Update technology costs for wind

Calculate grid extension costs for offshore based on weighted average distance
from weather cell to substation.

Snakefile

@@ -112,7 +112,6 @@ rule build_renewable_potentials:
 
 rule build_renewable_profiles:
     input:
-        base_network="networks/base.nc",
         potentials="resources/potentials_{technology}.nc",
         regions=lambda wildcards: ("resources/regions_onshore.geojson"
                                    if wildcards.technology in ('onwind', 'solar')

data/costs.csv

@@ -1,7 +1,7 @@
 technology,year,parameter,value,unit,source
 solar-rooftop,2030,discount rate,0.04,per unit,standard for decentral
-onwind,2030,lifetime,25,years,IEA2010
+onwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,lifetime,25,years,IEA2010
+offwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 solar,2030,lifetime,25,years,IEA2010
 solar-rooftop,2030,lifetime,25,years,IEA2010
 solar-utility,2030,lifetime,25,years,IEA2010
@@ -16,8 +16,10 @@ lignite,2030,lifetime,40,years,IEA2010
 geothermal,2030,lifetime,40,years,IEA2010
 biomass,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
 oil,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
-onwind,2030,investment,1182,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
+onwind,2030,investment,910,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,investment,2506,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
+offwind,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-grid,2030,investment,255,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node
+offwind-grid-perlength,2030,investment,0.97,EUR/kWel/km,Haertel 2017
 solar,2030,investment,600,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 biomass,2030,investment,2209,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 geothermal,2030,investment,3392,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
@@ -32,8 +34,8 @@ OCGT,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 nuclear,2030,investment,6000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 CCGT,2030,investment,800,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 oil,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
-onwind,2030,FOM,2.961083,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
+onwind,2030,FOM,2.450549,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,FOM,3.192338,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
+offwind,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 solar,2030,FOM,4.166667,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 solar-rooftop,2030,FOM,2,%/year,ETIP PV
 solar-utility,2030,FOM,3,%/year,ETIP PV
@@ -47,8 +49,8 @@ hydro,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 ror,2030,FOM,2,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 CCGT,2030,FOM,2.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 OCGT,2030,FOM,3.75,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
-onwind,2030,VOM,0.015,EUR/MWhel,RES costs made up to fix curtailment order
+onwind,2030,VOM,2.3,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,VOM,0.02,EUR/MWhel,RES costs made up to fix curtailment order
+offwind,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 solar,2030,VOM,0.01,EUR/MWhel,RES costs made up to fix curtailment order
 coal,2030,VOM,6,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
 lignite,2030,VOM,7,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348

scripts/add_electricity.py

@@ -161,6 +161,15 @@ def attach_wind_and_solar(n, costs):
 
         n.add("Carrier", name=tech)
         with xr.open_dataset(getattr(snakemake.input, 'profile_' + tech)) as ds:
+            capital_cost = costs.at[tech, 'capital_cost']
+            if tech + "-grid" in costs.index:
+                if tech + "-grid-perlength" in costs.index:
+                    grid_cost = costs.at[tech + "-grid", "capital_cost"] + costs.at[tech + "-grid-perlength", 'capital_cost'] * ds['average_distance'].to_pandas()
+                    logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(grid_cost.min(), grid_cost.max(), tech))
+                else:
+                    grid_cost = costs.at[tech + "-grid", "capital_cost"]
+                    logger.info("Added connection cost of {:0.0f} Eur/MW/a to {}".format(grid_cost, tech))
+                capital_cost = capital_cost + grid_cost
 
             n.madd("Generator", ds.indexes['bus'], ' ' + tech,
                    bus=ds.indexes['bus'],
@@ -169,7 +178,7 @@ def attach_wind_and_solar(n, costs):
                    p_nom_max=ds['p_nom_max'].to_pandas(),
                    weight=ds['weight'].to_pandas(),
                    marginal_cost=costs.at[tech, 'marginal_cost'],
-                   capital_cost=costs.at[tech, 'capital_cost'],
+                   capital_cost=capital_cost,
                    efficiency=costs.at[tech, 'efficiency'],
                    p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
 

scripts/build_bus_regions.py

@@ -24,6 +24,8 @@ for country in countries:
     onshore_shape = country_shapes[country]
     onshore_locs = n.buses.loc[c_b & n.buses.substation_lv, ["x", "y"]]
     onshore_regions.append(gpd.GeoDataFrame({
+            'x': onshore_locs['x'],
+            'y': onshore_locs['y'],
             'geometry': voronoi_partition_pts(onshore_locs.values, onshore_shape),
             'country': country
         }, index=onshore_locs.index))
@@ -32,6 +34,8 @@ for country in countries:
     offshore_shape = offshore_shapes[country]
     offshore_locs = n.buses.loc[c_b & n.buses.substation_off, ["x", "y"]]
     offshore_regions_c = gpd.GeoDataFrame({
+            'x': offshore_locs['x'],
+            'y': offshore_locs['y'],
             'geometry': voronoi_partition_pts(offshore_locs.values, offshore_shape),
             'country': country
         }, index=offshore_locs.index)

scripts/build_renewable_profiles.py

@@ -6,6 +6,8 @@ import numpy as np
 import xarray as xr
 import pandas as pd
 import geopandas as gpd
+from pypsa.geo import haversine
+from vresutils.array import spdiag
 
 import logging
 logger = logging.getLogger(__name__)
@@ -47,9 +49,21 @@ p_nom_max = xr.DataArray([np.nanmin(relativepotentials[row.nonzero()[1]])
                           for row in indicatormatrix.tocsr()],
                          [capacities.coords['bus']]) * capacities
 
+# Determine weighted average distance to substation
+matrix_weighted = indicatormatrix * spdiag(layout.stack(spatial=('y', 'x')))
+cell_coords = cutout.grid_coordinates()
+
+average_distance = []
+for i, bus in enumerate(regions.index):
+    row = matrix_weighted[i]
+    distances = haversine(regions.loc[bus, ['x', 'y']], cell_coords[row.indices])[0]
+    average_distance.append((distances * (row.data / row.data.sum())).sum())
+average_distance = xr.DataArray(average_distance, [regions.index.rename("bus")])
+
 ds = xr.merge([(correction_factor * profile).rename('profile'),
                capacities.rename('weight'),
                p_nom_max.rename('p_nom_max'),
-               layout.rename('potential')])
+               layout.rename('potential'),
+               average_distance.rename('average_distance')])
 (ds.sel(bus=ds['profile'].mean('time') > config.get('min_p_max_pu', 0.))
  .to_netcdf(snakemake.output.profile))