Merge branch 'master' into jrc-idees-2020
This commit is contained in:
lisazeyen
GitHub
commit
fe9185ef3f
@ -67,7 +67,6 @@ snapshots:
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#enable
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enable:
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retrieve: auto
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prepare_links_p_nom: false
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retrieve_databundle: true
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retrieve_cost_data: true
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build_cutout: false
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@ -370,6 +369,23 @@ biomass:
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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
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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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@ -410,6 +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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@ -492,7 +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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@ -722,7 +754,7 @@ 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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@ -1040,6 +1072,7 @@ 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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@ -53,7 +53,6 @@ extensions = [
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autodoc_mock_imports = [
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"atlite",
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"snakemake",
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"pycountry",
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"rioxarray",
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"country_converter",
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"tabula",
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@ -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
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share_sustainable_potential_available,--,Dictionary with planning horizons as keys., Share determines phase-in of ENSPRESO biomass potentials
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@ -1,6 +1,5 @@
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,Unit,Values,Description
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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."
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prepare_links_p_nom,bool,"{true, false}","Switch to retrieve current HVDC projects from `Wikipedia <https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_"
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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."
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retrieve_cost_data,bool,"{true, false}","Switch to retrieve technology cost data from `technology-data repository <https://github.com/PyPSA/technology-data>`_."
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build_cutout,bool,"{true, false}","Switch to enable the building of cutouts via the rule :mod:`build_cutout`."
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|
@ -1,162 +1,174 @@
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,Unit,Values,Description
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transport,--,"{true, false}",Flag to include transport sector.
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heating,--,"{true, false}",Flag to include heating sector.
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biomass,--,"{true, false}",Flag to include biomass sector.
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industry,--,"{true, false}",Flag to include industry sector.
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agriculture,--,"{true, false}",Flag to include agriculture sector.
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fossil_fuels,--,"{true, false}","Flag to include imports of fossil fuels ( [""coal"", ""gas"", ""oil"", ""lignite""])"
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district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_
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-- potential,--,float,maximum fraction of urban demand which can be supplied by district heating
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-- 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
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-- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses
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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.
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,,,
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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
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bev_dsm_restriction _time,--,float,Time at which SOC of BEV has to be dsm_restriction_value
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transport_heating _deadband_upper,°C,float,"The maximum temperature in the vehicle. At higher temperatures, the energy required for cooling in the vehicle increases."
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transport_heating _deadband_lower,°C,float,"The minimum temperature in the vehicle. At lower temperatures, the energy required for heating in the vehicle increases."
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,,,
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ICE_lower_degree_factor,--,float,Share increase in energy demand in internal combustion engine (ICE) for each degree difference between the cold environment and the minimum temperature.
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ICE_upper_degree_factor,--,float,Share increase in energy demand in internal combustion engine (ICE) for each degree difference between the hot environment and the maximum temperature.
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EV_lower_degree_factor,--,float,Share increase in energy demand in electric vehicles (EV) for each degree difference between the cold environment and the minimum temperature.
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EV_upper_degree_factor,--,float,Share increase in energy demand in electric vehicles (EV) for each degree difference between the hot environment and the maximum temperature.
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bev_dsm,--,"{true, false}",Add the option for battery electric vehicles (BEV) to participate in demand-side management (DSM)
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,,,
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bev_availability,--,float,The share for battery electric vehicles (BEV) that are able to do demand side management (DSM)
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bev_energy,--,float,The average size of battery electric vehicles (BEV) in MWh
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bev_charge_efficiency,--,float,Battery electric vehicles (BEV) charge and discharge efficiency
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bev_charge_rate,MWh,float,The power consumption for one electric vehicle (EV) in MWh. Value derived from 3-phase charger with 11 kW.
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bev_avail_max,--,float,The maximum share plugged-in availability for passenger electric vehicles.
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bev_avail_mean,--,float,The average share plugged-in availability for passenger electric vehicles.
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v2g,--,"{true, false}",Allows feed-in to grid from EV battery
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land_transport_fuel_cell _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses fuel cells in a given year
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land_transport_electric _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses electric vehicles (EV) in a given year
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land_transport_ice _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses internal combustion engines (ICE) in a given year. What is not EV or FCEV is oil-fuelled ICE.
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transport_electric_efficiency,MWh/100km,float,The conversion efficiencies of electric vehicles in transport
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transport_fuel_cell_efficiency,MWh/100km,float,The H2 conversion efficiencies of fuel cells in transport
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transport_ice_efficiency,MWh/100km,float,The oil conversion efficiencies of internal combustion engine (ICE) in transport
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agriculture_machinery _electric_share,--,float,The share for agricultural machinery that uses electricity
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agriculture_machinery _oil_share,--,float,The share for agricultural machinery that uses oil
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agriculture_machinery _fuel_efficiency,--,float,The efficiency of electric-powered machinery in the conversion of electricity to meet agricultural needs.
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agriculture_machinery _electric_efficiency,--,float,The efficiency of oil-powered machinery in the conversion of oil to meet agricultural needs.
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Mwh_MeOH_per_MWh_H2,LHV,float,"The energy amount of the produced methanol per energy amount of hydrogen. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, page 64."
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MWh_MeOH_per_tCO2,LHV,float,"The energy amount of the produced methanol per ton of CO2. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, page 66."
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MWh_MeOH_per_MWh_e,LHV,float,"The energy amount of the produced methanol per energy amount of electricity. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, page 64."
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shipping_hydrogen _liquefaction,--,"{true, false}",Whether to include liquefaction costs for hydrogen demand in shipping.
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,,,
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shipping_hydrogen_share,--,Dictionary with planning horizons as keys.,The share of ships powered by hydrogen in a given year
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shipping_methanol_share,--,Dictionary with planning horizons as keys.,The share of ships powered by methanol in a given year
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shipping_oil_share,--,Dictionary with planning horizons as keys.,The share of ships powered by oil in a given year
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shipping_methanol _efficiency,--,float,The efficiency of methanol-powered ships in the conversion of methanol to meet shipping needs (propulsion). The efficiency increase from oil can be 10-15% higher according to the `IEA <https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf>`_
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,,,
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shipping_oil_efficiency,--,float,The efficiency of oil-powered ships in the conversion of oil to meet shipping needs (propulsion). Base value derived from 2011
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aviation_demand_factor,--,float,The proportion of demand for aviation compared to today's consumption
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HVC_demand_factor,--,float,The proportion of demand for high-value chemicals compared to today's consumption
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,,,
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time_dep_hp_cop,--,"{true, false}",Consider the time dependent coefficient of performance (COP) of the heat pump
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heat_pump_sink_T,°C,float,The temperature heat sink used in heat pumps based on DTU / large area radiators. The value is conservatively high to cover hot water and space heating in poorly-insulated buildings
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reduce_space_heat _exogenously,--,"{true, false}",Influence on space heating demand by a certain factor (applied before losses in district heating).
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reduce_space_heat _exogenously_factor,--,Dictionary with planning horizons as keys.,"A positive factor can mean renovation or demolition of a building. If the factor is negative, it can mean an increase in floor area, increased thermal comfort, population growth. The default factors are determined by the `Eurocalc Homes and buildings decarbonization scenario <http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221>`_"
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retrofitting,,,
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-- retro_endogen,--,"{true, false}",Add retrofitting as an endogenous system which co-optimise space heat savings.
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-- cost_factor,--,float,Weight costs for building renovation
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-- interest_rate,--,float,The interest rate for investment in building components
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-- annualise_cost,--,"{true, false}",Annualise the investment costs of retrofitting
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-- tax_weighting,--,"{true, false}",Weight the costs of retrofitting depending on taxes in countries
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-- construction_index,--,"{true, false}",Weight the costs of retrofitting depending on labour/material costs per country
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tes,--,"{true, false}",Add option for storing thermal energy in large water pits associated with district heating systems and individual thermal energy storage (TES)
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tes_tau,,,The time constant used to calculate the decay of thermal energy in thermal energy storage (TES): 1- :math:`e^{-1/24τ}`.
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-- decentral,days,float,The time constant in decentralized thermal energy storage (TES)
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-- central,days,float,The time constant in centralized thermal energy storage (TES)
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boilers,--,"{true, false}",Add option for transforming gas into heat using gas boilers
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resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
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oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers
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biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers
|
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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.
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chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP)
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micro_chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) for decentral areas.
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solar_thermal,--,"{true, false}",Add option for using solar thermal to generate heat.
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solar_cf_correction,--,float,The correction factor for the value provided by the solar thermal profile calculations
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marginal_cost_storage,currency/MWh ,float,The marginal cost of discharging batteries in distributed grids
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methanation,--,"{true, false}",Add option for transforming hydrogen and CO2 into methane using methanation.
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coal_cc,--,"{true, false}",Add option for coal CHPs with carbon capture
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dac,--,"{true, false}",Add option for Direct Air Capture (DAC)
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co2_vent,--,"{true, false}",Add option for vent out CO2 from storages to the atmosphere.
|
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allam_cycle,--,"{true, false}",Add option to include `Allam cycle gas power plants <https://en.wikipedia.org/wiki/Allam_power_cycle>`_
|
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hydrogen_fuel_cell,--,"{true, false}",Add option to include hydrogen fuel cell for re-electrification. Assuming OCGT technology costs
|
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hydrogen_turbine,--,"{true, false}",Add option to include hydrogen turbine for re-electrification. Assuming OCGT technology costs
|
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SMR,--,"{true, false}",Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR)
|
||||
SMR CC,--,"{true, false}",Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR) and Carbon Capture (CC)
|
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regional_methanol_demand,--,"{true, false}",Spatially resolve methanol demand. Set to true if regional CO2 constraints needed.
|
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regional_oil_demand,--,"{true, false}",Spatially resolve oil demand. Set to true if regional CO2 constraints needed.
|
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regional_co2 _sequestration_potential,,,
|
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-- enable,--,"{true, false}",Add option for regionally-resolved geological carbon dioxide sequestration potentials based on `CO2StoP <https://setis.ec.europa.eu/european-co2-storage-database_en>`_.
|
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-- attribute,--,string or list,Name (or list of names) of the attribute(s) for the sequestration potential
|
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-- include_onshore,--,"{true, false}",Add options for including onshore sequestration potentials
|
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-- min_size,Gt ,float,Any sites with lower potential than this value will be excluded
|
||||
-- max_size,Gt ,float,The maximum sequestration potential for any one site.
|
||||
-- years_of_storage,years,float,The years until potential exhausted at optimised annual rate
|
||||
co2_sequestration_potential,MtCO2/a,float,The potential of sequestering CO2 in Europe per year
|
||||
co2_sequestration_cost,currency/tCO2,float,The cost of sequestering a ton of CO2
|
||||
co2_sequestration_lifetime,years,int,The lifetime of a CO2 sequestration site
|
||||
co2_spatial,--,"{true, false}","Add option to spatially resolve carrier representing stored carbon dioxide. This allows for more detailed modelling of CCUTS, e.g. regarding the capturing of industrial process emissions, usage as feedstock for electrofuels, transport of carbon dioxide, and geological sequestration sites."
|
||||
,,,
|
||||
co2network,--,"{true, false}",Add option for planning a new carbon dioxide transmission network
|
||||
co2_network_cost_factor,p.u.,float,The cost factor for the capital cost of the carbon dioxide transmission network
|
||||
,,,
|
||||
cc_fraction,--,float,The default fraction of CO2 captured with post-combustion capture
|
||||
hydrogen_underground _storage,--,"{true, false}",Add options for storing hydrogen underground. Storage potential depends regionally.
|
||||
hydrogen_underground _storage_locations,,"{onshore, nearshore, offshore}","The location where hydrogen underground storage can be located. Onshore, nearshore, offshore means it must be located more than 50 km away from the sea, within 50 km of the sea, or within the sea itself respectively."
|
||||
,,,
|
||||
ammonia,--,"{true, false, regional}","Add ammonia as a carrrier. It can be either true (copperplated NH3), false (no NH3 carrier) or ""regional"" (regionalised NH3 without network)"
|
||||
min_part_load_fischer _tropsch,per unit of p_nom ,float,The minimum unit dispatch (``p_min_pu``) for the Fischer-Tropsch process
|
||||
min_part_load _methanolisation,per unit of p_nom ,float,The minimum unit dispatch (``p_min_pu``) for the methanolisation process
|
||||
,,,
|
||||
use_fischer_tropsch _waste_heat,--,"{true, false}",Add option for using waste heat of Fischer Tropsch in district heating networks
|
||||
use_fuel_cell_waste_heat,--,"{true, false}",Add option for using waste heat of fuel cells in district heating networks
|
||||
use_electrolysis_waste _heat,--,"{true, false}",Add option for using waste heat of electrolysis in district heating networks
|
||||
electricity_transmission _grid,--,"{true, false}",Switch for enabling/disabling the electricity transmission grid.
|
||||
electricity_distribution _grid,--,"{true, false}",Add a simplified representation of the exchange capacity between transmission and distribution grid level through a link.
|
||||
electricity_distribution _grid_cost_factor,,,Multiplies the investment cost of the electricity distribution grid
|
||||
,,,
|
||||
electricity_grid _connection,--,"{true, false}",Add the cost of electricity grid connection for onshore wind and solar
|
||||
transmission_efficiency,,,Section to specify transmission losses or compression energy demands of bidirectional links. Splits them into two capacity-linked unidirectional links.
|
||||
-- {carrier},--,str,The carrier of the link.
|
||||
-- -- efficiency_static,p.u.,float,Length-independent transmission efficiency.
|
||||
-- -- efficiency_per_1000km,p.u. per 1000 km,float,Length-dependent transmission efficiency ($\eta^{\text{length}}$)
|
||||
-- -- compression_per_1000km,p.u. per 1000 km,float,Length-dependent electricity demand for compression ($\eta \cdot \text{length}$) implemented as multi-link to local electricity bus.
|
||||
H2_network,--,"{true, false}",Add option for new hydrogen pipelines
|
||||
gas_network,--,"{true, false}","Add existing natural gas infrastructure, incl. LNG terminals, production and entry-points. The existing gas network is added with a lossless transport model. A length-weighted `k-edge augmentation algorithm <https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation>`_ can be run to add new candidate gas pipelines such that all regions of the model can be connected to the gas network. When activated, all the gas demands are regionally disaggregated as well."
|
||||
H2_retrofit,--,"{true, false}",Add option for retrofiting existing pipelines to transport hydrogen.
|
||||
H2_retrofit_capacity _per_CH4,--,float,"The ratio for H2 capacity per original CH4 capacity of retrofitted pipelines. The `European Hydrogen Backbone (April, 2020) p.15 <https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf>`_ 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity."
|
||||
gas_network_connectivity _upgrade ,--,float,The number of desired edge connectivity (k) in the length-weighted `k-edge augmentation algorithm <https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation>`_ used for the gas network
|
||||
gas_distribution_grid,--,"{true, false}",Add a gas distribution grid
|
||||
gas_distribution_grid _cost_factor,,,Multiplier for the investment cost of the gas distribution grid
|
||||
,,,
|
||||
biomass_spatial,--,"{true, false}",Add option for resolving biomass demand regionally
|
||||
biomass_transport,--,"{true, false}",Add option for transporting solid biomass between nodes
|
||||
biogas_upgrading_cc,--,"{true, false}",Add option to capture CO2 from biomass upgrading
|
||||
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)
|
||||
-- max_growth,,,
|
||||
-- -- {carrier},GW,float,The historic maximum growth of a carrier
|
||||
-- max_relative_growth,,,
|
||||
-- -- {carrier},p.u.,float,The historic maximum relative growth of a carrier
|
||||
,,,
|
||||
enhanced_geothermal,,,
|
||||
-- enable,--,"{true, false}",Add option to include Enhanced Geothermal Systems
|
||||
-- flexible,--,"{true, false}",Add option for flexible operation (see Ricks et al. 2024)
|
||||
-- max_hours,--,int,The maximum hours the reservoir can be charged under flexible operation
|
||||
-- max_boost,--,float,The maximum boost in power output under flexible operation
|
||||
-- var_cf,--,"{true, false}",Add option for variable capacity factor (see Ricks et al. 2024)
|
||||
-- sustainability_factor,--,float,Share of sourced heat that is replenished by the earth's core (see details in `build_egs_potentials.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_egs_potentials.py>`_)
|
||||
solid_biomass_import,,,
|
||||
-- enable,--,"{true, false}",Add option to include solid biomass imports
|
||||
-- price,currency/MWh,float,Price for importing solid biomass
|
||||
-- max_amount,Twh,float,Maximum solid biomass import potential
|
||||
-- upstream_emissions_factor,p.u.,float,Upstream emissions of solid biomass imports
|
||||
,Unit,Values,Description
|
||||
transport,--,"{true, false}",Flag to include transport sector.
|
||||
heating,--,"{true, false}",Flag to include heating sector.
|
||||
biomass,--,"{true, false}",Flag to include biomass sector.
|
||||
industry,--,"{true, false}",Flag to include industry sector.
|
||||
agriculture,--,"{true, false}",Flag to include agriculture sector.
|
||||
fossil_fuels,--,"{true, false}","Flag to include imports of fossil fuels ( [""coal"", ""gas"", ""oil"", ""lignite""])"
|
||||
district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_
|
||||
-- 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
|
||||
bev_dsm_restriction _time,--,float,Time at which SOC of BEV has to be dsm_restriction_value
|
||||
transport_heating _deadband_upper,°C,float,"The maximum temperature in the vehicle. At higher temperatures, the energy required for cooling in the vehicle increases."
|
||||
transport_heating _deadband_lower,°C,float,"The minimum temperature in the vehicle. At lower temperatures, the energy required for heating in the vehicle increases."
|
||||
,,,
|
||||
ICE_lower_degree_factor,--,float,Share increase in energy demand in internal combustion engine (ICE) for each degree difference between the cold environment and the minimum temperature.
|
||||
ICE_upper_degree_factor,--,float,Share increase in energy demand in internal combustion engine (ICE) for each degree difference between the hot environment and the maximum temperature.
|
||||
EV_lower_degree_factor,--,float,Share increase in energy demand in electric vehicles (EV) for each degree difference between the cold environment and the minimum temperature.
|
||||
EV_upper_degree_factor,--,float,Share increase in energy demand in electric vehicles (EV) for each degree difference between the hot environment and the maximum temperature.
|
||||
bev_dsm,--,"{true, false}",Add the option for battery electric vehicles (BEV) to participate in demand-side management (DSM)
|
||||
,,,
|
||||
bev_availability,--,float,The share for battery electric vehicles (BEV) that are able to do demand side management (DSM)
|
||||
bev_energy,--,float,The average size of battery electric vehicles (BEV) in MWh
|
||||
bev_charge_efficiency,--,float,Battery electric vehicles (BEV) charge and discharge efficiency
|
||||
bev_charge_rate,MWh,float,The power consumption for one electric vehicle (EV) in MWh. Value derived from 3-phase charger with 11 kW.
|
||||
bev_avail_max,--,float,The maximum share plugged-in availability for passenger electric vehicles.
|
||||
bev_avail_mean,--,float,The average share plugged-in availability for passenger electric vehicles.
|
||||
v2g,--,"{true, false}",Allows feed-in to grid from EV battery
|
||||
land_transport_fuel_cell _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses fuel cells in a given year
|
||||
land_transport_electric _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses electric vehicles (EV) in a given year
|
||||
land_transport_ice _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses internal combustion engines (ICE) in a given year. What is not EV or FCEV is oil-fuelled ICE.
|
||||
transport_electric_efficiency,MWh/100km,float,The conversion efficiencies of electric vehicles in transport
|
||||
transport_fuel_cell_efficiency,MWh/100km,float,The H2 conversion efficiencies of fuel cells in transport
|
||||
transport_ice_efficiency,MWh/100km,float,The oil conversion efficiencies of internal combustion engine (ICE) in transport
|
||||
agriculture_machinery _electric_share,--,float,The share for agricultural machinery that uses electricity
|
||||
agriculture_machinery _oil_share,--,float,The share for agricultural machinery that uses oil
|
||||
agriculture_machinery _fuel_efficiency,--,float,The efficiency of electric-powered machinery in the conversion of electricity to meet agricultural needs.
|
||||
agriculture_machinery _electric_efficiency,--,float,The efficiency of oil-powered machinery in the conversion of oil to meet agricultural needs.
|
||||
Mwh_MeOH_per_MWh_H2,LHV,float,"The energy amount of the produced methanol per energy amount of hydrogen. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, page 64."
|
||||
MWh_MeOH_per_tCO2,LHV,float,"The energy amount of the produced methanol per ton of CO2. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, page 66."
|
||||
MWh_MeOH_per_MWh_e,LHV,float,"The energy amount of the produced methanol per energy amount of electricity. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, page 64."
|
||||
shipping_hydrogen _liquefaction,--,"{true, false}",Whether to include liquefaction costs for hydrogen demand in shipping.
|
||||
,,,
|
||||
shipping_hydrogen_share,--,Dictionary with planning horizons as keys.,The share of ships powered by hydrogen in a given year
|
||||
shipping_methanol_share,--,Dictionary with planning horizons as keys.,The share of ships powered by methanol in a given year
|
||||
shipping_oil_share,--,Dictionary with planning horizons as keys.,The share of ships powered by oil in a given year
|
||||
shipping_methanol _efficiency,--,float,The efficiency of methanol-powered ships in the conversion of methanol to meet shipping needs (propulsion). The efficiency increase from oil can be 10-15% higher according to the `IEA <https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf>`_
|
||||
,,,
|
||||
shipping_oil_efficiency,--,float,The efficiency of oil-powered ships in the conversion of oil to meet shipping needs (propulsion). Base value derived from 2011
|
||||
aviation_demand_factor,--,float,The proportion of demand for aviation compared to today's consumption
|
||||
HVC_demand_factor,--,float,The proportion of demand for high-value chemicals compared to today's consumption
|
||||
,,,
|
||||
time_dep_hp_cop,--,"{true, false}",Consider the time dependent coefficient of performance (COP) of the heat pump
|
||||
heat_pump_sink_T,°C,float,The temperature heat sink used in heat pumps based on DTU / large area radiators. The value is conservatively high to cover hot water and space heating in poorly-insulated buildings
|
||||
reduce_space_heat _exogenously,--,"{true, false}",Influence on space heating demand by a certain factor (applied before losses in district heating).
|
||||
reduce_space_heat _exogenously_factor,--,Dictionary with planning horizons as keys.,"A positive factor can mean renovation or demolition of a building. If the factor is negative, it can mean an increase in floor area, increased thermal comfort, population growth. The default factors are determined by the `Eurocalc Homes and buildings decarbonization scenario <http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221>`_"
|
||||
retrofitting,,,
|
||||
-- retro_endogen,--,"{true, false}",Add retrofitting as an endogenous system which co-optimise space heat savings.
|
||||
-- cost_factor,--,float,Weight costs for building renovation
|
||||
-- interest_rate,--,float,The interest rate for investment in building components
|
||||
-- annualise_cost,--,"{true, false}",Annualise the investment costs of retrofitting
|
||||
-- tax_weighting,--,"{true, false}",Weight the costs of retrofitting depending on taxes in countries
|
||||
-- construction_index,--,"{true, false}",Weight the costs of retrofitting depending on labour/material costs per country
|
||||
tes,--,"{true, false}",Add option for storing thermal energy in large water pits associated with district heating systems and individual thermal energy storage (TES)
|
||||
tes_tau,,,The time constant used to calculate the decay of thermal energy in thermal energy storage (TES): 1- :math:`e^{-1/24τ}`.
|
||||
-- decentral,days,float,The time constant in decentralized thermal energy storage (TES)
|
||||
-- central,days,float,The time constant in centralized thermal energy storage (TES)
|
||||
boilers,--,"{true, false}",Add option for transforming gas into heat using gas boilers
|
||||
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.
|
||||
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.
|
||||
solar_cf_correction,--,float,The correction factor for the value provided by the solar thermal profile calculations
|
||||
marginal_cost_storage,currency/MWh ,float,The marginal cost of discharging batteries in distributed grids
|
||||
methanation,--,"{true, false}",Add option for transforming hydrogen and CO2 into methane using methanation.
|
||||
coal_cc,--,"{true, false}",Add option for coal CHPs with carbon capture
|
||||
dac,--,"{true, false}",Add option for Direct Air Capture (DAC)
|
||||
co2_vent,--,"{true, false}",Add option for vent out CO2 from storages to the atmosphere.
|
||||
allam_cycle,--,"{true, false}",Add option to include `Allam cycle gas power plants <https://en.wikipedia.org/wiki/Allam_power_cycle>`_
|
||||
hydrogen_fuel_cell,--,"{true, false}",Add option to include hydrogen fuel cell for re-electrification. Assuming OCGT technology costs
|
||||
hydrogen_turbine,--,"{true, false}",Add option to include hydrogen turbine for re-electrification. Assuming OCGT technology costs
|
||||
SMR,--,"{true, false}",Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR)
|
||||
SMR CC,--,"{true, false}",Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR) and Carbon Capture (CC)
|
||||
regional_methanol_demand,--,"{true, false}",Spatially resolve methanol demand. Set to true if regional CO2 constraints needed.
|
||||
regional_oil_demand,--,"{true, false}",Spatially resolve oil demand. Set to true if regional CO2 constraints needed.
|
||||
regional_co2 _sequestration_potential,,,
|
||||
-- enable,--,"{true, false}",Add option for regionally-resolved geological carbon dioxide sequestration potentials based on `CO2StoP <https://setis.ec.europa.eu/european-co2-storage-database_en>`_.
|
||||
-- attribute,--,string or list,Name (or list of names) of the attribute(s) for the sequestration potential
|
||||
-- include_onshore,--,"{true, false}",Add options for including onshore sequestration potentials
|
||||
-- min_size,Gt ,float,Any sites with lower potential than this value will be excluded
|
||||
-- max_size,Gt ,float,The maximum sequestration potential for any one site.
|
||||
-- years_of_storage,years,float,The years until potential exhausted at optimised annual rate
|
||||
co2_sequestration_potential,MtCO2/a,float,The potential of sequestering CO2 in Europe per year
|
||||
co2_sequestration_cost,currency/tCO2,float,The cost of sequestering a ton of CO2
|
||||
co2_sequestration_lifetime,years,int,The lifetime of a CO2 sequestration site
|
||||
co2_spatial,--,"{true, false}","Add option to spatially resolve carrier representing stored carbon dioxide. This allows for more detailed modelling of CCUTS, e.g. regarding the capturing of industrial process emissions, usage as feedstock for electrofuels, transport of carbon dioxide, and geological sequestration sites."
|
||||
,,,
|
||||
co2network,--,"{true, false}",Add option for planning a new carbon dioxide transmission network
|
||||
co2_network_cost_factor,p.u.,float,The cost factor for the capital cost of the carbon dioxide transmission network
|
||||
,,,
|
||||
cc_fraction,--,float,The default fraction of CO2 captured with post-combustion capture
|
||||
hydrogen_underground _storage,--,"{true, false}",Add options for storing hydrogen underground. Storage potential depends regionally.
|
||||
hydrogen_underground _storage_locations,,"{onshore, nearshore, offshore}","The location where hydrogen underground storage can be located. Onshore, nearshore, offshore means it must be located more than 50 km away from the sea, within 50 km of the sea, or within the sea itself respectively."
|
||||
,,,
|
||||
ammonia,--,"{true, false, regional}","Add ammonia as a carrrier. It can be either true (copperplated NH3), false (no NH3 carrier) or ""regional"" (regionalised NH3 without network)"
|
||||
min_part_load_fischer _tropsch,per unit of p_nom ,float,The minimum unit dispatch (``p_min_pu``) for the Fischer-Tropsch process
|
||||
min_part_load _methanolisation,per unit of p_nom ,float,The minimum unit dispatch (``p_min_pu``) for the methanolisation process
|
||||
,,,
|
||||
use_fischer_tropsch _waste_heat,--,"{true, false}",Add option for using waste heat of Fischer Tropsch in district heating networks
|
||||
use_fuel_cell_waste_heat,--,"{true, false}",Add option for using waste heat of fuel cells in district heating networks
|
||||
use_electrolysis_waste _heat,--,"{true, false}",Add option for using waste heat of electrolysis in district heating networks
|
||||
electricity_transmission _grid,--,"{true, false}",Switch for enabling/disabling the electricity transmission grid.
|
||||
electricity_distribution _grid,--,"{true, false}",Add a simplified representation of the exchange capacity between transmission and distribution grid level through a link.
|
||||
electricity_distribution _grid_cost_factor,,,Multiplies the investment cost of the electricity distribution grid
|
||||
,,,
|
||||
electricity_grid _connection,--,"{true, false}",Add the cost of electricity grid connection for onshore wind and solar
|
||||
transmission_efficiency,,,Section to specify transmission losses or compression energy demands of bidirectional links. Splits them into two capacity-linked unidirectional links.
|
||||
-- {carrier},--,str,The carrier of the link.
|
||||
-- -- efficiency_static,p.u.,float,Length-independent transmission efficiency.
|
||||
-- -- efficiency_per_1000km,p.u. per 1000 km,float,Length-dependent transmission efficiency ($\eta^{\text{length}}$)
|
||||
-- -- compression_per_1000km,p.u. per 1000 km,float,Length-dependent electricity demand for compression ($\eta \cdot \text{length}$) implemented as multi-link to local electricity bus.
|
||||
H2_network,--,"{true, false}",Add option for new hydrogen pipelines
|
||||
gas_network,--,"{true, false}","Add existing natural gas infrastructure, incl. LNG terminals, production and entry-points. The existing gas network is added with a lossless transport model. A length-weighted `k-edge augmentation algorithm <https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation>`_ can be run to add new candidate gas pipelines such that all regions of the model can be connected to the gas network. When activated, all the gas demands are regionally disaggregated as well."
|
||||
H2_retrofit,--,"{true, false}",Add option for retrofiting existing pipelines to transport hydrogen.
|
||||
H2_retrofit_capacity _per_CH4,--,float,"The ratio for H2 capacity per original CH4 capacity of retrofitted pipelines. The `European Hydrogen Backbone (April, 2020) p.15 <https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf>`_ 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity."
|
||||
gas_network_connectivity _upgrade ,--,float,The number of desired edge connectivity (k) in the length-weighted `k-edge augmentation algorithm <https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation>`_ used for the gas network
|
||||
gas_distribution_grid,--,"{true, false}",Add a gas distribution grid
|
||||
gas_distribution_grid _cost_factor,,,Multiplier for the investment cost of the gas distribution grid
|
||||
,,,
|
||||
biomass_spatial,--,"{true, false}",Add option for resolving biomass demand regionally
|
||||
biomass_transport,--,"{true, false}",Add option for transporting solid biomass between nodes
|
||||
biogas_upgrading_cc,--,"{true, false}",Add option to capture CO2 from biomass upgrading
|
||||
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)
|
||||
-- max_growth,,,
|
||||
-- -- {carrier},GW,float,The historic maximum growth of a carrier
|
||||
-- max_relative_growth,,,
|
||||
-- -- {carrier},p.u.,float,The historic maximum relative growth of a carrier
|
||||
,,,
|
||||
enhanced_geothermal,,,
|
||||
-- enable,--,"{true, false}",Add option to include Enhanced Geothermal Systems
|
||||
-- flexible,--,"{true, false}",Add option for flexible operation (see Ricks et al. 2024)
|
||||
-- max_hours,--,int,The maximum hours the reservoir can be charged under flexible operation
|
||||
-- max_boost,--,float,The maximum boost in power output under flexible operation
|
||||
-- var_cf,--,"{true, false}",Add option for variable capacity factor (see Ricks et al. 2024)
|
||||
-- sustainability_factor,--,float,Share of sourced heat that is replenished by the earth's core (see details in `build_egs_potentials.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_egs_potentials.py>`_)
|
||||
solid_biomass_import,,,
|
||||
-- enable,--,"{true, false}",Add option to include solid biomass imports
|
||||
-- price,currency/MWh,float,Price for importing solid biomass
|
||||
-- max_amount,Twh,float,Maximum solid biomass import potential
|
||||
-- upstream_emissions_factor,p.u.,float,Upstream emissions of solid biomass imports
|
||||
|
||||
|
@ -41,11 +41,6 @@ Rule ``build_cutout``
|
||||
.. automodule:: build_cutout
|
||||
|
||||
|
||||
Rule ``prepare_links_p_nom``
|
||||
===============================
|
||||
|
||||
.. automodule:: prepare_links_p_nom
|
||||
|
||||
.. _base:
|
||||
|
||||
Rule ``base_network``
|
||||
|
||||
@ -19,6 +19,14 @@ Upcoming Release
|
||||
|
||||
* Add flag ``sector: fossil_fuels`` in config to remove the option of importing fossil fuels
|
||||
|
||||
* 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
|
||||
|
||||
@ -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:
|
||||
@ -943,7 +958,10 @@ 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",
|
||||
@ -223,6 +222,64 @@ 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__)
|
||||
|
||||
@ -60,6 +64,7 @@ def define_spatial(nodes, options):
|
||||
|
||||
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"
|
||||
@ -67,6 +72,7 @@ def define_spatial(nodes, options):
|
||||
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"]
|
||||
@ -1776,7 +1782,7 @@ def build_heat_demand(n):
|
||||
.unstack(level=1)
|
||||
)
|
||||
|
||||
sectors = ["residential", "services"]
|
||||
sectors = [sector.value for sector in HeatSector]
|
||||
uses = ["water", "space"]
|
||||
|
||||
heat_demand = {}
|
||||
@ -1804,10 +1810,21 @@ 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)
|
||||
|
||||
@ -1826,23 +1843,6 @@ 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)
|
||||
@ -1852,31 +1852,34 @@ 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,
|
||||
@ -1884,30 +1887,24 @@ 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()
|
||||
@ -1920,20 +1917,25 @@ 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"]
|
||||
)
|
||||
@ -1941,10 +1943,10 @@ 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"]
|
||||
@ -1954,59 +1956,65 @@ 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"]
|
||||
@ -2016,16 +2024,16 @@ 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"]
|
||||
@ -2035,22 +2043,26 @@ 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",
|
||||
@ -2107,16 +2119,20 @@ 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"],
|
||||
@ -2152,7 +2168,7 @@ 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"]
|
||||
@ -2248,8 +2264,14 @@ 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(
|
||||
@ -2258,11 +2280,27 @@ def add_biomass(n, costs):
|
||||
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")
|
||||
@ -2387,6 +2425,81 @@ 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,
|
||||
@ -3110,27 +3223,23 @@ 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",
|
||||
@ -4122,6 +4231,7 @@ 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(
|
||||
@ -4186,7 +4296,7 @@ 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