demandregio¶
The central module containing all code dealing with importing and adjusting data from demandRegio
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class
DemandRegio
(dependencies)[source]¶ Bases:
egon.data.datasets.Dataset
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class
EgonDemandRegioCtsInd
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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demand
¶
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nuts3
¶
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scenario
¶
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wz
¶
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year
¶
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class
EgonDemandRegioHH
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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demand
¶
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hh_size
¶
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nuts3
¶
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scenario
¶
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year
¶
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class
EgonDemandRegioHouseholds
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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hh_size
¶
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households
¶
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nuts3
¶
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year
¶
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class
EgonDemandRegioPopulation
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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nuts3
¶
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population
¶
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year
¶
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class
EgonDemandRegioTimeseriesCtsInd
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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load_curve
¶
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slp
¶
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wz
¶
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year
¶
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class
EgonDemandRegioWz
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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definition
¶
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sector
¶
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wz
¶
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adjust_cts_ind_nep
(ec_cts_ind, sector)[source]¶ Add electrical demand of new largescale CTS und industrial consumers according to NEP 2021, scneario C 2035. Values per federal state are linear distributed over all CTS branches and nuts3 regions.
Parameters: ec_cts_ind (pandas.DataFrame) – CTS or industry demand without new largescale consumers. Returns: ec_cts_ind (pandas.DataFrame) – CTS or industry demand including new largescale consumers.
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adjust_ind_pes
(ec_cts_ind)[source]¶ Adjust electricity demand of industrial consumers due to electrification of process heat based on assumptions of pypsa-eur-sec.
Parameters: ec_cts_ind (pandas.DataFrame) – Industrial demand without additional electrification Returns: ec_cts_ind (pandas.DataFrame) – Industrial demand with additional electrification
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data_in_boundaries
(df)[source]¶ Select rows with nuts3 code within boundaries, used for testmode
Parameters: df (pandas.DataFrame) – Data for all nuts3 regions Returns: pandas.DataFrame – Data for nuts3 regions within boundaries
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disagg_households_power
(scenario, year, weight_by_income=False, original=False, **kwargs)[source]¶ Perform spatial disaggregation of electric power in [GWh/a] by key and possibly weight by income. Similar to disaggregator.spatial.disagg_households_power
Parameters: - by (str) – must be one of [‘households’, ‘population’]
- weight_by_income (bool, optional) – Flag if to weight the results by the regional income (default False)
- orignal (bool, optional) – Throughput to function households_per_size, A flag if the results should be left untouched and returned in original form for the year 2011 (True) or if they should be scaled to the given year by the population in that year (False).
Returns: pd.DataFrame or pd.Series
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insert_cts_ind
(scenario, year, engine, target_values)[source]¶ Calculates electrical demands of CTS and industry using demandregio’s disaggregator, adjusts them according to resulting values of NEP 2021 or JRC IDEES and insert results into the database.
Parameters: - scenario (str) – Name of the corresponing scenario.
- year (int) – The number of households per region is taken from this year.
- target_values (dict) – List of target values for each scenario and sector.
Returns: None.
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insert_cts_ind_demands
()[source]¶ Insert electricity demands per nuts3-region in Germany according to demandregio using its disaggregator-tool in MWh
Returns: None.
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insert_cts_ind_wz_definitions
()[source]¶ Insert demandregio’s definitions of CTS and industrial branches
Returns: None.
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insert_hh_demand
(scenario, year, engine)[source]¶ Calculates electrical demands of private households using demandregio’s disaggregator and insert results into the database.
Parameters: - scenario (str) – Name of the corresponing scenario.
- year (int) – The number of households per region is taken from this year.
Returns: None.
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insert_household_demand
()[source]¶ Insert electrical demands for households according to demandregio using its disaggregator-tool in MWh
Returns: None.
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insert_society_data
()[source]¶ Insert population and number of households per nuts3-region in Germany according to demandregio using its disaggregator-tool
Returns: None.
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insert_timeseries_per_wz
(sector, year)[source]¶ Insert normalized electrical load time series for the selected sector
Parameters: - sector (str) – Name of the sector. [‘CTS’, ‘industry’]
- year (int) – Selected weather year
Returns: None.