    "retrieve_sector_databundle", True
 ):
    datafiles = [
-        "data/eea/UNFCCC_v23.csv",
-        "data/switzerland-sfoe/switzerland-new_format.csv",
-        "data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
-        "data/myb1-2017-nitro.xls",
-        "data/Industrial_Database.csv",
-        "data/emobility/KFZ__count",
-        "data/emobility/Pkw__count",
-        "data/h2_salt_caverns_GWh_per_sqkm.geojson",
-        directory("data/eurostat-energy_balances-june_2016_edition"),
-        directory("data/eurostat-energy_balances-may_2018_edition"),
-        directory("data/jrc-idees-2015"),
+        "eea/UNFCCC_v23.csv",
+        "switzerland-sfoe/switzerland-new_format.csv",
+        "nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
+        "myb1-2017-nitro.xls",
+        "Industrial_Database.csv",
+        "emobility/KFZ__count",
+        "emobility/Pkw__count",
+        "h2_salt_caverns_GWh_per_sqkm.geojson",
+    ]
+
+    datafolders = [
+        protected(
+            directory("data/bundle-sector/eurostat-energy_balances-june_2016_edition")
+        ),
+        protected(
+            directory("data/bundle-sector/eurostat-energy_balances-may_2018_edition")
+        ),
+        protected(directory("data/bundle-sector/jrc-idees-2015")),
    ]

    rule retrieve_sector_databundle:
        output:
-            *datafiles,
+            protected(expand("data/bundle-sector/{files}", files=datafiles)),
+            *datafolders,
        log:
            "logs/retrieve_sector_databundle.log",
        retries: 2
        conda:
-            "../envs/environment.yaml"
+            "../envs/retrieve.yaml"
        script:
            "../scripts/retrieve_sector_databundle.py"


-if config["enable"]["retrieve"] and (
-    config["sector"]["gas_network"] or config["sector"]["H2_retrofit"]
-):
+if config["enable"]["retrieve"]:
    datafiles = [
        "IGGIELGN_LNGs.geojson",
        "IGGIELGN_BorderPoints.geojson",
        "IGGIELGN_Productions.geojson",
+        "IGGIELGN_Storages.geojson",
        "IGGIELGN_PipeSegments.geojson",
    ]

    rule retrieve_gas_infrastructure_data:
        output:
-            expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles),
+            protected(
+                expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles)
+            ),
        log:
            "logs/retrieve_gas_infrastructure_data.log",
        retries: 2
        conda:
-            "../envs/environment.yaml"
+            "../envs/retrieve.yaml"
        script:
            "../scripts/retrieve_gas_infrastructure_data.py"

@ -179,7 +211,7 @@ if config["enable"]["retrieve"]:
                static=True,
            ),
        output:
-            "data/shipdensity_global.zip",
+            protected("data/shipdensity_global.zip"),
        log:
            "logs/retrieve_ship_raster.log",
        resources:
@ -187,6 +219,122 @@ if config["enable"]["retrieve"]:
        retries: 2
        run:
            move(input[0], output[0])
+            validate_checksum(output[0], input[0])
+
+
+if config["enable"]["retrieve"]:
+
+    # Downloading Copernicus Global Land Cover for land cover and land use:
+    # Website: https://land.copernicus.eu/global/products/lc
+    rule download_copernicus_land_cover:
+        input:
+            HTTP.remote(
+                "zenodo.org/record/3939050/files/PROBAV_LC100_global_v3.0.1_2019-nrt_Discrete-Classification-map_EPSG-4326.tif",
+                static=True,
+            ),
+        output:
+            "data/Copernicus_LC100_global_v3.0.1_2019-nrt_Discrete-Classification-map_EPSG-4326.tif",
+        run:
+            move(input[0], output[0])
+            validate_checksum(output[0], input[0])
+
+
+if config["enable"]["retrieve"]:
+
+    # Downloading LUISA Base Map for land cover and land use:
+    # Website: https://ec.europa.eu/jrc/en/luisa
+    rule retrieve_luisa_land_cover:
+        input:
+            HTTP.remote(
+                "jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/LUISA/EUROPE/Basemaps/LandUse/2018/LATEST/LUISA_basemap_020321_50m.tif",
+                static=True,
+            ),
+        output:
+            "data/LUISA_basemap_020321_50m.tif",
+        run:
+            move(input[0], output[0])
+
+
+if config["enable"]["retrieve"]:
+    # Some logic to find the correct file URL
+    # Sometimes files are released delayed or ahead of schedule, check which file is currently available
+
+    def check_file_exists(url):
+        response = requests.head(url)
+        return response.status_code == 200
+
+    # Basic pattern where WDPA files can be found
+    url_pattern = (
+        "https://d1gam3xoknrgr2.cloudfront.net/current/WDPA_{bYYYY}_Public_shp.zip"
+    )
+
+    # 3-letter month + 4 digit year for current/previous/next month to test
+    current_monthyear = datetime.now().strftime("%b%Y")
+    prev_monthyear = (datetime.now() - timedelta(30)).strftime("%b%Y")
+    next_monthyear = (datetime.now() + timedelta(30)).strftime("%b%Y")
+
+    # Test prioritised: current month -> previous -> next
+    for bYYYY in [current_monthyear, prev_monthyear, next_monthyear]:
+        if check_file_exists(url := url_pattern.format(bYYYY=bYYYY)):
+            break
+        else:
+            # If None of the three URLs are working
+            url = False
+
+    assert (
+        url
+    ), f"No WDPA files found at {url_pattern} for bY='{current_monthyear}, {prev_monthyear}, or {next_monthyear}'"
+
+    # Downloading protected area database from WDPA
+    # extract the main zip and then merge the contained 3 zipped shapefiles
+    # Website: https://www.protectedplanet.net/en/thematic-areas/wdpa
+    rule download_wdpa:
+        input:
+            HTTP.remote(
+                url,
+                static=True,
+                keep_local=True,
+            ),
+        params:
+            zip="data/WDPA_shp.zip",
+            folder=directory("data/WDPA"),
+        output:
+            gpkg=protected("data/WDPA.gpkg"),
+        run:
+            shell("cp {input} {params.zip}")
+            shell("unzip -o {params.zip} -d {params.folder}")
+            for i in range(3):
+                # vsizip is special driver for directly working with zipped shapefiles in ogr2ogr
+                layer_path = (
+                    f"/vsizip/{params.folder}/WDPA_{bYYYY}_Public_shp_{i}.zip"
+                )
+                print(f"Adding layer {i + 1} of 3 to combined output file.")
+                shell("ogr2ogr -f gpkg -update -append {output.gpkg} {layer_path}")
+
+    rule download_wdpa_marine:
+        # Downloading Marine protected area database from WDPA
+        # extract the main zip and then merge the contained 3 zipped shapefiles
+        # Website: https://www.protectedplanet.net/en/thematic-areas/marine-protected-areas
+        input:
+            HTTP.remote(
+                f"d1gam3xoknrgr2.cloudfront.net/current/WDPA_WDOECM_{bYYYY}_Public_marine_shp.zip",
+                static=True,
+                keep_local=True,
+            ),
+        params:
+            zip="data/WDPA_WDOECM_marine.zip",
+            folder=directory("data/WDPA_WDOECM_marine"),
+        output:
+            gpkg=protected("data/WDPA_WDOECM_marine.gpkg"),
+        run:
+            shell("cp {input} {params.zip}")
+            shell("unzip -o {params.zip} -d {params.folder}")
+            for i in range(3):
+                # vsizip is special driver for directly working with zipped shapefiles in ogr2ogr
+                layer_path = f"/vsizip/{params.folder}/WDPA_WDOECM_{bYYYY}_Public_marine_shp_{i}.zip"
+                print(f"Adding layer {i + 1} of 3 to combined output file.")
+                shell("ogr2ogr -f gpkg -update -append {output.gpkg} {layer_path}")
+


 if config["enable"]["retrieve"]:
@ -220,6 +368,6 @@ if config["enable"]["retrieve"]:
            mem_mb=5000,
        retries: 2
        conda:
-            "../envs/environment.yaml"
+            "../envs/retrieve.yaml"
        script:
            "../scripts/retrieve_monthly_fuel_prices.py"
--- a/rules/solve_electricity.smk
+++ b/rules/solve_electricity.smk
@ -11,6 +11,7 @@ rule solve_network:
        co2_sequestration_potential=config_provider(
            "sector", "co2_sequestration_potential", default=200
        ),
+        custom_extra_functionality=input_custom_extra_functionality,
    input:
        network=resources("networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"),
        config=RESULTS + "config.yaml",
@ -24,7 +25,7 @@ rule solve_network:
        + "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_python.log",
    benchmark:
        BENCHMARKS + "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
-    threads: 4
+    threads: solver_threads
    resources:
        mem_mb=memory,
        walltime=config_provider("solving", "walltime", default="12:00:00"),
--- a/rules/solve_myopic.smk
+++ b/rules/solve_myopic.smk
@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
+# SPDX-FileCopyrightText: : 2023-4 The PyPSA-Eur Authors
 #
 # SPDX-License-Identifier: MIT

@ -21,7 +21,7 @@ rule add_existing_baseyear:
        ),
        cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
        cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
-        existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
+        existing_heating_distribution=resources("existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"),
        existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
        existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
        existing_offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv",
@ -54,7 +54,16 @@ rule add_brownfield:
            "sector", "H2_retrofit_capacity_per_CH4"
        ),
        threshold_capacity=config_provider("existing_capacities", " threshold_capacity"),
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, # TODO: use config_provider
+        carriers=config_provider("electricity", "renewable_carriers"),
    input:
+        **{
+            f"profile_{tech}": RESOURCES + f"profile_{tech}.nc"
+            for tech in config["electricity"]["renewable_carriers"]
+            if tech != "hydro"
+        },
+        simplify_busmap=RESOURCES + "busmap_elec_s{simpl}.csv",
+        cluster_busmap=RESOURCES + "busmap_elec_s{simpl}_{clusters}.csv",
        network=RESULTS
        + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
        network_p=solved_previous_horizon,  #solved network at previous time step
@ -92,6 +101,7 @@ rule solve_sector_network_myopic:
        co2_sequestration_potential=config_provider(
            "sector", "co2_sequestration_potential", default=200
        ),
+        custom_extra_functionality=input_custom_extra_functionality,
    input:
        network=RESULTS
        + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@ -107,7 +117,7 @@ rule solve_sector_network_myopic:
        + "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
        python=LOGS
        + "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
-    threads: 4
+    threads: solver_threads
    resources:
        mem_mb=config_provider("solving", "mem"),
        walltime=config_provider("solving", "walltime", default="12:00:00"),
--- a/rules/solve_overnight.smk
+++ b/rules/solve_overnight.smk
@ -11,6 +11,7 @@ rule solve_sector_network:
        co2_sequestration_potential=config_provider(
            "sector", "co2_sequestration_potential", default=200
        ),
+        custom_extra_functionality=input_custom_extra_functionality,
    input:
        network=RESULTS
        + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@ -21,11 +22,13 @@ rule solve_sector_network:
    shadow:
        "shallow"
    log:
-        solver=LOGS
-        + "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
-        python=LOGS
-        + "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
-    threads: config["solving"]["solver"].get("threads", 4)
+        solver=RESULTS
+        + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
+        memory=RESULTS
+        + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_memory.log",
+        python=RESULTS
+        + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
+    threads: solver_threads
    resources:
        mem_mb=config_provider("solving", "mem"),
        walltime=config_provider("solving", "walltime", default="12:00:00"),
--- a/rules/solve_perfect.smk
+++ b/rules/solve_perfect.smk
@ -0,0 +1,162 @@
+# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+rule add_existing_baseyear:
+    params:
+        baseyear=config["scenario"]["planning_horizons"][0],
+        sector=config["sector"],
+        existing_capacities=config["existing_capacities"],
+        costs=config["costs"],
+    input:
+        network=RESULTS
+        + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
+        powerplants=RESOURCES + "powerplants.csv",
+        busmap_s=RESOURCES + "busmap_elec_s{simpl}.csv",
+        busmap=RESOURCES + "busmap_elec_s{simpl}_{clusters}.csv",
+        clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
+        costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
+        cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
+        cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc",
+        existing_heating_distribution=RESOURCES
+        + "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+        existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
+        existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
+        existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
+        existing_offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv",
+    output:
+        RESULTS
+        + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
+    wildcard_constraints:
+        planning_horizons=config["scenario"]["planning_horizons"][0],  #only applies to baseyear
+    threads: 1
+    resources:
+        mem_mb=2000,
+    log:
+        LOGS
+        + "add_existing_baseyear_elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.log",
+    benchmark:
+        (
+            BENCHMARKS
+            + "add_existing_baseyear/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
+        )
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/add_existing_baseyear.py"
+
+
+rule prepare_perfect_foresight:
+    input:
+        **{
+            f"network_{year}": RESULTS
+            + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_"
+            + f"{year}.nc"
+            for year in config["scenario"]["planning_horizons"][1:]
+        },
+        brownfield_network=lambda w: (
+            RESULTS
+            + "prenetworks-brownfield/"
+            + "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_"
+            + "{}.nc".format(str(config["scenario"]["planning_horizons"][0]))
+        ),
+    output:
+        RESULTS
+        + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
+    threads: 2
+    resources:
+        mem_mb=10000,
+    log:
+        LOGS
+        + "prepare_perfect_foresight{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}.log",
+    benchmark:
+        (
+            BENCHMARKS
+            + "prepare_perfect_foresight{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}"
+        )
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/prepare_perfect_foresight.py"
+
+
+rule solve_sector_network_perfect:
+    params:
+        solving=config["solving"],
+        foresight=config["foresight"],
+        sector=config["sector"],
+        planning_horizons=config["scenario"]["planning_horizons"],
+        co2_sequestration_potential=config["sector"].get(
+            "co2_sequestration_potential", 200
+        ),
+        custom_extra_functionality=input_custom_extra_functionality,
+    input:
+        network=RESULTS
+        + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
+        costs="data/costs_2030.csv",
+        config=RESULTS + "config.yaml",
+    output:
+        RESULTS
+        + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
+    threads: solver_threads
+    resources:
+        mem_mb=config["solving"]["mem"],
+    shadow:
+        "shallow"
+    log:
+        solver=RESULTS
+        + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_solver.log",
+        python=RESULTS
+        + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_python.log",
+        memory=RESULTS
+        + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_memory.log",
+    benchmark:
+        (
+            BENCHMARKS
+            + "solve_sector_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years}"
+        )
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/solve_network.py"
+
+
+rule make_summary_perfect:
+    input:
+        **{
+            f"networks_{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}": RESULTS
+            + f"postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc"
+            for simpl in config["scenario"]["simpl"]
+            for clusters in config["scenario"]["clusters"]
+            for opts in config["scenario"]["opts"]
+            for sector_opts in config["scenario"]["sector_opts"]
+            for ll in config["scenario"]["ll"]
+        },
+        costs="data/costs_2020.csv",
+    output:
+        nodal_costs=RESULTS + "csvs/nodal_costs.csv",
+        nodal_capacities=RESULTS + "csvs/nodal_capacities.csv",
+        nodal_cfs=RESULTS + "csvs/nodal_cfs.csv",
+        cfs=RESULTS + "csvs/cfs.csv",
+        costs=RESULTS + "csvs/costs.csv",
+        capacities=RESULTS + "csvs/capacities.csv",
+        curtailment=RESULTS + "csvs/curtailment.csv",
+        energy=RESULTS + "csvs/energy.csv",
+        supply=RESULTS + "csvs/supply.csv",
+        supply_energy=RESULTS + "csvs/supply_energy.csv",
+        prices=RESULTS + "csvs/prices.csv",
+        weighted_prices=RESULTS + "csvs/weighted_prices.csv",
+        market_values=RESULTS + "csvs/market_values.csv",
+        price_statistics=RESULTS + "csvs/price_statistics.csv",
+        metrics=RESULTS + "csvs/metrics.csv",
+        co2_emissions=RESULTS + "csvs/co2_emissions.csv",
+    threads: 2
+    resources:
+        mem_mb=10000,
+    log:
+        LOGS + "make_summary_perfect.log",
+    benchmark:
+        (BENCHMARKS + "make_summary_perfect")
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/make_summary_perfect.py"
--- a/rules/validate.smk
+++ b/rules/validate.smk
@ -17,7 +17,7 @@ rule build_electricity_production:
    The data is used for validation of the optimization results.
    """
    params:
-        snapshots=config["snapshots"],
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
        countries=config["countries"],
    output:
        resources("historical_electricity_production.csv"),
@ -35,7 +35,7 @@ rule build_cross_border_flows:
    The data is used for validation of the optimization results.
    """
    params:
-        snapshots=config["snapshots"],
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
        countries=config["countries"],
    input:
        network=resources("networks/base.nc"),
@ -55,7 +55,7 @@ rule build_electricity_prices:
    The data is used for validation of the optimization results.
    """
    params:
-        snapshots=config["snapshots"],
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
        countries=config["countries"],
    output:
        resources("historical_electricity_prices.csv"),
--- a/scripts/_benchmark.py
+++ b/scripts/_benchmark.py
@ -0,0 +1,256 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+
+"""
+
+from __future__ import absolute_import, print_function
+
+import logging
+import os
+import sys
+import time
+
+from memory_profiler import _get_memory, choose_backend
+
+logger = logging.getLogger(__name__)
+
+# TODO: provide alternative when multiprocessing is not available
+try:
+    from multiprocessing import Pipe, Process
+except ImportError:
+    from multiprocessing.dummy import Pipe, Process
+
+
+# The memory logging facilities have been adapted from memory_profiler
+class MemTimer(Process):
+    """
+    Write memory consumption over a time interval to file until signaled to
+    stop on the pipe.
+    """
+
+    def __init__(
+        self, monitor_pid, interval, pipe, filename, max_usage, backend, *args, **kw
+    ):
+        self.monitor_pid = monitor_pid
+        self.interval = interval
+        self.pipe = pipe
+        self.filename = filename
+        self.max_usage = max_usage
+        self.backend = backend
+
+        self.timestamps = kw.pop("timestamps", True)
+        self.include_children = kw.pop("include_children", True)
+
+        super(MemTimer, self).__init__(*args, **kw)
+
+    def run(self):
+        # get baseline memory usage
+        cur_mem = _get_memory(
+            self.monitor_pid,
+            self.backend,
+            timestamps=self.timestamps,
+            include_children=self.include_children,
+        )
+
+        n_measurements = 1
+        mem_usage = cur_mem if self.max_usage else [cur_mem]
+
+        if self.filename is not None:
+            stream = open(self.filename, "w")
+            stream.write("MEM {0:.6f} {1:.4f}\n".format(*cur_mem))
+            stream.flush()
+        else:
+            stream = None
+
+        self.pipe.send(0)  # we're ready
+        stop = False
+        while True:
+            cur_mem = _get_memory(
+                self.monitor_pid,
+                self.backend,
+                timestamps=self.timestamps,
+                include_children=self.include_children,
+            )
+
+            if stream is not None:
+                stream.write("MEM {0:.6f} {1:.4f}\n".format(*cur_mem))
+                stream.flush()
+
+            n_measurements += 1
+            if not self.max_usage:
+                mem_usage.append(cur_mem)
+            else:
+                mem_usage = max(cur_mem, mem_usage)
+
+            if stop:
+                break
+            stop = self.pipe.poll(self.interval)
+            # do one more iteration
+
+        if stream is not None:
+            stream.close()
+
+        self.pipe.send(mem_usage)
+        self.pipe.send(n_measurements)
+
+
+class memory_logger(object):
+    """
+    Context manager for taking and reporting memory measurements at fixed
+    intervals from a separate process, for the duration of a context.
+
+    Parameters
+    ----------
+    filename : None|str
+        Name of the text file to log memory measurements, if None no log is
+        created (defaults to None)
+    interval : float
+        Interval between measurements (defaults to 1.)
+    max_usage : bool
+        If True, only store and report the maximum value (defaults to True)
+    timestamps : bool
+        Whether to record tuples of memory usage and timestamps; if logging to
+        a file timestamps are always kept (defaults to True)
+    include_children : bool
+        Whether the memory of subprocesses is to be included (default: True)
+
+    Arguments
+    ---------
+    n_measurements : int
+        Number of measurements that have been taken
+    mem_usage : (float, float)|[(float, float)]
+        All memory measurements and timestamps (if timestamps was True) or only
+        the maximum memory usage and its timestamp
+
+    Note
+    ----
+    The arguments are only set after all the measurements, i.e. outside of the
+    with statement.
+
+    Example
+    -------
+    with memory_logger(filename="memory.log", max_usage=True) as mem:
+        # Do a lot of long running memory intensive stuff
+        hard_memory_bound_stuff()
+
+    max_mem, timestamp = mem.mem_usage
+    """
+
+    def __init__(
+        self,
+        filename=None,
+        interval=1.0,
+        max_usage=True,
+        timestamps=True,
+        include_children=True,
+    ):
+        if filename is not None:
+            timestamps = True
+
+        self.filename = filename
+        self.interval = interval
+        self.max_usage = max_usage
+        self.timestamps = timestamps
+        self.include_children = include_children
+
+    def __enter__(self):
+        backend = choose_backend()
+
+        self.child_conn, self.parent_conn = Pipe()  # this will store MemTimer's results
+        self.p = MemTimer(
+            os.getpid(),
+            self.interval,
+            self.child_conn,
+            self.filename,
+            backend=backend,
+            timestamps=self.timestamps,
+            max_usage=self.max_usage,
+            include_children=self.include_children,
+        )
+        self.p.start()
+        self.parent_conn.recv()  # wait until memory logging in subprocess is ready
+
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if exc_type is None:
+            self.parent_conn.send(0)  # finish timing
+
+            self.mem_usage = self.parent_conn.recv()
+            self.n_measurements = self.parent_conn.recv()
+        else:
+            self.p.terminate()
+
+        return False
+
+
+class timer(object):
+    level = 0
+    opened = False
+
+    def __init__(self, name="", verbose=True):
+        self.name = name
+        self.verbose = verbose
+
+    def __enter__(self):
+        if self.verbose:
+            if self.opened:
+                sys.stdout.write("\n")
+
+            if len(self.name) > 0:
+                sys.stdout.write((".. " * self.level) + self.name + ": ")
+            sys.stdout.flush()
+
+            self.__class__.opened = True
+
+        self.__class__.level += 1
+
+        self.start = time.time()
+        return self
+
+    def print_usec(self, usec):
+        if usec < 1000:
+            print("%.1f usec" % usec)
+        else:
+            msec = usec / 1000
+            if msec < 1000:
+                print("%.1f msec" % msec)
+            else:
+                sec = msec / 1000
+                print("%.1f sec" % sec)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.opened and self.verbose:
+            sys.stdout.write(".. " * self.level)
+
+        if exc_type is None:
+            stop = time.time()
+            self.usec = usec = (stop - self.start) * 1e6
+            if self.verbose:
+                self.print_usec(usec)
+        elif self.verbose:
+            print("failed")
+        sys.stdout.flush()
+
+        self.__class__.level -= 1
+        if self.verbose:
+            self.__class__.opened = False
+        return False
+
+
+class optional(object):
+    def __init__(self, variable, contextman):
+        self.variable = variable
+        self.contextman = contextman
+
+    def __enter__(self):
+        if self.variable:
+            return self.contextman.__enter__()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.variable:
+            return self.contextman.__exit__(exc_type, exc_val, exc_tb)
+        return False
--- a/scripts/_helpers.py
+++ b/scripts/_helpers.py
@ -4,6 +4,7 @@
 # SPDX-License-Identifier: MIT

 import contextlib
+import hashlib
 import logging
 import os
 import re
@ -13,6 +14,7 @@ from pathlib import Path

 import pandas as pd
 import pytz
+import requests
 import yaml
 from snakemake.utils import update_config
 from tqdm import tqdm
@ -90,6 +92,35 @@ def path_provider(dir, rdir, shared_resources):
    return partial(get_run_path, dir=dir, rdir=rdir, shared_resources=shared_resources)


+def get_opt(opts, expr, flags=None):
+    """
+    Return the first option matching the regular expression.
+
+    The regular expression is case-insensitive by default.
+    """
+    if flags is None:
+        flags = re.IGNORECASE
+    for o in opts:
+        match = re.match(expr, o, flags=flags)
+        if match:
+            return match.group(0)
+    return None
+
+
+def find_opt(opts, expr):
+    """
+    Return if available the float after the expression.
+    """
+    for o in opts:
+        if expr in o:
+            m = re.findall("[0-9]*\.?[0-9]+$", o)
+            if len(m) > 0:
+                return True, float(m[0])
+            else:
+                return True, None
+    return False, None
+
+
 # Define a context manager to temporarily mute print statements
@contextlib.contextmanager
 def mute_print():
@ -132,6 +163,7 @@ def configure_logging(snakemake, skip_handlers=False):
        Do (not) skip the default handlers created for redirecting output to STDERR and file.
    """
    import logging
+    import sys

    kwargs = snakemake.config.get("logging", dict()).copy()
    kwargs.setdefault("level", "INFO")
@ -155,6 +187,16 @@ def configure_logging(snakemake, skip_handlers=False):
        )
    logging.basicConfig(**kwargs)

+    # Setup a function to handle uncaught exceptions and include them with their stacktrace into logfiles
+    def handle_exception(exc_type, exc_value, exc_traceback):
+        # Log the exception
+        logger = logging.getLogger()
+        logger.error(
+            "Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback)
+        )
+
+    sys.excepthook = handle_exception
+

 def update_p_nom_max(n):
    # if extendable carriers (solar/onwind/...) have capacity >= 0,
@ -275,7 +317,13 @@ def progress_retrieve(url, file, disable=False):
            urllib.request.urlretrieve(url, file, reporthook=update_to)


-def mock_snakemake(rulename, configfiles=[], **wildcards):
+def mock_snakemake(
+    rulename,
+    root_dir=None,
+    configfiles=[],
+    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,
@ -287,8 +335,13 @@ def mock_snakemake(rulename, configfiles=[], **wildcards):
    ----------
    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.
@ -296,15 +349,20 @@ def mock_snakemake(rulename, configfiles=[], **wildcards):
    import os

    import snakemake as sm
-    from packaging.version import Version, parse
    from pypsa.descriptors import Dict
    from snakemake.script import Snakemake

    script_dir = Path(__file__).parent.resolve()
-    root_dir = script_dir.parent
+    if root_dir is None:
+        root_dir = script_dir.parent
+    else:
+        root_dir = Path(root_dir).resolve()

    user_in_script_dir = Path.cwd().resolve() == script_dir
-    if user_in_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:
        raise RuntimeError(
@ -316,13 +374,12 @@ def mock_snakemake(rulename, configfiles=[], **wildcards):
            if os.path.exists(p):
                snakefile = p
                break
-        kwargs = (
-            dict(rerun_triggers=[]) if parse(sm.__version__) > Version("7.7.0") else {}
-        )
        if isinstance(configfiles, str):
            configfiles = [configfiles]

-        workflow = sm.Workflow(snakefile, overwrite_configfiles=configfiles, **kwargs)
+        workflow = sm.Workflow(
+            snakefile, overwrite_configfiles=configfiles, rerun_triggers=[]
+        )
        workflow.include(snakefile)

        if configfiles:
@ -386,17 +443,89 @@ def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
    return week_df


-def parse(l):
-    if len(l) == 1:
-        return yaml.safe_load(l[0])
+def parse(infix):
+    """
+    Recursively parse a chained wildcard expression into a dictionary or a YAML
+    object.
+
+    Parameters
+    ----------
+    list_to_parse : list
+        The list to parse.
+
+    Returns
+    -------
+    dict or YAML object
+        The parsed list.
+    """
+    if len(infix) == 1:
+        return yaml.safe_load(infix[0])
    else:
-        return {l.pop(0): parse(l)}
+        return {infix.pop(0): parse(infix)}


 def update_config_with_sector_opts(config, sector_opts):
-    from snakemake.utils import update_config
-
    for o in sector_opts.split("-"):
        if o.startswith("CF+"):
-            l = o.split("+")[1:]
-            update_config(config, parse(l))
+            infix = o.split("+")[1:]
+            update_config(config, parse(infix))
+
+
+def get_checksum_from_zenodo(file_url):
+    parts = file_url.split("/")
+    record_id = parts[parts.index("record") + 1]
+    filename = parts[-1]
+
+    response = requests.get(f"https://zenodo.org/api/records/{record_id}", timeout=30)
+    response.raise_for_status()
+    data = response.json()
+
+    for file in data["files"]:
+        if file["key"] == filename:
+            return file["checksum"]
+    return None
+
+
+def validate_checksum(file_path, zenodo_url=None, checksum=None):
+    """
+    Validate file checksum against provided or Zenodo-retrieved checksum.
+    Calculates the hash of a file using 64KB chunks. Compares it against a
+    given checksum or one from a Zenodo URL.
+
+    Parameters
+    ----------
+    file_path : str
+        Path to the file for checksum validation.
+    zenodo_url : str, optional
+        URL of the file on Zenodo to fetch the checksum.
+    checksum : str, optional
+        Checksum (format 'hash_type:checksum_value') for validation.
+
+    Raises
+    ------
+    AssertionError
+        If the checksum does not match, or if neither `checksum` nor `zenodo_url` is provided.
+
+
+    Examples
+    --------
+    >>> validate_checksum("/path/to/file", checksum="md5:abc123...")
+    >>> validate_checksum(
+    ...     "/path/to/file",
+    ...     zenodo_url="https://zenodo.org/record/12345/files/example.txt",
+    ... )
+
+    If the checksum is invalid, an AssertionError will be raised.
+    """
+    assert checksum or zenodo_url, "Either checksum or zenodo_url must be provided"
+    if zenodo_url:
+        checksum = get_checksum_from_zenodo(zenodo_url)
+    hash_type, checksum = checksum.split(":")
+    hasher = hashlib.new(hash_type)
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(65536), b""):  # 64kb chunks
+            hasher.update(chunk)
+    calculated_checksum = hasher.hexdigest()
+    assert (
+        calculated_checksum == checksum
+    ), "Checksum is invalid. This may be due to an incomplete download. Delete the file and re-execute the rule."
--- a/scripts/add_brownfield.py
+++ b/scripts/add_brownfield.py
@ -8,16 +8,16 @@ Prepares brownfield data from previous planning horizon.

 import logging

-logger = logging.getLogger(__name__)
-
-import pandas as pd
-
-idx = pd.IndexSlice
-
 import numpy as np
+import pandas as pd
 import pypsa
+import xarray as xr
 from _helpers import update_config_with_sector_opts
 from add_existing_baseyear import add_build_year_to_new_assets
+from pypsa.clustering.spatial import normed_or_uniform
+
+logger = logging.getLogger(__name__)
+idx = pd.IndexSlice


 def add_brownfield(n, n_p, year):
@ -41,12 +41,9 @@ def add_brownfield(n, n_p, year):
        # remove assets if their optimized nominal capacity is lower than a threshold
        # since CHP heat Link is proportional to CHP electric Link, make sure threshold is compatible
        chp_heat = c.df.index[
-            (
-                c.df[attr + "_nom_extendable"]
-                & c.df.index.str.contains("urban central")
-                & c.df.index.str.contains("CHP")
-                & c.df.index.str.contains("heat")
-            )
+            (c.df[f"{attr}_nom_extendable"] & c.df.index.str.contains("urban central"))
+            & c.df.index.str.contains("CHP")
+            & c.df.index.str.contains("heat")
        ]

        threshold = snakemake.params.threshold_capacity
@ -60,21 +57,20 @@ def add_brownfield(n, n_p, year):
            )
            n_p.mremove(
                c.name,
-                chp_heat[c.df.loc[chp_heat, attr + "_nom_opt"] < threshold_chp_heat],
+                chp_heat[c.df.loc[chp_heat, f"{attr}_nom_opt"] < threshold_chp_heat],
            )

        n_p.mremove(
            c.name,
            c.df.index[
-                c.df[attr + "_nom_extendable"]
-                & ~c.df.index.isin(chp_heat)
-                & (c.df[attr + "_nom_opt"] < threshold)
+                (c.df[f"{attr}_nom_extendable"] & ~c.df.index.isin(chp_heat))
+                & (c.df[f"{attr}_nom_opt"] < threshold)
            ],
        )

        # copy over assets but fix their capacity
-        c.df[attr + "_nom"] = c.df[attr + "_nom_opt"]
-        c.df[attr + "_nom_extendable"] = False
+        c.df[f"{attr}_nom"] = c.df[f"{attr}_nom_opt"]
+        c.df[f"{attr}_nom_extendable"] = False

        n.import_components_from_dataframe(c.df, c.name)

@ -124,7 +120,82 @@ def add_brownfield(n, n_p, year):
            n.links.loc[new_pipes, "p_nom_min"] = 0.0


-# %%
+def disable_grid_expansion_if_LV_limit_hit(n):
+    if "lv_limit" not in n.global_constraints.index:
+        return
+
+    total_expansion = (
+        n.lines.eval("s_nom_min * length").sum()
+        + n.links.query("carrier == 'DC'").eval("p_nom_min * length").sum()
+    ).sum()
+
+    lv_limit = n.global_constraints.at["lv_limit", "constant"]
+
+    # allow small numerical differences
+    if lv_limit - total_expansion < 1:
+        logger.info("LV is already reached, disabling expansion and LV limit")
+        extendable_acs = n.lines.query("s_nom_extendable").index
+        n.lines.loc[extendable_acs, "s_nom_extendable"] = False
+        n.lines.loc[extendable_acs, "s_nom"] = n.lines.loc[extendable_acs, "s_nom_min"]
+
+        extendable_dcs = n.links.query("carrier == 'DC' and p_nom_extendable").index
+        n.links.loc[extendable_dcs, "p_nom_extendable"] = False
+        n.links.loc[extendable_dcs, "p_nom"] = n.links.loc[extendable_dcs, "p_nom_min"]
+
+        n.global_constraints.drop("lv_limit", inplace=True)
+
+
+def adjust_renewable_profiles(n, input_profiles, params, year):
+    """
+    Adjusts renewable profiles according to the renewable technology specified,
+    using the latest year below or equal to the selected year.
+    """
+
+    # spatial clustering
+    cluster_busmap = pd.read_csv(snakemake.input.cluster_busmap, index_col=0).squeeze()
+    simplify_busmap = pd.read_csv(
+        snakemake.input.simplify_busmap, index_col=0
+    ).squeeze()
+    clustermaps = simplify_busmap.map(cluster_busmap)
+    clustermaps.index = clustermaps.index.astype(str)
+
+    # temporal clustering
+    dr = pd.date_range(**params["snapshots"], freq="h")
+    snapshotmaps = (
+        pd.Series(dr, index=dr).where(lambda x: x.isin(n.snapshots), pd.NA).ffill()
+    )
+
+    for carrier in params["carriers"]:
+        if carrier == "hydro":
+            continue
+        with xr.open_dataset(getattr(input_profiles, "profile_" + carrier)) as ds:
+            if ds.indexes["bus"].empty or "year" not in ds.indexes:
+                continue
+
+            closest_year = max(
+                (y for y in ds.year.values if y <= year), default=min(ds.year.values)
+            )
+
+            p_max_pu = (
+                ds["profile"]
+                .sel(year=closest_year)
+                .transpose("time", "bus")
+                .to_pandas()
+            )
+
+            # spatial clustering
+            weight = ds["weight"].sel(year=closest_year).to_pandas()
+            weight = weight.groupby(clustermaps).transform(normed_or_uniform)
+            p_max_pu = (p_max_pu * weight).T.groupby(clustermaps).sum().T
+            p_max_pu.columns = p_max_pu.columns + f" {carrier}"
+
+            # temporal_clustering
+            p_max_pu = p_max_pu.groupby(snapshotmaps).mean()
+
+            # replace renewable time series
+            n.generators_t.p_max_pu.loc[:, p_max_pu.columns] = p_max_pu
+
+
 if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake
@ -135,7 +206,7 @@ if __name__ == "__main__":
            clusters="37",
            opts="",
            ll="v1.0",
-            sector_opts="168H-T-H-B-I-solar+p3-dist1",
+            sector_opts="168H-T-H-B-I-dist1",
            planning_horizons=2030,
        )

@ -149,11 +220,15 @@ if __name__ == "__main__":

    n = pypsa.Network(snakemake.input.network)

+    adjust_renewable_profiles(n, snakemake.input, snakemake.params, year)
+
    add_build_year_to_new_assets(n, year)

    n_p = pypsa.Network(snakemake.input.network_p)

    add_brownfield(n, n_p, year)

+    disable_grid_expansion_if_LV_limit_hit(n)
+
    n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
    n.export_to_netcdf(snakemake.output[0])
--- a/scripts/add_electricity.py
+++ b/scripts/add_electricity.py
@ -84,6 +84,7 @@ It further adds extendable ``generators`` with **zero** capacity for

 import logging
 from itertools import product
+from typing import Dict, List

 import geopandas as gpd
 import numpy as np
@ -177,6 +178,15 @@ def sanitize_carriers(n, config):
    n.carriers["color"] = n.carriers.color.where(n.carriers.color != "", colors)


+def sanitize_locations(n):
+    n.buses["x"] = n.buses.x.where(n.buses.x != 0, n.buses.location.map(n.buses.x))
+    n.buses["y"] = n.buses.y.where(n.buses.y != 0, n.buses.location.map(n.buses.y))
+    n.buses["country"] = n.buses.country.where(
+        n.buses.country.ne("") & n.buses.country.notnull(),
+        n.buses.location.map(n.buses.country),
+    )
+
+
 def add_co2_emissions(n, costs, carriers):
    """
    Add CO2 emissions to the network's carriers attribute.
@ -255,6 +265,7 @@ def load_powerplants(ppl_fn):
        "bioenergy": "biomass",
        "ccgt, thermal": "CCGT",
        "hard coal": "coal",
+        "natural gas": "OCGT",
    }
    return (
        pd.read_csv(ppl_fn, index_col=0, dtype={"bus": "str"})
@ -279,38 +290,43 @@ def shapes_to_shapes(orig, dest):
    return transfer


-def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.0):
+def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.0):
    substation_lv_i = n.buses.index[n.buses["substation_lv"]]
    regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
    opsd_load = pd.read_csv(load, index_col=0, parse_dates=True).filter(items=countries)

-    logger.info(f"Load data scaled with scalling factor {scaling}.")
+    ua_md_gdp = pd.read_csv(ua_md_gdp, dtype={"name": "str"}).set_index("name")
+
+    logger.info(f"Load data scaled by factor {scaling}.")
    opsd_load *= scaling

    nuts3 = gpd.read_file(nuts3_shapes).set_index("index")

    def upsample(cntry, group):
-        l = opsd_load[cntry]
-        if len(group) == 1:
-            return pd.DataFrame({group.index[0]: l})
-        else:
-            nuts3_cntry = nuts3.loc[nuts3.country == cntry]
-            transfer = shapes_to_shapes(group, nuts3_cntry.geometry).T.tocsr()
-            gdp_n = pd.Series(
-                transfer.dot(nuts3_cntry["gdp"].fillna(1.0).values), index=group.index
-            )
-            pop_n = pd.Series(
-                transfer.dot(nuts3_cntry["pop"].fillna(1.0).values), index=group.index
-            )
+        load = opsd_load[cntry]

-            # relative factors 0.6 and 0.4 have been determined from a linear
-            # regression on the country to continent load data
-            factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
-            return pd.DataFrame(
-                factors.values * l.values[:, np.newaxis],
-                index=l.index,
-                columns=factors.index,
-            )
+        if len(group) == 1:
+            return pd.DataFrame({group.index[0]: load})
+        nuts3_cntry = nuts3.loc[nuts3.country == cntry]
+        transfer = shapes_to_shapes(group, nuts3_cntry.geometry).T.tocsr()
+        gdp_n = pd.Series(
+            transfer.dot(nuts3_cntry["gdp"].fillna(1.0).values), index=group.index
+        )
+        pop_n = pd.Series(
+            transfer.dot(nuts3_cntry["pop"].fillna(1.0).values), index=group.index
+        )
+
+        # relative factors 0.6 and 0.4 have been determined from a linear
+        # regression on the country to continent load data
+        factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
+        if cntry in ["UA", "MD"]:
+            # overwrite factor because nuts3 provides no data for UA+MD
+            factors = normed(ua_md_gdp.loc[group.index, "GDP_PPP"].squeeze())
+        return pd.DataFrame(
+            factors.values * load.values[:, np.newaxis],
+            index=load.index,
+            columns=factors.index,
+        )

    load = pd.concat(
        [
@ -320,7 +336,9 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.0):
        axis=1,
    )

-    n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)
+    n.madd(
+        "Load", substation_lv_i, bus=substation_lv_i, p_set=load
+    )  # carrier="electricity"


 def update_transmission_costs(n, costs, length_factor=1.0):
@ -367,6 +385,10 @@ def attach_wind_and_solar(
            if ds.indexes["bus"].empty:
                continue

+            # if-statement for compatibility with old profiles
+            if "year" in ds.indexes:
+                ds = ds.sel(year=ds.year.min(), drop=True)
+
            supcar = car.split("-", 2)[0]
            if supcar == "offwind":
                underwater_fraction = ds["underwater_fraction"].to_pandas()
@ -406,6 +428,7 @@ def attach_wind_and_solar(
                capital_cost=capital_cost,
                efficiency=costs.at[supcar, "efficiency"],
                p_max_pu=ds["profile"].transpose("time", "bus").to_pandas(),
+                lifetime=costs.at[supcar, "lifetime"],
            )


@ -434,7 +457,7 @@ def attach_conventional_generators(
    ppl = (
        ppl.query("carrier in @carriers")
        .join(costs, on="carrier", rsuffix="_r")
-        .rename(index=lambda s: "C" + str(s))
+        .rename(index=lambda s: f"C{str(s)}")
    )
    ppl["efficiency"] = ppl.efficiency.fillna(ppl.efficiency_r)

@ -496,8 +519,8 @@ def attach_conventional_generators(
                    snakemake.input[f"conventional_{carrier}_{attr}"], index_col=0
                ).iloc[:, 0]
                bus_values = n.buses.country.map(values)
-                n.generators[attr].update(
-                    n.generators.loc[idx].bus.map(bus_values).dropna()
+                n.generators.update(
+                    {attr: n.generators.loc[idx].bus.map(bus_values).dropna()}
                )
            else:
                # Single value affecting all generators of technology k indiscriminantely of country
@ -511,7 +534,7 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **par
    ppl = (
        ppl.query('carrier == "hydro"')
        .reset_index(drop=True)
-        .rename(index=lambda s: str(s) + " hydro")
+        .rename(index=lambda s: f"{str(s)} hydro")
    )
    ror = ppl.query('technology == "Run-Of-River"')
    phs = ppl.query('technology == "Pumped Storage"')
@ -608,16 +631,13 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **par
        )
        if not missing_countries.empty:
            logger.warning(
-                "Assuming max_hours=6 for hydro reservoirs in the countries: {}".format(
-                    ", ".join(missing_countries)
-                )
+                f'Assuming max_hours=6 for hydro reservoirs in the countries: {", ".join(missing_countries)}'
            )
        hydro_max_hours = hydro.max_hours.where(
            hydro.max_hours > 0, hydro.country.map(max_hours_country)
        ).fillna(6)

-        flatten_dispatch = params.get("flatten_dispatch", False)
-        if flatten_dispatch:
+        if params.get("flatten_dispatch", False):
            buffer = params.get("flatten_dispatch_buffer", 0.2)
            average_capacity_factor = inflow_t[hydro.index].mean() / hydro["p_nom"]
            p_max_pu = (average_capacity_factor + buffer).clip(upper=1)
@ -642,78 +662,17 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **par
        )


-def attach_extendable_generators(n, costs, ppl, carriers):
-    logger.warning(
-        "The function `attach_extendable_generators` is deprecated in v0.5.0."
-    )
-    add_missing_carriers(n, carriers)
-    add_co2_emissions(n, costs, carriers)
+def attach_OPSD_renewables(n: pypsa.Network, tech_map: Dict[str, List[str]]) -> None:
+    """
+    Attach renewable capacities from the OPSD dataset to the network.

-    for tech in carriers:
-        if tech.startswith("OCGT"):
-            ocgt = (
-                ppl.query("carrier in ['OCGT', 'CCGT']")
-                .groupby("bus", as_index=False)
-                .first()
-            )
-            n.madd(
-                "Generator",
-                ocgt.index,
-                suffix=" OCGT",
-                bus=ocgt["bus"],
-                carrier=tech,
-                p_nom_extendable=True,
-                p_nom=0.0,
-                capital_cost=costs.at["OCGT", "capital_cost"],
-                marginal_cost=costs.at["OCGT", "marginal_cost"],
-                efficiency=costs.at["OCGT", "efficiency"],
-            )
+    Args:
+    - n: The PyPSA network to attach the capacities to.
+    - tech_map: A dictionary mapping fuel types to carrier names.

-        elif tech.startswith("CCGT"):
-            ccgt = (
-                ppl.query("carrier in ['OCGT', 'CCGT']")
-                .groupby("bus", as_index=False)
-                .first()
-            )
-            n.madd(
-                "Generator",
-                ccgt.index,
-                suffix=" CCGT",
-                bus=ccgt["bus"],
-                carrier=tech,
-                p_nom_extendable=True,
-                p_nom=0.0,
-                capital_cost=costs.at["CCGT", "capital_cost"],
-                marginal_cost=costs.at["CCGT", "marginal_cost"],
-                efficiency=costs.at["CCGT", "efficiency"],
-            )
-
-        elif tech.startswith("nuclear"):
-            nuclear = (
-                ppl.query("carrier == 'nuclear'").groupby("bus", as_index=False).first()
-            )
-            n.madd(
-                "Generator",
-                nuclear.index,
-                suffix=" nuclear",
-                bus=nuclear["bus"],
-                carrier=tech,
-                p_nom_extendable=True,
-                p_nom=0.0,
-                capital_cost=costs.at["nuclear", "capital_cost"],
-                marginal_cost=costs.at["nuclear", "marginal_cost"],
-                efficiency=costs.at["nuclear", "efficiency"],
-            )
-
-        else:
-            raise NotImplementedError(
-                "Adding extendable generators for carrier "
-                "'{tech}' is not implemented, yet. "
-                "Only OCGT, CCGT and nuclear are allowed at the moment."
-            )
-
-
-def attach_OPSD_renewables(n, tech_map):
+    Returns:
+    - None
+    """
    tech_string = ", ".join(sum(tech_map.values(), []))
    logger.info(f"Using OPSD renewable capacities for carriers {tech_string}.")

@ -734,11 +693,30 @@ def attach_OPSD_renewables(n, tech_map):
        caps = caps.groupby(["bus"]).Capacity.sum()
        caps = caps / gens_per_bus.reindex(caps.index, fill_value=1)

-        n.generators.p_nom.update(gens.bus.map(caps).dropna())
-        n.generators.p_nom_min.update(gens.bus.map(caps).dropna())
+        n.generators.update({"p_nom": gens.bus.map(caps).dropna()})
+        n.generators.update({"p_nom_min": gens.bus.map(caps).dropna()})


-def estimate_renewable_capacities(n, year, tech_map, expansion_limit, countries):
+def estimate_renewable_capacities(
+    n: pypsa.Network, year: int, tech_map: dict, expansion_limit: bool, countries: list
+) -> None:
+    """
+    Estimate a different between renewable capacities in the network and
+    reported country totals from IRENASTAT dataset. Distribute the difference
+    with a heuristic.
+
+    Heuristic: n.generators_t.p_max_pu.mean() * n.generators.p_nom_max
+
+    Args:
+    - n: The PyPSA network.
+    - year: The year of optimisation.
+    - tech_map: A dictionary mapping fuel types to carrier names.
+    - expansion_limit: Boolean value from config file
+    - countries: A list of country codes to estimate capacities for.
+
+    Returns:
+    - None
+    """
    if not len(countries) or not len(tech_map):
        return

@ -755,7 +733,10 @@ def estimate_renewable_capacities(n, year, tech_map, expansion_limit, countries)

    for ppm_technology, techs in tech_map.items():
        tech_i = n.generators.query("carrier in @techs").index
-        stats = capacities.loc[ppm_technology].reindex(countries, fill_value=0.0)
+        if ppm_technology in capacities.index.get_level_values("Technology"):
+            stats = capacities.loc[ppm_technology].reindex(countries, fill_value=0.0)
+        else:
+            stats = pd.Series(0.0, index=countries)
        country = n.generators.bus[tech_i].map(n.buses.country)
        existent = n.generators.p_nom[tech_i].groupby(country).sum()
        missing = stats - existent
@ -829,6 +810,7 @@ if __name__ == "__main__":
        snakemake.input.regions,
        snakemake.input.load,
        snakemake.input.nuts3_shapes,
+        snakemake.input.ua_md_gdp,
        params.countries,
        params.scaling_factor,
    )
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@ -8,25 +8,20 @@ horizon.
 """

 import logging
-
-logger = logging.getLogger(__name__)
-
-import pandas as pd
-
-idx = pd.IndexSlice
-
 from types import SimpleNamespace

 import country_converter as coco
 import numpy as np
+import pandas as pd
 import pypsa
 import xarray as xr
 from _helpers import update_config_with_sector_opts
 from add_electricity import sanitize_carriers
 from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs

+logger = logging.getLogger(__name__)
 cc = coco.CountryConverter()
-
+idx = pd.IndexSlice
 spatial = SimpleNamespace()


@ -45,7 +40,7 @@ def add_build_year_to_new_assets(n, baseyear):

        # add -baseyear to name
        rename = pd.Series(c.df.index, c.df.index)
-        rename[assets] += "-" + str(baseyear)
+        rename[assets] += f"-{str(baseyear)}"
        c.df.rename(index=rename, inplace=True)

        # rename time-dependent
@ -53,7 +48,7 @@ def add_build_year_to_new_assets(n, baseyear):
            "series"
        ) & n.component_attrs[c.name].status.str.contains("Input")
        for attr in n.component_attrs[c.name].index[selection]:
-            c.pnl[attr].rename(columns=rename, inplace=True)
+            c.pnl[attr] = c.pnl[attr].rename(columns=rename)


 def add_existing_renewables(df_agg):
@ -88,7 +83,9 @@ def add_existing_renewables(df_agg):
            ]
            cfs = n.generators_t.p_max_pu[gens].mean()
            cfs_key = cfs / cfs.sum()
-            nodal_fraction.loc[n.generators.loc[gens, "bus"]] = cfs_key.values
+            nodal_fraction.loc[n.generators.loc[gens, "bus"]] = cfs_key.groupby(
+                n.generators.loc[gens, "bus"]
+            ).sum()

        nodal_df = df.loc[n.buses.loc[elec_buses, "country"]]
        nodal_df.index = elec_buses
@ -252,7 +249,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
            if "m" in snakemake.wildcards.clusters:
                for ind in new_capacity.index:
                    # existing capacities are split evenly among regions in every country
-                    inv_ind = [i for i in inv_busmap[ind]]
+                    inv_ind = list(inv_busmap[ind])

                    # for offshore the splitting only includes coastal regions
                    inv_ind = [
@ -303,7 +300,19 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
        else:
            bus0 = vars(spatial)[carrier[generator]].nodes
            if "EU" not in vars(spatial)[carrier[generator]].locations:
-                bus0 = bus0.intersection(capacity.index + " gas")
+                bus0 = bus0.intersection(capacity.index + " " + carrier[generator])
+
+            # check for missing bus
+            missing_bus = pd.Index(bus0).difference(n.buses.index)
+            if not missing_bus.empty:
+                logger.info(f"add buses {bus0}")
+                n.madd(
+                    "Bus",
+                    bus0,
+                    carrier=generator,
+                    location=vars(spatial)[carrier[generator]].locations,
+                    unit="MWh_el",
+                )

            already_build = n.links.index.intersection(asset_i)
            new_build = asset_i.difference(n.links.index)
@ -393,104 +402,18 @@ def add_heating_capacities_installed_before_baseyear(
    """
    logger.debug(f"Adding heating capacities installed before {baseyear}")

-    # Add existing heating capacities, data comes from the study
-    # "Mapping and analyses of the current and future (2020 - 2030)
-    # heating/cooling fuel deployment (fossil/renewables) "
-    # https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
-    # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
-    # TODO start from original file
-
-    # retrieve existing heating capacities
-    techs = [
-        "gas boiler",
-        "oil boiler",
-        "resistive heater",
-        "air heat pump",
-        "ground heat pump",
-    ]
-    df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
-
-    # data for Albania, Montenegro and Macedonia not included in database
-    df.loc["Albania"] = np.nan
-    df.loc["Montenegro"] = np.nan
-    df.loc["Macedonia"] = np.nan
-
-    df.fillna(0.0, inplace=True)
-
-    # convert GW to MW
-    df *= 1e3
-
-    df.index = cc.convert(df.index, to="iso2")
-
-    # coal and oil boilers are assimilated to oil boilers
-    df["oil boiler"] = df["oil boiler"] + df["coal boiler"]
-    df.drop(["coal boiler"], axis=1, inplace=True)
-
-    # distribute technologies to nodes by population
-    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
-
-    nodal_df = df.loc[pop_layout.ct]
-    nodal_df.index = pop_layout.index
-    nodal_df = nodal_df.multiply(pop_layout.fraction, axis=0)
-
-    # split existing capacities between residential and services
-    # proportional to energy demand
-    p_set_sum = n.loads_t.p_set.sum()
-    ratio_residential = pd.Series(
-        [
-            (
-                p_set_sum[f"{node} residential rural heat"]
-                / (
-                    p_set_sum[f"{node} residential rural heat"]
-                    + p_set_sum[f"{node} services rural heat"]
-                )
-            )
-            # if rural heating demand for one of the nodes doesn't exist,
-            # then columns were dropped before and heating demand share should be 0.0
-            if all(
-                f"{node} {service} rural heat" in p_set_sum.index
-                for service in ["residential", "services"]
-            )
-            else 0.0
-            for node in nodal_df.index
-        ],
-        index=nodal_df.index,
+    existing_heating = pd.read_csv(
+        snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
    )

-    for tech in techs:
-        nodal_df["residential " + tech] = nodal_df[tech] * ratio_residential
-        nodal_df["services " + tech] = nodal_df[tech] * (1 - ratio_residential)
+    techs = existing_heating.columns.get_level_values(1).unique()

-    names = [
-        "residential rural",
-        "services rural",
-        "residential urban decentral",
-        "services urban decentral",
-        "urban central",
-    ]
-
-    nodes = {}
-    p_nom = {}
-    for name in names:
+    for name in existing_heating.columns.get_level_values(0).unique():
        name_type = "central" if name == "urban central" else "decentral"
-        nodes[name] = pd.Index(
-            [
-                n.buses.at[index, "location"]
-                for index in n.buses.index[
-                    n.buses.index.str.contains(name)
-                    & n.buses.index.str.contains("heat")
-                ]
-            ]
-        )
-        heat_pump_type = "air" if "urban" in name else "ground"
-        heat_type = "residential" if "residential" in name else "services"

-        if name == "urban central":
-            p_nom[name] = nodal_df["air heat pump"][nodes[name]]
-        else:
-            p_nom[name] = nodal_df[f"{heat_type} {heat_pump_type} heat pump"][
-                nodes[name]
-            ]
+        nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
+
+        heat_pump_type = "air" if "urban" in name else "ground"

        # Add heat pumps
        costs_name = f"decentral {heat_pump_type}-sourced heat pump"
@ -498,7 +421,7 @@ def add_heating_capacities_installed_before_baseyear(
        cop = {"air": ashp_cop, "ground": gshp_cop}

        if time_dep_hp_cop:
-            efficiency = cop[heat_pump_type][nodes[name]]
+            efficiency = cop[heat_pump_type][nodes]
        else:
            efficiency = costs.at[costs_name, "efficiency"]

@ -506,82 +429,90 @@ def add_heating_capacities_installed_before_baseyear(
            if int(grouping_year) + default_lifetime <= int(baseyear):
                continue

-            # installation is assumed to be linear for the past 25 years (default lifetime)
+            # installation is assumed to be linear for the past default_lifetime years
            ratio = (int(grouping_year) - int(grouping_years[i - 1])) / default_lifetime

            n.madd(
                "Link",
-                nodes[name],
+                nodes,
                suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
-                bus0=nodes[name],
-                bus1=nodes[name] + " " + name + " heat",
+                bus0=nodes,
+                bus1=nodes + " " + name + " heat",
                carrier=f"{name} {heat_pump_type} heat pump",
                efficiency=efficiency,
                capital_cost=costs.at[costs_name, "efficiency"]
                * costs.at[costs_name, "fixed"],
-                p_nom=p_nom[name] * ratio / costs.at[costs_name, "efficiency"],
+                p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")]
+                * ratio
+                / costs.at[costs_name, "efficiency"],
                build_year=int(grouping_year),
                lifetime=costs.at[costs_name, "lifetime"],
            )

            # add resistive heater, gas boilers and oil boilers
-            # (50% capacities to rural buses, 50% to urban buses)
            n.madd(
                "Link",
-                nodes[name],
+                nodes,
                suffix=f" {name} resistive heater-{grouping_year}",
-                bus0=nodes[name],
-                bus1=nodes[name] + " " + name + " heat",
+                bus0=nodes,
+                bus1=nodes + " " + name + " heat",
                carrier=name + " resistive heater",
-                efficiency=costs.at[name_type + " resistive heater", "efficiency"],
-                capital_cost=costs.at[name_type + " resistive heater", "efficiency"]
-                * costs.at[name_type + " resistive heater", "fixed"],
-                p_nom=0.5
-                * nodal_df[f"{heat_type} resistive heater"][nodes[name]]
-                * ratio
-                / costs.at[name_type + " resistive heater", "efficiency"],
+                efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
+                capital_cost=(
+                    costs.at[f"{name_type} resistive heater", "efficiency"]
+                    * costs.at[f"{name_type} resistive heater", "fixed"]
+                ),
+                p_nom=(
+                    existing_heating.loc[nodes, (name, "resistive heater")]
+                    * ratio
+                    / costs.at[f"{name_type} resistive heater", "efficiency"]
+                ),
                build_year=int(grouping_year),
-                lifetime=costs.at[costs_name, "lifetime"],
+                lifetime=costs.at[f"{name_type} resistive heater", "lifetime"],
            )

            n.madd(
                "Link",
-                nodes[name],
+                nodes,
                suffix=f" {name} gas boiler-{grouping_year}",
-                bus0=spatial.gas.nodes,
-                bus1=nodes[name] + " " + name + " heat",
+                bus0="EU gas" if "EU gas" in spatial.gas.nodes else nodes + " gas",
+                bus1=nodes + " " + name + " heat",
                bus2="co2 atmosphere",
                carrier=name + " gas boiler",
-                efficiency=costs.at[name_type + " gas boiler", "efficiency"],
+                efficiency=costs.at[f"{name_type} gas boiler", "efficiency"],
                efficiency2=costs.at["gas", "CO2 intensity"],
-                capital_cost=costs.at[name_type + " gas boiler", "efficiency"]
-                * costs.at[name_type + " gas boiler", "fixed"],
-                p_nom=0.5
-                * nodal_df[f"{heat_type} gas boiler"][nodes[name]]
-                * ratio
-                / costs.at[name_type + " gas boiler", "efficiency"],
+                capital_cost=(
+                    costs.at[f"{name_type} gas boiler", "efficiency"]
+                    * costs.at[f"{name_type} gas boiler", "fixed"]
+                ),
+                p_nom=(
+                    existing_heating.loc[nodes, (name, "gas boiler")]
+                    * ratio
+                    / costs.at[f"{name_type} gas boiler", "efficiency"]
+                ),
                build_year=int(grouping_year),
-                lifetime=costs.at[name_type + " gas boiler", "lifetime"],
+                lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
            )

            n.madd(
                "Link",
-                nodes[name],
+                nodes,
                suffix=f" {name} oil boiler-{grouping_year}",
                bus0=spatial.oil.nodes,
-                bus1=nodes[name] + " " + name + " heat",
+                bus1=nodes + " " + name + " heat",
                bus2="co2 atmosphere",
                carrier=name + " oil boiler",
                efficiency=costs.at["decentral oil boiler", "efficiency"],
                efficiency2=costs.at["oil", "CO2 intensity"],
                capital_cost=costs.at["decentral oil boiler", "efficiency"]
                * costs.at["decentral oil boiler", "fixed"],
-                p_nom=0.5
-                * nodal_df[f"{heat_type} oil boiler"][nodes[name]]
-                * ratio
-                / costs.at["decentral oil boiler", "efficiency"],
+                p_nom=(
+                    existing_heating.loc[nodes, (name, "oil boiler")]
+                    * ratio
+                    / costs.at["decentral oil boiler", "efficiency"]
+                ),
                build_year=int(grouping_year),
-                lifetime=costs.at[name_type + " gas boiler", "lifetime"],
+                lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
            )

            # delete links with p_nom=nan corresponding to extra nodes in country
@ -606,20 +537,19 @@ def add_heating_capacities_installed_before_baseyear(
            )


-# %%
 if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake(
            "add_existing_baseyear",
-            configfiles="config/test/config.myopic.yaml",
+            # configfiles="config/test/config.myopic.yaml",
            simpl="",
-            clusters="5",
-            ll="v1.5",
+            clusters="37",
+            ll="v1.0",
            opts="",
-            sector_opts="24H-T-H-B-I-A-solar+p3-dist1",
-            planning_horizons=2030,
+            sector_opts="1p7-4380H-T-H-B-I-A-dist1",
+            planning_horizons=2020,
        )

    logging.basicConfig(level=snakemake.config["logging"]["level"])
@ -662,7 +592,9 @@ if __name__ == "__main__":
            .to_pandas()
            .reindex(index=n.snapshots)
        )
-        default_lifetime = snakemake.params.costs["fill_values"]["lifetime"]
+        default_lifetime = snakemake.params.existing_capacities[
+            "default_heating_lifetime"
+        ]
        add_heating_capacities_installed_before_baseyear(
            n,
            baseyear,
--- a/scripts/add_extra_components.py
+++ b/scripts/add_extra_components.py
@ -56,7 +56,7 @@ import numpy as np
 import pandas as pd
 import pypsa
 from _helpers import configure_logging, set_scenario_config
-from add_electricity import load_costs, sanitize_carriers
+from add_electricity import load_costs, sanitize_carriers, sanitize_locations

 idx = pd.IndexSlice

@ -100,10 +100,9 @@ def attach_stores(n, costs, extendable_carriers):
    n.madd("Carrier", carriers)

    buses_i = n.buses.index
-    bus_sub_dict = {k: n.buses[k].values for k in ["x", "y", "country"]}

    if "H2" in carriers:
-        h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", **bus_sub_dict)
+        h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", location=buses_i)

        n.madd(
            "Store",
@ -143,7 +142,7 @@ def attach_stores(n, costs, extendable_carriers):

    if "battery" in carriers:
        b_buses_i = n.madd(
-            "Bus", buses_i + " battery", carrier="battery", **bus_sub_dict
+            "Bus", buses_i + " battery", carrier="battery", location=buses_i
        )

        n.madd(
@ -247,6 +246,7 @@ if __name__ == "__main__":
    attach_hydrogen_pipelines(n, costs, extendable_carriers)

    sanitize_carriers(n, snakemake.config)
+    sanitize_locations(n)

    n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
    n.export_to_netcdf(snakemake.output[0])
--- a/scripts/base_network.py
+++ b/scripts/base_network.py
@ -78,10 +78,13 @@ import shapely.prepared
 import shapely.wkt
 import yaml
 from _helpers import configure_logging, set_scenario_config
+from packaging.version import Version, parse
 from scipy import spatial
 from scipy.sparse import csgraph
 from shapely.geometry import LineString, Point

+PD_GE_2_2 = parse(pd.__version__) >= Version("2.2")
+
 logger = logging.getLogger(__name__)


@ -138,7 +141,9 @@ def _load_buses_from_eg(eg_buses, europe_shape, config_elec):
    )

    buses["carrier"] = buses.pop("dc").map({True: "DC", False: "AC"})
-    buses["under_construction"] = buses["under_construction"].fillna(False).astype(bool)
+    buses["under_construction"] = buses.under_construction.where(
+        lambda s: s.notnull(), False
+    ).astype(bool)

    # remove all buses outside of all countries including exclusive economic zones (offshore)
    europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
@ -151,9 +156,7 @@ def _load_buses_from_eg(eg_buses, europe_shape, config_elec):
        buses.v_nom.isin(config_elec["voltages"]) | buses.v_nom.isnull()
    )
    logger.info(
-        "Removing buses with voltages {}".format(
-            pd.Index(buses.v_nom.unique()).dropna().difference(config_elec["voltages"])
-        )
+        f'Removing buses with voltages {pd.Index(buses.v_nom.unique()).dropna().difference(config_elec["voltages"])}'
    )

    return pd.DataFrame(buses.loc[buses_in_europe_b & buses_with_v_nom_to_keep_b])
@ -368,6 +371,25 @@ def _apply_parameter_corrections(n, parameter_corrections):
                df.loc[inds, attr] = r[inds].astype(df[attr].dtype)


+def _reconnect_crimea(lines):
+    logger.info("Reconnecting Crimea to the Ukrainian grid.")
+    lines_to_crimea = pd.DataFrame(
+        {
+            "bus0": ["3065", "3181", "3181"],
+            "bus1": ["3057", "3055", "3057"],
+            "v_nom": [300, 300, 300],
+            "num_parallel": [1, 1, 1],
+            "length": [140, 120, 140],
+            "carrier": ["AC", "AC", "AC"],
+            "underground": [False, False, False],
+            "under_construction": [False, False, False],
+        },
+        index=["Melitopol", "Liubymivka left", "Luibymivka right"],
+    )
+
+    return pd.concat([lines, lines_to_crimea])
+
+
 def _set_electrical_parameters_lines(lines, config):
    v_noms = config["electricity"]["voltages"]
    linetypes = config["lines"]["types"]
@ -452,19 +474,15 @@ def _remove_dangling_branches(branches, buses):
    )


-def _remove_unconnected_components(network):
+def _remove_unconnected_components(network, threshold=6):
    _, labels = csgraph.connected_components(network.adjacency_matrix(), directed=False)
    component = pd.Series(labels, index=network.buses.index)

    component_sizes = component.value_counts()
-    components_to_remove = component_sizes.iloc[1:]
+    components_to_remove = component_sizes.loc[component_sizes < threshold]

    logger.info(
-        "Removing {} unconnected network components with less than {} buses. In total {} buses.".format(
-            len(components_to_remove),
-            components_to_remove.max(),
-            components_to_remove.sum(),
-        )
+        f"Removing {len(components_to_remove)} unconnected network components with less than {components_to_remove.max()} buses. In total {components_to_remove.sum()} buses."
    )

    return network[component == component_sizes.index[0]]
@ -509,12 +527,13 @@ def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
        )
        return pd.Series(key, index)

+    compat_kws = dict(include_groups=False) if PD_GE_2_2 else {}
    gb = buses.loc[substation_b].groupby(
        ["x", "y"], as_index=False, group_keys=False, sort=False
    )
-    bus_map_low = gb.apply(prefer_voltage, "min")
+    bus_map_low = gb.apply(prefer_voltage, "min", **compat_kws)
    lv_b = (bus_map_low == bus_map_low.index).reindex(buses.index, fill_value=False)
-    bus_map_high = gb.apply(prefer_voltage, "max")
+    bus_map_high = gb.apply(prefer_voltage, "max", **compat_kws)
    hv_b = (bus_map_high == bus_map_high.index).reindex(buses.index, fill_value=False)

    onshore_b = pd.Series(False, buses.index)
@ -547,7 +566,7 @@ def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
        ~buses["under_construction"]
    )

-    c_nan_b = buses.country.isnull()
+    c_nan_b = buses.country.fillna("na") == "na"
    if c_nan_b.sum() > 0:
        c_tag = _get_country(buses.loc[c_nan_b])
        c_tag.loc[~c_tag.isin(countries)] = np.nan
@ -705,15 +724,19 @@ def base_network(
    lines = _load_lines_from_eg(buses, eg_lines)
    transformers = _load_transformers_from_eg(buses, eg_transformers)

+    if config["lines"].get("reconnect_crimea", True) and "UA" in config["countries"]:
+        lines = _reconnect_crimea(lines)
+
    lines = _set_electrical_parameters_lines(lines, config)
    transformers = _set_electrical_parameters_transformers(transformers, config)
    links = _set_electrical_parameters_links(links, config, links_p_nom)
    converters = _set_electrical_parameters_converters(converters, config)
+    snapshots = snakemake.params.snapshots

    n = pypsa.Network()
    n.name = "PyPSA-Eur"

-    n.set_snapshots(pd.date_range(freq="h", **config["snapshots"]))
+    n.set_snapshots(pd.date_range(freq="h", **snapshots))
    n.madd("Carrier", ["AC", "DC"])

    n.import_components_from_dataframe(buses, "Bus")
--- a/scripts/build_biomass_potentials.py
+++ b/scripts/build_biomass_potentials.py
@ -7,9 +7,15 @@ Compute biogas and solid biomass potentials for each clustered model region
 using data from JRC ENSPRESO.
 """

+import logging
+
 import geopandas as gpd
+import numpy as np
 import pandas as pd

+logger = logging.getLogger(__name__)
+AVAILABLE_BIOMASS_YEARS = [2010, 2020, 2030, 2040, 2050]
+

 def build_nuts_population_data(year=2013):
    pop = pd.read_csv(
@ -126,14 +132,14 @@ def disaggregate_nuts0(bio):
    pop = build_nuts_population_data()

    # get population in nuts2
-    pop_nuts2 = pop.loc[pop.index.str.len() == 4]
+    pop_nuts2 = pop.loc[pop.index.str.len() == 4].copy()
    by_country = pop_nuts2.total.groupby(pop_nuts2.ct).sum()
    pop_nuts2["fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)

    # distribute nuts0 data to nuts2 by population
    bio_nodal = bio.loc[pop_nuts2.ct]
    bio_nodal.index = pop_nuts2.index
-    bio_nodal = bio_nodal.mul(pop_nuts2.fraction, axis=0)
+    bio_nodal = bio_nodal.mul(pop_nuts2.fraction, axis=0).astype(float)

    # update inplace
    bio.update(bio_nodal)
@ -208,13 +214,41 @@ if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

-        snakemake = mock_snakemake("build_biomass_potentials", simpl="", clusters="5")
+        snakemake = mock_snakemake(
+            "build_biomass_potentials",
+            simpl="",
+            clusters="5",
+            planning_horizons=2050,
+        )

+    overnight = snakemake.config["foresight"] == "overnight"
    params = snakemake.params.biomass
-    year = params["year"]
+    investment_year = int(snakemake.wildcards.planning_horizons)
+    year = params["year"] if overnight else investment_year
    scenario = params["scenario"]

-    enspreso = enspreso_biomass_potentials(year, scenario)
+    if year > 2050:
+        logger.info("No biomass potentials for years after 2050, using 2050.")
+        max_year = max(AVAILABLE_BIOMASS_YEARS)
+        enspreso = enspreso_biomass_potentials(max_year, scenario)
+
+    elif year not in AVAILABLE_BIOMASS_YEARS:
+        before = int(np.floor(year / 10) * 10)
+        after = int(np.ceil(year / 10) * 10)
+        logger.info(
+            f"No biomass potentials for {year}, interpolating linearly between {before} and {after}."
+        )
+
+        enspreso_before = enspreso_biomass_potentials(before, scenario)
+        enspreso_after = enspreso_biomass_potentials(after, scenario)
+
+        fraction = (year - before) / (after - before)
+
+        enspreso = enspreso_before + fraction * (enspreso_after - enspreso_before)
+
+    else:
+        logger.info(f"Using biomass potentials for {year}.")
+        enspreso = enspreso_biomass_potentials(year, scenario)

    enspreso = disaggregate_nuts0(enspreso)

@ -229,7 +263,7 @@ if __name__ == "__main__":
    df.to_csv(snakemake.output.biomass_potentials_all)

    grouper = {v: k for k, vv in params["classes"].items() for v in vv}
-    df = df.groupby(grouper, axis=1).sum()
+    df = df.T.groupby(grouper).sum().T

    df *= 1e6  # TWh/a to MWh/a
    df.index.name = "MWh/a"
--- a/scripts/build_biomass_transport_costs.py
+++ b/scripts/build_biomass_transport_costs.py
@ -80,4 +80,9 @@ def build_biomass_transport_costs():


 if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake("build_biomass_transport_costs")
+
    build_biomass_transport_costs()
--- a/scripts/build_clustered_population_layouts.py
+++ b/scripts/build_clustered_population_layouts.py
@ -25,13 +25,10 @@ if __name__ == "__main__":
    cutout = atlite.Cutout(snakemake.input.cutout)

    clustered_regions = (
-        gpd.read_file(snakemake.input.regions_onshore)
-        .set_index("name")
-        .buffer(0)
-        .squeeze()
+        gpd.read_file(snakemake.input.regions_onshore).set_index("name").buffer(0)
    )

-    I = cutout.indicatormatrix(clustered_regions)
+    I = cutout.indicatormatrix(clustered_regions)  # noqa: E741

    pop = {}
    for item in ["total", "urban", "rural"]:
--- a/scripts/build_daily_heat_demand.py
+++ b/scripts/build_daily_heat_demand.py
@ -18,7 +18,8 @@ if __name__ == "__main__":
        from _helpers import mock_snakemake

        snakemake = mock_snakemake(
-            "build_heat_demands",
+            "build_daily_heat_demands",
+            scope="total",
            simpl="",
            clusters=48,
        )
@ -31,13 +32,10 @@ if __name__ == "__main__":
    cutout = atlite.Cutout(snakemake.input.cutout).sel(time=time)

    clustered_regions = (
-        gpd.read_file(snakemake.input.regions_onshore)
-        .set_index("name")
-        .buffer(0)
-        .squeeze()
+        gpd.read_file(snakemake.input.regions_onshore).set_index("name").buffer(0)
    )

-    I = cutout.indicatormatrix(clustered_regions)
+    I = cutout.indicatormatrix(clustered_regions)  # noqa: E741

    pop_layout = xr.open_dataarray(snakemake.input.pop_layout)

--- a/scripts/build_district_heat_share.py
+++ b/scripts/build_district_heat_share.py
@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Build district heat shares at each node, depending on investment year.
+"""
+
+import logging
+
+import pandas as pd
+from prepare_sector_network import get
+
+logger = logging.getLogger(__name__)
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_district_heat_share",
+            simpl="",
+            clusters=48,
+            planning_horizons="2050",
+        )
+
+    investment_year = int(snakemake.wildcards.planning_horizons[-4:])
+
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
+
+    district_heat_share = pd.read_csv(snakemake.input.district_heat_share, index_col=0)[
+        "district heat share"
+    ]
+
+    # make ct-based share nodal
+    district_heat_share = district_heat_share.loc[pop_layout.ct]
+    district_heat_share.index = pop_layout.index
+
+    # total urban population per country
+    ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
+
+    # distribution of urban population within a country
+    pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
+
+    # fraction of node that is urban
+    urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
+
+    # maximum potential of urban demand covered by district heating
+    central_fraction = snakemake.config["sector"]["district_heating"]["potential"]
+
+    # district heating share at each node
+    dist_fraction_node = (
+        district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
+    )
+
+    # if district heating share larger than urban fraction -> set urban
+    # fraction to district heating share
+    urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1)
+
+    # difference of max potential and today's share of district heating
+    diff = (urban_fraction * central_fraction) - dist_fraction_node
+    progress = get(
+        snakemake.config["sector"]["district_heating"]["progress"], investment_year
+    )
+    dist_fraction_node += diff * progress
+    logger.info(
+        f"Increase district heating share by a progress factor of {progress:.2%} "
+        f"resulting in new average share of {dist_fraction_node.mean():.2%}"
+    )
+
+    df = pd.DataFrame(
+        {
+            "original district heat share": district_heat_share,
+            "district fraction of node": dist_fraction_node,
+            "urban fraction": urban_fraction,
+        },
+        dtype=float,
+    )
+
+    df.to_csv(snakemake.output.district_heat_share)
--- a/scripts/build_electricity_demand.py
+++ b/scripts/build_electricity_demand.py
@ -41,13 +41,13 @@ Outputs

 import logging

-logger = logging.getLogger(__name__)
-import dateutil
 import numpy as np
 import pandas as pd
 from _helpers import configure_logging, set_scenario_config
 from pandas import Timedelta as Delta

+logger = logging.getLogger(__name__)
+

 def load_timeseries(fn, years, countries):
    """
@ -69,7 +69,7 @@ def load_timeseries(fn, years, countries):
        Load time-series with UTC timestamps x ISO-2 countries
    """
    return (
-        pd.read_csv(fn, index_col=0, parse_dates=[0])
+        pd.read_csv(fn, index_col=0, parse_dates=[0], date_format="%Y-%m-%dT%H:%M:%SZ")
        .tz_localize(None)
        .dropna(how="all", axis=0)
        .rename(columns={"GB_UKM": "GB"})
@ -247,6 +247,14 @@ def manual_adjustment(load, fn_load):
    copy_timeslice(load, "LU", "2019-01-02 11:00", "2019-01-05 05:00", Delta(weeks=-1))
    copy_timeslice(load, "LU", "2019-02-05 20:00", "2019-02-06 19:00", Delta(weeks=-1))

+    if "UA" in countries:
+        copy_timeslice(
+            load, "UA", "2013-01-25 14:00", "2013-01-28 21:00", Delta(weeks=1)
+        )
+        copy_timeslice(
+            load, "UA", "2013-10-28 03:00", "2013-10-28 20:00", Delta(weeks=1)
+        )
+
    return load


@ -267,6 +275,20 @@ if __name__ == "__main__":

    load = load_timeseries(snakemake.input[0], years, countries)

+    if "UA" in countries:
+        # attach load of UA (best data only for entsoe transparency)
+        load_ua = load_timeseries(snakemake.input[0], "2018", ["UA"], False)
+        snapshot_year = str(snapshots.year.unique().item())
+        time_diff = pd.Timestamp("2018") - pd.Timestamp(snapshot_year)
+        load_ua.index -= (
+            time_diff  # hack indices (currently, UA is manually set to 2018)
+        )
+        load["UA"] = load_ua
+        # attach load of MD (no time-series available, use 2020-totals and distribute according to UA):
+        # https://www.iea.org/data-and-statistics/data-browser/?country=MOLDOVA&fuel=Energy%20consumption&indicator=TotElecCons
+        if "MD" in countries:
+            load["MD"] = 6.2e6 * (load_ua / load_ua.sum())
+
    if snakemake.params.load["manual_adjustments"]:
        load = manual_adjustment(load, snakemake.input[0])

--- a/scripts/build_electricity_production.py
+++ b/scripts/build_electricity_production.py
@ -59,7 +59,7 @@ if __name__ == "__main__":
            gen = client.query_generation(country, start=start, end=end, nett=True)
            gen = gen.tz_localize(None).resample("1h").mean()
            gen = gen.loc[start.tz_localize(None) : end.tz_localize(None)]
-            gen = gen.rename(columns=carrier_grouper).groupby(level=0, axis=1).sum()
+            gen = gen.rename(columns=carrier_grouper).T.groupby(level=0).sum().T
            generation.append(gen)
        except NoMatchingDataError:
            unavailable_countries.append(country)
--- a/scripts/build_energy_totals.py
+++ b/scripts/build_energy_totals.py
@ -7,9 +7,6 @@ Build total energy demands per country using JRC IDEES, eurostat, and EEA data.
 """

 import logging
-
-logger = logging.getLogger(__name__)
-
 import multiprocessing as mp
 from functools import partial

@ -21,7 +18,7 @@ from _helpers import mute_print
 from tqdm import tqdm

 cc = coco.CountryConverter()
-
+logger = logging.getLogger(__name__)
 idx = pd.IndexSlice


@ -172,8 +169,6 @@ def build_swiss(year):


 def idees_per_country(ct, year, base_dir):
-    ct_totals = {}
-
    ct_idees = idees_rename.get(ct, ct)
    fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
    fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
@ -183,20 +178,20 @@ def idees_per_country(ct, year, base_dir):

    df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)[year]

-    ct_totals["total residential space"] = df["Space heating"]
-
    rows = ["Advanced electric heating", "Conventional electric heating"]
-    ct_totals["electricity residential space"] = df[rows].sum()
-
+    ct_totals = {
+        "total residential space": df["Space heating"],
+        "electricity residential space": df[rows].sum(),
+    }
    ct_totals["total residential water"] = df.at["Water heating"]

    assert df.index[23] == "Electricity"
-    ct_totals["electricity residential water"] = df[23]
+    ct_totals["electricity residential water"] = df.iloc[23]

    ct_totals["total residential cooking"] = df["Cooking"]

    assert df.index[30] == "Electricity"
-    ct_totals["electricity residential cooking"] = df[30]
+    ct_totals["electricity residential cooking"] = df.iloc[30]

    df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year]

@ -204,13 +199,13 @@ def idees_per_country(ct, year, base_dir):
    ct_totals["total residential"] = df[row]

    assert df.index[47] == "Electricity"
-    ct_totals["electricity residential"] = df[47]
+    ct_totals["electricity residential"] = df.iloc[47]

    assert df.index[46] == "Derived heat"
-    ct_totals["derived heat residential"] = df[46]
+    ct_totals["derived heat residential"] = df.iloc[46]

    assert df.index[50] == "Thermal uses"
-    ct_totals["thermal uses residential"] = df[50]
+    ct_totals["thermal uses residential"] = df.iloc[50]

    # services

@ -224,12 +219,12 @@ def idees_per_country(ct, year, base_dir):
    ct_totals["total services water"] = df["Hot water"]

    assert df.index[24] == "Electricity"
-    ct_totals["electricity services water"] = df[24]
+    ct_totals["electricity services water"] = df.iloc[24]

    ct_totals["total services cooking"] = df["Catering"]

    assert df.index[31] == "Electricity"
-    ct_totals["electricity services cooking"] = df[31]
+    ct_totals["electricity services cooking"] = df.iloc[31]

    df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]

@ -237,13 +232,13 @@ def idees_per_country(ct, year, base_dir):
    ct_totals["total services"] = df[row]

    assert df.index[50] == "Electricity"
-    ct_totals["electricity services"] = df[50]
+    ct_totals["electricity services"] = df.iloc[50]

    assert df.index[49] == "Derived heat"
-    ct_totals["derived heat services"] = df[49]
+    ct_totals["derived heat services"] = df.iloc[49]

    assert df.index[53] == "Thermal uses"
-    ct_totals["thermal uses services"] = df[53]
+    ct_totals["thermal uses services"] = df.iloc[53]

    # agriculture, forestry and fishing

@ -284,28 +279,28 @@ def idees_per_country(ct, year, base_dir):
    ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"]

    assert df.index[19] == "Passenger cars"
-    ct_totals["total passenger cars"] = df[19]
+    ct_totals["total passenger cars"] = df.iloc[19]

    assert df.index[30] == "Battery electric vehicles"
-    ct_totals["electricity passenger cars"] = df[30]
+    ct_totals["electricity passenger cars"] = df.iloc[30]

    assert df.index[31] == "Motor coaches, buses and trolley buses"
-    ct_totals["total other road passenger"] = df[31]
+    ct_totals["total other road passenger"] = df.iloc[31]

    assert df.index[39] == "Battery electric vehicles"
-    ct_totals["electricity other road passenger"] = df[39]
+    ct_totals["electricity other road passenger"] = df.iloc[39]

    assert df.index[41] == "Light duty vehicles"
-    ct_totals["total light duty road freight"] = df[41]
+    ct_totals["total light duty road freight"] = df.iloc[41]

    assert df.index[49] == "Battery electric vehicles"
-    ct_totals["electricity light duty road freight"] = df[49]
+    ct_totals["electricity light duty road freight"] = df.iloc[49]

    row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
    ct_totals["total heavy duty road freight"] = df[row]

    assert df.index[61] == "Passenger cars"
-    ct_totals["passenger car efficiency"] = df[61]
+    ct_totals["passenger car efficiency"] = df.iloc[61]

    df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year]

@ -314,39 +309,39 @@ def idees_per_country(ct, year, base_dir):
    ct_totals["electricity rail"] = df["Electricity"]

    assert df.index[15] == "Passenger transport"
-    ct_totals["total rail passenger"] = df[15]
+    ct_totals["total rail passenger"] = df.iloc[15]

    assert df.index[16] == "Metro and tram, urban light rail"
    assert df.index[19] == "Electric"
    assert df.index[20] == "High speed passenger trains"
-    ct_totals["electricity rail passenger"] = df[[16, 19, 20]].sum()
+    ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()

    assert df.index[21] == "Freight transport"
-    ct_totals["total rail freight"] = df[21]
+    ct_totals["total rail freight"] = df.iloc[21]

    assert df.index[23] == "Electric"
-    ct_totals["electricity rail freight"] = df[23]
+    ct_totals["electricity rail freight"] = df.iloc[23]

    df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year]

    assert df.index[6] == "Passenger transport"
-    ct_totals["total aviation passenger"] = df[6]
+    ct_totals["total aviation passenger"] = df.iloc[6]

    assert df.index[10] == "Freight transport"
-    ct_totals["total aviation freight"] = df[10]
+    ct_totals["total aviation freight"] = df.iloc[10]

    assert df.index[7] == "Domestic"
-    ct_totals["total domestic aviation passenger"] = df[7]
+    ct_totals["total domestic aviation passenger"] = df.iloc[7]

    assert df.index[8] == "International - Intra-EU"
    assert df.index[9] == "International - Extra-EU"
-    ct_totals["total international aviation passenger"] = df[[8, 9]].sum()
+    ct_totals["total international aviation passenger"] = df.iloc[[8, 9]].sum()

    assert df.index[11] == "Domestic and International - Intra-EU"
-    ct_totals["total domestic aviation freight"] = df[11]
+    ct_totals["total domestic aviation freight"] = df.iloc[11]

    assert df.index[12] == "International - Extra-EU"
-    ct_totals["total international aviation freight"] = df[12]
+    ct_totals["total international aviation freight"] = df.iloc[12]

    ct_totals["total domestic aviation"] = (
        ct_totals["total domestic aviation freight"]
@ -366,7 +361,7 @@ def idees_per_country(ct, year, base_dir):
    df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year]

    assert df.index[85] == "Passenger cars"
-    ct_totals["passenger cars"] = df[85]
+    ct_totals["passenger cars"] = df.iloc[85]

    return pd.Series(ct_totals, name=ct)

@ -396,13 +391,6 @@ def build_idees(countries, year):
    # convert TWh/100km to kWh/km
    totals.loc["passenger car efficiency"] *= 10

-    # district heating share
-    district_heat = totals.loc[
-        ["derived heat residential", "derived heat services"]
-    ].sum()
-    total_heat = totals.loc[["thermal uses residential", "thermal uses services"]].sum()
-    totals.loc["district heat share"] = district_heat.div(total_heat)
-
    return totals.T


@ -481,7 +469,7 @@ def build_energy_totals(countries, eurostat, swiss, idees):
    # The main heating source for about 73 per cent of the households is based on electricity
    # => 26% is non-electric

-    if "NO" in df:
+    if "NO" in df.index:
        elec_fraction = 0.73

        no_norway = df.drop("NO")
@ -577,16 +565,36 @@ def build_energy_totals(countries, eurostat, swiss, idees):
        ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
        df.loc["BA", missing] = ratio * df.loc["RS", missing]

+    return df
+
+
+def build_district_heat_share(countries, idees):
+    # district heating share
+    district_heat = idees[["derived heat residential", "derived heat services"]].sum(
+        axis=1
+    )
+    total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
+        axis=1
+    )
+
+    district_heat_share = district_heat / total_heat
+
+    district_heat_share = district_heat_share.reindex(countries)
+
    # Missing district heating share
-    dh_share = pd.read_csv(
-        snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]
+    dh_share = (
+        pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1])
+        .div(100)
+        .squeeze()
    )
    # make conservative assumption and take minimum from both data sets
-    df["district heat share"] = pd.concat(
-        [df["district heat share"], dh_share.reindex(index=df.index) / 100], axis=1
+    district_heat_share = pd.concat(
+        [district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1
    ).min(axis=1)

-    return df
+    district_heat_share.name = "district heat share"
+
+    return district_heat_share


 def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
@ -755,6 +763,9 @@ if __name__ == "__main__":
    energy = build_energy_totals(countries, eurostat, swiss, idees)
    energy.to_csv(snakemake.output.energy_name)

+    district_heat_share = build_district_heat_share(countries, idees)
+    district_heat_share.to_csv(snakemake.output.district_heat_share)
+
    base_year_emissions = params["base_emissions_year"]
    emissions_scope = snakemake.params.energy["emissions"]
    eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
--- a/scripts/build_existing_heating_distribution.py
+++ b/scripts/build_existing_heating_distribution.py
@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Builds table of existing heat generation capacities for initial planning
+horizon.
+"""
+import country_converter as coco
+import numpy as np
+import pandas as pd
+
+cc = coco.CountryConverter()
+
+
+def build_existing_heating():
+    # retrieve existing heating capacities
+
+    # Add existing heating capacities, data comes from the study
+    # "Mapping and analyses of the current and future (2020 - 2030)
+    # heating/cooling fuel deployment (fossil/renewables) "
+    # https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
+    # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
+    # data is for buildings only (i.e. NOT district heating) and represents the year 2012
+    # TODO start from original file
+
+    existing_heating = pd.read_csv(
+        snakemake.input.existing_heating, index_col=0, header=0
+    )
+
+    # data for Albania, Montenegro and Macedonia not included in database
+    existing_heating.loc["Albania"] = np.nan
+    existing_heating.loc["Montenegro"] = np.nan
+    existing_heating.loc["Macedonia"] = np.nan
+
+    existing_heating.fillna(0.0, inplace=True)
+
+    # convert GW to MW
+    existing_heating *= 1e3
+
+    existing_heating.index = cc.convert(existing_heating.index, to="iso2")
+
+    # coal and oil boilers are assimilated to oil boilers
+    existing_heating["oil boiler"] = (
+        existing_heating["oil boiler"] + existing_heating["coal boiler"]
+    )
+    existing_heating.drop(["coal boiler"], axis=1, inplace=True)
+
+    # distribute technologies to nodes by population
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
+
+    nodal_heating = existing_heating.loc[pop_layout.ct]
+    nodal_heating.index = pop_layout.index
+    nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)
+
+    district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
+    dist_fraction = district_heat_info["district fraction of node"]
+    urban_fraction = district_heat_info["urban fraction"]
+
+    energy_layout = pd.read_csv(
+        snakemake.input.clustered_pop_energy_layout, index_col=0
+    )
+
+    uses = ["space", "water"]
+    sectors = ["residential", "services"]
+
+    nodal_sectoral_totals = pd.DataFrame(dtype=float)
+
+    for sector in sectors:
+        nodal_sectoral_totals[sector] = energy_layout[
+            [f"total {sector} {use}" for use in uses]
+        ].sum(axis=1)
+
+    nodal_sectoral_fraction = nodal_sectoral_totals.div(
+        nodal_sectoral_totals.sum(axis=1), axis=0
+    )
+
+    nodal_heat_name_fraction = pd.DataFrame(index=district_heat_info.index, dtype=float)
+
+    nodal_heat_name_fraction["urban central"] = 0.0
+
+    for sector in sectors:
+        nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[
+            sector
+        ] * (1 - urban_fraction)
+        nodal_heat_name_fraction[f"{sector} urban decentral"] = (
+            nodal_sectoral_fraction[sector] * urban_fraction
+        )
+
+    nodal_heat_name_tech = pd.concat(
+        {
+            name: nodal_heating.multiply(nodal_heat_name_fraction[name], axis=0)
+            for name in nodal_heat_name_fraction.columns
+        },
+        axis=1,
+        names=["heat name", "technology"],
+    )
+
+    # move all ground HPs to rural, all air to urban
+
+    for sector in sectors:
+        nodal_heat_name_tech[(f"{sector} rural", "ground heat pump")] += (
+            nodal_heat_name_tech[("urban central", "ground heat pump")]
+            * nodal_sectoral_fraction[sector]
+            + nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")]
+        )
+        nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] = 0.0
+
+        nodal_heat_name_tech[
+            (f"{sector} urban decentral", "air heat pump")
+        ] += nodal_heat_name_tech[(f"{sector} rural", "air heat pump")]
+        nodal_heat_name_tech[(f"{sector} rural", "air heat pump")] = 0.0
+
+    nodal_heat_name_tech[("urban central", "ground heat pump")] = 0.0
+
+    nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_existing_heating_distribution",
+            simpl="",
+            clusters=48,
+            planning_horizons=2050,
+        )
+
+    build_existing_heating()
--- a/scripts/build_gas_input_locations.py
+++ b/scripts/build_gas_input_locations.py
@ -9,12 +9,12 @@ production sites with data from SciGRID_gas and Global Energy Monitor.

 import logging

-logger = logging.getLogger(__name__)
-
 import geopandas as gpd
 import pandas as pd
 from cluster_gas_network import load_bus_regions

+logger = logging.getLogger(__name__)
+

 def read_scigrid_gas(fn):
    df = gpd.read_file(fn)
@ -23,13 +23,15 @@ def read_scigrid_gas(fn):
    return df


-def build_gem_lng_data(lng_fn):
-    df = pd.read_excel(lng_fn[0], sheet_name="LNG terminals - data")
+def build_gem_lng_data(fn):
+    df = pd.read_excel(fn[0], sheet_name="LNG terminals - data")
    df = df.set_index("ComboID")

-    remove_status = ["Cancelled"]
-    remove_country = ["Cyprus", "Turkey"]
-    remove_terminal = ["Puerto de la Luz LNG Terminal", "Gran Canaria LNG Terminal"]
+    remove_country = ["Cyprus", "Turkey"]  # noqa: F841
+    remove_terminal = [  # noqa: F841
+        "Puerto de la Luz LNG Terminal",
+        "Gran Canaria LNG Terminal",
+    ]

    df = df.query(
        "Status != 'Cancelled' \
@ -42,9 +44,50 @@ def build_gem_lng_data(lng_fn):
    return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")


-def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
+def build_gem_prod_data(fn):
+    df = pd.read_excel(fn[0], sheet_name="Gas extraction - main")
+    df = df.set_index("GEM Unit ID")
+
+    remove_country = ["Cyprus", "Türkiye"]  # noqa: F841
+    remove_fuel_type = ["oil"]  # noqa: F841
+
+    df = df.query(
+        "Status != 'shut in' \
+              & 'Fuel type' != 'oil' \
+              & Country != @remove_country \
+              & ~Latitude.isna() \
+              & ~Longitude.isna()"
+    ).copy()
+
+    p = pd.read_excel(fn[0], sheet_name="Gas extraction - production")
+    p = p.set_index("GEM Unit ID")
+    p = p[p["Fuel description"] == "gas"]
+
+    capacities = pd.DataFrame(index=df.index)
+    for key in ["production", "production design capacity", "reserves"]:
+        cap = (
+            p.loc[p["Production/reserves"] == key, "Quantity (converted)"]
+            .groupby("GEM Unit ID")
+            .sum()
+            .reindex(df.index)
+        )
+        # assume capacity such that 3% of reserves can be extracted per year (25% quantile)
+        annualization_factor = 0.03 if key == "reserves" else 1.0
+        capacities[key] = cap * annualization_factor
+
+    df["mcm_per_year"] = (
+        capacities["production"]
+        .combine_first(capacities["production design capacity"])
+        .combine_first(capacities["reserves"])
+    )
+
+    geometry = gpd.points_from_xy(df["Longitude"], df["Latitude"])
+    return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+
+
+def build_gas_input_locations(gem_fn, entry_fn, sto_fn, countries):
    # LNG terminals
-    lng = build_gem_lng_data(lng_fn)
+    lng = build_gem_lng_data(gem_fn)

    # Entry points from outside the model scope
    entry = read_scigrid_gas(entry_fn)
@ -55,25 +98,30 @@ def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
        | (entry.from_country == "NO")  # malformed datapoint  # entries from NO to GB
    ]

+    sto = read_scigrid_gas(sto_fn)
+    remove_country = ["RU", "UA", "TR", "BY"]  # noqa: F841
+    sto = sto.query("country_code not in @remove_country")
+
    # production sites inside the model scope
-    prod = read_scigrid_gas(prod_fn)
-    prod = prod.loc[
-        (prod.geometry.y > 35) & (prod.geometry.x < 30) & (prod.country_code != "DE")
-    ]
+    prod = build_gem_prod_data(gem_fn)

    mcm_per_day_to_mw = 437.5  # MCM/day to MWh/h
+    mcm_per_year_to_mw = 1.199  #  MCM/year to MWh/h
    mtpa_to_mw = 1649.224  # mtpa to MWh/h
-    lng["p_nom"] = lng["CapacityInMtpa"] * mtpa_to_mw
-    entry["p_nom"] = entry["max_cap_from_to_M_m3_per_d"] * mcm_per_day_to_mw
-    prod["p_nom"] = prod["max_supply_M_m3_per_d"] * mcm_per_day_to_mw
+    mcm_to_gwh = 11.36  # MCM to GWh
+    lng["capacity"] = lng["CapacityInMtpa"] * mtpa_to_mw
+    entry["capacity"] = entry["max_cap_from_to_M_m3_per_d"] * mcm_per_day_to_mw
+    prod["capacity"] = prod["mcm_per_year"] * mcm_per_year_to_mw
+    sto["capacity"] = sto["max_cushionGas_M_m3"] * mcm_to_gwh

    lng["type"] = "lng"
    entry["type"] = "pipeline"
    prod["type"] = "production"
+    sto["type"] = "storage"

-    sel = ["geometry", "p_nom", "type"]
+    sel = ["geometry", "capacity", "type"]

-    return pd.concat([prod[sel], entry[sel], lng[sel]], ignore_index=True)
+    return pd.concat([prod[sel], entry[sel], lng[sel], sto[sel]], ignore_index=True)


 if __name__ == "__main__":
@ -83,7 +131,7 @@ if __name__ == "__main__":
        snakemake = mock_snakemake(
            "build_gas_input_locations",
            simpl="",
-            clusters="37",
+            clusters="128",
        )

    logging.basicConfig(level=snakemake.config["logging"]["level"])
@ -104,9 +152,9 @@ if __name__ == "__main__":
    countries = regions.index.str[:2].unique().str.replace("GB", "UK")

    gas_input_locations = build_gas_input_locations(
-        snakemake.input.lng,
+        snakemake.input.gem,
        snakemake.input.entry,
-        snakemake.input.production,
+        snakemake.input.storage,
        countries,
    )

@ -116,9 +164,13 @@ if __name__ == "__main__":

    gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver="GeoJSON")

+    ensure_columns = ["lng", "pipeline", "production", "storage"]
    gas_input_nodes_s = (
-        gas_input_nodes.groupby(["bus", "type"])["p_nom"].sum().unstack()
+        gas_input_nodes.groupby(["bus", "type"])["capacity"]
+        .sum()
+        .unstack()
+        .reindex(columns=ensure_columns)
    )
-    gas_input_nodes_s.columns.name = "p_nom"
+    gas_input_nodes_s.columns.name = "capacity"

    gas_input_nodes_s.to_csv(snakemake.output.gas_input_nodes_simplified)
--- a/scripts/build_gas_network.py
+++ b/scripts/build_gas_network.py
@ -9,13 +9,13 @@ Preprocess gas network based on data from bthe SciGRID_gas project

 import logging

-logger = logging.getLogger(__name__)
-
 import geopandas as gpd
 import pandas as pd
 from pypsa.geo import haversine_pts
 from shapely.geometry import Point

+logger = logging.getLogger(__name__)
+

 def diameter_to_capacity(pipe_diameter_mm):
    """
@ -29,25 +29,25 @@ def diameter_to_capacity(pipe_diameter_mm):
    Based on p.15 of
    https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
    """
-    # slopes definitions
-    m0 = (1500 - 0) / (500 - 0)
    m1 = (5000 - 1500) / (600 - 500)
    m2 = (11250 - 5000) / (900 - 600)
-    m3 = (21700 - 11250) / (1200 - 900)
-
-    # intercept
-    a0 = 0
    a1 = -16000
    a2 = -7500
-    a3 = -20100
-
    if pipe_diameter_mm < 500:
+        # slopes definitions
+        m0 = (1500 - 0) / (500 - 0)
+        # intercept
+        a0 = 0
        return a0 + m0 * pipe_diameter_mm
    elif pipe_diameter_mm < 600:
        return a1 + m1 * pipe_diameter_mm
    elif pipe_diameter_mm < 900:
        return a2 + m2 * pipe_diameter_mm
    else:
+        m3 = (21700 - 11250) / (1200 - 900)
+
+        a3 = -20100
+
        return a3 + m3 * pipe_diameter_mm


@ -114,12 +114,10 @@ def prepare_dataset(
    df["p_nom_diameter"] = df.diameter_mm.apply(diameter_to_capacity)
    ratio = df.p_nom / df.p_nom_diameter
    not_nordstream = df.max_pressure_bar < 220
-    df.p_nom.update(
-        df.p_nom_diameter.where(
-            (df.p_nom <= 500)
-            | ((ratio > correction_threshold_p_nom) & not_nordstream)
-            | ((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
-        )
+    df["p_nom"] = df.p_nom_diameter.where(
+        (df.p_nom <= 500)
+        | ((ratio > correction_threshold_p_nom) & not_nordstream)
+        | ((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
    )

    # lines which have way too discrepant line lengths
@ -130,12 +128,10 @@ def prepare_dataset(
        axis=1,
    )
    ratio = df.eval("length / length_haversine")
-    df["length"].update(
-        df.length_haversine.where(
-            (df["length"] < 20)
-            | (ratio > correction_threshold_length)
-            | (ratio < 1 / correction_threshold_length)
-        )
+    df["length"] = df.length_haversine.where(
+        (df["length"] < 20)
+        | (ratio > correction_threshold_length)
+        | (ratio < 1 / correction_threshold_length)
    )

    return df
--- a/scripts/build_hourly_heat_demand.py
+++ b/scripts/build_hourly_heat_demand.py
@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Build hourly heat demand time series from daily ones.
+"""
+
+from itertools import product
+
+import pandas as pd
+import xarray as xr
+from _helpers import generate_periodic_profiles
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_hourly_heat_demands",
+            scope="total",
+            simpl="",
+            clusters=48,
+        )
+
+    snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
+
+    daily_space_heat_demand = (
+        xr.open_dataarray(snakemake.input.heat_demand)
+        .to_pandas()
+        .reindex(index=snapshots, method="ffill")
+    )
+
+    intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
+
+    sectors = ["residential", "services"]
+    uses = ["water", "space"]
+
+    heat_demand = {}
+    for sector, use in product(sectors, uses):
+        weekday = list(intraday_profiles[f"{sector} {use} weekday"])
+        weekend = list(intraday_profiles[f"{sector} {use} weekend"])
+        weekly_profile = weekday * 5 + weekend * 2
+        intraday_year_profile = generate_periodic_profiles(
+            daily_space_heat_demand.index.tz_localize("UTC"),
+            nodes=daily_space_heat_demand.columns,
+            weekly_profile=weekly_profile,
+        )
+
+        if use == "space":
+            heat_demand[f"{sector} {use}"] = (
+                daily_space_heat_demand * intraday_year_profile
+            )
+        else:
+            heat_demand[f"{sector} {use}"] = intraday_year_profile
+
+    heat_demand = pd.concat(heat_demand, axis=1, names=["sector use", "node"])
+
+    heat_demand.index.name = "snapshots"
+
+    ds = heat_demand.stack().to_xarray()
+
+    ds.to_netcdf(snakemake.output.heat_demand)
--- a/scripts/build_hydro_profile.py
+++ b/scripts/build_hydro_profile.py
@ -26,7 +26,7 @@ Relevant Settings
 Inputs
 ------

- ``data/bundle/EIA_hydro_generation_2000_2014.csv``: Hydroelectricity net generation per country and year (`EIA <https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016>`_)
+- ``data/bundle/eia_hydro_annual_generation.csv``: Hydroelectricity net generation per country and year (`EIA <https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016>`_)

    .. image:: img/hydrogeneration.png
        :scale: 33 %
@ -72,12 +72,14 @@ cc = coco.CountryConverter()

 def get_eia_annual_hydro_generation(fn, countries):
    # in billion kWh/a = TWh/a
-    df = pd.read_csv(fn, skiprows=2, index_col=1, na_values=[" ", "--"]).iloc[1:, 1:]
+    df = pd.read_csv(
+        fn, skiprows=2, index_col=1, na_values=[" ", "--"], decimal=","
+    ).iloc[1:, 1:]
    df.index = df.index.str.strip()

    former_countries = {
        "Former Czechoslovakia": dict(
-            countries=["Czech Republic", "Slovakia"], start=1980, end=1992
+            countries=["Czechia", "Slovakia"], start=1980, end=1992
        ),
        "Former Serbia and Montenegro": dict(
            countries=["Serbia", "Montenegro"], start=1992, end=2005
--- a/scripts/build_industrial_distribution_key.py
+++ b/scripts/build_industrial_distribution_key.py
@ -7,17 +7,14 @@ Build spatial distribution of industries from Hotmaps database.
 """

 import logging
-
-logger = logging.getLogger(__name__)
-
 import uuid
 from itertools import product

 import country_converter as coco
 import geopandas as gpd
 import pandas as pd
-from packaging.version import Version, parse

+logger = logging.getLogger(__name__)
 cc = coco.CountryConverter()


@ -32,7 +29,7 @@ def locate_missing_industrial_sites(df):
    try:
        from geopy.extra.rate_limiter import RateLimiter
        from geopy.geocoders import Nominatim
-    except:
+    except ImportError:
        raise ModuleNotFoundError(
            "Optional dependency 'geopy' not found."
            "Install via 'conda install -c conda-forge geopy'"
@ -86,12 +83,7 @@ def prepare_hotmaps_database(regions):

    gdf = gpd.GeoDataFrame(df, geometry="coordinates", crs="EPSG:4326")

-    kws = (
-        dict(op="within")
-        if parse(gpd.__version__) < Version("0.10")
-        else dict(predicate="within")
-    )
-    gdf = gpd.sjoin(gdf, regions, how="inner", **kws)
+    gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")

    gdf.rename(columns={"index_right": "bus"}, inplace=True)
    gdf["country"] = gdf.bus.str[:2]
@ -101,7 +93,7 @@ def prepare_hotmaps_database(regions):
        # get all duplicated entries
        duplicated_i = gdf.index[gdf.index.duplicated()]
        # convert from raw data country name to iso-2-code
-        code = cc.convert(gdf.loc[duplicated_i, "Country"], to="iso2")
+        code = cc.convert(gdf.loc[duplicated_i, "Country"], to="iso2")  # noqa: F841
        # screen out malformed country allocation
        gdf_filtered = gdf.loc[duplicated_i].query("country == @code")
        # concat not duplicated and filtered gdf
--- a/scripts/build_industrial_energy_demand_per_country_today.py
+++ b/scripts/build_industrial_energy_demand_per_country_today.py
@ -167,9 +167,7 @@ def industrial_energy_demand(countries, year):
    with mp.Pool(processes=nprocesses) as pool:
        demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))

-    demand = pd.concat(demand_l, keys=countries)
-
-    return demand
+    return pd.concat(demand_l, keys=countries)


 if __name__ == "__main__":
--- a/scripts/build_industrial_production_per_country.py
+++ b/scripts/build_industrial_production_per_country.py
@ -7,11 +7,8 @@ Build industrial production per country.
 """

 import logging
-from functools import partial
-
-logger = logging.getLogger(__name__)
-
 import multiprocessing as mp
+from functools import partial

 import country_converter as coco
 import numpy as np
@ -19,6 +16,7 @@ import pandas as pd
 from _helpers import mute_print
 from tqdm import tqdm

+logger = logging.getLogger(__name__)
 cc = coco.CountryConverter()

 tj_to_ktoe = 0.0238845
--- a/scripts/build_line_rating.py
+++ b/scripts/build_line_rating.py
@ -41,7 +41,7 @@ The following heat gains and losses are considered:

 - heat gain through resistive losses
 - heat gain through solar radiation
- heat loss through radiation of the trasnmission line
+- heat loss through radiation of the transmission line
 - heat loss through forced convection with wind
 - heat loss through natural convection

@ -50,7 +50,6 @@ With a heat balance considering the maximum temperature threshold of the transmi
 the maximal possible capacity factor "s_max_pu" for each transmission line at each time step is calculated.
 """

-import logging
 import re

 import atlite
@ -83,8 +82,7 @@ def calculate_resistance(T, R_ref, T_ref=293, alpha=0.00403):
    -------
    Resistance of at given temperature.
    """
-    R = R_ref * (1 + alpha * (T - T_ref))
-    return R
+    return R_ref * (1 + alpha * (T - T_ref))


 def calculate_line_rating(n, cutout):
@ -100,7 +98,7 @@ def calculate_line_rating(n, cutout):
    -------
    xarray DataArray object with maximal power.
    """
-    relevant_lines = n.lines[(n.lines["underground"] == False)]
+    relevant_lines = n.lines[~n.lines["underground"]].copy()
    buses = relevant_lines[["bus0", "bus1"]].values
    x = n.buses.x
    y = n.buses.y
@ -120,18 +118,17 @@ def calculate_line_rating(n, cutout):
            .apply(lambda x: int(re.findall(r"(\d+)-bundle", x)[0]))
        )
        # Set default number of bundles per line
-        relevant_lines["n_bundle"].fillna(1, inplace=True)
+        relevant_lines["n_bundle"] = relevant_lines["n_bundle"].fillna(1)
        R *= relevant_lines["n_bundle"]
        R = calculate_resistance(T=353, R_ref=R)
    Imax = cutout.line_rating(shapes, R, D=0.0218, Ts=353, epsilon=0.8, alpha=0.8)
    line_factor = relevant_lines.eval("v_nom * n_bundle * num_parallel") / 1e3  # in mW
-    da = xr.DataArray(
+    return xr.DataArray(
        data=np.sqrt(3) * Imax * line_factor.values.reshape(-1, 1),
        attrs=dict(
            description="Maximal possible power in MW for given line considering line rating"
        ),
    )
-    return da


 if __name__ == "__main__":
@ -149,8 +146,10 @@ if __name__ == "__main__":
    configure_logging(snakemake)
    set_scenario_config(snakemake)

+    snapshots = snakemake.params.snapshots
+
    n = pypsa.Network(snakemake.input.base_network)
-    time = pd.date_range(freq="h", **snakemake.config["snapshots"])
+    time = pd.date_range(freq="h", **snapshots)
    cutout = atlite.Cutout(snakemake.input.cutout).sel(time=time)

    da = calculate_line_rating(n, cutout)
--- a/scripts/build_monthly_prices.py
+++ b/scripts/build_monthly_prices.py
@ -6,11 +6,8 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Created on Tue May 16 10:37:35 2023.
-
-This script extracts monthly fuel prices of oil, gas, coal and lignite,
-as well as CO2 prices
-
+This script extracts monthly fuel prices of oil, gas, coal and lignite, as well
+as CO2 prices.

 Inputs
 ------
--- a/scripts/build_population_layouts.py
+++ b/scripts/build_population_layouts.py
@ -8,15 +8,14 @@ Build mapping between cutout grid cells and population (total, urban, rural).

 import logging

-logger = logging.getLogger(__name__)
-
-
 import atlite
 import geopandas as gpd
 import numpy as np
 import pandas as pd
 import xarray as xr

+logger = logging.getLogger(__name__)
+
 if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake
@ -34,7 +33,7 @@ if __name__ == "__main__":
    nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")

    # Indicator matrix NUTS3 -> grid cells
-    I = atlite.cutout.compute_indicatormatrix(nuts3.geometry, grid_cells)
+    I = atlite.cutout.compute_indicatormatrix(nuts3.geometry, grid_cells)  # noqa: E741

    # Indicator matrix grid_cells -> NUTS3; inprinciple Iinv*I is identity
    # but imprecisions mean not perfect
@ -84,7 +83,8 @@ if __name__ == "__main__":

        # correct for imprecision of Iinv*I
        pop_ct = nuts3.loc[nuts3.country == ct, "pop"].sum()
-        pop_cells_ct *= pop_ct / pop_cells_ct.sum()
+        if pop_cells_ct.sum() != 0:
+            pop_cells_ct *= pop_ct / pop_cells_ct.sum()

        # The first low density grid cells to reach rural fraction are rural
        asc_density_i = density_cells_ct.sort_values().index
--- a/scripts/build_powerplants.py
+++ b/scripts/build_powerplants.py
@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
+# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
 #
 # SPDX-License-Identifier: MIT

@ -10,6 +10,7 @@ Retrieves conventional powerplant capacities and locations from
 these to buses and creates a ``.csv`` file. It is possible to amend the
 powerplant database with custom entries provided in
 ``data/custom_powerplants.csv``.
+Lastly, for every substation, powerplants with zero-initial capacity can be added for certain fuel types automatically.

 Relevant Settings
 -----------------
@ -19,6 +20,7 @@ Relevant Settings
    electricity:
      powerplants_filter:
      custom_powerplants:
+      everywhere_powerplants:

 .. seealso::
    Documentation of the configuration file ``config/config.yaml`` at
@ -44,6 +46,7 @@ Description
 -----------

 The configuration options ``electricity: powerplants_filter`` and ``electricity: custom_powerplants`` can be used to control whether data should be retrieved from the original powerplants database or from custom amendmends. These specify `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ commands.
+In addition the configuration option ``electricity: everywhere_powerplants`` can be used to place powerplants with zero-initial capacity of certain fuel types at all substations.

 1. Adding all powerplants from custom:

@ -73,10 +76,18 @@ The configuration options ``electricity: powerplants_filter`` and ``electricity:

        powerplants_filter: Country not in ['Germany'] and YearCommissioned <= 2015
        custom_powerplants: YearCommissioned <= 2015
+
+4. Adding powerplants at all substations for 4 conventional carrier types:
+
+    .. code:: yaml
+
+        everywhere_powerplants: ['Natural Gas', 'Coal', 'nuclear', 'OCGT']
 """

+import itertools
 import logging

+import numpy as np
 import pandas as pd
 import powerplantmatching as pm
 import pypsa
@ -89,7 +100,7 @@ logger = logging.getLogger(__name__)
 def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
    if not custom_ppl_query:
        return ppl
-    add_ppls = pd.read_csv(custom_powerplants, index_col=0, dtype={"bus": "str"})
+    add_ppls = pd.read_csv(custom_powerplants, dtype={"bus": "str"})
    if isinstance(custom_ppl_query, str):
        add_ppls.query(custom_ppl_query, inplace=True)
    return pd.concat(
@ -97,6 +108,45 @@ def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
    )


+def add_everywhere_powerplants(ppl, substations, everywhere_powerplants):
+    # Create a dataframe with "everywhere_powerplants" of stated carriers at the location of all substations
+    everywhere_ppl = (
+        pd.DataFrame(
+            itertools.product(substations.index.values, everywhere_powerplants),
+            columns=["substation_index", "Fueltype"],
+        ).merge(
+            substations[["x", "y", "country"]],
+            left_on="substation_index",
+            right_index=True,
+        )
+    ).drop(columns="substation_index")
+
+    # PPL uses different columns names compared to substations dataframe -> rename
+    everywhere_ppl = everywhere_ppl.rename(
+        columns={"x": "lon", "y": "lat", "country": "Country"}
+    )
+
+    # Add default values for the powerplants
+    everywhere_ppl["Name"] = (
+        "Automatically added everywhere-powerplant " + everywhere_ppl.Fueltype
+    )
+    everywhere_ppl["Set"] = "PP"
+    everywhere_ppl["Technology"] = everywhere_ppl["Fueltype"]
+    everywhere_ppl["Capacity"] = 0.0
+
+    # Assign plausible values for the commissioning and decommissioning years
+    # required for multi-year models
+    everywhere_ppl["DateIn"] = ppl["DateIn"].min()
+    everywhere_ppl["DateOut"] = ppl["DateOut"].max()
+
+    # NaN values for efficiency will be replaced by the generic efficiency by attach_conventional_generators(...) in add_electricity.py later
+    everywhere_ppl["Efficiency"] = np.nan
+
+    return pd.concat(
+        [ppl, everywhere_ppl], sort=False, ignore_index=True, verify_integrity=True
+    )
+
+
 def replace_natural_gas_technology(df):
    mapping = {"Steam Turbine": "CCGT", "Combustion Engine": "OCGT"}
    tech = df.Technology.replace(mapping).fillna("CCGT")
@ -147,10 +197,14 @@ if __name__ == "__main__":
        ppl, snakemake.input.custom_powerplants, custom_ppl_query
    )

-    countries_wo_ppl = set(countries) - set(ppl.Country.unique())
-    if countries_wo_ppl:
+    if countries_wo_ppl := set(countries) - set(ppl.Country.unique()):
        logging.warning(f"No powerplants known in: {', '.join(countries_wo_ppl)}")

+    # Add "everywhere powerplants" to all bus locations
+    ppl = add_everywhere_powerplants(
+        ppl, n.buses.query("substation_lv"), snakemake.params.everywhere_powerplants
+    )
+
    substations = n.buses.query("substation_lv")
    ppl = ppl.dropna(subset=["lat", "lon"])
    ppl = map_country_bus(ppl, substations)
--- a/scripts/build_renewable_profiles.py
+++ b/scripts/build_renewable_profiles.py
@ -26,20 +26,9 @@ Relevant settings

    renewable:
        {technology}:
-            cutout:
-            corine:
-            grid_codes:
-            distance:
-            natura:
-            max_depth:
-            max_shore_distance:
-            min_shore_distance:
-            capacity_per_sqkm:
-            correction_factor:
-            potential:
-            min_p_max_pu:
-            clip_p_max_pu:
-            resource:
+            cutout: corine: luisa: grid_codes: distance: natura: max_depth:
+            max_shore_distance: min_shore_distance: capacity_per_sqkm:
+            correction_factor: min_p_max_pu: clip_p_max_pu: resource:

 .. seealso::
    Documentation of the configuration file ``config/config.yaml`` at
@ -48,21 +37,37 @@ Relevant settings
 Inputs
 ------

- ``data/bundle/corine/g250_clc06_V18_5.tif``: `CORINE Land Cover (CLC) <https://land.copernicus.eu/pan-european/corine-land-cover>`_ inventory on `44 classes <https://wiki.openstreetmap.org/wiki/Corine_Land_Cover#Tagging>`_ of land use (e.g. forests, arable land, industrial, urban areas).
+- ``data/bundle/corine/g250_clc06_V18_5.tif``: `CORINE Land Cover (CLC)
+  <https://land.copernicus.eu/pan-european/corine-land-cover>`_ inventory on `44
+  classes <https://wiki.openstreetmap.org/wiki/Corine_Land_Cover#Tagging>`_ of
+  land use (e.g. forests, arable land, industrial, urban areas) at 100m
+  resolution.

    .. image:: img/corine.png
        :scale: 33 %

- ``data/bundle/GEBCO_2014_2D.nc``: A `bathymetric <https://en.wikipedia.org/wiki/Bathymetry>`_ data set with a global terrain model for ocean and land at 15 arc-second intervals by the `General Bathymetric Chart of the Oceans (GEBCO) <https://www.gebco.net/data_and_products/gridded_bathymetry_data/>`_.
+- ``data/LUISA_basemap_020321_50m.tif``: `LUISA Base Map
+  <https://publications.jrc.ec.europa.eu/repository/handle/JRC124621>`_ land
+  coverage dataset at 50m resolution similar to CORINE. For codes in relation to
+  CORINE land cover, see `Annex 1 of the technical documentation
+  <https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_.
+
+- ``data/bundle/GEBCO_2014_2D.nc``: A `bathymetric
+  <https://en.wikipedia.org/wiki/Bathymetry>`_ data set with a global terrain
+  model for ocean and land at 15 arc-second intervals by the `General
+  Bathymetric Chart of the Oceans (GEBCO)
+  <https://www.gebco.net/data_and_products/gridded_bathymetry_data/>`_.

    .. image:: img/gebco_2019_grid_image.jpg
        :scale: 50 %

-    **Source:** `GEBCO <https://www.gebco.net/data_and_products/images/gebco_2019_grid_image.jpg>`_
+    **Source:** `GEBCO
+    <https://www.gebco.net/data_and_products/images/gebco_2019_grid_image.jpg>`_

 - ``resources/natura.tiff``: confer :ref:`natura`
 - ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
- ``resources/regions_onshore.geojson``: (if not offshore wind), confer :ref:`busregions`
+- ``resources/regions_onshore.geojson``: (if not offshore wind), confer
+  :ref:`busregions`
 - ``resources/regions_offshore.geojson``: (if offshore wind), :ref:`busregions`
 - ``"cutouts/" + params["renewable"][{technology}]['cutout']``: :ref:`cutout`
 - ``networks/base.nc``: :ref:`base`
@ -128,25 +133,26 @@ Description
 This script functions at two main spatial resolutions: the resolution of the
 network nodes and their `Voronoi cells
 <https://en.wikipedia.org/wiki/Voronoi_diagram>`_, and the resolution of the
-cutout grid cells for the weather data. Typically the weather data grid is
-finer than the network nodes, so we have to work out the distribution of
-generators across the grid cells within each Voronoi cell. This is done by
-taking account of a combination of the available land at each grid cell and the
-capacity factor there.
+cutout grid cells for the weather data. Typically the weather data grid is finer
+than the network nodes, so we have to work out the distribution of generators
+across the grid cells within each Voronoi cell. This is done by taking account
+of a combination of the available land at each grid cell and the capacity factor
+there.

 First the script computes how much of the technology can be installed at each
-cutout grid cell and each node using the `GLAES
-<https://github.com/FZJ-IEK3-VSA/glaes>`_ library. This uses the CORINE land use data,
-Natura2000 nature reserves and GEBCO bathymetry data.
+cutout grid cell and each node using the `atlite
+<https://github.com/pypsa/atlite>`_ library. This uses the CORINE land use data,
+LUISA land use data, Natura2000 nature reserves, GEBCO bathymetry data, and
+shipping lanes.

 .. image:: img/eligibility.png
    :scale: 50 %
    :align: center

-To compute the layout of generators in each node's Voronoi cell, the
-installable potential in each grid cell is multiplied with the capacity factor
-at each grid cell. This is done since we assume more generators are installed
-at cells with a higher capacity factor.
+To compute the layout of generators in each node's Voronoi cell, the installable
+potential in each grid cell is multiplied with the capacity factor at each grid
+cell. This is done since we assume more generators are installed at cells with a
+higher capacity factor.

 .. image:: img/offwinddc-gridcell.png
    :scale: 50 %
@ -164,20 +170,14 @@ at cells with a higher capacity factor.
    :scale: 50 %
    :align: center

-This layout is then used to compute the generation availability time series
-from the weather data cutout from ``atlite``.
+This layout is then used to compute the generation availability time series from
+the weather data cutout from ``atlite``.

-Two methods are available to compute the maximal installable potential for the
-node (`p_nom_max`): ``simple`` and ``conservative``:
-
- ``simple`` adds up the installable potentials of the individual grid cells.
-  If the model comes close to this limit, then the time series may slightly
-  overestimate production since it is assumed the geographical distribution is
-  proportional to capacity factor.
-
- ``conservative`` assertains the nodal limit by increasing capacities
-  proportional to the layout until the limit of an individual grid cell is
-  reached.
+The maximal installable potential for the node (`p_nom_max`) is computed by
+adding up the installable potentials of the individual grid cells. If the model
+comes close to this limit, then the time series may slightly overestimate
+production since it is assumed the geographical distribution is proportional to
+capacity factor.
 """
 import functools
 import logging
@ -200,9 +200,7 @@ if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

-        snakemake = mock_snakemake(
-            "build_renewable_profiles", technology="solar", run="network2019"
-        )
+        snakemake = mock_snakemake("build_renewable_profiles", technology="offwind-dc")
    configure_logging(snakemake)
    set_scenario_config(snakemake)

@ -211,12 +209,16 @@ if __name__ == "__main__":
    noprogress = noprogress or not snakemake.config["atlite"]["show_progress"]
    params = snakemake.params.renewable[snakemake.wildcards.technology]
    resource = params["resource"]  # pv panel params / wind turbine params
+
+    tech = next(t for t in ["panel", "turbine"] if t in resource)
+    models = resource[tech]
+    if not isinstance(models, dict):
+        models = {0: models}
+    resource[tech] = models[next(iter(models))]
+
    correction_factor = params.get("correction_factor", 1.0)
    capacity_per_sqkm = params["capacity_per_sqkm"]
-    p_nom_max_meth = params.get("potential", "conservative")
-
-    if isinstance(params.get("corine", {}), list):
-        params["corine"] = {"grid_codes": params["corine"]}
+    snapshots = snakemake.params.snapshots

    if correction_factor != 1.0:
        logger.info(f"correction_factor is set as {correction_factor}")
@ -226,7 +228,7 @@ if __name__ == "__main__":
    else:
        client = None

-    sns = pd.date_range(freq="h", **snakemake.config["snapshots"])
+    sns = pd.date_range(freq="h", **snapshots)
    cutout = atlite.Cutout(snakemake.input.cutout).sel(time=sns)
    regions = gpd.read_file(snakemake.input.regions)
    assert not regions.empty, (
@ -243,18 +245,31 @@ if __name__ == "__main__":
    if params["natura"]:
        excluder.add_raster(snakemake.input.natura, nodata=0, allow_no_overlap=True)

-    corine = params.get("corine", {})
-    if "grid_codes" in corine:
-        codes = corine["grid_codes"]
-        excluder.add_raster(snakemake.input.corine, codes=codes, invert=True, crs=3035)
-    if corine.get("distance", 0.0) > 0.0:
-        codes = corine["distance_grid_codes"]
-        buffer = corine["distance"]
-        excluder.add_raster(
-            snakemake.input.corine, codes=codes, buffer=buffer, crs=3035
-        )
+    for dataset in ["corine", "luisa"]:
+        kwargs = {"nodata": 0} if dataset == "luisa" else {}
+        settings = params.get(dataset, {})
+        if not settings:
+            continue
+        if dataset == "luisa" and res > 50:
+            logger.info(
+                "LUISA data is available at 50m resolution, "
+                f"but coarser {res}m resolution is used."
+            )
+        if isinstance(settings, list):
+            settings = {"grid_codes": settings}
+        if "grid_codes" in settings:
+            codes = settings["grid_codes"]
+            excluder.add_raster(
+                snakemake.input[dataset], codes=codes, invert=True, crs=3035, **kwargs
+            )
+        if settings.get("distance", 0.0) > 0.0:
+            codes = settings["distance_grid_codes"]
+            buffer = settings["distance"]
+            excluder.add_raster(
+                snakemake.input[dataset], codes=codes, buffer=buffer, crs=3035, **kwargs
+            )

-    if "ship_threshold" in params:
+    if params.get("ship_threshold"):
        shipping_threshold = (
            params["ship_threshold"] * 8760 * 6
        )  # approximation because 6 years of data which is hourly collected
@ -280,15 +295,22 @@ if __name__ == "__main__":
            snakemake.input.country_shapes, buffer=buffer, invert=True
        )

+    logger.info("Calculate landuse availability...")
+    start = time.time()
+
    kwargs = dict(nprocesses=nprocesses, disable_progressbar=noprogress)
-    if noprogress:
-        logger.info("Calculate landuse availabilities...")
-        start = time.time()
-        availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
-        duration = time.time() - start
-        logger.info(f"Completed availability calculation ({duration:2.2f}s)")
-    else:
-        availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
+    availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
+
+    duration = time.time() - start
+    logger.info(f"Completed landuse availability calculation ({duration:2.2f}s)")
+
+    # For Moldova and Ukraine: Overwrite parts not covered by Corine with
+    # externally determined available areas
+    if "availability_matrix_MD_UA" in snakemake.input.keys():
+        availability_MDUA = xr.open_dataarray(
+            snakemake.input["availability_matrix_MD_UA"]
+        )
+        availability.loc[availability_MDUA.coords] = availability_MDUA

    area = cutout.grid.to_crs(3035).area / 1e6
    area = xr.DataArray(
@ -299,28 +321,53 @@ if __name__ == "__main__":
    func = getattr(cutout, resource.pop("method"))
    if client is not None:
        resource["dask_kwargs"] = {"scheduler": client}
+
+    logger.info("Calculate average capacity factor...")
+    start = time.time()
+
    capacity_factor = correction_factor * func(capacity_factor=True, **resource)
    layout = capacity_factor * area * capacity_per_sqkm
-    profile, capacities = func(
-        matrix=availability.stack(spatial=["y", "x"]),
-        layout=layout,
-        index=buses,
-        per_unit=True,
-        return_capacity=True,
-        **resource,
-    )

-    logger.info(f"Calculating maximal capacity per bus (method '{p_nom_max_meth}')")
-    if p_nom_max_meth == "simple":
-        p_nom_max = capacity_per_sqkm * availability @ area
-    elif p_nom_max_meth == "conservative":
-        max_cap_factor = capacity_factor.where(availability != 0).max(["x", "y"])
-        p_nom_max = capacities / max_cap_factor
-    else:
-        raise AssertionError(
-            'Config key `potential` should be one of "simple" '
-            f'(default) or "conservative", not "{p_nom_max_meth}"'
+    duration = time.time() - start
+    logger.info(f"Completed average capacity factor calculation ({duration:2.2f}s)")
+
+    profiles = []
+    capacities = []
+    for year, model in models.items():
+
+        logger.info(
+            f"Calculate weighted capacity factor time series for model {model}..."
        )
+        start = time.time()
+
+        resource[tech] = model
+
+        profile, capacity = func(
+            matrix=availability.stack(spatial=["y", "x"]),
+            layout=layout,
+            index=buses,
+            per_unit=True,
+            return_capacity=True,
+            **resource,
+        )
+
+        dim = {"year": [year]}
+        profile = profile.expand_dims(dim)
+        capacity = capacity.expand_dims(dim)
+
+        profiles.append(profile.rename("profile"))
+        capacities.append(capacity.rename("weight"))
+
+        duration = time.time() - start
+        logger.info(
+            f"Completed weighted capacity factor time series calculation for model {model} ({duration:2.2f}s)"
+        )
+
+    profiles = xr.merge(profiles)
+    capacities = xr.merge(capacities)
+
+    logger.info("Calculating maximal capacity per bus")
+    p_nom_max = capacity_per_sqkm * availability @ area

    logger.info("Calculate average distances.")
    layoutmatrix = (layout * availability).stack(spatial=["y", "x"])
@ -344,8 +391,8 @@ if __name__ == "__main__":

    ds = xr.merge(
        [
-            (correction_factor * profile).rename("profile"),
-            capacities.rename("weight"),
+            correction_factor * profiles,
+            capacities,
            p_nom_max.rename("p_nom_max"),
            potential.rename("potential"),
            average_distance.rename("average_distance"),
@ -365,9 +412,13 @@ if __name__ == "__main__":
        ds["underwater_fraction"] = xr.DataArray(underwater_fraction, [buses])

    # select only buses with some capacity and minimal capacity factor
+    mean_profile = ds["profile"].mean("time")
+    if "year" in ds.indexes:
+        mean_profile = mean_profile.max("year")
+
    ds = ds.sel(
        bus=(
-            (ds["profile"].mean("time") > params.get("min_p_max_pu", 0.0))
+            (mean_profile > params.get("min_p_max_pu", 0.0))
            & (ds["p_nom_max"] > params.get("min_p_nom_max", 0.0))
        )
    )
--- a/scripts/build_retro_cost.py
+++ b/scripts/build_retro_cost.py
@ -102,7 +102,7 @@ solar_energy_transmittance = (
 )
 # solar global radiation [kWh/(m^2a)]
 solar_global_radiation = pd.Series(
-    [246, 401, 246, 148],
+    [271, 392, 271, 160],
    index=["east", "south", "west", "north"],
    name="solar_global_radiation [kWh/(m^2a)]",
 )
@ -164,6 +164,12 @@ def prepare_building_stock_data():
        },
        inplace=True,
    )
+    building_data["feature"].replace(
+        {
+            "Construction features (U-value)": "Construction features (U-values)",
+        },
+        inplace=True,
+    )

    building_data.country_code = building_data.country_code.str.upper()
    building_data["subsector"].replace(
@ -198,12 +204,14 @@ def prepare_building_stock_data():
        }
    )

+    building_data["country_code"] = building_data["country"].map(country_iso_dic)
+
    # heated floor area ----------------------------------------------------------
    area = building_data[
        (building_data.type == "Heated area [Mm²]")
        & (building_data.subsector != "Total")
    ]
-    area_tot = area.groupby(["country", "sector"]).sum()
+    area_tot = area[["country", "sector", "value"]].groupby(["country", "sector"]).sum()
    area = pd.concat(
        [
            area,
@ -223,7 +231,7 @@ def prepare_building_stock_data():
        usecols=[0, 1, 2, 3],
        encoding="ISO-8859-1",
    )
-    area_tot = area_tot.append(area_missing.unstack(level=-1).dropna().stack())
+    area_tot = pd.concat([area_tot, area_missing.unstack(level=-1).dropna().stack()])
    area_tot = area_tot.loc[~area_tot.index.duplicated(keep="last")]

    # for still missing countries calculate floor area by population size
@ -246,7 +254,7 @@ def prepare_building_stock_data():
        averaged_data.index = index
        averaged_data["estimated"] = 1
        if ct not in area_tot.index.levels[0]:
-            area_tot = area_tot.append(averaged_data, sort=True)
+            area_tot = pd.concat([area_tot, averaged_data], sort=True)
        else:
            area_tot.loc[averaged_data.index] = averaged_data

@ -272,7 +280,7 @@ def prepare_building_stock_data():
            ][x["bage"]].iloc[0],
            axis=1,
        )
-        data_PL_final = data_PL_final.append(data_PL)
+        data_PL_final = pd.concat([data_PL_final, data_PL])

    u_values = pd.concat([u_values, data_PL_final]).reset_index(drop=True)

@ -289,8 +297,8 @@ def prepare_building_stock_data():
        errors="ignore",
    )

-    u_values.subsector.replace(rename_sectors, inplace=True)
-    u_values.btype.replace(rename_sectors, inplace=True)
+    u_values["subsector"] = u_values.subsector.replace(rename_sectors)
+    u_values["btype"] = u_values.btype.replace(rename_sectors)

    # for missing weighting of surfaces of building types assume MFH
    u_values["assumed_subsector"] = u_values.subsector
@ -298,8 +306,8 @@ def prepare_building_stock_data():
        ~u_values.subsector.isin(rename_sectors.values()), "assumed_subsector"
    ] = "MFH"

-    u_values.country_code.replace({"UK": "GB"}, inplace=True)
-    u_values.bage.replace({"Berfore 1945": "Before 1945"}, inplace=True)
+    u_values["country_code"] = u_values.country_code.replace({"UK": "GB"})
+    u_values["bage"] = u_values.bage.replace({"Berfore 1945": "Before 1945"})
    u_values = u_values[~u_values.bage.isna()]

    u_values.set_index(["country_code", "subsector", "bage", "type"], inplace=True)
@ -525,16 +533,16 @@ def prepare_temperature_data():
    """
    temperature = xr.open_dataarray(snakemake.input.air_temperature).to_pandas()
    d_heat = (
-        temperature.groupby(temperature.columns.str[:2], axis=1)
+        temperature.T.groupby(temperature.columns.str[:2])
        .mean()
-        .resample("1D")
+        .T.resample("1D")
        .mean()
        < t_threshold
    ).sum()
    temperature_average_d_heat = (
-        temperature.groupby(temperature.columns.str[:2], axis=1)
+        temperature.T.groupby(temperature.columns.str[:2])
        .mean()
-        .apply(
+        .T.apply(
            lambda x: get_average_temperature_during_heating_season(x, t_threshold=15)
        )
    )
@ -546,7 +554,7 @@ def prepare_temperature_data():


 # windows ---------------------------------------------------------------
-def window_limit(l, window_assumptions):
+def window_limit(l, window_assumptions):  # noqa: E741
    """
    Define limit u value from which on window is retrofitted.
    """
@ -559,7 +567,7 @@ def window_limit(l, window_assumptions):
    return m * l + a


-def u_retro_window(l, window_assumptions):
+def u_retro_window(l, window_assumptions):  # noqa: E741
    """
    Define retrofitting value depending on renovation strength.
    """
@ -572,7 +580,7 @@ def u_retro_window(l, window_assumptions):
    return max(m * l + a, 0.8)


-def window_cost(u, cost_retro, window_assumptions):
+def window_cost(u, cost_retro, window_assumptions):  # noqa: E741
    """
    Get costs for new windows depending on u value.
    """
@ -592,34 +600,40 @@ def window_cost(u, cost_retro, window_assumptions):
    return window_cost


-def calculate_costs(u_values, l, cost_retro, window_assumptions):
+def calculate_costs(u_values, l, cost_retro, window_assumptions):  # noqa: E741
    """
    Returns costs for a given retrofitting strength weighted by the average
    surface/volume ratio of the component for each building type.
    """
    return u_values.apply(
        lambda x: (
-            cost_retro.loc[x.name[3], "cost_var"]
-            * 100
-            * float(l)
-            * l_weight.loc[x.name[3]][0]
-            + cost_retro.loc[x.name[3], "cost_fix"]
-        )
-        * x.A_element
-        / x.A_C_Ref
-        if x.name[3] != "Window"
-        else (
-            window_cost(x["new_U_{}".format(l)], cost_retro, window_assumptions)
+            (
+                cost_retro.loc[x.name[3], "cost_var"]
+                * 100
+                * float(l)
+                * l_weight.loc[x.name[3]].iloc[0]
+                + cost_retro.loc[x.name[3], "cost_fix"]
+            )
            * x.A_element
            / x.A_C_Ref
-            if x.value > window_limit(float(l), window_assumptions)
-            else 0
+            if x.name[3] != "Window"
+            else (
+                (
+                    (
+                        window_cost(x[f"new_U_{l}"], cost_retro, window_assumptions)
+                        * x.A_element
+                    )
+                    / x.A_C_Ref
+                )
+                if x.value > window_limit(float(l), window_assumptions)
+                else 0
+            )
        ),
        axis=1,
    )


-def calculate_new_u(u_values, l, l_weight, window_assumptions, k=0.035):
+def calculate_new_u(u_values, l, l_weight, window_assumptions, k=0.035):  # noqa: E741
    """
    Calculate U-values after building retrofitting, depending on the old
    U-values (u_values). This is for simple insulation measuers, adding an
@ -641,12 +655,14 @@ def calculate_new_u(u_values, l, l_weight, window_assumptions, k=0.035):
    k: thermal conductivity
    """
    return u_values.apply(
-        lambda x: k / ((k / x.value) + (float(l) * l_weight.loc[x.name[3]]))
-        if x.name[3] != "Window"
-        else (
-            min(x.value, u_retro_window(float(l), window_assumptions))
-            if x.value > window_limit(float(l), window_assumptions)
-            else x.value
+        lambda x: (
+            k / ((k / x.value) + (float(l) * l_weight.loc[x.name[3]]))
+            if x.name[3] != "Window"
+            else (
+                min(x.value, u_retro_window(float(l), window_assumptions))
+                if x.value > window_limit(float(l), window_assumptions)
+                else x.value
+            )
        ),
        axis=1,
    )
@ -713,6 +729,7 @@ def map_to_lstrength(l_strength, df):
        .swaplevel(axis=1)
        .dropna(axis=1)
    )
+
    return pd.concat([df.drop([2, 3], axis=1, level=1), l_strength_df], axis=1)


@ -738,13 +755,13 @@ def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor)
    """
    #  (1) by transmission
    # calculate new U values of building elements due to additional insulation
-    for l in l_strength:
-        u_values["new_U_{}".format(l)] = calculate_new_u(
+    for l in l_strength:  # noqa: E741
+        u_values[f"new_U_{l}"] = calculate_new_u(
            u_values, l, l_weight, window_assumptions
        )
    # surface area of building components [m^2]
    area_element = (
-        data_tabula[["A_{}".format(e) for e in u_values.index.levels[3]]]
+        data_tabula[[f"A_{e}" for e in u_values.index.levels[3]]]
        .rename(columns=lambda x: x[2:])
        .stack()
        .unstack(-2)
@ -756,7 +773,7 @@ def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor)

    # heat transfer H_tr_e [W/m^2K] through building element
    # U_e * A_e / A_C_Ref
-    columns = ["value"] + ["new_U_{}".format(l) for l in l_strength]
+    columns = ["value"] + [f"new_U_{l}" for l in l_strength]
    heat_transfer = pd.concat(
        [u_values[columns].mul(u_values.A_element, axis=0), u_values.A_element], axis=1
    )
@ -793,6 +810,7 @@ def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor)
        * data_tabula.A_envelope
        / data_tabula.A_C_Ref
    )
+
    heat_transfer_perm2 = pd.concat(
        [
            heat_transfer_perm2,
@ -829,9 +847,9 @@ def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor)
    F_red_temp = map_to_lstrength(l_strength, F_red_temp)

    Q_ht = (
-        heat_transfer_perm2.groupby(level=1, axis=1)
+        heat_transfer_perm2.T.groupby(level=1)
        .sum()
-        .mul(F_red_temp.droplevel(0, axis=1))
+        .T.mul(F_red_temp.droplevel(0, axis=1))
        .mul(temperature_factor.reindex(heat_transfer_perm2.index, level=0), axis=0)
    )

@ -871,14 +889,11 @@ def calculate_gain_utilisation_factor(heat_transfer_perm2, Q_ht, Q_gain):
    Calculates gain utilisation factor nu.
    """
    # time constant of the building tau [h] = c_m [Wh/(m^2K)] * 1 /(H_tr_e+H_tb*H_ve) [m^2 K /W]
-    tau = c_m / heat_transfer_perm2.groupby(level=1, axis=1).sum()
+    tau = c_m / heat_transfer_perm2.T.groupby(axis=1).sum().T
    alpha = alpha_H_0 + (tau / tau_H_0)
    # heat balance ratio
    gamma = (1 / Q_ht).mul(Q_gain.sum(axis=1), axis=0)
-    # gain utilisation factor
-    nu = (1 - gamma**alpha) / (1 - gamma ** (alpha + 1))
-
-    return nu
+    return (1 - gamma**alpha) / (1 - gamma ** (alpha + 1))


 def calculate_space_heat_savings(
@ -947,7 +962,8 @@ def sample_dE_costs_area(
            .rename(index=rename_sectors, level=2)
            .reset_index()
        )
-        .rename(columns={"country": "country_code"})
+        # if uncommented, leads to the second `country_code` column
+        # .rename(columns={"country": "country_code"})
        .set_index(["country_code", "subsector", "bage"])
    )

@ -960,13 +976,14 @@ def sample_dE_costs_area(
    )

    # map missing countries
-    for ct in countries.difference(cost_dE.index.levels[0]):
+    for ct in set(countries).difference(cost_dE.index.levels[0]):
        averaged_data = (
            cost_dE.reindex(index=map_for_missings[ct], level=0)
-            .mean(level=1)
+            .groupby(level=1)
+            .mean()
            .set_index(pd.MultiIndex.from_product([[ct], cost_dE.index.levels[1]]))
        )
-        cost_dE = cost_dE.append(averaged_data)
+        cost_dE = pd.concat([cost_dE, averaged_data])

    # weights costs after construction index
    if construction_index:
@ -983,24 +1000,23 @@ def sample_dE_costs_area(
    # drop not considered countries
    cost_dE = cost_dE.reindex(countries, level=0)
    # get share of residential and service floor area
-    sec_w = area_tot.value / area_tot.value.groupby(level=0).sum()
+    sec_w = area_tot.div(area_tot.groupby(level=0).transform("sum"))
    # get the total cost-energy-savings weight by sector area
    tot = (
-        cost_dE.mul(sec_w, axis=0)
-        .groupby(level="country_code")
+        # sec_w has columns "estimated" and "value"
+        cost_dE.mul(sec_w.value, axis=0)
+        # for some reasons names of the levels were lost somewhere
+        # .groupby(level="country_code")
+        .groupby(level=0)
        .sum()
-        .set_index(
-            pd.MultiIndex.from_product(
-                [cost_dE.index.unique(level="country_code"), ["tot"]]
-            )
-        )
+        .set_index(pd.MultiIndex.from_product([cost_dE.index.unique(level=0), ["tot"]]))
    )
-    cost_dE = cost_dE.append(tot).unstack().stack()
+    cost_dE = pd.concat([cost_dE, tot]).unstack().stack()

-    summed_area = pd.DataFrame(area_tot.groupby("country").sum()).set_index(
-        pd.MultiIndex.from_product([area_tot.index.unique(level="country"), ["tot"]])
+    summed_area = pd.DataFrame(area_tot.groupby(level=0).sum()).set_index(
+        pd.MultiIndex.from_product([area_tot.index.unique(level=0), ["tot"]])
    )
-    area_tot = area_tot.append(summed_area).unstack().stack()
+    area_tot = pd.concat([area_tot, summed_area]).unstack().stack()

    cost_per_saving = cost_dE["cost"] / (
        1 - cost_dE["dE"]
--- a/scripts/build_salt_cavern_potentials.py
+++ b/scripts/build_salt_cavern_potentials.py
@ -66,11 +66,7 @@ def salt_cavern_potential_by_region(caverns, regions):
        "capacity_per_area * share * area_caverns / 1000"
    )  # TWh

-    caverns_regions = (
-        overlay.groupby(["name", "storage_type"]).e_nom.sum().unstack("storage_type")
-    )
-
-    return caverns_regions
+    return overlay.groupby(["name", "storage_type"]).e_nom.sum().unstack("storage_type")


 if __name__ == "__main__":
--- a/Show More
+++ b/Show More