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resolve Kosovo (XK) as separate country (#1249)

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* resolve Kosovo (XK) as separate country

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fillna

* add missing key in data/existing_infrastructure/existing_heating_raw.csv

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Fabian Neumann 2024-08-30 15:36:03 +02:00 committed by GitHub
parent 4f8d583d38
commit 88d28de3a1
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
18 changed files with 52 additions and 39 deletions
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4
config/config.default.yaml
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@ -42,7 +42,7 @@ scenario:
ll: ll:
- vopt - vopt
clusters: clusters:
- 38 - 39
- 128 - 128
- 256 - 256
opts: opts:
@ -56,7 +56,7 @@ scenario:
- 2050 - 2050
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#countries # 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 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#snapshots
snapshots: snapshots:

4
config/examples/config.entsoe-all.yaml
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@ -15,7 +15,7 @@ scenario:
ll: ll:
- vopt - vopt
clusters: clusters:
- 39 - 41
- 128 - 128
- 256 - 256
opts: opts:
@ -26,7 +26,7 @@ scenario:
- '' - ''
# TODO add Turkey (TR) # 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: electricity:
custom_powerplants: true custom_powerplants: true

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

1 country share to satisfy heat demand (residential) in percent capacity[MWth]
25 GB 2
26 BY 70
27 EE 52 5406
28 KO XK 3 207
29 RO 23 9962
30 SK 54 15000
31 NL 4 9800

1
data/era5-annual-HDD-per-country.csv
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@ -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 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 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 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 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 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 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

1 name 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
29 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
30 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
31 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
32 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
33 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
34 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
35 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

1
data/era5-annual-runoff-per-country.csv
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@ -29,6 +29,7 @@ PL,19040.141287526196,13600.003649707409,4232.940272903036,6263.550300160282,117
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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 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 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

1 name 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
29 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
30 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
31 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
32 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
33 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
34 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
35 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

1
data/existing_infrastructure/existing_heating_raw.csv
View File

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

1 gas boiler coal boiler oil boiler resistive heater air heat pump ground heat pump
28 Norway 2.91 0.334
29 Switzerland 1 0.849
30 Serbia
31 Kosovo
32 Bosnia Herzegovina

2
doc/configtables/countries.csv
View File

@ -1,2 +1,2 @@
,Unit,Values,Description ,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."

1 Unit Values Description
2 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'} 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.

2
doc/release_notes.rst
View File

@ -11,6 +11,8 @@ Release Notes
Upcoming Release Upcoming Release
================ ================
* Represent Kosovo (XK) as separate country.
* Added data on the locations and capacities of ammonia plants in Europe. * 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. 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``. The data manually collected with sources noted in ``data/ammonia_plants.csv``.

3
scripts/_helpers.py
View File

@ -507,7 +507,8 @@ def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
week_df = pd.DataFrame(index=dt_index, columns=nodes) week_df = pd.DataFrame(index=dt_index, columns=nodes)
for node in 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) 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] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
week_df[node] = week_df[node].map(weekly_profile) week_df[node] = week_df[node].map(weekly_profile)

14
scripts/build_biomass_potentials.py
View File

@ -88,9 +88,11 @@ def build_nuts_population_data(year=2013):
pop = pd.concat([pop, pd.concat(swiss)]).to_frame("total") pop = pd.concat([pop, pd.concat(swiss)]).to_frame("total")
# add missing manually # add missing manually
pop["AL"] = 2893 pop["AL"] = 2778
pop["BA"] = 3871 pop["BA"] = 3234
pop["RS"] = 7210 pop["RS"] = 6664
pop["ME"] = 617
pop["XK"] = 1587
pop["ct"] = pop.index.str[:2] 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() 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 return bio
@ -199,7 +197,7 @@ def build_nuts2_shapes():
) )
countries = gpd.read_file(snakemake.input.country_shapes).set_index("name") 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] missing = countries.loc[missing_iso2]
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True) nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)

8
scripts/build_electricity_demand.py
View File

@ -192,9 +192,9 @@ def manual_adjustment(load, fn_load, countries):
if "ME" in load: if "ME" in load:
load["BA"] = load.HR * (11.0 / 16.2) 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: 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, "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)) 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.") logger.info("Supplement missing data with synthetic data.")
fn = snakemake.input.synthetic fn = snakemake.input.synthetic
synthetic_load = pd.read_csv(fn, index_col=0, parse_dates=True) synthetic_load = pd.read_csv(fn, index_col=0, parse_dates=True)
# "UA" does not appear in synthetic load data # UA, MD, XK do not appear in synthetic load data
countries = list(set(countries) - set(["UA", "MD"])) countries = list(set(countries) - set(["UA", "MD", "XK"]))
synthetic_load = synthetic_load.loc[snapshots, countries] synthetic_load = synthetic_load.loc[snapshots, countries]
load = load.combine_first(synthetic_load) load = load.combine_first(synthetic_load)

6
scripts/build_energy_totals.py
View File

@ -1102,7 +1102,7 @@ def build_co2_totals(
co2 = eea_co2.reindex(countries) co2 = eea_co2.reindex(countries)
for ct in pd.Index(countries).intersection( for ct in pd.Index(countries).intersection(
["BA", "RS", "AL", "ME", "MK", "UA", "MD"] ["BA", "RS", "XK", "AL", "ME", "MK", "UA", "MD"]
): ):
mappings = { mappings = {
"electricity": (ct, "+", "Electricity & heat generation", np.nan), "electricity": (ct, "+", "Electricity & heat generation", np.nan),
@ -1455,10 +1455,10 @@ def update_residential_from_eurostat(energy: pd.DataFrame) -> pd.DataFrame:
for nrg_name, (code, siec) in nrg_type.items(): for nrg_name, (code, siec) in nrg_type.items():
# Select energy balance type, rename columns and countries to match IDEES data, # 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} col_to_rename = {"geo": "country", "TIME_PERIOD": "year", "OBS_VALUE": nrg_name}
idx_to_rename = {v: k for k, v in idees_rename.items()} 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_data = eurostat_households.query(
"nrg_bal == @code and siec == @siec and geo not in @drop_geo and OBS_VALUE > 0" "nrg_bal == @code and siec == @siec and geo not in @drop_geo and OBS_VALUE > 0"
).copy() ).copy()

10
scripts/build_hydro_profile.py
View File

@ -82,7 +82,7 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
countries=["Czechia", "Slovakia"], start=1980, end=1992 countries=["Czechia", "Slovakia"], start=1980, end=1992
), ),
"Former Serbia and Montenegro": dict( "Former Serbia and Montenegro": dict(
countries=["Serbia", "Montenegro"], start=1992, end=2005 countries=["Serbia", "Montenegro", "Kosovo"], start=1992, end=2005
), ),
"Former Yugoslavia": dict( "Former Yugoslavia": dict(
countries=[ countries=[
@ -90,6 +90,7 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
"Croatia", "Croatia",
"Bosnia and Herzegovina", "Bosnia and Herzegovina",
"Serbia", "Serbia",
"Kosovo",
"Montenegro", "Montenegro",
"North Macedonia", "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["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 = 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 = cc.convert(df.index, to="iso2")
df.index.name = "countries" df.index.name = "countries"
@ -122,6 +122,8 @@ def get_eia_annual_hydro_generation(fn, countries, capacities=False):
factor = 1e3 if capacities else 1e6 factor = 1e3 if capacities else 1e6
df = df.T[countries] * factor df = df.T[countries] * factor
df.ffill(axis=0, inplace=True)
return df 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) cap = get_eia_annual_hydro_generation(fn, countries, capacities=True)
ratio = cap / cap.loc[baseyear] ratio = cap / cap.loc[baseyear]
eia_stats_corrected = eia_stats / ratio 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) to_correct = eia_stats_corrected.columns.difference(to_keep)
eia_stats.loc[:, to_correct] = eia_stats_corrected.loc[:, to_correct] eia_stats.loc[:, to_correct] = eia_stats_corrected.loc[:, to_correct]

2
scripts/build_industrial_production_per_country.py
View File

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

5
scripts/build_population_layouts.py
View File

@ -20,8 +20,6 @@ logger = logging.getLogger(__name__)
cc = coco.CountryConverter() cc = coco.CountryConverter()
coco.logging.getLogger().setLevel(coco.logging.CRITICAL)
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
@ -32,6 +30,7 @@ if __name__ == "__main__":
configure_logging(snakemake) configure_logging(snakemake)
set_scenario_config(snakemake) set_scenario_config(snakemake)
coco.logging.getLogger().setLevel(coco.logging.CRITICAL)
cutout = atlite.Cutout(snakemake.input.cutout) cutout = atlite.Cutout(snakemake.input.cutout)
@ -56,6 +55,8 @@ if __name__ == "__main__":
urban_fraction = ( urban_fraction = (
urban_fraction.query("iso2 in @countries").set_index("iso2")["2019"].div(100) urban_fraction.query("iso2 in @countries").set_index("iso2")["2019"].div(100)
) )
if "XK" in countries:
urban_fraction["XK"] = urban_fraction["RS"]
# population in each grid cell # population in each grid cell
pop_cells = pd.Series(I.dot(nuts3["pop"])) pop_cells = pd.Series(I.dot(nuts3["pop"]))

2
scripts/build_retro_cost.py
View File

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

22
scripts/build_shapes.py
View File

@ -106,8 +106,6 @@ def _simplify_polys(polys, minarea=0.1, tolerance=None, filterremote=True):
def countries(naturalearth, country_list): def countries(naturalearth, country_list):
if "RS" in country_list:
country_list.append("KV")
df = gpd.read_file(naturalearth) 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[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["name"] = reduce(lambda x, y: x.fillna(y), fieldnames, next(fieldnames)).str[:2]
df.replace({"name": {"KV": "XK"}}, inplace=True)
df = df.loc[ df = df.loc[
df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5)) df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5))
] ]
s = df.set_index("name")["geometry"].map(_simplify_polys).set_crs(df.crs) 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 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 = 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( excludenuts = pd.Index(
( (
@ -217,13 +214,18 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
"FR9", "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.index.difference(excludenuts)]
df = df.loc[~df.country.isin(excludecountry)] df = df.loc[~df.country.isin(excludecountry)]
manual = gpd.GeoDataFrame( 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"], columns=["NUTS_ID", "country", "pop"],
geometry=gpd.GeoSeries(), geometry=gpd.GeoSeries(),
crs=df.crs, 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 = pd.concat([df, manual], sort=False)
df.loc["ME000", "pop"] = 650.0 df.loc["ME000", "pop"] = 617.0
return df return df

2
scripts/prepare_sector_network.py
View File

@ -281,7 +281,7 @@ def co2_emissions_year(
eurostat = build_eurostat(input_eurostat, countries) 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) eurostat_co2 = build_eurostat_co2(eurostat, year)
co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2) co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2)