add_electricity: Add heuristic for estimating renewable capacities from country totals
Split per-country capacity totals reported in entsoe SO&AF 2016 in proportion to yearly generation potential at each bus, i.e. p_nom_max * mean(p_max_pu)
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Jonas Hoersch
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b9f1f5a8b5
commit
5c3fcb642c
@ -36,6 +36,11 @@ electricity:
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battery: 6
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H2: 168
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# estimate_renewable_capacities_from_capacity_stats:
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# # Wind is the Fueltype in ppm.data.Capacity_stats, onwind, offwind-{ac,dc} the carrier in PyPSA-Eur
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# Wind: [onwind, offwind-ac, offwind-dc]
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# Solar: [solar]
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conventional_carriers: [] # nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
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atlite:
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@ -18,6 +18,13 @@ from vresutils import transfer as vtransfer
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import pypsa
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try:
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import powerplantmatching as ppm
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from build_powerplants import country_alpha_2
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has_ppm = True
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except ImportError:
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has_ppm = False
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def normed(s): return s/s.sum()
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@ -396,6 +403,29 @@ def attach_storage(n, costs):
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# marginal_cost=options['marginal_cost_storage'],
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# p_nom_extendable=True)
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def estimate_renewable_capacities(n, tech_map=None):
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if tech_map is None:
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tech_map = snakemake.config['electricity'].get('estimate_renewable_capacities_from_capacity_stats', {})
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if len(tech_map) == 0: return
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assert has_ppm, "The estimation of renewable capacities needs the powerplantmatching package"
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capacities = ppm.data.Capacity_stats()
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capacities['alpha_2'] = capacities['Country'].map(country_alpha_2)
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capacities = capacities.loc[capacities.Energy_Source_Level_2].set_index(['Fueltype', 'alpha_2']).sort_index()
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countries = n.buses.country.unique()
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for ppm_fueltype, techs in tech_map.items():
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tech_capacities = capacities.loc[ppm_fueltype, 'Capacity'].reindex(countries, fill_value=0.)
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tech_b = n.generators.carrier.isin(techs)
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n.generators.loc[tech_b, 'p_nom'] = (
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(n.generators_t.p_max_pu.mean().loc[tech_b] * n.generators.loc[tech_b, 'p_nom_max']) # maximal yearly generation
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.groupby(n.generators.bus.map(n.buses.country)) # for each country
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.transform(lambda s: normed(s) * tech_capacities.at[s.name])
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.where(lambda s: s>0.1, 0.) # only capacities above 100kW
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)
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def add_co2limit(n, Nyears=1.):
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n.add("GlobalConstraint", "CO2Limit",
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@ -418,11 +448,12 @@ if __name__ == "__main__":
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snakemake = MockSnakemake(output=['networks/elec.nc'])
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snakemake.input = snakemake.expand(
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Dict(base_network='networks/base.nc',
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tech_costs='data/costs/costs.csv',
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tech_costs='data/costs.csv',
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regions="resources/regions_onshore.geojson",
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powerplants="resources/powerplants.csv",
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hydro_capacities='data/hydro_capacities.csv',
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opsd_load='data/time_series_60min_singleindex_filtered.csv',
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hydro_capacities='data/bundle/hydro_capacities.csv',
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opsd_load='data/bundle/time_series_60min_singleindex_filtered.csv',
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nuts3_shapes='resources/nuts3_shapes.geojson',
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**{'profile_' + t: "resources/profile_" + t + ".nc"
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for t in snakemake.config['renewable']})
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)
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@ -445,4 +476,6 @@ if __name__ == "__main__":
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attach_extendable_generators(n, costs, ppl)
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attach_storage(n, costs)
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estimate_renewable_capacities(n)
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n.export_to_netcdf(snakemake.output[0])
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@ -18,50 +18,51 @@ def country_alpha_2(name):
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cntry = pyc.countries.get(official_name=name)
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return cntry.alpha_2
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if 'snakemake' not in globals():
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from vresutils.snakemake import MockSnakemake, Dict
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from vresutils.snakemake import MockSnakemake, Dict
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snakemake = MockSnakemake(
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input=Dict(base_network='networks/base.nc'),
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output=['resources/powerplants.csv']
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)
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snakemake = MockSnakemake(
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input=Dict(base_network='networks/base.nc'),
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output=['resources/powerplants.csv']
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)
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logging.basicConfig(level=snakemake.config['logging_level'])
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logging.basicConfig(level=snakemake.config['logging_level'])
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n = pypsa.Network(snakemake.input.base_network)
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n = pypsa.Network(snakemake.input.base_network)
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ppl = (ppm.collection.matched_data()
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[lambda df : ~df.Fueltype.isin(('Solar', 'Wind'))]
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.pipe(ppm.cleaning.clean_technology)
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.assign(Fueltype=lambda df: (
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df.Fueltype.where(df.Fueltype != 'Natural Gas',
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df.Technology.replace('Steam Turbine', 'OCGT').fillna('OCGT'))))
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.pipe(ppm.utils.fill_geoposition))
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ppl = (ppm.collection.matched_data()
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[lambda df : ~df.Fueltype.isin(('Solar', 'Wind'))]
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.pipe(ppm.cleaning.clean_technology)
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.assign(Fueltype=lambda df: (
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df.Fueltype.where(df.Fueltype != 'Natural Gas',
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df.Technology.replace('Steam Turbine', 'OCGT').fillna('OCGT'))))
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.pipe(ppm.utils.fill_geoposition))
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# ppl.loc[(ppl.Fueltype == 'Other') & ppl.Technology.str.contains('CCGT'), 'Fueltype'] = 'CCGT'
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# ppl.loc[(ppl.Fueltype == 'Other') & ppl.Technology.str.contains('Steam Turbine'), 'Fueltype'] = 'CCGT'
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# ppl.loc[(ppl.Fueltype == 'Other') & ppl.Technology.str.contains('CCGT'), 'Fueltype'] = 'CCGT'
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# ppl.loc[(ppl.Fueltype == 'Other') & ppl.Technology.str.contains('Steam Turbine'), 'Fueltype'] = 'CCGT'
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ppl = ppl.loc[ppl.lon.notnull() & ppl.lat.notnull()]
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ppl = ppl.loc[ppl.lon.notnull() & ppl.lat.notnull()]
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ppl_country = ppl.Country.map(country_alpha_2)
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countries = n.buses.country.unique()
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cntries_without_ppl = []
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ppl_country = ppl.Country.map(country_alpha_2)
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countries = n.buses.country.unique()
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cntries_without_ppl = []
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for cntry in countries:
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substation_lv_i = n.buses.index[n.buses['substation_lv'] & (n.buses.country == cntry)]
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ppl_b = ppl_country == cntry
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if not ppl_b.any():
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cntries_without_ppl.append(cntry)
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continue
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for cntry in countries:
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substation_lv_i = n.buses.index[n.buses['substation_lv'] & (n.buses.country == cntry)]
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ppl_b = ppl_country == cntry
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if not ppl_b.any():
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cntries_without_ppl.append(cntry)
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continue
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kdtree = KDTree(n.buses.loc[substation_lv_i, ['x','y']].values)
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ppl.loc[ppl_b, 'bus'] = substation_lv_i[kdtree.query(ppl.loc[ppl_b, ['lon','lat']].values)[1]]
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kdtree = KDTree(n.buses.loc[substation_lv_i, ['x','y']].values)
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ppl.loc[ppl_b, 'bus'] = substation_lv_i[kdtree.query(ppl.loc[ppl_b, ['lon','lat']].values)[1]]
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if cntries_without_ppl:
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logging.warning("No powerplants known in: {}".format(", ".join(cntries_without_ppl)))
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if cntries_without_ppl:
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logging.warning("No powerplants known in: {}".format(", ".join(cntries_without_ppl)))
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bus_null_b = ppl["bus"].isnull()
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if bus_null_b.any():
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logging.warning("Couldn't find close bus for {} powerplants".format(bus_null_b.sum()))
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bus_null_b = ppl["bus"].isnull()
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if bus_null_b.any():
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logging.warning("Couldn't find close bus for {} powerplants".format(bus_null_b.sum()))
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ppl.to_csv(snakemake.output[0])
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ppl.to_csv(snakemake.output[0])
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