Merge remote-tracking branch 'upstream/master' into add_methanol_techs
This commit is contained in:
Philipp Glaum
commit
77385721b6
@ -67,7 +67,6 @@ snapshots:
|
||||
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#enable
|
||||
enable:
|
||||
retrieve: auto
|
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prepare_links_p_nom: false
|
||||
retrieve_databundle: true
|
||||
retrieve_cost_data: true
|
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build_cutout: false
|
||||
@ -355,7 +354,6 @@ biomass:
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- Secondary Forestry residues - woodchips
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- Sawdust
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- Residues from landscape care
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- Municipal waste
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not included:
|
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- Sugar from sugar beet
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- Rape seed
|
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@ -369,6 +367,25 @@ biomass:
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biogas:
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- Manure solid, liquid
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- Sludge
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municipal solid waste:
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- Municipal waste
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share_unsustainable_use_retained:
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2020: 1
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||||
2025: 0.66
|
||||
2030: 0.33
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2035: 0
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2040: 0
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2045: 0
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2050: 0
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share_sustainable_potential_available:
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2020: 0
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2025: 0.33
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2030: 0.66
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2035: 1
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2040: 1
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2045: 1
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2050: 1
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#solar-thermal
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solar_thermal:
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@ -409,6 +426,22 @@ sector:
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2045: 0.8
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2050: 1.0
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district_heating_loss: 0.15
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forward_temperature: 90 #C
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return_temperature: 50 #C
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heat_source_cooling: 6 #K
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heat_pump_cop_approximation:
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refrigerant: ammonia
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heat_exchanger_pinch_point_temperature_difference: 5 #K
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isentropic_compressor_efficiency: 0.8
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heat_loss: 0.0
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heat_pump_sources:
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urban central:
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- air
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urban decentral:
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- air
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rural:
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- air
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- ground
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cluster_heat_buses: true
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heat_demand_cutout: default
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bev_dsm_restriction_value: 0.75
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@ -491,7 +524,7 @@ sector:
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aviation_demand_factor: 1.
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HVC_demand_factor: 1.
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time_dep_hp_cop: true
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heat_pump_sink_T: 55.
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heat_pump_sink_T_individual_heating: 55.
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reduce_space_heat_exogenously: true
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reduce_space_heat_exogenously_factor:
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2020: 0.10 # this results in a space heat demand reduction of 10%
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@ -613,6 +646,7 @@ sector:
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biomass_to_liquid: false
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electrobiofuels: false
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biosng: false
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municipal_solid_waste: false
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limit_max_growth:
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enable: false
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# allowing 30% larger than max historic growth
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@ -640,6 +674,7 @@ sector:
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max_amount: 1390 # TWh
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upstream_emissions_factor: .1 #share of solid biomass CO2 emissions at full combustion
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#industry
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industry:
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St_primary_fraction:
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@ -733,7 +768,7 @@ industry:
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#costs
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costs:
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year: 2030
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version: v0.9.0
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version: v0.9.1
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social_discountrate: 0.02
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fill_values:
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FOM: 0
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@ -1036,6 +1071,7 @@ plotting:
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biogas: '#e3d37d'
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biomass: '#baa741'
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solid biomass: '#baa741'
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municipal solid waste: '#91ba41'
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solid biomass import: '#d5ca8d'
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solid biomass transport: '#baa741'
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solid biomass for industry: '#7a6d26'
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@ -1050,6 +1086,7 @@ plotting:
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services rural biomass boiler: '#c6cf98'
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services urban decentral biomass boiler: '#dde5b5'
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biomass to liquid: '#32CD32'
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unsustainable bioliquids: '#32CD32'
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electrobiofuels: 'red'
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BioSNG: '#123456'
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# power transmission
|
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|
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@ -5,7 +5,7 @@ Cross-Channel,France - Echingen 50°41′48″N 1°38′21″E / 50.69667
|
||||
Volgograd-Donbass,Russia - Volzhskaya 48°49′34″N 44°40′20″E / 48.82611°N 44.67222°E,Ukraine - Mikhailovskaya 48°39′13″N 38°33′56″E / 48.65361°N 38.56556°E,475(0/475),400,750.0,1965,Merc/Thyr,Shut down in 2014,[1],44.672222222222224,48.82611111111111,38.565555555555555,48.65361111111111
|
||||
Konti-Skan 1,Denmark - Vester Hassing 57°3′46″N 10°5′24″E / 57.06278°N 10.09000°E,Sweden - Stenkullen 57°48′15″N 12°19′13″E / 57.80417°N 12.32028°E,176(87/89),250,250.0,1965,Merc,Replaced in August 2006 by modern converters using thyristors,[1],10.09,57.062777777777775,12.320277777777777,57.80416666666667
|
||||
SACOI 1a,Italy - Suvereto 43°3′10″N 10°41′42″E / 43.05278°N 10.69500°E ( before 1992: Italy - San Dalmazio 43°15′43″N 10°55′05″E / 43.26194°N 10.91806°E),"France- Lucciana 42°31′40″N 9°26′59″E / 42.52778°N 9.44972°E",483(365/118),200,200.0,1965,Merc,"Replaced in 1986 by Thyr- multiterminal scheme",[1],10.695,43.05277777777778,9.449722222222222,42.52777777777778
|
||||
SACOI 1b,"France- Lucciana 42°31′40″N 9°26′59″E / 42.52778°N 9.44972°E", "Codrongianos- Italy 40°39′7″N 8°42′48″E / 40.65194°N 8.71333°E",483(365/118),200,200.0,1965,Merc,"Replaced in 1986 by Thyr- multiterminal scheme",[1],9.449722222222222,42.52777777777778,8.679351,40.65765
|
||||
SACOI 1b,"France- Lucciana 42°31′40″N 9°26′59″E / 42.52778°N 9.44972°E","Codrongianos- Italy 40°39′7″N 8°42′48″E / 40.65194°N 8.71333°E",483(365/118),200,200.0,1965,Merc,"Replaced in 1986 by Thyr- multiterminal scheme",[1],9.449722222222222,42.52777777777778,8.679351,40.65765
|
||||
Kingsnorth,UK - Kingsnorth 51°25′11″N 0°35′46″E / 51.41972°N 0.59611°E,UK - London-Beddington 51°22′23″N 0°7′38″W / 51.37306°N 0.12722°W,85(85/0),266,320.0,1975,Merc,Bipolar scheme Supplier: English Electric Shut down in 1987,[33],0.5961111111111111,51.41972222222222,-0.1272222222222222,51.37305555555555
|
||||
Skagerrak 1 + 2,Denmark - Tjele 56°28′44″N 9°34′1″E / 56.47889°N 9.56694°E,Norway - Kristiansand 58°15′36″N 7°53′55″E / 58.26000°N 7.89861°E,230(130/100),250,500.0,1977,Thyr,Supplier: STK(Nexans) Control system upgrade by ABB in 2007,[34][35][36],9.566944444444445,56.47888888888889,7.898611111111111,58.26
|
||||
Gotland 2,Sweden - Västervik 57°43′41″N 16°38′51″E / 57.72806°N 16.64750°E,Sweden - Yigne 57°35′13″N 18°11′44″E / 57.58694°N 18.19556°E,99.5(92.9/6.6),150,130.0,1983,Thyr,Supplier: ABB,,16.6475,57.72805555555556,18.195555555555554,57.58694444444444
|
||||
@ -23,7 +23,7 @@ Visby-Nas,Sweden - Nas 57°05′58″N 18°14′27″E / 57.09944°N 18.24
|
||||
SwePol,Poland - Wierzbięcin 54°30′8″N 16°53′28″E / 54.50222°N 16.89111°E,Sweden - Stärnö 56°9′11″N 14°50′29″E / 56.15306°N 14.84139°E,245(245/0),450,600.0,2000,Thyr,Supplier: ABB,[38],16.891111111111112,54.50222222222222,14.841388888888888,56.153055555555554
|
||||
Tjæreborg,Denmark - Tjæreborg/Enge 55°26′52″N 8°35′34″E / 55.44778°N 8.59278°E,Denmark - Tjæreborg/Substation 55°28′07″N 8°33′36″E / 55.46861°N 8.56000°E,4.3(4.3/0),9,7.0,2000,IGBT,Interconnection to wind power generating stations,,8.592777777777778,55.44777777777778,8.56,55.46861111111111
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||||
Italy-Greece,Greece - Arachthos 39°11′00″N 20°57′48″E / 39.18333°N 20.96333°E,Italy - Galatina 40°9′53″N 18°7′49″E / 40.16472°N 18.13028°E,310(200/110),400,500.0,2001,Thyr,,,20.963333333333335,39.18333333333333,18.130277777777778,40.164722222222224
|
||||
Moyle,UK - Auchencrosh 55°04′10″N 4°58′50″W / 55.06944°N 4.98056°W,UK - N. Ireland- Ballycronan More 54°50′34″N 5°46′11″W / 54.84278°N 5.76972°W,63.5(63.5/0),250,2501.0,2001,Thyr,"Supplier: Siemens- Nexans",[39],-4.980555555555556,55.06944444444444,-5.769722222222223,54.842777777777776
|
||||
Moyle,UK - Auchencrosh 55°04′10″N 4°58′50″W / 55.06944°N 4.98056°W,UK - N. Ireland- Ballycronan More 54°50′34″N 5°46′11″W / 54.84278°N 5.76972°W,63.5(63.5/0),250,500.0,2001,Thyr,"Supplier: Siemens- Nexans",[39],-4.980555555555556,55.06944444444444,-5.769722222222223,54.842777777777776
|
||||
HVDC Troll,Norway - Kollsnes 60°33′01″N 4°50′26″E / 60.55028°N 4.84056°E,Norway - Offshore platform Troll A 60°40′00″N 3°40′00″E / 60.66667°N 3.66667°E,70(70/0),60,80.0,2004,IGBT,Power supply for offshore gas compressor Supplier: ABB,[40],4.8405555555555555,60.55027777777778,3.6666666666666665,60.666666666666664
|
||||
Estlink,Finland - Espoo 60°12′14″N 24°33′06″E / 60.20389°N 24.55167°E,Estonia - Harku 59°23′5″N 24°33′37″E / 59.38472°N 24.56028°E,105(105/0),150,350.0,2006,IGBT,Supplier: ABB,[40],24.551666666666666,60.20388888888889,24.560277777777777,59.38472222222222
|
||||
NorNed,Netherlands - Eemshaven 53°26′4″N 6°51′57″E / 53.43444°N 6.86583°E,Norway - Feda 58°16′58″N 6°51′55″E / 58.28278°N 6.86528°E,580(580/0),450,700.0,2008,Thyr,"Supplier: ABB- Nexans",[40],6.865833333333334,53.434444444444445,6.865277777777778,58.28277777777778
|
||||
|
||||
|
@ -53,7 +53,6 @@ extensions = [
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||||
autodoc_mock_imports = [
|
||||
"atlite",
|
||||
"snakemake",
|
||||
"pycountry",
|
||||
"rioxarray",
|
||||
"country_converter",
|
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"tabula",
|
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@ -341,4 +340,6 @@ texinfo_documents = [
|
||||
|
||||
|
||||
# Example configuration for intersphinx: refer to the Python standard library.
|
||||
intersphinx_mapping = {"https://docs.python.org/": None}
|
||||
intersphinx_mapping = {
|
||||
"https://docs.python.org/": ("https://docs.python.org/3", None),
|
||||
}
|
||||
|
||||
@ -5,3 +5,5 @@ classes ,,,
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-- solid biomass,--,Array of biomass comodity,The comodity that are included as solid biomass
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-- not included,--,Array of biomass comodity,The comodity that are not included as a biomass potential
|
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-- biogas,--,Array of biomass comodity,The comodity that are included as biogas
|
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share_unsustainable_use_retained,--,Dictionary with planning horizons as keys., Share of unsustainable biomass use retained using primary production of Eurostat data as reference
|
||||
share_sustainable_potential_available,--,Dictionary with planning horizons as keys., Share determines phase-in of ENSPRESO biomass potentials
|
||||
|
||||
|
@ -1,6 +1,5 @@
|
||||
,Unit,Values,Description
|
||||
enable,str or bool,"{auto, true, false}","Switch to include (true) or exclude (false) the retrieve_* rules of snakemake into the workflow; 'auto' sets true|false based on availability of an internet connection to prevent issues with snakemake failing due to lack of internet connection."
|
||||
prepare_links_p_nom,bool,"{true, false}","Switch to retrieve current HVDC projects from `Wikipedia <https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_"
|
||||
retrieve_databundle,bool,"{true, false}","Switch to retrieve databundle from zenodo via the rule :mod:`retrieve_databundle` or whether to keep a custom databundle located in the corresponding folder."
|
||||
retrieve_cost_data,bool,"{true, false}","Switch to retrieve technology cost data from `technology-data repository <https://github.com/PyPSA/technology-data>`_."
|
||||
build_cutout,bool,"{true, false}","Switch to enable the building of cutouts via the rule :mod:`build_cutout`."
|
||||
|
||||
|
@ -9,6 +9,18 @@ district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-
|
||||
-- potential,--,float,maximum fraction of urban demand which can be supplied by district heating
|
||||
-- progress,--,Dictionary with planning horizons as keys., Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating
|
||||
-- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses
|
||||
-- forward_temperature,°C,float,Forward temperature in district heating
|
||||
-- return_temperature,°C,float,Return temperature in district heating. Must be lower than forward temperature
|
||||
-- heat_source_cooling,K,float,Cooling of heat source for heat pumps
|
||||
-- heat_pump_cop_approximation,,,
|
||||
-- -- refrigerant,--,"{ammonia, isobutane}",Heat pump refrigerant assumed for COP approximation
|
||||
-- -- heat_exchanger_pinch_point_temperature_difference,K,float,Heat pump pinch point temperature difference in heat exchangers assumed for approximation.
|
||||
-- -- isentropic_compressor_efficiency,--,float,Isentropic efficiency of heat pump compressor assumed for approximation. Must be between 0 and 1.
|
||||
-- -- heat_loss,--,float,Heat pump heat loss assumed for approximation. Must be between 0 and 1.
|
||||
-- heat_pump_sources,--,,
|
||||
-- -- urban central,--,List of heat sources for heat pumps in urban central heating,
|
||||
-- -- urban decentral,--,List of heat sources for heat pumps in urban decentral heating,
|
||||
-- -- rural,--,List of heat sources for heat pumps in rural heating,
|
||||
cluster_heat_buses,--,"{true, false}",Cluster residential and service heat buses in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_ to one to save memory.
|
||||
,,,
|
||||
bev_dsm_restriction _value,--,float,Adds a lower state of charge (SOC) limit for battery electric vehicles (BEV) to manage its own energy demand (DSM). Located in `build_transport_demand.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_transport_demand.py>`_. Set to 0 for no restriction on BEV DSM
|
||||
@ -72,7 +84,7 @@ boilers,--,"{true, false}",Add option for transforming gas into heat using gas b
|
||||
resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
|
||||
oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers
|
||||
biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers
|
||||
overdimension_individual_heating,--,"float",Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
|
||||
overdimension_individual_heating,--,float,Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
|
||||
chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP)
|
||||
micro_chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) for decentral areas.
|
||||
solar_thermal,--,"{true, false}",Add option for using solar thermal to generate heat.
|
||||
@ -139,6 +151,7 @@ biogas_upgrading_cc,--,"{true, false}",Add option to capture CO2 from biomass up
|
||||
conventional_generation,,,Add a more detailed description of conventional carriers. Any power generation requires the consumption of fuel from nodes representing that fuel.
|
||||
biomass_to_liquid,--,"{true, false}",Add option for transforming solid biomass into liquid fuel with the same properties as oil
|
||||
biosng,--,"{true, false}",Add option for transforming solid biomass into synthesis gas with the same properties as natural gas
|
||||
municipal_solid_waste,--,"{true, false}",Add option for municipal solid waste
|
||||
limit_max_growth,,,
|
||||
-- enable,--,"{true, false}",Add option to limit the maximum growth of a carrier
|
||||
-- factor,p.u.,float,The maximum growth factor of a carrier (e.g. 1.3 allows 30% larger than max historic growth)
|
||||
|
||||
|
@ -41,11 +41,6 @@ Rule ``build_cutout``
|
||||
.. automodule:: build_cutout
|
||||
|
||||
|
||||
Rule ``prepare_links_p_nom``
|
||||
===============================
|
||||
|
||||
.. automodule:: prepare_links_p_nom
|
||||
|
||||
.. _base:
|
||||
|
||||
Rule ``base_network``
|
||||
|
||||
@ -10,9 +10,19 @@ Release Notes
|
||||
Upcoming Release
|
||||
================
|
||||
|
||||
* 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``.
|
||||
|
||||
* The rule ``prepare_links_p_nom`` was removed since it was outdated and not used.
|
||||
|
||||
* Changed heat pump COP approximation for central heating to be based on `Jensen et al. (2018) <https://backend.orbit.dtu.dk/ws/portalfiles/portal/151965635/MAIN_Final.pdf>`__ and a default forward temperature of 90C. This is more realistic for district heating than the previously used approximation method.
|
||||
|
||||
* split solid biomass potentials into solid biomass and municipal solid waste. Add option to use municipal solid waste. This option is only activated in combination with the flag ``waste_to_energy``
|
||||
|
||||
* Add option to import solid biomass
|
||||
|
||||
* Add option to produce electrobiofuels (flag ``electrobiofuels`) from solid biomass and hydrogen, as a combination of BtL and Fischer-Tropsch to make more use of the biogenic carbon
|
||||
* Add option to produce electrobiofuels (flag ``electrobiofuels``) from solid biomass and hydrogen, as a combination of BtL and Fischer-Tropsch to make more use of the biogenic carbon
|
||||
|
||||
* Add flag ``sector: fossil_fuels`` in config to remove the option of importing fossil fuels
|
||||
|
||||
|
||||
@ -17,7 +17,6 @@ tabula-py
|
||||
|
||||
# cartopy
|
||||
scikit-learn
|
||||
pycountry
|
||||
pyyaml
|
||||
seaborn
|
||||
memory_profiler
|
||||
|
||||
@ -18,7 +18,6 @@ dependencies:
|
||||
# Dependencies of the workflow itself
|
||||
- xlrd
|
||||
- openpyxl!=3.1.1
|
||||
- pycountry
|
||||
- seaborn
|
||||
- snakemake-minimal>=8.14
|
||||
- memory_profiler
|
||||
|
||||
@ -2,21 +2,6 @@
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
if config["enable"].get("prepare_links_p_nom", False):
|
||||
|
||||
rule prepare_links_p_nom:
|
||||
output:
|
||||
"data/links_p_nom.csv",
|
||||
log:
|
||||
logs("prepare_links_p_nom.log"),
|
||||
threads: 1
|
||||
resources:
|
||||
mem_mb=1500,
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
"../scripts/prepare_links_p_nom.py"
|
||||
|
||||
|
||||
rule build_electricity_demand:
|
||||
params:
|
||||
@ -106,8 +91,8 @@ rule build_shapes:
|
||||
params:
|
||||
countries=config_provider("countries"),
|
||||
input:
|
||||
naturalearth=ancient("data/bundle/naturalearth/ne_10m_admin_0_countries.shp"),
|
||||
eez=ancient("data/bundle/eez/World_EEZ_v8_2014.shp"),
|
||||
naturalearth=ancient("data/naturalearth/ne_10m_admin_0_countries_deu.shp"),
|
||||
eez=ancient("data/eez/World_EEZ_v12_20231025_gpkg/eez_v12.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"),
|
||||
|
||||
@ -217,13 +217,27 @@ rule build_temperature_profiles:
|
||||
|
||||
rule build_cop_profiles:
|
||||
params:
|
||||
heat_pump_sink_T=config_provider("sector", "heat_pump_sink_T"),
|
||||
heat_pump_sink_T_decentral_heating=config_provider(
|
||||
"sector", "heat_pump_sink_T_individual_heating"
|
||||
),
|
||||
forward_temperature_central_heating=config_provider(
|
||||
"sector", "district_heating", "forward_temperature"
|
||||
),
|
||||
return_temperature_central_heating=config_provider(
|
||||
"sector", "district_heating", "return_temperature"
|
||||
),
|
||||
heat_source_cooling_central_heating=config_provider(
|
||||
"sector", "district_heating", "heat_source_cooling"
|
||||
),
|
||||
heat_pump_cop_approximation_central_heating=config_provider(
|
||||
"sector", "district_heating", "heat_pump_cop_approximation"
|
||||
),
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
input:
|
||||
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
output:
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
|
||||
resources:
|
||||
mem_mb=20000,
|
||||
log:
|
||||
@ -233,7 +247,7 @@ rule build_cop_profiles:
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
"../scripts/build_cop_profiles.py"
|
||||
"../scripts/build_cop_profiles/run.py"
|
||||
|
||||
|
||||
def solar_thermal_cutout(wildcards):
|
||||
@ -331,7 +345,8 @@ rule build_biomass_potentials:
|
||||
"https://zenodo.org/records/10356004/files/ENSPRESO_BIOMASS.xlsx",
|
||||
keep_local=True,
|
||||
),
|
||||
nuts2="data/bundle/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
|
||||
eurostat="data/eurostat/Balances-April2023",
|
||||
nuts2="data/bundle/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
nuts3_population=ancient("data/bundle/nama_10r_3popgdp.tsv.gz"),
|
||||
swiss_cantons=ancient("data/ch_cantons.csv"),
|
||||
@ -344,7 +359,7 @@ rule build_biomass_potentials:
|
||||
biomass_potentials=resources(
|
||||
"biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
threads: 1
|
||||
threads: 8
|
||||
resources:
|
||||
mem_mb=1000,
|
||||
log:
|
||||
@ -940,7 +955,10 @@ rule prepare_sector_network:
|
||||
countries=config_provider("countries"),
|
||||
adjustments=config_provider("adjustments", "sector"),
|
||||
emissions_scope=config_provider("energy", "emissions"),
|
||||
biomass=config_provider("biomass"),
|
||||
RDIR=RDIR,
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
heat_systems=config_provider("sector", "heat_systems"),
|
||||
input:
|
||||
unpack(input_profile_offwind),
|
||||
**rules.cluster_gas_network.output,
|
||||
@ -1020,8 +1038,7 @@ rule prepare_sector_network:
|
||||
),
|
||||
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
|
||||
solar_thermal_total=lambda w: (
|
||||
resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc")
|
||||
if config_provider("sector", "solar_thermal")(w)
|
||||
|
||||
@ -4,6 +4,7 @@
|
||||
|
||||
import requests
|
||||
from datetime import datetime, timedelta
|
||||
from shutil import move, unpack_archive
|
||||
|
||||
if config["enable"].get("retrieve", "auto") == "auto":
|
||||
config["enable"]["retrieve"] = has_internet_access()
|
||||
@ -15,8 +16,6 @@ if config["enable"]["retrieve"] is False:
|
||||
if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle", True):
|
||||
datafiles = [
|
||||
"je-e-21.03.02.xls",
|
||||
"eez/World_EEZ_v8_2014.shp",
|
||||
"naturalearth/ne_10m_admin_0_countries.shp",
|
||||
"NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp",
|
||||
"nama_10r_3popgdp.tsv.gz",
|
||||
"nama_10r_3gdp.tsv.gz",
|
||||
@ -53,6 +52,8 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
|
||||
log:
|
||||
"logs/retrieve_eurostat_data.log",
|
||||
retries: 2
|
||||
conda:
|
||||
"../envs/retrieve.yaml"
|
||||
script:
|
||||
"../scripts/retrieve_eurostat_data.py"
|
||||
|
||||
@ -62,6 +63,8 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
|
||||
log:
|
||||
"logs/retrieve_eurostat_household_data.log",
|
||||
retries: 2
|
||||
conda:
|
||||
"../envs/retrieve.yaml"
|
||||
script:
|
||||
"../scripts/retrieve_eurostat_household_data.py"
|
||||
|
||||
@ -211,6 +214,64 @@ if config["enable"]["retrieve"]:
|
||||
move(input[0], output[0])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
rule retrieve_eez:
|
||||
params:
|
||||
zip="data/eez/World_EEZ_v12_20231025_gpkg.zip",
|
||||
output:
|
||||
gpkg="data/eez/World_EEZ_v12_20231025_gpkg/eez_v12.gpkg",
|
||||
run:
|
||||
import os
|
||||
import requests
|
||||
from uuid import uuid4
|
||||
|
||||
name = str(uuid4())[:8]
|
||||
org = str(uuid4())[:8]
|
||||
|
||||
response = requests.post(
|
||||
"https://www.marineregions.org/download_file.php",
|
||||
params={"name": "World_EEZ_v12_20231025_gpkg.zip"},
|
||||
data={
|
||||
"name": name,
|
||||
"organisation": org,
|
||||
"email": f"{name}@{org}.org",
|
||||
"country": "Germany",
|
||||
"user_category": "academia",
|
||||
"purpose_category": "Research",
|
||||
"agree": "1",
|
||||
},
|
||||
)
|
||||
|
||||
with open(params["zip"], "wb") as f:
|
||||
f.write(response.content)
|
||||
output_folder = Path(params["zip"]).parent
|
||||
unpack_archive(params["zip"], output_folder)
|
||||
os.remove(params["zip"])
|
||||
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
# Download directly from naciscdn.org which is a redirect from naturalearth.com
|
||||
# (https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/)
|
||||
# Use point-of-view (POV) variant of Germany so that Crimea is included.
|
||||
rule retrieve_naturalearth_countries:
|
||||
input:
|
||||
storage(
|
||||
"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_admin_0_countries_deu.zip"
|
||||
),
|
||||
params:
|
||||
zip="data/naturalearth/ne_10m_admin_0_countries_deu.zip",
|
||||
output:
|
||||
countries="data/naturalearth/ne_10m_admin_0_countries_deu.shp",
|
||||
run:
|
||||
move(input[0], params["zip"])
|
||||
output_folder = Path(output["countries"]).parent
|
||||
unpack_archive(params["zip"], output_folder)
|
||||
os.remove(params["zip"])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
# Some logic to find the correct file URL
|
||||
# Sometimes files are released delayed or ahead of schedule, check which file is currently available
|
||||
|
||||
@ -9,6 +9,7 @@ rule add_existing_baseyear:
|
||||
sector=config_provider("sector"),
|
||||
existing_capacities=config_provider("existing_capacities"),
|
||||
costs=config_provider("costs"),
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
input:
|
||||
network=RESULTS
|
||||
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
@ -21,8 +22,7 @@ rule add_existing_baseyear:
|
||||
config_provider("scenario", "planning_horizons", 0)(w)
|
||||
)
|
||||
),
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
|
||||
existing_heating_distribution=resources(
|
||||
"existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
@ -69,6 +69,7 @@ rule add_brownfield:
|
||||
snapshots=config_provider("snapshots"),
|
||||
drop_leap_day=config_provider("enable", "drop_leap_day"),
|
||||
carriers=config_provider("electricity", "renewable_carriers"),
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
input:
|
||||
unpack(input_profile_tech_brownfield),
|
||||
simplify_busmap=resources("busmap_elec_s{simpl}.csv"),
|
||||
@ -77,8 +78,7 @@ rule add_brownfield:
|
||||
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
network_p=solved_previous_horizon, #solved network at previous time step
|
||||
costs=resources("costs_{planning_horizons}.csv"),
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
|
||||
output:
|
||||
RESULTS
|
||||
+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
|
||||
@ -7,6 +7,7 @@ rule add_existing_baseyear:
|
||||
sector=config_provider("sector"),
|
||||
existing_capacities=config_provider("existing_capacities"),
|
||||
costs=config_provider("costs"),
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
input:
|
||||
network=RESULTS
|
||||
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
@ -19,8 +20,7 @@ rule add_existing_baseyear:
|
||||
config_provider("scenario", "planning_horizons", 0)(w)
|
||||
)
|
||||
),
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
|
||||
existing_heating_distribution=resources(
|
||||
"existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
|
||||
@ -24,6 +24,10 @@ from _helpers import (
|
||||
from add_electricity import sanitize_carriers
|
||||
from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
|
||||
|
||||
from scripts.definitions.heat_sector import HeatSector
|
||||
from scripts.definitions.heat_system import HeatSystem
|
||||
from scripts.definitions.heat_system_type import HeatSystemType
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
cc = coco.CountryConverter()
|
||||
idx = pd.IndexSlice
|
||||
@ -416,14 +420,14 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
|
||||
|
||||
def add_heating_capacities_installed_before_baseyear(
|
||||
n,
|
||||
baseyear,
|
||||
grouping_years,
|
||||
ashp_cop,
|
||||
gshp_cop,
|
||||
time_dep_hp_cop,
|
||||
costs,
|
||||
default_lifetime,
|
||||
n: pypsa.Network,
|
||||
baseyear: int,
|
||||
grouping_years: list,
|
||||
cop: dict,
|
||||
time_dep_hp_cop: bool,
|
||||
costs: pd.DataFrame,
|
||||
default_lifetime: int,
|
||||
existing_heating: pd.DataFrame,
|
||||
):
|
||||
"""
|
||||
Parameters
|
||||
@ -435,141 +439,158 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
currently assumed heating capacities split between residential and
|
||||
services proportional to heating load in both 50% capacities
|
||||
in rural buses 50% in urban buses
|
||||
cop: xr.DataArray
|
||||
DataArray with time-dependent coefficients of performance (COPs) heat pumps. Coordinates are heat sources (see config), heat system types (see :file:`scripts/enums/HeatSystemType.py`), nodes and snapshots.
|
||||
time_dep_hp_cop: bool
|
||||
If True, time-dependent (dynamic) COPs are used for heat pumps
|
||||
"""
|
||||
logger.debug(f"Adding heating capacities installed before {baseyear}")
|
||||
|
||||
existing_heating = pd.read_csv(
|
||||
snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
|
||||
)
|
||||
for heat_system in existing_heating.columns.get_level_values(0).unique():
|
||||
heat_system = HeatSystem(heat_system)
|
||||
|
||||
for name in existing_heating.columns.get_level_values(0).unique():
|
||||
name_type = "central" if name == "urban central" else "decentral"
|
||||
nodes = pd.Index(
|
||||
n.buses.location[n.buses.index.str.contains(f"{heat_system} heat")]
|
||||
)
|
||||
|
||||
nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
|
||||
|
||||
if (name_type != "central") and options["electricity_distribution_grid"]:
|
||||
if (not heat_system == HeatSystem.URBAN_CENTRAL) and options[
|
||||
"electricity_distribution_grid"
|
||||
]:
|
||||
nodes_elec = nodes + " low voltage"
|
||||
else:
|
||||
nodes_elec = nodes
|
||||
|
||||
heat_pump_type = "air" if "urban" in name else "ground"
|
||||
|
||||
# Add heat pumps
|
||||
costs_name = f"decentral {heat_pump_type}-sourced heat pump"
|
||||
|
||||
cop = {"air": ashp_cop, "ground": gshp_cop}
|
||||
|
||||
if time_dep_hp_cop:
|
||||
efficiency = cop[heat_pump_type][nodes]
|
||||
else:
|
||||
efficiency = costs.at[costs_name, "efficiency"]
|
||||
|
||||
too_large_grouping_years = [gy for gy in grouping_years if gy >= int(baseyear)]
|
||||
if too_large_grouping_years:
|
||||
logger.warning(
|
||||
f"Grouping years >= baseyear are ignored. Dropping {too_large_grouping_years}."
|
||||
)
|
||||
valid_grouping_years = pd.Series(
|
||||
[
|
||||
int(grouping_year)
|
||||
for grouping_year in grouping_years
|
||||
if int(grouping_year) + default_lifetime > int(baseyear)
|
||||
and int(grouping_year) < int(baseyear)
|
||||
too_large_grouping_years = [
|
||||
gy for gy in grouping_years if gy >= int(baseyear)
|
||||
]
|
||||
)
|
||||
if too_large_grouping_years:
|
||||
logger.warning(
|
||||
f"Grouping years >= baseyear are ignored. Dropping {too_large_grouping_years}."
|
||||
)
|
||||
valid_grouping_years = pd.Series(
|
||||
[
|
||||
int(grouping_year)
|
||||
for grouping_year in grouping_years
|
||||
if int(grouping_year) + default_lifetime > int(baseyear)
|
||||
and int(grouping_year) < int(baseyear)
|
||||
]
|
||||
)
|
||||
|
||||
assert valid_grouping_years.is_monotonic_increasing
|
||||
assert valid_grouping_years.is_monotonic_increasing
|
||||
|
||||
# get number of years of each interval
|
||||
_years = valid_grouping_years.diff()
|
||||
# Fill NA from .diff() with value for the first interval
|
||||
_years[0] = valid_grouping_years[0] - baseyear + default_lifetime
|
||||
# Installation is assumed to be linear for the past
|
||||
ratios = _years / _years.sum()
|
||||
# get number of years of each interval
|
||||
_years = valid_grouping_years.diff()
|
||||
# Fill NA from .diff() with value for the first interval
|
||||
_years[0] = valid_grouping_years[0] - baseyear + default_lifetime
|
||||
# Installation is assumed to be linear for the past
|
||||
ratios = _years / _years.sum()
|
||||
|
||||
for ratio, grouping_year in zip(ratios, valid_grouping_years):
|
||||
# Add heat pumps
|
||||
for heat_source in snakemake.params.heat_pump_sources[
|
||||
heat_system.system_type.value
|
||||
]:
|
||||
costs_name = heat_system.heat_pump_costs_name(heat_source)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
|
||||
bus0=nodes_elec,
|
||||
bus1=nodes + " " + name + " heat",
|
||||
carrier=f"{name} {heat_pump_type} heat pump",
|
||||
efficiency=efficiency,
|
||||
capital_cost=costs.at[costs_name, "efficiency"]
|
||||
* costs.at[costs_name, "fixed"],
|
||||
p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")]
|
||||
* ratio
|
||||
/ costs.at[costs_name, "efficiency"],
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[costs_name, "lifetime"],
|
||||
)
|
||||
efficiency = (
|
||||
cop.sel(
|
||||
heat_system=heat_system.system_type.value,
|
||||
heat_source=heat_source,
|
||||
name=nodes,
|
||||
)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots)
|
||||
if time_dep_hp_cop
|
||||
else costs.at[costs_name, "efficiency"]
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
suffix=f" {heat_system} {heat_source} heat pump-{grouping_year}",
|
||||
bus0=nodes_elec,
|
||||
bus1=nodes + " " + heat_system.value + " heat",
|
||||
carrier=f"{heat_system} {heat_source} heat pump",
|
||||
efficiency=efficiency,
|
||||
capital_cost=costs.at[costs_name, "efficiency"]
|
||||
* costs.at[costs_name, "fixed"],
|
||||
p_nom=existing_heating.loc[
|
||||
nodes, (heat_system.value, f"{heat_source} heat pump")
|
||||
]
|
||||
* ratio
|
||||
/ costs.at[costs_name, "efficiency"],
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[costs_name, "lifetime"],
|
||||
)
|
||||
|
||||
# add resistive heater, gas boilers and oil boilers
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
suffix=f" {name} resistive heater-{grouping_year}",
|
||||
suffix=f" {heat_system} resistive heater-{grouping_year}",
|
||||
bus0=nodes_elec,
|
||||
bus1=nodes + " " + name + " heat",
|
||||
carrier=name + " resistive heater",
|
||||
efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
|
||||
bus1=nodes + " " + heat_system.value + " heat",
|
||||
carrier=heat_system.value + " resistive heater",
|
||||
efficiency=costs.at[
|
||||
heat_system.resistive_heater_costs_name, "efficiency"
|
||||
],
|
||||
capital_cost=(
|
||||
costs.at[f"{name_type} resistive heater", "efficiency"]
|
||||
* costs.at[f"{name_type} resistive heater", "fixed"]
|
||||
costs.at[heat_system.resistive_heater_costs_name, "efficiency"]
|
||||
* costs.at[heat_system.resistive_heater_costs_name, "fixed"]
|
||||
),
|
||||
p_nom=(
|
||||
existing_heating.loc[nodes, (name, "resistive heater")]
|
||||
existing_heating.loc[nodes, (heat_system.value, "resistive heater")]
|
||||
* ratio
|
||||
/ costs.at[f"{name_type} resistive heater", "efficiency"]
|
||||
/ costs.at[heat_system.resistive_heater_costs_name, "efficiency"]
|
||||
),
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[f"{name_type} resistive heater", "lifetime"],
|
||||
lifetime=costs.at[heat_system.resistive_heater_costs_name, "lifetime"],
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
suffix=f" {name} gas boiler-{grouping_year}",
|
||||
suffix=f"{heat_system} gas boiler-{grouping_year}",
|
||||
bus0="EU gas" if "EU gas" in spatial.gas.nodes else nodes + " gas",
|
||||
bus1=nodes + " " + name + " heat",
|
||||
bus1=nodes + " " + heat_system.value + " heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=name + " gas boiler",
|
||||
efficiency=costs.at[f"{name_type} gas boiler", "efficiency"],
|
||||
carrier=heat_system.value + " gas boiler",
|
||||
efficiency=costs.at[heat_system.gas_boiler_costs_name, "efficiency"],
|
||||
efficiency2=costs.at["gas", "CO2 intensity"],
|
||||
capital_cost=(
|
||||
costs.at[f"{name_type} gas boiler", "efficiency"]
|
||||
* costs.at[f"{name_type} gas boiler", "fixed"]
|
||||
costs.at[heat_system.gas_boiler_costs_name, "efficiency"]
|
||||
* costs.at[heat_system.gas_boiler_costs_name, "fixed"]
|
||||
),
|
||||
p_nom=(
|
||||
existing_heating.loc[nodes, (name, "gas boiler")]
|
||||
existing_heating.loc[nodes, (heat_system.value, "gas boiler")]
|
||||
* ratio
|
||||
/ costs.at[f"{name_type} gas boiler", "efficiency"]
|
||||
/ costs.at[heat_system.gas_boiler_costs_name, "efficiency"]
|
||||
),
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
|
||||
lifetime=costs.at[heat_system.gas_boiler_costs_name, "lifetime"],
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
suffix=f" {name} oil boiler-{grouping_year}",
|
||||
suffix=f" {heat_system} oil boiler-{grouping_year}",
|
||||
bus0=spatial.oil.nodes,
|
||||
bus1=nodes + " " + name + " heat",
|
||||
bus1=nodes + " " + heat_system.value + " heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=name + " oil boiler",
|
||||
efficiency=costs.at["decentral oil boiler", "efficiency"],
|
||||
carrier=heat_system.value + " oil boiler",
|
||||
efficiency=costs.at[heat_system.oil_boiler_costs_name, "efficiency"],
|
||||
efficiency2=costs.at["oil", "CO2 intensity"],
|
||||
capital_cost=costs.at["decentral oil boiler", "efficiency"]
|
||||
* costs.at["decentral oil boiler", "fixed"],
|
||||
capital_cost=costs.at[heat_system.oil_boiler_costs_name, "efficiency"]
|
||||
* costs.at[heat_system.oil_boiler_costs_name, "fixed"],
|
||||
p_nom=(
|
||||
existing_heating.loc[nodes, (name, "oil boiler")]
|
||||
existing_heating.loc[nodes, (heat_system.value, "oil boiler")]
|
||||
* ratio
|
||||
/ costs.at["decentral oil boiler", "efficiency"]
|
||||
/ costs.at[heat_system.oil_boiler_costs_name, "efficiency"]
|
||||
),
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
|
||||
lifetime=costs.at[
|
||||
f"{heat_system.central_or_decentral} gas boiler", "lifetime"
|
||||
],
|
||||
)
|
||||
|
||||
# delete links with p_nom=nan corresponding to extra nodes in country
|
||||
@ -639,29 +660,22 @@ if __name__ == "__main__":
|
||||
)
|
||||
|
||||
if options["heating"]:
|
||||
time_dep_hp_cop = options["time_dep_hp_cop"]
|
||||
ashp_cop = (
|
||||
xr.open_dataarray(snakemake.input.cop_air_total)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots)
|
||||
)
|
||||
gshp_cop = (
|
||||
xr.open_dataarray(snakemake.input.cop_soil_total)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots)
|
||||
)
|
||||
default_lifetime = snakemake.params.existing_capacities[
|
||||
"default_heating_lifetime"
|
||||
]
|
||||
|
||||
add_heating_capacities_installed_before_baseyear(
|
||||
n,
|
||||
baseyear,
|
||||
grouping_years_heat,
|
||||
ashp_cop,
|
||||
gshp_cop,
|
||||
time_dep_hp_cop,
|
||||
costs,
|
||||
default_lifetime,
|
||||
n=n,
|
||||
baseyear=baseyear,
|
||||
grouping_years=grouping_years_heat,
|
||||
cop=xr.open_dataarray(snakemake.input.cop_profiles),
|
||||
time_dep_hp_cop=options["time_dep_hp_cop"],
|
||||
costs=costs,
|
||||
default_lifetime=snakemake.params.existing_capacities[
|
||||
"default_heating_lifetime"
|
||||
],
|
||||
existing_heating=pd.read_csv(
|
||||
snakemake.input.existing_heating_distribution,
|
||||
header=[0, 1],
|
||||
index_col=0,
|
||||
),
|
||||
)
|
||||
|
||||
if options.get("cluster_heat_buses", False):
|
||||
|
||||
@ -13,11 +13,51 @@ import geopandas as gpd
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from _helpers import configure_logging, set_scenario_config
|
||||
from build_energy_totals import build_eurostat
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
AVAILABLE_BIOMASS_YEARS = [2010, 2020, 2030, 2040, 2050]
|
||||
|
||||
|
||||
def _calc_unsustainable_potential(df, df_unsustainable, share_unsus, resource_type):
|
||||
"""
|
||||
Calculate the unsustainable biomass potential for a given resource type or
|
||||
regex.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df : pd.DataFrame
|
||||
The dataframe with sustainable biomass potentials.
|
||||
df_unsustainable : pd.DataFrame
|
||||
The dataframe with unsustainable biomass potentials.
|
||||
share_unsus : float
|
||||
The share of unsustainable biomass potential retained.
|
||||
resource_type : str or regex
|
||||
The resource type to calculate the unsustainable potential for.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series
|
||||
The unsustainable biomass potential for the given resource type or regex.
|
||||
"""
|
||||
|
||||
if "|" in resource_type:
|
||||
resource_potential = df_unsustainable.filter(regex=resource_type).sum(axis=1)
|
||||
else:
|
||||
resource_potential = df_unsustainable[resource_type]
|
||||
|
||||
return (
|
||||
df.apply(
|
||||
lambda c: c.sum()
|
||||
/ df.loc[df.index.str[:2] == c.name[:2]].sum().sum()
|
||||
* resource_potential.loc[c.name[:2]],
|
||||
axis=1,
|
||||
)
|
||||
.mul(share_unsus)
|
||||
.clip(lower=0)
|
||||
)
|
||||
|
||||
|
||||
def build_nuts_population_data(year=2013):
|
||||
pop = pd.read_csv(
|
||||
snakemake.input.nuts3_population,
|
||||
@ -211,15 +251,104 @@ def convert_nuts2_to_regions(bio_nuts2, regions):
|
||||
return bio_regions
|
||||
|
||||
|
||||
def add_unsustainable_potentials(df):
|
||||
"""
|
||||
Add unsustainable biomass potentials to the given dataframe. The difference
|
||||
between the data of JRC and Eurostat is assumed to be unsustainable
|
||||
biomass.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df : pd.DataFrame
|
||||
The dataframe with sustainable biomass potentials.
|
||||
unsustainable_biomass : str
|
||||
Path to the file with unsustainable biomass potentials.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
The dataframe with added unsustainable biomass potentials.
|
||||
"""
|
||||
if "GB" in snakemake.config["countries"]:
|
||||
latest_year = 2019
|
||||
else:
|
||||
latest_year = 2021
|
||||
idees_rename = {"GR": "EL", "GB": "UK"}
|
||||
df_unsustainable = (
|
||||
build_eurostat(
|
||||
countries=snakemake.config["countries"],
|
||||
input_eurostat=snakemake.input.eurostat,
|
||||
nprocesses=int(snakemake.threads),
|
||||
)
|
||||
.xs(
|
||||
max(min(latest_year, int(snakemake.wildcards.planning_horizons)), 1990),
|
||||
level=1,
|
||||
)
|
||||
.xs("Primary production", level=2)
|
||||
.droplevel([1, 2, 3])
|
||||
)
|
||||
|
||||
df_unsustainable.index = df_unsustainable.index.str.strip()
|
||||
df_unsustainable = df_unsustainable.rename(
|
||||
{v: k for k, v in idees_rename.items()}, axis=0
|
||||
)
|
||||
|
||||
bio_carriers = [
|
||||
"Primary solid biofuels",
|
||||
"Biogases",
|
||||
"Renewable municipal waste",
|
||||
"Pure biogasoline",
|
||||
"Blended biogasoline",
|
||||
"Pure biodiesels",
|
||||
"Blended biodiesels",
|
||||
"Pure bio jet kerosene",
|
||||
"Blended bio jet kerosene",
|
||||
"Other liquid biofuels",
|
||||
]
|
||||
|
||||
df_unsustainable = df_unsustainable[bio_carriers]
|
||||
|
||||
# Phase out unsustainable biomass potentials linearly from 2020 to 2035 while phasing in sustainable potentials
|
||||
share_unsus = params.get("share_unsustainable_use_retained").get(investment_year)
|
||||
|
||||
df_wo_ch = df.drop(df.filter(regex="CH\d", axis=0).index)
|
||||
|
||||
# Calculate unsustainable solid biomass
|
||||
df_wo_ch["unsustainable solid biomass"] = _calc_unsustainable_potential(
|
||||
df_wo_ch, df_unsustainable, share_unsus, "Primary solid biofuels"
|
||||
)
|
||||
|
||||
# Calculate unsustainable biogas
|
||||
df_wo_ch["unsustainable biogas"] = _calc_unsustainable_potential(
|
||||
df_wo_ch, df_unsustainable, share_unsus, "Biogases"
|
||||
)
|
||||
|
||||
# Calculate unsustainable bioliquids
|
||||
df_wo_ch["unsustainable bioliquids"] = _calc_unsustainable_potential(
|
||||
df_wo_ch,
|
||||
df_unsustainable,
|
||||
share_unsus,
|
||||
resource_type="gasoline|diesel|kerosene|liquid",
|
||||
)
|
||||
|
||||
share_sus = params.get("share_sustainable_potential_available").get(investment_year)
|
||||
df *= share_sus
|
||||
|
||||
df = df.join(df_wo_ch.filter(like="unsustainable")).fillna(0)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_biomass_potentials",
|
||||
simpl="",
|
||||
clusters="5",
|
||||
planning_horizons=2050,
|
||||
clusters="37",
|
||||
planning_horizons=2020,
|
||||
)
|
||||
|
||||
configure_logging(snakemake)
|
||||
@ -269,6 +398,8 @@ if __name__ == "__main__":
|
||||
grouper = {v: k for k, vv in params["classes"].items() for v in vv}
|
||||
df = df.T.groupby(grouper).sum().T
|
||||
|
||||
df = add_unsustainable_potentials(df)
|
||||
|
||||
df *= 1e6 # TWh/a to MWh/a
|
||||
df.index.name = "MWh/a"
|
||||
|
||||
|
||||
@ -1,69 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
"""
|
||||
Build coefficient of performance (COP) time series for air- or ground-sourced
|
||||
heat pumps.
|
||||
|
||||
The COP is approximated as a quatratic function of the temperature difference between source and
|
||||
sink, based on Staffell et al. 2012.
|
||||
|
||||
This rule is executed in ``build_sector.smk``.
|
||||
|
||||
Relevant Settings
|
||||
-----------------
|
||||
|
||||
.. code:: yaml
|
||||
heat_pump_sink_T:
|
||||
|
||||
|
||||
Inputs:
|
||||
-------
|
||||
- ``resources/<run_name>/temp_soil_total_elec_s<simpl>_<clusters>.nc``: Soil temperature (total) time series.
|
||||
- ``resources/<run_name>/temp_air_total_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (total) time series.
|
||||
|
||||
Outputs:
|
||||
--------
|
||||
- ``resources/cop_soil_total_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (total).
|
||||
- ``resources/cop_air_total_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (total).
|
||||
|
||||
|
||||
References
|
||||
----------
|
||||
[1] Staffell et al., Energy & Environmental Science 11 (2012): A review of domestic heat pumps, https://doi.org/10.1039/C2EE22653G.
|
||||
"""
|
||||
|
||||
import xarray as xr
|
||||
from _helpers import set_scenario_config
|
||||
|
||||
|
||||
def coefficient_of_performance(delta_T, source="air"):
|
||||
if source == "air":
|
||||
return 6.81 - 0.121 * delta_T + 0.000630 * delta_T**2
|
||||
elif source == "soil":
|
||||
return 8.77 - 0.150 * delta_T + 0.000734 * delta_T**2
|
||||
else:
|
||||
raise NotImplementedError("'source' must be one of ['air', 'soil']")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_cop_profiles",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
)
|
||||
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
for source in ["air", "soil"]:
|
||||
source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_total"])
|
||||
|
||||
delta_T = snakemake.params.heat_pump_sink_T - source_T
|
||||
|
||||
cop = coefficient_of_performance(delta_T, source)
|
||||
|
||||
cop.to_netcdf(snakemake.output[f"cop_{source}_total"])
|
||||
111
scripts/build_cop_profiles/BaseCopApproximator.py
Normal file
111
scripts/build_cop_profiles/BaseCopApproximator.py
Normal file
@ -0,0 +1,111 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
|
||||
|
||||
class BaseCopApproximator(ABC):
|
||||
"""
|
||||
Abstract class for approximating the coefficient of performance (COP) of a
|
||||
heat pump.
|
||||
|
||||
Attributes:
|
||||
----------
|
||||
forward_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The forward temperature in Celsius.
|
||||
source_inlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source inlet temperature in Celsius.
|
||||
|
||||
Methods:
|
||||
-------
|
||||
__init__(self, forward_temperature_celsius, source_inlet_temperature_celsius)
|
||||
Initialize CopApproximator.
|
||||
approximate_cop(self)
|
||||
Approximate heat pump coefficient of performance (COP).
|
||||
celsius_to_kelvin(t_celsius)
|
||||
Convert temperature from Celsius to Kelvin.
|
||||
logarithmic_mean(t_hot, t_cold)
|
||||
Calculate the logarithmic mean temperature difference.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
forward_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_inlet_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
):
|
||||
"""
|
||||
Initialize CopApproximator.
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
forward_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The forward temperature in Celsius.
|
||||
source_inlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source inlet temperature in Celsius.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def approximate_cop(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Approximate heat pump coefficient of performance (COP).
|
||||
|
||||
Returns:
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The calculated COP values.
|
||||
"""
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def celsius_to_kelvin(
|
||||
t_celsius: Union[float, xr.DataArray, np.array]
|
||||
) -> Union[float, xr.DataArray, np.array]:
|
||||
"""
|
||||
Convert temperature from Celsius to Kelvin.
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
t_celsius : Union[float, xr.DataArray, np.array]
|
||||
Temperature in Celsius.
|
||||
|
||||
Returns:
|
||||
-------
|
||||
Union[float, xr.DataArray, np.array]
|
||||
Temperature in Kelvin.
|
||||
"""
|
||||
if (np.asarray(t_celsius) > 200).any():
|
||||
raise ValueError(
|
||||
"t_celsius > 200. Are you sure you are using the right units?"
|
||||
)
|
||||
return t_celsius + 273.15
|
||||
|
||||
@staticmethod
|
||||
def logarithmic_mean(
|
||||
t_hot: Union[float, xr.DataArray, np.ndarray],
|
||||
t_cold: Union[float, xr.DataArray, np.ndarray],
|
||||
) -> Union[float, xr.DataArray, np.ndarray]:
|
||||
"""
|
||||
Calculate the logarithmic mean temperature difference.
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
t_hot : Union[float, xr.DataArray, np.ndarray]
|
||||
Hot temperature.
|
||||
t_cold : Union[float, xr.DataArray, np.ndarray]
|
||||
Cold temperature.
|
||||
|
||||
Returns:
|
||||
-------
|
||||
Union[float, xr.DataArray, np.ndarray]
|
||||
Logarithmic mean temperature difference.
|
||||
"""
|
||||
if (np.asarray(t_hot <= t_cold)).any():
|
||||
raise ValueError("t_hot must be greater than t_cold")
|
||||
return (t_hot - t_cold) / np.log(t_hot / t_cold)
|
||||
392
scripts/build_cop_profiles/CentralHeatingCopApproximator.py
Normal file
392
scripts/build_cop_profiles/CentralHeatingCopApproximator.py
Normal file
@ -0,0 +1,392 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
from BaseCopApproximator import BaseCopApproximator
|
||||
|
||||
|
||||
class CentralHeatingCopApproximator(BaseCopApproximator):
|
||||
"""
|
||||
Approximate the coefficient of performance (COP) for a heat pump in a
|
||||
central heating system (district heating).
|
||||
|
||||
Uses an approximation method proposed by Jensen et al. (2018) and
|
||||
default parameters from Pieper et al. (2020). The method is based on
|
||||
a thermodynamic heat pump model with some hard-to-know parameters
|
||||
being approximated.
|
||||
|
||||
Attributes:
|
||||
----------
|
||||
forward_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The forward temperature in Celsius.
|
||||
return_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The return temperature in Celsius.
|
||||
source_inlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source inlet temperature in Celsius.
|
||||
source_outlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source outlet temperature in Celsius.
|
||||
delta_t_pinch_point : float, optional
|
||||
The pinch point temperature difference, by default 5.
|
||||
isentropic_compressor_efficiency : float, optional
|
||||
The isentropic compressor efficiency, by default 0.8.
|
||||
heat_loss : float, optional
|
||||
The heat loss, by default 0.0.
|
||||
|
||||
Methods:
|
||||
-------
|
||||
__init__(
|
||||
forward_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_inlet_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
return_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_outlet_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
delta_t_pinch_point: float = 5,
|
||||
isentropic_compressor_efficiency: float = 0.8,
|
||||
heat_loss: float = 0.0,
|
||||
) -> None:
|
||||
Initializes the CentralHeatingCopApproximator object.
|
||||
|
||||
approximate_cop(self) -> Union[xr.DataArray, np.array]:
|
||||
Calculate the coefficient of performance (COP) for the system.
|
||||
|
||||
_approximate_delta_t_refrigerant_source(
|
||||
self, delta_t_source: Union[xr.DataArray, np.array]
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
Approximates the temperature difference between the refrigerant and the source.
|
||||
|
||||
_approximate_delta_t_refrigerant_sink(
|
||||
self,
|
||||
refrigerant: str = "ammonia",
|
||||
a: float = {"ammonia": 0.2, "isobutane": -0.0011},
|
||||
b: float = {"ammonia": 0.2, "isobutane": 0.3},
|
||||
c: float = {"ammonia": 0.016, "isobutane": 2.4},
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
Approximates the temperature difference between the refrigerant and heat sink.
|
||||
|
||||
_ratio_evaporation_compression_work_approximation(
|
||||
self,
|
||||
refrigerant: str = "ammonia",
|
||||
a: float = {"ammonia": 0.0014, "isobutane": 0.0035},
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
Calculate the ratio of evaporation to compression work based on approximation.
|
||||
|
||||
_approximate_delta_t_refrigerant_sink(
|
||||
self,
|
||||
refrigerant: str = "ammonia",
|
||||
a: float = {"ammonia": 0.2, "isobutane": -0.0011},
|
||||
b: float = {"ammonia": 0.2, "isobutane": 0.3},
|
||||
c: float = {"ammonia": 0.016, "isobutane": 2.4},
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
Approximates the temperature difference between the refrigerant and heat sink.
|
||||
|
||||
_ratio_evaporation_compression_work_approximation(
|
||||
self,
|
||||
refrigerant: str = "ammonia",
|
||||
a: float = {"ammonia": 0.0014, "isobutane": 0.0035},
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
Calculate the ratio of evaporation to compression work based on approximation.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
forward_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_inlet_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
return_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_outlet_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
delta_t_pinch_point: float = 5,
|
||||
isentropic_compressor_efficiency: float = 0.8,
|
||||
heat_loss: float = 0.0,
|
||||
) -> None:
|
||||
"""
|
||||
Initializes the CentralHeatingCopApproximator object.
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
forward_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The forward temperature in Celsius.
|
||||
return_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The return temperature in Celsius.
|
||||
source_inlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source inlet temperature in Celsius.
|
||||
source_outlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source outlet temperature in Celsius.
|
||||
delta_t_pinch_point : float, optional
|
||||
The pinch point temperature difference, by default 5.
|
||||
isentropic_compressor_efficiency : float, optional
|
||||
The isentropic compressor efficiency, by default 0.8.
|
||||
heat_loss : float, optional
|
||||
The heat loss, by default 0.0.
|
||||
"""
|
||||
self.t_source_in_kelvin = BaseCopApproximator.celsius_to_kelvin(
|
||||
source_inlet_temperature_celsius
|
||||
)
|
||||
self.t_sink_out_kelvin = BaseCopApproximator.celsius_to_kelvin(
|
||||
forward_temperature_celsius
|
||||
)
|
||||
|
||||
self.t_sink_in_kelvin = BaseCopApproximator.celsius_to_kelvin(
|
||||
return_temperature_celsius
|
||||
)
|
||||
self.t_source_out = BaseCopApproximator.celsius_to_kelvin(
|
||||
source_outlet_temperature_celsius
|
||||
)
|
||||
|
||||
self.isentropic_efficiency_compressor_kelvin = isentropic_compressor_efficiency
|
||||
self.heat_loss = heat_loss
|
||||
self.delta_t_pinch = delta_t_pinch_point
|
||||
|
||||
def approximate_cop(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the coefficient of performance (COP) for the system.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
Union[xr.DataArray, np.array]: The calculated COP values.
|
||||
"""
|
||||
return (
|
||||
self.ideal_lorenz_cop
|
||||
* (
|
||||
(
|
||||
1
|
||||
+ (self.delta_t_refrigerant_sink + self.delta_t_pinch)
|
||||
/ self.t_sink_mean_kelvin
|
||||
)
|
||||
/ (
|
||||
1
|
||||
+ (
|
||||
self.delta_t_refrigerant_sink
|
||||
+ self.delta_t_refrigerant_source
|
||||
+ 2 * self.delta_t_pinch
|
||||
)
|
||||
/ self.delta_t_lift
|
||||
)
|
||||
)
|
||||
* self.isentropic_efficiency_compressor_kelvin
|
||||
* (1 - self.ratio_evaporation_compression_work)
|
||||
+ 1
|
||||
- self.isentropic_efficiency_compressor_kelvin
|
||||
- self.heat_loss
|
||||
)
|
||||
|
||||
@property
|
||||
def t_sink_mean_kelvin(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the logarithmic mean temperature difference between the cold
|
||||
and hot sinks.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The mean temperature difference.
|
||||
"""
|
||||
return BaseCopApproximator.logarithmic_mean(
|
||||
t_cold=self.t_sink_in_kelvin, t_hot=self.t_sink_out_kelvin
|
||||
)
|
||||
|
||||
@property
|
||||
def t_source_mean_kelvin(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the logarithmic mean temperature of the heat source.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The mean temperature of the heat source.
|
||||
"""
|
||||
return BaseCopApproximator.logarithmic_mean(
|
||||
t_hot=self.t_source_in_kelvin, t_cold=self.t_source_out
|
||||
)
|
||||
|
||||
@property
|
||||
def delta_t_lift(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the temperature lift as the difference between the
|
||||
logarithmic sink and source temperatures.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The temperature difference between the sink and source.
|
||||
"""
|
||||
return self.t_sink_mean_kelvin - self.t_source_mean_kelvin
|
||||
|
||||
@property
|
||||
def ideal_lorenz_cop(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Ideal Lorenz coefficient of performance (COP).
|
||||
|
||||
The ideal Lorenz COP is calculated as the ratio of the mean sink temperature
|
||||
to the lift temperature difference.
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.array
|
||||
The ideal Lorenz COP.
|
||||
"""
|
||||
return self.t_sink_mean_kelvin / self.delta_t_lift
|
||||
|
||||
@property
|
||||
def delta_t_refrigerant_source(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the temperature difference between the refrigerant source
|
||||
inlet and outlet.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The temperature difference between the refrigerant source inlet and outlet.
|
||||
"""
|
||||
return self._approximate_delta_t_refrigerant_source(
|
||||
delta_t_source=self.t_source_in_kelvin - self.t_source_out
|
||||
)
|
||||
|
||||
@property
|
||||
def delta_t_refrigerant_sink(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Temperature difference between the refrigerant and the sink based on
|
||||
approximation.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The temperature difference between the refrigerant and the sink.
|
||||
"""
|
||||
return self._approximate_delta_t_refrigerant_sink()
|
||||
|
||||
@property
|
||||
def ratio_evaporation_compression_work(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the ratio of evaporation to compression work based on
|
||||
approximation.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The calculated ratio of evaporation to compression work.
|
||||
"""
|
||||
return self._ratio_evaporation_compression_work_approximation()
|
||||
|
||||
@property
|
||||
def delta_t_sink(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the temperature difference at the sink.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The temperature difference at the sink.
|
||||
"""
|
||||
return self.t_sink_out_kelvin - self.t_sink_in_kelvin
|
||||
|
||||
def _approximate_delta_t_refrigerant_source(
|
||||
self, delta_t_source: Union[xr.DataArray, np.array]
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Approximates the temperature difference between the refrigerant and the
|
||||
source.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
delta_t_source : Union[xr.DataArray, np.array]
|
||||
The temperature difference for the refrigerant source.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The approximate temperature difference between the refrigerant and heat source.
|
||||
"""
|
||||
return delta_t_source / 2
|
||||
|
||||
def _approximate_delta_t_refrigerant_sink(
|
||||
self,
|
||||
refrigerant: str = "ammonia",
|
||||
a: float = {"ammonia": 0.2, "isobutane": -0.0011},
|
||||
b: float = {"ammonia": 0.2, "isobutane": 0.3},
|
||||
c: float = {"ammonia": 0.016, "isobutane": 2.4},
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Approximates the temperature difference between the refrigerant and
|
||||
heat sink.
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
refrigerant : str, optional
|
||||
The refrigerant used in the system. Either 'isobutane' or 'ammonia. Default is 'ammonia'.
|
||||
a : float, optional
|
||||
Coefficient for the temperature difference between the sink and source, default is 0.2.
|
||||
b : float, optional
|
||||
Coefficient for the temperature difference at the sink, default is 0.2.
|
||||
c : float, optional
|
||||
Constant term, default is 0.016.
|
||||
|
||||
Returns:
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The approximate temperature difference between the refrigerant and heat sink.
|
||||
|
||||
Notes:
|
||||
------
|
||||
This function assumes ammonia as the refrigerant.
|
||||
|
||||
The approximate temperature difference at the refrigerant sink is calculated using the following formula:
|
||||
a * (t_sink_out - t_source_out + 2 * delta_t_pinch) + b * delta_t_sink + c
|
||||
"""
|
||||
if refrigerant not in a.keys():
|
||||
raise ValueError(
|
||||
f"Invalid refrigerant '{refrigerant}'. Must be one of {a.keys()}"
|
||||
)
|
||||
return (
|
||||
a[refrigerant]
|
||||
* (self.t_sink_out_kelvin - self.t_source_out + 2 * self.delta_t_pinch)
|
||||
+ b[refrigerant] * self.delta_t_sink
|
||||
+ c[refrigerant]
|
||||
)
|
||||
|
||||
def _ratio_evaporation_compression_work_approximation(
|
||||
self,
|
||||
refrigerant: str = "ammonia",
|
||||
a: float = {"ammonia": 0.0014, "isobutane": 0.0035},
|
||||
b: float = {"ammonia": -0.0015, "isobutane": -0.0033},
|
||||
c: float = {"ammonia": 0.039, "isobutane": 0.053},
|
||||
) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Calculate the ratio of evaporation to compression work approximation.
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
refrigerant : str, optional
|
||||
The refrigerant used in the system. Either 'isobutane' or 'ammonia. Default is 'ammonia'.
|
||||
a : float, optional
|
||||
Coefficient 'a' in the approximation equation. Default is 0.0014.
|
||||
b : float, optional
|
||||
Coefficient 'b' in the approximation equation. Default is -0.0015.
|
||||
c : float, optional
|
||||
Coefficient 'c' in the approximation equation. Default is 0.039.
|
||||
|
||||
Returns:
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The approximated ratio of evaporation to compression work.
|
||||
|
||||
Notes:
|
||||
------
|
||||
This function assumes ammonia as the refrigerant.
|
||||
|
||||
The approximation equation used is:
|
||||
ratio = a * (t_sink_out - t_source_out + 2 * delta_t_pinch) + b * delta_t_sink + c
|
||||
"""
|
||||
if refrigerant not in a.keys():
|
||||
raise ValueError(
|
||||
f"Invalid refrigerant '{refrigerant}'. Must be one of {a.keys()}"
|
||||
)
|
||||
return (
|
||||
a[refrigerant]
|
||||
* (self.t_sink_out_kelvin - self.t_source_out + 2 * self.delta_t_pinch)
|
||||
+ b[refrigerant] * self.delta_t_sink
|
||||
+ c[refrigerant]
|
||||
)
|
||||
110
scripts/build_cop_profiles/DecentralHeatingCopApproximator.py
Normal file
110
scripts/build_cop_profiles/DecentralHeatingCopApproximator.py
Normal file
@ -0,0 +1,110 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
from BaseCopApproximator import BaseCopApproximator
|
||||
|
||||
|
||||
class DecentralHeatingCopApproximator(BaseCopApproximator):
|
||||
"""
|
||||
Approximate the coefficient of performance (COP) for a heat pump in a
|
||||
decentral heating system (individual/household heating).
|
||||
|
||||
Uses a quadratic regression on the temperature difference between the source and sink based on empirical data proposed by Staffell et al. 2012.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
forward_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The forward temperature in Celsius.
|
||||
source_inlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source inlet temperature in Celsius.
|
||||
source_type : str
|
||||
The source of the heat pump. Must be either 'air' or 'ground'.
|
||||
|
||||
Methods
|
||||
-------
|
||||
__init__(forward_temperature_celsius, source_inlet_temperature_celsius, source_type)
|
||||
Initialize the DecentralHeatingCopApproximator object.
|
||||
approximate_cop()
|
||||
Compute the COP values using quadratic regression for air-/ground-source heat pumps.
|
||||
_approximate_cop_air_source()
|
||||
Evaluate quadratic regression for an air-sourced heat pump.
|
||||
_approximate_cop_ground_source()
|
||||
Evaluate quadratic regression for a ground-sourced heat pump.
|
||||
|
||||
References
|
||||
----------
|
||||
[1] Staffell et al., Energy & Environmental Science 11 (2012): A review of domestic heat pumps, https://doi.org/10.1039/C2EE22653G.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
forward_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_inlet_temperature_celsius: Union[xr.DataArray, np.array],
|
||||
source_type: str,
|
||||
):
|
||||
"""
|
||||
Initialize the DecentralHeatingCopApproximator object.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
forward_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The forward temperature in Celsius.
|
||||
source_inlet_temperature_celsius : Union[xr.DataArray, np.array]
|
||||
The source inlet temperature in Celsius.
|
||||
source_type : str
|
||||
The source of the heat pump. Must be either 'air' or 'ground'.
|
||||
"""
|
||||
|
||||
self.delta_t = forward_temperature_celsius - source_inlet_temperature_celsius
|
||||
if source_type not in ["air", "ground"]:
|
||||
raise ValueError("'source_type' must be one of ['air', 'ground']")
|
||||
else:
|
||||
self.source_type = source_type
|
||||
|
||||
def approximate_cop(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Compute the COP values using quadratic regression for air-/ground-
|
||||
source heat pumps.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The calculated COP values.
|
||||
"""
|
||||
if self.source_type == "air":
|
||||
return self._approximate_cop_air_source()
|
||||
elif self.source_type == "ground":
|
||||
return self._approximate_cop_ground_source()
|
||||
|
||||
def _approximate_cop_air_source(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Evaluate quadratic regression for an air-sourced heat pump.
|
||||
|
||||
COP = 6.81 - 0.121 * delta_T + 0.000630 * delta_T^2
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The calculated COP values.
|
||||
"""
|
||||
return 6.81 - 0.121 * self.delta_t + 0.000630 * self.delta_t**2
|
||||
|
||||
def _approximate_cop_ground_source(self) -> Union[xr.DataArray, np.array]:
|
||||
"""
|
||||
Evaluate quadratic regression for a ground-sourced heat pump.
|
||||
|
||||
COP = 8.77 - 0.150 * delta_T + 0.000734 * delta_T^2
|
||||
|
||||
Returns
|
||||
-------
|
||||
Union[xr.DataArray, np.array]
|
||||
The calculated COP values.
|
||||
"""
|
||||
return 8.77 - 0.150 * self.delta_t + 0.000734 * self.delta_t**2
|
||||
95
scripts/build_cop_profiles/run.py
Normal file
95
scripts/build_cop_profiles/run.py
Normal file
@ -0,0 +1,95 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
from _helpers import set_scenario_config
|
||||
from CentralHeatingCopApproximator import CentralHeatingCopApproximator
|
||||
from DecentralHeatingCopApproximator import DecentralHeatingCopApproximator
|
||||
|
||||
from scripts.definitions.heat_system_type import HeatSystemType
|
||||
|
||||
sys.path.append("..")
|
||||
|
||||
|
||||
def get_cop(
|
||||
heat_system_type: str,
|
||||
heat_source: str,
|
||||
source_inlet_temperature_celsius: xr.DataArray,
|
||||
) -> xr.DataArray:
|
||||
"""
|
||||
Calculate the coefficient of performance (COP) for a heating system.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
heat_system_type : str
|
||||
The type of heating system.
|
||||
heat_source : str
|
||||
The heat source used in the heating system.
|
||||
source_inlet_temperature_celsius : xr.DataArray
|
||||
The inlet temperature of the heat source in Celsius.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The calculated coefficient of performance (COP) for the heating system.
|
||||
"""
|
||||
if HeatSystemType(heat_system_type).is_central:
|
||||
return CentralHeatingCopApproximator(
|
||||
forward_temperature_celsius=snakemake.params.forward_temperature_central_heating,
|
||||
return_temperature_celsius=snakemake.params.return_temperature_central_heating,
|
||||
source_inlet_temperature_celsius=source_inlet_temperature_celsius,
|
||||
source_outlet_temperature_celsius=source_inlet_temperature_celsius
|
||||
- snakemake.params.heat_source_cooling_central_heating,
|
||||
).approximate_cop()
|
||||
|
||||
else:
|
||||
return DecentralHeatingCopApproximator(
|
||||
forward_temperature_celsius=snakemake.params.heat_pump_sink_T_decentral_heating,
|
||||
source_inlet_temperature_celsius=source_inlet_temperature_celsius,
|
||||
source_type=heat_source,
|
||||
).approximate_cop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_cop_profiles",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
)
|
||||
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
cop_all_system_types = []
|
||||
for heat_system_type, heat_sources in snakemake.params.heat_pump_sources.items():
|
||||
cop_this_system_type = []
|
||||
for heat_source in heat_sources:
|
||||
source_inlet_temperature_celsius = xr.open_dataarray(
|
||||
snakemake.input[f"temp_{heat_source.replace('ground', 'soil')}_total"]
|
||||
)
|
||||
cop_da = get_cop(
|
||||
heat_system_type=heat_system_type,
|
||||
heat_source=heat_source,
|
||||
source_inlet_temperature_celsius=source_inlet_temperature_celsius,
|
||||
)
|
||||
cop_this_system_type.append(cop_da)
|
||||
cop_all_system_types.append(
|
||||
xr.concat(
|
||||
cop_this_system_type, dim=pd.Index(heat_sources, name="heat_source")
|
||||
)
|
||||
)
|
||||
|
||||
cop_dataarray = xr.concat(
|
||||
cop_all_system_types,
|
||||
dim=pd.Index(snakemake.params.heat_pump_sources.keys(), name="heat_system"),
|
||||
)
|
||||
|
||||
cop_dataarray.to_netcdf(snakemake.output.cop_profiles)
|
||||
@ -26,7 +26,7 @@ Inputs
|
||||
.. image:: img/countries.png
|
||||
:scale: 33 %
|
||||
|
||||
- ``data/bundle/eez/World_EEZ_v8_2014.shp``: World `exclusive economic zones <https://en.wikipedia.org/wiki/Exclusive_economic_zone>`_ (EEZ)
|
||||
- ``data/eez/World_EEZ_v12_20231025_gpkg/eez_v12.gpkg ``: World `exclusive economic zones <https://en.wikipedia.org/wiki/Exclusive_economic_zone>`_ (EEZ)
|
||||
|
||||
.. image:: img/eez.png
|
||||
:scale: 33 %
|
||||
@ -73,22 +73,16 @@ from functools import reduce
|
||||
from itertools import takewhile
|
||||
from operator import attrgetter
|
||||
|
||||
import country_converter as coco
|
||||
import geopandas as gpd
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pycountry as pyc
|
||||
from _helpers import configure_logging, set_scenario_config
|
||||
from shapely.geometry import MultiPolygon, Polygon
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _get_country(target, **keys):
|
||||
assert len(keys) == 1
|
||||
try:
|
||||
return getattr(pyc.countries.get(**keys), target)
|
||||
except (KeyError, AttributeError):
|
||||
return np.nan
|
||||
cc = coco.CountryConverter()
|
||||
|
||||
|
||||
def _simplify_polys(polys, minarea=0.1, tolerance=None, filterremote=True):
|
||||
@ -135,22 +129,15 @@ def countries(naturalearth, country_list):
|
||||
return s
|
||||
|
||||
|
||||
def eez(country_shapes, eez, country_list):
|
||||
def eez(eez, country_list):
|
||||
df = gpd.read_file(eez)
|
||||
df = df.loc[
|
||||
df["ISO_3digit"].isin(
|
||||
[_get_country("alpha_3", alpha_2=c) for c in country_list]
|
||||
)
|
||||
]
|
||||
df["name"] = df["ISO_3digit"].map(lambda c: _get_country("alpha_2", alpha_3=c))
|
||||
iso3_list = cc.convert(country_list, src="ISO2", to="ISO3")
|
||||
df = df.query("ISO_TER1 in @iso3_list and POL_TYPE == '200NM'").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)
|
||||
)
|
||||
s = gpd.GeoSeries(
|
||||
{k: v for k, v in s.items() if v.distance(country_shapes[k]) < 1e-3},
|
||||
crs=df.crs,
|
||||
)
|
||||
s = s.to_frame("geometry")
|
||||
s = s.to_frame("geometry").set_crs(df.crs)
|
||||
s.index.name = "name"
|
||||
return s
|
||||
|
||||
@ -262,9 +249,7 @@ if __name__ == "__main__":
|
||||
country_shapes = countries(snakemake.input.naturalearth, snakemake.params.countries)
|
||||
country_shapes.reset_index().to_file(snakemake.output.country_shapes)
|
||||
|
||||
offshore_shapes = eez(
|
||||
country_shapes, snakemake.input.eez, snakemake.params.countries
|
||||
)
|
||||
offshore_shapes = eez(snakemake.input.eez, snakemake.params.countries)
|
||||
offshore_shapes.reset_index().to_file(snakemake.output.offshore_shapes)
|
||||
|
||||
europe_shape = gpd.GeoDataFrame(
|
||||
|
||||
28
scripts/definitions/heat_sector.py
Normal file
28
scripts/definitions/heat_sector.py
Normal file
@ -0,0 +1,28 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class HeatSector(Enum):
|
||||
"""
|
||||
Enumeration class representing different heat sectors.
|
||||
|
||||
Attributes:
|
||||
RESIDENTIAL (str): Represents the residential heat sector.
|
||||
SERVICES (str): Represents the services heat sector.
|
||||
"""
|
||||
|
||||
RESIDENTIAL = "residential"
|
||||
SERVICES = "services"
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""
|
||||
Returns the string representation of the heat sector.
|
||||
|
||||
Returns:
|
||||
str: The string representation of the heat sector.
|
||||
"""
|
||||
return self.value
|
||||
267
scripts/definitions/heat_system.py
Normal file
267
scripts/definitions/heat_system.py
Normal file
@ -0,0 +1,267 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
from enum import Enum
|
||||
|
||||
from scripts.definitions.heat_sector import HeatSector
|
||||
from scripts.definitions.heat_system_type import HeatSystemType
|
||||
|
||||
|
||||
class HeatSystem(Enum):
|
||||
"""
|
||||
Enumeration representing different heat systems.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
RESIDENTIAL_RURAL : str
|
||||
Heat system for residential areas in rural locations.
|
||||
SERVICES_RURAL : str
|
||||
Heat system for service areas in rural locations.
|
||||
RESIDENTIAL_URBAN_DECENTRAL : str
|
||||
Heat system for residential areas in urban decentralized locations.
|
||||
SERVICES_URBAN_DECENTRAL : str
|
||||
Heat system for service areas in urban decentralized locations.
|
||||
URBAN_CENTRAL : str
|
||||
Heat system for urban central areas.
|
||||
|
||||
Methods
|
||||
-------
|
||||
__str__()
|
||||
Returns the string representation of the heat system.
|
||||
central_or_decentral()
|
||||
Returns whether the heat system is central or decentralized.
|
||||
system_type()
|
||||
Returns the type of the heat system.
|
||||
sector()
|
||||
Returns the sector of the heat system.
|
||||
rural()
|
||||
Returns whether the heat system is for rural areas.
|
||||
urban_decentral()
|
||||
Returns whether the heat system is for urban decentralized areas.
|
||||
urban()
|
||||
Returns whether the heat system is for urban areas.
|
||||
heat_demand_weighting(urban_fraction=None, dist_fraction=None)
|
||||
Calculates the heat demand weighting based on urban fraction and distribution fraction.
|
||||
heat_pump_costs_name(heat_source)
|
||||
Generates the name for the heat pump costs based on the heat source.
|
||||
"""
|
||||
|
||||
RESIDENTIAL_RURAL = "residential rural"
|
||||
SERVICES_RURAL = "services rural"
|
||||
RESIDENTIAL_URBAN_DECENTRAL = "residential urban decentral"
|
||||
SERVICES_URBAN_DECENTRAL = "services urban decentral"
|
||||
URBAN_CENTRAL = "urban central"
|
||||
|
||||
def __init__(self, *args):
|
||||
super().__init__(*args)
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""
|
||||
Returns the string representation of the heat system.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
The string representation of the heat system.
|
||||
"""
|
||||
return self.value
|
||||
|
||||
@property
|
||||
def central_or_decentral(self) -> str:
|
||||
"""
|
||||
Returns whether the heat system is central or decentralized.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"central" if the heat system is central, "decentral" otherwise.
|
||||
"""
|
||||
if self == HeatSystem.URBAN_CENTRAL:
|
||||
return "central"
|
||||
else:
|
||||
return "decentral"
|
||||
|
||||
@property
|
||||
def system_type(self) -> HeatSystemType:
|
||||
"""
|
||||
Returns the type of the heat system.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
The type of the heat system.
|
||||
|
||||
Raises
|
||||
------
|
||||
RuntimeError
|
||||
If the heat system is invalid.
|
||||
"""
|
||||
if self == HeatSystem.URBAN_CENTRAL:
|
||||
return HeatSystemType.URBAN_CENTRAL
|
||||
elif (
|
||||
self == HeatSystem.RESIDENTIAL_URBAN_DECENTRAL
|
||||
or self == HeatSystem.SERVICES_URBAN_DECENTRAL
|
||||
):
|
||||
return HeatSystemType.URBAN_DECENTRAL
|
||||
elif self == HeatSystem.RESIDENTIAL_RURAL or self == HeatSystem.SERVICES_RURAL:
|
||||
return HeatSystemType.RURAL
|
||||
else:
|
||||
raise RuntimeError(f"Invalid heat system: {self}")
|
||||
|
||||
@property
|
||||
def sector(self) -> HeatSector:
|
||||
"""
|
||||
Returns the sector of the heat system.
|
||||
|
||||
Returns
|
||||
-------
|
||||
HeatSector
|
||||
The sector of the heat system.
|
||||
"""
|
||||
if (
|
||||
self == HeatSystem.RESIDENTIAL_RURAL
|
||||
or self == HeatSystem.RESIDENTIAL_URBAN_DECENTRAL
|
||||
):
|
||||
return HeatSector.RESIDENTIAL
|
||||
elif (
|
||||
self == HeatSystem.SERVICES_RURAL
|
||||
or self == HeatSystem.SERVICES_URBAN_DECENTRAL
|
||||
):
|
||||
return HeatSector.SERVICES
|
||||
else:
|
||||
"tot"
|
||||
|
||||
@property
|
||||
def is_rural(self) -> bool:
|
||||
"""
|
||||
Returns whether the heat system is for rural areas.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
True if the heat system is for rural areas, False otherwise.
|
||||
"""
|
||||
if self == HeatSystem.RESIDENTIAL_RURAL or self == HeatSystem.SERVICES_RURAL:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
@property
|
||||
def is_urban_decentral(self) -> bool:
|
||||
"""
|
||||
Returns whether the heat system is for urban decentralized areas.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
True if the heat system is for urban decentralized areas, False otherwise.
|
||||
"""
|
||||
if (
|
||||
self == HeatSystem.RESIDENTIAL_URBAN_DECENTRAL
|
||||
or self == HeatSystem.SERVICES_URBAN_DECENTRAL
|
||||
):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
@property
|
||||
def is_urban(self) -> bool:
|
||||
"""
|
||||
Returns whether the heat system is for urban areas.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool True if the heat system is for urban areas, False otherwise.
|
||||
"""
|
||||
return not self.is_rural
|
||||
|
||||
def heat_demand_weighting(self, urban_fraction=None, dist_fraction=None) -> float:
|
||||
"""
|
||||
Calculates the heat demand weighting based on urban fraction and
|
||||
distribution fraction.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
urban_fraction : float, optional
|
||||
The fraction of urban heat demand.
|
||||
dist_fraction : float, optional
|
||||
The fraction of distributed heat demand.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
The heat demand weighting.
|
||||
|
||||
Raises
|
||||
------
|
||||
RuntimeError
|
||||
If the heat system is invalid.
|
||||
"""
|
||||
if "rural" in self.value:
|
||||
return 1 - urban_fraction
|
||||
elif "urban central" in self.value:
|
||||
return dist_fraction
|
||||
elif "urban decentral" in self.value:
|
||||
return urban_fraction - dist_fraction
|
||||
else:
|
||||
raise RuntimeError(f"Invalid heat system: {self}")
|
||||
|
||||
def heat_pump_costs_name(self, heat_source: str) -> str:
|
||||
"""
|
||||
Generates the name for the heat pump costs based on the heat source and
|
||||
system.
|
||||
Used to retrieve data from `technology-data <https://github.com/PyPSA/technology-data>`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
heat_source : str
|
||||
The heat source.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
The name for the heat pump costs.
|
||||
"""
|
||||
return f"{self.central_or_decentral} {heat_source}-sourced heat pump"
|
||||
|
||||
@property
|
||||
def resistive_heater_costs_name(self) -> str:
|
||||
"""
|
||||
Generates the name for the resistive heater costs based on the heat
|
||||
system.
|
||||
Used to retrieve data from `technology-data <https://github.com/PyPSA/technology-data>`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
The name for the heater costs.
|
||||
"""
|
||||
return f"{self.central_or_decentral} resistive heater"
|
||||
|
||||
@property
|
||||
def gas_boiler_costs_name(self) -> str:
|
||||
"""
|
||||
Generates the name for the gas boiler costs based on the heat system.
|
||||
Used to retrieve data from `technology-data <https://github.com/PyPSA/technology-data>`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
The name for the gas boiler costs.
|
||||
"""
|
||||
return f"{self.central_or_decentral} gas boiler"
|
||||
|
||||
@property
|
||||
def oil_boiler_costs_name(self) -> str:
|
||||
"""
|
||||
Generates the name for the oil boiler costs based on the heat system.
|
||||
Used to retrieve data from `technology-data <https://github.com/PyPSA/technology-data>`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
The name for the oil boiler costs.
|
||||
"""
|
||||
return "decentral oil boiler"
|
||||
35
scripts/definitions/heat_system_type.py
Normal file
35
scripts/definitions/heat_system_type.py
Normal file
@ -0,0 +1,35 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class HeatSystemType(Enum):
|
||||
"""
|
||||
Enumeration representing different types of heat systems.
|
||||
"""
|
||||
|
||||
URBAN_CENTRAL = "urban central"
|
||||
URBAN_DECENTRAL = "urban decentral"
|
||||
RURAL = "rural"
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""
|
||||
Returns the string representation of the heat system type.
|
||||
|
||||
Returns:
|
||||
str: The string representation of the heat system type.
|
||||
"""
|
||||
return self.value
|
||||
|
||||
@property
|
||||
def is_central(self) -> bool:
|
||||
"""
|
||||
Returns whether the heat system type is central.
|
||||
|
||||
Returns:
|
||||
bool: True if the heat system type is central, False otherwise.
|
||||
"""
|
||||
return self == HeatSystemType.URBAN_CENTRAL
|
||||
@ -1,95 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
"""
|
||||
Extracts capacities of HVDC links from `Wikipedia.
|
||||
|
||||
<https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_.
|
||||
|
||||
Relevant Settings
|
||||
-----------------
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
enable:
|
||||
prepare_links_p_nom:
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config/config.yaml`` at
|
||||
:ref:`toplevel_cf`
|
||||
|
||||
Inputs
|
||||
------
|
||||
|
||||
*None*
|
||||
|
||||
Outputs
|
||||
-------
|
||||
|
||||
- ``data/links_p_nom.csv``: A plain download of https://en.wikipedia.org/wiki/List_of_HVDC_projects#Europe plus extracted coordinates.
|
||||
|
||||
Description
|
||||
-----------
|
||||
|
||||
*None*
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
import pandas as pd
|
||||
from _helpers import configure_logging, set_scenario_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def multiply(s):
|
||||
return s.str[0].astype(float) * s.str[1].astype(float)
|
||||
|
||||
|
||||
def extract_coordinates(s):
|
||||
regex = (
|
||||
r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(N|S) " r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(E|W)"
|
||||
)
|
||||
e = s.str.extract(regex, expand=True)
|
||||
lat = (
|
||||
e[0].astype(float) + (e[1].astype(float) + e[2].astype(float) / 60.0) / 60.0
|
||||
) * e[3].map({"N": +1.0, "S": -1.0})
|
||||
lon = (
|
||||
e[4].astype(float) + (e[5].astype(float) + e[6].astype(float) / 60.0) / 60.0
|
||||
) * e[7].map({"E": +1.0, "W": -1.0})
|
||||
return lon, lat
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake # rule must be enabled in config
|
||||
|
||||
snakemake = mock_snakemake("prepare_links_p_nom", simpl="")
|
||||
configure_logging(snakemake)
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
links_p_nom = pd.read_html(
|
||||
"https://en.wikipedia.org/wiki/List_of_HVDC_projects", header=0, match="SwePol"
|
||||
)[0]
|
||||
|
||||
mw = "Power (MW)"
|
||||
m_b = links_p_nom[mw].str.contains("x").fillna(False)
|
||||
|
||||
links_p_nom.loc[m_b, mw] = links_p_nom.loc[m_b, mw].str.split("x").pipe(multiply)
|
||||
links_p_nom[mw] = (
|
||||
links_p_nom[mw].str.extract("[-/]?([\d.]+)", expand=False).astype(float)
|
||||
)
|
||||
|
||||
links_p_nom["x1"], links_p_nom["y1"] = extract_coordinates(
|
||||
links_p_nom["Converterstation 1"]
|
||||
)
|
||||
links_p_nom["x2"], links_p_nom["y2"] = extract_coordinates(
|
||||
links_p_nom["Converterstation 2"]
|
||||
)
|
||||
|
||||
links_p_nom.dropna(subset=["x1", "y1", "x2", "y2"]).to_csv(
|
||||
snakemake.output[0], index=False
|
||||
)
|
||||
@ -37,6 +37,10 @@ from pypsa.geo import haversine_pts
|
||||
from pypsa.io import import_components_from_dataframe
|
||||
from scipy.stats import beta
|
||||
|
||||
from scripts.definitions.heat_sector import HeatSector
|
||||
from scripts.definitions.heat_system import HeatSystem
|
||||
from scripts.definitions.heat_system_type import HeatSystemType
|
||||
|
||||
spatial = SimpleNamespace()
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -56,19 +60,27 @@ def define_spatial(nodes, options):
|
||||
# biomass
|
||||
|
||||
spatial.biomass = SimpleNamespace()
|
||||
spatial.msw = SimpleNamespace()
|
||||
|
||||
if options.get("biomass_spatial", options["biomass_transport"]):
|
||||
spatial.biomass.nodes = nodes + " solid biomass"
|
||||
spatial.biomass.bioliquids = nodes + " bioliquids"
|
||||
spatial.biomass.locations = nodes
|
||||
spatial.biomass.industry = nodes + " solid biomass for industry"
|
||||
spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
|
||||
spatial.msw.nodes = nodes + " municipal solid waste"
|
||||
spatial.msw.locations = nodes
|
||||
else:
|
||||
spatial.biomass.nodes = ["EU solid biomass"]
|
||||
spatial.biomass.bioliquids = ["EU unsustainable bioliquids"]
|
||||
spatial.biomass.locations = ["EU"]
|
||||
spatial.biomass.industry = ["solid biomass for industry"]
|
||||
spatial.biomass.industry_cc = ["solid biomass for industry CC"]
|
||||
spatial.msw.nodes = ["EU municipal solid waste"]
|
||||
spatial.msw.locations = ["EU"]
|
||||
|
||||
spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)
|
||||
spatial.msw.df = pd.DataFrame(vars(spatial.msw), index=nodes)
|
||||
|
||||
# co2
|
||||
|
||||
@ -2102,7 +2114,7 @@ def build_heat_demand(n):
|
||||
.unstack(level=1)
|
||||
)
|
||||
|
||||
sectors = ["residential", "services"]
|
||||
sectors = [sector.value for sector in HeatSector]
|
||||
uses = ["water", "space"]
|
||||
|
||||
heat_demand = {}
|
||||
@ -2130,10 +2142,21 @@ def build_heat_demand(n):
|
||||
return heat_demand
|
||||
|
||||
|
||||
def add_heat(n, costs):
|
||||
def add_heat(n: pypsa.Network, costs: pd.DataFrame, cop: xr.DataArray):
|
||||
"""
|
||||
Add heat sector to the network.
|
||||
|
||||
Parameters:
|
||||
n (pypsa.Network): The PyPSA network object.
|
||||
costs (pd.DataFrame): DataFrame containing cost information.
|
||||
cop (xr.DataArray): DataArray containing coefficient of performance (COP) values.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
logger.info("Add heat sector")
|
||||
|
||||
sectors = ["residential", "services"]
|
||||
sectors = [sector.value for sector in HeatSector]
|
||||
|
||||
heat_demand = build_heat_demand(n)
|
||||
|
||||
@ -2152,23 +2175,6 @@ def add_heat(n, costs):
|
||||
for sector in sectors:
|
||||
heat_demand[sector + " space"] = (1 - dE) * heat_demand[sector + " space"]
|
||||
|
||||
heat_systems = [
|
||||
"residential rural",
|
||||
"services rural",
|
||||
"residential urban decentral",
|
||||
"services urban decentral",
|
||||
"urban central",
|
||||
]
|
||||
|
||||
cop = {
|
||||
"air": xr.open_dataarray(snakemake.input.cop_air_total)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots),
|
||||
"ground": xr.open_dataarray(snakemake.input.cop_soil_total)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots),
|
||||
}
|
||||
|
||||
if options["solar_thermal"]:
|
||||
solar_thermal = (
|
||||
xr.open_dataarray(snakemake.input.solar_thermal_total)
|
||||
@ -2178,31 +2184,34 @@ def add_heat(n, costs):
|
||||
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
|
||||
solar_thermal = options["solar_cf_correction"] * solar_thermal / 1e3
|
||||
|
||||
for name in heat_systems:
|
||||
name_type = "central" if name == "urban central" else "decentral"
|
||||
for (
|
||||
heat_system
|
||||
) in (
|
||||
HeatSystem
|
||||
): # this loops through all heat systems defined in _entities.HeatSystem
|
||||
|
||||
if name == "urban central":
|
||||
if heat_system == HeatSystem.URBAN_CENTRAL:
|
||||
nodes = dist_fraction.index[dist_fraction > 0]
|
||||
else:
|
||||
nodes = pop_layout.index
|
||||
|
||||
n.add("Carrier", name + " heat")
|
||||
n.add("Carrier", f"{heat_system} heat")
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
nodes + f" {name} heat",
|
||||
nodes + f" {heat_system.value} heat",
|
||||
location=nodes,
|
||||
carrier=name + " heat",
|
||||
carrier=f"{heat_system.value} heat",
|
||||
unit="MWh_th",
|
||||
)
|
||||
|
||||
if name == "urban central" and options.get("central_heat_vent"):
|
||||
if heat_system == HeatSystem.URBAN_CENTRAL and options.get("central_heat_vent"):
|
||||
n.madd(
|
||||
"Generator",
|
||||
nodes + f" {name} heat vent",
|
||||
bus=nodes + f" {name} heat",
|
||||
nodes + f" {heat_system} heat vent",
|
||||
bus=nodes + f" {heat_system} heat",
|
||||
location=nodes,
|
||||
carrier=name + " heat vent",
|
||||
carrier=f"{heat_system} heat vent",
|
||||
p_nom_extendable=True,
|
||||
p_max_pu=0,
|
||||
p_min_pu=-1,
|
||||
@ -2210,30 +2219,24 @@ def add_heat(n, costs):
|
||||
)
|
||||
|
||||
## Add heat load
|
||||
factor = heat_system.heat_demand_weighting(
|
||||
urban_fraction=urban_fraction[nodes], dist_fraction=dist_fraction[nodes]
|
||||
)
|
||||
if not heat_system == HeatSystem.URBAN_CENTRAL:
|
||||
heat_load = (
|
||||
heat_demand[
|
||||
[
|
||||
heat_system.sector.value + " water",
|
||||
heat_system.sector.value + " space",
|
||||
]
|
||||
]
|
||||
.T.groupby(level=1)
|
||||
.sum()
|
||||
.T[nodes]
|
||||
.multiply(factor)
|
||||
)
|
||||
|
||||
for sector in sectors:
|
||||
# heat demand weighting
|
||||
if "rural" in name:
|
||||
factor = 1 - urban_fraction[nodes]
|
||||
elif "urban central" in name:
|
||||
factor = dist_fraction[nodes]
|
||||
elif "urban decentral" in name:
|
||||
factor = urban_fraction[nodes] - dist_fraction[nodes]
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f" {name} not in " f"heat systems: {heat_systems}"
|
||||
)
|
||||
|
||||
if sector in name:
|
||||
heat_load = (
|
||||
heat_demand[[sector + " water", sector + " space"]]
|
||||
.T.groupby(level=1)
|
||||
.sum()
|
||||
.T[nodes]
|
||||
.multiply(factor)
|
||||
)
|
||||
|
||||
if name == "urban central":
|
||||
if heat_system == HeatSystem.URBAN_CENTRAL:
|
||||
heat_load = (
|
||||
heat_demand.T.groupby(level=1)
|
||||
.sum()
|
||||
@ -2246,20 +2249,25 @@ def add_heat(n, costs):
|
||||
n.madd(
|
||||
"Load",
|
||||
nodes,
|
||||
suffix=f" {name} heat",
|
||||
bus=nodes + f" {name} heat",
|
||||
carrier=name + " heat",
|
||||
suffix=f" {heat_system} heat",
|
||||
bus=nodes + f" {heat_system} heat",
|
||||
carrier=f"{heat_system} heat",
|
||||
p_set=heat_load,
|
||||
)
|
||||
|
||||
## Add heat pumps
|
||||
|
||||
heat_pump_types = ["air"] if "urban" in name else ["ground", "air"]
|
||||
|
||||
for heat_pump_type in heat_pump_types:
|
||||
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
|
||||
for heat_source in snakemake.params.heat_pump_sources[
|
||||
heat_system.system_type.value
|
||||
]:
|
||||
costs_name = heat_system.heat_pump_costs_name(heat_source)
|
||||
efficiency = (
|
||||
cop[heat_pump_type][nodes]
|
||||
cop.sel(
|
||||
heat_system=heat_system.system_type.value,
|
||||
heat_source=heat_source,
|
||||
name=nodes,
|
||||
)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots)
|
||||
if options["time_dep_hp_cop"]
|
||||
else costs.at[costs_name, "efficiency"]
|
||||
)
|
||||
@ -2267,10 +2275,10 @@ def add_heat(n, costs):
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
suffix=f" {name} {heat_pump_type} heat pump",
|
||||
suffix=f" {heat_system} {heat_source} heat pump",
|
||||
bus0=nodes,
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=f"{name} {heat_pump_type} heat pump",
|
||||
bus1=nodes + f" {heat_system} heat",
|
||||
carrier=f"{heat_system} {heat_source} heat pump",
|
||||
efficiency=efficiency,
|
||||
capital_cost=costs.at[costs_name, "efficiency"]
|
||||
* costs.at[costs_name, "fixed"]
|
||||
@ -2280,59 +2288,65 @@ def add_heat(n, costs):
|
||||
)
|
||||
|
||||
if options["tes"]:
|
||||
n.add("Carrier", name + " water tanks")
|
||||
n.add("Carrier", f"{heat_system} water tanks")
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
nodes + f" {name} water tanks",
|
||||
nodes + f" {heat_system} water tanks",
|
||||
location=nodes,
|
||||
carrier=name + " water tanks",
|
||||
carrier=f"{heat_system} water tanks",
|
||||
unit="MWh_th",
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {name} water tanks charger",
|
||||
bus0=nodes + f" {name} heat",
|
||||
bus1=nodes + f" {name} water tanks",
|
||||
nodes + f" {heat_system} water tanks charger",
|
||||
bus0=nodes + f" {heat_system} heat",
|
||||
bus1=nodes + f" {heat_system} water tanks",
|
||||
efficiency=costs.at["water tank charger", "efficiency"],
|
||||
carrier=name + " water tanks charger",
|
||||
carrier=f"{heat_system} water tanks charger",
|
||||
p_nom_extendable=True,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {name} water tanks discharger",
|
||||
bus0=nodes + f" {name} water tanks",
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=name + " water tanks discharger",
|
||||
nodes + f" {heat_system} water tanks discharger",
|
||||
bus0=nodes + f" {heat_system} water tanks",
|
||||
bus1=nodes + f" {heat_system} heat",
|
||||
carrier=f"{heat_system} water tanks discharger",
|
||||
efficiency=costs.at["water tank discharger", "efficiency"],
|
||||
p_nom_extendable=True,
|
||||
)
|
||||
|
||||
tes_time_constant_days = options["tes_tau"][name_type]
|
||||
tes_time_constant_days = options["tes_tau"][
|
||||
heat_system.central_or_decentral
|
||||
]
|
||||
|
||||
n.madd(
|
||||
"Store",
|
||||
nodes + f" {name} water tanks",
|
||||
bus=nodes + f" {name} water tanks",
|
||||
nodes + f" {heat_system} water tanks",
|
||||
bus=nodes + f" {heat_system} water tanks",
|
||||
e_cyclic=True,
|
||||
e_nom_extendable=True,
|
||||
carrier=name + " water tanks",
|
||||
carrier=f"{heat_system} water tanks",
|
||||
standing_loss=1 - np.exp(-1 / 24 / tes_time_constant_days),
|
||||
capital_cost=costs.at[name_type + " water tank storage", "fixed"],
|
||||
lifetime=costs.at[name_type + " water tank storage", "lifetime"],
|
||||
capital_cost=costs.at[
|
||||
heat_system.central_or_decentral + " water tank storage", "fixed"
|
||||
],
|
||||
lifetime=costs.at[
|
||||
heat_system.central_or_decentral + " water tank storage", "lifetime"
|
||||
],
|
||||
)
|
||||
|
||||
if options["resistive_heaters"]:
|
||||
key = f"{name_type} resistive heater"
|
||||
key = f"{heat_system.central_or_decentral} resistive heater"
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {name} resistive heater",
|
||||
nodes + f" {heat_system} resistive heater",
|
||||
bus0=nodes,
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=name + " resistive heater",
|
||||
bus1=nodes + f" {heat_system} heat",
|
||||
carrier=f"{heat_system} resistive heater",
|
||||
efficiency=costs.at[key, "efficiency"],
|
||||
capital_cost=costs.at[key, "efficiency"]
|
||||
* costs.at[key, "fixed"]
|
||||
@ -2342,16 +2356,16 @@ def add_heat(n, costs):
|
||||
)
|
||||
|
||||
if options["boilers"]:
|
||||
key = f"{name_type} gas boiler"
|
||||
key = f"{heat_system.central_or_decentral} gas boiler"
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {name} gas boiler",
|
||||
nodes + f" {heat_system} gas boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes + f" {name} heat",
|
||||
bus1=nodes + f" {heat_system} heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=name + " gas boiler",
|
||||
carrier=f"{heat_system} gas boiler",
|
||||
efficiency=costs.at[key, "efficiency"],
|
||||
efficiency2=costs.at["gas", "CO2 intensity"],
|
||||
capital_cost=costs.at[key, "efficiency"]
|
||||
@ -2361,22 +2375,26 @@ def add_heat(n, costs):
|
||||
)
|
||||
|
||||
if options["solar_thermal"]:
|
||||
n.add("Carrier", name + " solar thermal")
|
||||
n.add("Carrier", f"{heat_system} solar thermal")
|
||||
|
||||
n.madd(
|
||||
"Generator",
|
||||
nodes,
|
||||
suffix=f" {name} solar thermal collector",
|
||||
bus=nodes + f" {name} heat",
|
||||
carrier=name + " solar thermal",
|
||||
suffix=f" {heat_system} solar thermal collector",
|
||||
bus=nodes + f" {heat_system} heat",
|
||||
carrier=f"{heat_system} solar thermal",
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at[name_type + " solar thermal", "fixed"]
|
||||
capital_cost=costs.at[
|
||||
heat_system.central_or_decentral + " solar thermal", "fixed"
|
||||
]
|
||||
* overdim_factor,
|
||||
p_max_pu=solar_thermal[nodes],
|
||||
lifetime=costs.at[name_type + " solar thermal", "lifetime"],
|
||||
lifetime=costs.at[
|
||||
heat_system.central_or_decentral + " solar thermal", "lifetime"
|
||||
],
|
||||
)
|
||||
|
||||
if options["chp"] and name == "urban central":
|
||||
if options["chp"] and heat_system == HeatSystem.URBAN_CENTRAL:
|
||||
# add gas CHP; biomass CHP is added in biomass section
|
||||
n.madd(
|
||||
"Link",
|
||||
@ -2433,16 +2451,20 @@ def add_heat(n, costs):
|
||||
lifetime=costs.at["central gas CHP", "lifetime"],
|
||||
)
|
||||
|
||||
if options["chp"] and options["micro_chp"] and name != "urban central":
|
||||
if (
|
||||
options["chp"]
|
||||
and options["micro_chp"]
|
||||
and heat_system.value != "urban central"
|
||||
):
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {name} micro gas CHP",
|
||||
nodes + f" {heat_system} micro gas CHP",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes,
|
||||
bus2=nodes + f" {name} heat",
|
||||
bus2=nodes + f" {heat_system} heat",
|
||||
bus3="co2 atmosphere",
|
||||
carrier=name + " micro gas CHP",
|
||||
carrier=heat_system.value + " micro gas CHP",
|
||||
efficiency=costs.at["micro CHP", "efficiency"],
|
||||
efficiency2=costs.at["micro CHP", "efficiency-heat"],
|
||||
efficiency3=costs.at["gas", "CO2 intensity"],
|
||||
@ -2478,7 +2500,7 @@ def add_heat(n, costs):
|
||||
) / heat_demand.T.groupby(level=[1]).sum().T
|
||||
|
||||
for name in n.loads[
|
||||
n.loads.carrier.isin([x + " heat" for x in heat_systems])
|
||||
n.loads.carrier.isin([x + " heat" for x in HeatSystem])
|
||||
].index:
|
||||
node = n.buses.loc[name, "location"]
|
||||
ct = pop_layout.loc[node, "ct"]
|
||||
@ -2632,19 +2654,83 @@ def add_biomass(n, costs):
|
||||
biogas_potentials_spatial = biomass_potentials["biogas"].rename(
|
||||
index=lambda x: x + " biogas"
|
||||
)
|
||||
unsustainable_biogas_potentials_spatial = biomass_potentials[
|
||||
"unsustainable biogas"
|
||||
].rename(index=lambda x: x + " biogas")
|
||||
else:
|
||||
biogas_potentials_spatial = biomass_potentials["biogas"].sum()
|
||||
unsustainable_biogas_potentials_spatial = biomass_potentials[
|
||||
"unsustainable biogas"
|
||||
].sum()
|
||||
|
||||
if options.get("biomass_spatial", options["biomass_transport"]):
|
||||
solid_biomass_potentials_spatial = biomass_potentials["solid biomass"].rename(
|
||||
index=lambda x: x + " solid biomass"
|
||||
)
|
||||
msw_biomass_potentials_spatial = biomass_potentials[
|
||||
"municipal solid waste"
|
||||
].rename(index=lambda x: x + " municipal solid waste")
|
||||
unsustainable_solid_biomass_potentials_spatial = biomass_potentials[
|
||||
"unsustainable solid biomass"
|
||||
].rename(index=lambda x: x + " solid biomass")
|
||||
|
||||
else:
|
||||
solid_biomass_potentials_spatial = biomass_potentials["solid biomass"].sum()
|
||||
msw_biomass_potentials_spatial = biomass_potentials[
|
||||
"municipal solid waste"
|
||||
].sum()
|
||||
unsustainable_solid_biomass_potentials_spatial = biomass_potentials[
|
||||
"unsustainable solid biomass"
|
||||
].sum()
|
||||
|
||||
if options["regional_oil_demand"]:
|
||||
unsustainable_liquid_biofuel_potentials_spatial = biomass_potentials[
|
||||
"unsustainable bioliquids"
|
||||
].rename(index=lambda x: x + " bioliquids")
|
||||
else:
|
||||
unsustainable_liquid_biofuel_potentials_spatial = biomass_potentials[
|
||||
"unsustainable bioliquids"
|
||||
].sum()
|
||||
|
||||
n.add("Carrier", "biogas")
|
||||
n.add("Carrier", "solid biomass")
|
||||
|
||||
if (
|
||||
options["municipal_solid_waste"]
|
||||
and not options["industry"]
|
||||
and cf_industry["waste_to_energy"]
|
||||
or cf_industry["waste_to_energy_cc"]
|
||||
):
|
||||
logger.warning(
|
||||
"Flag municipal_solid_waste can be only used with industry "
|
||||
"sector waste to energy."
|
||||
"Setting municipal_solid_waste=False."
|
||||
)
|
||||
options["municipal_solid_waste"] = False
|
||||
|
||||
if options["municipal_solid_waste"]:
|
||||
|
||||
n.add("Carrier", "municipal solid waste")
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
spatial.msw.nodes,
|
||||
location=spatial.msw.locations,
|
||||
carrier="municipal solid waste",
|
||||
)
|
||||
|
||||
e_max_pu = pd.Series([1] * (len(n.snapshots) - 1) + [0], index=n.snapshots)
|
||||
n.madd(
|
||||
"Store",
|
||||
spatial.msw.nodes,
|
||||
bus=spatial.msw.nodes,
|
||||
carrier="municipal solid waste",
|
||||
e_nom=msw_biomass_potentials_spatial,
|
||||
marginal_cost=0, # costs.at["municipal solid waste", "fuel"],
|
||||
e_max_pu=e_max_pu,
|
||||
e_initial=msw_biomass_potentials_spatial,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
spatial.gas.biogas,
|
||||
@ -2729,6 +2815,81 @@ def add_biomass(n, costs):
|
||||
p_nom_extendable=True,
|
||||
)
|
||||
|
||||
if biomass_potentials.filter(like="unsustainable").sum().sum() > 0:
|
||||
|
||||
# Create timeseries to force usage of unsustainable potentials
|
||||
e_max_pu = pd.DataFrame(1, index=n.snapshots, columns=spatial.gas.biogas)
|
||||
e_max_pu.iloc[-1] = 0
|
||||
|
||||
n.madd(
|
||||
"Store",
|
||||
spatial.gas.biogas,
|
||||
suffix=" unsustainable",
|
||||
bus=spatial.gas.biogas,
|
||||
carrier="unsustainable biogas",
|
||||
e_nom=unsustainable_biogas_potentials_spatial,
|
||||
marginal_cost=costs.at["biogas", "fuel"],
|
||||
e_initial=unsustainable_biogas_potentials_spatial,
|
||||
e_nom_extendable=False,
|
||||
e_max_pu=e_max_pu,
|
||||
)
|
||||
|
||||
e_max_pu = pd.DataFrame(1, index=n.snapshots, columns=spatial.biomass.nodes)
|
||||
e_max_pu.iloc[-1] = 0
|
||||
|
||||
n.madd(
|
||||
"Store",
|
||||
spatial.biomass.nodes,
|
||||
suffix=" unsustainable",
|
||||
bus=spatial.biomass.nodes,
|
||||
carrier="unsustainable solid biomass",
|
||||
e_nom=unsustainable_solid_biomass_potentials_spatial,
|
||||
marginal_cost=costs.at["fuelwood", "fuel"],
|
||||
e_initial=unsustainable_solid_biomass_potentials_spatial,
|
||||
e_nom_extendable=False,
|
||||
e_max_pu=e_max_pu,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
spatial.biomass.bioliquids,
|
||||
location=spatial.biomass.locations,
|
||||
carrier="unsustainable bioliquids",
|
||||
unit="MWh_LHV",
|
||||
)
|
||||
|
||||
e_max_pu = pd.DataFrame(
|
||||
1, index=n.snapshots, columns=spatial.biomass.bioliquids
|
||||
)
|
||||
e_max_pu.iloc[-1] = 0
|
||||
|
||||
n.madd(
|
||||
"Store",
|
||||
spatial.biomass.bioliquids,
|
||||
suffix=" unsustainable",
|
||||
bus=spatial.biomass.bioliquids,
|
||||
carrier="unsustainable bioliquids",
|
||||
e_nom=unsustainable_liquid_biofuel_potentials_spatial,
|
||||
marginal_cost=costs.at["biodiesel crops", "fuel"],
|
||||
e_initial=unsustainable_liquid_biofuel_potentials_spatial,
|
||||
e_nom_extendable=False,
|
||||
e_max_pu=e_max_pu,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
spatial.biomass.bioliquids,
|
||||
bus0=spatial.biomass.bioliquids,
|
||||
bus1=spatial.oil.nodes,
|
||||
bus2="co2 atmosphere",
|
||||
carrier="unsustainable bioliquids",
|
||||
efficiency=1,
|
||||
efficiency2=-costs.at["solid biomass", "CO2 intensity"]
|
||||
+ costs.at["BtL", "CO2 stored"],
|
||||
p_nom=unsustainable_liquid_biofuel_potentials_spatial,
|
||||
marginal_cost=costs.at["BtL", "VOM"],
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
spatial.gas.biogas_to_gas,
|
||||
@ -2800,6 +2961,19 @@ def add_biomass(n, costs):
|
||||
carrier="solid biomass transport",
|
||||
)
|
||||
|
||||
if options["municipal_solid_waste"]:
|
||||
n.madd(
|
||||
"Link",
|
||||
biomass_transport.index,
|
||||
bus0=biomass_transport.bus0 + " municipal solid waste",
|
||||
bus1=biomass_transport.bus1 + " municipal solid waste",
|
||||
p_nom_extendable=False,
|
||||
p_nom=5e4,
|
||||
length=biomass_transport.length.values,
|
||||
marginal_cost=biomass_transport.costs * biomass_transport.length.values,
|
||||
carrier="municipal solid waste transport",
|
||||
)
|
||||
|
||||
elif options["biomass_spatial"]:
|
||||
# add artificial biomass generators at nodes which include transport costs
|
||||
transport_costs = pd.read_csv(
|
||||
@ -2829,6 +3003,26 @@ def add_biomass(n, costs):
|
||||
type="operational_limit",
|
||||
)
|
||||
|
||||
if options["municipal_solid_waste"]:
|
||||
# Add municipal solid waste
|
||||
n.madd(
|
||||
"Generator",
|
||||
spatial.msw.nodes,
|
||||
bus=spatial.msw.nodes,
|
||||
carrier="municipal solid waste",
|
||||
p_nom=10000,
|
||||
marginal_cost=0 # costs.at["municipal solid waste", "fuel"]
|
||||
+ bus_transport_costs * average_distance,
|
||||
)
|
||||
n.add(
|
||||
"GlobalConstraint",
|
||||
"msw limit",
|
||||
carrier_attribute="municipal solid waste",
|
||||
sense="<=",
|
||||
constant=biomass_potentials["municipal solid waste"].sum(),
|
||||
type="operational_limit",
|
||||
)
|
||||
|
||||
# AC buses with district heating
|
||||
urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
|
||||
if not urban_central.empty and options["chp"]:
|
||||
@ -3420,27 +3614,23 @@ def add_industry(n, costs):
|
||||
if options["oil_boilers"]:
|
||||
nodes = pop_layout.index
|
||||
|
||||
for name in [
|
||||
"residential rural",
|
||||
"services rural",
|
||||
"residential urban decentral",
|
||||
"services urban decentral",
|
||||
]:
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {name} oil boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.oil.nodes,
|
||||
bus1=nodes + f" {name} heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=f"{name} oil boiler",
|
||||
efficiency=costs.at["decentral oil boiler", "efficiency"],
|
||||
efficiency2=costs.at["oil", "CO2 intensity"],
|
||||
capital_cost=costs.at["decentral oil boiler", "efficiency"]
|
||||
* costs.at["decentral oil boiler", "fixed"]
|
||||
* options["overdimension_individual_heating"],
|
||||
lifetime=costs.at["decentral oil boiler", "lifetime"],
|
||||
)
|
||||
for heat_system in HeatSystem:
|
||||
if not heat_system == HeatSystem.URBAN_CENTRAL:
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes + f" {heat_system} oil boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.oil.nodes,
|
||||
bus1=nodes + f" {heat_system} heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=f"{heat_system} oil boiler",
|
||||
efficiency=costs.at["decentral oil boiler", "efficiency"],
|
||||
efficiency2=costs.at["oil", "CO2 intensity"],
|
||||
capital_cost=costs.at["decentral oil boiler", "efficiency"]
|
||||
* costs.at["decentral oil boiler", "fixed"]
|
||||
* options["overdimension_individual_heating"],
|
||||
lifetime=costs.at["decentral oil boiler", "lifetime"],
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
@ -3535,6 +3725,17 @@ def add_industry(n, costs):
|
||||
efficiency3=process_co2_per_naphtha,
|
||||
)
|
||||
|
||||
if options.get("biomass", True) and options["municipal_solid_waste"]:
|
||||
n.madd(
|
||||
"Link",
|
||||
spatial.msw.locations,
|
||||
bus0=spatial.msw.nodes,
|
||||
bus1=non_sequestered_hvc_locations,
|
||||
carrier="municipal solid waste",
|
||||
p_nom_extendable=True,
|
||||
efficiency=1.0,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
spatial.oil.demand_locations,
|
||||
@ -4427,13 +4628,14 @@ def add_enhanced_geothermal(n, egs_potentials, egs_overlap, costs):
|
||||
# %%
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"prepare_sector_network",
|
||||
simpl="",
|
||||
opts="",
|
||||
clusters="1",
|
||||
clusters="37",
|
||||
ll="vopt",
|
||||
sector_opts="",
|
||||
planning_horizons="2050",
|
||||
@ -4492,7 +4694,7 @@ if __name__ == "__main__":
|
||||
add_land_transport(n, costs)
|
||||
|
||||
if options["heating"]:
|
||||
add_heat(n, costs)
|
||||
add_heat(n=n, costs=costs, cop=xr.open_dataarray(snakemake.input.cop_profiles))
|
||||
|
||||
if options["biomass"]:
|
||||
add_biomass(n, costs)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user