diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 72eabf9e..8b9bf2e0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -51,7 +51,7 @@ repos:
 
   # Formatting with "black" coding style
 - repo: https://github.com/psf/black-pre-commit-mirror
-  rev: 24.4.2
+  rev: 24.8.0
   hooks:
       # Format Python files
   - id: black
diff --git a/config/config.default.yaml b/config/config.default.yaml
index 8f9e6d7a..8f22b02f 100644
--- a/config/config.default.yaml
+++ b/config/config.default.yaml
@@ -155,6 +155,7 @@ renewable:
     resource:
       method: wind
       turbine: Vestas_V112_3MW
+      smooth: true
       add_cutout_windspeed: true
     capacity_per_sqkm: 3
     # correction_factor: 0.93
@@ -174,6 +175,7 @@ renewable:
     resource:
       method: wind
       turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
+      smooth: true
       add_cutout_windspeed: true
     capacity_per_sqkm: 2
     correction_factor: 0.8855
@@ -190,6 +192,7 @@ renewable:
     resource:
       method: wind
       turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
+      smooth: true
       add_cutout_windspeed: true
     capacity_per_sqkm: 2
     correction_factor: 0.8855
@@ -206,6 +209,8 @@ renewable:
     resource:
       method: wind
       turbine: NREL_ReferenceTurbine_5MW_offshore
+      smooth: true
+      add_cutout_windspeed: true
     # ScholzPhd Tab 4.3.1: 10MW/km^2
     capacity_per_sqkm: 2
     correction_factor: 0.8855
@@ -281,7 +286,7 @@ lines:
   max_extension: 20000 #MW
   length_factor: 1.25
   reconnect_crimea: true
-  under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
+  under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity for lines in grid extract
   dynamic_line_rating:
     activate: false
     cutout: europe-2013-sarah3-era5
@@ -294,8 +299,25 @@ links:
   p_max_pu: 1.0
   p_nom_max: .inf
   max_extension: 30000 #MW
-  include_tyndp: true
-  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
+  under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity for lines in grid extract
+
+# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#transmission_projects
+transmission_projects:
+  enable: true
+  include:
+    tyndp2020: true
+    nep: true
+    manual: true
+  skip:
+  - upgraded_lines
+  - upgraded_links
+  status:
+  - under_construction
+  - in_permitting
+  - confirmed
+    #- planned_not_yet_permitted
+    #- under_consideration
+  new_link_capacity: zero #keep or zero
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#transformers
 transformers:
@@ -760,7 +782,7 @@ industry:
   MWh_CH4_per_tMeOH: 10.25
   MWh_MeOH_per_tMeOH: 5.528
   hotmaps_locate_missing: false
-  reference_year: 2015
+  reference_year: 2019
 
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#costs
diff --git a/data/ch_industrial_production_per_subsector.csv b/data/ch_industrial_production_per_subsector.csv
new file mode 100644
index 00000000..acb7ed81
--- /dev/null
+++ b/data/ch_industrial_production_per_subsector.csv
@@ -0,0 +1,16 @@
+sector,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023
+Nahrung,19035,17728,16486,16782,16261,17062,17132,17335,15625,15713
+Textil / Leder,1845,1742,1450,1497,1433,1488,1331,1496,1371,1112
+Papier / Druck,12407,12257,11100,11086,9530,9436,8482,9186,8850,7657
+Chemie / Pharma,27253,27050,27501,26953,26506,26055,24889,25595,25028,23240
+Zement / Beton,15513,13307,13576,13429,13263,13221,12612,12843,12919,11400
+Andere NE-Mineralien,4029,3820,3884,4254,3546,3577,3475,3602,3603,3178
+Metall / Eisen,7841,7889,7638,8049,8053,7188,7051,6872,6782,5531
+NE-Metall,3386,3037,2833,2813,2743,2931,2719,2992,2896,2653
+Metall / Geräte,14652,14993,14272,14689,14300,15037,13946,15042,13862,12578
+Maschinen,4561,4724,4861,4957,4365,4364,4061,4290,3698,3545
+Andere Industrien,10750,10825,10225,9833,9689,9545,8696,9371,8823,8157
+current electricity,55142,53760,51302,52173,51604,51389,48933,51730,50926,46969
+,,,,,,,,,,
+,,,,,,,,,,
+"source: https://pubdb.bfe.admin.ch/de/publication/download/11817, accessed August 2024",,,,,,,,,,
diff --git a/data/district_heat_share.csv b/data/district_heat_share.csv
index 5afd65c8..07d4f51d 100644
--- a/data/district_heat_share.csv
+++ b/data/district_heat_share.csv
@@ -22,7 +22,7 @@ RS,25,5821
 SI,8.86,1739
 ES,0.251589260787732,1273
 SE,50.4,
-UK,2,
+GB,2,
 BY,70,
 EE,52,5406
 KO,3,207
diff --git a/data/entsoegridkit/links.csv b/data/entsoegridkit/links.csv
index 00a488dd..abcaf0cc 100644
--- a/data/entsoegridkit/links.csv
+++ b/data/entsoegridkit/links.csv
@@ -6,7 +6,7 @@ link_id,bus0,bus1,length,underground,under_construction,tags,geometry
 5587,1377,2382,76847.0139826037,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"32533", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"200", "symbol"=>"DC-Line", "country"=>"IT", "t9_code"=>"0", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(8.67675371049727 40.6777653795244,9.03900099999999 40.979898,9.22164899999999 41.133159,9.19977299501706 41.2082924934473)'
 5640,1422,1638,234733.218840324,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"32590", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"Rómulo", "symbol"=>"DC-Line", "country"=>"ES", "t9_code"=>"0", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(2.48932993486183 39.561252379133,1.13159199999999 39.610978,0 39.710356,-0.234388957535875 39.7314420592468)'
 13589,2262,7428,316517.539537871,f,f,,'LINESTRING(9.17009350125146 41.2967653544603,9.38095099999999 41.331451,9.858856 41.352072,10.70755 41.479776,11.25 41.448903,12.100067 41.432431,12.380219 41.426253,12.418671 41.401536,12.704315 41.347948,12.805939 41.368564,12.9016442293009 41.3921592955445)'
-14802,2258,7029,391819.608605717,f,t,,'LINESTRING(14.0986517070226 42.4133438660838,14.412689 42.431566,15.115814 42.363618,16.269379 42.067646,16.875 42.126747,16.962891 42.135913,18.531189 42.271212,18.7271798293119 42.3522936900005)'
+14802,2258,7029,391819.608605717,f,f,,'LINESTRING(14.0986517070226 42.4133438660838,14.412689 42.431566,15.115814 42.363618,16.269379 42.067646,16.875 42.126747,16.962891 42.135913,18.531189 42.271212,18.7271798293119 42.3522936900005)'
 14668,2333,3671,146536.932669904,f,t,,'LINESTRING(6.04271995139229 45.4637174756646,6.16607700000001 45.327048,6.351471 45.183973,6.54922499999999 45.148148,6.62338299999999 45.101638,6.642609 45.089036,6.70440700000001 45.05121,6.980438 45.089036,7.00653099999999 45.092914,7.21939099999999 45.094853,7.223511 45.089036,7.378693 44.871443,7.32136143270145 44.8385424366672)'
 14808,2379,2383,103628.671904731,f,f,,'LINESTRING(9.37725891362686 42.7057449479108,9.79980499999999 42.799431,10.5931379465185 42.9693952059839)'
 5575,2379,2380,24868.4258834249,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"32521", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>" ", "symbol"=>"DC-Line", "country"=>"FR", "t9_code"=>"0", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(9.37679000208623 42.7053229039427,9.357605 42.552069,9.45054814341409 42.5389781005166)'
@@ -15,7 +15,7 @@ link_id,bus0,bus1,length,underground,under_construction,tags,geometry
 5583,2382,7428,11623.019620339,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"32529", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>" ", "symbol"=>"DC-Line", "country"=>"IT", "t9_code"=>"FR-IT-01", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"1", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"1.555323123e+12", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(9.17008474107272 41.2967639130447,9.168091 41.303603,9.18319700000001 41.250968,9.1995514318356 41.2089447559651)'
 14825,2476,2585,45367.7245799963,f,f,,'LINESTRING(2.98259070757654 42.2776059846425,2.90313700000001 42.397094,2.867432 42.467032,2.77404800000001 42.655172)'
 8745,3611,8302,9361.61122972312,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"120591", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"None", "symbol"=>"DC-Line", "country"=>"CH", "t9_code"=>"None", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"1", "CreatedDate"=>"1.556535027e+12", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(7.95410166666667 47.5542867377085,7.928009 47.555214,7.937622 47.526475,7.96895162362761 47.4961125343931)'
-14801,4709,4781,50206.4589537583,f,t,,'LINESTRING(6.43068069229957 50.8136946409214,6.020508 50.766865,5.925751 50.755572,5.73118285928413 50.7304278585398)'
+14801,4709,4781,50206.4589537583,f,f,,'LINESTRING(6.43068069229957 50.8136946409214,6.020508 50.766865,5.925751 50.755572,5.73118285928413 50.7304278585398)'
 14814,4972,5062,232745.802729813,f,f,,'LINESTRING(4.04528166772434 51.9611233898246,2.41561900000001 51.702353,0.794192405058928 51.4189824547604)'
 5558,4975,7427,45665.1050240866,f,t,'"MW"=>"None", "TSO"=>"None", "oid"=>"32502", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>" ", "symbol"=>"DC-Line", "country"=>"UK", "t9_code"=>" BE-UK-01", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"1", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"1.555407949e+12", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(1.92947399999999 51.251601,1.27623412238205 51.2327009391635)'
 14826,4977,4983,52725.5506558225,f,f,,'LINESTRING(1.75051314494826 50.9186901861196,1.43508900000001 50.970535,1.02353536683349 51.0370060560335)'
@@ -33,16 +33,16 @@ link_id,bus0,bus1,length,underground,under_construction,tags,geometry
 5571,5743,7074,89346.6337548304,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"32517", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"HelWin1", "symbol"=>"DC-Line", "country"=>"DE", "t9_code"=>"0", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"1.545224101e+12", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(8.12610708224912 54.310749538123,8.238373 54.256401,9.32699442549698 53.9319562532009)'
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-14803,5751,5803,280301.445474794,f,t,,'LINESTRING(6.75668661933496 53.437616158174,6.838989 53.664171,6.96258499999999 53.785238,7.34298700000001 53.882488,7.80029300000001 54.517096,8.20678699999999 55.297102,8.86005375885099 55.4336013425692)'
+14803,5751,5803,280301.445474794,f,f,,'LINESTRING(6.75668661933496 53.437616158174,6.838989 53.664171,6.96258499999999 53.785238,7.34298700000001 53.882488,7.80029300000001 54.517096,8.20678699999999 55.297102,8.86005375885099 55.4336013425692)'
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 12932,5770,5773,6905.52230262641,f,t,,'LINESTRING(7.15460523215685 53.4027398808691,7.24823000000001 53.375956)'
-14848,5858,6358,574884.998052791,f,t,,'LINESTRING(6.81690675921544 58.6338502746805,6.63024900000001 58.249559,6.78268399999999 57.579197,7.17544599999999 56.532986,7.17407200000001 56.5345,7.46521000000001 55.776573,7.46521000000001 55.776573,7.64099100000001 55.312736,8.458099 54.316523,9.394684 53.934262)'
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+14804,5949,6684,695432.776022422,f,f,,'LINESTRING(6.64773945778347 59.5995729910866,6.483307 59.539192,6.374817 59.538495,6.24847399999999 59.510636,6.196289 59.448566,5.898285 59.321981,5.64697299999999 59.234284,5.62500000000001 59.223042,4.81338500000001 58.813742,2.03384400000001 57.374679,0 56.170023,-0.650940000000012 55.776573,-1.55838055228731 55.2221613174321)'
 5635,6300,6348,93313.2906756649,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"32585", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"150", "symbol"=>"DC-Line", "country"=>"SE", "t9_code"=>"0", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(18.2272491895352 57.5711315582343,17.274628 57.645401,16.875 57.674052,16.6818074486274 57.692364166947)'
 14819,6311,6416,122337.134741418,f,f,,'LINESTRING(10.2163282994747 57.1311139024238,10.567474 57.20771,10.737762 57.192832,10.972595 57.230016,11.25 57.33171,11.532898 57.436081,11.867981 57.556366,12.0227165657676 57.561507168045)'
 14809,6311,6416,122935.90852816,f,f,,'LINESTRING(10.2163571716117 57.1310010356663,10.366974 57.123569,10.578461 57.16678,10.740509 57.15263,11.001434 57.197296,11.174469 57.255281,11.25 57.282754,11.56723 57.399104,12.0227887239052 57.5613889668514)'
@@ -58,6 +58,6 @@ link_id,bus0,bus1,length,underground,under_construction,tags,geometry
 14818,6586,6618,257364.279393886,f,f,,'LINESTRING(21.3559064590049 61.0800030227353,21.303864 61.005076,20.946808 60.801394,18.153534 60.501202,18.007965 60.483615,17.171631 60.503906,17.0593630437863 60.5503864910584)'
 14817,6589,6618,197128.229552834,f,f,,'LINESTRING(21.3557421230034 61.0800501553429,20.902863 60.846249,18.224945 60.556604,18.0193872312079 60.533018071939)'
 14812,6620,6623,140169.735736189,f,f,,'LINESTRING(22.3045576957813 60.4368452717433,21.404114 60.329667,19.8472351583549 60.129935739173)'
-8394,6684,6696,21158.5735245602,f,t,'"MW"=>"None", "TSO"=>"None", "oid"=>"89791", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"None", "symbol"=>"DC-Line", "country"=>"NO", "t9_code"=>"None", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"1.518010133e+12", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(6.64851407057135 59.5996162767494,6.99238592942864 59.5246589234811)'
+8394,6684,6696,21158.5735245602,f,f,'"MW"=>"None", "TSO"=>"None", "oid"=>"89791", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"None", "symbol"=>"DC-Line", "country"=>"NO", "t9_code"=>"None", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"1.518010133e+12", "DeletedDate"=>"None", "ModifiedDate"=>"None", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(6.64851407057135 59.5996162767494,6.99238592942864 59.5246589234811)'
 5569,5787,8272,38561.1931761179,f,t,'"MW"=>"None", "TSO"=>"None", "oid"=>"32514", "ext1"=>"None", "EIC_2"=>"None", "EIC_3"=>"None", "EIC_4"=>"None", "text_"=>"DolWin 3", "symbol"=>"DC-Line", "country"=>"DE", "t9_code"=>"0", "visible"=>"1", "EIC_code"=>"None", "tie_line"=>"0", "oneCircuit"=>"0", "CreatedDate"=>"None", "DeletedDate"=>"None", "ModifiedDate"=>"1.489072219e+12", "Internalcomments"=>"None", "visible_on_printed"=>"1"','LINESTRING(7.223511 53.1805270078955,7.223511 53.179704,7.21527100000001 53.121229,7.24273699999999 52.932086,7.16056753068224 52.8486333236236)'
 14813,7053,7430,192856.020480538,f,f,,'LINESTRING(10.8823542109264 53.948125809387,11.25 54.061,11.657867 54.186548,12.208557 54.386955,12.236023 54.402946,12.43515 54.541003,12.602692 54.684153,12.745514 54.844199,12.744141 54.842618,12.87735 54.979978,12.947388 55.077581,12.9299984288384 55.0630403498842)'
diff --git a/data/links_tyndp.csv b/data/links_tyndp.csv
deleted file mode 100644
index 43030be5..00000000
--- a/data/links_tyndp.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-Name,Converterstation 1,Converterstation 2,Length (given) (km),Length (distance*1.2) (km),Power (MW),status,replaces,Ref,x1,y1,x2,y2
-Biscay Gulf,Gatica (ES),Cubnezais (FR),370,,2200,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/16,-2.867,43.367,-0.408943,45.074191
-Italy-France,Piossasco (IT),Grand Ile (FR),190,,1000,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/21,7.468,44.9898,6.045,45.472
-IFA2,Tourbe (FR),Chilling (GB),,247.2,1000,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/25,-0.172042,49.083593,-1.277269,50.839338
-Italy-Montenegro,Villanova (IT),Latsva (MT),445,,1200,under construction,Link.14539,https://tyndp.entsoe.eu/tyndp2018/projects/projects/28,14.125,42.3947222222222,18.7947222222222,42.3175
-NordLink,Tonstad (NO),Wilster (DE),514,,1400,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/37,6.716948,58.662631,9.373979,53.922479
-COBRA cable,Endrup (DK),Eemshaven (NL),325,,700,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/71,8.718392,55.523115,6.835494,53.438589
-Thames Estuary Cluster (NEMO-Link),Richborough (GB),Gezelle (BE),140,,1000,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/74,1.324854,51.295891,3.23043,51.24902
-Anglo-Scottish -1,Hunterston (UK),Deeside (UK),422,,2400,built,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/77,-4.898329,55.723331,-3.032972,53.199735
-ALEGrO,Lixhe (BE),Oberzier (DE),100,,1000,built,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/92,5.67933,50.7567965,6.474704,50.867532
-North Sea Link,Kvilldal (NO),Blythe (GB),720,,1400,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/110,6.637527,59.515096,-1.510277,55.126957
-HVDC SuedOstLink,Wolmirstedt (DE),Isar (DE),,557,2000,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/130,11.629014,52.252137,12.091596,48.080837
-HVDC Line A-North,Emden East (DE),Osterath (DE),,284,2000,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/132,7.206009,53.359403,6.619451,51.272935
-France-Alderney-Britain,Exeter (UK),Menuel (FR),220,,1400,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/153,-3.533899,50.718412,-1.469216,49.509594
-Viking DKW-GB,Bicker Fen (GB),Revsing (DK),,807,1400,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/167,-0.203587,52.93979,9.178363,55.509166
-ElecLink,Sellindge (UK),Mandarins (FR),,72,1000,under construction,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/172,0.975555555555556,51.1058333333333,1.78472222222222,50.9030555555556
-Greenconnector,Verderio (IT),Sils i.D. (CH),150,,1000,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/174,9.439781,45.668539,9.76569,46.432156
-Hansa PowerBridge I,Hurva (SE),Guestrow (DE),,283,700,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/176,13.6022222222222,55.8330555555556,12.189538,53.803155
-NorthConnect,Simadalen (NO),Peterhead (UK),650,,1400,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/190,7.16027,60.500527,-1.784066,57.508123
-HVDC SuedLink,Wilster (DE),Großgartach (DE),,637,4000,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/235,9.373979,53.922479,9.117193,49.145157
-AQUIND Interconnector,Lovedean (GB),Barnabos (FR),254,,2000,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/247,-1.020512,50.908244,0.991736,49.656631
-HVDC Ultranet,Osterath (DE),Philippsburg (DE),,314,600,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/254,6.619451,51.272935,8.458036,49.235253
-Gridlink,Kingsnorth (UK),Warande (FR),160,,1400,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/285,0.596111111111111,51.41972,2.376776,51.034368
-NeuConnect,Grain (UK),Fedderwarden (DE),680,,1400,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/309,0.716666666666667,51.44,8.046524,53.562763
-NordBalt,Klaipeda (LT),Nybro (SE),450,,700,built,,https://en.wikipedia.org/wiki/NordBalt,21.256667,55.681667,15.854167,56.767778
-Estlink 1,Harku (EE),Espoo (FI),105,,350,built,,https://en.wikipedia.org/wiki/Estlink,24.560278,59.384722,24.551667,60.203889
-Greenlink,Waterford (IE),Pembroke (UK),,180,500,under construction,,https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/286,-6.987,52.260,-4.986,51.686
-Celtic Interconnector,Aghada (IE),La Martyre (FR),,572,700,under consideration,,https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/107,-8.16642,51.91413,-4.184,48.459
-GiLA,Bordeaux (FR),Nantes (FR),,312,640,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,-1.209,46.901,-0.576,44.960
-HG North Tyrrhenian Corridor,Milan (IT),Viterbo (IT),,500,2000,in permitting,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,9.409,45.553,12.015,42.244
-HG Adriatic Corridor,Ferrara (IT),Foggia (IT),,582,2000,in permitting,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,11.661,44.855,15.550,41.513
-SAPEI 2,Fioumesanto (IT),Montalto (IT),,390,1000,in permitting,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,8.283,40.790,11.602,42.331
-HG Ionian-Tyrrhenian Corridor,Rossano (IT),Latina (IT),,496,2000,in permitting,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,16.629,39.568,12.779,41.430
-HG Ionian-Tyrrhenian Corridor 2,Rossano (IT),Catania (IT),,330,2000,in permitting,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,16.629,39.568,15.049,37.408
-Germany-UK Hybrid Interconnector,Fetteresso (UK),Emden (DE),800,,2000,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,-2.383,56.991,7.207,53.376
-NU-Link Interconnector,Hornsea (UK),Moerdijk (NL),,460,1200,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,-0.261,53.655,4.586,51.661
-APOLLO-LINK,La Farga (ES),La Spezia (IT),,725,2091,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,2.883,42.062,9.884,44.107
-Baltic WindConnector (BWC),Lubmin (DE),Lihula (EE),,960,2000,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,13.686,54.139,23.818,58.675
-High-Voltage Direct Current Interconnector Project Romania-Hungary,Constanta (RO),Albertirsa (HU),,930,2500,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,28.588,44.201,19.584,47.224
-Rhine-Main-Link,Ovelgönne (DE),Marxheim (DE),,433,4000,in permitting,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,8.379,53.315,8.435,50.078
-Green Aegean Interconnector,Arachthos (GR),Ottenhofen (DE),,600,3000,under consideration,,https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx,20.967,39.185,11.868,48.207
diff --git a/data/parameter_corrections.yaml b/data/parameter_corrections.yaml
index df15738a..3d19bed8 100644
--- a/data/parameter_corrections.yaml
+++ b/data/parameter_corrections.yaml
@@ -15,6 +15,7 @@ Link:
       "115000": 1200 # Caithness Moray HVDC
     index:
       "14804": 1400 # North-Sea link (NSN Link)
+      "8394": 1400 # North-Sea Link (NSN Link) continuation
       "14822": 700 # NO-DK Skagerrak 4
       "14827": 440 # NO-DK Skagerrak 3
       "14810": 500 # NO-DK Skagerrak 1-2
diff --git a/data/transmission_projects/manual/new_links.csv b/data/transmission_projects/manual/new_links.csv
new file mode 100644
index 00000000..901f9593
--- /dev/null
+++ b/data/transmission_projects/manual/new_links.csv
@@ -0,0 +1,21 @@
+,bus0,bus1,length,p_nom,project_status,tags,x0,y0,x1,y1
+TYNDP2018_154,Exeter (UK),Menuel (FR),220,1400,in_permitting,"{name:France-Alderney-Britain, url:https://tyndp.entsoe.eu/tyndp2018/projects/projects/153}",-3.533899,50.718412,-1.469216,49.509594
+TYNDP2018_25,Tourbe (FR),Chilling (GB),247.2,1000,under_construction,"{name:IFA2, url:https://tyndp.entsoe.eu/tyndp2018/projects/projects/25]",-0.172042,49.083593,-1.277269,50.839338
+TYNDP2018_285,Kingsnorth (UK),Warande (FR),160,1400,in_permitting,"{name:Gridlink, url:https://tyndp.entsoe.eu/tyndp2018/projects/projects/285}",0.596111111111111,51.41972,2.376776,51.034368
+TYNDP2018_190,Simadalen (NO),Peterhead (UK),650,1400,in_permitting,"{name:NorthConnect, url:https://tyndp.entsoe.eu/tyndp2018/projects/projects/190}",7.16027,60.500527,-1.784066,57.508123
+TYNDP2018_16,Gatica (ES),Cubnezais (FR),370,2200,in_permitting,"{name:Biscay Gulf, url:https://tyndp.entsoe.eu/tyndp2018/projects/projects/16}",-2.867,43.367,-0.408943,45.074191
+TYNDP2018_21,Piossasco (IT),Grand Ile (FR),190,1000,under_construction,"{name:Italy-France, url:https://tyndp.entsoe.eu/tyndp2018/projects/projects/21}",7.468,44.9898,6.045,45.472
+TYNDP2022_286,Waterford (IE),Pembroke (UK),180,500,under_construction,"{name:Greenlink,url:{name:TYNDP2022_286,url:https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/286}}",-6.987,52.26,-4.986,51.686
+TYNDP2024_1134,Bordeaux (FR),Nantes (FR),312,640,under_consideration,"{name:GiLA,url:{name:TYNDP2024_1134,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",-1.209,46.901,-0.576,44.96
+TYNDP2024_1157,Milan (IT),Viterbo (IT),500,2000,in_permitting,"{name:HG North Tyrrhenian Corridor,url:{name:TYNDP2024_1157,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",9.409,45.553,12.015,42.244
+TYNDP2024_1166,Ferrara (IT),Foggia (IT),582,2000,in_permitting,"{name:HG Adriatic Corridor,url:{name:TYNDP2024_1166,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",11.661,44.855,15.55,41.513
+TYNDP2024_1169,Fioumesanto (IT),Montalto (IT),390,1000,in_permitting,"{name:SAPEI 2,url:{name:TYNDP2024_1169,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",8.283,40.79,11.602,42.331
+TYNDP2024_1168,Rossano (IT),Latina (IT),496,2000,in_permitting,"{name:HG Ionian-Tyrrhenian Corridor,url:{name:TYNDP2024_1168,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",16.629,39.568,12.779,41.43
+TYNDP2024_1168_1,Rossano (IT),Catania (IT),330,2000,in_permitting,"{name:HG Ionian-Tyrrhenian Corridor 2,url:{name:TYNDP2024_1168_1,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",16.629,39.568,15.049,37.408
+TYNDP2024_1192,Fetteresso (UK),Emden (DE),800,2000,under_consideration,"{name:Germany-UK Hybrid Interconnector,url:{name:TYNDP2024_1192,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",-2.383,56.991,7.207,53.376
+TYNDP2024_1197,Hornsea (UK),Moerdijk (NL),460,1200,under_consideration,"{name:NU-Link Interconnector,url:{name:TYNDP2024_1197,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",-0.261,53.655,4.586,51.661
+TYNDP2024_1210,La Farga (ES),La Spezia (IT),725,2091,under_consideration,"{name:APOLLO-LINK,url:{name:TYNDP2024_1210,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",2.883,42.062,9.884,44.107
+TYNDP2024_1211,Lubmin (DE),Sindi (EE),960,2000,under_consideration,"{name:Baltic WindConnector (BWC),url:{name:TYNDP2024_1211,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",13.686,54.139,24.671903,58.429161
+TYNDP2024_1216,Constanta (RO),Albertirsa (HU),930,2500,under_consideration,"{name:High-Voltage Direct Current Interconnector Project Romania-Hungary,url:{name:TYNDP2024_1216,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",28.588,44.201,19.584,47.224
+TYNDP2024_1229,Ovelgönne (DE),Marxheim (DE),433,4000,in_permitting,"{name:Rhine-Main-Link,url:{name:TYNDP2024_1229,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",8.379,53.315,8.435,50.078
+TYNDP2024_1231,Arachthos (GR),Ottenhofen (DE),600,3000,under_consideration,"{name:Green Aegean Interconnector,url:{name:TYNDP2024_1231,url:https://eepublicdownloads.blob.core.windows.net/public-cdn-container/tyndp-documents/TYNDP2024/240220_TYNDP2024_project_portfolio.xlsx}}",20.967,39.185,11.868,48.207
diff --git a/data/transmission_projects/nep/new_lines.csv b/data/transmission_projects/nep/new_lines.csv
new file mode 100644
index 00000000..19c50116
--- /dev/null
+++ b/data/transmission_projects/nep/new_lines.csv
@@ -0,0 +1,16 @@
+,bus0,bus1,length,v_nom,num_parallel,project_status,build_year,tags,x0,y0,x1,y1,type
+P43,Dipperz/Hessen,Bergrheinfeld//Bayern,85,380.0,2.0,confirmed,2031,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Dipperz/Hessen"",""location1"":""Bergrheinfeld//Bayern""",9.807198712581199,50.54115855,10.1810033,50.0082136,Al/St 490/64 4-bundle 380.0
+P71,Kiel/Schleswig-Holstein,Göhl/Schleswig-Holstein,85,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Kiel/Schleswig-Holstein"",""location1"":""Göhl/Schleswig-Holstein""",10.135555,54.3227085,10.9399634,54.2861191,Al/St 490/64 4-bundle 380.0
+P84,Hamburg,Sahms/Schleswig-Holstein,35,380.0,2.0,confirmed,2031,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Hamburg"",""location1"":""Sahms/Schleswig-Holstein""",10.000654,53.550341,10.5331297,53.5252973,Al/St 490/64 4-bundle 380.0
+P227,Lübeck/Schleswig-Holstein,Sahms/Schleswig-Holstein,52,380.0,2.0,confirmed,2030,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Lübeck/Schleswig-Holstein"",""location1"":""Sahms/Schleswig-Holstein""",10.684738,53.866444,10.5331297,53.5252973,Al/St 490/64 4-bundle 380.0
+P402,Westerkappeln/Nordrhein-Westfalen,Gersteinwerk/Nordrhein-Westfalen,89,380.0,2.0,confirmed,2033,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Westerkappeln/Nordrhein-Westfalen"",""location1"":""Gersteinwerk/Nordrhein-Westfalen""",7.8772237,52.3141716,7.722207887811252,51.6749132,Al/St 490/64 4-bundle 380.0
+P470,Emden/Niedersachsen,Dörpen/West /Niedersachsen,66,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Emden/Niedersachsen"",""location1"":""Dörpen/West /Niedersachsen""",7.2058304,53.3670541,7.256622642835093,52.9807372,Al/St 490/64 4-bundle 380.0
+P476,Hochwöhrden/Schleswig-Holstein,Mehlbek/Schleswig-Holstein,39,380.0,2.0,confirmed,2032,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Hochwöhrden/Schleswig-Holstein"",""location1"":""Mehlbek/Schleswig-Holstein""",9.0323968,54.158602,9.4346536,54.0045912,Al/St 490/64 4-bundle 380.0
+P478,Pöschendorf/Schleswig-Holstein,Alfstedt/Niedersachsen,37,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Pöschendorf/Schleswig-Holstein"",""location1"":""Alfstedt/Niedersachsen""",9.4944931,54.0409041,9.0666915,53.5499782,Al/St 490/64 4-bundle 380.0
+P485,Herlasgrün/Sachsen,Marktleuthen/Bayern,69,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Herlasgrün/Sachsen"",""location1"":""Marktleuthen/Bayern""",12.2299537,50.5680975,11.9945062,50.1279651,Al/St 490/64 4-bundle 380.0
+P490,Petersgmünd /Bayern,Goldshöfe /Baden-Württemberg,104,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Petersgmünd /Bayern"",""location1"":""Goldshöfe /Baden-Württemberg""",11.0252473,49.185644,10.1279814,48.8940723,Al/St 490/64 4-bundle 380.0
+P540,Eisfeld/Thüringen,Grafenrheinfeld /Bayern,126,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Eisfeld/Thüringen"",""location1"":""Grafenrheinfeld /Bayern""",10.908994,50.4269984,10.1966808,50.0055317,Al/St 490/64 4-bundle 380.0
+P625,Streumen/Sachsen,Schmölln/Sachsen,92,380.0,2.0,confirmed,2035,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Streumen/Sachsen"",""location1"":""Schmölln/Sachsen""",13.4054041,51.3582767,14.2347005,51.1244043,Al/St 490/64 4-bundle 380.0
+P627,Schossin/Mecklenburg-Vorpommern,Perleberg/Brandenburg,89,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Schossin/Mecklenburg-Vorpommern"",""location1"":""Perleberg/Brandenburg""",11.2823593,53.5284043,11.8627933,53.0762716,Al/St 490/64 4-bundle 380.0
+P635,Grabowhöfe/Mecklenburg-Vorpommern,Marke/Sachsen-Anhalt,280,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Grabowhöfe/Mecklenburg-Vorpommern"",""location1"":""Marke/Sachsen-Anhalt""",12.594171,53.5683057,12.252458735300388,51.7301547,Al/St 490/64 4-bundle 380.0
+P636,Delitzsch/Sachsen,Eula/Sachsen,58,380.0,2.0,confirmed,2037,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Delitzsch/Sachsen"",""location1"":""Eula/Sachsen""",12.342857,51.5255661,12.5130329,51.1496022,Al/St 490/64 4-bundle 380.0
diff --git a/data/transmission_projects/nep/new_links.csv b/data/transmission_projects/nep/new_links.csv
new file mode 100644
index 00000000..d237893f
--- /dev/null
+++ b/data/transmission_projects/nep/new_links.csv
@@ -0,0 +1,12 @@
+,bus0,bus1,p_nom,length,project_status,build_year,underground,tags,x0,y0,x1,y1
+DC21,Wilhelmshaven/Niedersachsen,Lippetal/Nordrhein-Westfalen,2000.0,270,confirmed,2032,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Wilhelmshaven/Niedersachsen"",""location1"":""Lippetal/Nordrhein-Westfalen""",8.106301,53.5278793,8.0787633,51.6516157
+DC25,Heide/Schleswig-Holstein,Polsum/Nordrhein-Westfalen,2000.0,440,confirmed,2032,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Heide/Schleswig-Holstein"",""location1"":""Polsum/Nordrhein-Westfalen""",9.0929015,54.1948456,7.0528322,51.6264919
+DC31,Hemmingstedt/Schleswig-Holstein,Stralendorf/Mecklenburg-Vorpommern,2000.0,212,confirmed,2032,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Hemmingstedt/Schleswig-Holstein"",""location1"":""Stralendorf/Mecklenburg-Vorpommern""",9.08263,54.1506435,11.3010418,53.572637
+DC32,Pöschendorf/Schleswig-Holstein,Stralendorf/Mecklenburg-Vorpommern,2000.0,170,confirmed,2034,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Pöschendorf/Schleswig-Holstein"",""location1"":""Stralendorf/Mecklenburg-Vorpommern""",9.4944931,54.0409041,11.3010418,53.572637
+DC34,Ovelgönne/Niedersachsen,Bürstadt/Hessen,2000.0,523,confirmed,2033,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Ovelgönne/Niedersachsen"",""location1"":""Bürstadt/Hessen""",8.4209977,53.3427799,8.4747076,49.6531823
+DC35,Ovelgönne/Niedersachsen,Marxheim/Taunus,2000.0,461,confirmed,2035,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Ovelgönne/Niedersachsen"",""location1"":""Marxheim/Taunus""",8.4209977,53.3427799,8.4320722,50.0724011
+DC40,Nüttermoor/Niedersachsen,Streumen/Sachsen,2000.0,594,confirmed,2037,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Nüttermoor/Niedersachsen"",""location1"":""Streumen/Sachsen""",7.4360347,53.2625382,13.4054041,51.3582767
+DC40plus,Dörpen/West,Klostermansfeld/Sachsen-Anhalt,2000.0,426,confirmed,2037,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Dörpen/West"",""location1"":""Klostermansfeld/Sachsen-Anhalt""",7.256622642835093,52.9807372,11.4933395,51.5841525
+DC41,Alfstedt/Niedersachsen,Hüffenhardt/Baden-Württemberg,2000.0,607,confirmed,2037,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Alfstedt/Niedersachsen"",""location1"":""Hüffenhardt/Baden-Württemberg""",9.0666915,53.5499782,9.0827453,49.2908862
+DC42,Sahms/Schleswig-Holstein,Böblingen/Baden-Württemberg,2000.0,737,confirmed,2037,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Sahms/Schleswig-Holstein"",""location1"":""Böblingen/Baden-Württemberg""",10.5331297,53.5252973,9.0113444,48.684969
+DC42plus,Sahms/Schleswig-Holstein,Trennfeld/Bayern,2000.0,546,confirmed,2037,True,"{""url"":""https://data.netzausbau.de/2037-2023/NEP/NEP_2037_2045_Bestaetigung.pdf"", ""status"":""confirmed"", ""origin"":""NEP"",""version"":2024,""location0"":""Sahms/Schleswig-Holstein"",""location1"":""Trennfeld/Bayern""",10.5331297,53.5252973,9.6151453,49.7950676
diff --git a/data/transmission_projects/template/new_lines.csv b/data/transmission_projects/template/new_lines.csv
new file mode 100644
index 00000000..2f75428c
--- /dev/null
+++ b/data/transmission_projects/template/new_lines.csv
@@ -0,0 +1,5 @@
+,project_status,length,build_year,underground,v_nom,tags,x0,y0,x1,y1,num_parallel
+New_line_1,in_permitting,100,2025,False,380,"{link to source, name of project, further custom information}",-7.793621146853301,41.52102394053344,-8.38943499999999,40.95916,2
+New_line_2,under_construction,100,2023,False,380,"{link to source, name of project, further custom information}",,,,,
+New_line_3,in_planning,100,2040,False,380,"{link to source, name of project, further custom information}",,,,,
+New_line_4,under_consideration,100,2050,true,380,"{link to source, name of project, further custom information}",,,,,
diff --git a/data/transmission_projects/template/new_links.csv b/data/transmission_projects/template/new_links.csv
new file mode 100644
index 00000000..ff2d652e
--- /dev/null
+++ b/data/transmission_projects/template/new_links.csv
@@ -0,0 +1,5 @@
+,project_status,length,build_year,underground,p_nom,tags,x0,y0,x1,y1
+New_link_1,in_permitting,100,2025,False,1000,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
+New_link_2,under_construction,100,2023,False,600,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
+New_link_3,in_planning,100,2040,False,2000,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
+New_link_4,under_consideration,100,2050,true,600,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
diff --git a/data/transmission_projects/template/upgraded_lines.csv b/data/transmission_projects/template/upgraded_lines.csv
new file mode 100644
index 00000000..86192a3f
--- /dev/null
+++ b/data/transmission_projects/template/upgraded_lines.csv
@@ -0,0 +1,5 @@
+,project_status,length,build_year,underground,v_nom,tags,x0,y0,x1,y1,num_parallel
+Upgraded_line_1,in_permitting,100,2025,False,380,"{link to source, name of project, further custom information}",-7.793621146853301,41.52102394053344,-8.38943499999999,40.95916,2
+Upgraded_line_2,under_construction,100,2023,False,380,"{link to source, name of project, further custom information}",,,,,
+Upgraded_line_3,in_planning,100,2040,False,380,"{link to source, name of project, further custom information}",,,,,
+Upgraded_line_4,under_consideration,100,2050,true,380,"{link to source, name of project, further custom information}",,,,,
diff --git a/data/transmission_projects/template/upgraded_links.csv b/data/transmission_projects/template/upgraded_links.csv
new file mode 100644
index 00000000..65270cc3
--- /dev/null
+++ b/data/transmission_projects/template/upgraded_links.csv
@@ -0,0 +1,5 @@
+,project_status,length,build_year,underground,p_nom,tags,x0,y0,x1,y1
+Upgraded_link_1,in_permitting,100,2025,False,1000,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
+Upgraded_link_2,under_construction,100,2023,False,600,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
+Upgraded_link_3,in_planning,100,2040,False,2000,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
+Upgraded_link_4,under_consideration,100,2050,true,600,"{link to source, name of project, further custom information}",-7.793621,41.52145,-8.38944,40.95915
diff --git a/data/transmission_projects/tyndp2020/new_lines.csv b/data/transmission_projects/tyndp2020/new_lines.csv
new file mode 100644
index 00000000..24729655
--- /dev/null
+++ b/data/transmission_projects/tyndp2020/new_lines.csv
@@ -0,0 +1,119 @@
+,project_status,length,build_year,underground,v_nom,tags,x0,y0,x1,y1,num_parallel,type
+TYNDP2020_0,in_permitting,131.0,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/1"", ""tyndp2020_proj_id""=>""1"", ""tyndp2020_invest_id""=>""4"", ""tyndp_status""=>""in_permitting""",-7.793621146853301,41.52102394053344,-8.38943499999999,40.95916,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_1,in_permitting,30.0,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/4"", ""tyndp2020_proj_id""=>""4"", ""tyndp2020_invest_id""=>""18"", ""tyndp_status""=>""in_permitting""",-8.273869552394737,42.46748640601422,-7.94998199999999,42.383908,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_2,in_permitting,140.21,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/4"", ""tyndp2020_proj_id""=>""4"", ""tyndp2020_invest_id""=>""496"", ""tyndp_status""=>""in_permitting""",-7.94998199999999,42.383908,-8.582626164089064,41.764651842353146,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_3,in_permitting,140.21,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/4"", ""tyndp2020_proj_id""=>""4"", ""tyndp2020_invest_id""=>""496"", ""tyndp_status""=>""in_permitting""",-8.582626164089064,41.764651842353146,-8.67645299999999,41.303603,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_4,under_construction,23.0,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/23"", ""tyndp2020_proj_id""=>""23"", ""tyndp2020_invest_id""=>""60"", ""tyndp_status""=>""under_construction""",3.08990499999999,50.50906,3.429108,50.773813,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_6,in_permitting,80.0,2023,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/33"", ""tyndp2020_proj_id""=>""33"", ""tyndp2020_invest_id""=>""90"", ""tyndp_status""=>""in_permitting""",11.126404,43.872158,11.413422,44.580687,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_8,in_permitting,120.5,2026,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/35"", ""tyndp2020_proj_id""=>""35"", ""tyndp2020_invest_id""=>""313"", ""tyndp_status""=>""in_permitting""",14.3524,49.1584,15.687103,49.531448,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_9,in_permitting,117.0,2028,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/35"", ""tyndp2020_proj_id""=>""35"", ""tyndp2020_invest_id""=>""315"", ""tyndp_status""=>""in_permitting""",14.3524,49.1584,13.282471,49.60804,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_11,under_construction,110.0,2020,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/39"", ""tyndp2020_proj_id""=>""39"", ""tyndp2020_invest_id""=>""144"", ""tyndp_status""=>""under_construction""",9.75860599999998,54.306108,9.29580699999999,55.075223,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_12,planned_not_yet_permitting,157.0,2027,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/47"", ""tyndp2020_proj_id""=>""47"", ""tyndp2020_invest_id""=>""689"", ""tyndp_status""=>""planned_not_yet_permitting""",10.06485,48.282279,10.897064,47.236355,2,Al/St 240/40 4-bundle 380.0
+TYNDP2020_13,under_construction,110.0,2020,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/48"", ""tyndp2020_proj_id""=>""48"", ""tyndp2020_invest_id""=>""1500"", ""tyndp_status""=>""under_construction""",17.539673,47.882276,17.786865,47.653363,2,Al/St 240/40 4-bundle 380.0
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diff --git a/data/transmission_projects/tyndp2020/new_links.csv b/data/transmission_projects/tyndp2020/new_links.csv
new file mode 100644
index 00000000..902cc9b3
--- /dev/null
+++ b/data/transmission_projects/tyndp2020/new_links.csv
@@ -0,0 +1,49 @@
+,project_status,length,build_year,underground,p_nom,tags,x0,y0,x1,y1
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diff --git a/data/transmission_projects/tyndp2020/upgraded_lines.csv b/data/transmission_projects/tyndp2020/upgraded_lines.csv
new file mode 100644
index 00000000..ecf07b6a
--- /dev/null
+++ b/data/transmission_projects/tyndp2020/upgraded_lines.csv
@@ -0,0 +1,27 @@
+,project_status,length,build_year,underground,v_nom,tags,x0,y0,x1,y1,num_parallel,type
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+14758,under_consideration,12.0,2030,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/377"", ""tyndp2020_proj_id""=>""377"", ""tyndp2020_invest_id""=>""1561"", ""tyndp_status""=>""under_consideration""",5.71701000000001,51.094036,5.89691199999999,51.140586,2,Al/St 240/40 4-bundle 380.0
+8621,in_permitting,131.0,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/1"", ""tyndp2020_proj_id""=>""1"", ""tyndp2020_invest_id""=>""4"", ""tyndp_status""=>""in_permitting""",-7.72338899999999,41.614416,-7.793621146853301,41.52102394053344,2,Al/St 240/40 4-bundle 380.0
+6036,in_permitting,50.0,2025,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/103"", ""tyndp2020_proj_id""=>""103"", ""tyndp2020_invest_id""=>""1540"", ""tyndp_status""=>""in_permitting""",5.46295200000001,51.422333,5.89691199999999,51.140586,2,Al/St 240/40 4-bundle 380.0
+3715,in_permitting,94.0,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/142"", ""tyndp2020_proj_id""=>""142"", ""tyndp2020_invest_id""=>""257"", ""tyndp_status""=>""in_permitting""",26.003265,42.200038,24.483032,42.09007,2,Al/St 240/40 4-bundle 380.0
+8260,in_permitting,74.0,2025,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/144"", ""tyndp2020_proj_id""=>""144"", ""tyndp2020_invest_id""=>""270"", ""tyndp_status""=>""in_permitting""",21.88777,45.30413,21.2815,45.7426,2,Al/St 240/40 4-bundle 380.0
+8367,in_permitting,274.0,2025,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/144"", ""tyndp2020_proj_id""=>""144"", ""tyndp2020_invest_id""=>""270"", ""tyndp_status""=>""in_permitting""",21.2815,45.7426,21.409607,46.176978,2,Al/St 240/40 4-bundle 380.0
+1693,in_permitting,,2026,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/227"", ""tyndp2020_proj_id""=>""227"", ""tyndp2020_invest_id""=>""630"", ""tyndp_status""=>""in_permitting""",20.187378,44.664746,19.500315,43.953021,2,Al/St 240/40 4-bundle 380.0
+12696,in_permitting,147.0,2022,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/230"", ""tyndp2020_proj_id""=>""230"", ""tyndp2020_invest_id""=>""1232"", ""tyndp_status""=>""in_permitting""",15.038505,52.76751,16.729431,52.32359,2,Al/St 240/40 4-bundle 380.0
+3966,planned_not_yet_permitting,70.0,2029,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/276"", ""tyndp2020_proj_id""=>""276"", ""tyndp2020_invest_id""=>""1207"", ""tyndp_status""=>""planned_not_yet_permitting""",-0.812988000000009,43.979969,-0.547943,44.563077,2,Al/St 240/40 4-bundle 380.0
+14277,in_permitting,15.0,2025,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/297"", ""tyndp2020_proj_id""=>""297"", ""tyndp2020_invest_id""=>""604"", ""tyndp_status""=>""in_permitting""",4.27917500000001,51.25504,4.177551,51.162122,2,Al/St 240/40 4-bundle 380.0
+14594,under_consideration,70.0,2034,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/325"", ""tyndp2020_proj_id""=>""325"", ""tyndp2020_invest_id""=>""1483"", ""tyndp_status""=>""under_consideration""",14.5912685597917,46.536256583087,15.119934,46.263443,2,Al/St 240/40 4-bundle 380.0
+12655,under_construction,161.0,2020,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/350"", ""tyndp2020_proj_id""=>""350"", ""tyndp2020_invest_id""=>""1622"", ""tyndp_status""=>""under_construction""",21.475525,41.045181,20.725963,41.239159,2,Al/St 240/40 4-bundle 380.0
+6355,under_consideration,90.0,2036,False,380,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/1074"", ""tyndp2020_proj_id""=>""1074"", ""tyndp2020_invest_id""=>""1744"", ""tyndp_status""=>""under_consideration""",19.756165,45.527517,19.609222,45.021127,2,Al/St 240/40 4-bundle 380.0
diff --git a/data/transmission_projects/tyndp2020/upgraded_links.csv b/data/transmission_projects/tyndp2020/upgraded_links.csv
new file mode 100644
index 00000000..5fe56694
--- /dev/null
+++ b/data/transmission_projects/tyndp2020/upgraded_links.csv
@@ -0,0 +1,8 @@
+,project_status,length,build_year,underground,p_nom,tags,x0,y0,x1,y1
+T0,in_permitting,370.0,2027,True,2000.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/16"", ""tyndp2020_proj_id""=>""16"", ""tyndp2020_invest_id""=>""38"", ""tyndp_status""=>""in_permitting""",-2.880286,43.349776,-0.341949000000001,45.030833
+14801,under_construction,90.0,2020,True,1000.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/92"", ""tyndp2020_proj_id""=>""92"", ""tyndp2020_invest_id""=>""146"", ""tyndp_status""=>""under_construction""",6.46545399999999,50.831096,5.688171,50.729502
+T17,in_permitting,665.0,2024,True,1400.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/190"", ""tyndp2020_proj_id""=>""190"", ""tyndp2020_invest_id""=>""1382"", ""tyndp_status""=>""in_permitting""",7.23331357825616,60.5160767478361,-1.815491,57.484833
+T25,planned_not_yet_permitting,195.0,2023,True,500.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/286"", ""tyndp2020_proj_id""=>""286"", ""tyndp2020_invest_id""=>""1385"", ""tyndp_status""=>""planned_not_yet_permitting""",-6.96670499999999,52.27404,-4.850464,51.728729
+T12,in_permitting,220.0,2025,True,1400.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/153"", ""tyndp2020_proj_id""=>""153"", ""tyndp2020_invest_id""=>""987"", ""tyndp_status""=>""in_permitting""",-1.47628799999999,49.460984,-3.357697,50.739062
+T13,under_construction,770.0,2023,True,1400.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/167"", ""tyndp2020_proj_id""=>""167"", ""tyndp2020_invest_id""=>""998"", ""tyndp_status""=>""under_construction""",9.22851599999999,55.481188,-0.208961173685554,52.9600676434916
+5635,under_consideration,125.0,2036,True,700.0,"""url""=>""https://tyndp2020-project-platform.azurewebsites.net/projectsheets/transmission/1068"", ""tyndp2020_proj_id""=>""1068"", ""tyndp2020_invest_id""=>""1741"", ""tyndp_status""=>""under_consideration""",16.659393,57.43682,18.227692,57.571097
diff --git a/doc/configtables/links.csv b/doc/configtables/links.csv
index c1ffb427..9dedc36a 100644
--- a/doc/configtables/links.csv
+++ b/doc/configtables/links.csv
@@ -2,5 +2,4 @@
 p_max_pu,--,"Value in [0.,1.]","Correction factor for link capacities ``p_nom``."
 p_nom_max,MW,"float","Global upper limit for the maximum capacity of each extendable DC link."
 max_extension,MW,"float","Upper limit for the extended capacity of each extendable DC link."
-include_tyndp,bool,"{'true', 'false'}","Specifies whether to add HVDC link projects from the `TYNDP 2018 <https://tyndp.entsoe.eu/tyndp2018/projects/>`_ which are at least in permitting."
 under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity}","Specifies how to handle lines which are currently under construction."
diff --git a/doc/configtables/offwind-ac.csv b/doc/configtables/offwind-ac.csv
index 9ba2fa7e..c9c7c843 100644
--- a/doc/configtables/offwind-ac.csv
+++ b/doc/configtables/offwind-ac.csv
@@ -3,6 +3,7 @@ cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
 -- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_.  Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
+-- smooth,--,"{True, False}","Switch to apply a gaussian kernel density smoothing to the power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."
diff --git a/doc/configtables/offwind-dc.csv b/doc/configtables/offwind-dc.csv
index e55d8944..86bf0cdc 100644
--- a/doc/configtables/offwind-dc.csv
+++ b/doc/configtables/offwind-dc.csv
@@ -3,6 +3,7 @@ cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
 -- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
+-- smooth,--,"{True, False}","Switch to apply a gaussian kernel density smoothing to the power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."
diff --git a/doc/configtables/onwind.csv b/doc/configtables/onwind.csv
index a801d83c..676494f4 100644
--- a/doc/configtables/onwind.csv
+++ b/doc/configtables/onwind.csv
@@ -3,6 +3,7 @@ cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
 -- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
+-- smooth,--,"{True, False}","Switch to apply a gaussian kernel density smoothing to the power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 corine,,,
 -- grid_codes,--,"Any subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for wind turbine placement."
diff --git a/doc/configtables/transmission_projects.csv b/doc/configtables/transmission_projects.csv
new file mode 100644
index 00000000..acba4842
--- /dev/null
+++ b/doc/configtables/transmission_projects.csv
@@ -0,0 +1,9 @@
+,Unit,Values,Description
+enable,bool,"{true,false}",Whether to integrate this transmission projects or not.
+include,--,,"Name of the transmission projects. They should be unique and have to be provided in the `data/transmission_projects` folder."
+-- tyndp2020,bool,"{true,false}",Whether to integrate the TYNDP 2020 dataset.
+-- nep,bool,"{true,false}",Whether to integrate the German network development plan dataset.
+-- manual,bool,"{true,false}",Whether to integrate the manually added transmission projects. They are taken from the previously existing links_tyndp.csv file.
+skip,list,,"Type of lines to skip from all transmission projects. Possible values are: ``upgraded_lines``, ``upgraded_links``, ``new_lines``, ``new_links``."
+status,list or dict,,"Status to include into the model as list or as dict with name of project and status to include. Possible values for status are ``under_construction``, ``in_permitting``, ``confirmed``, ``planned_not_yet_permitted``, ``under_consideration``."
+new_link_capacity,--,"{zero,keep}",Whether to set the new link capacity to the provided capacity or set it to zero.
diff --git a/doc/configuration.rst b/doc/configuration.rst
index 8695d4bb..e140261a 100644
--- a/doc/configuration.rst
+++ b/doc/configuration.rst
@@ -379,6 +379,23 @@ overwrite the existing values.
 
 .. _transformers_cf:
 
+``transmission projects``
+=======================
+
+Allows to define additional transmission projects that will be added to the base network, e.g., from the TYNDP 2020 dataset. The projects are read in from the CSV files in the subfolder of ``data/transmission_projects/``. New transmission projects, e.g. from TYNDP 2024, can be added in a new subfolder of transmission projects, e.g. ``data/transmission_projects/tyndp2024`` while extending the list of ``transmission_projects`` in the ``config.yaml`` by ``tyndp2024``. The CSV files in the project folder should have the same columns as the CSV files in the template folder ``data/transmission_projects/template``.
+
+.. literalinclude:: ../config/config.default.yaml
+   :language: yaml
+   :start-at: transmission_projects:
+   :end-before: # docs
+
+.. csv-table::
+   :header-rows: 1
+   :widths: 22,7,22,33
+   :file: configtables/transmission_projects.csv
+
+.. _transformers_cf:
+
 ``transformers``
 ================
 
diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 9d059e7a..97c84b64 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -11,6 +11,12 @@ Upcoming Release
 ================
 * Add technology options for methanol, like electricity production from methanol, biomass to methanol, methanol to kerosene, ...
 
+* More modular and flexible handling of transmission projects. One can now add new transmission projects in a subfolder of `data/transmission projects` similar to the files in the template folder. After adding the new files and updating the config section `transmission_projects:`, transmission projects will be included if they are not duplicates of existing lines or other projects.
+
+* Add option to apply a gaussian kernel density smoothing to wind turbine power curves.
+
+* 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.
+
 * Added unsustainable biomass potentials for solid, gaseous, and liquid biomass. The potentials can be phased-out and/or
   substituted by the phase-in of sustainable biomass types using the config parameters
   ``biomass: share_unsustainable_use_retained`` and ``biomass: share_sustainable_potential_available``.
diff --git a/envs/environment.fixed.yaml b/envs/environment.fixed.yaml
index 67fff66f..917deab7 100644
--- a/envs/environment.fixed.yaml
+++ b/envs/environment.fixed.yaml
@@ -335,7 +335,7 @@ dependencies:
 - pyomo=6.6.1
 - pyparsing=3.1.2
 - pyproj=3.6.1
-- pypsa=0.28.0
+- pypsa=0.29.0
 - pyqt=5.15.9
 - pyqt5-sip=12.12.2
 - pyscipopt=5.0.1
diff --git a/envs/environment.yaml b/envs/environment.yaml
index c8d8a633..c87de77a 100644
--- a/envs/environment.yaml
+++ b/envs/environment.yaml
@@ -11,7 +11,7 @@ dependencies:
 - pip
 
 - atlite>=0.2.9
-- pypsa>=0.28
+- pypsa>=0.29
 - linopy
 - dask
 
diff --git a/rules/build_electricity.smk b/rules/build_electricity.smk
index 34472f27..f3505091 100644
--- a/rules/build_electricity.smk
+++ b/rules/build_electricity.smk
@@ -56,7 +56,6 @@ rule base_network:
         snapshots=config_provider("snapshots"),
         drop_leap_day=config_provider("enable", "drop_leap_day"),
         lines=config_provider("lines"),
-        links=config_provider("links"),
         transformers=config_provider("transformers"),
     input:
         eg_buses="data/entsoegridkit/buses.csv",
@@ -66,7 +65,6 @@ rule base_network:
         eg_transformers="data/entsoegridkit/transformers.csv",
         parameter_corrections="data/parameter_corrections.yaml",
         links_p_nom="data/links_p_nom.csv",
-        links_tyndp="data/links_tyndp.csv",
         country_shapes=resources("country_shapes.geojson"),
         offshore_shapes=resources("offshore_shapes.geojson"),
         europe_shape=resources("europe_shape.geojson"),
@@ -92,7 +90,7 @@ rule build_shapes:
         countries=config_provider("countries"),
     input:
         naturalearth=ancient("data/naturalearth/ne_10m_admin_0_countries_deu.shp"),
-        eez=ancient("data/eez/World_EEZ_v12_20231025_gpkg/eez_v12.gpkg"),
+        eez=ancient("data/eez/World_EEZ_v12_20231025_LR/eez_v12_lowres.gpkg"),
         nuts3=ancient("data/bundle/NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp"),
         nuts3pop=ancient("data/bundle/nama_10r_3popgdp.tsv.gz"),
         nuts3gdp=ancient("data/bundle/nama_10r_3gdp.tsv.gz"),
@@ -342,6 +340,40 @@ rule build_line_rating:
         "../scripts/build_line_rating.py"
 
 
+rule build_transmission_projects:
+    params:
+        transmission_projects=config_provider("transmission_projects"),
+        line_factor=config_provider("lines", "length_factor"),
+    input:
+        base_network=resources("networks/base.nc"),
+        offshore_shapes=resources("offshore_shapes.geojson"),
+        europe_shape=resources("europe_shape.geojson"),
+        transmission_projects=lambda w: [
+            "data/transmission_projects/" + name
+            for name, include in config_provider("transmission_projects", "include")(
+                w
+            ).items()
+            if include
+        ],
+    output:
+        new_lines=resources("transmission_projects/new_lines.csv"),
+        new_links=resources("transmission_projects/new_links.csv"),
+        adjust_lines=resources("transmission_projects/adjust_lines.csv"),
+        adjust_links=resources("transmission_projects/adjust_links.csv"),
+        new_buses=resources("transmission_projects/new_buses.csv"),
+    log:
+        logs("build_transmission_projects.log"),
+    benchmark:
+        benchmarks("build_transmission_projects")
+    resources:
+        mem_mb=2000,
+    threads: 1
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_transmission_projects.py"
+
+
 def input_profile_tech(w):
     return {
         f"profile_{tech}": resources(f"profile_{tech}.nc")
@@ -402,6 +434,7 @@ rule add_electricity:
         costs=config_provider("costs"),
         foresight=config_provider("foresight"),
         drop_leap_day=config_provider("enable", "drop_leap_day"),
+        transmission_projects=config_provider("transmission_projects"),
     input:
         unpack(input_profile_tech),
         unpack(input_conventional),
@@ -412,6 +445,17 @@ rule add_electricity:
             if config_provider("lines", "dynamic_line_rating", "activate")(w)
             else resources("networks/base.nc")
         ),
+        transmission_projects=lambda w: (
+            [
+                resources("transmission_projects/new_buses.csv"),
+                resources("transmission_projects/new_lines.csv"),
+                resources("transmission_projects/new_links.csv"),
+                resources("transmission_projects/adjust_lines.csv"),
+                resources("transmission_projects/adjust_links.csv"),
+            ]
+            if config_provider("transmission_projects", "enable")(w)
+            else []
+        ),
         tech_costs=lambda w: resources(
             f"costs_{config_provider('costs', 'year')(w)}.csv"
         ),
diff --git a/rules/build_sector.smk b/rules/build_sector.smk
index 9136cf7c..cb8f3de7 100644
--- a/rules/build_sector.smk
+++ b/rules/build_sector.smk
@@ -296,11 +296,12 @@ rule build_energy_totals:
         co2="data/bundle/eea/UNFCCC_v23.csv",
         swiss="data/switzerland-new_format-all_years.csv",
         swiss_transport="data/gr-e-11.03.02.01.01-cc.csv",
-        idees="data/bundle/jrc-idees-2015",
+        idees="data/jrc-idees-2021",
         district_heat_share="data/district_heat_share.csv",
         eurostat="data/eurostat/Balances-April2023",
         eurostat_households="data/eurostat/eurostat-household_energy_balances-february_2024.csv",
     output:
+        transformation_output_coke=resources("transformation_output_coke.csv"),
         energy_name=resources("energy_totals.csv"),
         co2_name=resources("co2_totals.csv"),
         transport_name=resources("transport_data.csv"),
@@ -444,7 +445,9 @@ rule build_salt_cavern_potentials:
 
 rule build_ammonia_production:
     input:
-        usgs="data/bundle/myb1-2017-nitro.xls",
+        usgs=storage(
+            "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/media/files/myb1-2022-nitro-ert.xlsx"
+        ),
     output:
         ammonia_production=resources("ammonia_production.csv"),
     threads: 1
@@ -466,7 +469,7 @@ rule build_industry_sector_ratios:
         ammonia=config_provider("sector", "ammonia", default=False),
     input:
         ammonia_production=resources("ammonia_production.csv"),
-        idees="data/bundle/jrc-idees-2015",
+        idees="data/jrc-idees-2021",
     output:
         industry_sector_ratios=resources("industry_sector_ratios.csv"),
     threads: 1
@@ -515,8 +518,9 @@ rule build_industrial_production_per_country:
         industry=config_provider("industry"),
         countries=config_provider("countries"),
     input:
+        ch_industrial_production="data/ch_industrial_production_per_subsector.csv",
         ammonia_production=resources("ammonia_production.csv"),
-        jrc="data/bundle/jrc-idees-2015",
+        jrc="data/jrc-idees-2021",
         eurostat="data/eurostat/Balances-April2023",
     output:
         industrial_production_per_country=resources(
@@ -663,7 +667,8 @@ rule build_industrial_energy_demand_per_country_today:
         countries=config_provider("countries"),
         industry=config_provider("industry"),
     input:
-        jrc="data/bundle/jrc-idees-2015",
+        transformation_output_coke=resources("transformation_output_coke.csv"),
+        jrc="data/jrc-idees-2021",
         industrial_production_per_country=resources(
             "industrial_production_per_country.csv"
         ),
diff --git a/rules/retrieve.smk b/rules/retrieve.smk
index 86c6b998..b2382f51 100644
--- a/rules/retrieve.smk
+++ b/rules/retrieve.smk
@@ -22,7 +22,6 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
         "corine/g250_clc06_V18_5.tif",
         "eea/UNFCCC_v23.csv",
         "nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
-        "myb1-2017-nitro.xls",
         "emobility/KFZ__count",
         "emobility/Pkw__count",
         "h2_salt_caverns_GWh_per_sqkm.geojson",
@@ -57,6 +56,15 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
         script:
             "../scripts/retrieve_eurostat_data.py"
 
+    rule retrieve_jrc_idees:
+        output:
+            directory("data/jrc-idees-2021"),
+        log:
+            "logs/retrieve_jrc_idees.log",
+        retries: 2
+        script:
+            "../scripts/retrieve_jrc_idees.py"
+
     rule retrieve_eurostat_household_data:
         output:
             "data/eurostat/eurostat-household_energy_balances-february_2024.csv",
@@ -218,9 +226,9 @@ if config["enable"]["retrieve"]:
 
     rule retrieve_eez:
         params:
-            zip="data/eez/World_EEZ_v12_20231025_gpkg.zip",
+            zip="data/eez/World_EEZ_v12_20231025_LR.zip",
         output:
-            gpkg="data/eez/World_EEZ_v12_20231025_gpkg/eez_v12.gpkg",
+            gpkg="data/eez/World_EEZ_v12_20231025_LR/eez_v12_lowres.gpkg",
         run:
             import os
             import requests
@@ -231,7 +239,7 @@ if config["enable"]["retrieve"]:
 
             response = requests.post(
                 "https://www.marineregions.org/download_file.php",
-                params={"name": "World_EEZ_v12_20231025_gpkg.zip"},
+                params={"name": "World_EEZ_v12_20231025_LR.zip"},
                 data={
                     "name": name,
                     "organisation": org,
diff --git a/scripts/add_electricity.py b/scripts/add_electricity.py
index 49510953..076eb84e 100755
--- a/scripts/add_electricity.py
+++ b/scripts/add_electricity.py
@@ -84,6 +84,7 @@ It further adds extendable ``generators`` with **zero** capacity for
 
 import logging
 from itertools import product
+from pathlib import Path
 from typing import Dict, List
 
 import geopandas as gpd
@@ -799,6 +800,25 @@ def attach_line_rating(
     n.lines_t.s_max_pu *= s_max_pu
 
 
+def add_transmission_projects(n, transmission_projects):
+    logger.info(f"Adding transmission projects to network.")
+    for path in transmission_projects:
+        path = Path(path)
+        df = pd.read_csv(path, index_col=0, dtype={"bus0": str, "bus1": str})
+        if df.empty:
+            continue
+        if "new_buses" in path.name:
+            n.madd("Bus", df.index, **df)
+        elif "new_lines" in path.name:
+            n.madd("Line", df.index, **df)
+        elif "new_links" in path.name:
+            n.madd("Link", df.index, **df)
+        elif "adjust_lines":
+            n.lines.update(df)
+        elif "adjust_links":
+            n.links.update(df)
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -811,6 +831,9 @@ if __name__ == "__main__":
 
     n = pypsa.Network(snakemake.input.base_network)
 
+    if params["transmission_projects"]["enable"]:
+        add_transmission_projects(n, snakemake.input.transmission_projects)
+
     time = get_snapshots(snakemake.params.snapshots, snakemake.params.drop_leap_day)
     n.set_snapshots(time)
 
diff --git a/scripts/base_network.py b/scripts/base_network.py
index 8172b332..78cf6984 100644
--- a/scripts/base_network.py
+++ b/scripts/base_network.py
@@ -25,7 +25,6 @@ Relevant Settings
     links:
         p_max_pu:
         under_construction:
-        include_tyndp:
 
     transformers:
         x:
@@ -43,7 +42,6 @@ Inputs
 - ``data/entsoegridkit``:  Extract from the geographical vector data of the online `ENTSO-E Interactive Map <https://www.entsoe.eu/data/map/>`_ by the `GridKit <https://github.com/martacki/gridkit>`_ toolkit dating back to March 2022.
 - ``data/parameter_corrections.yaml``: Corrections for ``data/entsoegridkit``
 - ``data/links_p_nom.csv``: confer :ref:`links`
-- ``data/links_tyndp.csv``: List of projects in the `TYNDP 2018 <https://tyndp.entsoe.eu/tyndp2018/>`_ that are at least *in permitting* with fields for start- and endpoint (names and coordinates), length, capacity, construction status, and project reference ID.
 - ``resources/country_shapes.geojson``: confer :ref:`shapes`
 - ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
 - ``resources/europe_shape.geojson``: confer :ref:`shapes`
@@ -222,112 +220,6 @@ def _load_links_from_eg(buses, eg_links):
     return links
 
 
-def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
-    links_tyndp = pd.read_csv(links_tyndp)
-
-    # remove all links from list which lie outside all of the desired countries
-    europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
-    europe_shape_prepped = shapely.prepared.prep(europe_shape)
-    x1y1_in_europe_b = links_tyndp[["x1", "y1"]].apply(
-        lambda p: europe_shape_prepped.contains(Point(p)), axis=1
-    )
-    x2y2_in_europe_b = links_tyndp[["x2", "y2"]].apply(
-        lambda p: europe_shape_prepped.contains(Point(p)), axis=1
-    )
-    is_within_covered_countries_b = x1y1_in_europe_b & x2y2_in_europe_b
-
-    if not is_within_covered_countries_b.all():
-        logger.info(
-            "TYNDP links outside of the covered area (skipping): "
-            + ", ".join(links_tyndp.loc[~is_within_covered_countries_b, "Name"])
-        )
-
-        links_tyndp = links_tyndp.loc[is_within_covered_countries_b]
-        if links_tyndp.empty:
-            return buses, links
-
-    has_replaces_b = links_tyndp.replaces.notnull()
-    oids = dict(Bus=_get_oid(buses), Link=_get_oid(links))
-    keep_b = dict(
-        Bus=pd.Series(True, index=buses.index), Link=pd.Series(True, index=links.index)
-    )
-    for reps in links_tyndp.loc[has_replaces_b, "replaces"]:
-        for comps in reps.split(":"):
-            oids_to_remove = comps.split(".")
-            c = oids_to_remove.pop(0)
-            keep_b[c] &= ~oids[c].isin(oids_to_remove)
-    buses = buses.loc[keep_b["Bus"]]
-    links = links.loc[keep_b["Link"]]
-
-    links_tyndp["j"] = _find_closest_links(
-        links, links_tyndp, distance_upper_bound=0.20
-    )
-    # Corresponds approximately to 20km tolerances
-
-    if links_tyndp["j"].notnull().any():
-        logger.info(
-            "TYNDP links already in the dataset (skipping): "
-            + ", ".join(links_tyndp.loc[links_tyndp["j"].notnull(), "Name"])
-        )
-        links_tyndp = links_tyndp.loc[links_tyndp["j"].isnull()]
-        if links_tyndp.empty:
-            return buses, links
-
-    tree_buses = buses.query("carrier=='AC'")
-    tree = KDTree(tree_buses[["x", "y"]])
-    _, ind0 = tree.query(links_tyndp[["x1", "y1"]])
-    ind0_b = ind0 < len(tree_buses)
-    links_tyndp.loc[ind0_b, "bus0"] = tree_buses.index[ind0[ind0_b]]
-
-    _, ind1 = tree.query(links_tyndp[["x2", "y2"]])
-    ind1_b = ind1 < len(tree_buses)
-    links_tyndp.loc[ind1_b, "bus1"] = tree_buses.index[ind1[ind1_b]]
-
-    links_tyndp_located_b = (
-        links_tyndp["bus0"].notnull() & links_tyndp["bus1"].notnull()
-    )
-    if not links_tyndp_located_b.all():
-        logger.warning(
-            "Did not find connected buses for TYNDP links (skipping): "
-            + ", ".join(links_tyndp.loc[~links_tyndp_located_b, "Name"])
-        )
-        links_tyndp = links_tyndp.loc[links_tyndp_located_b]
-
-    logger.info("Adding the following TYNDP links: " + ", ".join(links_tyndp["Name"]))
-
-    links_tyndp = links_tyndp[["bus0", "bus1"]].assign(
-        carrier="DC",
-        p_nom=links_tyndp["Power (MW)"],
-        length=links_tyndp["Length (given) (km)"].fillna(
-            links_tyndp["Length (distance*1.2) (km)"]
-        ),
-        under_construction=True,
-        underground=False,
-        geometry=(
-            links_tyndp[["x1", "y1", "x2", "y2"]].apply(
-                lambda s: str(LineString([[s.x1, s.y1], [s.x2, s.y2]])), axis=1
-            )
-        ),
-        tags=(
-            '"name"=>"'
-            + links_tyndp["Name"]
-            + '", '
-            + '"ref"=>"'
-            + links_tyndp["Ref"]
-            + '", '
-            + '"status"=>"'
-            + links_tyndp["status"]
-            + '"'
-        ),
-    )
-
-    links_tyndp.index = "T" + links_tyndp.index.astype(str)
-
-    links = pd.concat([links, links_tyndp], sort=True)
-
-    return buses, links
-
-
 def _load_lines_from_eg(buses, eg_lines):
     lines = (
         pd.read_csv(
@@ -731,7 +623,6 @@ def base_network(
     eg_lines,
     eg_links,
     links_p_nom,
-    links_tyndp,
     europe_shape,
     country_shapes,
     offshore_shapes,
@@ -741,8 +632,6 @@ def base_network(
     buses = _load_buses_from_eg(eg_buses, europe_shape, config["electricity"])
 
     links = _load_links_from_eg(buses, eg_links)
-    if config["links"].get("include_tyndp"):
-        buses, links = _add_links_from_tyndp(buses, links, links_tyndp, europe_shape)
 
     converters = _load_converters_from_eg(buses, eg_converters)
 
@@ -911,7 +800,6 @@ if __name__ == "__main__":
         snakemake.input.eg_lines,
         snakemake.input.eg_links,
         snakemake.input.links_p_nom,
-        snakemake.input.links_tyndp,
         snakemake.input.europe_shape,
         snakemake.input.country_shapes,
         snakemake.input.offshore_shapes,
diff --git a/scripts/build_ammonia_production.py b/scripts/build_ammonia_production.py
index 37692f2f..e0e2de5e 100644
--- a/scripts/build_ammonia_production.py
+++ b/scripts/build_ammonia_production.py
@@ -18,7 +18,8 @@ Outputs
 Description
 -------
 
-This functions takes data from the `Minerals Yearbook <https://www.usgs.gov/centers/national-minerals-information-center/nitrogen-statistics-and-information>`_ (June 2024) published by the US Geological Survey (USGS) and the National Minerals Information Center and extracts the annual ammonia production per country in ktonN/a. The data is converted to ktonNH3/a.
+This functions takes data from the `Minerals Yearbook <https://www.usgs.gov/centers/national-minerals-information-center/nitrogen-statistics-and-information>`_
+ (July 2024) published by the US Geological Survey (USGS) and the National Minerals Information Center and extracts the annual ammonia production per country in ktonN/a. The data is converted to ktonNH3/a.
 """
 
 import country_converter as coco
@@ -42,15 +43,15 @@ if __name__ == "__main__":
         skiprows=5,
         header=0,
         index_col=0,
-        skipfooter=19,
+        skipfooter=7,
         na_values=["--"],
     )
 
     ammonia.index = cc.convert(ammonia.index, to="iso2")
 
-    years = [str(i) for i in range(2013, 2018)]
+    years = [str(i) for i in range(2018, 2023)]
 
-    ammonia = ammonia[years]
+    ammonia = ammonia.rename(columns=lambda x: str(x))[years]
 
     # convert from ktonN to ktonNH3
     ammonia *= 17 / 14
diff --git a/scripts/build_energy_totals.py b/scripts/build_energy_totals.py
index a476ec65..48004ea2 100644
--- a/scripts/build_energy_totals.py
+++ b/scripts/build_energy_totals.py
@@ -25,7 +25,7 @@ Inputs
 - `data/bundle/eea_UNFCCC_v23.csv`: CO2 emissions data from EEA.
 - `data/switzerland-new_format-all_years.csv`: Swiss energy data.
 - `data/gr-e-11.03.02.01.01-cc.csv`: Swiss transport data
-- `data/bundle/jrc-idees`: JRC IDEES data.
+- `data/jrc-idees`: JRC IDEES data.
 - `data/district_heat_share.csv`: District heating shares.
 - `data/eurostat/Balances-April2023`: Eurostat energy balances.
 - `data/eurostat/eurostat-household_energy_balances-february_2024.csv`: Eurostat household energy balances.
@@ -110,7 +110,7 @@ def reverse(dictionary: dict) -> dict:
 idees_rename = {"GR": "EL", "GB": "UK"}
 
 eu28 = cc.EU28as("ISO2").ISO2.tolist()
-
+eu27 = cc.EU27as("ISO2").ISO2.tolist()
 eu28_eea = eu28.copy()
 eu28_eea.remove("GB")
 eu28_eea.append("UK")
@@ -170,6 +170,7 @@ def eurostat_per_country(input_eurostat: str, country: str) -> pd.DataFrame:
         sheet_name=None,
         skiprows=4,
         index_col=list(range(4)),
+        na_values=":",
     )
     sheet.pop("Cover")
     return pd.concat(sheet)
@@ -329,9 +330,9 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     """
 
     ct_idees = idees_rename.get(ct, ct)
-    fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
-    fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
-    fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
+    fn_residential = f"{base_dir}/{ct_idees}/JRC-IDEES-2021_Residential_{ct_idees}.xlsx"
+    fn_tertiary = f"{base_dir}/{ct_idees}/JRC-IDEES-2021_Tertiary_{ct_idees}.xlsx"
+    fn_transport = f"{base_dir}/{ct_idees}/JRC-IDEES-2021_Transport_{ct_idees}.xlsx"
 
     ct_totals = {}
 
@@ -357,14 +358,14 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     row = "Energy consumption by fuel - Eurostat structure (ktoe)"
     ct_totals["total residential"] = df.loc[row]
 
-    assert df.index[47] == "Electricity"
-    ct_totals["electricity residential"] = df.iloc[47]
+    assert df.index[40] == "Electricity"
+    ct_totals["electricity residential"] = df.iloc[40]
 
-    assert df.index[46] == "Derived heat"
-    ct_totals["derived heat residential"] = df.iloc[46]
+    assert df.index[39] == "Distributed heat"
+    ct_totals["distributed heat residential"] = df.iloc[39]
 
-    assert df.index[50] == "Thermal uses"
-    ct_totals["thermal uses residential"] = df.iloc[50]
+    assert df.index[43] == "Thermal uses"
+    ct_totals["thermal uses residential"] = df.iloc[43]
 
     # services
 
@@ -390,14 +391,14 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     row = "Energy consumption by fuel - Eurostat structure (ktoe)"
     ct_totals["total services"] = df.loc[row]
 
-    assert df.index[50] == "Electricity"
-    ct_totals["electricity services"] = df.iloc[50]
+    assert df.index[43] == "Electricity"
+    ct_totals["electricity services"] = df.iloc[43]
 
-    assert df.index[49] == "Derived heat"
-    ct_totals["derived heat services"] = df.iloc[49]
+    assert df.index[42] == "Distributed heat"
+    ct_totals["distributed heat services"] = df.iloc[42]
 
-    assert df.index[53] == "Thermal uses"
-    ct_totals["thermal uses services"] = df.iloc[53]
+    assert df.index[46] == "Thermal uses"
+    ct_totals["thermal uses services"] = df.iloc[46]
 
     # agriculture, forestry and fishing
 
@@ -410,7 +411,7 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
         "Lighting",
         "Ventilation",
         "Specific electricity uses",
-        "Pumping devices (electric)",
+        "Pumping devices (electricity)",
     ]
     ct_totals["total agriculture electricity"] = df.loc[rows].sum()
 
@@ -419,8 +420,8 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
 
     rows = [
         "Motor drives",
-        "Farming machine drives (diesel oil incl. biofuels)",
-        "Pumping devices (diesel oil incl. biofuels)",
+        "Farming machine drives (diesel oil and liquid biofuels)",
+        "Pumping devices (diesel oil and liquid biofuels)",
     ]
     ct_totals["total agriculture machinery"] = df.loc[rows].sum()
 
@@ -435,7 +436,7 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
 
     ct_totals["electricity road"] = df.loc["Electricity"]
 
-    ct_totals["total two-wheel"] = df.loc["Powered 2-wheelers (Gasoline)"]
+    ct_totals["total two-wheel"] = df.loc["Powered two-wheelers (Gasoline)"]
 
     assert df.index[19] == "Passenger cars"
     ct_totals["total passenger cars"] = df.iloc[19]
@@ -449,13 +450,13 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     assert df.index[39] == "Battery electric vehicles"
     ct_totals["electricity other road passenger"] = df.iloc[39]
 
-    assert df.index[41] == "Light duty vehicles"
+    assert df.index[41] == "Light commercial vehicles"
     ct_totals["total light duty road freight"] = df.iloc[41]
 
     assert df.index[49] == "Battery electric vehicles"
     ct_totals["electricity light duty road freight"] = df.iloc[49]
 
-    row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
+    row = "Heavy goods vehicles (Diesel oil incl. biofuels)"
     ct_totals["total heavy duty road freight"] = df.loc[row]
 
     assert df.index[61] == "Passenger cars"
@@ -463,44 +464,45 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
 
     df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)
 
-    ct_totals["total rail"] = df.loc["by fuel (EUROSTAT DATA)"]
+    ct_totals["total rail"] = df.loc["by fuel"]
 
     ct_totals["electricity rail"] = df.loc["Electricity"]
 
-    assert df.index[15] == "Passenger transport"
-    ct_totals["total rail passenger"] = df.iloc[15]
+    assert df.index[9] == "Passenger transport"
+    ct_totals["total rail passenger"] = df.iloc[9]
 
-    assert df.index[16] == "Metro and tram, urban light rail"
-    assert df.index[19] == "Electric"
-    assert df.index[20] == "High speed passenger trains"
-    ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()
+    assert df.index[10] == "Metro and tram, urban light rail"
+    assert df.index[13] == "Electric"
+    assert df.index[14] == "High speed passenger trains"
+    ct_totals["electricity rail passenger"] = df.iloc[[10, 13, 14]].sum()
 
-    assert df.index[21] == "Freight transport"
-    ct_totals["total rail freight"] = df.iloc[21]
+    assert df.index[15] == "Freight transport"
+    ct_totals["total rail freight"] = df.iloc[15]
 
-    assert df.index[23] == "Electric"
-    ct_totals["electricity rail freight"] = df.iloc[23]
+    assert df.index[17] == "Electric"
+    ct_totals["electricity rail freight"] = df.iloc[17]
 
     df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)
 
-    assert df.index[6] == "Passenger transport"
-    ct_totals["total aviation passenger"] = df.iloc[6]
+    assert df.index[4] == "Passenger transport"
+    ct_totals["total aviation passenger"] = df.iloc[4]
 
-    assert df.index[10] == "Freight transport"
-    ct_totals["total aviation freight"] = df.iloc[10]
+    assert df.index[8] == "Freight transport"
+    ct_totals["total aviation freight"] = df.iloc[8]
 
-    assert df.index[7] == "Domestic"
-    ct_totals["total domestic aviation passenger"] = df.iloc[7]
+    assert df.index[2] == "Domestic"
+    ct_totals["total domestic aviation passenger"] = df.iloc[2]
 
-    assert df.index[8] == "International - Intra-EU"
-    assert df.index[9] == "International - Extra-EU"
-    ct_totals["total international aviation passenger"] = df.iloc[[8, 9]].sum()
+    assert df.index[6] == "International - Intra-EEAwUK"
+    assert df.index[7] == "International - Extra-EEAwUK"
+    ct_totals["total international aviation passenger"] = df.iloc[[6, 7]].sum()
 
-    assert df.index[11] == "Domestic and International - Intra-EU"
-    ct_totals["total domestic aviation freight"] = df.iloc[11]
+    assert df.index[9] == "Domestic"
+    assert df.index[10] == "International - Intra-EEAwUK"
+    ct_totals["total domestic aviation freight"] = df.iloc[[9, 10]].sum()
 
-    assert df.index[12] == "International - Extra-EU"
-    ct_totals["total international aviation freight"] = df.iloc[12]
+    assert df.index[11] == "International - Extra-EEAwUK"
+    ct_totals["total international aviation freight"] = df.iloc[11]
 
     ct_totals["total domestic aviation"] = (
         ct_totals["total domestic aviation freight"]
@@ -515,7 +517,7 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
     df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)
 
     # coastal and inland
-    ct_totals["total domestic navigation"] = df.loc["by fuel (EUROSTAT DATA)"]
+    ct_totals["total domestic navigation"] = df.loc["Energy consumption (ktoe)"]
 
     df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)
 
@@ -567,15 +569,51 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
         names=["country", "year"],
     )
 
+    # clean up dataframe
+    years = np.arange(2000, 2022)
+    totals = totals[totals.index.get_level_values(1).isin(years)]
+
     # efficiency kgoe/100km -> ktoe/100km so that after conversion TWh/100km
     totals.loc[:, "passenger car efficiency"] /= 1e6
     # convert ktoe to TWh
     exclude = totals.columns.str.fullmatch("passenger cars")
+    totals = totals.copy()
     totals.loc[:, ~exclude] *= 11.63 / 1e3
 
     return totals
 
 
+def fill_missing_years(fill_values: pd.Series) -> pd.Series:
+    """
+    Fill missing years for some countries by first using forward fill (ffill)
+    and then backward fill (bfill).
+
+    Parameters
+    ----------
+    fill_values : pd.Series
+        A pandas Series with a MultiIndex (levels: country and year) representing
+        energy values, where some values may be zero and need to be filled.
+
+    Returns
+    -------
+    pd.Series
+        A pandas Series with zero values replaced by the forward-filled and
+        backward-filled values of the corresponding country.
+
+    Notes
+    -----
+    - The function groups the data by the 'country' level and performs forward fill
+      and backward fill to fill zero values.
+    - Zero values in the original Series are replaced by the ffilled and bfilled
+      value of their respective country group.
+    """
+
+    # Forward fill and then backward fill within each country group
+    fill_values = fill_values.groupby(level="country").ffill().bfill()
+
+    return fill_values
+
+
 def build_energy_totals(
     countries: List[str],
     eurostat: pd.DataFrame,
@@ -629,6 +667,8 @@ def build_energy_totals(
 
     slicer = idx[in_eurostat, :, :, "Bunkers", :]
     fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
+    # fill missing years for some countries by mean over the other years
+    fill_values = fill_missing_years(fill_values)
     df.loc[in_eurostat, "total international navigation"] = fill_values
 
     # add swiss energy data
@@ -636,6 +676,36 @@ def build_energy_totals(
     df = pd.concat([df.drop("CH", errors="ignore"), swiss]).sort_index()
 
     # get values for missing countries based on Eurostat EnergyBalances
+
+    # agriculture
+
+    to_fill = df.index[
+        df["total agriculture"].isna()
+        & df.index.get_level_values("country").isin(eurostat_countries)
+    ]
+    c = to_fill.get_level_values("country")
+    y = to_fill.get_level_values("year")
+
+    # take total final energy consumption from Eurostat
+    eurostat_sector = "Agriculture & forestry"
+    slicer = idx[c, y, :, :, eurostat_sector]
+
+    fill_values = eurostat.loc[slicer]["Total all products"].groupby(level=[0, 1]).sum()
+    # fill missing years for some countries by mean over the other years
+    fill_values = fill_missing_years(fill_values)
+    df.loc[to_fill, "total agriculture"] = fill_values
+
+    # split into end uses by average EU data from IDEES
+    uses = ["electricity", "heat", "machinery"]
+
+    for use in uses:
+        avg = (
+            idees["total agriculture electricity"] / idees["total agriculture"]
+        ).mean()
+        df.loc[to_fill, f"total agriculture {use}"] = (
+            df.loc[to_fill, "total agriculture"] * avg
+        )
+
     # divide cooking/space/water according to averages in EU28
 
     uses = ["space", "cooking", "water"]
@@ -657,6 +727,8 @@ def build_energy_totals(
             fill_values = (
                 eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0, 1]).sum()
             )
+            # fill missing years for some countries by mean over the other years
+            fill_values = fill_missing_years(fill_values)
             df.loc[to_fill, f"{fuel} {sector}"] = fill_values
 
     for sector in ["residential", "services"]:
@@ -664,7 +736,9 @@ def build_energy_totals(
 
         for use in uses:
             fuel_use = df[f"electricity {sector} {use}"]
-            fuel = df[f"electricity {sector}"]
+            fuel = (
+                df[f"electricity {sector}"].replace(0, np.nan).infer_objects(copy=False)
+            )
             avg = fuel_use.div(fuel).mean()
             logger.debug(
                 f"{sector}: average fraction of electricity for {use} is {avg:.3f}"
@@ -680,6 +754,7 @@ def build_energy_totals(
                 df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
             )
             nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"]
+            nonelectric = nonelectric.copy().replace(0, np.nan)
             avg = nonelectric_use.div(nonelectric).mean()
             logger.debug(
                 f"{sector}: average fraction of non-electric for {use} is {avg:.3f}"
@@ -716,6 +791,7 @@ def build_energy_totals(
                 nonelectric = (
                     no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
                 )
+                nonelectric = nonelectric.copy().replace(0, np.nan)
                 fraction = nonelectric_use.div(nonelectric).mean()
                 df.loc["NO", f"total {sector} {use}"] = (
                     total_heating * fraction
@@ -728,16 +804,22 @@ def build_energy_totals(
 
     slicer = idx[c, y, :, :, "Domestic aviation"]
     fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
+    # fill missing years for some countries by mean over the other years
+    fill_values = fill_missing_years(fill_values)
     df.loc[to_fill, "total domestic aviation"] = fill_values
 
     slicer = idx[c, y, :, :, "International aviation"]
     fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
+    # fill missing years for some countries by mean over the other years
+    fill_values = fill_missing_years(fill_values)
     df.loc[to_fill, "total international aviation"] = fill_values
 
     # missing domestic navigation
 
     slicer = idx[c, y, :, :, "Domestic Navigation"]
     fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
+    # fill missing years for some countries by mean over the other years
+    fill_values = fill_missing_years(fill_values)
     df.loc[to_fill, "total domestic navigation"] = fill_values
 
     # split road traffic for non-IDEES
@@ -793,7 +875,9 @@ def build_energy_totals(
         mean_BA = df.loc["BA"].loc[2014:2021, "total residential"].mean()
         mean_RS = df.loc["RS"].loc[2014:2021, "total residential"].mean()
         ratio = mean_BA / mean_RS
-        df.loc["BA"] = df.loc["BA"].replace(0.0, np.nan).values
+        df.loc["BA"] = (
+            df.loc["BA"].replace(0.0, np.nan).infer_objects(copy=False).values
+        )
         df.loc["BA"] = df.loc["BA"].combine_first(ratio * df.loc["RS"]).values
 
     return df
@@ -817,18 +901,20 @@ def build_district_heat_share(countries: List[str], idees: pd.DataFrame) -> pd.S
 
     Notes
     -----
-    - The function calculates the district heating share as the sum of residential and services derived heat, divided by the sum of residential and services thermal uses.
+    - The function calculates the district heating share as the sum of residential and services distributed heat, divided by the sum of residential and services thermal uses.
     - The district heating share is then reindexed to match the provided list of countries.
     - Missing district heating shares are filled from `data/district_heat_share.csv`.
     - The function makes a conservative assumption and takes the minimum district heating share from both the IDEES data and `data/district_heat_share.csv`.
     """
 
     # district heating share
-    district_heat = idees[["derived heat residential", "derived heat services"]].sum(
-        axis=1
-    )
-    total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
-        axis=1
+    district_heat = idees[
+        ["distributed heat residential", "distributed heat services"]
+    ].sum(axis=1)
+    total_heat = (
+        idees[["thermal uses residential", "thermal uses services"]]
+        .sum(axis=1)
+        .replace(0, np.nan)
     )
 
     district_heat_share = district_heat / total_heat
@@ -842,10 +928,15 @@ def build_district_heat_share(countries: List[str], idees: pd.DataFrame) -> pd.S
         .squeeze()
     )
     # make conservative assumption and take minimum from both data sets
+    new_index = pd.MultiIndex.from_product(
+        [dh_share.index, district_heat_share.index.get_level_values(1).unique()]
+    )
     district_heat_share = pd.concat(
-        [district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1
+        [district_heat_share, dh_share.reindex(new_index, level=0)], axis=1
     ).min(axis=1)
 
+    district_heat_share = district_heat_share.reindex(countries, level=0)
+
     district_heat_share.name = "district heat share"
 
     # restrict to available years
@@ -1086,6 +1177,12 @@ def build_transport_data(
 
     transport_data.rename(columns={"passenger cars": "number cars"}, inplace=True)
 
+    # clean up dataframe
+    years = np.arange(2000, 2022)
+    transport_data = transport_data[
+        transport_data.index.get_level_values(1).isin(years)
+    ]
+
     missing = transport_data.index[transport_data["number cars"].isna()]
     if not missing.empty:
         logger.info(
@@ -1123,8 +1220,8 @@ def rescale_idees_from_eurostat(
     idees_countries: List[str], energy: pd.DataFrame, eurostat: pd.DataFrame
 ) -> pd.DataFrame:
     """
-    Takes JRC IDEES data from 2015 and rescales it by the ratio of the Eurostat
-    data and the 2015 Eurostat data.
+    Takes JRC IDEES data from 2021 and rescales it by the ratio of the Eurostat
+    data and the 2021 Eurostat data.
     Missing data: ['passenger car efficiency', 'passenger cars']
 
     Parameters
@@ -1154,9 +1251,9 @@ def rescale_idees_from_eurostat(
 
     main_cols = ["Total all products", "Electricity"]
     # read in the eurostat data for 2015
-    eurostat_2015 = eurostat.xs(2015, level="year")[main_cols]
+    eurostat_2021 = eurostat.xs(2021, level="year")[main_cols]
     # calculate the ratio of the two data sets
-    ratio = eurostat[main_cols] / eurostat_2015
+    ratio = eurostat[main_cols] / eurostat_2021
     ratio = ratio.droplevel([2, 5])
     cols_rename = {"Total all products": "total", "Electricity": "ele"}
     index_rename = {v: k for k, v in idees_rename.items()}
@@ -1169,7 +1266,7 @@ def rescale_idees_from_eurostat(
                 "total residential water",
                 "total residential cooking",
                 "total residential",
-                "derived heat residential",
+                "distributed heat residential",
                 "thermal uses residential",
             ],
             "elec": [
@@ -1185,7 +1282,7 @@ def rescale_idees_from_eurostat(
                 "total services water",
                 "total services cooking",
                 "total services",
-                "derived heat services",
+                "distributed heat services",
                 "thermal uses services",
             ],
             "elec": [
@@ -1376,6 +1473,33 @@ def update_residential_from_eurostat(energy: pd.DataFrame) -> pd.DataFrame:
     )
 
 
+def build_transformation_output_coke(eurostat, fn):
+    """
+    Extracts and builds the transformation output data for coke ovens from the
+    Eurostat dataset.
+
+    This function specifically filters the Eurostat data to extract
+    transformation output related to coke ovens.
+    Since the transformation output for coke ovens
+    is not included in the final energy consumption of the iron and steel sector,
+    it needs to be processed and added separately. The filtered data is saved
+    as a CSV file.
+
+    Parameters:
+    eurostat (pd.DataFrame): A pandas DataFrame containing Eurostat data with
+                             a multi-level index
+    fn (str): The file path where the resulting CSV file should be saved.
+
+    Output:
+    The resulting transformation output data for coke ovens is saved as a CSV
+    file at the path specified in fn.
+    """
+    slicer = pd.IndexSlice[:, :, :, "Coke ovens", "Other sources", :]
+    df = eurostat.loc[slicer, :].droplevel(level=[2, 3, 4, 5])
+    df.to_csv(fn)
+
+
+# %%
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -1391,7 +1515,7 @@ if __name__ == "__main__":
     population = nuts3["pop"].groupby(nuts3.country).sum()
 
     countries = snakemake.params.countries
-    idees_countries = pd.Index(countries).intersection(eu28)
+    idees_countries = pd.Index(countries).intersection(eu27)
 
     input_eurostat = snakemake.input.eurostat
     eurostat = build_eurostat(
@@ -1400,15 +1524,16 @@ if __name__ == "__main__":
         nprocesses=snakemake.threads,
         disable_progressbar=snakemake.config["run"].get("disable_progressbar", False),
     )
+
+    build_transformation_output_coke(
+        eurostat, snakemake.output.transformation_output_coke
+    )
+
     swiss = build_swiss()
     idees = build_idees(idees_countries)
 
     energy = build_energy_totals(countries, eurostat, swiss, idees)
 
-    # Data from IDEES only exists from 2000-2015.
-    logger.info("Extrapolate IDEES data based on eurostat for years 2015-2021.")
-    energy = rescale_idees_from_eurostat(idees_countries, energy, eurostat)
-
     update_residential_from_eurostat(energy)
 
     energy.to_csv(snakemake.output.energy_name)
diff --git a/scripts/build_industrial_energy_demand_per_country_today.py b/scripts/build_industrial_energy_demand_per_country_today.py
index b77ba8d6..ef559efa 100644
--- a/scripts/build_industrial_energy_demand_per_country_today.py
+++ b/scripts/build_industrial_energy_demand_per_country_today.py
@@ -8,7 +8,7 @@ Build industrial energy demand per country.
 Inputs
 -------
 
-- ``data/bundle/jrc-idees-2015``
+- ``data/jrc-idees-2021``
 - ``industrial_production_per_country.csv``
 
 Outputs
@@ -34,9 +34,9 @@ For each country and each subcategory of
 - Glass production
 - HVC
 - Integrated steelworks
-- Machinery Equipment
+- Machinery equipment
 - Methanol
-- Other Industrial Sectors
+- Other industrial sectors
 - Other chemicals
 - Other non-ferrous metals
 - Paper production
@@ -44,7 +44,7 @@ For each country and each subcategory of
 - Printing and media reproduction
 - Pulp production
 - Textiles and leather
-- Transport Equipment
+- Transport equipment
 - Wood and wood products
 
 the output file contains the energy demand in TWh/a for the following carriers
@@ -74,53 +74,56 @@ ktoe_to_twh = 0.011630
 
 # name in JRC-IDEES Energy Balances
 sector_sheets = {
-    "Integrated steelworks": "cisb",
-    "Electric arc": "cise",
-    "Alumina production": "cnfa",
-    "Aluminium - primary production": "cnfp",
-    "Aluminium - secondary production": "cnfs",
-    "Other non-ferrous metals": "cnfo",
-    "Basic chemicals": "cbch",
-    "Other chemicals": "coch",
-    "Pharmaceutical products etc.": "cpha",
-    "Basic chemicals feedstock": "cpch",
-    "Cement": "ccem",
-    "Ceramics & other NMM": "ccer",
-    "Glass production": "cgla",
-    "Pulp production": "cpul",
-    "Paper production": "cpap",
-    "Printing and media reproduction": "cprp",
-    "Food, beverages and tobacco": "cfbt",
-    "Transport Equipment": "ctre",
-    "Machinery Equipment": "cmae",
-    "Textiles and leather": "ctel",
-    "Wood and wood products": "cwwp",
-    "Mining and quarrying": "cmiq",
-    "Construction": "ccon",
-    "Non-specified": "cnsi",
+    "Integrated steelworks": "FC_IND_IS_BF_E",
+    "Electric arc": "FC_IND_IS_EA_E",
+    "Alumina production": "FC_IND_NFM_AM_E",
+    "Aluminium - primary production": "FC_IND_NFM_PA_E",
+    "Aluminium - secondary production": "FC_IND_NFM_SA_E",
+    "Other non-ferrous metals": "FC_IND_NFM_OM_E",
+    "Basic chemicals": "FC_IND_CPC_BC_E",
+    "Other chemicals": "FC_IND_CPC_OC_E",
+    "Pharmaceutical products etc.": "FC_IND_CPC_PH_E",
+    "Basic chemicals feedstock": "FC_IND_CPC_NE",
+    "Cement": "FC_IND_NMM_CM_E",
+    "Ceramics & other NMM": "FC_IND_NMM_CR_E",
+    "Glass production": "FC_IND_NMM_GL_E",
+    "Pulp production": "FC_IND_PPP_PU_E",
+    "Paper production": "FC_IND_PPP_PA_E",
+    "Printing and media reproduction": "FC_IND_PPP_PR_E",
+    "Food, beverages and tobacco": "FC_IND_FBT_E",
+    "Transport equipment": "FC_IND_TE_E",
+    "Machinery equipment": "FC_IND_MAC_E",
+    "Textiles and leather": "FC_IND_TL_E",
+    "Wood and wood products": "FC_IND_WP_E",
+    "Mining and quarrying": "FC_IND_MQ_E",
+    "Construction": "FC_IND_CON_E",
+    "Non-specified": "FC_IND_NSP_E",
 }
 
 
 fuels = {
-    "All Products": "all",
-    "Solid Fuels": "solid",
-    "Total petroleum products (without biofuels)": "liquid",
-    "Gases": "gas",
+    "Total": "all",
+    "Solid fossil fuels": "solid",
+    "Peat and peat products": "solid",
+    "Oil shale and oil sands": "solid",
+    "Oil and petroleum products": "liquid",
+    "Manufactured gases": "gas",
+    "Natural gas": "gas",
     "Nuclear heat": "heat",
-    "Derived heat": "heat",
-    "Biomass and Renewable wastes": "biomass",
-    "Wastes (non-renewable)": "waste",
+    "Heat": "heat",
+    "Renewables and biofuels": "biomass",
+    "Non-renewable waste": "waste",
     "Electricity": "electricity",
 }
 
-eu28 = cc.EU28as("ISO2").ISO2.tolist()
+eu27 = cc.EU27as("ISO2").ISO2.tolist()
 
 jrc_names = {"GR": "EL", "GB": "UK"}
 
 
 def industrial_energy_demand_per_country(country, year, jrc_dir):
     jrc_country = jrc_names.get(country, country)
-    fn = f"{jrc_dir}/JRC-IDEES-2015_EnergyBalance_{jrc_country}.xlsx"
+    fn = f"{jrc_dir}/{jrc_country}/JRC-IDEES-2021_EnergyBalance_{jrc_country}.xlsx"
 
     sheets = list(sector_sheets.values())
     df_dict = pd.read_excel(fn, sheet_name=sheets, index_col=0)
@@ -139,7 +142,7 @@ def industrial_energy_demand_per_country(country, year, jrc_dir):
     )
 
     sel = ["Mining and quarrying", "Construction", "Non-specified"]
-    df["Other Industrial Sectors"] = df[sel].sum(axis=1)
+    df["Other industrial sectors"] = df[sel].sum(axis=1)
     df["Basic chemicals"] += df["Basic chemicals feedstock"]
 
     df.drop(columns=sel + ["Basic chemicals feedstock"], index="all", inplace=True)
@@ -189,20 +192,20 @@ def separate_basic_chemicals(demand, production):
     return demand
 
 
-def add_non_eu28_industrial_energy_demand(countries, demand, production):
-    non_eu28 = countries.difference(eu28)
-    if non_eu28.empty:
+def add_non_eu27_industrial_energy_demand(countries, demand, production):
+    non_eu27 = countries.difference(eu27)
+    if non_eu27.empty:
         return demand
 
-    eu28_production = production.loc[countries.intersection(eu28)].sum()
-    eu28_energy = demand.groupby(level=1).sum()
-    eu28_averages = eu28_energy / eu28_production
+    eu27_production = production.loc[countries.intersection(eu27)].sum()
+    eu27_energy = demand.groupby(level=1).sum()
+    eu27_averages = eu27_energy / eu27_production
 
-    demand_non_eu28 = pd.concat(
-        {k: v * eu28_averages for k, v in production.loc[non_eu28].iterrows()}
+    demand_non_eu27 = pd.concat(
+        {k: v * eu27_averages for k, v in production.loc[non_eu27].iterrows()}
     )
 
-    return pd.concat([demand, demand_non_eu28])
+    return pd.concat([demand, demand_non_eu27])
 
 
 def industrial_energy_demand(countries, year):
@@ -224,6 +227,48 @@ def industrial_energy_demand(countries, year):
     return pd.concat(demand_l, keys=countries)
 
 
+def add_coke_ovens(demand, fn, year, factor=0.75):
+    """
+    Adds the energy consumption of coke ovens to the energy demand for
+    integrated steelworks.
+
+    This function reads the energy consumption data for coke ovens from a
+    CSV file, processes it to match the structure of the `demand` DataFrame,
+    and then adds a specified share of  this energy consumption to the energy
+    demand for integrated steelworks.
+
+    The `factor`  parameter controls what proportion of the coke ovens' energy
+    consumption should be attributed to the iron and steel production.
+    The default value of 75% is based on https://doi.org/10.1016/j.erss.2022.102565
+
+    Parameters:
+    demand (pd.DataFrame): A pandas DataFrame containing energy demand data
+                           with a multi-level column index where one of the
+                           levels corresponds to "Integrated steelworks".
+    fn (str): The file path to the CSV file containing the coke ovens energy
+              consumption data.
+    year (int): The year for which the coke ovens data should be selected.
+    factor (float, optional): The proportion of coke ovens energy consumption to add to the
+                              integrated steelworks demand. Defaults to 0.75.
+
+    Returns:
+    pd.DataFrame: The updated `demand` DataFrame with the coke ovens energy
+    consumption added to the integrated steelworks energy demand.
+    """
+
+    df = pd.read_csv(fn, index_col=[0, 1]).xs(year, level=1)
+    df = df.rename(columns={"Total all products": "Total"})[fuels.keys()]
+    df = df.rename(columns=fuels).T.groupby(level=0).sum().T
+    df["other"] = df["all"] - df.loc[:, df.columns != "all"].sum(axis=1)
+    df = df.T.reindex_like(demand.xs("Integrated steelworks", axis=1, level=1)).fillna(
+        0
+    )
+    sel = demand.columns.get_level_values(1) == "Integrated steelworks"
+    demand.loc[:, sel] += factor * df.values
+
+    return demand
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -232,10 +277,10 @@ if __name__ == "__main__":
     set_scenario_config(snakemake)
 
     params = snakemake.params.industry
-    year = params.get("reference_year", 2015)
+    year = params.get("reference_year", 2019)
     countries = pd.Index(snakemake.params.countries)
 
-    demand = industrial_energy_demand(countries.intersection(eu28), year)
+    demand = industrial_energy_demand(countries.intersection(eu27), year)
 
     # output in MtMaterial/a
     production = (
@@ -245,11 +290,14 @@ if __name__ == "__main__":
 
     demand = separate_basic_chemicals(demand, production)
 
-    demand = add_non_eu28_industrial_energy_demand(countries, demand, production)
+    demand = add_non_eu27_industrial_energy_demand(countries, demand, production)
 
     # for format compatibility
     demand = demand.stack(future_stack=True).unstack(level=[0, 2])
 
+    # add energy consumption of coke ovens
+    demand = add_coke_ovens(demand, snakemake.input.transformation_output_coke, year)
+
     # style and annotation
     demand.index.name = "TWh/a"
     demand.sort_index(axis=1, inplace=True)
diff --git a/scripts/build_industrial_production_per_country.py b/scripts/build_industrial_production_per_country.py
index ec86a78d..ccb4feca 100644
--- a/scripts/build_industrial_production_per_country.py
+++ b/scripts/build_industrial_production_per_country.py
@@ -16,7 +16,7 @@ Relevant Settings
 Inputs
 -------
 - ``resources/ammonia_production.csv``
-- ``data/bundle-sector/jrc-idees-2015``
+- ``data/bundle-sector/jrc-idees-2021``
 - ``data/eurostat``
 
 Outputs
@@ -31,7 +31,7 @@ The industrial production is taken from the `JRC-IDEES <https://joint-research-c
 This dataset provides detailed information about the consumption of energy for various processes.
 If the country is not part of the EU28, the energy consumption in the industrial sectors is taken from the `Eurostat <https://ec.europa.eu/eurostat/de/data/database>` dataset. The industrial production is calculated for the year specified in the config["industry"]["reference_year"].
 
-The ammonia production is provided by the rule `build_ammonia_production <https://pypsa-eur.readthedocs.io/en/latest/sector.html#module-build_ammonia_production>`. Since Switzerland is not part of the EU28 nor reported by eurostat, the energy consumption in the industrial sectors is taken from the `BFE <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken/energieverbrauch-nach-verwendungszweck.html> dataset.
+The ammonia production is provided by the rule `build_ammonia_production <https://pypsa-eur.readthedocs.io/en/latest/sector.html#module-build_ammonia_production>`. Since Switzerland is not part of the EU28 nor reported by eurostat, the energy consumption in the industrial sectors is taken from the `BFE <https://pubdb.bfe.admin.ch/de/publication/download/11817> dataset.
 After the industrial production is calculated, the basic chemicals are separated into ammonia, chlorine, methanol and HVC. The production of these chemicals is assumed to be proportional to the production of basic chemicals without ammonia.
 
 The following subcategories [kton/a] are considered:
@@ -50,11 +50,11 @@ The following subcategories [kton/a] are considered:
 - Aluminium - primary production
 - Aluminium - secondary production
 - Other non-ferrous metals
-- Transport Equipment
-- Machinery Equipment
+- Transport equipment
+- Machinery equipment
 - Textiles and leather
 - Wood and wood products
-- Other Industrial Sectors
+- Other industrial sectors
 - Ammonia
 - HVC
 - Chlorine
@@ -79,25 +79,25 @@ ktoe_to_twh = 0.01163
 
 sub_sheet_name_dict = {
     "Iron and steel": "ISI",
-    "Chemicals Industry": "CHI",
+    "Chemical industry": "CHI",
     "Non-metallic mineral products": "NMM",
     "Pulp, paper and printing": "PPA",
     "Food, beverages and tobacco": "FBT",
     "Non Ferrous Metals": "NFM",
-    "Transport Equipment": "TRE",
-    "Machinery Equipment": "MAE",
+    "Transport equipment": "TRE",
+    "Machinery equipment": "MAE",
     "Textiles and leather": "TEL",
     "Wood and wood products": "WWP",
-    "Other Industrial Sectors": "OIS",
+    "Other industrial sectors": "OIS",
 }
 
-eu28 = cc.EU28as("ISO2").ISO2.values
+eu27 = cc.EU27as("ISO2").ISO2.values
 
 jrc_names = {"GR": "EL", "GB": "UK"}
 
 sect2sub = {
     "Iron and steel": ["Electric arc", "Integrated steelworks"],
-    "Chemicals Industry": [
+    "Chemical industry": [
         "Basic chemicals",
         "Other chemicals",
         "Pharmaceutical products etc.",
@@ -119,11 +119,11 @@ sect2sub = {
         "Aluminium - secondary production",
         "Other non-ferrous metals",
     ],
-    "Transport Equipment": ["Transport Equipment"],
-    "Machinery Equipment": ["Machinery Equipment"],
+    "Transport equipment": ["Transport equipment"],
+    "Machinery equipment": ["Machinery equipment"],
     "Textiles and leather": ["Textiles and leather"],
     "Wood and wood products": ["Wood and wood products"],
-    "Other Industrial Sectors": ["Other Industrial Sectors"],
+    "Other industrial sectors": ["Other industrial sectors"],
 }
 
 sub2sect = {v: k for k, vv in sect2sub.items() for v in vv}
@@ -145,47 +145,41 @@ fields = {
     "Aluminium - primary production": "Aluminium - primary production",
     "Aluminium - secondary production": "Aluminium - secondary production",
     "Other non-ferrous metals": "Other non-ferrous metals (kt lead eq.)",
-    "Transport Equipment": "Physical output (index)",
-    "Machinery Equipment": "Physical output (index)",
+    "Transport equipment": "Physical output (index)",
+    "Machinery equipment": "Physical output (index)",
     "Textiles and leather": "Physical output (index)",
     "Wood and wood products": "Physical output (index)",
-    "Other Industrial Sectors": "Physical output (index)",
+    "Other industrial sectors": "Physical output (index)",
 }
 
 eb_sectors = {
     "Iron & steel": "Iron and steel",
-    "Chemical & petrochemical": "Chemicals Industry",
+    "Chemical & petrochemical": "Chemical industry",
     "Non-ferrous metals": "Non-metallic mineral products",
     "Paper, pulp & printing": "Pulp, paper and printing",
     "Food, beverages & tobacco": "Food, beverages and tobacco",
     "Non-metallic minerals": "Non Ferrous Metals",
-    "Transport equipment": "Transport Equipment",
-    "Machinery": "Machinery Equipment",
+    "Transport equipment": "Transport equipment",
+    "Machinery": "Machinery equipment",
     "Textile & leather": "Textiles and leather",
     "Wood & wood products": "Wood and wood products",
-    "Not elsewhere specified (industry)": "Other Industrial Sectors",
+    "Not elsewhere specified (industry)": "Other industrial sectors",
 }
 
-# TODO: this should go in a csv in `data`
-# Annual energy consumption in Switzerland by sector in 2015 (in TJ)
-# From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
-# http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
-e_switzerland = pd.Series(
-    {
-        "Iron and steel": 7889.0,
-        "Chemicals Industry": 26871.0,
-        "Non-metallic mineral products": 15513.0 + 3820.0,
-        "Pulp, paper and printing": 12004.0,
-        "Food, beverages and tobacco": 17728.0,
-        "Non Ferrous Metals": 3037.0,
-        "Transport Equipment": 14993.0,
-        "Machinery Equipment": 4724.0,
-        "Textiles and leather": 1742.0,
-        "Wood and wood products": 0.0,
-        "Other Industrial Sectors": 10825.0,
-        "current electricity": 53760.0,
-    }
-)
+
+ch_mapping = {
+    "Nahrung": "Food, beverages and tobacco",
+    "Textil / Leder": "Textiles and leather",
+    "Papier / Druck": "Pulp, paper and printing",
+    "Chemie / Pharma": "Chemical industry",
+    "Zement / Beton": "Non-metallic mineral products",
+    "Andere NE-Mineralien": "Other non-ferrous metals",
+    "Metall / Eisen": "Iron and steel",
+    "NE-Metall": "Non Ferrous Metals",
+    "Metall / Geräte": "Transport equipment",
+    "Maschinen": "Machinery equipment",
+    "Andere Industrien": "Other industrial sectors",
+}
 
 
 def find_physical_output(df):
@@ -197,32 +191,39 @@ def find_physical_output(df):
 
 def get_energy_ratio(country, eurostat_dir, jrc_dir, year):
     if country == "CH":
-        e_country = e_switzerland * tj_to_ktoe
+        # data ranges between 2014-2023
+        e_country = pd.read_csv(
+            snakemake.input.ch_industrial_production, index_col=0
+        ).dropna()
+        e_country = e_country.rename(index=ch_mapping).groupby(level=0).sum()
+        e_country = e_country[str(min(2019, year))]
+        e_country *= tj_to_ktoe
     else:
+        ct_eurostat = country.replace("GB", "UK")
         # estimate physical output, energy consumption in the sector and country
-        fn = f"{eurostat_dir}/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
+        fn = f"{eurostat_dir}/{ct_eurostat}-Energy-balance-sheets-April-2023-edition.xlsb"
         df = pd.read_excel(
             fn,
-            sheet_name=str(min(2021, year)),
+            sheet_name=str(min(2019, year)),
             index_col=2,
             header=0,
             skiprows=4,
         )
         e_country = df.loc[eb_sectors.keys(), "Total"].rename(eb_sectors)
 
-    fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx"
+    fn = f"{jrc_dir}/EU27/JRC-IDEES-2021_Industry_EU27.xlsx"
 
     with mute_print():
         df = pd.read_excel(fn, sheet_name="Ind_Summary", index_col=0, header=0).squeeze(
             "columns"
         )
 
-    assert df.index[48] == "by sector"
+    assert df.index[49] == "by sector"
     year_i = df.columns.get_loc(year)
-    e_eu28 = df.iloc[49:76, year_i]
-    e_eu28.index = e_eu28.index.str.lstrip()
+    e_eu27 = df.iloc[50:77, year_i]
+    e_eu27.index = e_eu27.index.str.lstrip()
 
-    e_ratio = e_country / e_eu28
+    e_ratio = e_country / e_eu27
 
     return pd.Series({k: e_ratio[v] for k, v in sub2sect.items()})
 
@@ -230,7 +231,7 @@ def get_energy_ratio(country, eurostat_dir, jrc_dir, year):
 def industry_production_per_country(country, year, eurostat_dir, jrc_dir):
     def get_sector_data(sector, country):
         jrc_country = jrc_names.get(country, country)
-        fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx"
+        fn = f"{jrc_dir}/{jrc_country}/JRC-IDEES-2021_Industry_{jrc_country}.xlsx"
         sheet = sub_sheet_name_dict[sector]
         with mute_print():
             df = pd.read_excel(fn, sheet_name=sheet, index_col=0, header=0).squeeze(
@@ -245,10 +246,10 @@ def industry_production_per_country(country, year, eurostat_dir, jrc_dir):
 
         return df
 
-    ct = "EU28" if country not in eu28 else country
+    ct = "EU27" if country not in eu27 else country
     demand = pd.concat([get_sector_data(s, ct) for s in sect2sub])
 
-    if country not in eu28:
+    if country not in eu27:
         demand *= get_energy_ratio(country, eurostat_dir, jrc_dir, year)
 
     demand.name = country
@@ -287,6 +288,7 @@ def separate_basic_chemicals(demand, year):
     """
     Separate basic chemicals into ammonia, chlorine, methanol and HVC.
     """
+    # ammonia data from 2018-2022
     ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
 
     there = ammonia.index.intersection(demand.index)
@@ -296,7 +298,12 @@ def separate_basic_chemicals(demand, year):
 
     demand["Ammonia"] = 0.0
 
-    demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
+    year_to_use = min(max(year, 2018), 2022)
+    if year_to_use != year:
+        logger.info(
+            f"Year {year} outside data range. Using data from {year_to_use} for ammonia production."
+        )
+    demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year_to_use)]
 
     demand["Basic chemicals"] -= demand["Ammonia"]
 
diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py
index b7a62d91..760dca76 100644
--- a/scripts/build_industry_sector_ratios.py
+++ b/scripts/build_industry_sector_ratios.py
@@ -17,7 +17,7 @@ Relevant Settings
 Inputs
 -------
 - ``resources/ammonia_production.csv``
-- ``data/bundle-sector/jrc-idees-2015``
+- ``data/bundle-sector/jrc-idees-2021``
 
 Outputs
 -------
@@ -48,42 +48,17 @@ If the `config["industry"]["ammonia"] <https://pypsa-eur.readthedocs.io/en/lates
 The unit of the specific energy consumption is MWh/t material and tCO2/t material for process emissions.
 """
 
+import country_converter as coco
 import pandas as pd
 from _helpers import mute_print, set_scenario_config
 
+cc = coco.CountryConverter()
+
 # GWh/ktoe OR MWh/toe
 toe_to_MWh = 11.630
 
-eu28 = [
-    "FR",
-    "DE",
-    "GB",
-    "IT",
-    "ES",
-    "PL",
-    "SE",
-    "NL",
-    "BE",
-    "FI",
-    "DK",
-    "PT",
-    "RO",
-    "AT",
-    "BG",
-    "EE",
-    "GR",
-    "LV",
-    "CZ",
-    "HU",
-    "IE",
-    "SK",
-    "LT",
-    "HR",
-    "LU",
-    "SI",
-    "CY",
-    "MT",
-]
+eu27 = cc.EU27as("ISO2").ISO2.tolist()
+
 
 sheet_names = {
     "Iron and steel": "ISI",
@@ -92,11 +67,11 @@ sheet_names = {
     "Pulp, paper and printing": "PPA",
     "Food, beverages and tobacco": "FBT",
     "Non Ferrous Metals": "NFM",
-    "Transport Equipment": "TRE",
-    "Machinery Equipment": "MAE",
+    "Transport equipment": "TRE",
+    "Machinery equipment": "MAE",
     "Textiles and leather": "TEL",
     "Wood and wood products": "WWP",
-    "Other Industrial Sectors": "OIS",
+    "Other industrial sectors": "OIS",
 }
 
 
@@ -116,7 +91,7 @@ index = [
 ]
 
 
-def load_idees_data(sector, country="EU28"):
+def load_idees_data(sector, country="EU27"):
     suffixes = {"out": "", "fec": "_fec", "ued": "_ued", "emi": "_emi"}
     sheets = {k: sheet_names[sector] + v for k, v in suffixes.items()}
 
@@ -125,7 +100,7 @@ def load_idees_data(sector, country="EU28"):
 
     with mute_print():
         idees = pd.read_excel(
-            f"{snakemake.input.idees}/JRC-IDEES-2015_Industry_{country}.xlsx",
+            f"{snakemake.input.idees}/{country}/JRC-IDEES-2021_Industry_{country}.xlsx",
             sheet_name=list(sheets.values()),
             index_col=0,
             header=0,
@@ -134,15 +109,26 @@ def load_idees_data(sector, country="EU28"):
 
     for k, v in sheets.items():
         idees[k] = idees.pop(v).squeeze()
+        idees[k] = idees[k][year]
 
     return idees
 
 
 def iron_and_steel():
-    # There are two different approaches to produce iron and steel:
-    # i.e., integrated steelworks and electric arc.
-    # Electric arc approach has higher efficiency and relies more on electricity.
-    # We assume that integrated steelworks will be replaced by electric arc entirely.
+    """
+    This function calculates the energy consumption and emissions for different
+    approaches to producing iron and steel. The two primary approaches are
+    integrated steelworks and electric arc furnaces (EAF). The function assumes
+    that integrated steelworks will be replaced entirely by electric arc
+    furnaces due to their higher efficiency and greater reliance on
+    electricity.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
+                      and process emissions (in tCO2/t material) for different steel
+                      production approaches, including electric arc, DRI + electric arc,
+                      and integrated steelworks.
+    """
 
     sector = "Iron and steel"
     idees = load_idees_data(sector)
@@ -155,51 +141,51 @@ def iron_and_steel():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][51:57]
+    s_fec = idees["fec"][52:68]
     assert s_fec.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
-    df.at["elec", sector] += s_fec[sel].sum()
+    df.at["elec", sector] += s_fec.loc[sel].sum()
 
-    df.at["heat", sector] += s_fec["Low enthalpy heat"]
+    df.at["heat", sector] += s_fec.loc["Low-enthalpy heat"]
 
     subsector = "Steel: Smelters"
-    s_fec = idees["fec"][61:67]
-    s_ued = idees["ued"][61:67]
+    s_fec = idees["fec"][63:68]
+    s_ued = idees["ued"][63:68]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
     # efficiency changes due to transforming all the smelters into methane
-    key = "Natural gas (incl. biogas)"
-    eff_met = s_ued[key] / s_fec[key]
+    key = "Natural gas and biogas"
+    eff_met = s_ued.loc[key] / s_fec.loc[key]
 
     df.at["methane", sector] += s_ued[subsector] / eff_met
 
     subsector = "Steel: Electric arc"
-    s_fec = idees["fec"][67:68]
+    s_fec = idees["fec"][69:70]
     assert s_fec.index[0] == subsector
 
     df.at["elec", sector] += s_fec[subsector]
 
-    subsector = "Steel: Furnaces, Refining and Rolling"
-    s_fec = idees["fec"][68:75]
-    s_ued = idees["ued"][68:75]
+    subsector = "Steel: Furnaces, refining and rolling"
+    s_fec = idees["fec"][70:77]
+    s_ued = idees["ued"][70:77]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    key = "Steel: Furnaces, Refining and Rolling - Electric"
+    key = "Steel: Furnaces, refining and rolling - Electric"
     eff = s_ued[key] / s_fec[key]
 
     # assume fully electrified, other processes scaled by used energy
     df.at["elec", sector] += s_ued[subsector] / eff
 
-    subsector = "Steel: Products finishing"
-    s_fec = idees["fec"][75:92]
-    s_ued = idees["ued"][75:92]
+    subsector = "Steel: Product finishing"
+    s_fec = idees["fec"][77:95]
+    s_ued = idees["ued"][77:95]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    key = "Steel: Products finishing - Electric"
+    key = "Steel: Product finishing - Electric"
     eff = s_ued[key] / s_fec[key]
 
     # assume fully electrified
@@ -207,7 +193,7 @@ def iron_and_steel():
 
     # Process emissions (per physical output)
 
-    s_emi = idees["emi"][51:93]
+    s_emi = idees["emi"][52:95]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][7:8]
@@ -242,63 +228,63 @@ def iron_and_steel():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:9]
+    s_fec = idees["fec"][3:50]
     assert s_fec.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
-    subsector = "Steel: Sinter/Pellet making"
+    subsector = "Steel: Sinter/Pellet-making"
 
-    s_fec = idees["fec"][13:19]
-    s_ued = idees["ued"][13:19]
+    s_fec = idees["fec"][14:20]
+    s_ued = idees["ued"][14:20]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
     df.loc["elec", sector] += s_fec["Electricity"]
 
-    sel = ["Natural gas (incl. biogas)", "Residual fuel oil"]
+    sel = ["Natural gas and biogas", "Fuel oil"]
     df.loc["methane", sector] += s_fec[sel].sum()
 
     df.loc["coal", sector] += s_fec["Solids"]
 
     subsector = "Steel: Blast /Basic oxygen furnace"
 
-    s_fec = idees["fec"][19:25]
-    s_ued = idees["ued"][19:25]
+    s_fec = idees["fec"][20:26]
+    s_ued = idees["ued"][20:26]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    sel = ["Natural gas (incl. biogas)", "Residual fuel oil"]
+    sel = ["Natural gas and biogas", "Fuel oil"]
     df.loc["methane", sector] += s_fec[sel].sum()
 
     df.loc["coal", sector] += s_fec["Solids"]
 
     df.loc["coke", sector] = s_fec["Coke"]
 
-    subsector = "Steel: Furnaces, Refining and Rolling"
+    subsector = "Steel: Furnaces, refining and rolling"
 
-    s_fec = idees["fec"][25:32]
-    s_ued = idees["ued"][25:32]
+    s_fec = idees["fec"][26:33]
+    s_ued = idees["ued"][26:33]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    key = "Steel: Furnaces, Refining and Rolling - Electric"
+    key = "Steel: Furnaces, refining and rolling - Electric"
     eff = s_ued[key] / s_fec[key]
 
     # assume fully electrified, other processes scaled by used energy
     df.loc["elec", sector] += s_ued[subsector] / eff
 
-    subsector = "Steel: Products finishing"
+    subsector = "Steel: Product finishing"
 
-    s_fec = idees["fec"][32:49]
-    s_ued = idees["ued"][32:49]
+    s_fec = idees["fec"][33:50]
+    s_ued = idees["ued"][33:50]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    key = "Steel: Products finishing - Electric"
+    key = "Steel: Product finishing - Electric"
     eff = s_ued[key] / s_fec[key]
 
     # assume fully electrified
@@ -306,7 +292,7 @@ def iron_and_steel():
 
     # Process emissions (per physical output)
 
-    s_emi = idees["emi"][3:50]
+    s_emi = idees["emi"][3:51]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][6:7]
@@ -323,6 +309,18 @@ def iron_and_steel():
 
 
 def chemicals_industry():
+    """
+    This function calculates the energy consumption and emissions for the
+    chemicals industry, focusing on various subsectors such as basic chemicals,
+    steam processing, furnaces, and process cooling. The function also accounts
+    for specific processes in ammonia, chlorine, methanol production, and other
+    chemicals.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
+                      and process emissions (in tCO2/t material) for various subsectors
+                      within the chemicals industry.
+    """
     sector = "Chemicals Industry"
     idees = load_idees_data(sector)
 
@@ -340,15 +338,15 @@ def chemicals_industry():
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     subsector = "Chemicals: Feedstock (energy used as raw material)"
-    # There are Solids, Refinery gas, LPG, Diesel oil, Residual fuel oil,
+    # There are Solids, Refinery gas, LPG, Diesel oil, Fuel oil,
     # Other liquids, Naphtha, Natural gas for feedstock.
     # Naphta represents 47%, methane 17%. LPG (18%) solids, refinery gas,
-    # diesel oil, residual fuel oils and other liquids are assimilated to Naphtha
+    # diesel oil, Fuel oils and other liquids are assimilated to Naphtha
 
-    s_fec = idees["fec"][13:22]
+    s_fec = idees["fec"][14:23]
     assert s_fec.index[0] == subsector
 
     df.loc["naphtha", sector] += s_fec["Naphtha"]
@@ -362,7 +360,7 @@ def chemicals_industry():
         "Refinery gas",
         "LPG",
         "Diesel oil",
-        "Residual fuel oil",
+        "Fuel oil",
         "Other liquids",
     ]
     df.loc["naphtha", sector] += s_fec[sel].sum()
@@ -372,21 +370,21 @@ def chemicals_industry():
     # converted to methane, since we need >1000 C temperatures here.
     # The current efficiency of methane is assumed in the conversion.
 
-    s_fec = idees["fec"][22:33]
-    s_ued = idees["ued"][22:33]
+    s_fec = idees["fec"][23:34]
+    s_ued = idees["ued"][23:34]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
     # efficiency of natural gas
-    eff_ch4 = s_ued["Natural gas (incl. biogas)"] / s_fec["Natural gas (incl. biogas)"]
+    eff_ch4 = s_ued["Natural gas and biogas"] / s_fec["Natural gas and biogas"]
 
     # replace all fec by methane
     df.loc["methane", sector] += s_ued[subsector] / eff_ch4
 
     subsector = "Chemicals: Furnaces"
 
-    s_fec = idees["fec"][33:41]
-    s_ued = idees["ued"][33:41]
+    s_fec = idees["fec"][34:42]
+    s_ued = idees["ued"][34:42]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
@@ -399,8 +397,8 @@ def chemicals_industry():
 
     subsector = "Chemicals: Process cooling"
 
-    s_fec = idees["fec"][41:55]
-    s_ued = idees["ued"][41:55]
+    s_fec = idees["fec"][42:56]
+    s_ued = idees["ued"][42:56]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
@@ -412,17 +410,17 @@ def chemicals_industry():
 
     subsector = "Chemicals: Generic electric process"
 
-    s_fec = idees["fec"][55:56]
+    s_fec = idees["fec"][56:57]
     assert s_fec.index[0] == subsector
 
     df.loc["elec", sector] += s_fec[subsector]
 
     # Process emissions
 
-    # Correct everything by subtracting 2015's ammonia demand and
+    # Correct everything by subtracting 2019's ammonia demand and
     # putting in ammonia demand for H2 and electricity separately
 
-    s_emi = idees["emi"][3:57]
+    s_emi = idees["emi"][3:58]
     assert s_emi.index[0] == sector
 
     # convert from MtHVC/a to ktHVC/a
@@ -447,7 +445,7 @@ def chemicals_industry():
 
     # subtract ammonia energy demand (in ktNH3/a)
     ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
-    ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
+    ammonia_total = ammonia.loc[ammonia.index.intersection(eu27), str(year)].sum()
     df.loc["methane", sector] -= ammonia_total * params["MWh_CH4_per_tNH3_SMR"]
     df.loc["elec", sector] -= ammonia_total * params["MWh_elec_per_tNH3_SMR"]
 
@@ -507,22 +505,22 @@ def chemicals_industry():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][58:64]
+    s_fec = idees["fec"][59:65]
     assert s_fec.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
-    subsector = "Chemicals: High enthalpy heat  processing"
+    subsector = "Chemicals: High-enthalpy heat processing"
 
-    s_fec = idees["fec"][68:81]
-    s_ued = idees["ued"][68:81]
+    s_fec = idees["fec"][70:83]
+    s_ued = idees["ued"][70:83]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    key = "High enthalpy heat  processing - Electric (microwave)"
+    key = "High-enthalpy heat processing - Electric (microwave)"
     eff_elec = s_ued[key] / s_fec[key]
 
     # assume fully electrified
@@ -530,8 +528,8 @@ def chemicals_industry():
 
     subsector = "Chemicals: Furnaces"
 
-    s_fec = idees["fec"][81:89]
-    s_ued = idees["ued"][81:89]
+    s_fec = idees["fec"][83:92]
+    s_ued = idees["ued"][83:92]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
@@ -543,8 +541,8 @@ def chemicals_industry():
 
     subsector = "Chemicals: Process cooling"
 
-    s_fec = idees["fec"][89:103]
-    s_ued = idees["ued"][89:103]
+    s_fec = idees["fec"][91:105]
+    s_ued = idees["ued"][91:105]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
@@ -556,14 +554,14 @@ def chemicals_industry():
 
     subsector = "Chemicals: Generic electric process"
 
-    s_fec = idees["fec"][103:104]
+    s_fec = idees["fec"][105:106]
     assert s_fec.index[0] == subsector
 
     df.loc["elec", sector] += s_fec[subsector]
 
     # Process emissions
 
-    s_emi = idees["emi"][58:105]
+    s_emi = idees["emi"][59:107]
     s_out = idees["out"][9:10]
     assert s_emi.index[0] == sector
     assert sector in str(s_out.index)
@@ -581,22 +579,22 @@ def chemicals_industry():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][106:112]
+    s_fec = idees["fec"][108:114]
     assert s_fec.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
-    subsector = "Chemicals: High enthalpy heat  processing"
+    subsector = "Chemicals: High-enthalpy heat processing"
 
-    s_fec = idees["fec"][116:129]
-    s_ued = idees["ued"][116:129]
+    s_fec = idees["fec"][119:132]
+    s_ued = idees["ued"][119:132]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    key = "High enthalpy heat  processing - Electric (microwave)"
+    key = "High-enthalpy heat processing - Electric (microwave)"
     eff_elec = s_ued[key] / s_fec[key]
 
     # assume fully electrified
@@ -604,8 +602,8 @@ def chemicals_industry():
 
     subsector = "Chemicals: Furnaces"
 
-    s_fec = idees["fec"][129:137]
-    s_ued = idees["ued"][129:137]
+    s_fec = idees["fec"][132:140]
+    s_ued = idees["ued"][132:140]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
@@ -617,8 +615,8 @@ def chemicals_industry():
 
     subsector = "Chemicals: Process cooling"
 
-    s_fec = idees["fec"][137:151]
-    s_ued = idees["ued"][137:151]
+    s_fec = idees["fec"][140:154]
+    s_ued = idees["ued"][140:154]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
@@ -630,7 +628,7 @@ def chemicals_industry():
 
     subsector = "Chemicals: Generic electric process"
 
-    s_fec = idees["fec"][151:152]
+    s_fec = idees["fec"][154:155]
     s_out = idees["out"][10:11]
     assert s_fec.index[0] == subsector
     assert sector in str(s_out.index)
@@ -648,8 +646,17 @@ def chemicals_industry():
 
 
 def nonmetalic_mineral_products():
-    # This includes cement, ceramic and glass production.
-    # This includes process emissions related to the fabrication of clinker.
+    """
+    This function calculates the energy consumption and emissions for the non-
+    metallic mineral products industry, focusing on three main sectors: cement,
+    ceramics, and glass production. It takes into account the specific
+    processes and their associated energy types and emissions.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
+                      and process emissions (in tCO2/t material) for the cement, ceramics,
+                      and glass production sectors.
+    """
 
     sector = "Non-metallic mineral products"
     idees = load_idees_data(sector)
@@ -681,27 +688,27 @@ def nonmetalic_mineral_products():
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # pre-processing: keep existing elec and biomass, rest to methane
     df.loc["elec", sector] += s_fec["Cement: Grinding, milling of raw material"]
-    df.loc["biomass", sector] += s_fec["Biomass"]
+    df.loc["biomass", sector] += s_fec["Biomass and waste"]
     df.loc["methane", sector] += (
-        s_fec["Cement: Pre-heating and pre-calcination"] - s_fec["Biomass"]
+        s_fec["Cement: Pre-heating and pre-calcination"] - s_fec["Biomass and waste"]
     )
 
     subsector = "Cement: Clinker production (kilns)"
 
-    s_fec = idees["fec"][34:43]
-    s_ued = idees["ued"][34:43]
+    s_fec = idees["fec"][23:32]
+    s_ued = idees["ued"][23:32]
     assert s_fec.index[0] == subsector
     assert s_ued.index[0] == subsector
 
-    df.loc["biomass", sector] += s_fec["Biomass"]
+    df.loc["biomass", sector] += s_fec["Biomass and waste"]
     df.loc["methane", sector] += (
-        s_fec["Cement: Clinker production (kilns)"] - s_fec["Biomass"]
+        s_fec["Cement: Clinker production (kilns)"] - s_fec["Biomass and waste"]
     )
-    df.loc["elec", sector] += s_fec["Cement: Grinding, packaging"]
+    df.loc["elec", sector] += s_fec["Cement: Grinding, packaging and precasting"]
 
     # Process emissions
 
@@ -709,7 +716,7 @@ def nonmetalic_mineral_products():
     # Calcium carbonate -> lime + CO2
     # CaCO3  -> CaO + CO2
 
-    s_emi = idees["emi"][3:44]
+    s_emi = idees["emi"][3:45]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][7:8]
@@ -737,19 +744,21 @@ def nonmetalic_mineral_products():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][45:94]
-    s_ued = idees["ued"][45:94]
+    s_fec = idees["fec"][46:95]
+    s_ued = idees["ued"][46:95]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Ceramics: Microwave drying and sintering"
-    eff_elec = s_ued[key] / s_fec[key]
+    # the values are zero in new JRC-data -> assume here value from JRC-2015
+    # eff_elec = s_ued[key] / s_fec[key]
+    eff_elec = 11.6 / 26
 
     sel = [
         "Ceramics: Mixing of raw material",
@@ -767,7 +776,7 @@ def nonmetalic_mineral_products():
 
     df.loc["elec", sector] += s_ued["Ceramics: Product finishing"] / eff_elec
 
-    s_emi = idees["emi"][45:94]
+    s_emi = idees["emi"][46:96]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][8:9]
@@ -795,15 +804,15 @@ def nonmetalic_mineral_products():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][95:123]
-    s_ued = idees["ued"][95:123]
+    s_fec = idees["fec"][97:126]
+    s_ued = idees["ued"][97:126]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Glass: Electric melting tank"
@@ -817,7 +826,7 @@ def nonmetalic_mineral_products():
     sel = ["Glass: Forming", "Glass: Annealing", "Glass: Finishing processes"]
     df.loc["elec", sector] += s_ued[sel].sum() / eff_elec
 
-    s_emi = idees["emi"][95:124]
+    s_emi = idees["emi"][97:127]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][9:10]
@@ -834,8 +843,15 @@ def nonmetalic_mineral_products():
 
 
 def pulp_paper_printing():
-    # Pulp, paper and printing can be completely electrified.
-    # There are no process emissions associated to this sector.
+    """
+    Models the energy consumption for the pulp, paper, and printing sector,
+    assuming complete electrification of all processes. This sector does not
+    have any process emissions associated with it.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
+                      for the pulp, paper, and printing sector.
+    """
 
     sector = "Pulp, paper and printing"
     idees = load_idees_data(sector)
@@ -857,15 +873,15 @@ def pulp_paper_printing():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:28]
-    s_ued = idees["ued"][3:28]
+    s_fec = idees["fec"][3:29]
+    s_ued = idees["ued"][3:29]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Industry-specific
     sel = [
@@ -876,7 +892,7 @@ def pulp_paper_printing():
     df.loc["elec", sector] += s_fec[sel].sum()
 
     # Efficiency changes due to biomass
-    eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
+    eff_bio = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
     df.loc["biomass", sector] += s_ued["Pulp: Pulping thermal"] / eff_bio
 
     s_out = idees["out"][8:9]
@@ -906,15 +922,15 @@ def pulp_paper_printing():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][29:78]
-    s_ued = idees["ued"][29:78]
+    s_fec = idees["fec"][30:80]
+    s_ued = idees["ued"][30:80]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Industry-specific
     df.loc["elec", sector] += s_fec["Paper: Stock preparation"]
@@ -925,22 +941,22 @@ def pulp_paper_printing():
     # add electricity from process that is already electrified
     df.loc["elec", sector] += s_fec["Paper: Product finishing - Electricity"]
 
-    s_fec = idees["fec"][53:64]
-    s_ued = idees["ued"][53:64]
+    s_fec = idees["fec"][55:66]
+    s_ued = idees["ued"][55:66]
     assert s_fec.index[0] == "Paper: Paper machine - Steam use"
     assert s_ued.index[0] == "Paper: Paper machine - Steam use"
 
     # Efficiency changes due to biomass
-    eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
+    eff_bio = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
     df.loc["biomass", sector] += s_ued["Paper: Paper machine - Steam use"] / eff_bio
 
-    s_fec = idees["fec"][66:77]
-    s_ued = idees["ued"][66:77]
+    s_fec = idees["fec"][68:79]
+    s_ued = idees["ued"][68:79]
     assert s_fec.index[0] == "Paper: Product finishing - Steam use"
     assert s_ued.index[0] == "Paper: Product finishing - Steam use"
 
     # Efficiency changes due to biomass
-    eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
+    eff_bio = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
     df.loc["biomass", sector] += s_ued["Paper: Product finishing - Steam use"] / eff_bio
 
     s_out = idees["out"][9:10]
@@ -959,8 +975,8 @@ def pulp_paper_printing():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][79:90]
-    s_ued = idees["ued"][79:90]
+    s_fec = idees["fec"][81:93]
+    s_ued = idees["ued"][81:93]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
@@ -968,8 +984,8 @@ def pulp_paper_printing():
     df.loc["elec", sector] += s_fec[sel].sum()
     df.loc["elec", sector] += s_ued[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
-    df.loc["heat", sector] += s_ued["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
+    df.loc["heat", sector] += s_ued["Low-enthalpy heat"]
 
     # Industry-specific
     df.loc["elec", sector] += s_fec["Printing and publishing"]
@@ -986,8 +1002,15 @@ def pulp_paper_printing():
 
 
 def food_beverages_tobacco():
-    # Food, beverages and tobaco can be completely electrified.
-    # There are no process emissions associated to this sector.
+    """
+    Calculates the energy consumption for the food, beverages, and tobacco
+    sector, assuming complete electrification of all processes. This sector
+    does not have any process emissions associated with it.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the energy consumption (in MWh/t material)
+                      for the food, beverages, and tobacco sector.
+    """
 
     sector = "Food, beverages and tobacco"
     idees = load_idees_data(sector)
@@ -996,15 +1019,15 @@ def food_beverages_tobacco():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:78]
-    s_ued = idees["ued"][3:78]
+    s_fec = idees["fec"][3:79]
+    s_ued = idees["ued"][3:79]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
 
@@ -1052,7 +1075,7 @@ def non_ferrous_metals():
 
     # Alumina
 
-    # High enthalpy heat is converted to methane.
+    # High-enthalpy heat is converted to methane.
     # Process heat at T>500C is required here.
     # Refining is electrified.
     # There are no process emissions associated to Alumina manufacturing.
@@ -1069,24 +1092,24 @@ def non_ferrous_metals():
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # High-enthalpy heat is transformed into methane
 
-    s_fec = idees["fec"][13:24]
-    s_ued = idees["ued"][13:24]
-    assert s_fec.index[0] == "Alumina production: High enthalpy heat"
-    assert s_ued.index[0] == "Alumina production: High enthalpy heat"
+    s_fec = idees["fec"][14:25]
+    s_ued = idees["ued"][14:25]
+    assert s_fec.index[0] == "Alumina production: High-enthalpy heat"
+    assert s_ued.index[0] == "Alumina production: High-enthalpy heat"
 
-    eff_met = s_ued["Natural gas (incl. biogas)"] / s_fec["Natural gas (incl. biogas)"]
+    eff_met = s_ued["Natural gas and biogas"] / s_fec["Natural gas and biogas"]
     df.loc["methane", sector] += (
-        s_fec["Alumina production: High enthalpy heat"] / eff_met
+        s_fec["Alumina production: High-enthalpy heat"] / eff_met
     )
 
     # Efficiency changes due to electrification
 
-    s_fec = idees["fec"][24:30]
-    s_ued = idees["ued"][24:30]
+    s_fec = idees["fec"][25:31]
+    s_ued = idees["ued"][25:31]
     assert s_fec.index[0] == "Alumina production: Refining"
     assert s_ued.index[0] == "Alumina production: Refining"
 
@@ -1111,15 +1134,15 @@ def non_ferrous_metals():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][31:66]
-    s_ued = idees["ued"][31:66]
+    s_fec = idees["fec"][32:68]
+    s_ued = idees["ued"][32:68]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Add aluminium  electrolysis (smelting
     df.loc["elec", sector] += s_fec["Aluminium electrolysis (smelting)"]
@@ -1135,7 +1158,7 @@ def non_ferrous_metals():
     eff_elec = s_ued[key] / s_fec[key]
     df.loc["elec", sector] += s_ued["Aluminium finishing"] / eff_elec
 
-    s_emi = idees["emi"][31:67]
+    s_emi = idees["emi"][32:69]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][11:12]
@@ -1160,15 +1183,15 @@ def non_ferrous_metals():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][68:109]
-    s_ued = idees["ued"][68:109]
+    s_fec = idees["fec"][70:112]
+    s_ued = idees["ued"][70:112]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Secondary aluminium - Electric"
@@ -1200,15 +1223,15 @@ def non_ferrous_metals():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][110:152]
-    s_ued = idees["ued"][110:152]
+    s_fec = idees["fec"][113:156]
+    s_ued = idees["ued"][113:156]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Metal production - Electric"
@@ -1224,7 +1247,7 @@ def non_ferrous_metals():
     eff_elec = s_ued[key] / s_fec[key]
     df.loc["elec", sector] += s_ued["Metal finishing"] / eff_elec
 
-    s_emi = idees["emi"][110:153]
+    s_emi = idees["emi"][113:157]
     assert s_emi.index[0] == sector
 
     s_out = idees["out"][13:14]
@@ -1247,22 +1270,22 @@ def non_ferrous_metals():
 
 
 def transport_equipment():
-    sector = "Transport Equipment"
+    sector = "Transport equipment"
     idees = load_idees_data(sector)
 
     df = pd.DataFrame(index=index)
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:45]
-    s_ued = idees["ued"][3:45]
+    s_fec = idees["fec"][3:46]
+    s_ued = idees["ued"][3:46]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Trans. Eq.: Electric Foundries"
@@ -1281,7 +1304,7 @@ def transport_equipment():
     df.loc["elec", sector] += s_fec["Trans. Eq.: Product finishing"]
 
     # Steam processing is supplied with biomass
-    eff_biomass = s_ued["Biomass"] / s_fec["Biomass"]
+    eff_biomass = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
     df.loc["biomass", sector] += s_ued["Trans. Eq.: Steam processing"] / eff_biomass
 
     s_out = idees["out"][3:4]
@@ -1297,7 +1320,7 @@ def transport_equipment():
 
 
 def machinery_equipment():
-    sector = "Machinery Equipment"
+    sector = "Machinery equipment"
 
     idees = load_idees_data(sector)
 
@@ -1305,15 +1328,15 @@ def machinery_equipment():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:45]
-    s_ued = idees["ued"][3:45]
+    s_fec = idees["fec"][3:46]
+    s_ued = idees["ued"][3:46]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Mach. Eq.: Electric Foundries"
@@ -1333,7 +1356,7 @@ def machinery_equipment():
     df.loc["elec", sector] += s_fec["Mach. Eq.: Product finishing"]
 
     # Steam processing is supplied with biomass
-    eff_biomass = s_ued["Biomass"] / s_fec["Biomass"]
+    eff_biomass = s_ued["Biomass and waste"] / s_fec["Biomass and waste"]
     df.loc["biomass", sector] += s_ued["Mach. Eq.: Steam processing"] / eff_biomass
 
     s_out = idees["out"][3:4]
@@ -1357,26 +1380,28 @@ def textiles_and_leather():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:57]
-    s_ued = idees["ued"][3:57]
+    s_fec = idees["fec"][3:58]
+    s_ued = idees["ued"][3:58]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Textiles: Electric drying"
-    eff_elec = s_ued[key] / s_fec[key]
+    # in new JRC data zero assume old data
+    # eff_elec = s_ued[key] / s_fec[key]
+    eff_elec = 73.7 / 146.6
     df.loc["elec", sector] += s_ued["Textiles: Drying"] / eff_elec
 
     df.loc["elec", sector] += s_fec["Textiles: Electric general machinery"]
     df.loc["elec", sector] += s_fec["Textiles: Finishing Electric"]
 
     # Steam processing is supplied with biomass
-    eff_biomass = s_ued[15:26]["Biomass"] / s_fec[15:26]["Biomass"]
+    eff_biomass = s_ued[15:26]["Biomass and waste"] / s_fec[15:26]["Biomass and waste"]
     df.loc["biomass", sector] += (
         s_ued["Textiles: Pretreatment with steam"] / eff_biomass
     )
@@ -1405,15 +1430,15 @@ def wood_and_wood_products():
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:46]
-    s_ued = idees["ued"][3:46]
+    s_fec = idees["fec"][3:47]
+    s_ued = idees["ued"][3:47]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Wood: Electric drying"
@@ -1424,7 +1449,7 @@ def wood_and_wood_products():
     df.loc["elec", sector] += s_fec["Wood: Finishing Electric"]
 
     # Steam processing is supplied with biomass
-    eff_biomass = s_ued[15:25]["Biomass"] / s_fec[15:25]["Biomass"]
+    eff_biomass = s_ued[15:25]["Biomass and waste"] / s_fec[15:25]["Biomass and waste"]
     df.loc["biomass", sector] += (
         s_ued["Wood: Specific processes with steam"] / eff_biomass
     )
@@ -1442,23 +1467,23 @@ def wood_and_wood_products():
 
 
 def other_industrial_sectors():
-    sector = "Other Industrial Sectors"
 
+    sector = "Other industrial sectors"
     idees = load_idees_data(sector)
 
     df = pd.DataFrame(index=index)
 
     df[sector] = 0.0
 
-    s_fec = idees["fec"][3:67]
-    s_ued = idees["ued"][3:67]
+    s_fec = idees["fec"][3:68]
+    s_ued = idees["ued"][3:68]
     assert s_fec.index[0] == sector
     assert s_ued.index[0] == sector
 
     sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
     df.loc["elec", sector] += s_fec[sel].sum()
 
-    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
+    df.loc["heat", sector] += s_fec["Low-enthalpy heat"]
 
     # Efficiency changes due to electrification
     key = "Other Industrial sectors: Electric processing"
@@ -1484,7 +1509,7 @@ def other_industrial_sectors():
     df.loc["elec", sector] += s_fec[key]
 
     # Steam processing is supplied with biomass
-    eff_biomass = s_ued[15:25]["Biomass"] / s_fec[15:25]["Biomass"]
+    eff_biomass = s_ued[15:25]["Biomass and waste"] / s_fec[15:25]["Biomass and waste"]
     df.loc["biomass", sector] += (
         s_ued["Other Industrial sectors: Steam processing"] / eff_biomass
     )
@@ -1508,11 +1533,10 @@ if __name__ == "__main__":
         snakemake = mock_snakemake("build_industry_sector_ratios")
     set_scenario_config(snakemake)
 
-    # TODO make params option
-    year = 2015
-
     params = snakemake.params.industry
 
+    year = params["reference_year"]
+
     df = pd.concat(
         [
             iron_and_steel(),
diff --git a/scripts/build_industry_sector_ratios_intermediate.py b/scripts/build_industry_sector_ratios_intermediate.py
index ebbabdb2..42e8a7dc 100644
--- a/scripts/build_industry_sector_ratios_intermediate.py
+++ b/scripts/build_industry_sector_ratios_intermediate.py
@@ -56,8 +56,8 @@ For each bus, the following industry subcategories
 - Aluminium - primary production
 - Aluminium - secondary production
 - Other non-ferrous metals
-- Transport Equipment
-- Machinery Equipment
+- Transport equipment
+- Machinery equipment
 - Textiles and leather
 - Wood and wood products
 - Other Industrial Sectors
diff --git a/scripts/build_shapes.py b/scripts/build_shapes.py
index 411d56a4..4de5370a 100644
--- a/scripts/build_shapes.py
+++ b/scripts/build_shapes.py
@@ -132,7 +132,8 @@ def countries(naturalearth, country_list):
 def eez(eez, country_list):
     df = gpd.read_file(eez)
     iso3_list = cc.convert(country_list, src="ISO2", to="ISO3")
-    df = df.query("ISO_TER1 in @iso3_list and POL_TYPE == '200NM'").copy()
+    pol_type = ["200NM", "Overlapping claim"]
+    df = df.query("ISO_TER1 in @iso3_list and POL_TYPE in @pol_type").copy()
     df["name"] = cc.convert(df.ISO_TER1, src="ISO3", to="ISO2")
     s = df.set_index("name").geometry.map(
         lambda s: _simplify_polys(s, filterremote=False)
diff --git a/scripts/build_transmission_projects.py b/scripts/build_transmission_projects.py
new file mode 100644
index 00000000..ba185d17
--- /dev/null
+++ b/scripts/build_transmission_projects.py
@@ -0,0 +1,564 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+
+# coding: utf-8
+"""
+Gets the transmission projects defined in the config file, concatenates and
+deduplicates them. Projects are later included in :mod:`add_electricity.py`.
+
+Relevant Settings
+-----------------
+
+.. code:: yaml
+
+transmission_projects:
+  include:
+    #tyndp: true # For later, when other TYNDP projects are combined with new version
+    nep: true
+  status:
+  - confirmed
+  - in_permitting
+  - under_construction
+    #- under_consideration
+  link_under_construction: zero
+
+.. seealso::
+    Documentation of the configuration file ``config/config.yaml`` at
+    :ref:`transmission_projects`
+Inputs
+------
+
+- ``networks/base_network.nc``:  Base network topology for the electricity grid. This is processed in :mod:`base_network.py`.
+- ``data/transmission_projects/"project_name"/``: Takes the transmission projects from the subfolder of data/transmission_projects. The subfolder name is the project name.
+- ``offshore_shapes.geojson``: Shapefile containing the offshore regions. Used to determine if a new bus should be added for a new line or link.
+- ``europe_shape.geojson``: Shapefile containing the shape of Europe. Used to determine if a project is within the considered countries.
+
+Outputs
+-------
+
+- ``transmission_projects/new_lines.csv``: New project lines to be added to the network. This includes new lines and upgraded lines.
+- ``transmission_projects/new_links.csv``: New project links to be added to the network. This includes new links and upgraded links.
+- ``transmission_projects/adjust_lines.csv``: For lines which are upgraded, the decommissioning year of the existing line is adjusted to the build year of the upgraded line.
+- ``transmission_projects/adjust_links.csv``: For links which are upgraded, the decommissioning year of the existing link is adjusted to the build year of the upgraded link.
+- ``transmission_projects/new_buses.csv``: For some links, we have to add new buses (e.g. North Sea Wind Power Hub).
+"""
+import logging
+import os
+from pathlib import Path
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import pypsa
+import shapely
+from _helpers import configure_logging, set_scenario_config
+from pypsa.descriptors import nominal_attrs
+from scipy import spatial
+from shapely.geometry import LineString, Point
+
+logger = logging.getLogger(__name__)
+
+
+def add_new_buses(n, new_ports):
+    # Add new buses for the ports which do not have an existing bus close by. If there are multiple ports at the same location, only one bus is added.
+    duplicated = new_ports.duplicated(subset=["x", "y"], keep="first")
+    to_add = new_ports[~duplicated]
+    added_buses = n.madd(
+        "Bus",
+        names=to_add.index,
+        suffix=" bus",
+        x=to_add.x,
+        y=to_add.y,
+        v_nom=380,
+        under_construction=True,
+        symbol="substation",
+        substation_off=True,
+        substation_lv=False,
+        carrier="AC",
+    )
+    new_buses = n.buses.loc[added_buses].copy().dropna(axis=1, how="all")
+    new_ports.loc[to_add.index, "neighbor"] = added_buses
+    new_ports["neighbor"] = new_ports.groupby(["x", "y"])["neighbor"].transform("first")
+    return new_ports, new_buses
+
+
+def find_country_for_bus(bus, shapes):
+    """
+    Find the country of a bus based on its coordinates and the provided
+    shapefile.
+
+    Shapefile must contain a column "country" with the country names.
+    """
+    point = Point(bus.x, bus.y)
+    country = shapes.loc[shapes.contains(point), "country"]
+    return country.values[0]
+
+
+def connect_new_lines(
+    lines,
+    n,
+    new_buses_df,
+    offshore_shapes=None,
+    distance_upper_bound=np.inf,
+    bus_carrier="AC",
+):
+    """
+    Find the closest existing bus to the port of each line.
+
+    If closest bus is further away than distance_upper_bound and is
+    inside an offshore region, a new bus is created. and the line is
+    connected to it.
+    """
+    bus_carrier = np.atleast_1d(bus_carrier)
+    buses = n.buses.query("carrier in @bus_carrier").copy()
+    bus_tree = spatial.KDTree(buses[["x", "y"]])
+
+    for port in [0, 1]:
+        lines_port = lines["geometry"].apply(
+            lambda x: pd.Series(
+                get_bus_coords_from_port(x, port=port), index=["x", "y"]
+            )
+        )
+        distances, indices = bus_tree.query(lines_port)
+        # Series of lines with closest bus in the existing network and whether they match the distance criterion
+        lines_port["neighbor"] = buses.iloc[indices].index
+        lines_port["match_distance"] = distances < distance_upper_bound
+        # For buses which are not close to any existing bus, only add a new bus if the line is going offshore (e.g. North Sea Wind Power Hub)
+        if not lines_port.match_distance.all() and offshore_shapes.union_all():
+            potential_new_buses = lines_port[~lines_port.match_distance]
+            is_offshore = potential_new_buses.apply(
+                lambda x: offshore_shapes.union_all().contains(Point(x.x, x.y)), axis=1
+            )
+            new_buses = potential_new_buses[is_offshore]
+            if not new_buses.empty:
+                new_port, new_buses = add_new_buses(n, new_buses)
+                new_buses["country"] = new_buses.apply(
+                    lambda bus: find_country_for_bus(bus, offshore_shapes), axis=1
+                )
+                lines_port.loc[new_port.index, "match_distance"] = True
+                lines_port.loc[new_port.index, "neighbor"] = new_port.neighbor
+                new_buses_df = pd.concat([new_buses_df, new_buses])
+
+        if not lines_port.match_distance.all():
+            logging.warning(
+                "Could not find bus close enough to connect the the following lines:\n"
+                + str(lines_port[~lines_port.match_distance].index.to_list())
+                + "\n Lines will be ignored."
+            )
+            lines.drop(lines_port[~lines_port.match_distance].index, inplace=True)
+            lines_port = lines_port[lines_port.match_distance]
+
+        lines.loc[lines_port.index, f"bus{port}"] = lines_port["neighbor"]
+
+    lines = lines.assign(under_construction=True)
+
+    return lines, new_buses_df
+
+
+def get_branch_coords_from_geometry(linestring, reversed=False):
+    """
+    Reduces a linestring to its start and end points. Used to simplify the
+    linestring which can have more than two points.
+
+    Parameters:
+    linestring: Shapely linestring
+    reversed (bool, optional): If True, returns the end and start points instead of the start and end points.
+                               Defaults to False.
+
+    Returns:
+    numpy.ndarray: Flattened array of start and end coordinates.
+    """
+    coords = np.asarray(linestring.coords)
+    ind = [0, -1] if not reversed else [-1, 0]
+    start_end_coords = coords[ind]
+    return start_end_coords.flatten()
+
+
+def get_branch_coords_from_buses(line):
+    """
+    Gets line string for branch component in an pypsa network.
+
+    Parameters:
+    linestring: shapely linestring
+    reversed (bool, optional): If True, returns the end and start points instead of the start and end points.
+                               Defaults to False.
+
+    Returns:
+    numpy.ndarray: Flattened array of start and end coordinates.
+    """
+    start_coords = n.buses.loc[line.bus0, ["x", "y"]].values
+    end_coords = n.buses.loc[line.bus1, ["x", "y"]].values
+    return np.array([start_coords, end_coords]).flatten()
+
+
+def get_bus_coords_from_port(linestring, port=0):
+    """
+    Extracts the coordinates of a specified port from a given linestring.
+
+    Parameters:
+    linestring: The shapely linestring.
+    port (int): The index of the port to extract coordinates from. Default is 0.
+
+    Returns:
+    tuple: The coordinates of the specified port as a tuple (x, y).
+    """
+    coords = np.asarray(linestring.coords)
+    ind = [0, -1]
+    coords = coords[ind]
+    coords = coords[port]
+    return coords
+
+
+def find_closest_lines(lines, new_lines, distance_upper_bound=0.1, type="new"):
+    """
+    Find the closest lines in the existing set of lines to a set of new lines.
+
+    Parameters:
+    lines (pandas.DataFrame): DataFrame of the existing lines.
+    new_lines (pandas.DataFrame): DataFrame with column geometry containing the new lines.
+    distance_upper_bound (float, optional): Maximum distance to consider a line as a match. Defaults to 0.1 which corresponds to approximately 15 km.
+
+    Returns:
+    pandas.Series: Series containing with index the new lines and values providing closest existing line.
+    """
+
+    # get coordinates of start and end points of all lines, for new lines we need to check both directions
+    treelines = lines.apply(get_branch_coords_from_buses, axis=1)
+    querylines = pd.concat(
+        [
+            new_lines["geometry"].apply(get_branch_coords_from_geometry),
+            new_lines["geometry"].apply(get_branch_coords_from_geometry, reversed=True),
+        ]
+    )
+    treelines = np.vstack(treelines)
+    querylines = np.vstack(querylines)
+    tree = spatial.KDTree(treelines)
+    dist, ind = tree.query(querylines, distance_upper_bound=distance_upper_bound)
+    found_b = ind < len(lines)
+    # since the new lines are checked in both directions, we need to find the correct index of the new line
+    found_i = np.arange(len(querylines))[found_b] % len(new_lines)
+    # create a DataFrame with the distances, new line and its closest existing line
+    line_map = pd.DataFrame(
+        dict(D=dist[found_b], existing_line=lines.index[ind[found_b] % len(lines)]),
+        index=new_lines.index[found_i].rename("new_lines"),
+    )
+    if type == "new":
+        if len(found_i) != 0:
+            logger.warning(
+                "Found new lines similar to existing lines:\n"
+                + str(line_map["existing_line"].to_dict())
+                + "\n Lines are assumed to be duplicated and will be ignored."
+            )
+    elif type == "upgraded":
+        if len(found_i) < len(new_lines):
+            not_found = new_lines.index.difference(line_map.index)
+            logger.warning(
+                "Could not find upgraded lines close enough to existing lines:\n"
+                + str(not_found.to_list())
+                + "\n Lines will be ignored."
+            )
+    # only keep the closer line of the new line pair (since lines are checked in both directions)
+    line_map = line_map.sort_values(by="D")[
+        lambda ds: ~ds.index.duplicated(keep="first")
+    ].sort_index()["existing_line"]
+    return line_map
+
+
+def adjust_decommissioning(upgraded_lines, line_map):
+    """
+    Adjust the decommissioning year of the existing lines to the built year of
+    the upgraded lines.
+    """
+    to_update = pd.DataFrame(index=line_map)
+    to_update["build_year"] = (
+        1990  # dummy build_year to make existing lines decommissioned when upgraded lines are built
+    )
+    to_update["lifetime"] = (
+        upgraded_lines.rename(line_map)["build_year"] - 1990
+    )  # set lifetime to the difference between build year of upgraded line and existing line
+    return to_update
+
+
+def get_upgraded_lines(branch_component, n, upgraded_lines, line_map):
+    """
+    Get upgraded lines by merging information of existing line and upgraded
+    line.
+    """
+    # get first the information of the existing lines which will be upgraded
+    lines_to_add = n.df(branch_component).loc[line_map].copy()
+    # get columns of upgraded lines which are not in existing lines
+    new_columns = upgraded_lines.columns.difference(lines_to_add.columns)
+    # rename upgraded lines to match existing lines
+    upgraded_lines = upgraded_lines.rename(line_map)
+    # set the same index names to be able to merge
+    upgraded_lines.index.name = lines_to_add.index.name
+    # merge upgraded lines with existing lines
+    lines_to_add.update(upgraded_lines)
+    # add column which was added in upgraded lines
+    lines_to_add = pd.concat([lines_to_add, upgraded_lines[new_columns]], axis=1)
+    # only consider columns of original upgraded lines and bus0 and bus1
+    lines_to_add = lines_to_add.loc[:, ["bus0", "bus1", *upgraded_lines.columns]]
+    # set capacity of upgraded lines to capacity of existing lines
+    lines_to_add[nominal_attrs[branch_component]] = n.df(branch_component).loc[
+        line_map, nominal_attrs[branch_component]
+    ]
+    # change index of new lines to avoid duplicates
+    lines_to_add.index = lines_to_add.index.astype(str) + "_upgraded"
+    return lines_to_add
+
+
+def get_project_files(path, skip=[]):
+    path = Path(path)
+    lines = {}
+    files = [
+        p
+        for p in path.iterdir()
+        if p.is_file()
+        and p.suffix == ".csv"
+        and not any(substring in p.name for substring in skip)
+    ]
+    if not files:
+        logger.warning(f"No projects found for {path.parent.name}")
+        return lines
+    for file in files:
+        df = pd.read_csv(file, index_col=0)
+        df["geometry"] = df.apply(
+            lambda x: LineString([[x.x0, x.y0], [x.x1, x.y1]]), axis=1
+        )
+        df.drop(columns=["x0", "y0", "x1", "y1"], inplace=True)
+        lines[file.stem] = df
+    return lines
+
+
+def remove_projects_outside_countries(lines, europe_shape):
+    """
+    Remove projects which are not in the considered countries.
+    """
+    europe_shape_prepped = shapely.prepared.prep(europe_shape)
+    is_within_covered_countries = lines["geometry"].apply(
+        lambda x: europe_shape_prepped.contains(x)
+    )
+
+    if not is_within_covered_countries.all():
+        logger.warning(
+            "Project lines outside of the covered area (skipping): "
+            + ", ".join(str(i) for i in lines.loc[~is_within_covered_countries].index)
+        )
+
+    lines = lines.loc[is_within_covered_countries]
+    return lines
+
+
+def is_similar(ds1, ds2, percentage=10):
+    """
+    Check if values in series ds2 are within a specified percentage of series
+    ds1.
+
+    Returns:
+    - A boolean series where True indicates ds2 values are within the percentage range of ds2.
+    """
+    lower_bound = ds1 * (1 - percentage / 100)
+    upper_bound = ds1 * (1 + percentage / 100)
+    return np.logical_and(ds2 >= lower_bound, ds2 <= upper_bound)
+
+
+def set_underwater_fraction(new_links, offshore_shapes):
+    new_links_gds = gpd.GeoSeries(new_links["geometry"])
+    new_links["underwater_fraction"] = (
+        new_links_gds.intersection(offshore_shapes.union_all()).length
+        / new_links_gds.length
+    ).round(2)
+
+
+def add_projects(
+    n,
+    new_lines_df,
+    new_links_df,
+    adjust_lines_df,
+    adjust_links_df,
+    new_buses_df,
+    europe_shape,
+    offshore_shapes,
+    path,
+    plan,
+    status=["confirmed", "under construction"],
+    skip=[],
+):
+    lines_dict = get_project_files(path, skip=skip)
+    for key, lines in lines_dict.items():
+        logging.info(f"Processing {key.replace('_', ' ')} projects from {plan}.")
+        lines = remove_projects_outside_countries(lines, europe_shape)
+        if isinstance(status, dict):
+            status = status[plan]
+        lines = lines.loc[lines.project_status.isin(status)]
+        if lines.empty:
+            continue
+        if key == "new_lines":
+            new_lines, new_buses_df = connect_new_lines(
+                lines, n, new_buses_df, bus_carrier="AC"
+            )
+            duplicate_lines = find_closest_lines(
+                n.lines, new_lines, distance_upper_bound=0.10, type="new"
+            )
+            # TODO: think about using build_year instead of v_nom
+            # ignore duplicates where v_nom is not within a tolerance of 10%
+            to_ignore = is_similar(
+                new_lines.loc[duplicate_lines.index, "v_nom"],
+                duplicate_lines.map(n.lines["v_nom"]),
+            )
+            duplicate_lines = duplicate_lines[~to_ignore]
+            new_lines = new_lines.drop(duplicate_lines.index, errors="ignore")
+            new_lines_df = pd.concat([new_lines_df, new_lines])
+            # add new lines to network to be able to find added duplicates
+            n.madd("Line", new_lines.index, **new_lines)
+        elif key == "new_links":
+            new_links, new_buses_df = connect_new_lines(
+                lines,
+                n,
+                new_buses_df,
+                offshore_shapes=offshore_shapes,
+                distance_upper_bound=0.4,
+                bus_carrier=["AC", "DC"],
+            )
+            duplicate_links = find_closest_lines(
+                n.links, new_links, distance_upper_bound=0.10, type="new"
+            )
+            # TODO: think about using build_year instead of p_nom
+            # ignore duplicates where p_nom is not within a tolerance of 10%
+            to_ignore = is_similar(
+                new_links.loc[duplicate_links.index, "p_nom"],
+                duplicate_links.map(n.links["p_nom"]),
+            )
+            duplicate_links = duplicate_links[~to_ignore]
+            new_links = new_links.drop(duplicate_links.index, errors="ignore")
+            set_underwater_fraction(new_links, offshore_shapes)
+            new_links_df = pd.concat([new_links_df, new_links])
+            # add new links to network to be able to find added duplicates
+            n.madd("Link", new_links.index, **new_links)
+        elif key == "upgraded_lines":
+            line_map = find_closest_lines(
+                n.lines, lines, distance_upper_bound=0.30, type="upgraded"
+            )
+            upgraded_lines = lines.loc[line_map.index]
+            lines_to_adjust = adjust_decommissioning(upgraded_lines, line_map)
+            adjust_lines_df = pd.concat([adjust_lines_df, lines_to_adjust])
+            upgraded_lines = get_upgraded_lines("Line", n, upgraded_lines, line_map)
+            new_lines_df = pd.concat([new_lines_df, upgraded_lines])
+        elif key == "upgraded_links":
+            line_map = find_closest_lines(
+                n.links.query("carrier=='DC'"),
+                lines,
+                distance_upper_bound=0.30,
+                type="upgraded",
+            )
+            upgraded_links = lines.loc[line_map.index]
+            links_to_adjust = adjust_decommissioning(upgraded_links, line_map)
+            adjust_links_df = pd.concat([adjust_links_df, links_to_adjust])
+            upgraded_links = get_upgraded_lines("Link", n, upgraded_links, line_map)
+            new_links_df = pd.concat([new_links_df, upgraded_links])
+            set_underwater_fraction(new_links_df, offshore_shapes)
+        else:
+            logger.warning(f"Unknown project type {key}")
+            continue
+    return new_lines_df, new_links_df, adjust_lines_df, adjust_links_df, new_buses_df
+
+
+def fill_length_from_geometry(line, line_factor=1.2):
+    if not pd.isna(line.length):
+        return line.length
+    length = gpd.GeoSeries(line["geometry"], crs=4326).to_crs(3035).length.values[0]
+    length = length / 1000 * line_factor
+    return round(length, 1)
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake("build_transmission_projects", run="all")
+    configure_logging(snakemake)
+    set_scenario_config(snakemake)
+
+    line_factor = snakemake.params.line_factor
+
+    n = pypsa.Network(snakemake.input.base_network)
+
+    new_lines_df = pd.DataFrame()
+    new_links_df = pd.DataFrame()
+    adjust_lines_df = pd.DataFrame()
+    adjust_links_df = pd.DataFrame()
+    new_buses_df = pd.DataFrame()
+
+    europe_shape = gpd.read_file(snakemake.input.europe_shape).loc[0, "geometry"]
+    offshore_shapes = gpd.read_file(snakemake.input.offshore_shapes).rename(
+        {"name": "country"}, axis=1
+    )
+
+    transmission_projects = snakemake.params.transmission_projects
+    projects = [
+        project
+        for project, include in transmission_projects["include"].items()
+        if include
+    ]
+    paths = snakemake.input.transmission_projects
+    for project in projects:
+        path = list(filter(lambda path: project in path, paths))[0]
+        new_lines_df, new_links_df, adjust_lines_df, adjust_links_df, new_buses_df = (
+            add_projects(
+                n,
+                new_lines_df,
+                new_links_df,
+                adjust_lines_df,
+                adjust_links_df,
+                new_buses_df,
+                europe_shape,
+                offshore_shapes,
+                path=path,
+                plan=project,
+                status=transmission_projects["status"],
+                skip=transmission_projects["skip"],
+            )
+        )
+    if not new_lines_df.empty:
+        line_type = "Al/St 240/40 4-bundle 380.0"
+        # Add new line type for new lines
+        new_lines_df["type"] = new_lines_df["type"].fillna(line_type)
+        new_lines_df["num_parallel"] = new_lines_df["num_parallel"].fillna(2)
+        if "underground" in new_lines_df.columns:
+            new_lines_df["underground"] = (
+                new_lines_df["underground"].astype("bool").fillna(False)
+            )
+        # Add carrier types of lines
+        new_lines_df["carrier"] = "AC"
+        # Fill empty length values with length calculated from geometry
+        new_lines_df["length"] = new_lines_df.apply(
+            fill_length_from_geometry, args=(line_factor,), axis=1
+        )
+        # get s_nom from line type
+        new_lines_df["s_nom"] = (
+            np.sqrt(3)
+            * n.line_types.loc[new_lines_df["type"], "i_nom"].values
+            * new_lines_df["v_nom"]
+            * new_lines_df["num_parallel"]
+        ).round(2)
+    if not new_links_df.empty:
+        # Add carrier types of lines and links
+        new_links_df["carrier"] = "DC"
+        # Fill empty length values with length calculated from geometry
+        new_links_df["length"] = new_links_df.apply(
+            fill_length_from_geometry, args=(line_factor,), axis=1
+        )
+        # Whether to keep existing link capacity or set to zero
+        not_upgraded = ~new_links_df.index.str.contains("upgraded")
+        if transmission_projects["new_link_capacity"] == "keep":
+            new_links_df.loc[not_upgraded, "p_nom"] = new_links_df["p_nom"].fillna(0)
+        elif transmission_projects["new_link_capacity"] == "zero":
+            new_links_df.loc[not_upgraded, "p_nom"] = 0
+    # export csv files for new buses, lines, links and adjusted lines and links
+    new_lines_df.to_csv(snakemake.output.new_lines)
+    new_links_df.to_csv(snakemake.output.new_links)
+    adjust_lines_df.to_csv(snakemake.output.adjust_lines)
+    adjust_links_df.to_csv(snakemake.output.adjust_links)
+    new_buses_df.to_csv(snakemake.output.new_buses)
diff --git a/scripts/build_transport_demand.py b/scripts/build_transport_demand.py
index 4a29667a..074dc260 100644
--- a/scripts/build_transport_demand.py
+++ b/scripts/build_transport_demand.py
@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 def build_nodal_transport_data(fn, pop_layout, year):
     # get numbers of car and fuel efficiency per country
     transport_data = pd.read_csv(fn, index_col=[0, 1])
-    transport_data = transport_data.xs(min(2015, year), level="year")
+    transport_data = transport_data.xs(year, level="year")
 
     # break number of cars down to nodal level based on population density
     nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
diff --git a/scripts/cluster_network.py b/scripts/cluster_network.py
index da7bd178..79184167 100644
--- a/scripts/cluster_network.py
+++ b/scripts/cluster_network.py
@@ -410,6 +410,7 @@ def clustering_for_n_clusters(
         generator_strategies=generator_strategies,
         one_port_strategies=one_port_strategies,
         scale_link_capital_costs=False,
+        custom_line_groupers=["build_year"],
     )
 
     if not n.links.empty:
diff --git a/scripts/retrieve_jrc_idees.py b/scripts/retrieve_jrc_idees.py
new file mode 100644
index 00000000..0e23b82c
--- /dev/null
+++ b/scripts/retrieve_jrc_idees.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2024- The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Retrieve and extract JRC IDEES 2021 data.
+"""
+
+import logging
+import os
+import zipfile
+from pathlib import Path
+
+from _helpers import configure_logging, progress_retrieve, set_scenario_config
+
+logger = logging.getLogger(__name__)
+
+# Define the base URL
+url_jrc = "https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/JRC-IDEES/JRC-IDEES-2021_v1/JRC-IDEES-2021.zip"
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake("retrieve_jrc_idees")
+        rootpath = ".."
+    else:
+        rootpath = "."
+
+    configure_logging(snakemake)
+    set_scenario_config(snakemake)
+    disable_progress = snakemake.config["run"].get("disable_progressbar", False)
+
+    to_fn = snakemake.output[0]
+    to_fn_zp = to_fn + ".zip"
+
+    # download .zip file
+    logger.info(f"Downloading JRC IDEES from {url_jrc}.")
+    progress_retrieve(url_jrc, to_fn_zp, disable=disable_progress)
+
+    # extract
+    logger.info("Extracting JRC IDEES data.")
+    with zipfile.ZipFile(to_fn_zp, "r") as zip_ref:
+        zip_ref.extractall(to_fn)
+
+    logger.info(f"JRC IDEES data available in '{to_fn}'.")
diff --git a/scripts/simplify_network.py b/scripts/simplify_network.py
index 558e4cf2..9ab0cb23 100644
--- a/scripts/simplify_network.py
+++ b/scripts/simplify_network.py
@@ -124,7 +124,7 @@ def simplify_network_to_380(n):
 
     n.buses["v_nom"] = 380.0
 
-    (linetype_380,) = n.lines.loc[n.lines.v_nom == 380.0, "type"].unique()
+    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]
@@ -298,13 +298,33 @@ def simplify_links(
     _, labels = connected_components(adjacency_matrix, directed=False)
     labels = pd.Series(labels, n.buses.index)
 
-    G = n.graph()
+    # Only span graph over the DC link components
+    G = n.graph(branch_components=["Link"])
 
     def split_links(nodes):
         nodes = frozenset(nodes)
 
         seen = set()
-        supernodes = {m for m in nodes if len(G.adj[m]) > 2 or (set(G.adj[m]) - nodes)}
+
+        # Corsica substation
+        node_corsica = find_closest_bus(
+            n,
+            x=9.44802,
+            y=42.52842,
+            tol=2000,  # Tolerance needed to only return the bus if the region is actually modelled
+        )
+
+        # Supernodes are endpoints of links, identified by having lass then two neighbours or being an AC Bus
+        # An example for the latter is if two different links are connected to the same AC bus.
+        supernodes = {
+            m
+            for m in nodes
+            if (
+                (len(G.adj[m]) < 2 or (set(G.adj[m]) - nodes))
+                or (n.buses.loc[m, "carrier"] == "AC")
+                or (m == node_corsica)
+            )
+        }
 
         for u in supernodes:
             for m, ls in G.adj[u].items():
@@ -519,11 +539,47 @@ def cluster(
     return clustering.network, clustering.busmap
 
 
+def find_closest_bus(n, x, y, tol=2000):
+    """
+    Find the index of the closest bus to the given coordinates within a specified tolerance.
+    Parameters:
+        n (pypsa.Network): The network object.
+        x (float): The x-coordinate (longitude) of the target location.
+        y (float): The y-coordinate (latitude) of the target location.
+        tol (float): The distance tolerance in meters. Default is 2000 meters.
+
+    Returns:
+        int: The index of the closest bus to the target location within the tolerance.
+             Returns None if no bus is within the tolerance.
+    """
+    # Conversion factors
+    meters_per_degree_lat = 111139  # Meters per degree of latitude
+    meters_per_degree_lon = 111139 * np.cos(
+        np.radians(y)
+    )  # Meters per degree of longitude at the given latitude
+
+    x0 = np.array(n.buses.x)
+    y0 = np.array(n.buses.y)
+
+    # Calculate distances in meters
+    dist = np.sqrt(
+        ((x - x0) * meters_per_degree_lon) ** 2
+        + ((y - y0) * meters_per_degree_lat) ** 2
+    )
+
+    # Find the closest bus within the tolerance
+    min_dist = dist.min()
+    if min_dist <= tol:
+        return n.buses.index[dist.argmin()]
+    else:
+        return None
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
 
-        snakemake = mock_snakemake("simplify_network", simpl="")
+        snakemake = mock_snakemake("simplify_network", simpl="", run="all")
     configure_logging(snakemake)
     set_scenario_config(snakemake)
 
@@ -599,6 +655,7 @@ if __name__ == "__main__":
         "substation_off",
         "geometry",
         "underground",
+        "project_status",
     ]
     n.buses.drop(remove, axis=1, inplace=True, errors="ignore")
     n.lines.drop(remove, axis=1, errors="ignore", inplace=True)