renewable_feedin

Central module containing all code dealing with processing era5 weather data.

class MapZensusWeatherCell(**kwargs)

Bases: Base

w_id

zensus_population_id

class RenewableFeedin(dependencies)

Bases: Dataset

Calculate possible feedin time series for renewable energy generators

This dataset calculates possible feedin timeseries for fluctuation renewable generators and coefficient of performance time series for heat pumps. Relevant input is the downloaded weather data. Parameters for the time series calcultaion are also defined by representative types of pv plants and wind turbines that are selected within this dataset. The resulting profiles are stored in the database.

Dependencies

• WeatherData

• Vg250

• ZensusVg250

Resulting tables

• supply.egon_era5_renewable_feedin is filled

name: str = 'RenewableFeedin'

sources: DatasetSources = DatasetSources(tables={'weather_cells': 'supply.egon_era5_weather_cells', 'vg250_lan_union': 'boundaries.vg250_lan_union'}, files={}, urls={})

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

targets: DatasetTargets = DatasetTargets(tables={'feedin_table': 'supply.egon_era5_renewable_feedin', 'map_zensus_weather_cell': 'boundaries.egon_map_zensus_weather_cell'}, files={})

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

version: str = '0.0.12'

add_metadata()

Add metdata to supply.egon_era5_renewable_feedin

Returns:

None.

federal_states_per_weather_cell()

Assings a federal state to each weather cell in Germany.

Sets the federal state to the weather celss using the centroid. Weather cells at the borders whoes centroid is not inside Germany are assinged to the closest federal state.

Returns:

GeoPandas.GeoDataFrame – Index, points and federal state of weather cells inside Germany

feedin_per_turbine()

Calculate feedin timeseries per turbine type and weather cell

Returns:

gdf (GeoPandas.GeoDataFrame) – Feed-in timeseries per turbine type and weather cell

heat_pump_cop()

Calculate coefficient of performance for heat pumps according to T. Brown et al: “Synergies of sector coupling and transmission reinforcement in a cost-optimised, highlyrenewable European energy system”, 2018, p. 8

Returns:

None.

insert_feedin(data, carrier, weather_year)

Insert feedin data into database

Parameters:

• data (xarray.core.dataarray.DataArray) – Feedin timeseries data

• carrier (str) – Name of energy carrier

• weather_year (int) – Selected weather year

Returns:

None.

mapping_zensus_weather()

Perform mapping between era5 weather cell and zensus grid

offshore_weather_cells(geom_column='geom')

Get weather cells which intersect with Germany

Returns:

GeoPandas.GeoDataFrame – Index and points of weather cells inside Germany

pv()

Insert feed-in timeseries for pv plants to database

Returns:

None.

solar_thermal()

Insert feed-in timeseries for pv plants to database

Returns:

None.

turbine_per_weather_cell()

Assign wind onshore turbine types to weather cells

Returns:

weather_cells (GeoPandas.GeoDataFrame) – Weather cells in Germany including turbine type

weather_cells_in_germany(geom_column='geom')

Get weather cells which intersect with Germany

Returns:

GeoPandas.GeoDataFrame – Index and points of weather cells inside Germany

wind()

Insert feed-in timeseries for wind onshore turbines to database

Returns:

None.

wind_offshore()

Insert feed-in timeseries for wind offshore turbines to database

Returns:

None.