Update technology costs for wind
Calculate grid extension costs for offshore based on weighted average distance from weather cell to substation.
This commit is contained in:
Jonas Hörsch
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ee6085ed7e
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@ -112,7 +112,6 @@ rule build_renewable_potentials:
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rule build_renewable_profiles:
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input:
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base_network="networks/base.nc",
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potentials="resources/potentials_{technology}.nc",
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regions=lambda wildcards: ("resources/regions_onshore.geojson"
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if wildcards.technology in ('onwind', 'solar')
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@ -1,7 +1,7 @@
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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,25,years,IEA2010
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offwind,2030,lifetime,25,years,IEA2010
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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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@ -16,8 +16,10 @@ 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,1182,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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offwind,2030,investment,2506,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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onwind,2030,investment,910,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-grid,2030,investment,255,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node
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offwind-grid-perlength,2030,investment,0.97,EUR/kWel/km,Haertel 2017
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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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@ -32,8 +34,8 @@ 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,2.961083,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
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offwind,2030,FOM,3.192338,%/year,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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@ -47,8 +49,8 @@ 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,0.015,EUR/MWhel,RES costs made up to fix curtailment order
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offwind,2030,VOM,0.02,EUR/MWhel,RES costs made up to fix curtailment order
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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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@ -161,6 +161,15 @@ def attach_wind_and_solar(n, costs):
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n.add("Carrier", name=tech)
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with xr.open_dataset(getattr(snakemake.input, 'profile_' + tech)) as ds:
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capital_cost = costs.at[tech, 'capital_cost']
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if tech + "-grid" in costs.index:
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if tech + "-grid-perlength" in costs.index:
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grid_cost = costs.at[tech + "-grid", "capital_cost"] + costs.at[tech + "-grid-perlength", 'capital_cost'] * ds['average_distance'].to_pandas()
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logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(grid_cost.min(), grid_cost.max(), tech))
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else:
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grid_cost = costs.at[tech + "-grid", "capital_cost"]
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logger.info("Added connection cost of {:0.0f} Eur/MW/a to {}".format(grid_cost, tech))
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capital_cost = capital_cost + grid_cost
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n.madd("Generator", ds.indexes['bus'], ' ' + tech,
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bus=ds.indexes['bus'],
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@ -169,7 +178,7 @@ def attach_wind_and_solar(n, costs):
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p_nom_max=ds['p_nom_max'].to_pandas(),
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weight=ds['weight'].to_pandas(),
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marginal_cost=costs.at[tech, 'marginal_cost'],
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capital_cost=costs.at[tech, 'capital_cost'],
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capital_cost=capital_cost,
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efficiency=costs.at[tech, 'efficiency'],
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p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
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@ -24,6 +24,8 @@ for country in countries:
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onshore_shape = country_shapes[country]
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onshore_locs = n.buses.loc[c_b & n.buses.substation_lv, ["x", "y"]]
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onshore_regions.append(gpd.GeoDataFrame({
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'x': onshore_locs['x'],
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'y': onshore_locs['y'],
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'geometry': voronoi_partition_pts(onshore_locs.values, onshore_shape),
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'country': country
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}, index=onshore_locs.index))
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@ -32,6 +34,8 @@ for country in countries:
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offshore_shape = offshore_shapes[country]
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offshore_locs = n.buses.loc[c_b & n.buses.substation_off, ["x", "y"]]
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offshore_regions_c = gpd.GeoDataFrame({
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'x': offshore_locs['x'],
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'y': offshore_locs['y'],
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'geometry': voronoi_partition_pts(offshore_locs.values, offshore_shape),
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'country': country
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}, index=offshore_locs.index)
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@ -6,6 +6,8 @@ import numpy as np
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import xarray as xr
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import pandas as pd
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import geopandas as gpd
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from pypsa.geo import haversine
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from vresutils.array import spdiag
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import logging
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logger = logging.getLogger(__name__)
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@ -47,9 +49,21 @@ p_nom_max = xr.DataArray([np.nanmin(relativepotentials[row.nonzero()[1]])
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for row in indicatormatrix.tocsr()],
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[capacities.coords['bus']]) * capacities
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# Determine weighted average distance to substation
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matrix_weighted = indicatormatrix * spdiag(layout.stack(spatial=('y', 'x')))
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cell_coords = cutout.grid_coordinates()
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average_distance = []
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for i, bus in enumerate(regions.index):
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row = matrix_weighted[i]
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distances = haversine(regions.loc[bus, ['x', 'y']], cell_coords[row.indices])[0]
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average_distance.append((distances * (row.data / row.data.sum())).sum())
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average_distance = xr.DataArray(average_distance, [regions.index.rename("bus")])
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ds = xr.merge([(correction_factor * profile).rename('profile'),
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capacities.rename('weight'),
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p_nom_max.rename('p_nom_max'),
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layout.rename('potential')])
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layout.rename('potential'),
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average_distance.rename('average_distance')])
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(ds.sel(bus=ds['profile'].mean('time') > config.get('min_p_max_pu', 0.))
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.to_netcdf(snakemake.output.profile))
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