Merge remote-tracking branch 'upstream/master' into add_methanol_techs

.pre-commit-config.yaml

@@ -33,7 +33,7 @@ repos:
   rev: v2.3.0
   hooks:
   - id: codespell
-    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
+    args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom,appartment,bage,ore,setis,tabacco,berfore,vor,pris'] # Ignore capital case words, e.g. country codes
     types_or: [python, rst, markdown]
     files: ^(scripts|doc)/
 

config/config.default.yaml

@@ -42,7 +42,7 @@ scenario:
   ll:
   - vopt
   clusters:
-  - 38
+  - 39
   - 128
   - 256
   opts:
@@ -56,7 +56,7 @@ scenario:
   - 2050
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#countries
-countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']
+countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK', 'XK']
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#snapshots
 snapshots:
@@ -87,7 +87,7 @@ co2_budget:
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#electricity
 electricity:
   voltages: [200., 220., 300., 380., 500., 750.]
-  base_network: entsoegridkit
+  base_network: osm-prebuilt
   gaslimit_enable: false
   gaslimit: false
   co2limit_enable: false
@@ -449,19 +449,26 @@ sector:
       2045: 0.8
       2050: 1.0
     district_heating_loss: 0.15
-    # check these numbers!
-    forward_temperature:
-      default: 90
-      DK: 70
-      SE: 70
-      NO: 70
-      FI: 70
-    return_temperature:
-      default: 50
-      DK: 40
-      SE: 40
-      NO: 40
-      FI: 40
+    supply_temperature_approximation:
+      max_forward_temperature:
+        default: 90
+        DK: 70
+        SE: 70
+        NO: 70
+      min_forward_temperature:
+        default: 68
+        DK: 54
+        SE: 54
+        NO: 54
+      return_temperature:
+        default: 50
+        DK: 40
+        SE: 40
+        NO: 40
+        FI: 40
+      lower_threshold_ambient_temperature: 0
+      upper_threshold_ambient_temperature: 10
+      rolling_window_ambient_temperature: 72
     heat_source_cooling: 6 #K
     heat_pump_cop_approximation:
       refrigerant: ammonia

config/examples/config.entsoe-all.yaml

@@ -15,7 +15,7 @@ scenario:
   ll:
   - vopt
   clusters:
-  - 39
+  - 41
   - 128
   - 256
   opts:
@@ -26,7 +26,7 @@ scenario:
   - ''
 
 # TODO add Turkey (TR)
-countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MD', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK', 'UA']
+countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MD', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK', 'UA', 'XK']
 
 electricity:
   custom_powerplants: true

data/ammonia_plants.csv

@@ -0,0 +1,37 @@
+"Plant","Ammonia [kt/a]","Urea [kt/a]","AN [kt/a]","UAN [kt/a]","CAN [kt/a]","Nitrogen fertilisers [kt/a]","MAP [kt/a]","Country","Latitude","Longitude","Date","Source","Comment"
+"Yara Sluiskil","1700","1300",,,,,,"Netherlands","51.27186","3.84896","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"OCI Geleen","550",,,,,,,"Netherlands","50.99738","5.78497","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"CF Fertilisers Billingham","590",,,,,,,"United Kingdom","53.28149","-2.79744","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/; https://www.cfindustries.com/what-we-do/ammonia-productiont",
+"Yara Tertre","420",,,,,,,"Belgium","50.47842","3.80285","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"BASF Antwerp","650",,,,,,,"Belgium","51.35583","4.27811","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Yara Gonfreville","400",,,,,,,"France","49.47982","0.19587","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Borealis Rouen",,,,,,,,"France","49.41916","1.02584","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"BASF Ludwigshafen","880",,,,,,,"Germany","49.51171","8.42009","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"SKW","1300",,,,,,,"Germany","51.87746","12.5858","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Yara Brunsbuettel","750",,,,,,,"Germany","53.91152","9.20982","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Yara Porsgrunn","500",,,,,,,"Norway","59.12444","9.61922","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Fertiberia Puertollano","400","250",,"200",,,,"Spain","38.67276","-4.0608","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/; https://www.icis.com/explore/resources/news/2021/10/25/10698264/spain-s-fertiberia-urea-ammonia-plants-in-palos-to-remain-shut-on-gas-prices/",
+"Fertiberia Huelva","200","130",,,,,,"Spain","37.22572","-6.94742","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/; https://www.icis.com/explore/resources/news/2021/10/25/10698264/spain-s-fertiberia-urea-ammonia-plants-in-palos-to-remain-shut-on-gas-prices/",
+"Yara Ferrara","600","600",,,,,,"Italy","44.86206","11.57922","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Petrokemija",,"500",,,,,,"Croatia","45.46977","16.78944","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Neochim","450",,"710",,,,,"Bulgaria","42.20514","25.61547","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Agopolychim",,,"400","800",,,"300","Bulgaria","43.20652","27.65983","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/; https://ammoniaenergy.org/articles/agropolychim-investing-in-ammonia-distribution-from-bulgaria/","no own ammonia plant"
+"Azomures","1600",,,,,,,"Romania","46.76187","24.42361","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/","Total for ammonia, AN, NPK, UAN, Urea"
+"Nitrogenmuvek","497",,,,"1300",,,"Hungary","47.16691","18.12844","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"DUSLO","530",,,,,,,"Slovakia","48.18705","17.9301","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Grupa Azoty","524","375",,,,,,"Poland","51.45179","21.98174","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Anwil",,,,,,"965",,"Poland","52.70438","18.96606","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Achema","1100","785","651","1300","540",,,"Lithuania","55.081","24.32377","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Lifosa",,,,,,"3241",,"Lithuania","55.27567","24.01652","13-01-2023","https://www.icis.com/explore/resources/news/2023/01/18/10846094/insight-poor-demand-high-costs-stifle-europe-industry-despite-falling-gas-prices/",
+"Unknown (Linz)",,,,,,,,"Austria","48.28749","14.32369","15-08-2024",,"most likely location"
+"Unknown (Kothla-Järve)",,,,,,,,"Estonia","59.39984","27.25401","15-08-2024",,"most likely location"
+"Unknown (Oulu)",,,,,,,,"Finland","65.0033","25.43731","15-08-2024",,"most likely location"
+"Unknown (Ptolemaida)",,,,,,,,"Greece","40.50701","21.71414","15-08-2024",,"most likely location"
+"Unknown (Sabac)",,,,,,,,"Serbia","44.76298","19.68426","15-08-2024",,"most likely location"
+"Unknown (Lugano)",,,,,,,,"Switzerland","46.02709","8.9641","15-08-2024",,"most likely location"
+"Rivneazot","420",,"540","300","470 ",,,"Ukraine","50.70502","26.1630","28-08-2024","https://interfax.com/newsroom/top-stories/100959/; https://interfax.com/newsroom/top-stories/103386/; https://gmk.center/en/news/ostchem-plants-produced-520-6-thousand-tons-of-fertilizers-in-q1/; http://www.ostchem.com/en/o-kompanii/proizvodstvo/rovno",
+"Stirol (Horlivka)","1470","940 ","693",,,,,"Ukraine","48.29697","38.10412","28-08-2024","http://ostchem.com/en/o-kompanii/proizvodstvo/stirol",
+"Severodonetsk Azot","1020","390","550",,,,,"Ukraine","48.94185","38.47399","28-08-2024",,
+"Odessa Port Plant ","1160","942",,,,,,"Ukraine","46.49460","30.73222","28-08-2024","https://www.spfu.gov.ua/userfiles/files/OPP_Teaser_Eng.pdf",
+"Cherkazy Azot","963",,"970","500",,,,"Ukraine","49.38241","32.05719","28-08-2024","https://gmk.center/en/news/ostchem-plants-produced-520-6-thousand-tons-of-fertilizers-in-q1/https://gmk.center/en/news/ostchem-plants-produced-520-6-thousand-tons-of-fertilizers-in-q1/; http://ostchem.com/en/o-kompanii/proizvodstvo/azot",
+"DneproAZOT","660",,,,,,,"Ukraine","48.49004","34.66667","28-08-2024","https://www.alfalaval.com/media/stories/fertilizers/increased-plant-capacity-and-reduced-maintenance-at-fertilizer-plant/",

data/cement-plants-noneu.csv

@@ -0,0 +1,26 @@
+"Company","Site","Cement [kt/a]","Country","Latitude","Longitude","Date","Source","Comment"
+"Titan Cementarnica USJE AD","Skopje",910,"MK",41.96816,21.45604,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Colacem Albania Sh.p.k","Balldre plant, Lezhe",500,"AL",41.83689,19.63339,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Fushe Kruje Cement Factory, Sh.p.k.","Elbasan cement plant",300,"AL",41.12184,20.04347,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Fushe Kruje Cement Factory, Sh.p.k.","Fushe Kruje plant",1330,"AL",40.46403,19.49948,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Antea Cement Sh.A","Boka e Kuqe, Burizane",1500,"AL",41.54838,19.72574,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Beocinska Fabrika Cementa","Beocin",2000,"RS",45.20952,19.71043,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Holcim (Srbija) a.d.","Novi Popovac",1400,"RS",43.90633,21.50378,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Titan Cementara Kosjerić Ltd.","Kosjeric",750,"RS",44.0125,19.88831,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Sharrcem Sh.p.k.","Hani i Elezit,",850,"XK",42.14704,21.29863,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Fabrika Cementa Lukavac d.d.","Lukavac",800,"BA",44.52596,18.52811,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Tvornica Cementa Kakanj d.d. ","Kakanj",400,"BA",44.11922,18.10897,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"Lafarge Ciment Moldova SA","Rezina",1400,"MD",47.79094,28.9553,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"ZAO Rybnitsa Cement Comple","Rybnitsa",1100,"MD",47.78115,29.02078,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"ChAO Nikolayevtsemen","Mykolayiv",900,"UA",46.94555,32.06641,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information; https://cemark.ua/en/zavodi/prat-mikolajivcement ",
+"OAO KyivCement ","Kyiv",175,"UA",50.38525,30.55205,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information https://www.dyckerhoff.com.ua/en/dyckerhoff-cement-ukraine ",
+"OAO YuGCement ","Mykolayiv",1250,"UA",46.98512,31.9317,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information, https://www.dyckerhoff.com.ua/en/yug-cemen",
+"OOO Cement","Odessa",550,"UA",46.50818,30.67422,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information https://se.ua/en/projects/llc-cement-odessa-cement-plant/ ",
+"Overin Ltd","Amvrosievka",800,"UA",47.83453,38.46961,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information","assumed equal split"
+"Overin Ltd","Kamenskoye",800,"UA",48.51783,34.63382,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information","assumed equal split"
+"Overin Ltd","Kriviy Rih",800,"UA",47.87454,33.43586,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information","assumed equal split"
+"PAO Eurocement Ukraine","Balakleya, Kharkivs'ka",1000,"UA",49.49314,36.74926,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information https://www.globalcement.com/news/item/13733-update-on-ukraine-february-2022 ",
+"PAO Ivano-Frankovsktsement","Ivano-Frankivsk ",3600,"UA",48.97709,24.71227,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information",
+"PAO Kramatorkiy Tsementnyi Zavod PUSHKA ","Kramatorsk, Donets'ka",138,"UA",48.72802,37.54362,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information  https://www.cemnet.com/News/story/144668/pushka-ups-cement-production-56-6p-per-cent-to138-400t-in-q1.html ",
+"PAO Podolsk Cement","Khmel'nyts'ka Oblast' ",2050,"UA",48.7461,26.64664,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information https://cemark.ua/en/zavodi/at-podilskiy-cement ",
+"PAO Volyn'Cement","Volyn'ska Oblast'",2000,"UA",50.54731,26.25774,"28-08-2024","https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information https://www.dyckerhoff.com.ua/volyn-cement ",

data/district_heat_share.csv

@@ -25,7 +25,7 @@ SE,50.4,
 GB,2,
 BY,70,
 EE,52,5406
-KO,3,207
+XK,3,207
 RO,23,9962
 SK,54,15000
 NL,4,9800

data/era5-annual-HDD-per-country.csv

@@ -29,6 +29,7 @@ PL,1615,1584,1275,1356,1364,1378,1517,1290,1230,1296,1249,1438,1294,1478,1446,15
 PT,114,114,102,113,86,110,95,79,94,96,107,103,99,109,80,131,106,93,93,97,78,106,104,115,103,98,103,97,112,110,115,116,106,109,109,109,99,139,107,102,89,90,95,104,99,108,85,89,75,87,104,96,104,85,66,88,62,82,96,90,90,78,87,97,103,90,91,91,84,96,80,101,100,81,79,87,79,96,81,72,78,98,75
 RO,931,939,801,813,874,851,839,815,799,780,736,811,883,948,822,968,810,789,826,731,744,826,890,867,873,706,791,776,858,792,787,798,838,782,752,855,772,836,780,880,801,811,763,807,917,815,882,827,710,700,846,801,848,701,806,869,842,805,740,710,760,721,844,754,799,780,684,695,695,757,793,781,709,662,681,721,720,690,624,648,740,973,611
 RS,292,305,246,274,285,276,265,263,261,239,222,252,273,300,249,313,248,249,254,231,226,270,283,271,274,233,253,243,267,254,251,246,272,242,243,265,233,265,241,273,251,255,244,253,275,256,268,254,231,220,275,243,263,223,250,270,259,250,237,217,234,217,262,238,265,247,217,214,222,229,251,244,214,192,222,223,232,211,197,212,234,306,192
+XK,292,305,246,274,285,276,265,263,261,239,222,252,273,300,249,313,248,249,254,231,226,270,283,271,274,233,253,243,267,254,251,246,272,242,243,265,233,265,241,273,251,255,244,253,275,256,268,254,231,220,275,243,263,223,250,270,259,250,237,217,234,217,262,238,265,247,217,214,222,229,251,244,214,192,222,223,232,211,197,212,234,306,192
 SK,221,219,199,207,209,201,212,202,189,189,178,210,195,217,208,228,192,193,185,185,178,208,213,211,219,179,187,192,203,200,192,188,195,178,178,197,188,204,193,215,191,190,180,193,212,198,207,187,172,173,202,186,191,171,186,204,193,184,177,162,184,174,190,184,191,182,163,161,170,188,175,179,174,142,161,170,176,157,152,158,180,231,150
 SI,81,77,66,74,72,71,72,69,61,65,61,72,67,73,69,78,65,66,62,61,59,75,75,73,73,62,65,67,71,69,67,68,71,62,61,69,62,71,68,74,67,65,65,69,73,71,71,63,59,58,70,61,63,55,61,71,63,62,62,54,59,55,65,63,67,61,54,57,57,65,58,59,59,46,55,57,58,53,52,53,61,77,51
 SE,4509,4537,3713,3939,4134,4059,4374,3918,3633,4015,3891,4219,3560,3919,4426,4488,3950,4223,3662,3988,3814,4451,4260,4021,4358,4613,3929,4280,4255,4254,4043,3806,3975,3634,3625,4238,4132,4314,4246,4287,4301,3913,3840,3819,4588,4139,4376,3931,3476,3446,3785,3695,3893,3991,3916,4073,3757,3950,3781,3446,3898,3778,3755,3769,3632,3561,3606,3590,3806,4397,3474,3935,3675,3452,3421,3635,3693,3705,3689,3247,3807,5084,3769

data/era5-annual-runoff-per-country.csv

@@ -29,6 +29,7 @@ PL,19040.141287526196,13600.003649707409,4232.940272903036,6263.550300160282,117
 PT,17389.910347609384,7956.13238827081,8339.728858321132,2075.556471824434,3081.9400195534536,5428.965709165979,13519.093138173732,8266.183051247071,2305.611210847845,4751.414112923152,8372.129135066036,5969.31083523099,2397.6586063764958,3916.9962424030286,9654.699566273128,9603.363148222781,4163.162870337118,9179.465624121007,12131.854156488425,23406.646477036124,10635.077955993413,11029.404714084018,16475.917880350513,12087.55769685171,9804.492049893286,26178.184180984652,5669.820254013018,7559.528320921835,15405.930172416956,11163.387795923936,7088.025310785357,10191.330496756716,7128.069257230898,9335.631563569985,5236.13128329125,4572.251664471752,32605.577182903042,149323.86523586328,34294.81803755196,6061.11721308257,5836.654497517657,4843.7020037722705,7814.782411245904,9395.759000250582,14057.157961109715,8192.288977642056,7655.760207241933,9203.645337709624,7637.7450966202805,7971.812432879156,7421.37678917759,2935.2871895485287,6329.939163930326,8820.796560748415,8944.109217212255,14719.27363438586,11050.779819799696,8379.75073967695,5590.525909815882,10485.131539563748,20476.028147427794,7715.162648805314,11939.81827695821,5116.2596877306005,3047.0903835389504,9273.489011971804,5135.313604526994,3331.418010762715,6338.048693933388,12105.616460835923,7097.468638389797,3348.213951094401,11773.920694867731,13465.586888738457,4379.602415677836,11394.463621230614,3104.8789319036796,7216.150544702967,6653.765864361187,5720.580737490315,6685.493238874142,8486.079863634486,8329.192253601708
 RO,46545.50823036229,35517.718755954746,12007.97895282227,19448.903832688356,20440.973051620913,15132.394486734484,21523.767710605425,32075.68337603852,20312.088233477134,14653.654680967496,19346.344374357534,19295.50358149612,27702.617554877004,24644.67023787785,52797.882559506776,42262.7162454671,34689.01143849651,42583.74114324521,28973.147767939397,36882.64868179707,29063.471368254675,39575.626278602576,31898.858414072816,36283.98261870408,42207.50410750205,41714.957670180156,43270.313090823794,40474.54716426176,53565.91349208634,74865.02611452111,48190.11154819992,57257.26135564704,46523.80416861758,44300.83791323246,54901.1701096736,47361.27151969814,49156.09266449866,56461.30009557872,56062.45724028393,58506.511677323855,48499.732173446595,45901.64074770707,33171.12855766741,38310.49272614435,39377.41586450999,28638.803870927226,29424.553978129992,39429.14347226504,39300.73151290347,27525.04968129815,47095.77682827757,30948.90232192865,32939.14262154816,30155.99148400138,35067.04977301419,35592.25066656211,45790.67404045973,42596.77718301701,39223.35618808953,25767.93476452563,30963.38609617001,30034.908201595936,23323.05819720473,28517.276888551194,41560.85308154171,43999.15897330077,21427.19650317167,25084.131072037453,18450.764512377853,38182.39266803458,20422.517191285966,14090.243196787245,21998.55223596247,30809.187151501046,22071.90474198888,31906.454754357772,20763.073093214778,25620.747974742615,19248.368008063957,23289.011422937554,26669.846131314298,27207.74766143675,29386.08741874402
 RS,12898.821957973714,11760.375115749226,5762.823681912337,10868.645457667955,6851.647476795101,6489.091511565737,8016.427124526303,11096.059611708006,8784.092785314235,6844.044739629848,5968.673897869134,7450.873145908507,8457.218084424714,10466.134979346216,18700.932768467817,18783.264457747777,9647.157790116991,14650.396388121844,12360.14810912279,11640.673528541236,8882.446980807292,14762.619600006037,18399.930168952076,13646.806400326906,14489.119148714197,14104.804002759012,15462.34970923992,10894.228431882033,15095.088711819322,17853.281396922546,12916.46431873845,14207.379133135786,14418.834370720482,12752.5957180177,15544.588217112372,15820.904121022999,13534.055257847373,15215.146639151637,12996.626380040323,13029.919161637963,10196.668003968522,10284.440766030819,7022.693160251435,8401.249663323453,8435.803252725058,10265.915943082338,8623.546341166757,7782.429841191159,9309.259412625106,4849.32865233776,9480.40891035497,8237.330612702019,6688.9341497267815,7113.796951355988,8945.392918392941,11391.406447517316,8770.78174795334,7797.074404297926,11205.567652241964,7490.638744688466,6890.626375097134,8721.152535625733,6986.899718851523,10014.515393386338,11863.685949406314,12576.12226693591,6930.903954576986,6933.68818306879,8436.351253425808,11680.836931112251,5673.257881932308,4512.930178739657,6094.8735580904795,11616.485592765608,8423.704814584986,9582.260510301834,6280.960312130538,10196.683253538626,5869.428152205924,6637.126459584064,8223.324863109141,9163.49600405833,11744.973584261521
+XK,12898.821957973714,11760.375115749226,5762.823681912337,10868.645457667955,6851.647476795101,6489.091511565737,8016.427124526303,11096.059611708006,8784.092785314235,6844.044739629848,5968.673897869134,7450.873145908507,8457.218084424714,10466.134979346216,18700.932768467817,18783.264457747777,9647.157790116991,14650.396388121844,12360.14810912279,11640.673528541236,8882.446980807292,14762.619600006037,18399.930168952076,13646.806400326906,14489.119148714197,14104.804002759012,15462.34970923992,10894.228431882033,15095.088711819322,17853.281396922546,12916.46431873845,14207.379133135786,14418.834370720482,12752.5957180177,15544.588217112372,15820.904121022999,13534.055257847373,15215.146639151637,12996.626380040323,13029.919161637963,10196.668003968522,10284.440766030819,7022.693160251435,8401.249663323453,8435.803252725058,10265.915943082338,8623.546341166757,7782.429841191159,9309.259412625106,4849.32865233776,9480.40891035497,8237.330612702019,6688.9341497267815,7113.796951355988,8945.392918392941,11391.406447517316,8770.78174795334,7797.074404297926,11205.567652241964,7490.638744688466,6890.626375097134,8721.152535625733,6986.899718851523,10014.515393386338,11863.685949406314,12576.12226693591,6930.903954576986,6933.68818306879,8436.351253425808,11680.836931112251,5673.257881932308,4512.930178739657,6094.8735580904795,11616.485592765608,8423.704814584986,9582.260510301834,6280.960312130538,10196.683253538626,5869.428152205924,6637.126459584064,8223.324863109141,9163.49600405833,11744.973584261521
 SK,10526.703178382486,6581.841291454129,2779.6437301689157,6043.293021386589,8068.711762338472,4716.572547513843,4263.788112757618,7436.446538154413,5733.7942336519345,5091.94458281141,6885.47211985414,6956.591212284293,7116.928783228905,4573.269245439668,8169.130903254093,6799.092645889461,7190.199390340817,8819.311158887818,6679.080394471699,8681.76846617681,6714.688996801623,8780.135433744465,9674.052829336924,7510.89080672348,12315.672504913706,11551.288938768861,10658.095118642217,7760.467099195778,7334.701885838525,11743.053621084162,9101.455526104724,11252.254293590808,7089.2297957961755,11692.575497480175,11870.620362608648,8320.411050776946,12664.612697391287,10840.053026355938,9544.145158945727,8285.851845920657,6554.955003499832,6014.385824757616,5119.229272436076,5566.197178944401,8283.751441606055,4576.739002618681,5529.727054286627,4884.285979430572,7030.767074231632,6380.185010419688,6092.06334940043,4789.6919845964085,3988.728835990378,6100.445127127096,6174.336930810738,5839.492530664778,6739.897695678873,6073.547495791424,6239.663814901599,5985.626769812626,5946.910319392802,5350.4706690989715,2897.742723724252,3812.447551889599,5110.734824948982,5466.7571969447445,3822.1224333717755,4189.745228198809,3608.9397299109723,8925.718578619328,5520.297117401385,2338.023634704523,3634.014208205775,5079.021357670912,3789.3939110245783,3599.858750509022,4222.707040620564,3096.1701009538024,2158.8092391216674,4542.203594136759,4220.210061470592,2961.7796555712353,3705.0055429796116
 SI,8932.252857660058,6769.672897729603,3971.443731895907,6727.0196008878875,5996.060296331706,4552.96378417224,7787.375795346372,9548.071392837826,4890.204133819321,7094.028640574649,11752.798116380785,9378.602450871665,7169.30972105447,8221.267552376507,10568.142173580174,7705.119699907013,8559.440940655051,9496.2682906945,10641.418485889739,13644.09405840664,8756.865265119286,10877.994032024846,13095.473005855994,10408.222250010325,17110.767148034232,13257.44359305617,10163.420442559087,10743.809603872312,11213.570708968457,12419.028940460184,8518.188189494229,14975.496301661744,10217.449212514946,10521.007791805137,10759.209828867228,9699.803414833696,14530.647692616923,14530.715084460895,12439.143395173185,11990.046930204517,9001.647305603035,10111.423546501868,6824.172499114327,10267.968760960974,10233.709492652128,9562.175707725224,11231.941186997657,8178.692540046128,8568.526412713521,8103.883097000008,9639.895318751553,9157.43571368136,6814.50158997294,8089.913064615838,8170.645053083449,10765.044719853075,6713.170108103339,8936.8362631332,9542.147586182518,8964.110093884974,8653.114721452737,8353.75428643612,4558.589088593759,9432.405903987477,6927.92152111369,7408.760392367348,5834.871486466167,9336.912851251047,8839.494837985663,10766.693939731143,5073.937522450624,6475.598951785893,9482.419717110955,13414.771212079502,5906.5584945059145,9942.151265372298,7696.092255607837,9521.835825354157,8476.124990692872,6361.010018809483,7504.908031281684,6349.444363482254,12494.86342976942
 SE,97729.33612275522,104893.13349064675,152164.30605691054,147567.81837152236,172633.569885874,142454.06079467706,109264.12507808255,145966.80578917114,158733.59492028368,161670.6171803483,134822.12256461562,136958.6213484757,175679.6273876132,133962.36617697845,126787.36233921513,130861.49290945902,151133.2982760823,140247.52694114187,130508.52271890236,137421.63579450184,164132.0529341604,142467.57286064525,135886.87305352974,163762.3962091317,161128.38161511483,148196.28633931337,167892.87677861055,132017.696130452,105953.99289036485,119407.22923207263,147139.6039546916,151309.55114963037,164893.61323375857,137608.18237925606,149284.78771947857,108749.15747050248,131859.531763871,117290.48017785426,131751.97960784068,111117.27409251402,110551.615114603,81550.1135393761,114677.940027603,102356.46697333359,128977.80283219383,94483.98267503879,114937.96457142044,93256.91327642264,96751.29026217168,91074.34139788701,87931.96567208324,96560.83822983531,116746.94389793588,64626.25905820224,75156.91767736348,51489.37512699482,74729.26978432719,134673.01513808029,110440.65771030058,149033.9827088587,143576.83069821307,93040.02354987273,64075.160200006496,102493.08906248632,106664.82884715112,90351.43771324471,94801.81222609026,92445.93458012305,88919.20770998915,108111.72872590217,127440.80298482065,124444.06618883039,98961.91573013023,87913.90602563386,115956.48217115598,95526.84265190935,103459.08985036932,86058.90377748368,99040.9103276256,110493.22773914765,94182.7249029169,169752.66741900297,99896.09236620825

data/existing_infrastructure/existing_heating_raw.csv

@@ -28,4 +28,5 @@ United Kingdom,160.49,1.26,7.39,13.81,0.81,0.21
 Norway,,,,,2.91,0.334
 Switzerland,,,,,1,0.849
 Serbia,,,,,,
+Kosovo,,,,,,
 Bosnia Herzegovina,,,,,,

data/geth2015_hydro_capacities.csv

@@ -1,32 +0,0 @@
-# Table 25 from F. Geth et al., An overview of large-scale stationary electricity storage plants in Europe  (2015) 1212–1227
-country,n,p_nom_discharge,p_nom_charge,e_stor
-AT,19,4.051,3.246,132.41
-BE,3,1.301,1.196,5.71
-BG,3,1.399,0.93,11.13
-HR,3,0.281,0.246,2.34
-CY,0,-,-,-
-CZ,3,1.119,1.145,5.72
-DK,0,-,-,-
-EE,0,-,-,-
-FI     ,0,-,-,-
-FR,10,5.512,4.317,83.37
-DE,34,6.805,6.417,39.12
-GR,2,0.735,-,4.97
-HU,0,-,-,-
-IE,1,0.292,-,1.8
-IT,25,7.833,7.64,68.27
-LV,0,-,-,-
-LT,1,0.9,0.88,10.8
-LU,1,1.296,1.05,4.92
-MT,0,-,-,-
-NL,0,-,-,-
-PL,6,1.757,1.647,7.96
-PT,7,1.279,-,40.77
-RO,5,0.285,0.2,10.2
-SK,4,1.016,0.79,3.63
-SI,1,0.185,0.18,0.5
-ES,26,6.358,5.859,70
-SE,2,0.091,-,72.12
-GB,4,2.788,2.65,26.7
-NO,8,1.273,0.892,399.39
-CH,20,2.291,1.512,311.48

data/nuclear_p_max_pu.csv

@@ -1,16 +1,17 @@
 country,factor
-BE,0.796
-BG,0.894
-CZ,0.827
-FI,0.936
-FR,0.71
-DE,0.871
-HU,0.913
-NL,0.868
-RO,0.909
-SK,0.9
-SI,0.913
-ES,0.897
-SE,0.851
-CH,0.87
-GB,0.656
+BE,0.883
+BG,0.876
+CZ,0.839
+FI,0.924
+FR,0.616
+DE,0.926
+HU,0.891
+NL,0.901
+RO,0.906
+SK,0.908
+SI,0.884
+ES,0.883
+SE,0.817
+CH,0.834
+GB,0.684
+UA,0.701

data/refineries-noneu.csv

@@ -0,0 +1,15 @@
+"Site","Capacity [bbl/day]","Country","Latitude","Longitude","Date","Source","Comment"
+"Modrica",1,"BA",44.95978,18.31294,"29-08-2024","https://modricaoil.com/Content/Read/onama?lang=en-US","the only one"
+"OKTA, Skopje",1,"MK",42.0022,21.65532,"29-08-2024","https://en.wikipedia.org/wiki/OKTA","the only one"
+"Ballsh",20000,"AL",40.60468,19.75231,"29-08-2024","https://www.trade.gov/country-commercial-guides/albania-oil-and-gas",
+"Fier",10000,"AL",40.70095,19.54698,"29-08-2024","https://www.trade.gov/country-commercial-guides/albania-oil-and-gas",
+"Elbasan",5000,"AL",41.08621,20.00762,"29-08-2024","https://www.trade.gov/country-commercial-guides/albania-oil-and-gas",
+"Pančevo",96000,"RS",44.83051,20.68127,"29-08-2024","https://en.wikipedia.org/wiki/Naftna_Industrija_Srbije",
+"Novi Sad",52000,"RS",45.27559,19.86733,"29-08-2024","https://en.wikipedia.org/wiki/Naftna_Industrija_Srbije",
+"Halychyna Refinery (Pryvat), Drohobych",40000,"UA",49.34,23.4851,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries",
+"Kherson Refinery (Alliance)",36000,"UA",46.65946,32.57479,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries",
+"Kremenchuk Refinery (Ukrtatnafta)",368500,"UA",49.15299,33.4327,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries",
+"LINOS Refinery, Lysychansk Oil Refinery (TNK-BP)",320000,"UA",48.84876,38.29979,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries",
+"Lviv Oil Research & Refinery",18000,"UA",49.80662,24.04376,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries","capacity estimated based on number of employees"
+"Naftokhimik Prykarpattya (Pryvat) Nadvirna",39000,"UA",48.62873,24.59924,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries",
+"Odesa Refinery (LUKOIL)",70000,"UA",46.51189,30.68805,"29-08-2024","https://en.wikipedia.org/wiki/List_of_oil_refineries",

data/urban_percent.csv

@@ -1,30 +0,0 @@
-AT,66
-BA,40
-BE,98
-BG,74
-CH,74
-CZ,73
-DE,75
-DK,88
-EE,68
-ES,80
-FI,84
-FR,80
-GB,83
-GR,78
-HR,59
-HU,71
-IE,63
-IT,69
-LT,67
-LU,90
-LV,67
-NL,90
-NO,80
-PL,61
-PT,63
-RO,55
-RS,56
-SE,86
-SI,50
-SK,54

doc/configtables/countries.csv

@@ -1,2 +1,2 @@
  ,Unit,Values,Description
-countries,--,"Subset of {'AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK'}","European countries defined by their `Two-letter country codes (ISO 3166-1) <https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2>`_ which should be included in the energy system model."
+countries,--,"Subset of {'AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK', 'XK'}","European countries defined by their `Two-letter country codes (ISO 3166-1) <https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2>`_ which should be included in the energy system model."

doc/configtables/licenses-sector.csv

@@ -1,6 +1,5 @@
 description,file/folder,licence,source
 JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
-urban/rural fraction,urban_percent.csv,unknown,unknown
 JRC ENSPRESO biomass potentials,remote,CC BY 4.0,https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f
 EEA emission statistics,eea/UNFCCC_v23.csv,EEA standard re-use policy,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
 Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances

doc/configtables/sector.csv

@@ -9,8 +9,13 @@ district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-
 -- potential,--,float,maximum fraction of urban demand which can be supplied by district heating
 -- progress,--,Dictionary with planning horizons as keys., Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating
 -- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses
--- forward_temperature,°C,Dictionary with country codes as keys. One key must be 'default'.,Forward temperature in district heating
--- return_temperature,°C,Dictionary with country codes as keys. One key must be 'default'.,Return temperature in district heating. Must be lower than forward temperature
+-- supply_temperature_approximation,,,
+-- -- max_forward_temperature,°C,Dictionary with country codes as keys. One key must be 'default'., Max. forward temperature in district heating (if ambient temperature lower-or-equal `lower_threshold_ambient_temperature`)
+-- -- min_forward_temperature,°C,Dictionary with country codes as keys. One key must be 'default'., Min. forward temperature in district heating (if ambient temperature higher-or-equal `upper_threshold_ambient_temperature`)
+-- -- return_temperature,°C,Dictionary with country codes as keys. One key must be 'default'.,Return temperature in district heating. Must be lower than forward temperature
+-- -- lower_threshold_ambient_temperature,°C,float, Assume `max_forward_temperature` if ambient temperature is below this threshold
+-- -- upper_threshold_ambient_temperature,°C,float, Assume `min_forward_temperature` if ambient temperature is above this threshold
+-- -- rolling_window_ambient_temperature, h, int, Rolling window size for averaging ambient temperature when approximating supply temperature
 -- heat_source_cooling,K,float,Cooling of heat source for heat pumps
 -- heat_pump_cop_approximation,,,
 -- -- refrigerant,--,"{ammonia, isobutane}",Heat pump refrigerant assumed for COP approximation

doc/release_notes.rst

@@ -1,3 +1,4 @@
+
 ..
   SPDX-FileCopyrightText: 2019-2024 The PyPSA-Eur Authors
 
@@ -11,6 +12,46 @@ Upcoming Release
 ================
 * Add technology options for methanol, like electricity production from methanol, biomass to methanol, methanol to kerosene, ...
 
+* Bugfix for passing function arguments in rule :mod:`solve_operations_network`.
+
+* Represent Kosovo (XK) as separate country.
+
+* Added data on the locations and capacities of ammonia plants in Europe.
+  This data is used as a spatial distribution key for the ammonia demand.
+  The data manually collected with sources noted in ``data/ammonia_plants.csv``.
+
+* Added data on the locations and capacities of cement plants in Europe that are
+  not included in the Hotmaps industrial database. The data sourced from the
+  `USGS 2019 Minerals Yearbooks
+  <https://www.usgs.gov/centers/national-minerals-information-center/international-minerals-statistics-and-information>`__
+  of specific countries is used as a spatial distribution key for the cement
+  demand. The data is stored in ``data/cement-plants-noneu.csv``.
+
+* Added data on the locations and capacities of refineries in Europe that are
+  not included in the Hotmaps industrial database. The data is mostly sourced
+  from the `Wikipedia list of oil refineries
+  <https://en.wikipedia.org/wiki/List_of_oil_refineries>`__. The data is stored
+  in ``data/refineries-noneu.csv``.
+
+* Included data from the `Global Steel Plant Tracker
+  <https://globalenergymonitor.org/projects/global-steel-plant-tracker/>`__
+  provided by Global Energy Monitor. The data includes among other attributes
+  the locations, ages, operating status, relining dates, manufacturing process
+  and capacities of steel plants in Europe. This data is used as a spatial
+  distribution key for the steel production, which is now separated by process
+  type (EAF, DRI + EAF, integrated).
+
+* Retrieve share of urban population from `World Bank API
+  <https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS>`__. The data
+  originates from the United Nations Population Division. Previously, a file
+  ``data/urban_percent.csv`` with an undocumented source was used.
+
+* Updated country-specific Energy Availability Factors (EAFs) for nuclear power
+  plants based on `IAEA 2021-2023 reported country averages
+  <https://pris.iaea.org/PRIS/WorldStatistics/ThreeYrsEnergyAvailabilityFactor.aspx>`__.
+
+* Update GEM Europe Gas Tracker to May 2024 version.
+
 * Add investment period dependent CO2 sequestration potentials
 
 * Add option to produce hydrogen from solid biomass (flag ``solid biomass to hydrogen``), combined with carbon capture
@@ -23,6 +64,8 @@ Upcoming Release
 
 * Update JRC-IDEES-2015 to `JRC-IDEES-2021 <https://publications.jrc.ec.europa.eu/repository/handle/JRC137809>`__. The reference year is changed from 2015 to 2019.
 
+* Made central heating supply temperatures dynamic based on an adaptation of a reference curve from Pieper et al. (2019) (https://www.sciencedirect.com/science/article/pii/S0360544219305857?via%3Dihub).
+
 * Added option to use country-specific district heating forward and return temperatures. Defaults to lower temperatures in Scandinavia.
 
 * Added unsustainable biomass potentials for solid, gaseous, and liquid biomass. The potentials can be phased-out and/or
@@ -76,7 +119,7 @@ Upcoming Release
 
 * Enable parallelism in :mod:`determine_availability_matrix_MD_UA.py` and remove plots. This requires the use of temporary files.
 
-* Added new major feature to create the base_network from OpenStreetMap (OSM) data (PR https://github.com/PyPSA/pypsa-eur/pull/1079). Note that a heuristics based cleaning process is used for lines and links where electrical parameters are incomplete, missing, or ambiguous. Through ``electricity["base_network"]``, the base network can be set to "entsoegridkit" (original default setting, deprecated soon), "osm-prebuilt" (which downloads the latest prebuilt snapshot based on OSM data from Zenodo), or "osm-raw" which retrieves (once) and cleans the raw OSM data and subsequently builds the network. Note that this process may take a few minutes.
+* Added new major feature to create the base_network from OpenStreetMap (OSM) data (PR https://github.com/PyPSA/pypsa-eur/pull/1079). Note that a heuristics based cleaning process is used for lines and links where electrical parameters are incomplete, missing, or ambiguous. Through ``electricity["base_network"]``, the base network can be set to "entsoegridkit" (now deprecated), "osm-prebuilt" (default, downloads the latest prebuilt snapshot based on OSM data from Zenodo), or "osm-raw" which retrieves (once) and cleans the raw OSM data and subsequently builds the network. Note that this process may take a few minutes.
 
 * Updated pre-built `weather data cutouts
   <https://zenodo.org/records/12791128>`__. These are now merged cutouts with

rules/build_electricity.smk

@@ -373,7 +373,7 @@ rule build_transmission_projects:
     benchmark:
         benchmarks("build_transmission_projects")
     resources:
-        mem_mb=2000,
+        mem_mb=4000,
     threads: 1
     conda:
         "../envs/environment.yaml"
@@ -469,7 +469,6 @@ rule add_electricity:
         regions=resources("regions_onshore.geojson"),
         powerplants=resources("powerplants.csv"),
         hydro_capacities=ancient("data/hydro_capacities.csv"),
-        geth_hydro_capacities="data/geth2015_hydro_capacities.csv",
         unit_commitment="data/unit_commitment.csv",
         fuel_price=lambda w: (
             resources("monthly_fuel_price.csv")

rules/build_sector.smk

@@ -6,7 +6,7 @@
 rule build_population_layouts:
     input:
         nuts3_shapes=resources("nuts3_shapes.geojson"),
-        urban_percent="data/urban_percent.csv",
+        urban_percent="data/worldbank/API_SP.URB.TOTL.IN.ZS_DS2_en_csv_v2_3403768.csv",
         cutout=lambda w: "cutouts/"
         + CDIR
         + config_provider("atlite", "default_cutout")(w)
@@ -93,10 +93,7 @@ rule build_gas_network:
 
 rule build_gas_input_locations:
     input:
-        gem=storage(
-            "https://globalenergymonitor.org/wp-content/uploads/2023/07/Europe-Gas-Tracker-2023-03-v3.xlsx",
-            keep_local=True,
-        ),
+        gem="data/gem/Europe-Gas-Tracker-2024-05.xlsx",
         entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
         storage="data/gas_network/scigrid-gas/data/IGGIELGN_Storages.geojson",
         regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
@@ -215,17 +212,72 @@ rule build_temperature_profiles:
         "../scripts/build_temperature_profiles.py"
 
 
+rule build_central_heating_temperature_profiles:
+    params:
+        max_forward_temperature_central_heating=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "max_forward_temperature",
+        ),
+        min_forward_temperature_central_heating=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "min_forward_temperature",
+        ),
+        return_temperature_central_heating=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "return_temperature",
+        ),
+        snapshots=config_provider("snapshots"),
+        lower_threshold_ambient_temperature=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "lower_threshold_ambient_temperature",
+        ),
+        upper_threshold_ambient_temperature=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "upper_threshold_ambient_temperature",
+        ),
+        rolling_window_ambient_temperature=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "rolling_window_ambient_temperature",
+        ),
+    input:
+        temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
+        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
+    output:
+        central_heating_forward_temperature_profiles=resources(
+            "central_heating_forward_temperature_profiles_elec_s{simpl}_{clusters}.nc"
+        ),
+        central_heating_return_temperature_profiles=resources(
+            "central_heating_return_temperature_profiles_elec_s{simpl}_{clusters}.nc"
+        ),
+    resources:
+        mem_mb=20000,
+    log:
+        logs("build_central_heating_temperature_profiles_s{simpl}_{clusters}.log"),
+    benchmark:
+        benchmarks("build_central_heating_temperature_profiles/s{simpl}_{clusters}")
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_central_heating_temperature_profiles/run.py"
+
+
 rule build_cop_profiles:
     params:
         heat_pump_sink_T_decentral_heating=config_provider(
             "sector", "heat_pump_sink_T_individual_heating"
         ),
-        forward_temperature_central_heating=config_provider(
-            "sector", "district_heating", "forward_temperature"
-        ),
-        return_temperature_central_heating=config_provider(
-            "sector", "district_heating", "return_temperature"
-        ),
         heat_source_cooling_central_heating=config_provider(
             "sector", "district_heating", "heat_source_cooling"
         ),
@@ -235,6 +287,12 @@ rule build_cop_profiles:
         heat_pump_sources=config_provider("sector", "heat_pump_sources"),
         snapshots=config_provider("snapshots"),
     input:
+        central_heating_forward_temperature_profiles=resources(
+            "central_heating_forward_temperature_profiles_elec_s{simpl}_{clusters}.nc"
+        ),
+        central_heating_return_temperature_profiles=resources(
+            "central_heating_return_temperature_profiles_elec_s{simpl}_{clusters}.nc"
+        ),
         temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
         temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
         regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
@@ -578,10 +636,14 @@ rule build_industrial_distribution_key:
     input:
         regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
         clustered_pop_layout=resources("pop_layout_elec_s{simpl}_{clusters}.csv"),
-        hotmaps_industrial_database=storage(
+        hotmaps=storage(
             "https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database/-/raw/master/data/Industrial_Database.csv",
             keep_local=True,
         ),
+        gem_gspt="data/gem/Global-Steel-Plant-Tracker-April-2024-Standard-Copy-V1.xlsx",
+        ammonia="data/ammonia_plants.csv",
+        cement_supplement="data/cement-plants-noneu.csv",
+        refineries_supplement="data/refineries-noneu.csv",
     output:
         industrial_distribution_key=resources(
             "industrial_distribution_key_elec_s{simpl}_{clusters}.csv"

rules/retrieve.smk

@@ -259,6 +259,30 @@ if config["enable"]["retrieve"]:
 
 
 
+if config["enable"]["retrieve"]:
+
+    rule retrieve_worldbank_urban_population:
+        params:
+            zip="data/worldbank/API_SP.URB.TOTL.IN.ZS_DS2_en_csv_v2_3403768.zip",
+        output:
+            gpkg="data/worldbank/API_SP.URB.TOTL.IN.ZS_DS2_en_csv_v2_3403768.csv",
+        run:
+            import os
+            import requests
+
+            response = requests.get(
+                "https://api.worldbank.org/v2/en/indicator/SP.URB.TOTL.IN.ZS?downloadformat=csv",
+                params={"name": "API_SP.URB.TOTL.IN.ZS_DS2_en_csv_v2_3403768.zip"},
+            )
+
+            with open(params["zip"], "wb") as f:
+                f.write(response.content)
+            output_folder = Path(params["zip"]).parent
+            unpack_archive(params["zip"], output_folder)
+            os.remove(params["zip"])
+
+
+
 if config["enable"]["retrieve"]:
 
     # Download directly from naciscdn.org which is a redirect from naturalearth.com
@@ -280,6 +304,40 @@ if config["enable"]["retrieve"]:
             os.remove(params["zip"])
 
 
+if config["enable"]["retrieve"]:
+
+    rule retrieve_gem_europe_gas_tracker:
+        output:
+            "data/gem/Europe-Gas-Tracker-2024-05.xlsx",
+        run:
+            import requests
+
+            response = requests.get(
+                "https://globalenergymonitor.org/wp-content/uploads/2024/05/Europe-Gas-Tracker-2024-05.xlsx",
+                headers={"User-Agent": "Mozilla/5.0"},
+            )
+            with open(output[0], "wb") as f:
+                f.write(response.content)
+
+
+
+if config["enable"]["retrieve"]:
+
+    rule retrieve_gem_steel_plant_tracker:
+        output:
+            "data/gem/Global-Steel-Plant-Tracker-April-2024-Standard-Copy-V1.xlsx",
+        run:
+            import requests
+
+            response = requests.get(
+                "https://globalenergymonitor.org/wp-content/uploads/2024/04/Global-Steel-Plant-Tracker-April-2024-Standard-Copy-V1.xlsx",
+                headers={"User-Agent": "Mozilla/5.0"},
+            )
+            with open(output[0], "wb") as f:
+                f.write(response.content)
+
+
+
 if config["enable"]["retrieve"]:
     # Some logic to find the correct file URL
     # Sometimes files are released delayed or ahead of schedule, check which file is currently available

rules/solve_electricity.smk

@@ -41,6 +41,13 @@ rule solve_network:
 rule solve_operations_network:
     params:
         options=config_provider("solving", "options"),
+        solving=config_provider("solving"),
+        foresight=config_provider("foresight"),
+        planning_horizons=config_provider("scenario", "planning_horizons"),
+        co2_sequestration_potential=config_provider(
+            "sector", "co2_sequestration_potential", default=200
+        ),
+        custom_extra_functionality=input_custom_extra_functionality,
     input:
         network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
     output:

scripts/_helpers.py

@@ -507,7 +507,8 @@ def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
     week_df = pd.DataFrame(index=dt_index, columns=nodes)
 
     for node in nodes:
-        timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
+        ct = node[:2] if node[:2] != "XK" else "RS"
+        timezone = pytz.timezone(pytz.country_timezones[ct][0])
         tz_dt_index = dt_index.tz_convert(timezone)
         week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
         week_df[node] = week_df[node].map(weekly_profile)

scripts/add_electricity.py

@@ -51,7 +51,6 @@ Inputs
     .. image:: img/hydrocapacities.png
         :scale: 34 %
 
-- ``data/geth2015_hydro_capacities.csv``: alternative to capacities above; not currently used!
 - ``resources/electricity_demand.csv`` Hourly per-country electricity demand profiles.
 - ``resources/regions_onshore.geojson``: confer :ref:`busregions`
 - ``resources/nuts3_shapes.geojson``: confer :ref:`shapes`

scripts/base_network.py

@@ -1008,12 +1008,12 @@ if __name__ == "__main__":
     )
 
     shapes.to_file(snakemake.output.regions_onshore)
-    append_bus_shapes(n, shapes, "onshore")
+    # append_bus_shapes(n, shapes, "onshore")
 
     if offshore_regions:
         shapes = pd.concat(offshore_regions, ignore_index=True)
         shapes.to_file(snakemake.output.regions_offshore)
-        append_bus_shapes(n, shapes, "offshore")
+        # append_bus_shapes(n, shapes, "offshore")
     else:
         offshore_shapes.to_frame().to_file(snakemake.output.regions_offshore)
 

scripts/build_biomass_potentials.py

@@ -88,9 +88,11 @@ def build_nuts_population_data(year=2013):
     pop = pd.concat([pop, pd.concat(swiss)]).to_frame("total")
 
     # add missing manually
-    pop["AL"] = 2893
-    pop["BA"] = 3871
-    pop["RS"] = 7210
+    pop["AL"] = 2778
+    pop["BA"] = 3234
+    pop["RS"] = 6664
+    pop["ME"] = 617
+    pop["XK"] = 1587
 
     pop["ct"] = pop.index.str[:2]
 
@@ -149,10 +151,6 @@ def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
 
     bio = dff.groupby(["NUTS2", "commodity"]).potential.sum().unstack()
 
-    # currently Serbia and Kosovo not split, so aggregate
-    bio.loc["RS"] += bio.loc["XK"]
-    bio.drop("XK", inplace=True)
-
     return bio
 
 
@@ -199,7 +197,7 @@ def build_nuts2_shapes():
     )
 
     countries = gpd.read_file(snakemake.input.country_shapes).set_index("name")
-    missing_iso2 = countries.index.intersection(["AL", "RS", "BA"])
+    missing_iso2 = countries.index.intersection(["AL", "RS", "XK", "BA"])
     missing = countries.loc[missing_iso2]
 
     nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)

scripts/build_central_heating_temperature_profiles/central_heating_temperature_approximator.py

@@ -0,0 +1,218 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+
+import pandas as pd
+import xarray as xr
+
+
+class CentralHeatingTemperatureApproximator:
+    """
+    A class to approximate central heating temperatures based on ambient
+    temperature.
+
+    Attributes
+    ----------
+    ambient_temperature : xr.DataArray
+        The ambient temperature data.
+    max_forward_temperature : xr.DataArray
+        The maximum forward temperature.
+    min_forward_temperature : xr.DataArray
+        The minimum forward temperature.
+    fixed_return_temperature : xr.DataArray
+        The fixed return temperature.
+    lower_threshold_ambient_temperature : float
+        Forward temperature is `max_forward_temperature` for ambient temperatures lower-or-equal this threshold.
+    upper_threshold_ambient_temperature : float
+        Forward temperature is `min_forward_temperature` for ambient temperatures higher-or-equal this threshold.
+    """
+
+    def __init__(
+        self,
+        ambient_temperature: xr.DataArray,
+        max_forward_temperature: float,
+        min_forward_temperature: float,
+        fixed_return_temperature: float,
+        lower_threshold_ambient_temperature: float,
+        upper_threshold_ambient_temperature: float,
+        rolling_window_ambient_temperature: int,
+    ) -> None:
+        """
+        Initialize the CentralHeatingTemperatureApproximator.
+
+        Parameters
+        ----------
+        ambient_temperature : xr.DataArray
+            The ambient temperature data.
+        max_forward_temperature : xr.DataArray
+            The maximum forward temperature.
+        min_forward_temperature : xr.DataArray
+            The minimum forward temperature.
+        fixed_return_temperature : xr.DataArray
+            The fixed return temperature.
+        lower_threshold_ambient_temperature : float
+            Forward temperature is `max_forward_temperature` for ambient temperatures lower-or-equal this threshold.
+        upper_threshold_ambient_temperature : float
+            Forward temperature is `min_forward_temperature` for ambient temperatures higher-or-equal this threshold.
+        rolling_window_ambient_temperature : int
+            Rolling window size for averaging ambient temperature.
+        """
+        self._ambient_temperature = ambient_temperature
+        self.max_forward_temperature = max_forward_temperature
+        self.min_forward_temperature = min_forward_temperature
+        self.fixed_return_temperature = fixed_return_temperature
+        self.lower_threshold_ambient_temperature = lower_threshold_ambient_temperature
+        self.upper_threshold_ambient_temperature = upper_threshold_ambient_temperature
+        self.rolling_window_ambient_temperature = rolling_window_ambient_temperature
+
+    def ambient_temperature_rolling_mean(self) -> xr.DataArray:
+        """
+        Property to get ambient temperature.
+
+        Returns
+        -------
+        xr.DataArray
+            Rolling mean of ambient temperature input.
+        """
+        # bfill to avoid NAs in the beginning
+        return (
+            self._ambient_temperature.rolling(
+                time=self.rolling_window_ambient_temperature
+            )
+            .mean(skip_na=True)
+            .bfill(dim="time")
+        )
+
+    @property
+    def forward_temperature(self) -> xr.DataArray:
+        """
+        Property to get dynamic forward temperature.
+
+        Returns
+        -------
+        xr.DataArray
+            Dynamic forward temperatures
+        """
+        return self._approximate_forward_temperature()
+
+    @property
+    def return_temperature(self) -> float:
+        """
+        Property to get return temperature.
+
+        Returns
+        -------
+        float
+            Return temperature.
+        """
+        return self._approximate_return_temperature()
+
+    def _approximate_forward_temperature(self) -> xr.DataArray:
+        """
+        Approximate dynamic forward temperature based on reference curve. Adapted from [Pieper et al. (2019)](https://doi.org/10.1016/j.energy.2019.03.165).
+
+        Returns
+        -------
+        xr.DataArray
+            Dynamic forward temperatures.
+        """
+
+        forward_temperature = xr.where(
+            self.ambient_temperature_rolling_mean()
+            <= self.lower_threshold_ambient_temperature,
+            self.max_forward_temperature,
+            xr.where(
+                self.ambient_temperature_rolling_mean()
+                >= self.upper_threshold_ambient_temperature,
+                self.min_forward_temperature,
+                self.min_forward_temperature
+                + (self.max_forward_temperature - self.min_forward_temperature)
+                * (
+                    self.upper_threshold_ambient_temperature
+                    - self.ambient_temperature_rolling_mean()
+                )
+                / (
+                    self.upper_threshold_ambient_temperature
+                    - self.lower_threshold_ambient_temperature
+                ),
+            ),
+        )
+        return forward_temperature
+
+    def _approximate_return_temperature(self) -> float:
+        """
+        Approximate return temperature.
+
+        Returns
+        -------
+        float
+            Return temperature.
+        """
+        return self.fixed_return_temperature
+
+    @property
+    def forward_temperature(self) -> xr.DataArray:
+        """
+        Property to get dynamic forward temperature.
+
+        Returns
+        -------
+        xr.DataArray
+            Dynamic forward temperatures.
+        """
+        return self._approximate_forward_temperature()
+
+    @property
+    def return_temperature(self) -> float:
+        """
+        Property to get return temperature.
+
+        Returns
+        -------
+        float
+            Return temperature.
+        """
+        return self._approximate_return_temperature()
+
+    def _approximate_forward_temperature(self) -> xr.DataArray:
+        """
+        Approximate dynamic forward temperature.
+
+        Returns
+        -------
+        xr.DataArray
+            Dynamic forward temperatures.
+        """
+        forward_temperature = xr.where(
+            self.ambient_temperature_rolling_mean()
+            <= self.lower_threshold_ambient_temperature,
+            self.max_forward_temperature,
+            xr.where(
+                self.ambient_temperature_rolling_mean()
+                >= self.upper_threshold_ambient_temperature,
+                self.min_forward_temperature,
+                self.min_forward_temperature
+                + (self.max_forward_temperature - self.min_forward_temperature)
+                * (
+                    self.upper_threshold_ambient_temperature
+                    - self.ambient_temperature_rolling_mean()
+                )
+                / (
+                    self.upper_threshold_ambient_temperature
+                    - self.lower_threshold_ambient_temperature
+                ),
+            ),
+        )
+        return forward_temperature
+
+    def _approximate_return_temperature(self) -> float:
+        """
+        Approximate return temperature.
+
+        Returns
+        -------
+        float
+            Return temperature.
+        """
+        return self.fixed_return_temperature

scripts/build_central_heating_temperature_profiles/run.py

@@ -0,0 +1,147 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Approximate district heating forward and return temperature profiles based on
+ambient temperature. The method is based on a reference curve from Pieper et
+al. 2019, where for ambient temperatures below 0C, the highest possible forward
+temperature is assumed and vice versa for temperatures above 10C. Between these
+threshold levels, forward temperatures are linearly interpolated.
+
+By default, temperature levels are increased for non-Scandinavian countries.
+The default ratios between min. and max. forward temperatures is based on AGFW-Hauptbericht 2022.
+
+Relevant Settings
+-----------------
+
+.. code:: yaml
+    sector:
+        district_heating:
+            max_forward_temperature:
+            min_forward_temperature:
+            return_temperature:
+Inputs
+------
+- `resources/<run_name>/temp_air_total`: Air temperature
+
+Outputs
+-------
+- `resources/<run_name>/central_heating_temperature_profiles.nc`:
+
+References
+----------
+- Pieper, et al. (2019): "Assessment of a combination of three heat sources for heat pumps to supply district heating" (https://doi.org/10.1016/j.energy.2019.03.165).
+- AGFW (2022): "Hauptbericht 2022" (https://www.agfw.de/zahlen-und-statistiken/agfw-hauptbericht)
+"""
+
+import sys
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import xarray as xr
+from _helpers import set_scenario_config
+from central_heating_temperature_approximator import (
+    CentralHeatingTemperatureApproximator,
+)
+
+
+def get_country_from_node_name(node_name: str) -> str:
+    return node_name[:2]
+
+
+def map_temperature_dict_to_onshore_regions(
+    supply_temperature_by_country: dict,
+    regions_onshore: pd.Index,
+    snapshots: pd.DatetimeIndex,
+) -> xr.DataArray:
+    """
+    Map dictionary of temperatures to onshore regions.
+
+    Parameters:
+    ----------
+    supply_temperature_by_country : dictionary
+        Dictionary with temperatures as values and country keys as keys. One key must be named "default"
+    regions_onshore : pd.Index
+        Names of onshore regions
+    snapshots : pd.DatetimeIndex
+        Time stamps
+
+    Returns:
+    -------
+    xr.DataArray
+        The dictionary values mapped to onshore regions with onshore regions as coordinates.
+    """
+    return xr.DataArray(
+        [
+            [
+                (
+                    supply_temperature_by_country[get_country_from_node_name(node_name)]
+                    if get_country_from_node_name(node_name)
+                    in supply_temperature_by_country.keys()
+                    else supply_temperature_by_country["default"]
+                )
+                for node_name in regions_onshore.values
+            ]
+            # pass both nodes and snapshots as dimensions to preserve correct data structure
+            for _ in snapshots
+        ],
+        dims=["time", "name"],
+        coords={"time": snapshots, "name": regions_onshore},
+    )
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_cop_profiles",
+            simpl="",
+            clusters=48,
+        )
+
+    set_scenario_config(snakemake)
+
+    # map forward and return temperatures specified on country-level to onshore regions
+    regions_onshore = gpd.read_file(snakemake.input.regions_onshore)["name"]
+    snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
+    max_forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
+        map_temperature_dict_to_onshore_regions(
+            supply_temperature_by_country=snakemake.params.max_forward_temperature_central_heating,
+            regions_onshore=regions_onshore,
+            snapshots=snapshots,
+        )
+    )
+    min_forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
+        map_temperature_dict_to_onshore_regions(
+            supply_temperature_by_country=snakemake.params.min_forward_temperature_central_heating,
+            regions_onshore=regions_onshore,
+            snapshots=snapshots,
+        )
+    )
+    return_temperature_central_heating_by_node_and_time: xr.DataArray = (
+        map_temperature_dict_to_onshore_regions(
+            supply_temperature_by_country=snakemake.params.return_temperature_central_heating,
+            regions_onshore=regions_onshore,
+            snapshots=snapshots,
+        )
+    )
+
+    central_heating_temperature_approximator = CentralHeatingTemperatureApproximator(
+        ambient_temperature=xr.open_dataarray(snakemake.input.temp_air_total),
+        max_forward_temperature=max_forward_temperature_central_heating_by_node_and_time,
+        min_forward_temperature=min_forward_temperature_central_heating_by_node_and_time,
+        fixed_return_temperature=return_temperature_central_heating_by_node_and_time,
+        lower_threshold_ambient_temperature=snakemake.params.lower_threshold_ambient_temperature,
+        upper_threshold_ambient_temperature=snakemake.params.upper_threshold_ambient_temperature,
+        rolling_window_ambient_temperature=snakemake.params.rolling_window_ambient_temperature,
+    )
+
+    central_heating_temperature_approximator.forward_temperature.to_netcdf(
+        snakemake.output.central_heating_forward_temperature_profiles
+    )
+    central_heating_temperature_approximator.return_temperature.to_netcdf(
+        snakemake.output.central_heating_return_temperature_profiles
+    )

scripts/build_cop_profiles/run.py

@@ -53,47 +53,6 @@ from DecentralHeatingCopApproximator import DecentralHeatingCopApproximator
 from scripts.definitions.heat_system_type import HeatSystemType
 
 
-def map_temperature_dict_to_onshore_regions(
-    supply_temperature_by_country: dict,
-    regions_onshore: pd.Index,
-    snapshots: pd.DatetimeIndex,
-) -> xr.DataArray:
-    """
-    Map dictionary of temperatures to onshore regions.
-
-    Parameters:
-    ----------
-    supply_temperature_by_country : dictionary
-        Dictionary with temperatures as values and country keys as keys. One key must be named "default"
-    regions_onshore : pd.Index
-        Names of onshore regions
-    snapshots : pd.DatetimeIndex
-        Time stamps
-
-    Returns:
-    -------
-    xr.DataArray
-        The dictionary values mapped to onshore regions with onshore regions as coordinates.
-    """
-    return xr.DataArray(
-        [
-            [
-                (
-                    supply_temperature_by_country[get_country_from_node_name(node_name)]
-                    if get_country_from_node_name(node_name)
-                    in supply_temperature_by_country.keys()
-                    else supply_temperature_by_country["default"]
-                )
-                for node_name in regions_onshore.values
-            ]
-            # pass both nodes and snapshots as dimensions to preserve correct data structure
-            for _ in snapshots
-        ],
-        dims=["time", "name"],
-        coords={"time": snapshots, "name": regions_onshore},
-    )
-
-
 def get_cop(
     heat_system_type: str,
     heat_source: str,
@@ -154,20 +113,13 @@ if __name__ == "__main__":
     # map forward and return temperatures specified on country-level to onshore regions
     regions_onshore = gpd.read_file(snakemake.input.regions_onshore)["name"]
     snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
-    forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
-        map_temperature_dict_to_onshore_regions(
-            supply_temperature_by_country=snakemake.params.forward_temperature_central_heating,
-            regions_onshore=regions_onshore,
-            snapshots=snapshots,
-        )
+    central_heating_forward_temperature: xr.DataArray = xr.open_dataarray(
+        snakemake.input.central_heating_forward_temperature_profiles
     )
-    return_temperature_central_heating_by_node_and_time: xr.DataArray = (
-        map_temperature_dict_to_onshore_regions(
-            supply_temperature_by_country=snakemake.params.return_temperature_central_heating,
-            regions_onshore=regions_onshore,
-            snapshots=snapshots,
-        )
+    central_heating_return_temperature: xr.DataArray = xr.open_dataarray(
+        snakemake.input.central_heating_return_temperature_profiles
     )
+
     cop_all_system_types = []
     for heat_system_type, heat_sources in snakemake.params.heat_pump_sources.items():
         cop_this_system_type = []
@@ -179,8 +131,8 @@ if __name__ == "__main__":
                 heat_system_type=heat_system_type,
                 heat_source=heat_source,
                 source_inlet_temperature_celsius=source_inlet_temperature_celsius,
-                forward_temperature_by_node_and_time=forward_temperature_central_heating_by_node_and_time,
-                return_temperature_by_node_and_time=return_temperature_central_heating_by_node_and_time,
+                forward_temperature_by_node_and_time=central_heating_forward_temperature,
+                return_temperature_by_node_and_time=central_heating_return_temperature,
             )
             cop_this_system_type.append(cop_da)
         cop_all_system_types.append(

scripts/build_electricity_demand.py

@@ -192,9 +192,9 @@ def manual_adjustment(load, fn_load, countries):
         if "ME" in load:
             load["BA"] = load.HR * (11.0 / 16.2)
 
-    if ("KV" not in load or load.KV.isnull().values.all()) and "KV" in countries:
+    if "XK" not in load and "XK" in countries:
         if "RS" in load:
-            load["KV"] = load["RS"] * (4.8 / 27.0)
+            load["XK"] = load["RS"] * (4.8 / 27.0)
 
     copy_timeslice(load, "GR", "2015-08-11 21:00", "2015-08-15 20:00", Delta(weeks=1))
     copy_timeslice(load, "AT", "2018-12-31 22:00", "2019-01-01 22:00", Delta(days=2))
@@ -311,8 +311,8 @@ if __name__ == "__main__":
         logger.info("Supplement missing data with synthetic data.")
         fn = snakemake.input.synthetic
         synthetic_load = pd.read_csv(fn, index_col=0, parse_dates=True)
-        # "UA" does not appear in synthetic load data
-        countries = list(set(countries) - set(["UA", "MD"]))
+        # UA, MD, XK do not appear in synthetic load data
+        countries = list(set(countries) - set(["UA", "MD", "XK"]))
         synthetic_load = synthetic_load.loc[snapshots, countries]
         load = load.combine_first(synthetic_load)
 

scripts/build_energy_totals.py

@@ -1065,9 +1065,11 @@ def build_eurostat_co2(eurostat: pd.DataFrame, year: int = 1990) -> pd.Series:
     specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
 
     # emissions in tCO2_equiv per MWh_th
-    specific_emissions["Solid fuels"] = 0.36  # Approximates coal
-    specific_emissions["Oil (total)"] = 0.285  # Average of distillate and residue
-    specific_emissions["Gas"] = 0.2  # For natural gas
+    specific_emissions["Solid fossil fuels"] = 0.36  # Approximates coal
+    specific_emissions["Oil and petroleum products"] = (
+        0.285  # Average of distillate and residue
+    )
+    specific_emissions["Natural gas"] = 0.2  # For natural gas
 
     return eurostat_year.multiply(specific_emissions).sum(axis=1)
 
@@ -1099,7 +1101,9 @@ def build_co2_totals(
 
     co2 = eea_co2.reindex(countries)
 
-    for ct in pd.Index(countries).intersection(["BA", "RS", "AL", "ME", "MK"]):
+    for ct in pd.Index(countries).intersection(
+        ["BA", "RS", "XK", "AL", "ME", "MK", "UA", "MD"]
+    ):
         mappings = {
             "electricity": (ct, "+", "Electricity & heat generation", np.nan),
             "residential non-elec": (ct, "+", "+", "Residential"),
@@ -1451,10 +1455,10 @@ def update_residential_from_eurostat(energy: pd.DataFrame) -> pd.DataFrame:
     for nrg_name, (code, siec) in nrg_type.items():
 
         # Select energy balance type, rename columns and countries to match IDEES data,
-        # convert TJ to TWh, and drop XK data already since included in RS data
+        # convert TJ to TWh
         col_to_rename = {"geo": "country", "TIME_PERIOD": "year", "OBS_VALUE": nrg_name}
         idx_to_rename = {v: k for k, v in idees_rename.items()}
-        drop_geo = ["EU27_2020", "EA20", "XK"]
+        drop_geo = ["EU27_2020", "EA20"]
         nrg_data = eurostat_households.query(
             "nrg_bal == @code and siec == @siec and geo not in @drop_geo and OBS_VALUE > 0"
         ).copy()

scripts/build_gas_input_locations.py

@@ -36,15 +36,20 @@ def build_gem_lng_data(fn):
         "Gran Canaria LNG Terminal",
     ]
 
+    status_list = ["Operating", "Construction"]  # noqa: F841
+
     df = df.query(
-        "Status != 'Cancelled' \
+        "Status in @status_list \
+              & FacilityType == 'Import' \
               & Country != @remove_country \
               & TerminalName != @remove_terminal \
-              & CapacityInMtpa != '--'"
+              & CapacityInMtpa != '--' \
+              & CapacityInMtpa != 0"
     )
 
     geometry = gpd.points_from_xy(df["Longitude"], df["Latitude"])
-    return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+    return gdf
 
 
 def build_gem_prod_data(fn):
@@ -54,8 +59,10 @@ def build_gem_prod_data(fn):
     remove_country = ["Cyprus", "Türkiye"]  # noqa: F841
     remove_fuel_type = ["oil"]  # noqa: F841
 
+    status_list = ["operating", "in development"]  # noqa: F841
+
     df = df.query(
-        "Status != 'shut in' \
+        "Status in @status_list \
               & 'Fuel type' != 'oil' \
               & Country != @remove_country \
               & ~Latitude.isna() \
@@ -64,7 +71,7 @@ def build_gem_prod_data(fn):
 
     p = pd.read_excel(fn, sheet_name="Gas extraction - production")
     p = p.set_index("GEM Unit ID")
-    p = p[p["Fuel description"] == "gas"]
+    p = p[p["Fuel description"].str.contains("gas")]
 
     capacities = pd.DataFrame(index=df.index)
     for key in ["production", "production design capacity", "reserves"]:
@@ -85,7 +92,8 @@ def build_gem_prod_data(fn):
     )
 
     geometry = gpd.points_from_xy(df["Longitude"], df["Latitude"])
-    return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+    return gdf
 
 
 def build_gas_input_locations(gem_fn, entry_fn, sto_fn, countries):
@@ -134,8 +142,7 @@ if __name__ == "__main__":
         snakemake = mock_snakemake(
             "build_gas_input_locations",
             simpl="",
-            clusters="5",
-            configfiles="config/test/config.overnight.yaml",
+            clusters="128",
         )
 
     configure_logging(snakemake)

scripts/build_hydro_profile.py

@@ -82,7 +82,7 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
             countries=["Czechia", "Slovakia"], start=1980, end=1992
         ),
         "Former Serbia and Montenegro": dict(
-            countries=["Serbia", "Montenegro"], start=1992, end=2005
+            countries=["Serbia", "Montenegro", "Kosovo"], start=1992, end=2005
         ),
         "Former Yugoslavia": dict(
             countries=[
@@ -90,6 +90,7 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
                 "Croatia",
                 "Bosnia and Herzegovina",
                 "Serbia",
+                "Kosovo",
                 "Montenegro",
                 "North Macedonia",
             ],
@@ -111,9 +112,8 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
     )
 
     df.loc["Germany"] = df.filter(like="Germany", axis=0).sum()
-    df.loc["Serbia"] += df.loc["Kosovo"].fillna(0.0)
     df = df.loc[~df.index.str.contains("Former")]
-    df.drop(["Europe", "Germany, West", "Germany, East", "Kosovo"], inplace=True)
+    df.drop(["Europe", "Germany, West", "Germany, East"], inplace=True)
 
     df.index = cc.convert(df.index, to="iso2")
     df.index.name = "countries"
@@ -122,6 +122,8 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
     factor = 1e3 if capacities else 1e6
     df = df.T[countries] * factor
 
+    df.ffill(axis=0, inplace=True)
+
     return df
 
 
@@ -129,7 +131,7 @@ def correct_eia_stats_by_capacity(eia_stats, fn, countries, baseyear=2019):
     cap = get_eia_annual_hydro_generation(fn, countries, capacities=True)
     ratio = cap / cap.loc[baseyear]
     eia_stats_corrected = eia_stats / ratio
-    to_keep = ["AL", "AT", "CH", "DE", "GB", "NL", "RS", "RO", "SK"]
+    to_keep = ["AL", "AT", "CH", "DE", "GB", "NL", "RS", "XK", "RO", "SK"]
     to_correct = eia_stats_corrected.columns.difference(to_keep)
     eia_stats.loc[:, to_correct] = eia_stats_corrected.loc[:, to_correct]
 

scripts/build_industrial_distribution_key.py

@@ -100,7 +100,7 @@ def prepare_hotmaps_database(regions):
     """
     Load hotmaps database of industrial sites and map onto bus regions.
     """
-    df = pd.read_csv(snakemake.input.hotmaps_industrial_database, sep=";", index_col=0)
+    df = pd.read_csv(snakemake.input.hotmaps, sep=";", index_col=0)
 
     df[["srid", "coordinates"]] = df.geom.str.split(";", expand=True)
 
@@ -133,7 +133,97 @@ def prepare_hotmaps_database(regions):
     return gdf
 
 
-def build_nodal_distribution_key(hotmaps, regions, countries):
+def prepare_gem_database(regions):
+    """
+    Load GEM database of steel plants and map onto bus regions.
+    """
+
+    df = pd.read_excel(
+        snakemake.input.gem_gspt,
+        sheet_name="Steel Plants",
+        na_values=["N/A", "unknown", ">0"],
+    ).query("Region == 'Europe'")
+
+    df["Retired Date"] = pd.to_numeric(
+        df["Retired Date"].combine_first(df["Idled Date"]), errors="coerce"
+    )
+    df["Start date"] = pd.to_numeric(
+        df["Start date"].str.split("-").str[0], errors="coerce"
+    )
+
+    latlon = (
+        df["Coordinates"]
+        .str.split(", ", expand=True)
+        .rename(columns={0: "lat", 1: "lon"})
+    )
+    geometry = gpd.points_from_xy(latlon["lon"], latlon["lat"])
+    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+
+    gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")
+
+    gdf.rename(columns={"name": "bus"}, inplace=True)
+    gdf["country"] = gdf.bus.str[:2]
+
+    return gdf
+
+
+def prepare_ammonia_database(regions):
+    """
+    Load ammonia database of plants and map onto bus regions.
+    """
+    df = pd.read_csv(snakemake.input.ammonia, index_col=0)
+
+    geometry = gpd.points_from_xy(df.Longitude, df.Latitude)
+    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+
+    gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")
+
+    gdf.rename(columns={"name": "bus"}, inplace=True)
+    gdf["country"] = gdf.bus.str[:2]
+
+    return gdf
+
+
+def prepare_cement_supplement(regions):
+    """
+    Load supplementary cement plants from non-EU-(NO-CH) and map onto bus
+    regions.
+    """
+
+    df = pd.read_csv(snakemake.input.cement_supplement, index_col=0)
+
+    geometry = gpd.points_from_xy(df.Longitude, df.Latitude)
+    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+
+    gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")
+
+    gdf.rename(columns={"name": "bus"}, inplace=True)
+    gdf["country"] = gdf.bus.str[:2]
+
+    return gdf
+
+
+def prepare_refineries_supplement(regions):
+    """
+    Load supplementary refineries from non-EU-(NO-CH) and map onto bus regions.
+    """
+
+    df = pd.read_csv(snakemake.input.refineries_supplement, index_col=0)
+
+    geometry = gpd.points_from_xy(df.Longitude, df.Latitude)
+    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
+
+    gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")
+
+    gdf.rename(columns={"name": "bus"}, inplace=True)
+    gdf["country"] = gdf.bus.str[:2]
+
+    return gdf
+
+
+def build_nodal_distribution_key(
+    hotmaps, gem, ammonia, cement, refineries, regions, countries
+):
     """
     Build nodal distribution keys for each sector.
     """
@@ -158,15 +248,137 @@ def build_nodal_distribution_key(hotmaps, regions, countries):
             if emissions.sum() == 0:
                 key = pd.Series(1 / len(facilities), facilities.index)
             else:
-                # BEWARE: this is a strong assumption
-                emissions = emissions.fillna(emissions.mean())
+                # assume 20% quantile for missing values
+                emissions = emissions.fillna(emissions.quantile(0.2))
                 key = emissions / emissions.sum()
             key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.0)
+        elif sector == "Cement" and country in cement.country.unique():
+            facilities = cement.query("country == @country")
+            production = facilities["Cement [kt/a]"]
+            if production.sum() == 0:
+                key = pd.Series(1 / len(facilities), facilities.index)
+            else:
+                key = production / production.sum()
+            key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.0)
+        elif sector == "Refineries" and country in refineries.country.unique():
+            facilities = refineries.query("country == @country")
+            production = facilities["Capacity [bbl/day]"]
+            if production.sum() == 0:
+                key = pd.Series(1 / len(facilities), facilities.index)
+            else:
+                key = production / production.sum()
+            key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.0)
         else:
             key = keys.loc[regions_ct, "population"]
 
         keys.loc[regions_ct, sector] = key
 
+    # add specific steel subsectors
+    steel_processes = ["EAF", "DRI + EAF", "Integrated steelworks"]
+    for process, country in product(steel_processes, countries):
+        regions_ct = regions.index[regions.index.str.contains(country)]
+
+        facilities = gem.query("country == @country")
+
+        if process == "EAF":
+            status_list = [
+                "construction",
+                "operating",
+                "operating pre-retirement",
+                "retired",
+            ]
+            capacities = facilities.loc[
+                facilities["Capacity operating status"].isin(status_list)
+                & (
+                    facilities["Retired Date"].isna()
+                    | facilities["Retired Date"].gt(2025)
+                ),
+                "Nominal EAF steel capacity (ttpa)",
+            ].dropna()
+        elif process == "DRI + EAF":
+            status_list = [
+                "construction",
+                "operating",
+                "operating pre-retirement",
+                "retired",
+                "announced",
+            ]
+            sel = [
+                "Nominal BOF steel capacity (ttpa)",
+                "Nominal OHF steel capacity (ttpa)",
+                "Nominal iron capacity (ttpa)",
+            ]
+            status_filter = facilities["Capacity operating status"].isin(status_list)
+            retirement_filter = facilities["Retired Date"].isna() | facilities[
+                "Retired Date"
+            ].gt(2030)
+            start_filter = (
+                facilities["Start date"].isna()
+                & ~facilities["Capacity operating status"].eq("announced")
+            ) | facilities["Start date"].le(2030)
+            capacities = (
+                facilities.loc[status_filter & retirement_filter & start_filter, sel]
+                .sum(axis=1)
+                .dropna()
+            )
+        elif process == "Integrated steelworks":
+            status_list = [
+                "construction",
+                "operating",
+                "operating pre-retirement",
+                "retired",
+            ]
+            sel = [
+                "Nominal BOF steel capacity (ttpa)",
+                "Nominal OHF steel capacity (ttpa)",
+            ]
+            capacities = (
+                facilities.loc[
+                    facilities["Capacity operating status"].isin(status_list)
+                    & (
+                        facilities["Retired Date"].isna()
+                        | facilities["Retired Date"].gt(2025)
+                    ),
+                    sel,
+                ]
+                .sum(axis=1)
+                .dropna()
+            )
+        else:
+            raise ValueError(f"Unknown process {process}")
+
+        if not capacities.empty:
+            if capacities.sum() == 0:
+                key = pd.Series(1 / len(capacities), capacities.index)
+            else:
+                key = capacities / capacities.sum()
+            buses = facilities.loc[capacities.index, "bus"]
+            key = key.groupby(buses).sum().reindex(regions_ct, fill_value=0.0)
+        else:
+            key = keys.loc[regions_ct, "population"]
+
+        keys.loc[regions_ct, process] = key
+
+    # add ammonia
+    for country in countries:
+        regions_ct = regions.index[regions.index.str.contains(country)]
+
+        facilities = ammonia.query("country == @country")
+
+        if not facilities.empty:
+            production = facilities["Ammonia [kt/a]"]
+            if production.sum() == 0:
+                key = pd.Series(1 / len(facilities), facilities.index)
+            else:
+                # assume 50% of the minimum production for missing values
+                production = production.fillna(0.5 * facilities["Ammonia [kt/a]"].min())
+                key = production / production.sum()
+            key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.0)
+        else:
+            key = 0.0
+
+        keys.loc[regions_ct, "Ammonia"] = key
+
     return keys
 
 
@@ -188,6 +400,16 @@ if __name__ == "__main__":
 
     hotmaps = prepare_hotmaps_database(regions)
 
-    keys = build_nodal_distribution_key(hotmaps, regions, countries)
+    gem = prepare_gem_database(regions)
+
+    ammonia = prepare_ammonia_database(regions)
+
+    cement = prepare_cement_supplement(regions)
+
+    refineries = prepare_refineries_supplement(regions)
+
+    keys = build_nodal_distribution_key(
+        hotmaps, gem, ammonia, cement, refineries, regions, countries
+    )
 
     keys.to_csv(snakemake.output.industrial_distribution_key)

scripts/build_industrial_energy_demand_per_node_today.py

@@ -33,10 +33,10 @@ from _helpers import set_scenario_config
 
 # map JRC/our sectors to hotmaps sector, where mapping exist
 sector_mapping = {
-    "Electric arc": "Iron and steel",
-    "Integrated steelworks": "Iron and steel",
-    "DRI + Electric arc": "Iron and steel",
-    "Ammonia": "Chemical industry",
+    "Electric arc": "EAF",
+    "Integrated steelworks": "Integrated steelworks",
+    "DRI + Electric arc": "DRI + EAF",
+    "Ammonia": "Ammonia",
     "Basic chemicals (without ammonia)": "Chemical industry",
     "Other chemicals": "Chemical industry",
     "Pharmaceutical products etc.": "Chemical industry",

scripts/build_industrial_production_per_country.py

@@ -345,5 +345,7 @@ if __name__ == "__main__":
 
     separate_basic_chemicals(demand, year)
 
+    demand.fillna(0.0, inplace=True)
+
     fn = snakemake.output.industrial_production_per_country
     demand.to_csv(fn, float_format="%.2f")

scripts/build_industrial_production_per_node.py

@@ -32,10 +32,10 @@ from _helpers import set_scenario_config
 
 # map JRC/our sectors to hotmaps sector, where mapping exist
 sector_mapping = {
-    "Electric arc": "Iron and steel",
-    "Integrated steelworks": "Iron and steel",
-    "DRI + Electric arc": "Iron and steel",
-    "Ammonia": "Chemical industry",
+    "Electric arc": "EAF",
+    "Integrated steelworks": "Integrated steelworks",
+    "DRI + Electric arc": "DRI + EAF",
+    "Ammonia": "Ammonia",
     "HVC": "Chemical industry",
     "HVC (mechanical recycling)": "Chemical industry",
     "HVC (chemical recycling)": "Chemical industry",

scripts/build_population_layouts.py

@@ -9,6 +9,7 @@ Build mapping between cutout grid cells and population (total, urban, rural).
 import logging
 
 import atlite
+import country_converter as coco
 import geopandas as gpd
 import numpy as np
 import pandas as pd
@@ -17,6 +18,8 @@ from _helpers import configure_logging, set_scenario_config
 
 logger = logging.getLogger(__name__)
 
+cc = coco.CountryConverter()
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -27,6 +30,7 @@ if __name__ == "__main__":
 
     configure_logging(snakemake)
     set_scenario_config(snakemake)
+    coco.logging.getLogger().setLevel(coco.logging.CRITICAL)
 
     cutout = atlite.Cutout(snakemake.input.cutout)
 
@@ -45,19 +49,14 @@ if __name__ == "__main__":
 
     countries = np.sort(nuts3.country.unique())
 
+    urban_fraction = pd.read_csv(snakemake.input.urban_percent, skiprows=4)
+    iso3 = urban_fraction["Country Code"]
+    urban_fraction["iso2"] = cc.convert(names=iso3, src="ISO3", to="ISO2")
     urban_fraction = (
-        pd.read_csv(
-            snakemake.input.urban_percent, header=None, index_col=0, names=["fraction"]
-        ).squeeze()
-        / 100.0
+        urban_fraction.query("iso2 in @countries").set_index("iso2")["2019"].div(100)
     )
-
-    # fill missing Balkans values
-    missing = ["AL", "ME", "MK"]
-    reference = ["RS", "BA"]
-    average = urban_fraction[reference].mean()
-    fill_values = pd.Series({ct: average for ct in missing})
-    urban_fraction = pd.concat([urban_fraction, fill_values])
+    if "XK" in countries:
+        urban_fraction["XK"] = urban_fraction["RS"]
 
     # population in each grid cell
     pop_cells = pd.Series(I.dot(nuts3["pop"]))

scripts/build_powerplants.py

@@ -206,12 +206,11 @@ if __name__ == "__main__":
 
     # Add "everywhere powerplants" to all bus locations
     ppl = add_everywhere_powerplants(
-        ppl, n.buses.query("substation_lv"), snakemake.params.everywhere_powerplants
+        ppl, n.buses, snakemake.params.everywhere_powerplants
     )
 
-    substations = n.buses.query("substation_lv")
     ppl = ppl.dropna(subset=["lat", "lon"])
-    ppl = map_country_bus(ppl, substations)
+    ppl = map_country_bus(ppl, n.buses)
 
     bus_null_b = ppl["bus"].isnull()
     if bus_null_b.any():

scripts/build_retro_cost.py

@@ -198,6 +198,7 @@ def prepare_building_stock_data():
             "Iceland": "IS",
             "Montenegro": "ME",
             "Serbia": "RS",
+            "Kosovo": "XK",
             "Albania": "AL",
             "United Kingdom": "GB",
             "Bosnia and Herzegovina": "BA",
@@ -1073,6 +1074,7 @@ if __name__ == "__main__":
         "AL": ["BG", "RO", "GR"],
         "BA": ["HR"],
         "RS": ["BG", "RO", "HR", "HU"],
+        "KV": ["RS"],
         "MK": ["BG", "GR"],
         "ME": ["BA", "AL", "RS", "HR"],
         "CH": ["SE", "DE"],

scripts/build_shapes.py

@@ -106,8 +106,6 @@ def _simplify_polys(polys, minarea=0.1, tolerance=None, filterremote=True):
 
 
 def countries(naturalearth, country_list):
-    if "RS" in country_list:
-        country_list.append("KV")
 
     df = gpd.read_file(naturalearth)
 
@@ -116,15 +114,12 @@ def countries(naturalearth, country_list):
         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[:2]
+    df.replace({"name": {"KV": "XK"}}, inplace=True)
 
     df = df.loc[
         df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5))
     ]
     s = df.set_index("name")["geometry"].map(_simplify_polys).set_crs(df.crs)
-    if "RS" in country_list:
-        s["RS"] = s["RS"].union(s.pop("KV"))
-        # cleanup shape union
-        s["RS"] = Polygon(s["RS"].exterior.coords)
 
     return s
 
@@ -195,7 +190,9 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
 
     df = df.join(pd.DataFrame(dict(pop=pop, gdp=gdp)))
 
-    df["country"] = df.index.to_series().str[:2].replace(dict(UK="GB", EL="GR"))
+    df["country"] = (
+        df.index.to_series().str[:2].replace(dict(UK="GB", EL="GR", KV="XK"))
+    )
 
     excludenuts = pd.Index(
         (
@@ -217,13 +214,18 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
             "FR9",
         )
     )
-    excludecountry = pd.Index(("MT", "TR", "LI", "IS", "CY", "KV"))
+    excludecountry = pd.Index(("MT", "TR", "LI", "IS", "CY"))
 
     df = df.loc[df.index.difference(excludenuts)]
     df = df.loc[~df.country.isin(excludecountry)]
 
     manual = gpd.GeoDataFrame(
-        [["BA1", "BA", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]],
+        [
+            ["BA1", "BA", 3234.0],
+            ["RS1", "RS", 6664.0],
+            ["AL1", "AL", 2778.0],
+            ["XK1", "XK", 1587.0],
+        ],
         columns=["NUTS_ID", "country", "pop"],
         geometry=gpd.GeoSeries(),
         crs=df.crs,
@@ -234,7 +236,7 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
 
     df = pd.concat([df, manual], sort=False)
 
-    df.loc["ME000", "pop"] = 650.0
+    df.loc["ME000", "pop"] = 617.0
 
     return df
 

scripts/cluster_network.py

@@ -557,12 +557,12 @@ if __name__ == "__main__":
     ):  # also available: linemap_positive, linemap_negative
         getattr(clustering, attr).to_csv(snakemake.output[attr])
 
-    nc.shapes = n.shapes.copy()
+    # nc.shapes = n.shapes.copy()
     for which in ["regions_onshore", "regions_offshore"]:
         regions = gpd.read_file(snakemake.input[which])
         clustered_regions = cluster_regions((clustering.busmap,), regions)
         clustered_regions.to_file(snakemake.output[which])
-        append_bus_shapes(nc, clustered_regions, type=which.split("_")[1])
+        # append_bus_shapes(nc, clustered_regions, type=which.split("_")[1])
 
     nc.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     nc.export_to_netcdf(snakemake.output.network)

scripts/prepare_sector_network.py

@@ -281,7 +281,7 @@ def co2_emissions_year(
 
     eurostat = build_eurostat(input_eurostat, countries)
 
-    # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
+    # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK, XK
     eurostat_co2 = build_eurostat_co2(eurostat, year)
 
     co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2)
@@ -539,7 +539,24 @@ def add_carrier_buses(n, carrier, nodes=None):
 
     unit = "MWh_LHV" if carrier == "gas" else "MWh_th"
     # preliminary value for non-gas carriers to avoid zeros
-    capital_cost = costs.at["gas storage", "fixed"] if carrier == "gas" else 0.02
+    if carrier == "gas":
+        capital_cost = costs.at["gas storage", "fixed"]
+    elif carrier == "oil":
+        # based on https://www.engineeringtoolbox.com/fuels-higher-calorific-values-d_169.html
+        mwh_per_m3 = 44.9 * 724 * 0.278 * 1e-3  # MJ/kg * kg/m3 * kWh/MJ * MWh/kWh
+        capital_cost = (
+            costs.at["General liquid hydrocarbon storage (product)", "fixed"]
+            / mwh_per_m3
+        )
+    elif carrier == "methanol":
+        # based on https://www.engineeringtoolbox.com/fossil-fuels-energy-content-d_1298.html
+        mwh_per_m3 = 5.54 * 791 * 1e-3  # kWh/kg * kg/m3 * MWh/kWh
+        capital_cost = (
+            costs.at["General liquid hydrocarbon storage (product)", "fixed"]
+            / mwh_per_m3
+        )
+    else:
+        capital_cost = 0.1
 
     n.madd("Bus", nodes, location=location, carrier=carrier, unit=unit)
 
@@ -2639,8 +2656,7 @@ def add_biomass(n, costs):
     if (
         options["municipal_solid_waste"]
         and not options["industry"]
-        and cf_industry["waste_to_energy"]
-        or cf_industry["waste_to_energy_cc"]
+        and (cf_industry["waste_to_energy"] or cf_industry["waste_to_energy_cc"])
     ):
         logger.warning(
             "Flag municipal_solid_waste can be only used with industry "
@@ -2660,7 +2676,12 @@ def add_biomass(n, costs):
             carrier="municipal solid waste",
         )
 
-        e_max_pu = pd.Series([1] * (len(n.snapshots) - 1) + [0], index=n.snapshots)
+        e_max_pu = np.array(
+            len(spatial.msw.nodes) * [[1] * (len(n.snapshots) - 1) + [0]]
+        ).T
+        e_max_pu = pd.DataFrame(
+            e_max_pu, index=n.snapshots, columns=spatial.msw.nodes
+        ).astype(float)
         n.madd(
             "Store",
             spatial.msw.nodes,
@@ -2757,7 +2778,7 @@ def add_biomass(n, costs):
         )
 
     if biomass_potentials.filter(like="unsustainable").sum().sum() > 0:
-
+        add_carrier_buses(n, "oil")
         # Create timeseries to force usage of unsustainable potentials
         e_max_pu = pd.DataFrame(1, index=n.snapshots, columns=spatial.gas.biogas)
         e_max_pu.iloc[-1] = 0
@@ -2905,13 +2926,15 @@ def add_biomass(n, costs):
         if options["municipal_solid_waste"]:
             n.madd(
                 "Link",
-                biomass_transport.index,
-                bus0=biomass_transport.bus0 + " municipal solid waste",
-                bus1=biomass_transport.bus1 + " municipal solid waste",
+                biomass_transport.index + " municipal solid waste",
+                bus0=biomass_transport.bus0.values + " municipal solid waste",
+                bus1=biomass_transport.bus1.values + " municipal solid waste",
                 p_nom_extendable=False,
                 p_nom=5e4,
                 length=biomass_transport.length.values,
-                marginal_cost=biomass_transport.costs * biomass_transport.length.values,
+                marginal_cost=(
+                    biomass_transport.costs * biomass_transport.length
+                ).values,
                 carrier="municipal solid waste transport",
             )
 
@@ -3041,6 +3064,7 @@ def add_biomass(n, costs):
 
     # Solid biomass to liquid fuel
     if options["biomass_to_liquid"]:
+        add_carrier_buses(n, "oil")
         n.madd(
             "Link",
             spatial.biomass.nodes,
@@ -3084,7 +3108,7 @@ def add_biomass(n, costs):
     # Combination of efuels and biomass to liquid, both based on Fischer-Tropsch.
     # Experimental version - use with caution
     if options["electrobiofuels"]:
-
+        add_carrier_buses(n, "oil")
         efuel_scale_factor = costs.at["BtL", "C stored"]
         name = (
             pd.Index(spatial.biomass.nodes)
@@ -3196,6 +3220,10 @@ def add_biomass(n, costs):
 
 def add_industry(n, costs):
     logger.info("Add industrial demand")
+    # add oil buses for shipping, aviation and naptha for industry
+    add_carrier_buses(n, "oil")
+    # add methanol buses for industry
+    add_carrier_buses(n, "methanol")
 
     nodes = pop_layout.index
     nhours = n.snapshot_weightings.generators.sum()
@@ -3520,38 +3548,8 @@ def add_industry(n, costs):
             ],  # CO2 intensity methanol based on stoichiometric calculation with 22.7 GJ/t methanol (32 g/mol), CO2 (44 g/mol), 277.78 MWh/TJ = 0.218 t/MWh
         )
 
-    if "oil" not in n.buses.carrier.unique():
-        n.madd(
-            "Bus",
-            spatial.oil.nodes,
-            location=spatial.oil.locations,
-            carrier="oil",
-            unit="MWh_LHV",
-        )
-
-    if "oil" not in n.stores.carrier.unique():
-        # could correct to e.g. 0.001 EUR/kWh * annuity and O&M
-        n.madd(
-            "Store",
-            spatial.oil.nodes,
-            suffix=" Store",
-            bus=spatial.oil.nodes,
-            e_nom_extendable=True,
-            e_cyclic=True,
-            carrier="oil",
-        )
-
-    if options.get("fossil_fuels", True) and "oil" not in n.generators.carrier.unique():
-        n.madd(
-            "Generator",
-            spatial.oil.nodes,
-            bus=spatial.oil.nodes,
-            p_nom_extendable=True,
-            carrier="oil",
-            marginal_cost=costs.at["oil", "fuel"],
-        )
-
     if shipping_oil_share:
+
         p_set_oil = shipping_oil_share * p_set.rename(lambda x: x + " shipping oil")
 
         if not options["regional_oil_demand"]:

scripts/simplify_network.py

@@ -124,7 +124,6 @@ def simplify_network_to_380(n, linetype_380):
 
     n.buses["v_nom"] = 380.0
 
-    linetype_380 = n.lines["type"].mode()[0]
     n.lines["type"] = linetype_380
     n.lines["v_nom"] = 380
     n.lines["i_nom"] = n.line_types.i_nom[linetype_380]
@@ -670,7 +669,7 @@ if __name__ == "__main__":
     n.lines.drop(remove, axis=1, errors="ignore", inplace=True)
 
     if snakemake.wildcards.simpl:
-        shapes = n.shapes
+        # shapes = n.shapes
         n, cluster_map = cluster(
             n,
             int(snakemake.wildcards.simpl),
@@ -680,7 +679,7 @@ if __name__ == "__main__":
             params.simplify_network["feature"],
             params.aggregation_strategies,
         )
-        n.shapes = shapes
+        # n.shapes = shapes
         busmaps.append(cluster_map)
 
     update_p_nom_max(n)
@@ -692,7 +691,7 @@ if __name__ == "__main__":
         regions = gpd.read_file(snakemake.input[which])
         clustered_regions = cluster_regions(busmaps, regions)
         clustered_regions.to_file(snakemake.output[which])
-        append_bus_shapes(n, clustered_regions, type=which.split("_")[1])
+        # append_bus_shapes(n, clustered_regions, type=which.split("_")[1])
 
     n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output.network)

scripts/solve_network.py

@@ -1055,8 +1055,8 @@ def extra_functionality(n, snapshots):
     if config["sector"]["enhanced_geothermal"]["enable"]:
         add_flexible_egs_constraint(n)
 
-    if snakemake.params.custom_extra_functionality:
-        source_path = snakemake.params.custom_extra_functionality
+    if n.params.custom_extra_functionality:
+        source_path = n.params.custom_extra_functionality
         assert os.path.exists(source_path), f"{source_path} does not exist"
         sys.path.append(os.path.dirname(source_path))
         module_name = os.path.splitext(os.path.basename(source_path))[0]
@@ -1065,7 +1065,7 @@ def extra_functionality(n, snapshots):
         custom_extra_functionality(n, snapshots, snakemake)
 
 
-def solve_network(n, config, solving, **kwargs):
+def solve_network(n, config, params, solving, **kwargs):
     set_of_options = solving["solver"]["options"]
     cf_solving = solving["options"]
 
@@ -1093,6 +1093,7 @@ def solve_network(n, config, solving, **kwargs):
 
     # add to network for extra_functionality
     n.config = config
+    n.params = params
 
     if rolling_horizon and snakemake.rule == "solve_operations_network":
         kwargs["horizon"] = cf_solving.get("horizon", 365)
@@ -1165,6 +1166,7 @@ if __name__ == "__main__":
         n = solve_network(
             n,
             config=snakemake.config,
+            params=snakemake.params,
             solving=snakemake.params.solving,
             log_fn=snakemake.log.solver,
         )

scripts/solve_operations_network.py

@@ -49,7 +49,13 @@ if __name__ == "__main__":
 
     n.optimize.fix_optimal_capacities()
     n = prepare_network(n, solve_opts, config=snakemake.config)
-    n = solve_network(n, config=snakemake.config, log_fn=snakemake.log.solver)
+    n = solve_network(
+        n,
+        config=snakemake.config,
+        params=snakemake.params,
+        solving=snakemake.params.solving,
+        log_fn=snakemake.log.solver,
+    )
 
     n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output[0])