Merge pull request #675 from PyPSA/fneum/line-rating
Dynamic line rating
This commit is contained in:
Fabian Hofmann
GitHub
commit
e91dfdcf97
@ -227,6 +227,12 @@ lines:
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max_extension: .inf
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length_factor: 1.25
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under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
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dynamic_line_rating:
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activate: false
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cutout: europe-2013-era5
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correction_factor: 0.95
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max_voltage_difference: false
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max_line_rating: false
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# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#links
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links:
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@ -60,6 +60,12 @@ renewable:
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clustering:
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exclude_carriers: ["OCGT", "offwind-ac", "coal"]
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lines:
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dynamic_line_rating:
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activate: true
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cutout: be-03-2013-era5
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max_line_rating: 1.3
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solving:
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solver:
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@ -62,6 +62,11 @@ renewable:
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solar:
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cutout: be-03-2013-era5
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lines:
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dynamic_line_rating:
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activate: true
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max_line_rating: 1.3
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industry:
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St_primary_fraction:
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2030: 0.6
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@ -59,6 +59,11 @@ renewable:
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solar:
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cutout: be-03-2013-era5
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lines:
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dynamic_line_rating:
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activate: true
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max_line_rating: 1.3
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sector:
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gas_network: true
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H2_retrofit: true
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195
data/costs.csv
195
data/costs.csv
@ -1,195 +0,0 @@
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technology,year,parameter,value,unit,source
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solar-rooftop,2030,discount rate,0.04,per unit,standard for decentral
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onwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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solar,2030,lifetime,25,years,IEA2010
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solar-rooftop,2030,lifetime,25,years,IEA2010
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solar-utility,2030,lifetime,25,years,IEA2010
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PHS,2030,lifetime,80,years,IEA2010
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hydro,2030,lifetime,80,years,IEA2010
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ror,2030,lifetime,80,years,IEA2010
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OCGT,2030,lifetime,30,years,IEA2010
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nuclear,2030,lifetime,45,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
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CCGT,2030,lifetime,30,years,IEA2010
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coal,2030,lifetime,40,years,IEA2010
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lignite,2030,lifetime,40,years,IEA2010
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geothermal,2030,lifetime,40,years,IEA2010
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biomass,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
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oil,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
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onwind,2030,investment,1040,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-ac-station,2030,investment,250,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-ac-connection-submarine,2030,investment,2685,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-ac-connection-underground,2030,investment,1342,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-dc-station,2030,investment,400,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
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offwind-dc-connection-submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
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offwind-dc-connection-underground,2030,investment,1000,EUR/MW/km,Haertel 2017; average + 13% learning reduction
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solar,2030,investment,600,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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biomass,2030,investment,2209,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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geothermal,2030,investment,3392,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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coal,2030,investment,1300,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
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lignite,2030,investment,1500,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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solar-rooftop,2030,investment,725,EUR/kWel,ETIP PV
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solar-utility,2030,investment,425,EUR/kWel,ETIP PV
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PHS,2030,investment,2000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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hydro,2030,investment,2000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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ror,2030,investment,3000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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OCGT,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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nuclear,2030,investment,6000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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CCGT,2030,investment,800,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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oil,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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onwind,2030,FOM,2.450549,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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solar,2030,FOM,4.166667,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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solar-rooftop,2030,FOM,2,%/year,ETIP PV
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solar-utility,2030,FOM,3,%/year,ETIP PV
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biomass,2030,FOM,4.526935,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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geothermal,2030,FOM,2.358491,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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coal,2030,FOM,1.923076,%/year,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
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lignite,2030,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
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oil,2030,FOM,1.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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PHS,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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hydro,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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ror,2030,FOM,2,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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CCGT,2030,FOM,2.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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OCGT,2030,FOM,3.75,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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onwind,2030,VOM,2.3,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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solar,2030,VOM,0.01,EUR/MWhel,RES costs made up to fix curtailment order
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coal,2030,VOM,6,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
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lignite,2030,VOM,7,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
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CCGT,2030,VOM,4,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
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OCGT,2030,VOM,3,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
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nuclear,2030,VOM,8,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
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gas,2030,fuel,21.6,EUR/MWhth,IEA2011b
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uranium,2030,fuel,3,EUR/MWhth,DIW DataDoc http://hdl.handle.net/10419/80348
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oil,2030,VOM,3,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
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nuclear,2030,fuel,3,EUR/MWhth,IEA2011b
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biomass,2030,fuel,7,EUR/MWhth,IEA2011b
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coal,2030,fuel,8.4,EUR/MWhth,IEA2011b
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lignite,2030,fuel,2.9,EUR/MWhth,IEA2011b
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oil,2030,fuel,50,EUR/MWhth,IEA WEM2017 97USD/boe = http://www.iea.org/media/weowebsite/2017/WEM_Documentation_WEO2017.pdf
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PHS,2030,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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hydro,2030,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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ror,2030,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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OCGT,2030,efficiency,0.39,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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CCGT,2030,efficiency,0.5,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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biomass,2030,efficiency,0.468,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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geothermal,2030,efficiency,0.239,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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nuclear,2030,efficiency,0.337,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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gas,2030,CO2 intensity,0.187,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
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coal,2030,efficiency,0.464,per unit,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
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lignite,2030,efficiency,0.447,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
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oil,2030,efficiency,0.393,per unit,DIW DataDoc http://hdl.handle.net/10419/80348 CT
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coal,2030,CO2 intensity,0.354,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
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lignite,2030,CO2 intensity,0.334,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
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oil,2030,CO2 intensity,0.248,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
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geothermal,2030,CO2 intensity,0.026,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
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electrolysis,2030,investment,350,EUR/kWel,Palzer Thesis
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electrolysis,2030,FOM,4,%/year,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
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electrolysis,2030,lifetime,18,years,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
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electrolysis,2030,efficiency,0.8,per unit,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
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fuel cell,2030,investment,339,EUR/kWel,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
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fuel cell,2030,FOM,3,%/year,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
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fuel cell,2030,lifetime,20,years,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
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fuel cell,2030,efficiency,0.58,per unit,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013 conservative 2020
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hydrogen storage,2030,investment,11.2,USD/kWh,budischak2013
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hydrogen storage,2030,lifetime,20,years,budischak2013
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hydrogen underground storage,2030,investment,0.5,EUR/kWh,maximum from https://www.nrel.gov/docs/fy10osti/46719.pdf
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hydrogen underground storage,2030,lifetime,40,years,http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Materialien/ESYS_Technologiesteckbrief_Energiespeicher.pdf
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H2 pipeline,2030,investment,267,EUR/MW/km,Welder et al https://doi.org/10.1016/j.ijhydene.2018.12.156
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H2 pipeline,2030,lifetime,40,years,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
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H2 pipeline,2030,FOM,5,%/year,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
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H2 pipeline,2030,efficiency,0.98,per unit,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
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methanation,2030,investment,1000,EUR/kWH2,Schaber thesis
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methanation,2030,lifetime,25,years,Schaber thesis
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methanation,2030,FOM,3,%/year,Schaber thesis
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methanation,2030,efficiency,0.6,per unit,Palzer; Breyer for DAC
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helmeth,2030,investment,1000,EUR/kW,no source
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helmeth,2030,lifetime,25,years,no source
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helmeth,2030,FOM,3,%/year,no source
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helmeth,2030,efficiency,0.8,per unit,HELMETH press release
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DAC,2030,investment,250,EUR/(tCO2/a),Fasihi/Climeworks
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DAC,2030,lifetime,30,years,Fasihi
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DAC,2030,FOM,4,%/year,Fasihi
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battery inverter,2030,investment,411,USD/kWel,budischak2013
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battery inverter,2030,lifetime,20,years,budischak2013
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battery inverter,2030,efficiency,0.9,per unit charge/discharge,budischak2013; Lund and Kempton (2008) http://dx.doi.org/10.1016/j.enpol.2008.06.007
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battery inverter,2030,FOM,3,%/year,budischak2013
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battery storage,2030,investment,192,USD/kWh,budischak2013
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battery storage,2030,lifetime,15,years,budischak2013
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decentral air-sourced heat pump,2030,investment,1050,EUR/kWth,HP; Palzer thesis
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decentral air-sourced heat pump,2030,lifetime,20,years,HP; Palzer thesis
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decentral air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
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decentral air-sourced heat pump,2030,efficiency,3,per unit,default for costs
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decentral air-sourced heat pump,2030,discount rate,0.04,per unit,Palzer thesis
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decentral ground-sourced heat pump,2030,investment,1400,EUR/kWth,Palzer thesis
|
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decentral ground-sourced heat pump,2030,lifetime,20,years,Palzer thesis
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decentral ground-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
|
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decentral ground-sourced heat pump,2030,efficiency,4,per unit,default for costs
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decentral ground-sourced heat pump,2030,discount rate,0.04,per unit,Palzer thesis
|
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central air-sourced heat pump,2030,investment,700,EUR/kWth,Palzer thesis
|
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central air-sourced heat pump,2030,lifetime,20,years,Palzer thesis
|
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central air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
|
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central air-sourced heat pump,2030,efficiency,3,per unit,default for costs
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retrofitting I,2030,discount rate,0.04,per unit,Palzer thesis
|
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retrofitting I,2030,lifetime,50,years,Palzer thesis
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retrofitting I,2030,FOM,1,%/year,Palzer thesis
|
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retrofitting I,2030,investment,50,EUR/m2/fraction reduction,Palzer thesis
|
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retrofitting II,2030,discount rate,0.04,per unit,Palzer thesis
|
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retrofitting II,2030,lifetime,50,years,Palzer thesis
|
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retrofitting II,2030,FOM,1,%/year,Palzer thesis
|
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retrofitting II,2030,investment,250,EUR/m2/fraction reduction,Palzer thesis
|
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water tank charger,2030,efficiency,0.9,per unit,HP
|
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water tank discharger,2030,efficiency,0.9,per unit,HP
|
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decentral water tank storage,2030,investment,860,EUR/m3,IWES Interaktion
|
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decentral water tank storage,2030,FOM,1,%/year,HP
|
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decentral water tank storage,2030,lifetime,20,years,HP
|
||||
decentral water tank storage,2030,discount rate,0.04,per unit,Palzer thesis
|
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central water tank storage,2030,investment,30,EUR/m3,IWES Interaktion
|
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central water tank storage,2030,FOM,1,%/year,HP
|
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central water tank storage,2030,lifetime,40,years,HP
|
||||
decentral resistive heater,2030,investment,100,EUR/kWhth,Schaber thesis
|
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decentral resistive heater,2030,lifetime,20,years,Schaber thesis
|
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decentral resistive heater,2030,FOM,2,%/year,Schaber thesis
|
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decentral resistive heater,2030,efficiency,0.9,per unit,Schaber thesis
|
||||
decentral resistive heater,2030,discount rate,0.04,per unit,Palzer thesis
|
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central resistive heater,2030,investment,100,EUR/kWhth,Schaber thesis
|
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central resistive heater,2030,lifetime,20,years,Schaber thesis
|
||||
central resistive heater,2030,FOM,2,%/year,Schaber thesis
|
||||
central resistive heater,2030,efficiency,0.9,per unit,Schaber thesis
|
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decentral gas boiler,2030,investment,175,EUR/kWhth,Palzer thesis
|
||||
decentral gas boiler,2030,lifetime,20,years,Palzer thesis
|
||||
decentral gas boiler,2030,FOM,2,%/year,Palzer thesis
|
||||
decentral gas boiler,2030,efficiency,0.9,per unit,Palzer thesis
|
||||
decentral gas boiler,2030,discount rate,0.04,per unit,Palzer thesis
|
||||
central gas boiler,2030,investment,63,EUR/kWhth,Palzer thesis
|
||||
central gas boiler,2030,lifetime,22,years,Palzer thesis
|
||||
central gas boiler,2030,FOM,1,%/year,Palzer thesis
|
||||
central gas boiler,2030,efficiency,0.9,per unit,Palzer thesis
|
||||
decentral CHP,2030,lifetime,25,years,HP
|
||||
decentral CHP,2030,investment,1400,EUR/kWel,HP
|
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decentral CHP,2030,FOM,3,%/year,HP
|
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decentral CHP,2030,discount rate,0.04,per unit,Palzer thesis
|
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central CHP,2030,lifetime,25,years,HP
|
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central CHP,2030,investment,650,EUR/kWel,HP
|
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central CHP,2030,FOM,3,%/year,HP
|
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decentral solar thermal,2030,discount rate,0.04,per unit,Palzer thesis
|
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decentral solar thermal,2030,FOM,1.3,%/year,HP
|
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decentral solar thermal,2030,investment,270000,EUR/1000m2,HP
|
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decentral solar thermal,2030,lifetime,20,years,HP
|
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central solar thermal,2030,FOM,1.4,%/year,HP
|
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central solar thermal,2030,investment,140000,EUR/1000m2,HP
|
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central solar thermal,2030,lifetime,20,years,HP
|
||||
HVAC overhead,2030,investment,400,EUR/MW/km,Hagspiel
|
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HVAC overhead,2030,lifetime,40,years,Hagspiel
|
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HVAC overhead,2030,FOM,2,%/year,Hagspiel
|
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HVDC overhead,2030,investment,400,EUR/MW/km,Hagspiel
|
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HVDC overhead,2030,lifetime,40,years,Hagspiel
|
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HVDC overhead,2030,FOM,2,%/year,Hagspiel
|
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HVDC submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
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HVDC submarine,2030,lifetime,40,years,Hagspiel
|
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HVDC submarine,2030,FOM,2,%/year,Hagspiel
|
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HVDC inverter pair,2030,investment,150000,EUR/MW,Hagspiel
|
||||
HVDC inverter pair,2030,lifetime,40,years,Hagspiel
|
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HVDC inverter pair,2030,FOM,2,%/year,Hagspiel
|
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|
@ -5,3 +5,9 @@ s_nom_max,MW,"float","Global upper limit for the maximum capacity of each extend
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max_extension,MW,"float","Upper limit for the extended capacity of each extendable line."
|
||||
length_factor,--,float,"Correction factor to account for the fact that buses are *not* connected by lines through air-line distance."
|
||||
under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity}","Specifies how to handle lines which are currently under construction."
|
||||
dynamic_line_rating,,,
|
||||
-- activate,bool,"true or false","Whether to take dynamic line rating into account"
|
||||
-- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
-- correction_factor,--,"float","Factor to compensate for overestimation of wind speeds in hourly averaged wind data"
|
||||
-- max_voltage_difference,deg,"float","Maximum voltage angle difference in degrees or 'false' to disable"
|
||||
-- max_line_rating,--,"float","Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable"
|
||||
|
||||
|
@ -53,6 +53,7 @@ dependencies:
|
||||
- descartes
|
||||
- rasterio!=1.2.10
|
||||
|
||||
|
||||
- pip:
|
||||
- tsam>=1.1.0
|
||||
- git+https://github.com/PyPSA/PyPSA.git@master
|
||||
|
||||
@ -274,6 +274,30 @@ rule build_hydro_profile:
|
||||
"../scripts/build_hydro_profile.py"
|
||||
|
||||
|
||||
if config["lines"]["dynamic_line_rating"]["activate"]:
|
||||
|
||||
rule build_line_rating:
|
||||
input:
|
||||
base_network=RESOURCES + "networks/base.nc",
|
||||
cutout="cutouts/"
|
||||
+ CDIR
|
||||
+ config["lines"]["dynamic_line_rating"]["cutout"]
|
||||
+ ".nc",
|
||||
output:
|
||||
output=RESOURCES + "networks/line_rating.nc",
|
||||
log:
|
||||
LOGS + "build_line_rating.log",
|
||||
benchmark:
|
||||
BENCHMARKS + "build_line_rating"
|
||||
threads: ATLITE_NPROCESSES
|
||||
resources:
|
||||
mem_mb=ATLITE_NPROCESSES * 1000,
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
"../scripts/build_line_rating.py"
|
||||
|
||||
|
||||
rule add_electricity:
|
||||
params:
|
||||
length_factor=config["lines"]["length_factor"],
|
||||
@ -295,6 +319,9 @@ rule add_electricity:
|
||||
if str(fn).startswith("data/")
|
||||
},
|
||||
base_network=RESOURCES + "networks/base.nc",
|
||||
line_rating=RESOURCES + "networks/line_rating.nc"
|
||||
if config["lines"]["dynamic_line_rating"]["activate"]
|
||||
else RESOURCES + "networks/base.nc",
|
||||
tech_costs=COSTS,
|
||||
regions=RESOURCES + "regions_onshore.geojson",
|
||||
powerplants=RESOURCES + "powerplants.csv",
|
||||
|
||||
@ -2,8 +2,6 @@
|
||||
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# coding: utf-8
|
||||
"""
|
||||
Adds electrical generators and existing hydro storage units to a base network.
|
||||
|
||||
@ -752,6 +750,30 @@ def estimate_renewable_capacities(n, year, tech_map, expansion_limit, countries)
|
||||
)
|
||||
|
||||
|
||||
def attach_line_rating(
|
||||
n, rating, s_max_pu, correction_factor, max_voltage_difference, max_line_rating
|
||||
):
|
||||
# TODO: Only considers overhead lines
|
||||
n.lines_t.s_max_pu = (rating / n.lines.s_nom[rating.columns]) * correction_factor
|
||||
if max_voltage_difference:
|
||||
x_pu = (
|
||||
n.lines.type.map(n.line_types["x_per_length"])
|
||||
* n.lines.length
|
||||
/ (n.lines.v_nom**2)
|
||||
)
|
||||
# need to clip here as cap values might be below 1
|
||||
# -> would mean the line cannot be operated at actual given pessimistic ampacity
|
||||
s_max_pu_cap = (
|
||||
np.deg2rad(max_voltage_difference) / (x_pu * n.lines.s_nom)
|
||||
).clip(lower=1)
|
||||
n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(
|
||||
lower=1, upper=s_max_pu_cap, axis=1
|
||||
)
|
||||
if max_line_rating:
|
||||
n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(upper=max_line_rating)
|
||||
n.lines_t.s_max_pu *= s_max_pu
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
@ -834,6 +856,23 @@ if __name__ == "__main__":
|
||||
|
||||
update_p_nom_max(n)
|
||||
|
||||
line_rating_config = snakemake.config["lines"]["dynamic_line_rating"]
|
||||
if line_rating_config["activate"]:
|
||||
rating = xr.open_dataarray(snakemake.input.line_rating).to_pandas().transpose()
|
||||
s_max_pu = snakemake.config["lines"]["s_max_pu"]
|
||||
correction_factor = line_rating_config["correction_factor"]
|
||||
max_voltage_difference = line_rating_config["max_voltage_difference"]
|
||||
max_line_rating = line_rating_config["max_line_rating"]
|
||||
|
||||
attach_line_rating(
|
||||
n,
|
||||
rating,
|
||||
s_max_pu,
|
||||
correction_factor,
|
||||
max_voltage_difference,
|
||||
max_line_rating,
|
||||
)
|
||||
|
||||
sanitize_carriers(n, snakemake.config)
|
||||
|
||||
n.meta = snakemake.config
|
||||
|
||||
155
scripts/build_line_rating.py
Executable file
155
scripts/build_line_rating.py
Executable file
@ -0,0 +1,155 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# coding: utf-8
|
||||
"""
|
||||
Adds dynamic line rating timeseries to the base network.
|
||||
|
||||
Relevant Settings
|
||||
-----------------
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
lines:
|
||||
cutout:
|
||||
line_rating:
|
||||
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config.yaml`
|
||||
Inputs
|
||||
------
|
||||
|
||||
- ``data/cutouts``:
|
||||
- ``networks/base.nc``: confer :ref:`base`
|
||||
|
||||
Outputs
|
||||
-------
|
||||
|
||||
- ``resources/line_rating.nc``
|
||||
|
||||
|
||||
Description
|
||||
-----------
|
||||
|
||||
The rule :mod:`build_line_rating` calculates the line rating for transmission lines.
|
||||
The line rating provides the maximal capacity of a transmission line considering the heat exchange with the environment.
|
||||
|
||||
The following heat gains and losses are considered:
|
||||
|
||||
- heat gain through resistive losses
|
||||
- heat gain through solar radiation
|
||||
- heat loss through radiation of the trasnmission line
|
||||
- heat loss through forced convection with wind
|
||||
- heat loss through natural convection
|
||||
|
||||
|
||||
With a heat balance considering the maximum temperature threshold of the transmission line,
|
||||
the maximal possible capacity factor "s_max_pu" for each transmission line at each time step is calculated.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import re
|
||||
|
||||
import atlite
|
||||
import geopandas as gpd
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pypsa
|
||||
import xarray as xr
|
||||
from _helpers import configure_logging
|
||||
from shapely.geometry import LineString as Line
|
||||
from shapely.geometry import Point
|
||||
|
||||
|
||||
def calculate_resistance(T, R_ref, T_ref=293, alpha=0.00403):
|
||||
"""
|
||||
Calculates the resistance at other temperatures than the reference
|
||||
temperature.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
T : Temperature at which resistance is calculated in [°C] or [K]
|
||||
R_ref : Resistance at reference temperature in [Ohm] or [Ohm/Per Length Unit]
|
||||
T_ref : Reference temperature in [°C] or [K]
|
||||
alpha: Temperature coefficient in [1/K]
|
||||
Defaults are:
|
||||
* T_ref : 20 °C
|
||||
* alpha : 0.00403 1/K
|
||||
|
||||
Returns
|
||||
-------
|
||||
Resistance of at given temperature.
|
||||
"""
|
||||
R = R_ref * (1 + alpha * (T - T_ref))
|
||||
return R
|
||||
|
||||
|
||||
def calculate_line_rating(n, cutout):
|
||||
"""
|
||||
Calculates the maximal allowed power flow in each line for each time step
|
||||
considering the maximal temperature.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : pypsa.Network object containing information on grid
|
||||
|
||||
Returns
|
||||
-------
|
||||
xarray DataArray object with maximal power.
|
||||
"""
|
||||
relevant_lines = n.lines[(n.lines["underground"] == False)]
|
||||
buses = relevant_lines[["bus0", "bus1"]].values
|
||||
x = n.buses.x
|
||||
y = n.buses.y
|
||||
shapes = [Line([Point(x[b0], y[b0]), Point(x[b1], y[b1])]) for (b0, b1) in buses]
|
||||
shapes = gpd.GeoSeries(shapes, index=relevant_lines.index)
|
||||
if relevant_lines.r_pu.eq(0).all():
|
||||
# Overwrite standard line resistance with line resistance obtained from line type
|
||||
r_per_length = n.line_types["r_per_length"]
|
||||
R = (
|
||||
relevant_lines.join(r_per_length, on=["type"])["r_per_length"] / 1000
|
||||
) # in meters
|
||||
# If line type with bundles is given retrieve number of conductors per bundle
|
||||
relevant_lines["n_bundle"] = (
|
||||
relevant_lines["type"]
|
||||
.where(relevant_lines["type"].str.contains("bundle"))
|
||||
.dropna()
|
||||
.apply(lambda x: int(re.findall(r"(\d+)-bundle", x)[0]))
|
||||
)
|
||||
# Set default number of bundles per line
|
||||
relevant_lines["n_bundle"].fillna(1, inplace=True)
|
||||
R *= relevant_lines["n_bundle"]
|
||||
R = calculate_resistance(T=353, R_ref=R)
|
||||
Imax = cutout.line_rating(shapes, R, D=0.0218, Ts=353, epsilon=0.8, alpha=0.8)
|
||||
line_factor = relevant_lines.eval("v_nom * n_bundle * num_parallel") / 1e3 # in mW
|
||||
da = xr.DataArray(
|
||||
data=np.sqrt(3) * Imax * line_factor.values.reshape(-1, 1),
|
||||
attrs=dict(
|
||||
description="Maximal possible power in MW for given line considering line rating"
|
||||
),
|
||||
)
|
||||
return da
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_line_rating",
|
||||
network="elec",
|
||||
simpl="",
|
||||
clusters="5",
|
||||
ll="v1.0",
|
||||
opts="Co2L-4H",
|
||||
)
|
||||
configure_logging(snakemake)
|
||||
|
||||
n = pypsa.Network(snakemake.input.base_network)
|
||||
cutout = atlite.Cutout(snakemake.input.cutout)
|
||||
|
||||
da = calculate_line_rating(n, cutout)
|
||||
da.to_netcdf(snakemake.output[0])
|
||||
Loading…
Reference in New Issue
Block a user