electricity_demand

The central module containing all code dealing with processing data from demandRegio

class CtsElectricityDemand(dependencies)[source]

Bases: Dataset

Create table and store data on cts electricity demands per census cell

Creates a table to store data on electricity demands from the cts sector on census cell level. For a spatial distribution of electricity demands data from DemandRegio, which provides the data on NUT3-level, is used and distributed to census cells according to heat demand data from Peta. Annual demands are then aggregated per MV grid district and a corresponding time series is created taking the shares of different cts branches and their specific standard load profiles into account.

Dependencies
Resulting tables
name: str = 'CtsElectricityDemand'
sources: DatasetSources = DatasetSources(tables={'demandregio': 'demand.egon_demandregio_cts_ind', 'demandregio_wz': 'demand.egon_demandregio_wz', 'demandregio_cts': 'demand.egon_demandregio_cts_ind', 'heat_demand_cts': 'demand.egon_peta_heat', 'map_zensus_vg250': 'boundaries.egon_map_zensus_vg250', 'demandregio_timeseries': 'demand.egon_demandregio_timeseries_cts_ind', 'map_grid_districts': 'boundaries.egon_map_zensus_grid_districts', 'map_vg250': 'boundaries.egon_map_zensus_vg250', 'zensus_electricity': 'demand.egon_demandregio_zensus_electricity'}, files={}, urls={})

The sources used by the datasets. Could be tables, files and urls

targets: DatasetTargets = DatasetTargets(tables={'cts_demands_zensus': 'demand.egon_demandregio_zensus_electricity', 'cts_demand_curves': 'demand.egon_etrago_electricity_cts'}, files={})

The targets created by the datasets. Could be tables and files

version: str = '0.0.4'
class EgonDemandRegioZensusElectricity(**kwargs)[source]

Bases: Base

demand
scenario
sector
zensus_population_id
class HouseholdElectricityDemand(dependencies)[source]

Bases: Dataset

Create table and store data on household electricity demands per census cell

Create a table to store the annual electricity demand for households on census cell level. In a next step the annual electricity demand per cell is determined by executing function get_annual_household_el_demand_cells()

Dependencies
Resulting tables
name: str = 'HouseholdElectricityDemand'
targets: DatasetTargets = DatasetTargets(tables={'household_demands_zensus': 'demand.egon_demandregio_zensus_electricity'}, files={})

The targets created by the datasets. Could be tables and files

version: str = '0.0.8'
create_tables()[source]

Create tables for demandregio data :returns: None.

distribute_cts_demands()[source]

Distribute electrical demands for cts to zensus cells.

The demands on nuts3-level from demandregio are linear distributed to the heat demand of cts in each zensus cell.

Returns:

None.

get_annual_household_el_demand_cells()[source]

Annual electricity demand per cell is determined

Timeseries for every cell are accumulated, the maximum value determined and with the respective nuts3 factor scaled for 2035 and 2050 scenario.

Note

In test-mode ‘SH’ the iteration takes place by ‘cell_id’ to avoid intensive RAM usage. For whole Germany ‘nuts3’ are taken and RAM > 32GB is necessary.