pv_rooftop_buildings

Distribute MaStR PV rooftop capacities to OSM and synthetic buildings. Generate new PV rooftop generators for scenarios eGon2035 and eGon100RE.

See documentation section PV ground mounted for more information.

class EgonPowerPlantPvRoofBuilding(**kwargs)[source]

Bases: Base

Class definition of table supply.egon_power_plants_pv_roof_building.

building_id
bus_id
capacity
gens_id
index
orientation_primary
orientation_primary_angle
orientation_uniform
scenario
voltage_level
weather_cell_id
class OsmBuildingsFiltered(**kwargs)[source]

Bases: Base

Class definition of table openstreetmap.osm_buildings_filtered.

amenity
area
building
geom
geom_point
id
name
osm_id
tags
class Vg250Lan(**kwargs)[source]

Bases: Base

Class definition of table boundaries.vg250_lan.

ade
ags
ags_0
ars
ars_0
bem
bez
bsg
debkg_id
fk_s3
gen
geometry
gf
ibz
id
nbd
nuts
rs
rs_0
sdv_ars
sdv_rs
sn_g
sn_k
sn_l
sn_r
sn_v1
sn_v2
wsk
add_ags_to_buildings(buildings_gdf: GeoDataFrame, municipalities_gdf: GeoDataFrame) → GeoDataFrame[source]

Add information about AGS ID to buildings.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

  • municipalities_gdf (geopandas.GeoDataFrame) – GeoDataFrame with municipality data.

Returns:

gepandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with AGS ID added.

add_ags_to_gens(mastr_gdf: GeoDataFrame, municipalities_gdf: GeoDataFrame) → GeoDataFrame[source]

Add information about AGS ID to generators.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame with valid and cleaned MaStR data.

  • municipalities_gdf (geopandas.GeoDataFrame) – GeoDataFrame with municipality data.

Returns:

gepandas.GeoDataFrame – GeoDataFrame with valid and cleaned MaStR data with AGS ID added.

add_buildings_meta_data(buildings_gdf: GeoDataFrame, prob_dict: dict, seed: int) → GeoDataFrame[source]

Randomly add additional metadata to desaggregated PV plants.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data with desaggregated PV plants.

  • prob_dict (dict) – Dictionary with values and probabilities per capacity range.

  • seed (int) – Seed to use for random operations with NumPy and pandas.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM building data with desaggregated PV plants.

add_bus_ids_sq(buildings_gdf: GeoDataFrame) → GeoDataFrame[source]

Add bus ids for status_quo units

Parameters:

buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data with desaggregated PV plants.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM building data with bus_id per generator.

add_commissioning_date(buildings_gdf: GeoDataFrame, start: Timestamp, end: Timestamp, seed: int)[source]

Randomly and linear add start-up date to new pv generators.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data with desaggregated PV plants.

  • start (pandas.Timestamp) – Minimum Timestamp to use.

  • end (pandas.Timestamp) – Maximum Timestamp to use.

  • seed (int) – Seed to use for random operations with NumPy and pandas.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with start-up date added.

add_metadata()[source]
add_overlay_id_to_buildings(buildings_gdf: GeoDataFrame, grid_federal_state_gdf: GeoDataFrame) → GeoDataFrame[source]

Add information about overlay ID to buildings.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

  • grid_federal_state_gdf (geopandas.GeoDataFrame) – GeoDataFrame with intersection shapes between counties and grid districts.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with overlay ID added.

add_weather_cell_id(buildings_gdf: GeoDataFrame) → GeoDataFrame[source]
allocate_pv(q_mastr_gdf: GeoDataFrame, q_buildings_gdf: GeoDataFrame, seed: int) → tuple[GeoDataFrame, GeoDataFrame][source]

Allocate the MaStR pv generators to the OSM buildings. This will determine a building for each pv generator if there are more buildings than generators within a given AGS. Primarily generators are distributed with the same qunatile as the buildings. Multiple assignment is excluded.

Parameters:

  • q_mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded and qcut MaStR data.

  • q_buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing qcut OSM buildings data.

  • seed (int) – Seed to use for random operations with NumPy and pandas.

Returns:

tuple with two geopandas.GeoDataFrame s – GeoDataFrame containing MaStR data allocated to building IDs. GeoDataFrame containing building data allocated to MaStR IDs.

allocate_scenarios(mastr_gdf: GeoDataFrame, valid_buildings_gdf: GeoDataFrame, last_scenario_gdf: GeoDataFrame, scenario: str)[source]

Desaggregate and allocate scenario pv rooftop ramp-ups onto buildings.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • valid_buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

  • last_scenario_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings matched with pv generators from temporally preceding scenario.

  • scenario (str) – Scenario to desaggrgate and allocate.

Returns:

tuple –

geopandas.GeoDataFrame

GeoDataFrame containing OSM buildings matched with pv generators.

pandas.DataFrame

DataFrame containing pv rooftop capacity per grid id.

allocate_to_buildings(mastr_gdf: GeoDataFrame, buildings_gdf: GeoDataFrame) → tuple[GeoDataFrame, GeoDataFrame][source]

Allocate status quo pv rooftop generators to buildings.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing MaStR data with geocoded locations.

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data with buildings without an AGS ID dropped.

Returns:

tuple with two geopandas.GeoDataFrame s – GeoDataFrame containing MaStR data allocated to building IDs. GeoDataFrame containing building data allocated to MaStR IDs.

building_area_range_per_cap_range(mastr_gdf: GeoDataFrame, cap_ranges: list[tuple[int | float, int | float]] | None = None, min_building_size: int | float = 10.0, upper_quantile: float = 0.95, lower_quantile: float = 0.05) → dict[tuple[int | float, int | float], tuple[int | float, int | float]][source]

Estimate normal building area range per capacity range. Calculate the mean roof load factor per capacity range from existing PV plants.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • cap_ranges (list(tuple(int, int))) – List of capacity ranges to distinguish between. The first tuple should start with a zero and the last one should end with infinite.

  • min_building_size (int, float) – Minimal building size to consider for PV plants.

  • upper_quantile (float) – Upper quantile to estimate maximum building size per capacity range.

  • lower_quantile (float) – Lower quantile to estimate minimum building size per capacity range.

Returns:

dict – Dictionary with estimated normal building area range per capacity range.

calculate_building_load_factor(mastr_gdf: GeoDataFrame, buildings_gdf: GeoDataFrame, rounding: int = 4) → GeoDataFrame[source]

Calculate the roof load factor from existing PV systems.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

  • rounding (int) – Rounding to use for load factor.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing geocoded MaStR data with calculated load factor.

calculate_max_pv_cap_per_building(buildings_gdf: GeoDataFrame, mastr_gdf: GeoDataFrame, pv_cap_per_sq_m: float | int, roof_factor: float | int) → GeoDataFrame[source]

Calculate the estimated maximum possible PV capacity per building.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • pv_cap_per_sq_m (float, int) – Average expected, installable PV capacity per square meter.

  • roof_factor (float, int) – Average for PV usable roof area share.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with estimated maximum PV capacity.

cap_per_bus_id(scenario: str) → DataFrame[source]

Get table with total pv rooftop capacity per grid district.

Parameters:

scenario (str) – Scenario name.

Returns:

pandas.DataFrame – DataFrame with total rooftop capacity per mv grid.

cap_share_per_cap_range(mastr_gdf: GeoDataFrame, cap_ranges: list[tuple[int | float, int | float]] | None = None) → dict[tuple[int | float, int | float], float][source]

Calculate the share of PV capacity from the total PV capacity within capacity ranges.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • cap_ranges (list(tuple(int, int))) – List of capacity ranges to distinguish between. The first tuple should start with a zero and the last one should end with infinite.

Returns:

dict – Dictionary with share of PV capacity from the total PV capacity within capacity ranges.

clean_mastr_data(mastr_gdf: GeoDataFrame, max_realistic_pv_cap: int | float, min_realistic_pv_cap: int | float, seed: int) → GeoDataFrame[source]

Clean the MaStR data from implausible data.

  • Drop MaStR ID duplicates.

  • Drop generators with implausible capacities.

Parameters:

  • mastr_gdf (pandas.DataFrame) – DataFrame containing MaStR data.

  • max_realistic_pv_cap (int or float) – Maximum capacity, which is considered to be realistic.

  • min_realistic_pv_cap (int or float) – Minimum capacity, which is considered to be realistic.

  • seed (int) – Seed to use for random operations with NumPy and pandas.

Returns:

pandas.DataFrame – DataFrame containing cleaned MaStR data.

create_scenario_table(buildings_gdf)[source]

Create mapping table pv_unit <-> building for scenario

desaggregate_pv(buildings_gdf: GeoDataFrame, cap_df: DataFrame, **kwargs) → GeoDataFrame[source]

Desaggregate PV capacity on buildings within a given grid district.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

  • cap_df (pandas.DataFrame) – DataFrame with total rooftop capacity per mv grid.

Other Parameters:

  • prob_dict (dict) – Dictionary with values and probabilities per capacity range.

  • cap_share_dict (dict) – Dictionary with share of PV capacity from the total PV capacity within capacity ranges.

  • building_area_range_dict (dict) – Dictionary with estimated normal building area range per capacity range.

  • load_factor_dict (dict) – Dictionary with mean roof load factor per capacity range.

  • seed (int) – Seed to use for random operations with NumPy and pandas.

  • pv_cap_per_sq_m (float, int) – Average expected, installable PV capacity per square meter.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM building data with desaggregated PV plants.

desaggregate_pv_in_mv_grid(buildings_gdf: GeoDataFrame, pv_cap: float | int, **kwargs) → GeoDataFrame[source]

Desaggregate PV capacity on buildings within a given grid district.

Parameters:

  • buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing buildings within the grid district.

  • pv_cap (float, int) – PV capacity to desaggregate.

Other Parameters:

  • prob_dict (dict) – Dictionary with values and probabilities per capacity range.

  • cap_share_dict (dict) – Dictionary with share of PV capacity from the total PV capacity within capacity ranges.

  • building_area_range_dict (dict) – Dictionary with estimated normal building area range per capacity range.

  • load_factor_dict (dict) – Dictionary with mean roof load factor per capacity range.

  • seed (int) – Seed to use for random operations with NumPy and pandas.

  • pv_cap_per_sq_m (float, int) – Average expected, installable PV capacity per square meter.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM building data with desaggregated PV plants.

determine_end_of_life_gens(mastr_gdf: GeoDataFrame, scenario_timestamp: Timestamp, pv_rooftop_lifetime: Timedelta) → GeoDataFrame[source]

Determine if an old PV system has reached its end of life.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • scenario_timestamp (pandas.Timestamp) – Timestamp at which the scenario takes place.

  • pv_rooftop_lifetime (pandas.Timedelta) – Average expected lifetime of PV rooftop systems.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing geocoded MaStR data and info if the system has reached its end of life.

drop_buildings_outside_grids(buildings_gdf: GeoDataFrame) → GeoDataFrame[source]

Drop all buildings outside of grid areas.

Parameters:

buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

Returns:

gepandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with buildings without an bus ID dropped.

drop_buildings_outside_muns(buildings_gdf: GeoDataFrame) → GeoDataFrame[source]

Drop all buildings outside of municipalities.

Parameters:

buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing OSM buildings data.

Returns:

gepandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with buildings without an AGS ID dropped.

drop_gens_outside_muns(mastr_gdf: GeoDataFrame) → GeoDataFrame[source]

Drop all generators outside of municipalities.

Parameters:

mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame with valid and cleaned MaStR data.

Returns:

gepandas.GeoDataFrame – GeoDataFrame with valid and cleaned MaStR data with generatos without an AGS ID dropped.

drop_unallocated_gens(gdf: GeoDataFrame) → GeoDataFrame[source]

Drop generators which did not get allocated.

Parameters:

gdf (geopandas.GeoDataFrame) – GeoDataFrame containing MaStR data allocated to building IDs.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing MaStR data with generators dropped which did not get allocated.

egon_building_peak_loads()[source]
federal_state_data(to_crs: CRS) → GeoDataFrame[source]

Get feder state data from eGo^n Database.

Parameters:

to_crs (pyproj.crs.crs.CRS) – CRS to transform geometries to.

Returns:

geopandas.GeoDataFrame – GeoDataFrame with federal state data.

frame_to_numeric(df: DataFrame | GeoDataFrame) → DataFrame | GeoDataFrame[source]

Try to convert all columns of a DataFrame to numeric ignoring errors.

Parameters:

df (pandas.DataFrame or geopandas.GeoDataFrame)

Returns:

pandas.DataFrame or geopandas.GeoDataFrame

get_probability_for_property(mastr_gdf: GeoDataFrame, cap_range: tuple[int | float, int | float], prop: str) → tuple[array, array][source]

Calculate the probability of the different options of a property of the existing PV plants.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • cap_range (tuple(int, int)) – Capacity range of PV plants to look at.

  • prop (str) – Property to calculate probabilities for. String needs to be in columns of mastr_gdf.

Returns:

tuple –

numpy.array

Unique values of property.

numpy.array

Probabilties per unique value.

grid_districts(epsg: int) → GeoDataFrame[source]

Load mv grid district geo data from eGo^n Database as geopandas.GeoDataFrame.

Parameters:

epsg (int) – EPSG ID to use as CRS.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing mv grid district ID and geo shapes data.

infer_voltage_level(units_gdf: GeoDataFrame) → GeoDataFrame[source]

Infer nan values in voltage level derived from generator capacity to the power plants.

Parameters:

  • units_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing units with voltage levels from MaStR

  • Returnsunits_gdf (gpd.GeoDataFrame)

  • ——-

  • geopandas.GeoDataFrame – GeoDataFrame containing units all having assigned a voltage level.

load_building_data()[source]

Read buildings from DB Tables:

  • openstreetmap.osm_buildings_filtered (from OSM)

  • openstreetmap.osm_buildings_synthetic (synthetic, created by us)

Use column id for both as it is unique hence you concat both datasets. If INCLUDE_SYNTHETIC_BUILDINGS is False synthetic buildings will not be loaded.

Returns:

gepandas.GeoDataFrame – GeoDataFrame containing OSM buildings data with buildings without an AGS ID dropped.

load_mastr_data()[source]

Read PV rooftop data from MaStR CSV Note: the source will be replaced as soon as the MaStR data is available in DB.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing MaStR data with geocoded locations.

mastr_data(index_col: str | int | list[str] | list[int]) → GeoDataFrame[source]

Read MaStR data from database.

Parameters:

index_col (str, int or list of str or int) – Column(s) to use as the row labels of the DataFrame.

Returns:

pandas.DataFrame – DataFrame containing MaStR data.

mean_load_factor_per_cap_range(mastr_gdf: GeoDataFrame, cap_ranges: list[tuple[int | float, int | float]] | None = None) → dict[tuple[int | float, int | float], float][source]

Calculate the mean roof load factor per capacity range from existing PV plants.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • cap_ranges (list(tuple(int, int))) – List of capacity ranges to distinguish between. The first tuple should start with a zero and the last one should end with infinite.

Returns:

dict – Dictionary with mean roof load factor per capacity range.

municipality_data() → GeoDataFrame[source]

Get municipality data from eGo^n Database. :returns: gepandas.GeoDataFrame – GeoDataFrame with municipality data.

osm_buildings(to_crs: CRS) → GeoDataFrame[source]

Read OSM buildings data from eGo^n Database.

Parameters:

to_crs (pyproj.crs.crs.CRS) – CRS to transform geometries to.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM buildings data.

overlay_grid_districts_with_counties(mv_grid_district_gdf: GeoDataFrame, federal_state_gdf: GeoDataFrame) → GeoDataFrame[source]

Calculate the intersections of mv grid districts and counties.

Parameters:

  • mv_grid_district_gdf (gpd.GeoDataFrame) – GeoDataFrame containing mv grid district ID and geo shapes data.

  • federal_state_gdf (gpd.GeoDataFrame) – GeoDataFrame with federal state data.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM buildings data.

probabilities(mastr_gdf: GeoDataFrame, cap_ranges: list[tuple[int | float, int | float]] | None = None, properties: list[str] | None = None) → dict[source]

Calculate the probability of the different options of properties of the existing PV plants.

Parameters:

  • mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing geocoded MaStR data.

  • cap_ranges (list(tuple(int, int))) – List of capacity ranges to distinguish between. The first tuple should start with a zero and the last one should end with infinite.

  • properties (list(str)) – List of properties to calculate probabilities for. Strings need to be in columns of mastr_gdf.

Returns:

dict – Dictionary with values and probabilities per capacity range.

pv_rooftop_to_buildings()[source]

Main script, executed as task

scenario_data(carrier: str = 'solar_rooftop', scenario: str = 'eGon2035') → DataFrame[source]

Get scenario capacity data from eGo^n Database.

Parameters:

  • carrier (str) – Carrier type to filter table by.

  • scenario (str) – Scenario to filter table by.

Returns:

geopandas.GeoDataFrame – GeoDataFrame with scenario capacity data in GW.

sort_and_qcut_df(df: DataFrame | GeoDataFrame, col: str, q: int) → DataFrame | GeoDataFrame[source]

Determine the quantile of a given attribute in a (Geo)DataFrame. Sort the (Geo)DataFrame in ascending order for the given attribute.

Parameters:

  • df (pandas.DataFrame or geopandas.GeoDataFrame) – (Geo)DataFrame to sort and qcut.

  • col (str) – Name of the attribute to sort and qcut the (Geo)DataFrame on.

  • q (int) – Number of quantiles.

Returns:

pandas.DataFrame or gepandas.GeoDataFrame – Sorted and qcut (Geo)DataFrame.

synthetic_buildings(to_crs: CRS) → GeoDataFrame[source]

Read synthetic buildings data from eGo^n Database.

Parameters:

to_crs (pyproj.crs.crs.CRS) – CRS to transform geometries to.

Returns:

geopandas.GeoDataFrame – GeoDataFrame containing OSM buildings data.

timer_func(func)[source]
validate_output(desagg_mastr_gdf: DataFrame | GeoDataFrame, desagg_buildings_gdf: DataFrame | GeoDataFrame) → None[source]

Validate output.

  • Validate that there are exactly as many buildings with a pv system as there are pv systems with a building

  • Validate that the building IDs with a pv system are the same building IDs as assigned to the pv systems

  • Validate that the pv system IDs with a building are the same pv system IDs as assigned to the buildings

Parameters:

  • desagg_mastr_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing MaStR data allocated to building IDs.

  • desagg_buildings_gdf (geopandas.GeoDataFrame) – GeoDataFrame containing building data allocated to MaStR IDs.