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"""Create a basic scenario from the internal data structure. |
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SPDX-FileCopyrightText: 2016-2019 Uwe Krien <[email protected]> |
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SPDX-License-Identifier: MIT |
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""" |
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import calendar |
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import logging |
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import os |
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import pandas as pd |
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from reegis import demand_elec |
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from reegis import demand_heat |
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from scenario_builder import config as cfg |
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def get_heat_profiles_deflex( |
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deflex_geo, year, time_index=None, weather_year=None, keep_unit=False |
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): |
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""" |
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Parameters |
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---------- |
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year |
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deflex_geo |
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time_index |
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weather_year |
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keep_unit |
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Returns |
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------- |
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""" |
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# separate_regions=keep all demand connected to the region |
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separate_regions = cfg.get_list("demand_heat", "separate_heat_regions") |
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# Add lower and upper cases to be not case sensitive |
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separate_regions = ([x.upper() for x in separate_regions] + |
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[x.lower() for x in separate_regions]) |
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# add second fuel to first |
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combine_fuels = cfg.get_dict("combine_heat_fuels") |
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# fuels to be dissolved per region |
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region_fuels = cfg.get_list("demand_heat", "local_fuels") |
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fn = os.path.join( |
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cfg.get("paths", "demand"), |
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"heat_profiles_{year}_{map}".format(year=year, map=deflex_geo.name), |
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) |
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demand_region = ( |
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demand_heat.get_heat_profiles_by_region( |
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deflex_geo, year, to_csv=fn, weather_year=weather_year |
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) |
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.groupby(level=[0, 1], axis=1) |
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.sum() |
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) |
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# Decentralised demand is combined to a nation-wide demand if not part |
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# of region_fuels. |
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regions = list( |
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set(demand_region.columns.get_level_values(0).unique()) |
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- set(separate_regions) |
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) |
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# If region_fuels is 'all' fetch all fuels to be local. |
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if "all" in region_fuels: |
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region_fuels = demand_region.columns.get_level_values(1).unique() |
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for fuel in demand_region.columns.get_level_values(1).unique(): |
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demand_region["DE_demand", fuel] = 0 |
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for region in regions: |
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for f1, f2 in combine_fuels.items(): |
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demand_region[region, f1] += demand_region[region, f2] |
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demand_region.drop((region, f2), axis=1, inplace=True) |
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cols = list(set(demand_region[region].columns) - set(region_fuels)) |
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for col in cols: |
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demand_region["DE_demand", col] += demand_region[region, col] |
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demand_region.drop((region, col), axis=1, inplace=True) |
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if time_index is not None: |
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demand_region.index = time_index |
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if not keep_unit: |
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msg = ( |
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"The unit of the source is 'TJ'. " |
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"Will be divided by {0} to get 'MWh'." |
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) |
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converter = 0.0036 |
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demand_region = demand_region.div(converter) |
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logging.debug(msg.format(converter)) |
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demand_region.sort_index(1, inplace=True) |
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for c in demand_region.columns: |
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if demand_region[c].sum() == 0: |
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demand_region.drop(c, axis=1, inplace=True) |
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return demand_region |
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def scenario_demand(regions, year, name, weather_year=None): |
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""" |
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Parameters |
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---------- |
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regions |
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year |
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name |
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weather_year |
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Returns |
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------- |
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Examples |
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-------- |
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>>> regions=geometries.deflex_regions(rmap="de21") # doctest: +SKIP |
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>>> my_demand=scenario_demand(regions, 2014, "de21") # doctest: +SKIP |
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>>> int(my_demand["DE01", "district heating"].sum()) # doctest: +SKIP |
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18639262 |
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>>> int(my_demand["DE05", "electrical_load"].sum()) # doctest: +SKIP |
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10069304 |
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""" |
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demand_series = scenario_elec_demand( |
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pd.DataFrame(), regions, year, name, weather_year=weather_year |
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) |
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if cfg.get("basic", "heat"): |
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demand_series = scenario_heat_demand( |
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demand_series, regions, year, weather_year=weather_year |
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) |
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return demand_series |
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def scenario_heat_demand(table, regions, year, weather_year=None): |
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""" |
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Parameters |
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---------- |
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table |
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regions |
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year |
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weather_year |
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Returns |
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------- |
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""" |
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idx = table.index # Use the index of the existing time series |
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table = pd.concat( |
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[ |
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table, |
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get_heat_profiles_deflex( |
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regions, year, idx, weather_year=weather_year |
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), |
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], |
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axis=1, |
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) |
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return table.sort_index(1) |
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def scenario_elec_demand(table, regions, year, name, weather_year=None): |
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""" |
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Parameters |
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---------- |
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table |
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regions |
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year |
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name |
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weather_year |
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Returns |
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------- |
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""" |
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if weather_year is None: |
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demand_year = year |
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else: |
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demand_year = weather_year |
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df = demand_elec.get_entsoe_profile_by_region( |
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regions, demand_year, name, annual_demand="bmwi" |
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) |
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df = pd.concat([df], axis=1, keys=["electrical_load"]).swaplevel(0, 1, 1) |
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df = df.reset_index(drop=True) |
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if not calendar.isleap(year) and len(df) > 8760: |
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df = df.iloc[:8760] |
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return pd.concat([table, df], axis=1).sort_index(1) |
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if __name__ == "__main__": |
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pass |
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reegis /
scenario_builder
