Merge https://github.com/pypsa/pypsa-eur

.github/workflows/ci.yaml

@@ -83,6 +83,7 @@ jobs:
         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 all --configfile config/test/config.perfect.yaml --rerun-triggers=mtime
 
     - name: Upload artifacts
       uses: actions/upload-artifact@v3

.gitignore

@@ -8,6 +8,7 @@ __pycache__
 *dconf
 gurobi.log
 .vscode
+*.orig
 
 /bak
 /resources

.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
@@ -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
+  rev: 23.10.1
   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: v2.11.0
   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: v0.8.5
   hooks:
   - id: snakefmt
 

.readthedocs.yml

@@ -14,4 +14,3 @@ build:
 python:
   install:
   - requirements: doc/requirements.txt
-  system_packages: false

Snakefile

@@ -66,13 +66,31 @@ 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:
+        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:

config/config.default.yaml

@@ -68,6 +68,7 @@ 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
@@ -452,6 +453,7 @@ sector:
   hydrogen_fuel_cell: true
   hydrogen_turbine: false
   SMR: true
+  SMR_cc: true
   regional_co2_sequestration_potential:
     enable: false
     attribute: 'conservative estimate Mt'
@@ -461,6 +463,7 @@ sector:
     years_of_storage: 25
   co2_sequestration_potential: 200
   co2_sequestration_cost: 10
+  co2_sequestration_lifetime: 50
   co2_spatial: false
   co2network: false
   cc_fraction: 0.9
@@ -491,6 +494,20 @@ sector:
     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
   enhanced_geothermal: true
   enhanced_geothermal_optimism: false # if true, egs costs are reducing towards 2050 according to Aghahosseini et al., (2020)
   enhanced_geothermal_performant: true # if true, adds only the cheapest patch of EGS potential to each region
@@ -546,11 +563,13 @@ 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
   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
@@ -764,6 +783,7 @@ 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 +802,7 @@ plotting:
     Coal: '#545454'
     coal: '#545454'
     Coal marginal: '#545454'
+    coal for industry: '#343434'
     solid: '#545454'
     Lignite: '#826837'
     lignite: '#826837'
@@ -895,6 +916,7 @@ plotting:
     H2 for shipping: "#ebaee0"
     H2: '#bf13a0'
     hydrogen: '#bf13a0'
+    retrofitted H2 boiler: '#e5a0d9'
     SMR: '#870c71'
     SMR CC: '#4f1745'
     H2 liquefaction: '#d647bd'

config/config.perfect.yaml

@@ -0,0 +1,43 @@
+# 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-solar+p3-dist1
+  - 1p7-4380H-T-H-B-I-A-solar+p3-dist1
+  - 2p0-4380H-T-H-B-I-A-solar+p3-dist1
+  planning_horizons:
+  - 2020
+  - 2030
+  - 2040
+  - 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

config/test/config.perfect.yaml

@@ -0,0 +1,91 @@
+# 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: true
+  shared_cutouts: true
+
+foresight: perfect
+
+scenario:
+  ll:
+  - v1.0
+  clusters:
+  - 5
+  sector_opts:
+  - 8760H-T-H-B-I-A-solar+p3-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

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
+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,,,,,,,,,,,,,,,,,,,
+Albania,,,,,,,,,,,,,,,,,,,,,,,
-Austria,,,,,,,,,,,,,,,,,,,
+Austria,,,,,,,,,,,,,,,,,,,,,,,
-Belgium,,,,,,,,,,31.5,196.5,196.5,381,707.7,707.7,712,712.2,877.2,1185.9
+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,,,,,,,,,,,,,,,,,,,
+Bosnia Herzg,,,,,,,,,,,,,,,,,,,,,,,
-Bulgaria,,,,,,,,,,,,,,,,,,,
+Bulgaria,,,,,,,,,,,,,,,,,,,,,,,
-Croatia,,,,,,,,,,,,,,,,,,,
+Croatia,,,,,,,,,,,,,,,,,,,,,,,
-Czechia,,,,,,,,,,,,,,,,,,,
+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
+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,,,,,,,,,,,,,,,,,,,
+Estonia,,,,,,,,,,,,,,,,,,,,,,,
-Finland,,,,,,,,,24,24,26.3,26.3,26.3,26.3,26.3,32,32,72.7,72.7
+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,,,,,,,,,,,,,,,,,,2,2
+France,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,2.0,482.0
-Germany,,,,,,,,,,35,80,188,268,508,994,3283,4132,5406,6396
+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,,,,,,,,,,,,,,,,,,,
+Greece,,,,,,,,,,,,,,,,,,,,,,,
-Hungary,,,,,,,,,,,,,,,,,,,
+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
+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,,,,,,,,,,,,,,,,,,,
+Italy,,,,,,,,,,,,,,,,,,,,,,,30.0
-Latvia,,,,,,,,,,,,,,,,,,,
+Latvia,,,,,,,,,,,,,,,,,,,,,,,
-Lithuania,,,,,,,,,,,,,,,,,,,
+Lithuania,,,,,,,,,,,,,,,,,,,,,,,
-Luxembourg,,,,,,,,,,,,,,,,,,,
+Luxembourg,,,,,,,,,,,,,,,,,,,,,,,
-Montenegro,,,,,,,,,,,,,,,,,,,
+Montenegro,,,,,,,,,,,,,,,,,,,,,,,
-Netherlands,,,,,,,108,108,228,228,228,228,228,228,228,357,957,957,957
+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,,,,,,,,,,,,,,,,,,,
+North Macedonia,,,,,,,,,,,,,,,,,,,,,,,
-Norway,,,,,,,,,,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3
+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,,,,,,,,,,,,,,,,,,,
+Poland,,,,,,,,,,,,,,,,,,,,,,,
-Portugal,,,,,,,,,,,,1.9,2,2,2,2,,,
+Portugal,,,,,,,,,,,,1.86,2.0,2.0,2.0,2.0,,,,,25.0,25.0,25.0
-Romania,,,,,,,,,,,,,,,,,,,
+Romania,,,,,,,,,,,,,,,,,,,,,,,
-Serbia,,,,,,,,,,,,,,,,,,,
+Serbia,,,,,,,,,,,,,,,,,,,,,,,
-Slovakia,,,,,,,,,,,,,,,,,,,
+Slovakia,,,,,,,,,,,,,,,,,,,,,,,
-Slovenia,,,,,,,,,,,,,,,,,,,
+Slovenia,,,,,,,,,,,,,,,,,,,,,,,
-Spain,,,,,,,,,,,,,,5,5,5,5,5,5
+Spain,,,,,,,,,,,,,,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0
-Sweden,13,22,22,22,22,22,22,131,133,163,163,163,163,212,213,213,203,203,203
+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,,,,,,,,,,,,,,,,,,,
+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
+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

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
+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,,,,,,,,,,,,,,,,,,,
+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
+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,14,26,31,67,96,167,212,276,324,576.5,715.5,872.5,989,1072.3,1236.3,1464,1657.8,1919.3,2074.8
+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,,,,,,,,,,,,0.3,0.3,0.3,0.3,0.3,0.3,0.3,50.9
+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,,,,,1,8,27,30,114,333,488,541,677,683,699,699,699,698.4,698.9
+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,,,,,6,6,17,17,17,70,79,130,180,254,339,418,483,576.1,586.3
+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,,6.4,10.6,16.5,22,43.5,113.8,150,193,213,213,258,262,278,281,282,308.2,316.2
+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,2340.1,2447.2,2680.6,2696.6,2700.4,2704.5,2712.3,2700.9,2739.5,2821.2,2934,3080.5,3240.1,3547.9,3615.4,3805.9,3974.5,4225.8,4419.8
+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,,,1,3,7,31,31,50,77,104,108,180,266,248,275,300,310,311.8,310
+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,38,39,43,52,82,82,86,110,119,123,170.7,172.7,230.7,420.7,600.7,973,1533,1971.3,1968.3
+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,38,66,138,218,358,690,1412,2223,3403,4582,5912,6758,7607.5,8156,9201.4,10298.2,11566.6,13497.4,14898.1
+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,6095,8754,12001,14381,16419,18248,20474,22116,22794,25697,26823,28524,30711,32969,37620,41297,45303,50174,52447
+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,226,270,287,371,470,491,749,846,1022,1171,1298,1640,1753,1809,1978,2091,2370,2624,2877.5
+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,,1,1,3,3,17,33,61,134,203,293,331,325,329,329,329,329,329,329
+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,116.5,122.9,134.8,210.3,311.2,468.1,651.3,715.3,917.1,1226.1,1365.2,1559.4,1679.2,1983,2258.1,2426,2760.8,3292.8,3650.9
+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,363,664,780,874,1127,1635,1902,2702,3525,4879,5794,6918,8102,8542,8683,9137,9384,9736.6,10230.2
+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,2,2,22,26,26,26,26,26,28,29,30,36,59,65.9,68.9,68.2,69.9,77.1,78.2
+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,,,,,1,1,31,47,54,98,133,202,275,279,288,436,509,518,533
+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,14,13.9,13.9,20.5,34.9,34.9,34.9,34.9,42.9,42.9,43.7,44.5,58.3,58.3,58.3,63.8,119.7,119.7,122.9
+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,,,,,,,,,,,,,,,,,,72,118
+Montenegro,,,,,,,,,,,,,,,,,,72.0,72.0,118.0,118.0,118.0,118.0
-Netherlands,447,486,672,905,1075,1224,1453,1641,1921,1994,2009,2088,2205,2485,2637,3034,3300,3245,3436
+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,,,,,,,,,,,,,,,37,37,37,37,37
+North Macedonia,,,,,,,,,,,,,,,37.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0
-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
+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,4,19,32,35,40,121,172,306,526,709,1108,1800,2564,3429,3836,4886,5747,5759.4,5766.1
+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,83,125,190,268,553,1064,1681,2201,2857,3326,3796,4254.4,4409.6,4607.9,4854.6,4934.8,5124.1,5124.1,5172.4
+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,,,,,,1,1,3,5,15,389,988,1822,2773,3244,3130,3025,3029.8,3032.3
+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,,,,,,,,,,,,,0.5,0.5,0.5,10.4,17,25,25
+Serbia,,,,,,,,,,,,,0.5,0.5,0.5,10.4,17.0,25.0,227.0,398.0,398.0,398.0,398.0
-Slovakia,,,,3,3,5,5,5,5,3,3,3,3,5,3,3,3,4,3
+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,,,,,,,,,,,,,,4,4,5,5,5,5.2
+Slovenia,,,,,,,,,,,,,2.0,2.0,3.0,3.0,3.0,3.3,3.3,3.3,3.3,3.33,3.33
-Spain,2206,3397,4891,5945,8317,9918,11722,14820,16555,19176,20693,21529,22789,22953,22920,22938,22985,23119.5,23400.1
+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,196,273,335,395,453,500,563,692,956,1312,1854,2601,3443,3982,4875,5606,6232,6408,7097
+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,3,5,5,5,9,12,12,12,14,18,42,46,49,60,60,60,75,75,75
+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,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
+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

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
+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,,0.1,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.3,0.4,0.6,0.7,0.8,0.9,1.1,1,1,1
+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,5,7,9,23,27,21,22.4,24.2,30.1,48.9,88.8,174.1,337.5,626,785.2,937.1,1096,1269,1437.6
+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,,,1,1,1,2,2,20,62,386,1007,1979,2647,2902,3015.2,3131.7,3327,3616.2,3986.5
+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,,,,0.1,0.2,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,1.3,7.2,8.2,14.1,16,18.2
+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,,,,,,,,0,0.1,2,25,154,1013,1020,1026,1029,1028,1035.6,1032.7
+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,,,,,,,,,,0.3,0.3,0.3,4,19,33,47.8,55.8,60,67.7
+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,0.1,0.1,0.2,0.3,0.4,0.6,0.8,4,39.5,464.6,1727,1913,2022,2063.5,2067.4,2074.9,2067.9,2069.5,2075.1
+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,1,1,2,2,2,3,3,3,3,5,7,17,402,571,607,782.1,851,906.4,998
+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,,,,,,,,,,0.1,0.1,0.2,0.4,1.5,3.3,6.5,10,15,31.9
+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,2,3,3,3,4,4,5,5,6,6,7,7,8,9,11,17,39,82,140
+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,7,7,8,9,11,13,15,26,80,277,1044,3003.6,4358.8,5277.3,6034.4,7137.5,7702.1,8610.4,9617
+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,114,195,260,435,1105,2056,2899,4170,6120,10564,18004,25914,34075,36708,37898,39222,40677,42291,45179
+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,,1,1,1,1,1,5,9,12,46,202,612,1536,2579,2596,2604,2604,2605.5,2651.6
+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,,,,,,,,0.4,1,1,2,4,12,35,89,172,235,344,726
+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,,,,,,,,,,0.6,0.7,0.8,0.9,1,1.6,2.4,5.9,15.7,24.2
+Ireland,,,,,,,,,,,,,,,,,,,,,,,
-Italy,19,20,22,26,31,34,45,110,483,1264,3592,13131,16785,18185,18594,18901,19283,19682.3,20107.6
+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,,,,,,,,,,,,,0.2,0.2,0.2,0.2,0.7,0.7,2
+Latvia,,,,,,,,,,,,,,,,,0.69,0.69,1.96,3.3,5.1,7.16,56.16
-Lithuania,,,,,,,,,0.1,0.1,0.1,0.3,7,68,69,69,70,73.8,82
+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,,0.2,1.6,14.2,23.6,23.6,23.7,23.9,24.6,26.4,29.5,40.7,74.7,95,109.9,116.3,121.9,128.1,130.6
+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,,,,,,,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
+Montenegro,,,,,,,,,,,,,,,,,,,,,2.57,2.57,22.2
-Netherlands,13,21,26,46,50,51,53,54,59,69,90,149,369,746,1048,1515,2049,2903,4522
+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,,,,,,,,,,,0,2,4,7,15,17,16.7,16.7,20.6
+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,6,6,6,7,7,7,8,8,8.3,8.7,9.1,9.5,10,11,13,15,26.7,44.9,68.4
+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,,,,,,,,,,,,1.1,1.3,2.4,27.2,107.8,187.2,287.1,562
+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,1,1,1,2,2,2,3,24,59,115,134,172,238,296,415,447,512.8,579.2,667.4
+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,,,,,,,,,0.1,0.1,0.1,1,41,761,1293,1326,1372,1374.1,1385.8
+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,,,,,,0.1,0.2,0.4,0.9,1.2,1.3,1.5,3.1,4.7,6,9,11,10,10
+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,,,,,,,,,,,19,496,513,533,533,533,533,528,472
+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,,,0,0,0,0,0.2,0.6,1,4,12,57,142,187,223,238,233,246.8,221.3
+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,10,13,17,22,33,52,130,494,3384,3423,3873,4283,4569,4690,4697,4704,4713,4723,4763.5
+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,3,3,3,4,4,4,5,6,8,9,11,12,24,43,60,104,153,402,492
+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,16,18,20,22,24,28,30,37,49,79,125,223,437,756,1061,1394,1664,1906,2171
+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,2,3,4,6,8,11,14,18,23,27,95,1000,1753,2937,5528,9601.2,11930.5,12781.8,13118.3
+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

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`."

doc/configtables/sector.csv

@@ -79,6 +79,7 @@ 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_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

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

doc/index.rst

@@ -185,7 +185,7 @@ 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: ::

doc/release_notes.rst

@@ -20,8 +20,29 @@ Upcoming Release
 
 * Files extracted from sector-coupled data bundle have been moved from ``data/`` to ``data/sector-bundle``.
 
+* New feature multi-decade optimisation with  perfect foresight.
+
+* 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.
+
+* In pathway mode, the biomass potential is linked to the investment year.
+
+* Rule ``purge`` now initiates a dialog to confirm if purge is desired.
+
+* Rule ``retrieve_irena`` get updated values for renewables capacities.
+
+* Split configuration to enable SMR and SMR CC.
+
+* The ``mock_snakemake`` function can now be used with a Snakefile from a different directory using the new ``root_dir`` argument.
+
 * Added Enhanced Geothermal Systems for electricity generation.
 
+
+**Bugs and Compatibility**
+
+* A bug preventing custom powerplants specified in ``data/custom_powerplants.csv`` was fixed. (https://github.com/PyPSA/pypsa-eur/pull/732)
+
+
 PyPSA-Eur 0.8.1 (27th July 2023)
 ================================
 

doc/retrieve.rst

@@ -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``
 ================================
 

doc/tutorial.rst

@@ -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"];
+            0[label = "solve_network", color = "0.33 0.6 0.85", style="rounded"];
-        1[label = "prepare_network\nll: copt\nopts: Co2L-24H", color = "0.02 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 = "0.37 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 = "0.39 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 = "0.11 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 = "0.23 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: onwind", color = "0.57 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 = "0.09 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 = "0.41 0.6 0.85", style="rounded"];
+            8[label = "build_shapes", color = "0.07 0.6 0.85", style="rounded,dashed"];
-        9[label = "retrieve_databundle", color = "0.28 0.6 0.85", style="rounded"];
+            9[label = "retrieve_databundle", color = "0.60 0.6 0.85", style="rounded"];
-        10[label = "retrieve_natura_raster", color = "0.62 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 = "0.53 0.6 0.85", style="rounded"];
+            11[label = "build_bus_regions", color = "0.09 0.6 0.85", style="rounded,dashed"];
-        12[label = "retrieve_cutout\ncutout: europe-2013-era5", color = "0.05 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 = "0.57 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 = "0.64 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 = "0.07 0.6 0.85", style="rounded,dashed"];
+            15[label = "retrieve_ship_raster", color = "0.40 0.6 0.85", style="rounded"];
-        16[label = "retrieve_cutout\ncutout: europe-2013-sarah", color = "0.05 0.6 0.85", style="rounded,dashed"];
+            16[label = "build_renewable_profiles\ntechnology: offwind-dc", color = "0.15 0.6 0.85", style="rounded"];
-        17[label = "build_renewable_profiles\ntechnology: offwind-dc", color = "0.57 0.6 0.85", style="rounded"];
+            17[label = "build_line_rating", color = "0.32 0.6 0.85", style="rounded"];
-        18[label = "build_renewable_profiles\ntechnology: solar", color = "0.57 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_hydro_profile", color = "0.44 0.6 0.85", style="rounded"];
+            19[label = "build_powerplants", color = "0.64 0.6 0.85", style="rounded,dashed"];
-        20[label = "retrieve_cost_data", color = "0.30 0.6 0.85", style="rounded"];
+            20[label = "build_electricity_demand", color = "0.13 0.6 0.85", style="rounded,dashed"];
-        21[label = "build_powerplants", color = "0.16 0.6 0.85", style="rounded"];
+            21[label = "retrieve_electricity_demand", color = "0.31 0.6 0.85", style="rounded"];
-        22[label = "build_electricity_demand", color = "0.00 0.6 0.85", style="rounded"];
+            22[label = "copy_config", color = "0.23 0.6 0.85", style="rounded"];
-        23[label = "retrieve_electricity_demand", color = "0.34 0.6 0.85", style="rounded,dashed"];
             1 -> 0
+            22 -> 0
             2 -> 1
-        20 -> 1
+            18 -> 1
             3 -> 2
-        20 -> 2
+            18 -> 2
             4 -> 3
-        20 -> 3
+            18 -> 3
             5 -> 4
-        20 -> 4
+            18 -> 4
             11 -> 4
             6 -> 5
+            12 -> 5
             13 -> 5
+            16 -> 5
+            7 -> 5
             17 -> 5
             18 -> 5
-        19 -> 5
-        7 -> 5
-        20 -> 5
             11 -> 5
-        21 -> 5
+            19 -> 5
             9 -> 5
-        22 -> 5
+            20 -> 5
             8 -> 5
             7 -> 6
             9 -> 6
             10 -> 6
             8 -> 6
             11 -> 6
-        12 -> 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
-        12 -> 13
             15 -> 14
-        12 -> 14
+            7 -> 16
-        16 -> 14
+            9 -> 16
+            10 -> 16
+            14 -> 16
+            8 -> 16
+            11 -> 16
             7 -> 17
-        9 -> 17
+            7 -> 19
-        10 -> 17
+            21 -> 20
-        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
     }
 
 |

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:
 

envs/environment.yaml

@@ -27,7 +27,7 @@ dependencies:
 - numpy
 - pandas>=1.4
 - geopandas>=0.11.0
-- xarray
+- xarray<=2023.8.0
 - rioxarray
 - netcdf4
 - networkx
@@ -55,5 +55,5 @@ dependencies:
 
 
 - pip:
-  - tsam>=1.1.0
+  - tsam>=2.3.1
-  - pypsa>=0.25.1
+  - pypsa>=0.25.2

rules/build_sector.smk

@@ -280,15 +280,16 @@ 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:
@@ -608,7 +609,7 @@ if config["sector"]["retrofitting"]["retro_endogen"]:
             countries=config["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",
@@ -753,7 +754,12 @@ rule prepare_sector_network:
         dsm_profile=RESOURCES + "dsm_profile_s{simpl}_{clusters}.csv",
         co2_totals_name=RESOURCES + "co2_totals.csv",
         co2="data/bundle-sector/eea/UNFCCC_v23.csv",
-        biomass_potentials=RESOURCES + "biomass_potentials_s{simpl}_{clusters}.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",
         heat_profile="data/heat_load_profile_BDEW.csv",
         costs="data/costs_{}.csv".format(config["costs"]["year"])
         if config["foresight"] == "overnight"

rules/collect.smk

@@ -14,12 +14,6 @@ localrules:
     plot_networks,
 
 
-rule all:
-    input:
-        RESULTS + "graphs/costs.pdf",
-    default_target: True
-
-
 rule cluster_networks:
     input:
         expand(RESOURCES + "networks/elec_s{simpl}_{clusters}.nc", **config["scenario"]),
@@ -66,6 +60,15 @@ rule solve_sector_networks:
         ),
 
 
+rule solve_sector_networks_perfect:
+    input:
+        expand(
+            RESULTS
+            + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
+            **config["scenario"]
+        ),
+
+
 rule plot_networks:
     input:
         expand(

rules/postprocess.smk

@@ -8,6 +8,8 @@ localrules:
     copy_conda_env,
 
 
+if config["foresight"] != "perfect":
+
     rule plot_network:
         params:
             foresight=config["foresight"],
@@ -35,6 +37,35 @@ rule plot_network:
             "../scripts/plot_network.py"
 
 
+if config["foresight"] == "perfect":
+
+    rule plot_network:
+        params:
+            foresight=config["foresight"],
+            plotting=config["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["scenario"]["planning_horizons"]
+            },
+        threads: 2
+        resources:
+            mem_mb=10000,
+        benchmark:
+            BENCHMARKS
+            +"postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_benchmark"
+        conda:
+            "../envs/environment.yaml"
+        script:
+            "../scripts/plot_network.py"
+
+
 rule copy_config:
     params:
         RDIR=RDIR,

rules/retrieve.smk

@@ -39,6 +39,24 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
             "../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/environment.yaml"
+        script:
+            "../scripts/retrieve_irena.py"
+
+
 if config["enable"]["retrieve"] and config["enable"].get("retrieve_cutout", True):
 
     rule retrieve_cutout:

rules/solve_perfect.smk

@@ -0,0 +1,194 @@
+# 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="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 add_brownfield:
+    params:
+        H2_retrofit=config["sector"]["H2_retrofit"],
+        H2_retrofit_capacity_per_CH4=config["sector"]["H2_retrofit_capacity_per_CH4"],
+        threshold_capacity=config["existing_capacities"]["threshold_capacity"],
+    input:
+        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
+        costs="data/costs_{planning_horizons}.csv",
+        cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
+        cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc",
+    output:
+        RESULTS
+        + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
+    threads: 4
+    resources:
+        mem_mb=10000,
+    log:
+        LOGS
+        + "add_brownfield_elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.log",
+    benchmark:
+        (
+            BENCHMARKS
+            + "add_brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
+        )
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/add_brownfield.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
+        ),
+    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: 4
+    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"
+
+
+ruleorder: add_existing_baseyear > add_brownfield

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
+
+logger = logging.getLogger(__name__)
+
+# TODO: provide alternative when multiprocessing is not available
+try:
+    from multiprocessing import Pipe, Process
+except ImportError:
+    from multiprocessing.dummy import Process, Pipe
+
+from memory_profiler import _get_memory, choose_backend
+
+
+# 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

scripts/_helpers.py

@@ -191,7 +191,7 @@ 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=[], **wildcards):
     """
     This function is expected to be executed from the 'scripts'-directory of '
     the snakemake project. It returns a snakemake.script.Snakemake object,
@@ -203,6 +203,8 @@ 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
     **wildcards:
@@ -217,7 +219,10 @@ def mock_snakemake(rulename, configfiles=[], **wildcards):
     from snakemake.script import Snakemake
 
     script_dir = Path(__file__).parent.resolve()
+    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:
@@ -303,10 +308,7 @@ def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
 
 
 def parse(l):
-    if len(l) == 1:
+    return yaml.safe_load(l[0]) if len(l) == 1 else {l.pop(0): parse(l)}
-        return yaml.safe_load(l[0])
-    else:
-        return {l.pop(0): parse(l)}
 
 
 def update_config_with_sector_opts(config, sector_opts):

scripts/add_brownfield.py

@@ -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[f"{attr}_nom_extendable"] & c.df.index.str.contains("urban central"))
-                c.df[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[f"{attr}_nom_extendable"] & ~c.df.index.isin(chp_heat))
-                & ~c.df.index.isin(chp_heat)
+                & (c.df[f"{attr}_nom_opt"] < threshold)
-                & (c.df[attr + "_nom_opt"] < threshold)
             ],
         )
 
         # copy over assets but fix their capacity
-        c.df[attr + "_nom"] = c.df[attr + "_nom_opt"]
+        c.df[f"{attr}_nom"] = c.df[f"{attr}_nom_opt"]
-        c.df[attr + "_nom_extendable"] = False
+        c.df[f"{attr}_nom_extendable"] = False
 
         n.import_components_from_dataframe(c.df, c.name)
 
@@ -124,7 +120,6 @@ def add_brownfield(n, n_p, year):
             n.links.loc[new_pipes, "p_nom_min"] = 0.0
 
 
-# %%
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake

scripts/add_electricity.py

@@ -293,7 +293,6 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.0):
         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(
@@ -406,6 +405,7 @@ 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 +434,7 @@ 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)
 
@@ -511,7 +511,7 @@ 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 +608,13 @@ 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(
+                f'Assuming max_hours=6 for hydro reservoirs in the countries: {", ".join(missing_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 := params.get("flatten_dispatch", False):
-        if flatten_dispatch:
             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)

scripts/add_existing_baseyear.py

@@ -45,7 +45,7 @@ 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
@@ -252,7 +252,7 @@ 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 = [
@@ -305,6 +305,18 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
             if "EU" not in vars(spatial)[carrier[generator]].locations:
                 bus0 = bus0.intersection(capacity.index + " gas")
 
+            # 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)
             lifetime_assets = lifetime.loc[grouping_year, generator].dropna()
@@ -533,13 +545,17 @@ def add_heating_capacities_installed_before_baseyear(
                 bus0=nodes[name],
                 bus1=nodes[name] + " " + name + " heat",
                 carrier=name + " resistive heater",
-                efficiency=costs.at[name_type + " resistive heater", "efficiency"],
+                efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
-                capital_cost=costs.at[name_type + " resistive heater", "efficiency"]
+                capital_cost=(
-                * costs.at[name_type + " resistive heater", "fixed"],
+                    costs.at[f"{name_type} resistive heater", "efficiency"]
-                p_nom=0.5
+                    * costs.at[f"{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"],
+                    / costs.at[f"{name_type} resistive heater", "efficiency"]
+                ),
                 build_year=int(grouping_year),
                 lifetime=costs.at[costs_name, "lifetime"],
             )
@@ -552,16 +568,20 @@ def add_heating_capacities_installed_before_baseyear(
                 bus1=nodes[name] + " " + 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"]
+                capital_cost=(
-                * costs.at[name_type + " gas boiler", "fixed"],
+                    costs.at[f"{name_type} gas boiler", "efficiency"]
-                p_nom=0.5
+                    * costs.at[f"{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"],
+                    / 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(
@@ -581,7 +601,7 @@ def add_heating_capacities_installed_before_baseyear(
                 * 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
@@ -605,21 +625,24 @@ def add_heating_capacities_installed_before_baseyear(
                 ],
             )
 
+            # drop assets which are at the end of their lifetime
+            links_i = n.links[(n.links.build_year + n.links.lifetime <= baseyear)].index
+            n.mremove("Link", links_i)
+
 
-# %%
 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",
+            clusters="37",
-            ll="v1.5",
+            ll="v1.0",
             opts="",
-            sector_opts="24H-T-H-B-I-A-solar+p3-dist1",
+            sector_opts="1p7-4380H-T-H-B-I-A-solar+p3-dist1",
-            planning_horizons=2030,
+            planning_horizons=2020,
         )
 
     logging.basicConfig(level=snakemake.config["logging"]["level"])

scripts/base_network.py

@@ -151,9 +151,7 @@ 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(
+        f'Removing buses with voltages {pd.Index(buses.v_nom.unique()).dropna().difference(config_elec["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])
@@ -460,11 +458,7 @@ def _remove_unconnected_components(network):
     components_to_remove = component_sizes.iloc[1:]
 
     logger.info(
-        "Removing {} unconnected network components with less than {} buses. In total {} buses.".format(
+        f"Removing {len(components_to_remove)} unconnected network components with less than {components_to_remove.max()} buses. In total {components_to_remove.sum()} buses."
-            len(components_to_remove),
-            components_to_remove.max(),
-            components_to_remove.sum(),
-        )
     )
 
     return network[component == component_sizes.index[0]]

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
+
+logger = logging.getLogger(__name__)
 import geopandas as gpd
+import numpy as np
 import pandas as pd
 
+AVAILABLE_BIOMASS_YEARS = [2010, 2020, 2030, 2040, 2050]
+
 
 def build_nuts_population_data(year=2013):
     pop = pd.read_csv(
@@ -208,12 +214,40 @@ 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"]
 
+    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)

scripts/build_energy_totals.py

@@ -172,8 +172,6 @@ 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"
@@ -183,11 +181,11 @@ 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"

scripts/build_gas_network.py

@@ -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
 
 

scripts/build_industrial_distribution_key.py

@@ -154,7 +154,7 @@ if __name__ == "__main__":
         snakemake = mock_snakemake(
             "build_industrial_distribution_key",
             simpl="",
-            clusters=48,
+            clusters=128,
         )
 
     logging.basicConfig(level=snakemake.config["logging"]["level"])

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 pd.concat(demand_l, keys=countries)
-
-    return demand
 
 
 if __name__ == "__main__":

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
 
@@ -83,8 +83,7 @@ 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_ref * (1 + alpha * (T - T_ref))
-    return R
 
 
 def calculate_line_rating(n, cutout):
@@ -125,13 +124,12 @@ def calculate_line_rating(n, cutout):
         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__":

scripts/build_powerplants.py

@@ -89,7 +89,7 @@ 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(
@@ -146,8 +146,7 @@ if __name__ == "__main__":
         ppl, snakemake.input.custom_powerplants, custom_ppl_query
     )
 
-    countries_wo_ppl = set(countries) - set(ppl.Country.unique())
+    if countries_wo_ppl := set(countries) - set(ppl.Country.unique()):
-    if countries_wo_ppl:
         logging.warning(f"No powerplants known in: {', '.join(countries_wo_ppl)}")
 
     substations = n.buses.query("substation_lv")

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)
 
@@ -609,12 +617,11 @@ def calculate_costs(u_values, l, cost_retro, window_assumptions):
         / x.A_C_Ref
         if x.name[3] != "Window"
         else (
-            window_cost(x["new_U_{}".format(l)], cost_retro, window_assumptions)
+            (window_cost(x[f"new_U_{l}"], cost_retro, window_assumptions) * x.A_element)
-            * x.A_element
             / x.A_C_Ref
+        )
         if x.value > window_limit(float(l), window_assumptions)
-            else 0
+        else 0,
-        ),
         axis=1,
     )
 
@@ -739,12 +746,12 @@ 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(
+        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 +763,7 @@ 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
     )
@@ -875,10 +882,7 @@ def calculate_gain_utilisation_factor(heat_transfer_perm2, Q_ht, Q_gain):
     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
+    return (1 - gamma**alpha) / (1 - gamma ** (alpha + 1))
-    nu = (1 - gamma**alpha) / (1 - gamma ** (alpha + 1))
-
-    return nu
 
 
 def calculate_space_heat_savings(
@@ -947,7 +951,8 @@ 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 +965,14 @@ 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 +989,23 @@ 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)
+        # sec_w has columns "estimated" and "value"
-        .groupby(level="country_code")
+        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(
+        .set_index(pd.MultiIndex.from_product([cost_dE.index.unique(level=0), ["tot"]]))
-            pd.MultiIndex.from_product(
-                [cost_dE.index.unique(level="country_code"), ["tot"]]
     )
-        )
+    cost_dE = pd.concat([cost_dE, tot]).unstack().stack()
-    )
-    cost_dE = cost_dE.append(tot).unstack().stack()
 
-    summed_area = pd.DataFrame(area_tot.groupby("country").sum()).set_index(
+    summed_area = pd.DataFrame(area_tot.groupby(level=0).sum()).set_index(
-        pd.MultiIndex.from_product([area_tot.index.unique(level="country"), ["tot"]])
+        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"]

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

scripts/build_shapes.py

@@ -119,7 +119,7 @@ def countries(naturalearth, country_list):
     fieldnames = (
         df[x].where(lambda s: s != "-99") for x in ("ISO_A2", "WB_A2", "ADM0_A3")
     )
-    df["name"] = reduce(lambda x, y: x.fillna(y), fieldnames, next(fieldnames)).str[0:2]
+    df["name"] = reduce(lambda x, y: x.fillna(y), fieldnames, next(fieldnames)).str[:2]
 
     df = df.loc[
         df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5))

scripts/build_transport_demand.py

@@ -81,14 +81,12 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
         - pop_weighted_energy_totals["electricity rail"]
     )
 
-    transport = (
+    return (
         (transport_shape.multiply(energy_totals_transport) * 1e6 * nyears)
         .divide(efficiency_gain * ice_correction)
         .multiply(1 + dd_EV)
     )
 
-    return transport
-
 
 def transport_degree_factor(
     temperature,
@@ -132,14 +130,12 @@ def bev_availability_profile(fn, snapshots, nodes, options):
         traffic.mean() - traffic.min()
     )
 
-    avail_profile = generate_periodic_profiles(
+    return generate_periodic_profiles(
         dt_index=snapshots,
         nodes=nodes,
         weekly_profile=avail.values,
     )
 
-    return avail_profile
-
 
 def bev_dsm_profile(snapshots, nodes, options):
     dsm_week = np.zeros((24 * 7,))
@@ -148,14 +144,12 @@ def bev_dsm_profile(snapshots, nodes, options):
         "bev_dsm_restriction_value"
     ]
 
-    dsm_profile = generate_periodic_profiles(
+    return generate_periodic_profiles(
         dt_index=snapshots,
         nodes=nodes,
         weekly_profile=dsm_week,
     )
 
-    return dsm_profile
-
 
 if __name__ == "__main__":
     if "snakemake" not in globals():

scripts/cluster_network.py

@@ -322,9 +322,9 @@ def busmap_for_n_clusters(
                 neighbor_bus = n.lines.query(
                     "bus0 == @disconnected_bus or bus1 == @disconnected_bus"
                 ).iloc[0][["bus0", "bus1"]]
-                new_country = list(
+                new_country = list(set(n.buses.loc[neighbor_bus].country) - {country})[
-                    set(n.buses.loc[neighbor_bus].country) - set([country])
+                    0
-                )[0]
+                ]
 
                 logger.info(
                     f"overwriting country `{country}` of bus `{disconnected_bus}` "

scripts/make_summary.py

@@ -33,10 +33,7 @@ def assign_locations(n):
         ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
         for i in ifind.unique():
             names = ifind.index[ifind == i]
-            if i == -1:
+            c.df.loc[names, "location"] = "" if i == -1 else names.str[:i]
-                c.df.loc[names, "location"] = ""
-            else:
-                c.df.loc[names, "location"] = names.str[:i]
 
 
 def calculate_nodal_cfs(n, label, nodal_cfs):
@@ -397,7 +394,7 @@ def calculate_supply_energy(n, label, supply_energy):
 
         for c in n.iterate_components(n.branch_components):
             for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
-                items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
+                items = c.df.index[c.df[f"bus{str(end)}"].map(bus_map).fillna(False)]
 
                 if len(items) == 0:
                     continue
@@ -493,7 +490,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
         "H2": ["Sabatier", "H2 Fuel Cell"],
     }
 
-    for carrier in link_loads:
+    for carrier, value in link_loads.items():
         if carrier == "electricity":
             suffix = ""
         elif carrier[:5] == "space":
@@ -515,14 +512,14 @@ def calculate_weighted_prices(n, label, weighted_prices):
         else:
             load = n.loads_t.p_set[buses]
 
-        for tech in link_loads[carrier]:
+        for tech in value:
             names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
 
-            if names.empty:
+            if not names.empty:
-                continue
-
                 load += (
-                n.links_t.p0[names].groupby(n.links.loc[names, "bus0"], axis=1).sum()
+                    n.links_t.p0[names]
+                    .groupby(n.links.loc[names, "bus0"], axis=1)
+                    .sum()
                 )
 
         # Add H2 Store when charging
@@ -650,11 +647,7 @@ def make_summaries(networks_dict):
         networks_dict.keys(), names=["cluster", "ll", "opt", "planning_horizon"]
     )
 
-    df = {}
+    df = {output: pd.DataFrame(columns=columns, dtype=float) for output in outputs}
-
-    for output in outputs:
-        df[output] = pd.DataFrame(columns=columns, dtype=float)
-
     for label, filename in networks_dict.items():
         logger.info(f"Make summary for scenario {label}, using {filename}")
 

scripts/make_summary_perfect.py

@@ -0,0 +1,744 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Create summary CSV files for all scenario runs with perfect foresight including
+costs, capacities, capacity factors, curtailment, energy balances, prices and
+other metrics.
+"""
+
+
+import numpy as np
+import pandas as pd
+import pypsa
+from make_summary import (
+    assign_carriers,
+    assign_locations,
+    calculate_cfs,
+    calculate_nodal_cfs,
+    calculate_nodal_costs,
+)
+from prepare_sector_network import prepare_costs
+from pypsa.descriptors import get_active_assets, nominal_attrs
+from six import iteritems
+
+idx = pd.IndexSlice
+
+opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
+
+
+def calculate_costs(n, label, costs):
+    investments = n.investment_periods
+    cols = pd.MultiIndex.from_product(
+        [
+            costs.columns.levels[0],
+            costs.columns.levels[1],
+            costs.columns.levels[2],
+            investments,
+        ],
+        names=costs.columns.names[:3] + ["year"],
+    )
+    costs = costs.reindex(cols, axis=1)
+
+    for c in n.iterate_components(
+        n.branch_components | n.controllable_one_port_components ^ {"Load"}
+    ):
+        capital_costs = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
+        active = pd.concat(
+            [
+                get_active_assets(n, c.name, inv_p).rename(inv_p)
+                for inv_p in investments
+            ],
+            axis=1,
+        ).astype(int)
+        capital_costs = active.mul(capital_costs, axis=0)
+        discount = (
+            n.investment_period_weightings["objective"]
+            / n.investment_period_weightings["years"]
+        )
+        capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum().mul(discount)
+
+        capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
+        capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
+
+        costs = costs.reindex(capital_costs_grouped.index.union(costs.index))
+
+        costs.loc[capital_costs_grouped.index, label] = capital_costs_grouped.values
+
+        if c.name == "Link":
+            p = (
+                c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0)
+                .groupby(level=0)
+                .sum()
+            )
+        elif c.name == "Line":
+            continue
+        elif c.name == "StorageUnit":
+            p_all = c.pnl.p.multiply(n.snapshot_weightings.stores, axis=0)
+            p_all[p_all < 0.0] = 0.0
+            p = p_all.groupby(level=0).sum()
+        else:
+            p = (
+                round(c.pnl.p, ndigits=2)
+                .multiply(n.snapshot_weightings.generators, axis=0)
+                .groupby(level=0)
+                .sum()
+            )
+
+        # correct sequestration cost
+        if c.name == "Store":
+            items = c.df.index[
+                (c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.0)
+            ]
+            c.df.loc[items, "marginal_cost"] = -20.0
+
+        marginal_costs = p.mul(c.df.marginal_cost).T
+        # marginal_costs = active.mul(marginal_costs, axis=0)
+        marginal_costs_grouped = (
+            marginal_costs.groupby(c.df.carrier).sum().mul(discount)
+        )
+
+        marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
+        marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
+
+        costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
+
+        costs.loc[marginal_costs_grouped.index, label] = marginal_costs_grouped.values
+
+    # add back in all hydro
+    # costs.loc[("storage_units","capital","hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro","p_nom"].sum()
+    # costs.loc[("storage_units","capital","PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS","p_nom"].sum()
+    # costs.loc[("generators","capital","ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror","p_nom"].sum()
+
+    return costs
+
+
+def calculate_cumulative_cost():
+    planning_horizons = snakemake.config["scenario"]["planning_horizons"]
+
+    cumulative_cost = pd.DataFrame(
+        index=df["costs"].sum().index,
+        columns=pd.Series(data=np.arange(0, 0.1, 0.01), name="social discount rate"),
+    )
+
+    # discount cost and express them in money value of planning_horizons[0]
+    for r in cumulative_cost.columns:
+        cumulative_cost[r] = [
+            df["costs"].sum()[index] / ((1 + r) ** (index[-1] - planning_horizons[0]))
+            for index in cumulative_cost.index
+        ]
+
+    # integrate cost throughout the transition path
+    for r in cumulative_cost.columns:
+        for cluster in cumulative_cost.index.get_level_values(level=0).unique():
+            for lv in cumulative_cost.index.get_level_values(level=1).unique():
+                for sector_opts in cumulative_cost.index.get_level_values(
+                    level=2
+                ).unique():
+                    cumulative_cost.loc[
+                        (cluster, lv, sector_opts, "cumulative cost"), r
+                    ] = np.trapz(
+                        cumulative_cost.loc[
+                            idx[cluster, lv, sector_opts, planning_horizons], r
+                        ].values,
+                        x=planning_horizons,
+                    )
+
+    return cumulative_cost
+
+
+def calculate_nodal_capacities(n, label, nodal_capacities):
+    # Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
+    for c in n.iterate_components(
+        n.branch_components | n.controllable_one_port_components ^ {"Load"}
+    ):
+        nodal_capacities_c = c.df.groupby(["location", "carrier"])[
+            opt_name.get(c.name, "p") + "_nom_opt"
+        ].sum()
+        index = pd.MultiIndex.from_tuples(
+            [(c.list_name,) + t for t in nodal_capacities_c.index.to_list()]
+        )
+        nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
+        nodal_capacities.loc[index, label] = nodal_capacities_c.values
+
+    return nodal_capacities
+
+
+def calculate_capacities(n, label, capacities):
+    investments = n.investment_periods
+    cols = pd.MultiIndex.from_product(
+        [
+            capacities.columns.levels[0],
+            capacities.columns.levels[1],
+            capacities.columns.levels[2],
+            investments,
+        ],
+        names=capacities.columns.names[:3] + ["year"],
+    )
+    capacities = capacities.reindex(cols, axis=1)
+
+    for c in n.iterate_components(
+        n.branch_components | n.controllable_one_port_components ^ {"Load"}
+    ):
+        active = pd.concat(
+            [
+                get_active_assets(n, c.name, inv_p).rename(inv_p)
+                for inv_p in investments
+            ],
+            axis=1,
+        ).astype(int)
+        caps = c.df[opt_name.get(c.name, "p") + "_nom_opt"]
+        caps = active.mul(caps, axis=0)
+        capacities_grouped = (
+            caps.groupby(c.df.carrier).sum().drop("load", errors="ignore")
+        )
+        capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
+
+        capacities = capacities.reindex(
+            capacities_grouped.index.union(capacities.index)
+        )
+
+        capacities.loc[capacities_grouped.index, label] = capacities_grouped.values
+
+    return capacities
+
+
+def calculate_curtailment(n, label, curtailment):
+    avail = (
+        n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt)
+        .sum()
+        .groupby(n.generators.carrier)
+        .sum()
+    )
+    used = n.generators_t.p.sum().groupby(n.generators.carrier).sum()
+
+    curtailment[label] = (((avail - used) / avail) * 100).round(3)
+
+    return curtailment
+
+
+def calculate_energy(n, label, energy):
+    investments = n.investment_periods
+    cols = pd.MultiIndex.from_product(
+        [
+            energy.columns.levels[0],
+            energy.columns.levels[1],
+            energy.columns.levels[2],
+            investments,
+        ],
+        names=energy.columns.names[:3] + ["year"],
+    )
+    energy = energy.reindex(cols, axis=1)
+
+    for c in n.iterate_components(n.one_port_components | n.branch_components):
+        if c.name in n.one_port_components:
+            c_energies = (
+                c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
+                .groupby(level=0)
+                .sum()
+                .multiply(c.df.sign)
+                .groupby(c.df.carrier, axis=1)
+                .sum()
+            )
+        else:
+            c_energies = pd.DataFrame(
+                0.0, columns=c.df.carrier.unique(), index=n.investment_periods
+            )
+            for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
+                totals = (
+                    c.pnl["p" + port]
+                    .multiply(n.snapshot_weightings.generators, axis=0)
+                    .groupby(level=0)
+                    .sum()
+                )
+                # remove values where bus is missing (bug in nomopyomo)
+                no_bus = c.df.index[c.df["bus" + port] == ""]
+                totals[no_bus] = float(
+                    n.component_attrs[c.name].loc["p" + port, "default"]
+                )
+                c_energies -= totals.groupby(c.df.carrier, axis=1).sum()
+
+        c_energies = pd.concat([c_energies.T], keys=[c.list_name])
+
+        energy = energy.reindex(c_energies.index.union(energy.index))
+
+        energy.loc[c_energies.index, label] = c_energies.values
+
+    return energy
+
+
+def calculate_supply(n, label, supply):
+    """
+    Calculate the max dispatch of each component at the buses aggregated by
+    carrier.
+    """
+
+    bus_carriers = n.buses.carrier.unique()
+
+    for i in bus_carriers:
+        bus_map = n.buses.carrier == i
+        bus_map.at[""] = False
+
+        for c in n.iterate_components(n.one_port_components):
+            items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
+
+            if len(items) == 0:
+                continue
+
+            s = (
+                c.pnl.p[items]
+                .max()
+                .multiply(c.df.loc[items, "sign"])
+                .groupby(c.df.loc[items, "carrier"])
+                .sum()
+            )
+            s = pd.concat([s], keys=[c.list_name])
+            s = pd.concat([s], keys=[i])
+
+            supply = supply.reindex(s.index.union(supply.index))
+            supply.loc[s.index, label] = s
+
+        for c in n.iterate_components(n.branch_components):
+            for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
+                items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
+
+                if len(items) == 0:
+                    continue
+
+                # lots of sign compensation for direction and to do maximums
+                s = (-1) ** (1 - int(end)) * (
+                    (-1) ** int(end) * c.pnl["p" + end][items]
+                ).max().groupby(c.df.loc[items, "carrier"]).sum()
+                s.index = s.index + end
+                s = pd.concat([s], keys=[c.list_name])
+                s = pd.concat([s], keys=[i])
+
+                supply = supply.reindex(s.index.union(supply.index))
+                supply.loc[s.index, label] = s
+
+    return supply
+
+
+def calculate_supply_energy(n, label, supply_energy):
+    """
+    Calculate the total energy supply/consuption of each component at the buses
+    aggregated by carrier.
+    """
+
+    investments = n.investment_periods
+    cols = pd.MultiIndex.from_product(
+        [
+            supply_energy.columns.levels[0],
+            supply_energy.columns.levels[1],
+            supply_energy.columns.levels[2],
+            investments,
+        ],
+        names=supply_energy.columns.names[:3] + ["year"],
+    )
+    supply_energy = supply_energy.reindex(cols, axis=1)
+
+    bus_carriers = n.buses.carrier.unique()
+
+    for i in bus_carriers:
+        bus_map = n.buses.carrier == i
+        bus_map.at[""] = False
+
+        for c in n.iterate_components(n.one_port_components):
+            items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
+
+            if len(items) == 0:
+                continue
+
+            if c.name == "Generator":
+                weightings = n.snapshot_weightings.generators
+            else:
+                weightings = n.snapshot_weightings.stores
+
+            if i in ["oil", "co2", "H2"]:
+                if c.name == "Load":
+                    c.df.loc[items, "carrier"] = [
+                        load.split("-202")[0] for load in items
+                    ]
+                if i == "oil" and c.name == "Generator":
+                    c.df.loc[items, "carrier"] = "imported oil"
+            s = (
+                c.pnl.p[items]
+                .multiply(weightings, axis=0)
+                .groupby(level=0)
+                .sum()
+                .multiply(c.df.loc[items, "sign"])
+                .groupby(c.df.loc[items, "carrier"], axis=1)
+                .sum()
+                .T
+            )
+            s = pd.concat([s], keys=[c.list_name])
+            s = pd.concat([s], keys=[i])
+
+            supply_energy = supply_energy.reindex(
+                s.index.union(supply_energy.index, sort=False)
+            )
+            supply_energy.loc[s.index, label] = s.values
+
+        for c in n.iterate_components(n.branch_components):
+            for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
+                items = c.df.index[c.df[f"bus{str(end)}"].map(bus_map).fillna(False)]
+
+                if len(items) == 0:
+                    continue
+
+                s = (
+                    (-1)
+                    * c.pnl["p" + end]
+                    .reindex(items, axis=1)
+                    .multiply(n.snapshot_weightings.objective, axis=0)
+                    .groupby(level=0)
+                    .sum()
+                    .groupby(c.df.loc[items, "carrier"], axis=1)
+                    .sum()
+                ).T
+                s.index = s.index + end
+                s = pd.concat([s], keys=[c.list_name])
+                s = pd.concat([s], keys=[i])
+
+                supply_energy = supply_energy.reindex(
+                    s.index.union(supply_energy.index, sort=False)
+                )
+
+                supply_energy.loc[s.index, label] = s.values
+
+    return supply_energy
+
+
+def calculate_metrics(n, label, metrics):
+    metrics = metrics.reindex(
+        pd.Index(
+            [
+                "line_volume",
+                "line_volume_limit",
+                "line_volume_AC",
+                "line_volume_DC",
+                "line_volume_shadow",
+                "co2_shadow",
+            ]
+        ).union(metrics.index)
+    )
+
+    metrics.at["line_volume_DC", label] = (n.links.length * n.links.p_nom_opt)[
+        n.links.carrier == "DC"
+    ].sum()
+    metrics.at["line_volume_AC", label] = (n.lines.length * n.lines.s_nom_opt).sum()
+    metrics.at["line_volume", label] = metrics.loc[
+        ["line_volume_AC", "line_volume_DC"], label
+    ].sum()
+
+    if hasattr(n, "line_volume_limit"):
+        metrics.at["line_volume_limit", label] = n.line_volume_limit
+        metrics.at["line_volume_shadow", label] = n.line_volume_limit_dual
+
+    if "CO2Limit" in n.global_constraints.index:
+        metrics.at["co2_shadow", label] = n.global_constraints.at["CO2Limit", "mu"]
+
+    return metrics
+
+
+def calculate_prices(n, label, prices):
+    prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
+
+    # WARNING: this is time-averaged, see weighted_prices for load-weighted average
+    prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
+
+    return prices
+
+
+def calculate_weighted_prices(n, label, weighted_prices):
+    # Warning: doesn't include storage units as loads
+
+    weighted_prices = weighted_prices.reindex(
+        pd.Index(
+            [
+                "electricity",
+                "heat",
+                "space heat",
+                "urban heat",
+                "space urban heat",
+                "gas",
+                "H2",
+            ]
+        )
+    )
+
+    link_loads = {
+        "electricity": [
+            "heat pump",
+            "resistive heater",
+            "battery charger",
+            "H2 Electrolysis",
+        ],
+        "heat": ["water tanks charger"],
+        "urban heat": ["water tanks charger"],
+        "space heat": [],
+        "space urban heat": [],
+        "gas": ["OCGT", "gas boiler", "CHP electric", "CHP heat"],
+        "H2": ["Sabatier", "H2 Fuel Cell"],
+    }
+
+    for carrier, value in link_loads.items():
+        if carrier == "electricity":
+            suffix = ""
+        elif carrier[:5] == "space":
+            suffix = carrier[5:]
+        else:
+            suffix = " " + carrier
+
+        buses = n.buses.index[n.buses.index.str[2:] == suffix]
+
+        if buses.empty:
+            continue
+
+        load = (
+            pd.DataFrame(index=n.snapshots, columns=buses, data=0.0)
+            if carrier in ["H2", "gas"]
+            else n.loads_t.p_set.reindex(buses, axis=1)
+        )
+        for tech in value:
+            names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
+
+            if names.empty:
+                continue
+
+            load += (
+                n.links_t.p0[names].groupby(n.links.loc[names, "bus0"], axis=1).sum()
+            )
+
+        # Add H2 Store when charging
+        # if carrier == "H2":
+        #    stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store","bus"],axis=1).sum(axis=1)
+        #    stores[stores > 0.] = 0.
+        #    load += -stores
+
+        weighted_prices.loc[carrier, label] = (
+            load * n.buses_t.marginal_price[buses]
+        ).sum().sum() / load.sum().sum()
+
+        if carrier[:5] == "space":
+            print(load * n.buses_t.marginal_price[buses])
+
+    return weighted_prices
+
+
+def calculate_market_values(n, label, market_values):
+    # Warning: doesn't include storage units
+
+    carrier = "AC"
+
+    buses = n.buses.index[n.buses.carrier == carrier]
+
+    ## First do market value of generators ##
+
+    generators = n.generators.index[n.buses.loc[n.generators.bus, "carrier"] == carrier]
+
+    techs = n.generators.loc[generators, "carrier"].value_counts().index
+
+    market_values = market_values.reindex(market_values.index.union(techs))
+
+    for tech in techs:
+        gens = generators[n.generators.loc[generators, "carrier"] == tech]
+
+        dispatch = (
+            n.generators_t.p[gens]
+            .groupby(n.generators.loc[gens, "bus"], axis=1)
+            .sum()
+            .reindex(columns=buses, fill_value=0.0)
+        )
+
+        revenue = dispatch * n.buses_t.marginal_price[buses]
+
+        market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
+
+    ## Now do market value of links ##
+
+    for i in ["0", "1"]:
+        all_links = n.links.index[n.buses.loc[n.links["bus" + i], "carrier"] == carrier]
+
+        techs = n.links.loc[all_links, "carrier"].value_counts().index
+
+        market_values = market_values.reindex(market_values.index.union(techs))
+
+        for tech in techs:
+            links = all_links[n.links.loc[all_links, "carrier"] == tech]
+
+            dispatch = (
+                n.links_t["p" + i][links]
+                .groupby(n.links.loc[links, "bus" + i], axis=1)
+                .sum()
+                .reindex(columns=buses, fill_value=0.0)
+            )
+
+            revenue = dispatch * n.buses_t.marginal_price[buses]
+
+            market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
+
+    return market_values
+
+
+def calculate_price_statistics(n, label, price_statistics):
+    price_statistics = price_statistics.reindex(
+        price_statistics.index.union(
+            pd.Index(["zero_hours", "mean", "standard_deviation"])
+        )
+    )
+
+    buses = n.buses.index[n.buses.carrier == "AC"]
+
+    threshold = 0.1  # higher than phoney marginal_cost of wind/solar
+
+    df = pd.DataFrame(data=0.0, columns=buses, index=n.snapshots)
+
+    df[n.buses_t.marginal_price[buses] < threshold] = 1.0
+
+    price_statistics.at["zero_hours", label] = df.sum().sum() / (
+        df.shape[0] * df.shape[1]
+    )
+
+    price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].mean().mean()
+
+    price_statistics.at["standard_deviation", label] = (
+        n.buses_t.marginal_price[buses].droplevel(0).unstack().std()
+    )
+
+    return price_statistics
+
+
+def calculate_co2_emissions(n, label, df):
+    carattr = "co2_emissions"
+    emissions = n.carriers.query(f"{carattr} != 0")[carattr]
+
+    if emissions.empty:
+        return
+
+    weightings = n.snapshot_weightings.generators.mul(
+        n.investment_period_weightings["years"]
+        .reindex(n.snapshots)
+        .fillna(method="bfill")
+        .fillna(1.0),
+        axis=0,
+    )
+
+    # generators
+    gens = n.generators.query("carrier in @emissions.index")
+    if not gens.empty:
+        em_pu = gens.carrier.map(emissions) / gens.efficiency
+        em_pu = (
+            weightings["generators"].to_frame("weightings")
+            @ em_pu.to_frame("weightings").T
+        )
+        emitted = n.generators_t.p[gens.index].mul(em_pu)
+
+        emitted_grouped = (
+            emitted.groupby(level=0).sum().groupby(n.generators.carrier, axis=1).sum().T
+        )
+
+        df = df.reindex(emitted_grouped.index.union(df.index))
+
+        df.loc[emitted_grouped.index, label] = emitted_grouped.values
+
+    if any(n.stores.carrier == "co2"):
+        co2_i = n.stores[n.stores.carrier == "co2"].index
+        df[label] = n.stores_t.e.groupby(level=0).last()[co2_i].iloc[:, 0]
+
+    return df
+
+
+outputs = [
+    "nodal_costs",
+    "nodal_capacities",
+    "nodal_cfs",
+    "cfs",
+    "costs",
+    "capacities",
+    "curtailment",
+    "energy",
+    "supply",
+    "supply_energy",
+    "prices",
+    "weighted_prices",
+    "price_statistics",
+    "market_values",
+    "metrics",
+    "co2_emissions",
+]
+
+
+def make_summaries(networks_dict):
+    columns = pd.MultiIndex.from_tuples(
+        networks_dict.keys(), names=["cluster", "lv", "opt"]
+    )
+    df = {}
+
+    for output in outputs:
+        df[output] = pd.DataFrame(columns=columns, dtype=float)
+
+    for label, filename in iteritems(networks_dict):
+        print(label, filename)
+        try:
+            n = pypsa.Network(filename)
+        except OSError:
+            print(label, " not solved yet.")
+            continue
+            # del networks_dict[label]
+
+        if not hasattr(n, "objective"):
+            print(label, " not solved correctly. Check log if infeasible or unbounded.")
+            continue
+        assign_carriers(n)
+        assign_locations(n)
+
+        for output in outputs:
+            df[output] = globals()["calculate_" + output](n, label, df[output])
+
+    return df
+
+
+def to_csv(df):
+    for key in df:
+        df[key] = df[key].apply(lambda x: pd.to_numeric(x))
+        df[key].to_csv(snakemake.output[key])
+
+
+if __name__ == "__main__":
+    # Detect running outside of snakemake and mock snakemake for testing
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake("make_summary_perfect")
+
+    run = snakemake.config["run"]["name"]
+    if run != "":
+        run += "/"
+
+    networks_dict = {
+        (clusters, lv, opts + sector_opts): "results/"
+        + run
+        + f"postnetworks/elec_s{simpl}_{clusters}_l{lv}_{opts}_{sector_opts}_brownfield_all_years.nc"
+        for simpl in snakemake.config["scenario"]["simpl"]
+        for clusters in snakemake.config["scenario"]["clusters"]
+        for opts in snakemake.config["scenario"]["opts"]
+        for sector_opts in snakemake.config["scenario"]["sector_opts"]
+        for lv in snakemake.config["scenario"]["ll"]
+    }
+
+    print(networks_dict)
+
+    nyears = 1
+    costs_db = prepare_costs(
+        snakemake.input.costs,
+        snakemake.config["costs"],
+        nyears,
+    )
+
+    df = make_summaries(networks_dict)
+
+    df["metrics"].loc["total costs"] = df["costs"].sum().groupby(level=[0, 1, 2]).sum()
+
+    to_csv(df)

scripts/plot_network.py

@@ -24,7 +24,7 @@ from make_summary import assign_carriers
 from plot_summary import preferred_order, rename_techs
 from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
 
-plt.style.use(["ggplot", "matplotlibrc"])
+plt.style.use(["ggplot"])
 
 
 def rename_techs_tyndp(tech):
@@ -145,12 +145,12 @@ def plot_map(
     ac_color = "rosybrown"
     dc_color = "darkseagreen"
 
+    title = "added grid"
+
     if snakemake.wildcards["ll"] == "v1.0":
         # should be zero
         line_widths = n.lines.s_nom_opt - n.lines.s_nom
         link_widths = n.links.p_nom_opt - n.links.p_nom
-        title = "added grid"
-
         if transmission:
             line_widths = n.lines.s_nom_opt
             link_widths = n.links.p_nom_opt
@@ -160,8 +160,6 @@ def plot_map(
     else:
         line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
         link_widths = n.links.p_nom_opt - n.links.p_nom_min
-        title = "added grid"
-
         if transmission:
             line_widths = n.lines.s_nom_opt
             link_widths = n.links.p_nom_opt
@@ -262,12 +260,7 @@ def group_pipes(df, drop_direction=False):
         lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
         axis=1,
     )
-    # group pipe lines connecting the same buses and rename them for plotting
+    return df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
-    pipe_capacity = df.groupby(level=0).agg(
-        {"p_nom_opt": sum, "bus0": "first", "bus1": "first"}
-    )
-
-    return pipe_capacity
 
 
 def plot_h2_map(network, regions):
@@ -766,11 +759,13 @@ def plot_series(network, carrier="AC", name="test"):
             supply = pd.concat(
                 (
                     supply,
-                    (-1)
+                    (
-                    * c.pnl["p" + str(i)]
+                        -1
-                    .loc[:, c.df.index[c.df["bus" + str(i)].isin(buses)]]
+                        * c.pnl[f"p{str(i)}"]
+                        .loc[:, c.df.index[c.df[f"bus{str(i)}"].isin(buses)]]
                         .groupby(c.df.carrier, axis=1)
-                    .sum(),
+                        .sum()
+                    ),
                 ),
                 axis=1,
             )
@@ -913,6 +908,158 @@ def plot_series(network, carrier="AC", name="test"):
     )
 
 
+def plot_map_perfect(
+    network,
+    components=["Link", "Store", "StorageUnit", "Generator"],
+    bus_size_factor=1.7e10,
+):
+    n = network.copy()
+    assign_location(n)
+    # Drop non-electric buses so they don't clutter the plot
+    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
+    # investment periods
+    investments = n.snapshots.levels[0]
+
+    costs = {}
+    for comp in components:
+        df_c = n.df(comp)
+        if df_c.empty:
+            continue
+        df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
+
+        attr = "e_nom_opt" if comp == "Store" else "p_nom_opt"
+
+        active = pd.concat(
+            [n.get_active_assets(comp, inv_p).rename(inv_p) for inv_p in investments],
+            axis=1,
+        ).astype(int)
+        capital_cost = n.df(comp)[attr] * n.df(comp).capital_cost
+        capital_cost_t = (
+            (active.mul(capital_cost, axis=0))
+            .groupby([n.df(comp).location, n.df(comp).nice_group])
+            .sum()
+        )
+
+        capital_cost_t.drop("load", level=1, inplace=True, errors="ignore")
+
+        costs[comp] = capital_cost_t
+
+    costs = pd.concat(costs).groupby(level=[1, 2]).sum()
+    costs.drop(costs[costs.sum(axis=1) == 0].index, inplace=True)
+
+    new_columns = preferred_order.intersection(costs.index.levels[1]).append(
+        costs.index.levels[1].difference(preferred_order)
+    )
+    costs = costs.reindex(new_columns, level=1)
+
+    for item in new_columns:
+        if item not in snakemake.config["plotting"]["tech_colors"]:
+            print(
+                "Warning!",
+                item,
+                "not in config/plotting/tech_colors, assign random color",
+            )
+            snakemake.config["plotting"]["tech_colors"] = "pink"
+
+    n.links.drop(
+        n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")],
+        inplace=True,
+    )
+
+    # drop non-bus
+    to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
+    if len(to_drop) != 0:
+        print("dropping non-buses", to_drop)
+        costs.drop(to_drop, level=0, inplace=True, axis=0, errors="ignore")
+
+    # make sure they are removed from index
+    costs.index = pd.MultiIndex.from_tuples(costs.index.values)
+
+    # PDF has minimum width, so set these to zero
+    line_lower_threshold = 500.0
+    line_upper_threshold = 1e4
+    linewidth_factor = 2e3
+    ac_color = "gray"
+    dc_color = "m"
+
+    line_widths = n.lines.s_nom_opt
+    link_widths = n.links.p_nom_opt
+    linewidth_factor = 2e3
+    line_lower_threshold = 0.0
+    title = "Today's transmission"
+
+    line_widths[line_widths < line_lower_threshold] = 0.0
+    link_widths[link_widths < line_lower_threshold] = 0.0
+
+    line_widths[line_widths > line_upper_threshold] = line_upper_threshold
+    link_widths[link_widths > line_upper_threshold] = line_upper_threshold
+
+    for year in costs.columns:
+        fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
+        fig.set_size_inches(7, 6)
+        fig.suptitle(year)
+
+        n.plot(
+            bus_sizes=costs[year] / bus_size_factor,
+            bus_colors=snakemake.config["plotting"]["tech_colors"],
+            line_colors=ac_color,
+            link_colors=dc_color,
+            line_widths=line_widths / linewidth_factor,
+            link_widths=link_widths / linewidth_factor,
+            ax=ax,
+            **map_opts,
+        )
+
+        sizes = [20, 10, 5]
+        labels = [f"{s} bEUR/a" for s in sizes]
+        sizes = [s / bus_size_factor * 1e9 for s in sizes]
+
+        legend_kw = dict(
+            loc="upper left",
+            bbox_to_anchor=(0.01, 1.06),
+            labelspacing=0.8,
+            frameon=False,
+            handletextpad=0,
+            title="system cost",
+        )
+
+        add_legend_circles(
+            ax,
+            sizes,
+            labels,
+            srid=n.srid,
+            patch_kw=dict(facecolor="lightgrey"),
+            legend_kw=legend_kw,
+        )
+
+        sizes = [10, 5]
+        labels = [f"{s} GW" for s in sizes]
+        scale = 1e3 / linewidth_factor
+        sizes = [s * scale for s in sizes]
+
+        legend_kw = dict(
+            loc="upper left",
+            bbox_to_anchor=(0.27, 1.06),
+            frameon=False,
+            labelspacing=0.8,
+            handletextpad=1,
+            title=title,
+        )
+
+        add_legend_lines(
+            ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
+        )
+
+        legend_kw = dict(
+            bbox_to_anchor=(1.52, 1.04),
+            frameon=False,
+        )
+
+        fig.savefig(
+            snakemake.output[f"map_{year}"], transparent=True, bbox_inches="tight"
+        )
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -921,10 +1068,9 @@ if __name__ == "__main__":
             "plot_network",
             simpl="",
             opts="",
-            clusters="5",
+            clusters="37",
-            ll="v1.5",
+            ll="v1.0",
-            sector_opts="CO2L0-1H-T-H-B-I-A-solar+p3-dist1",
+            sector_opts="4380H-T-H-B-I-A-solar+p3-dist1",
-            planning_horizons="2030",
         )
 
     logging.basicConfig(level=snakemake.config["logging"]["level"])
@@ -938,6 +1084,13 @@ if __name__ == "__main__":
     if map_opts["boundaries"] is None:
         map_opts["boundaries"] = regions.total_bounds[[0, 2, 1, 3]] + [-1, 1, -1, 1]
 
+    if snakemake.params["foresight"] == "perfect":
+        plot_map_perfect(
+            n,
+            components=["Link", "Store", "StorageUnit", "Generator"],
+            bus_size_factor=2e10,
+        )
+    else:
         plot_map(
             n,
             components=["generators", "links", "stores", "storage_units"],

scripts/plot_statistics.py

@@ -33,8 +33,6 @@ if __name__ == "__main__":
         lambda s: s != "", "lightgrey"
     )
 
-    # %%
-
     def rename_index(ds):
         specific = ds.index.map(lambda x: f"{x[1]}\n({x[0]})")
         generic = ds.index.get_level_values("carrier")

scripts/plot_summary.py

@@ -49,6 +49,10 @@ def rename_techs(label):
         # "H2 Fuel Cell": "hydrogen storage",
         # "H2 pipeline": "hydrogen storage",
         "battery": "battery storage",
+        "H2 for industry": "H2 for industry",
+        "land transport fuel cell": "land transport fuel cell",
+        "land transport oil": "land transport oil",
+        "oil shipping": "shipping oil",
         # "CC": "CC"
     }
 
@@ -157,11 +161,11 @@ def plot_costs():
         df.index.difference(preferred_order)
     )
 
-    new_columns = df.sum().sort_values().index
+    # new_columns = df.sum().sort_values().index
 
     fig, ax = plt.subplots(figsize=(12, 8))
 
-    df.loc[new_index, new_columns].T.plot(
+    df.loc[new_index].T.plot(
         kind="bar",
         ax=ax,
         stacked=True,
@@ -213,17 +217,22 @@ def plot_energy():
 
     logger.info(f"Total energy of {round(df.sum()[0])} TWh/a")
 
+    if df.empty:
+        fig, ax = plt.subplots(figsize=(12, 8))
+        fig.savefig(snakemake.output.energy, bbox_inches="tight")
+        return
+
     new_index = preferred_order.intersection(df.index).append(
         df.index.difference(preferred_order)
     )
 
-    new_columns = df.columns.sort_values()
+    # new_columns = df.columns.sort_values()
 
     fig, ax = plt.subplots(figsize=(12, 8))
 
-    logger.debug(df.loc[new_index, new_columns])
+    logger.debug(df.loc[new_index])
 
-    df.loc[new_index, new_columns].T.plot(
+    df.loc[new_index].T.plot(
         kind="bar",
         ax=ax,
         stacked=True,
@@ -267,8 +276,6 @@ def plot_balances():
         i for i in balances_df.index.levels[0] if i not in co2_carriers
     ]
 
-    fig, ax = plt.subplots(figsize=(12, 8))
-
     for k, v in balances.items():
         df = balances_df.loc[v]
         df = df.groupby(df.index.get_level_values(2)).sum()
@@ -279,7 +286,7 @@ def plot_balances():
         # remove trailing link ports
         df.index = [
             i[:-1]
-            if ((i not in ["co2", "NH3"]) and (i[-1:] in ["0", "1", "2", "3"]))
+            if ((i not in ["co2", "NH3", "H2"]) and (i[-1:] in ["0", "1", "2", "3"]))
             else i
             for i in df.index
         ]
@@ -290,11 +297,7 @@ def plot_balances():
             df.abs().max(axis=1) < snakemake.params.plotting["energy_threshold"] / 10
         ]
 
-        if v[0] in co2_carriers:
+        units = "MtCO2/a" if v[0] in co2_carriers else "TWh/a"
-            units = "MtCO2/a"
-        else:
-            units = "TWh/a"
-
         logger.debug(
             f"Dropping technology energy balance smaller than {snakemake.params['plotting']['energy_threshold']/10} {units}"
         )
@@ -313,6 +316,8 @@ def plot_balances():
 
         new_columns = df.columns.sort_values()
 
+        fig, ax = plt.subplots(figsize=(12, 8))
+
         df.loc[new_index, new_columns].T.plot(
             kind="bar",
             ax=ax,
@@ -345,8 +350,6 @@ def plot_balances():
 
         fig.savefig(snakemake.output.balances[:-10] + k + ".pdf", bbox_inches="tight")
 
-        plt.cla()
-
 
 def historical_emissions(countries):
     """
@@ -354,8 +357,7 @@ def historical_emissions(countries):
     """
     # https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
     # downloaded 201228 (modified by EEA last on 201221)
-    fn = "data/bundle-sector/eea/UNFCCC_v23.csv"
+    df = pd.read_csv(snakemake.input.co2, encoding="latin-1", low_memory=False)
-    df = pd.read_csv(fn, encoding="latin-1")
     df.loc[df["Year"] == "1985-1987", "Year"] = 1986
     df["Year"] = df["Year"].astype(int)
     df = df.set_index(
@@ -379,18 +381,21 @@ def historical_emissions(countries):
     e["waste management"] = "5 - Waste management"
     e["other"] = "6 - Other Sector"
     e["indirect"] = "ind_CO2 - Indirect CO2"
-    e["total wL"] = "Total (with LULUCF)"
+    e["other LULUCF"] = "4.H - Other LULUCF"
-    e["total woL"] = "Total (without LULUCF)"
 
     pol = ["CO2"]  # ["All greenhouse gases - (CO2 equivalent)"]
     if "GB" in countries:
         countries.remove("GB")
         countries.append("UK")
 
-    # remove countries which are not included in eea historical emission dataset
+    year = df.index.levels[0][df.index.levels[0] >= 1990]
-    countries_to_remove = {"AL", "BA", "ME", "MK", "RS"}
+
-    countries = list(set(countries) - countries_to_remove)
+    missing = pd.Index(countries).difference(df.index.levels[2])
-    year = np.arange(1990, 2018).tolist()
+    if not missing.empty:
+        logger.warning(
+            f"The following countries are missing and not considered when plotting historic CO2 emissions: {missing}"
+        )
+        countries = pd.Index(df.index.levels[2]).intersection(countries)
 
     idx = pd.IndexSlice
     co2_totals = (
@@ -453,18 +458,12 @@ def plot_carbon_budget_distribution(input_eurostat):
     plt.rcParams["xtick.labelsize"] = 20
     plt.rcParams["ytick.labelsize"] = 20
 
-    plt.figure(figsize=(10, 7))
-    gs1 = gridspec.GridSpec(1, 1)
-    ax1 = plt.subplot(gs1[0, 0])
-    ax1.set_ylabel("CO$_2$ emissions (Gt per year)", fontsize=22)
-    ax1.set_ylim([0, 5])
-    ax1.set_xlim([1990, snakemake.params.planning_horizons[-1] + 1])
-
-    path_cb = "results/" + snakemake.params.RDIR + "csvs/"
-    countries = snakemake.params.countries
     emissions_scope = snakemake.params.emissions_scope
     report_year = snakemake.params.eurostat_report_year
     input_co2 = snakemake.input.co2
+
+    # historic emissions
+    countries = snakemake.params.countries
     e_1990 = co2_emissions_year(
         countries,
         input_eurostat,
@@ -474,15 +473,37 @@ def plot_carbon_budget_distribution(input_eurostat):
         input_co2,
         year=1990,
     )
-    CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)
-
-    ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None)
-
     emissions = historical_emissions(countries)
+    # add other years https://sdi.eea.europa.eu/data/0569441f-2853-4664-a7cd-db969ef54de0
+    emissions.loc[2019] = 2.971372
+    emissions.loc[2020] = 2.691958
+    emissions.loc[2021] = 2.869355
+
+    if snakemake.config["foresight"] == "myopic":
+        path_cb = "results/" + snakemake.params.RDIR + "/csvs/"
+        co2_cap = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)[
+            ["cb"]
+        ]
+        co2_cap *= e_1990
+    else:
+        supply_energy = pd.read_csv(
+            snakemake.input.balances, index_col=[0, 1, 2], header=[0, 1, 2, 3]
+        )
+        co2_cap = (
+            supply_energy.loc["co2"].droplevel(0).drop("co2").sum().unstack().T / 1e9
+        )
+        co2_cap.rename(index=lambda x: int(x), inplace=True)
+
+    plt.figure(figsize=(10, 7))
+    gs1 = gridspec.GridSpec(1, 1)
+    ax1 = plt.subplot(gs1[0, 0])
+    ax1.set_ylabel("CO$_2$ emissions \n [Gt per year]", fontsize=22)
+    # ax1.set_ylim([0, 5])
+    ax1.set_xlim([1990, snakemake.params.planning_horizons[-1] + 1])
 
     ax1.plot(emissions, color="black", linewidth=3, label=None)
 
-    # plot committed and uder-discussion targets
+    # plot committed and under-discussion targets
     # (notice that historical emissions include all countries in the
     # network, but targets refer to EU)
     ax1.plot(
@@ -499,7 +520,7 @@ def plot_carbon_budget_distribution(input_eurostat):
         [0.45 * emissions[1990]],
         marker="*",
         markersize=12,
-        markerfacecolor="white",
+        markerfacecolor="black",
         markeredgecolor="black",
     )
 
@@ -523,21 +544,7 @@ def plot_carbon_budget_distribution(input_eurostat):
 
     ax1.plot(
         [2050],
-        [0.01 * emissions[1990]],
+        [0.0 * emissions[1990]],
-        marker="*",
-        markersize=12,
-        markerfacecolor="white",
-        linewidth=0,
-        markeredgecolor="black",
-        label="EU under-discussion target",
-        zorder=10,
-        clip_on=False,
-    )
-
-    ax1.plot(
-        [2050],
-        [0.125 * emissions[1990]],
-        "ro",
         marker="*",
         markersize=12,
         markerfacecolor="black",
@@ -545,12 +552,16 @@ def plot_carbon_budget_distribution(input_eurostat):
         label="EU committed target",
     )
 
+    for col in co2_cap.columns:
+        ax1.plot(co2_cap[col], linewidth=3, label=col)
+
     ax1.legend(
         fancybox=True, fontsize=18, loc=(0.01, 0.01), facecolor="white", frameon=True
     )
 
-    path_cb_plot = "results/" + snakemake.params.RDIR + "graphs/"
+    plt.grid(axis="y")
-    plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
+    path = snakemake.output.balances.split("balances")[0] + "carbon_budget.pdf"
+    plt.savefig(path, bbox_inches="tight")
 
 
 if __name__ == "__main__":
@@ -571,6 +582,5 @@ if __name__ == "__main__":
 
     for sector_opts in snakemake.params.sector_opts:
         opts = sector_opts.split("-")
-        for o in opts:
+        if any("cb" in o for o in opts) or snakemake.config["foresight"] == "perfect":
-            if "cb" in o:
             plot_carbon_budget_distribution(snakemake.input.eurostat)

scripts/plot_validation_cross_border_flows.py

@@ -84,13 +84,9 @@ def cross_border_time_series(countries, data):
             df_neg.plot.area(
                 ax=ax[axis], stacked=True, linewidth=0.0, color=color, ylim=[-1, 1]
             )
-            if (axis % 2) == 0:
+            title = "Historic" if (axis % 2) == 0 else "Optimized"
-                title = "Historic"
-            else:
-                title = "Optimized"
-
             ax[axis].set_title(
-                title + " Import / Export for " + cc.convert(country, to="name_short")
+                f"{title} Import / Export for " + cc.convert(country, to="name_short")
             )
 
             # Custom legend elements
@@ -137,16 +133,12 @@ def cross_border_bar(countries, data):
             df_country = sort_one_country(country, df)
             df_neg, df_pos = df_country.clip(upper=0), df_country.clip(lower=0)
 
-            if (order % 2) == 0:
+            title = "Historic" if (order % 2) == 0 else "Optimized"
-                title = "Historic"
-            else:
-                title = "Optimized"
-
             df_positive_new = pd.DataFrame(data=df_pos.sum()).T.rename(
-                {0: title + " " + cc.convert(country, to="name_short")}
+                {0: f"{title} " + cc.convert(country, to="name_short")}
             )
             df_negative_new = pd.DataFrame(data=df_neg.sum()).T.rename(
-                {0: title + " " + cc.convert(country, to="name_short")}
+                {0: f"{title} " + cc.convert(country, to="name_short")}
             )
 
             df_positive = pd.concat([df_positive_new, df_positive])

scripts/prepare_network.py

@@ -274,7 +274,6 @@ def set_line_nom_max(
     n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True)
 
 
-# %%
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake

scripts/prepare_perfect_foresight.py

@@ -0,0 +1,548 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Concats pypsa networks of single investment periods to one network.
+"""
+
+import logging
+import re
+
+import numpy as np
+import pandas as pd
+import pypsa
+from _helpers import update_config_with_sector_opts
+from add_existing_baseyear import add_build_year_to_new_assets
+from pypsa.descriptors import expand_series
+from pypsa.io import import_components_from_dataframe
+from six import iterkeys
+
+logger = logging.getLogger(__name__)
+
+
+# helper functions ---------------------------------------------------
+def get_missing(df, n, c):
+    """
+    Get in network n missing assets of df for component c.
+
+    Input:
+        df: pandas DataFrame, static values of pypsa components
+        n : pypsa Network to which new assets should be added
+        c : string, pypsa component.list_name (e.g. "generators")
+    Return:
+        pd.DataFrame with static values of missing assets
+    """
+    df_final = getattr(n, c)
+    missing_i = df.index.difference(df_final.index)
+    return df.loc[missing_i]
+
+
+def get_social_discount(t, r=0.01):
+    """
+    Calculate for a given time t and social discount rate r [per unit] the
+    social discount.
+    """
+    return 1 / (1 + r) ** t
+
+
+def get_investment_weighting(time_weighting, r=0.01):
+    """
+    Define cost weighting.
+
+    Returns cost weightings depending on the the time_weighting
+    (pd.Series) and the social discountrate r
+    """
+    end = time_weighting.cumsum()
+    start = time_weighting.cumsum().shift().fillna(0)
+    return pd.concat([start, end], axis=1).apply(
+        lambda x: sum(get_social_discount(t, r) for t in range(int(x[0]), int(x[1]))),
+        axis=1,
+    )
+
+
+def add_year_to_constraints(n, baseyear):
+    """
+    Add investment period to global constraints and rename index.
+
+    Parameters
+    ----------
+    n : pypsa.Network
+    baseyear : int
+        year in which optimized assets are built
+    """
+
+    for c in n.iterate_components(["GlobalConstraint"]):
+        c.df["investment_period"] = baseyear
+        c.df.rename(index=lambda x: x + "-" + str(baseyear), inplace=True)
+
+
+def hvdc_transport_model(n):
+    """
+    Convert AC lines to DC links for multi-decade optimisation with line
+    expansion.
+
+    Losses of DC links are assumed to be 3% per 1000km
+    """
+
+    logger.info("Convert AC lines to DC links to perform multi-decade optimisation.")
+
+    n.madd(
+        "Link",
+        n.lines.index,
+        bus0=n.lines.bus0,
+        bus1=n.lines.bus1,
+        p_nom_extendable=True,
+        p_nom=n.lines.s_nom,
+        p_nom_min=n.lines.s_nom,
+        p_min_pu=-1,
+        efficiency=1 - 0.03 * n.lines.length / 1000,
+        marginal_cost=0,
+        carrier="DC",
+        length=n.lines.length,
+        capital_cost=n.lines.capital_cost,
+    )
+
+    # Remove AC lines
+    logger.info("Removing AC lines")
+    lines_rm = n.lines.index
+    n.mremove("Line", lines_rm)
+
+    # Set efficiency of all DC links to include losses depending on length
+    n.links.loc[n.links.carrier == "DC", "efficiency"] = (
+        1 - 0.03 * n.links.loc[n.links.carrier == "DC", "length"] / 1000
+    )
+
+
+def adjust_electricity_grid(n, year, years):
+    """
+    Add carrier to lines. Replace AC lines with DC links in case of line
+    expansion. Add lifetime to DC links in case of line expansion.
+
+    Parameters
+    ----------
+    n    : pypsa.Network
+    year : int
+           year in which optimized assets are built
+    years: list
+           investment periods
+    """
+    n.lines["carrier"] = "AC"
+    links_i = n.links[n.links.carrier == "DC"].index
+    if n.lines.s_nom_extendable.any() or n.links.loc[links_i, "p_nom_extendable"].any():
+        hvdc_transport_model(n)
+        links_i = n.links[n.links.carrier == "DC"].index
+        n.links.loc[links_i, "lifetime"] = 100
+        if year != years[0]:
+            n.links.loc[links_i, "p_nom_min"] = 0
+            n.links.loc[links_i, "p_nom"] = 0
+
+
+# --------------------------------------------------------------------
+def concat_networks(years):
+    """
+    Concat given pypsa networks and adds build_year.
+
+    Return:
+        n : pypsa.Network for the whole planning horizon
+    """
+
+    # input paths of sector coupling networks
+    network_paths = [snakemake.input.brownfield_network] + [
+        snakemake.input[f"network_{year}"] for year in years[1:]
+    ]
+    # final concatenated network
+    n = pypsa.Network()
+
+    # iterate over single year networks and concat to perfect foresight network
+    for i, network_path in enumerate(network_paths):
+        year = years[i]
+        network = pypsa.Network(network_path)
+        adjust_electricity_grid(network, year, years)
+        add_build_year_to_new_assets(network, year)
+
+        # static ----------------------------------
+        # (1) add buses and carriers
+        for component in network.iterate_components(["Bus", "Carrier"]):
+            df_year = component.df
+            # get missing assets
+            missing = get_missing(df_year, n, component.list_name)
+            import_components_from_dataframe(n, missing, component.name)
+        # (2) add generators, links, stores and loads
+        for component in network.iterate_components(
+            ["Generator", "Link", "Store", "Load", "Line", "StorageUnit"]
+        ):
+            df_year = component.df.copy()
+            missing = get_missing(df_year, n, component.list_name)
+
+            import_components_from_dataframe(n, missing, component.name)
+
+        # time variant --------------------------------------------------
+        network_sns = pd.MultiIndex.from_product([[year], network.snapshots])
+        snapshots = n.snapshots.drop("now", errors="ignore").union(network_sns)
+        n.set_snapshots(snapshots)
+
+        for component in network.iterate_components():
+            pnl = getattr(n, component.list_name + "_t")
+            for k in iterkeys(component.pnl):
+                pnl_year = component.pnl[k].copy().reindex(snapshots, level=1)
+                if pnl_year.empty and ~(component.name == "Load" and k == "p_set"):
+                    continue
+                if component.name == "Load":
+                    static_load = network.loads.loc[network.loads.p_set != 0]
+                    static_load_t = expand_series(static_load.p_set, network_sns).T
+                    pnl_year = pd.concat(
+                        [pnl_year.reindex(network_sns), static_load_t], axis=1
+                    )
+                    columns = (pnl[k].columns.union(pnl_year.columns)).unique()
+                    pnl[k] = pnl[k].reindex(columns=columns)
+                    pnl[k].loc[pnl_year.index, pnl_year.columns] = pnl_year
+
+                else:
+                    # this is to avoid adding multiple times assets with
+                    # infinite lifetime as ror
+                    cols = pnl_year.columns.difference(pnl[k].columns)
+                    pnl[k] = pd.concat([pnl[k], pnl_year[cols]], axis=1)
+
+        n.snapshot_weightings.loc[year, :] = network.snapshot_weightings.values
+
+        # (3) global constraints
+        for component in network.iterate_components(["GlobalConstraint"]):
+            add_year_to_constraints(network, year)
+            import_components_from_dataframe(n, component.df, component.name)
+
+    # set investment periods
+    n.investment_periods = n.snapshots.levels[0]
+    # weighting of the investment period -> assuming last period same weighting as the period before
+    time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill")
+    n.investment_period_weightings["years"] = time_w
+    # set objective weightings
+    objective_w = get_investment_weighting(
+        n.investment_period_weightings["years"], social_discountrate
+    )
+    n.investment_period_weightings["objective"] = objective_w
+    # all former static loads are now time-dependent -> set static = 0
+    n.loads["p_set"] = 0
+    n.loads_t.p_set.fillna(0, inplace=True)
+
+    return n
+
+
+def adjust_stores(n):
+    """
+    Make sure that stores still behave cyclic over one year and not whole
+    modelling horizon.
+    """
+    # cyclic constraint
+    cyclic_i = n.stores[n.stores.e_cyclic].index
+    n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True
+    n.stores.loc[cyclic_i, "e_cyclic"] = False
+    # non cyclic store assumptions
+    non_cyclic_store = ["co2", "co2 stored", "solid biomass", "biogas", "Li ion"]
+    co2_i = n.stores[n.stores.carrier.isin(non_cyclic_store)].index
+    n.stores.loc[co2_i, "e_cyclic_per_period"] = False
+    n.stores.loc[co2_i, "e_cyclic"] = False
+    # e_initial at beginning of each investment period
+    e_initial_store = ["solid biomass", "biogas"]
+    co2_i = n.stores[n.stores.carrier.isin(e_initial_store)].index
+    n.stores.loc[co2_i, "e_initial_per_period"] = True
+    # n.stores.loc[co2_i, "e_initial"] *= 10
+    # n.stores.loc[co2_i, "e_nom"] *= 10
+    e_initial_store = ["co2 stored"]
+    co2_i = n.stores[n.stores.carrier.isin(e_initial_store)].index
+    n.stores.loc[co2_i, "e_initial_per_period"] = True
+
+    return n
+
+
+def set_phase_out(n, carrier, ct, phase_out_year):
+    """
+    Set planned phase outs for given carrier,country (ct) and planned year of
+    phase out (phase_out_year).
+    """
+    df = n.links[(n.links.carrier.isin(carrier)) & (n.links.bus1.str[:2] == ct)]
+    # assets which are going to be phased out before end of their lifetime
+    assets_i = df[df[["build_year", "lifetime"]].sum(axis=1) > phase_out_year].index
+    build_year = n.links.loc[assets_i, "build_year"]
+    # adjust lifetime
+    n.links.loc[assets_i, "lifetime"] = (phase_out_year - build_year).astype(float)
+
+
+def set_all_phase_outs(n):
+    # TODO move this to a csv or to the config
+    planned = [
+        (["nuclear"], "DE", 2022),
+        (["nuclear"], "BE", 2025),
+        (["nuclear"], "ES", 2027),
+        (["coal", "lignite"], "DE", 2030),
+        (["coal", "lignite"], "ES", 2027),
+        (["coal", "lignite"], "FR", 2022),
+        (["coal", "lignite"], "GB", 2024),
+        (["coal", "lignite"], "IT", 2025),
+        (["coal", "lignite"], "DK", 2030),
+        (["coal", "lignite"], "FI", 2030),
+        (["coal", "lignite"], "HU", 2030),
+        (["coal", "lignite"], "SK", 2030),
+        (["coal", "lignite"], "GR", 2030),
+        (["coal", "lignite"], "IE", 2030),
+        (["coal", "lignite"], "NL", 2030),
+        (["coal", "lignite"], "RS", 2030),
+    ]
+    for carrier, ct, phase_out_year in planned:
+        set_phase_out(n, carrier, ct, phase_out_year)
+    # remove assets which are already phased out
+    remove_i = n.links[n.links[["build_year", "lifetime"]].sum(axis=1) < years[0]].index
+    n.mremove("Link", remove_i)
+
+
+def set_carbon_constraints(n, opts):
+    """
+    Add global constraints for carbon emissions.
+    """
+    budget = None
+    for o in opts:
+        # other budgets
+        m = re.match(r"^\d+p\d$", o, re.IGNORECASE)
+        if m is not None:
+            budget = snakemake.config["co2_budget"][m.group(0)] * 1e9
+    if budget != None:
+        logger.info(f"add carbon budget of {budget}")
+        n.add(
+            "GlobalConstraint",
+            "Budget",
+            type="Co2Budget",
+            carrier_attribute="co2_emissions",
+            sense="<=",
+            constant=budget,
+            investment_period=n.investment_periods[-1],
+        )
+
+        # drop other CO2 limits
+        drop_i = n.global_constraints[n.global_constraints.type == "co2_limit"].index
+        n.mremove("GlobalConstraint", drop_i)
+
+        n.add(
+            "GlobalConstraint",
+            "carbon_neutral",
+            type="co2_limit",
+            carrier_attribute="co2_emissions",
+            sense="<=",
+            constant=0,
+            investment_period=n.investment_periods[-1],
+        )
+
+    # set minimum CO2 emission constraint to avoid too fast reduction
+    if "co2min" in opts:
+        emissions_1990 = 4.53693
+        emissions_2019 = 3.344096
+        target_2030 = 0.45 * emissions_1990
+        annual_reduction = (emissions_2019 - target_2030) / 11
+        first_year = n.snapshots.levels[0][0]
+        time_weightings = n.investment_period_weightings.loc[first_year, "years"]
+        co2min = emissions_2019 - ((first_year - 2019) * annual_reduction)
+        logger.info(f"add minimum emissions for {first_year} of {co2min} t CO2/a")
+        n.add(
+            "GlobalConstraint",
+            f"Co2Min-{first_year}",
+            type="Co2min",
+            carrier_attribute="co2_emissions",
+            sense=">=",
+            investment_period=first_year,
+            constant=co2min * 1e9 * time_weightings,
+        )
+
+    return n
+
+
+def adjust_lvlimit(n):
+    """
+    Convert global constraints for single investment period to one uniform if
+    all attributes stay the same.
+    """
+    c = "GlobalConstraint"
+    cols = ["carrier_attribute", "sense", "constant", "type"]
+    glc_type = "transmission_volume_expansion_limit"
+    if (n.df(c)[n.df(c).type == glc_type][cols].nunique() == 1).all():
+        glc = n.df(c)[n.df(c).type == glc_type][cols].iloc[[0]]
+        glc.index = pd.Index(["lv_limit"])
+        remove_i = n.df(c)[n.df(c).type == glc_type].index
+        n.mremove(c, remove_i)
+        import_components_from_dataframe(n, glc, c)
+
+    return n
+
+
+def adjust_CO2_glc(n):
+    c = "GlobalConstraint"
+    glc_name = "CO2Limit"
+    glc_type = "primary_energy"
+    mask = (n.df(c).index.str.contains(glc_name)) & (n.df(c).type == glc_type)
+    n.df(c).loc[mask, "type"] = "co2_limit"
+
+    return n
+
+
+def add_H2_boilers(n):
+    """
+    Gas boilers can be retrofitted to run with H2.
+
+    Add H2 boilers for heating for all existing gas boilers.
+    """
+    c = "Link"
+    logger.info("Add H2 boilers.")
+    # existing gas boilers
+    mask = n.links.carrier.str.contains("gas boiler") & ~n.links.p_nom_extendable
+    gas_i = n.links[mask].index
+    df = n.links.loc[gas_i]
+    # adjust bus 0
+    df["bus0"] = df.bus1.map(n.buses.location) + " H2"
+    # rename carrier and index
+    df["carrier"] = df.carrier.apply(
+        lambda x: x.replace("gas boiler", "retrofitted H2 boiler")
+    )
+    df.rename(
+        index=lambda x: x.replace("gas boiler", "retrofitted H2 boiler"), inplace=True
+    )
+    # todo, costs for retrofitting
+    df["capital_costs"] = 100
+    # set existing capacity to zero
+    df["p_nom"] = 0
+    df["p_nom_extendable"] = True
+    # add H2 boilers to network
+    import_components_from_dataframe(n, df, c)
+
+
+def apply_time_segmentation_perfect(
+    n, segments, solver_name="cbc", overwrite_time_dependent=True
+):
+    """
+    Aggregating time series to segments with different lengths.
+
+    Input:
+        n: pypsa Network
+        segments: (int) number of segments in which the typical period should be
+                  subdivided
+        solver_name: (str) name of solver
+        overwrite_time_dependent: (bool) overwrite time dependent data of pypsa network
+        with typical time series created by tsam
+    """
+    try:
+        import tsam.timeseriesaggregation as tsam
+    except:
+        raise ModuleNotFoundError(
+            "Optional dependency 'tsam' not found." "Install via 'pip install tsam'"
+        )
+
+    # get all time-dependent data
+    columns = pd.MultiIndex.from_tuples([], names=["component", "key", "asset"])
+    raw = pd.DataFrame(index=n.snapshots, columns=columns)
+    for c in n.iterate_components():
+        for attr, pnl in c.pnl.items():
+            # exclude e_min_pu which is used for SOC of EVs in the morning
+            if not pnl.empty and attr != "e_min_pu":
+                df = pnl.copy()
+                df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
+                raw = pd.concat([raw, df], axis=1)
+    raw = raw.dropna(axis=1)
+    sn_weightings = {}
+
+    for year in raw.index.levels[0]:
+        logger.info(f"Find representative snapshots for {year}.")
+        raw_t = raw.loc[year]
+        # normalise all time-dependent data
+        annual_max = raw_t.max().replace(0, 1)
+        raw_t = raw_t.div(annual_max, level=0)
+        # get representative segments
+        agg = tsam.TimeSeriesAggregation(
+            raw_t,
+            hoursPerPeriod=len(raw_t),
+            noTypicalPeriods=1,
+            noSegments=int(segments),
+            segmentation=True,
+            solver=solver_name,
+        )
+        segmented = agg.createTypicalPeriods()
+
+        weightings = segmented.index.get_level_values("Segment Duration")
+        offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
+        timesteps = [raw_t.index[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
+        snapshots = pd.DatetimeIndex(timesteps)
+        sn_weightings[year] = pd.Series(
+            weightings, index=snapshots, name="weightings", dtype="float64"
+        )
+
+    sn_weightings = pd.concat(sn_weightings)
+    n.set_snapshots(sn_weightings.index)
+    n.snapshot_weightings = n.snapshot_weightings.mul(sn_weightings, axis=0)
+
+    return n
+
+
+def set_temporal_aggregation_SEG(n, opts, solver_name):
+    """
+    Aggregate network temporally with tsam.
+    """
+    for o in opts:
+        # segments with package tsam
+        m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
+        if m is not None:
+            segments = int(m[1])
+            logger.info(f"Use temporal segmentation with {segments} segments")
+            n = apply_time_segmentation_perfect(n, segments, solver_name=solver_name)
+            break
+    return n
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "prepare_perfect_foresight",
+            simpl="",
+            opts="",
+            clusters="37",
+            ll="v1.5",
+            sector_opts="1p7-4380H-T-H-B-I-A-solar+p3-dist1",
+        )
+
+    update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
+    # parameters -----------------------------------------------------------
+    years = snakemake.config["scenario"]["planning_horizons"]
+    opts = snakemake.wildcards.sector_opts.split("-")
+    social_discountrate = snakemake.config["costs"]["social_discountrate"]
+    for o in opts:
+        if "sdr" in o:
+            social_discountrate = float(o.replace("sdr", "")) / 100
+
+    logger.info(
+        f"Concat networks of investment period {years} with social discount rate of {social_discountrate * 100}%"
+    )
+
+    # concat prenetworks of planning horizon to single network ------------
+    n = concat_networks(years)
+
+    # temporal aggregate
+    opts = snakemake.wildcards.sector_opts.split("-")
+    solver_name = snakemake.config["solving"]["solver"]["name"]
+    n = set_temporal_aggregation_SEG(n, opts, solver_name)
+
+    # adjust global constraints lv limit if the same for all years
+    n = adjust_lvlimit(n)
+    # adjust global constraints CO2 limit
+    n = adjust_CO2_glc(n)
+    # adjust stores to multi period investment
+    n = adjust_stores(n)
+
+    # set phase outs
+    set_all_phase_outs(n)
+
+    # add H2 boiler
+    add_H2_boilers(n)
+
+    # set carbon constraints
+    opts = snakemake.wildcards.sector_opts.split("-")
+    n = set_carbon_constraints(n, opts)
+
+    # export network
+    n.export_to_netcdf(snakemake.output[0])

scripts/prepare_sector_network.py

@@ -184,10 +184,7 @@ def get(item, investment_year=None):
     """
     Check whether item depends on investment year.
     """
-    if isinstance(item, dict):
+    return item[investment_year] if isinstance(item, dict) else item
-        return item[investment_year]
-    else:
-        return item
 
 
 def co2_emissions_year(
@@ -220,7 +217,7 @@ def co2_emissions_year(
 
 
 # TODO: move to own rule with sector-opts wildcard?
-def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year, input_co2):
+def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year):
     """
     Distribute carbon budget following beta or exponential transition path.
     """
@@ -413,11 +410,9 @@ def update_wind_solar_costs(n, costs):
             # e.g. clusters == 37m means that VRE generators are left
             # at clustering of simplified network, but that they are
             # connected to 37-node network
-            if snakemake.wildcards.clusters[-1:] == "m":
+            genmap = (
-                genmap = busmap_s
+                busmap_s if snakemake.wildcards.clusters[-1:] == "m" else clustermaps
-            else:
+            )
-                genmap = clustermaps
-
             connection_cost = (connection_cost * weight).groupby(
                 genmap
             ).sum() / weight.groupby(genmap).sum()
@@ -505,8 +500,7 @@ def remove_non_electric_buses(n):
     """
     Remove buses from pypsa-eur with carriers which are not AC buses.
     """
-    to_drop = list(n.buses.query("carrier not in ['AC', 'DC']").carrier.unique())
+    if to_drop := list(n.buses.query("carrier not in ['AC', 'DC']").carrier.unique()):
-    if to_drop:
         logger.info(f"Drop buses from PyPSA-Eur with carrier: {to_drop}")
         n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
 
@@ -577,6 +571,7 @@ def add_co2_tracking(n, options):
         capital_cost=options["co2_sequestration_cost"],
         carrier="co2 stored",
         bus=spatial.co2.nodes,
+        lifetime=options["co2_sequestration_lifetime"],
     )
 
     n.add("Carrier", "co2 stored")
@@ -1231,11 +1226,9 @@ def add_storage_and_grids(n, costs):
 
         # apply k_edge_augmentation weighted by length of complement edges
         k_edge = options.get("gas_network_connectivity_upgrade", 3)
-        augmentation = list(
+        if augmentation := list(
             k_edge_augmentation(G, k_edge, avail=complement_edges.values)
-        )
+        ):
-
-        if augmentation:
             new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
             new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
 
@@ -1399,7 +1392,7 @@ def add_storage_and_grids(n, costs):
             lifetime=costs.at["coal", "lifetime"],
         )
 
-    if options["SMR"]:
+    if options["SMR_cc"]:
         n.madd(
             "Link",
             spatial.nodes,
@@ -1417,6 +1410,7 @@ def add_storage_and_grids(n, costs):
             lifetime=costs.at["SMR CC", "lifetime"],
         )
 
+    if options["SMR"]:
         n.madd(
             "Link",
             nodes + " SMR",
@@ -1960,7 +1954,7 @@ def add_heat(n, costs):
         # demand 'dE' [per unit of original heat demand] for each country and
         # different retrofitting strengths [additional insulation thickness in m]
         retro_data = pd.read_csv(
-            snakemake.input.retro_cost_energy,
+            snakemake.input.retro_cost,
             index_col=[0, 1],
             skipinitialspace=True,
             header=[0, 1],
@@ -2893,6 +2887,30 @@ def add_industry(n, costs):
             p_set=p_set,
         )
 
+    primary_steel = get(
+        snakemake.config["industry"]["St_primary_fraction"], investment_year
+    )
+    dri_steel = get(snakemake.config["industry"]["DRI_fraction"], investment_year)
+    bof_steel = primary_steel - dri_steel
+
+    if bof_steel > 0:
+        add_carrier_buses(n, "coal")
+
+        mwh_coal_per_mwh_coke = 1.366  # from eurostat energy balance
+        p_set = (
+            industrial_demand["coal"].sum()
+            + mwh_coal_per_mwh_coke * industrial_demand["coke"].sum()
+        ) / nhours
+
+        n.madd(
+            "Load",
+            spatial.coal.nodes,
+            suffix=" for industry",
+            bus=spatial.coal.nodes,
+            carrier="coal for industry",
+            p_set=p_set,
+        )
+
 
 def add_waste_heat(n):
     # TODO options?
@@ -3438,7 +3456,7 @@ if __name__ == "__main__":
 
     spatial = define_spatial(pop_layout.index, options)
 
-    if snakemake.params.foresight == "myopic":
+    if snakemake.params.foresight in ["myopic", "perfect"]:
         add_lifetime_wind_solar(n, costs)
 
         conventional = snakemake.params.conventional_carriers
@@ -3515,7 +3533,7 @@ if __name__ == "__main__":
         if "cb" not in o:
             continue
         limit_type = "carbon budget"
-        fn = "results/" + snakemake.params.RDIR + "csvs/carbon_budget_distribution.csv"
+        fn = "results/" + snakemake.params.RDIR + "/csvs/carbon_budget_distribution.csv"
         if not os.path.exists(fn):
             emissions_scope = snakemake.params.emissions_scope
             report_year = snakemake.params.eurostat_report_year
@@ -3559,7 +3577,7 @@ if __name__ == "__main__":
     if options["electricity_grid_connection"]:
         add_electricity_grid_connection(n, costs)
 
-    first_year_myopic = (snakemake.params.foresight == "myopic") and (
+    first_year_myopic = (snakemake.params.foresight in ["myopic", "perfect"]) and (
         snakemake.params.planning_horizons[0] == investment_year
     )
 

scripts/retrieve_irena.py

@@ -0,0 +1,107 @@
+# -*- coding: utf-8 -*-
+# Copyright 2023 Thomas Gilon (Climact)
+# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+This rule downloads the existing capacities from `IRENASTAT <https://www.irena.org/Data/Downloads/IRENASTAT>`_ and extracts it in the ``data/existing_capacities`` sub-directory.
+
+**Relevant Settings**
+
+.. code:: yaml
+
+    enable:
+        retrieve_irena:
+
+.. seealso::
+    Documentation of the configuration file ``config.yaml`` at
+    :ref:`enable_cf`
+
+**Outputs**
+
+- ``data/existing_capacities``: existing capacities for offwind, onwind and solar
+
+"""
+
+import logging
+
+import pandas as pd
+from _helpers import configure_logging
+
+logger = logging.getLogger(__name__)
+
+REGIONS = [
+    "Albania",
+    "Austria",
+    "Belgium",
+    "Bosnia and Herzegovina",
+    "Bulgaria",
+    "Croatia",
+    "Czechia",
+    "Denmark",
+    "Estonia",
+    "Finland",
+    "France",
+    "Germany",
+    "Greece",
+    "Hungary",
+    "Ireland",
+    "Italy",
+    "Latvia",
+    "Lithuania",
+    "Luxembourg",
+    "Montenegro",
+    # "Netherlands",
+    "Netherlands (Kingdom of the)",
+    "North Macedonia",
+    "Norway",
+    "Poland",
+    "Portugal",
+    "Romania",
+    "Serbia",
+    "Slovakia",
+    "Slovenia",
+    "Spain",
+    "Sweden",
+    "Switzerland",
+    # "United Kingdom",
+    "United Kingdom of Great Britain and Northern Ireland (the)",
+]
+
+REGIONS_DICT = {
+    "Bosnia and Herzegovina": "Bosnia Herzg",
+    "Netherlands (Kingdom of the)": "Netherlands",
+    "United Kingdom of Great Britain and Northern Ireland (the)": "UK",
+}
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake("retrieve_irena")
+    configure_logging(snakemake)
+
+    irena_raw = pd.read_csv(
+        "https://pxweb.irena.org:443/sq/99e64b12-fe03-4a7b-92ea-a22cc3713b92",
+        skiprows=2,
+        index_col=[0, 1, 3],
+        encoding="latin-1",
+    )
+
+    var = "Installed electricity capacity (MW)"
+    irena = irena_raw[var].unstack(level=2).reset_index(level=1).replace(0, "")
+
+    irena = irena[irena.index.isin(REGIONS)]
+    irena.rename(index=REGIONS_DICT, inplace=True)
+
+    df_offwind = irena[irena.Technology.str.contains("Offshore")].drop(
+        columns=["Technology"]
+    )
+    df_onwind = irena[irena.Technology.str.contains("Onshore")].drop(
+        columns=["Technology"]
+    )
+    df_pv = irena[irena.Technology.str.contains("Solar")].drop(columns=["Technology"])
+
+    df_offwind.to_csv(snakemake.output["offwind"])
+    df_onwind.to_csv(snakemake.output["onwind"])
+    df_pv.to_csv(snakemake.output["solar"])

scripts/simplify_network.py

@@ -152,11 +152,8 @@ def _prepare_connection_costs_per_link(n, costs, renewable_carriers, length_fact
     if n.links.empty:
         return {}
 
-    connection_costs_per_link = {}
+    return {
-
+        tech: (
-    for tech in renewable_carriers:
-        if tech.startswith("offwind"):
-            connection_costs_per_link[tech] = (
             n.links.length
             * length_factor
             * (
@@ -166,8 +163,9 @@ def _prepare_connection_costs_per_link(n, costs, renewable_carriers, length_fact
                 * costs.at[tech + "-connection-underground", "capital_cost"]
             )
         )
-
+        for tech in renewable_carriers
-    return connection_costs_per_link
+        if tech.startswith("offwind")
+    }
 
 
 def _compute_connection_costs_to_bus(

scripts/solve_network.py

@@ -33,7 +33,9 @@ import numpy as np
 import pandas as pd
 import pypsa
 import xarray as xr
+from _benchmark import memory_logger
 from _helpers import configure_logging, update_config_with_sector_opts
+from pypsa.descriptors import get_activity_mask
 
 logger = logging.getLogger(__name__)
 pypsa.pf.logger.setLevel(logging.WARNING)
@@ -47,6 +49,69 @@ def add_land_use_constraint(n, planning_horizons, config):
         _add_land_use_constraint(n)
 
 
+def add_land_use_constraint_perfect(n):
+    """
+    Add global constraints for tech capacity limit.
+    """
+    logger.info("Add land-use constraint for perfect foresight")
+
+    def compress_series(s):
+        def process_group(group):
+            if group.nunique() == 1:
+                return pd.Series(group.iloc[0], index=[None])
+            else:
+                return group
+
+        return s.groupby(level=[0, 1]).apply(process_group)
+
+    def new_index_name(t):
+        # Convert all elements to string and filter out None values
+        parts = [str(x) for x in t if x is not None]
+        # Join with space, but use a dash for the last item if not None
+        return " ".join(parts[:2]) + (f"-{parts[-1]}" if len(parts) > 2 else "")
+
+    def check_p_min_p_max(p_nom_max):
+        p_nom_min = n.generators[ext_i].groupby(grouper).sum().p_nom_min
+        p_nom_min = p_nom_min.reindex(p_nom_max.index)
+        check = (
+            p_nom_min.groupby(level=[0, 1]).sum()
+            > p_nom_max.groupby(level=[0, 1]).min()
+        )
+        if check.sum():
+            logger.warning(
+                f"summed p_min_pu values at node larger than technical potential {check[check].index}"
+            )
+
+    grouper = [n.generators.carrier, n.generators.bus, n.generators.build_year]
+    ext_i = n.generators.p_nom_extendable
+    # get technical limit per node and investment period
+    p_nom_max = n.generators[ext_i].groupby(grouper).min().p_nom_max
+    # drop carriers without tech limit
+    p_nom_max = p_nom_max[~p_nom_max.isin([np.inf, np.nan])]
+    # carrier
+    carriers = p_nom_max.index.get_level_values(0).unique()
+    gen_i = n.generators[(n.generators.carrier.isin(carriers)) & (ext_i)].index
+    n.generators.loc[gen_i, "p_nom_min"] = 0
+    # check minimum capacities
+    check_p_min_p_max(p_nom_max)
+    # drop multi entries in case p_nom_max stays constant in different periods
+    # p_nom_max = compress_series(p_nom_max)
+    # adjust name to fit syntax of nominal constraint per bus
+    df = p_nom_max.reset_index()
+    df["name"] = df.apply(
+        lambda row: f"nom_max_{row['carrier']}"
+        + (f"_{row['build_year']}" if row["build_year"] is not None else ""),
+        axis=1,
+    )
+
+    for name in df.name.unique():
+        df_carrier = df[df.name == name]
+        bus = df_carrier.bus
+        n.buses.loc[bus, name] = df_carrier.p_nom_max.values
+
+    return n
+
+
 def _add_land_use_constraint(n):
     # warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
 
@@ -82,19 +147,13 @@ def _add_land_use_constraint(n):
 def _add_land_use_constraint_m(n, planning_horizons, config):
     # if generators clustering is lower than network clustering, land_use accounting is at generators clusters
 
-    planning_horizons = param["planning_horizons"]
     grouping_years = config["existing_capacities"]["grouping_years"]
     current_horizon = snakemake.wildcards.planning_horizons
 
     for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
         existing = n.generators.loc[n.generators.carrier == carrier, "p_nom"]
         ind = list(
-            set(
+            {i.split(sep=" ")[0] + " " + i.split(sep=" ")[1] for i in existing.index}
-                [
-                    i.split(sep=" ")[0] + " " + i.split(sep=" ")[1]
-                    for i in existing.index
-                ]
-            )
         )
 
         previous_years = [
@@ -116,7 +175,7 @@ def _add_land_use_constraint_m(n, planning_horizons, config):
     n.generators.p_nom_max.clip(lower=0, inplace=True)
 
 
-def add_co2_sequestration_limit(n, limit=200):
+def add_co2_sequestration_limit(n, config, limit=200):
     """
     Add a global constraint on the amount of Mt CO2 that can be sequestered.
     """
@@ -130,16 +189,146 @@ def add_co2_sequestration_limit(n, limit=200):
         limit = float(o[o.find("seq") + 3 :]) * 1e6
         break
 
-    n.add(
+    if not n.investment_periods.empty:
+        periods = n.investment_periods
+        names = pd.Index([f"co2_sequestration_limit-{period}" for period in periods])
+    else:
+        periods = [np.nan]
+        names = pd.Index(["co2_sequestration_limit"])
+
+    n.madd(
         "GlobalConstraint",
-        "co2_sequestration_limit",
+        names,
         sense="<=",
         constant=limit,
         type="primary_energy",
         carrier_attribute="co2_absorptions",
+        investment_period=periods,
     )
 
 
+def add_carbon_constraint(n, snapshots):
+    glcs = n.global_constraints.query('type == "co2_limit"')
+    if glcs.empty:
+        return
+    for name, glc in glcs.iterrows():
+        carattr = glc.carrier_attribute
+        emissions = n.carriers.query(f"{carattr} != 0")[carattr]
+
+        if emissions.empty:
+            continue
+
+        # stores
+        n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
+        stores = n.stores.query("carrier in @emissions.index and not e_cyclic")
+        if not stores.empty:
+            last = n.snapshot_weightings.reset_index().groupby("period").last()
+            last_i = last.set_index([last.index, last.timestep]).index
+            final_e = n.model["Store-e"].loc[last_i, stores.index]
+            time_valid = int(glc.loc["investment_period"])
+            time_i = pd.IndexSlice[time_valid, :]
+            lhs = final_e.loc[time_i, :] - final_e.shift(snapshot=1).loc[time_i, :]
+
+            rhs = glc.constant
+            n.model.add_constraints(lhs <= rhs, name=f"GlobalConstraint-{name}")
+
+
+def add_carbon_budget_constraint(n, snapshots):
+    glcs = n.global_constraints.query('type == "Co2Budget"')
+    if glcs.empty:
+        return
+    for name, glc in glcs.iterrows():
+        carattr = glc.carrier_attribute
+        emissions = n.carriers.query(f"{carattr} != 0")[carattr]
+
+        if emissions.empty:
+            continue
+
+        # stores
+        n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
+        stores = n.stores.query("carrier in @emissions.index and not e_cyclic")
+        if not stores.empty:
+            last = n.snapshot_weightings.reset_index().groupby("period").last()
+            last_i = last.set_index([last.index, last.timestep]).index
+            final_e = n.model["Store-e"].loc[last_i, stores.index]
+            time_valid = int(glc.loc["investment_period"])
+            time_i = pd.IndexSlice[time_valid, :]
+            weighting = n.investment_period_weightings.loc[time_valid, "years"]
+            lhs = final_e.loc[time_i, :] * weighting
+
+            rhs = glc.constant
+            n.model.add_constraints(lhs <= rhs, name=f"GlobalConstraint-{name}")
+
+
+def add_max_growth(n, config):
+    """
+    Add maximum growth rates for different carriers.
+    """
+
+    opts = snakemake.params["sector"]["limit_max_growth"]
+    # take maximum yearly difference between investment periods since historic growth is per year
+    factor = n.investment_period_weightings.years.max() * opts["factor"]
+    for carrier in opts["max_growth"].keys():
+        max_per_period = opts["max_growth"][carrier] * factor
+        logger.info(
+            f"set maximum growth rate per investment period of {carrier} to {max_per_period} GW."
+        )
+        n.carriers.loc[carrier, "max_growth"] = max_per_period * 1e3
+
+    for carrier in opts["max_relative_growth"].keys():
+        max_r_per_period = opts["max_relative_growth"][carrier]
+        logger.info(
+            f"set maximum relative growth per investment period of {carrier} to {max_r_per_period}."
+        )
+        n.carriers.loc[carrier, "max_relative_growth"] = max_r_per_period
+
+    return n
+
+
+def add_retrofit_gas_boiler_constraint(n, snapshots):
+    """
+    Allow retrofitting of existing gas boilers to H2 boilers.
+    """
+    c = "Link"
+    logger.info("Add constraint for retrofitting gas boilers to H2 boilers.")
+    # existing gas boilers
+    mask = n.links.carrier.str.contains("gas boiler") & ~n.links.p_nom_extendable
+    gas_i = n.links[mask].index
+    mask = n.links.carrier.str.contains("retrofitted H2 boiler")
+    h2_i = n.links[mask].index
+
+    n.links.loc[gas_i, "p_nom_extendable"] = True
+    p_nom = n.links.loc[gas_i, "p_nom"]
+    n.links.loc[gas_i, "p_nom"] = 0
+
+    # heat profile
+    cols = n.loads_t.p_set.columns[
+        n.loads_t.p_set.columns.str.contains("heat")
+        & ~n.loads_t.p_set.columns.str.contains("industry")
+        & ~n.loads_t.p_set.columns.str.contains("agriculture")
+    ]
+    profile = n.loads_t.p_set[cols].div(
+        n.loads_t.p_set[cols].groupby(level=0).max(), level=0
+    )
+    # to deal if max value is zero
+    profile.fillna(0, inplace=True)
+    profile.rename(columns=n.loads.bus.to_dict(), inplace=True)
+    profile = profile.reindex(columns=n.links.loc[gas_i, "bus1"])
+    profile.columns = gas_i
+
+    rhs = profile.mul(p_nom)
+
+    dispatch = n.model["Link-p"]
+    active = get_activity_mask(n, c, snapshots, gas_i)
+    rhs = rhs[active]
+    p_gas = dispatch.sel(Link=gas_i)
+    p_h2 = dispatch.sel(Link=h2_i)
+
+    lhs = p_gas + p_h2
+
+    n.model.add_constraints(lhs == rhs, name="gas_retrofit")
+
+
 def prepare_network(
     n,
     solve_opts=None,
@@ -156,8 +345,7 @@ def prepare_network(
         ):
             df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)
 
-    load_shedding = solve_opts.get("load_shedding")
+    if load_shedding := solve_opts.get("load_shedding"):
-    if load_shedding:
         # intersect between macroeconomic and surveybased willingness to pay
         # http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
         # TODO: retrieve color and nice name from config
@@ -200,9 +388,14 @@ def prepare_network(
     if foresight == "myopic":
         add_land_use_constraint(n, planning_horizons, config)
 
+    if foresight == "perfect":
+        n = add_land_use_constraint_perfect(n)
+        if snakemake.params["sector"]["limit_max_growth"]["enable"]:
+            n = add_max_growth(n, config)
+
     if n.stores.carrier.eq("co2 stored").any():
         limit = co2_sequestration_potential
-        add_co2_sequestration_limit(n, limit=limit)
+        add_co2_sequestration_limit(n, config, limit=limit)
 
     return n
 
@@ -594,12 +787,17 @@ def extra_functionality(n, snapshots):
             add_EQ_constraints(n, o)
     add_battery_constraints(n)
     add_pipe_retrofit_constraint(n)
+    if n._multi_invest:
+        add_carbon_constraint(n, snapshots)
+        add_carbon_budget_constraint(n, snapshots)
+        add_retrofit_gas_boiler_constraint(n, snapshots)
 
 
 def solve_network(n, config, solving, opts="", **kwargs):
     set_of_options = solving["solver"]["options"]
     cf_solving = solving["options"]
 
+    kwargs["multi_investment_periods"] = config["foresight"] == "perfect"
     kwargs["solver_options"] = (
         solving["solver_options"][set_of_options] if set_of_options else {}
     )
@@ -671,13 +869,14 @@ if __name__ == "__main__":
         from _helpers import mock_snakemake
 
         snakemake = mock_snakemake(
-            "solve_network",
+            "solve_sector_network_perfect",
+            configfiles="../config/test/config.perfect.yaml",
             simpl="",
-            opts="Ept",
+            opts="",
-            clusters="37",
+            clusters="5",
-            ll="v1.0",
+            ll="v1.5",
-            sector_opts="",
+            sector_opts="8760H-T-H-B-I-A-solar+p3-dist1",
-            planning_horizons="2020",
+            planning_horizons="2030",
         )
     configure_logging(snakemake)
     if "sector_opts" in snakemake.wildcards.keys():
@@ -704,6 +903,9 @@ if __name__ == "__main__":
         co2_sequestration_potential=snakemake.params["co2_sequestration_potential"],
     )
 
+    with memory_logger(
+        filename=getattr(snakemake.log, "memory", None), interval=30.0
+    ) as mem:
         n = solve_network(
             n,
             config=snakemake.config,
@@ -712,5 +914,7 @@ if __name__ == "__main__":
             log_fn=snakemake.log.solver,
         )
 
+    logger.info(f"Maximum memory usage: {mem.mem_usage}")
+
     n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output[0])

scripts/solve_operations_network.py

@@ -7,6 +7,7 @@ Solves linear optimal dispatch in hourly resolution using the capacities of
 previous capacity expansion in rule :mod:`solve_network`.
 """
 
+
 import logging
 
 import numpy as np
@@ -35,7 +36,7 @@ if __name__ == "__main__":
     configure_logging(snakemake)
     update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
 
-    opts = (snakemake.wildcards.opts + "-" + snakemake.wildcards.sector_opts).split("-")
+    opts = f"{snakemake.wildcards.opts}-{snakemake.wildcards.sector_opts}".split("-")
     opts = [o for o in opts if o != ""]
     solve_opts = snakemake.params.options