power_plants

The central module containing all code dealing with power plant data.

class EgonPowerPlants(**kwargs)[source]

Bases: sqlalchemy.ext.declarative.api.Base

bus_id
carrier
el_capacity
geom
id
scenario
source_id
sources
voltage_level
weather_cell_id
class PowerPlants(dependencies)[source]

Bases: egon.data.datasets.Dataset

This module creates all electrical generators for different scenarios. It also calculates the weather area for each weather dependent generator.

Dependencies
  • Chp
  • :py:class:`CtsElectricityDemand

<egon.data.datasets.electricity_demand.CtsElectricityDemand>` * HouseholdElectricityDemand * mastr_data * define_mv_grid_districts * RePotentialAreas * ZensusVg250 * ScenarioCapacities * ScenarioParameters * Setup * substation_extraction * Vg250MvGridDistricts * ZensusMvGridDistricts

Resulting tables
  • :py:class:`supply.egon_power_plants

<egon.data.datasets.power_plants.EgonPowerPlants>` is filled

name = 'PowerPlants'
version = '0.0.18'
allocate_conventional_non_chp_power_plants()[source]
allocate_other_power_plants()[source]
assign_bus_id(power_plants, cfg)[source]

Assigns bus_ids to power plants according to location and voltage level

Parameters:power_plants (pandas.DataFrame) – Power plants including voltage level
Returns:power_plants (pandas.DataFrame) – Power plants including voltage level and bus_id
assign_voltage_level(mastr_loc, cfg, mastr_working_dir)[source]

Assigns voltage level to power plants.

If location data inluding voltage level is available from Marktstammdatenregister, this is used. Otherwise the voltage level is assigned according to the electrical capacity.

Parameters:mastr_loc (pandas.DataFrame) – Power plants listed in MaStR with geometry inside German boundaries
Returns:pandas.DataFrame – Power plants including voltage_level
assign_voltage_level_by_capacity(mastr_loc)[source]
create_tables()[source]

Create tables for power plant data :returns: None.

filter_mastr_geometry(mastr, federal_state=None)[source]

Filter data from MaStR by geometry

Parameters:
  • mastr (pandas.DataFrame) – All power plants listed in MaStR
  • federal_state (str or None) – Name of federal state whoes power plants are returned. If None, data for Germany is returned
Returns:

mastr_loc (pandas.DataFrame) – Power plants listed in MaStR with geometry inside German boundaries
insert_biomass_plants(scenario)[source]

Insert biomass power plants of future scenario

Parameters:scenario (str) – Name of scenario.
Returns:None.
insert_hydro_biomass()[source]

Insert hydro and biomass power plants in database

Returns:None.
insert_hydro_plants(scenario)[source]

Insert hydro power plants of future scenario.

Hydro power plants are diveded into run_of_river and reservoir plants according to Marktstammdatenregister. Additional hydro technologies (e.g. turbines inside drinking water systems) are not considered.

Parameters:scenario (str) – Name of scenario.
Returns:None.
scale_prox2now(df, target, level='federal_state')[source]

Scale installed capacities linear to status quo power plants

Parameters:
  • df (pandas.DataFrame) – Status Quo power plants
  • target (pandas.Series) – Target values for future scenario
  • level (str, optional) – Scale per ‘federal_state’ or ‘country’. The default is ‘federal_state’.
Returns:

df (pandas.DataFrame) – Future power plants
select_target(carrier, scenario)[source]

Select installed capacity per scenario and carrier

Parameters:
  • carrier (str) – Name of energy carrier
  • scenario (str) – Name of scenario
Returns:

pandas.Series – Target values for carrier and scenario