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import pandas as pd |
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from reegis import geometries as geo_reegis |
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from deflex import geometries as geo_deflex |
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from reegis import land_availability_glaes, demand_disaggregator |
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from disaggregator import data |
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def get_cost_emission_scenario_data(path_to_data): |
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commodity_sources = dict.fromkeys({'StatusQuo', 'NEP2030', 'AllElectric', 'SynFuel'}) |
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for n in commodity_sources.keys(): |
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cost_data = pd.read_excel(path_to_data, n) |
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cost_data.set_index('Unnamed: 0', drop=True, inplace=True) |
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cost_data.drop(['costs', 'emission_cost', 'carbon_price'], inplace=True) |
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commodity_sources[n] = cost_data |
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return commodity_sources |
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def return_normalized_domestic_profiles(regions, df): |
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test = df.groupby(level=[0,2], axis=1).sum() |
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profile_domestic = pd.DataFrame(index=test.index, columns=regions.index[0:18] ) |
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for reg in regions.index[0:18]: |
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profile_domestic[reg] = test[reg]["domestic"] + test[reg]["retail"] |
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profile_domestic = profile_domestic.div(profile_domestic.sum()) |
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return profile_domestic |
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def return_normalized_industrial_profiles(regions,df): |
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test = df.groupby(level=[0, 2], axis=1).sum() |
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profile_domestic = pd.DataFrame(index=test.index, columns=regions.index[0:18]) |
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for reg in regions.index[0:18]: |
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profile_domestic[reg] = test[reg]["industrial"] |
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profile_industrial = profile_domestic.div(profile_domestic.sum()) |
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return profile_industrial |
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def transform_NEP_capacities_to_de21(path_to_NEP_capacities): |
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de21 = geo_deflex.deflex_regions(rmap='de21') |
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fed_states = geo_reegis.get_federal_states_polygon() |
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nuts3_index = data.database_shapes().index |
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# Load NEP capacities |
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NEP2030_capacity = pd.read_excel(path_to_NEP_capacities) |
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NEP2030_capacity.set_index('fedstate', drop=True, inplace=True) |
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NEP2030_capacity = NEP2030_capacity.multiply(1e3) |
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#Fetch GLAES RES capacities and compare to NEP data |
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glaes_capacity = land_availability_glaes.aggregate_capacity_by_region(fed_states) |
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compare_RES = pd.concat([glaes_capacity, NEP2030_capacity['onshore'], NEP2030_capacity['offshore'], |
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NEP2030_capacity['solar pv']], axis=1) |
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compare_RES.drop(['N0', 'N1', 'O0', 'P0'], axis=0, inplace=True) |
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scaling_wind = compare_RES['onshore'] / compare_RES['P_wind'] |
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scaling_pv = compare_RES['solar pv'] / compare_RES['P_pv'] |
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mapped_nuts = demand_disaggregator.get_nutslist_for_regions(fed_states) |
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res_capacity_nuts3 = land_availability_glaes.get_pv_wind_capacity_potential_by_nuts3() |
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# Zuordnung der installierten Leistungen zu den jeweiligen Landkreisen |
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P_NEP_nuts3 = pd.DataFrame(index=nuts3_index, columns=['onshore', 'pv']) |
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for zone in compare_RES.index: |
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region_pick = mapped_nuts.loc[zone] |
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for nuts3 in region_pick.iloc[0]: |
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P_NEP_nuts3.loc[nuts3]['onshore'] = res_capacity_nuts3['P_wind'][nuts3] * scaling_wind[zone] |
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P_NEP_nuts3.loc[nuts3]['pv'] = res_capacity_nuts3['P_pv'][nuts3] * scaling_pv[zone] |
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NEP_capacity_de21 = pd.DataFrame( |
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index=de21.index, columns=["P_wind", "P_pv"] |
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) |
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nuts3_list_de21 = demand_disaggregator.get_nutslist_for_regions(de21) |
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for zone in de21.index: |
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idx = nuts3_list_de21.loc[zone]["nuts"] |
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NEP_capacity_de21.loc[zone]["P_wind"] = P_NEP_nuts3["onshore"][idx].sum() |
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NEP_capacity_de21.loc[zone]["P_pv"] = P_NEP_nuts3["pv"][idx].sum() |
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return NEP_capacity_de21 |
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reegis /
scenario_builder
