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""" |
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Functions related to the four different use cases |
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""" |
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from __future__ import annotations |
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from loguru import logger |
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import geopandas as gpd |
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import numpy as np |
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import pandas as pd |
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from egon.data import config |
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DATASET_CFG = config.datasets()["charging_infrastructure"] |
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def hpc(hpc_points: gpd.GeoDataFrame, uc_dict: dict) -> gpd.GeoDataFrame: |
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""" |
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Calculate placements and energy distribution for use case hpc. |
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:param hpc_points: gpd.GeoDataFrame |
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GeoDataFrame of possible hpc locations |
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:param uc_dict: dict |
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contains basic run info like region boundary and save directory |
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""" |
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uc_id = "hpc" |
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logger.debug(f"Use case: {uc_id}") |
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num_hpc = 10**6 |
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energy_sum = 1 |
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# filter hpc points by region |
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in_region_bool = hpc_points["geometry"].within(uc_dict["region"].iat[0]) |
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in_region = hpc_points.loc[in_region_bool] |
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if "has_hpc" in in_region.columns: |
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in_region = in_region.loc[in_region["has_hpc"]] |
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cols = [ |
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"geometry", |
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"hpc_count", |
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"potential", |
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"new_hpc_index", |
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"new_hpc_tag", |
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] |
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in_region = in_region[cols] |
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# select all hpc points tagged 0 (all registered points) |
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real_mask = in_region["new_hpc_tag"] == 0 |
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real_in_region = in_region.loc[real_mask] |
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num_hpc_real = real_in_region["hpc_count"].sum() |
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if num_hpc_real < num_hpc: |
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sim_in_region = in_region.loc[~real_mask] |
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sim_in_region = sim_in_region.loc[in_region["new_hpc_index"] > 0] |
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sim_in_region_sorted = sim_in_region.sort_values( |
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"potential", ascending=False |
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) |
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additional_hpc = int( |
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min(num_hpc - num_hpc_real, len(sim_in_region.index)) |
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) |
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selected_hpc = sim_in_region_sorted.iloc[:additional_hpc] |
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real_in_region = pd.concat([real_in_region, selected_hpc]) |
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if not len(real_in_region.index): |
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logger.warning( |
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f"No potential charging points found in region {uc_dict['key']}!" |
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) |
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else: |
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real_in_region["potential"] = ( |
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real_in_region["potential"] * real_in_region["hpc_count"] |
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) |
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total_potential = real_in_region["potential"].sum() |
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real_in_region = real_in_region.assign( |
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share=real_in_region["potential"] / total_potential |
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).round(6) |
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real_in_region["exists"] = real_in_region["new_hpc_tag"] == 0 |
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# outputs |
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logger.debug( |
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f"{round(energy_sum, 1)} kWh got fastcharged in region {uc_dict['key']}." |
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) |
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return gpd.GeoDataFrame(real_in_region) |
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def public( |
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public_points: gpd.GeoDataFrame, |
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public_data: gpd.GeoDataFrame, |
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uc_dict: dict, |
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) -> gpd.GeoDataFrame: |
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""" |
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Calculate placements and energy distribution for use case hpc. |
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:param public_points: gpd.GeoDataFrame |
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existing public charging points |
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:param public_data: gpd.GeoDataFrame |
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clustered POI |
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:param uc_dict: dict |
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contains basic run info like region boundary and save directory |
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""" |
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uc_id = "public" |
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logger.debug(f"Use case: {uc_id}") |
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num_public = 10**6 |
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energy_sum = 1 |
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# filter hpc points by region |
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in_region_bool = public_points["geometry"].within(uc_dict["region"].iat[0]) |
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in_region = public_points.loc[in_region_bool] |
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poi_in_region_bool = public_data["geometry"].within( |
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uc_dict["region"].iat[0] |
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) |
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poi_in_region = public_data.loc[poi_in_region_bool] |
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num_public_real = in_region["count"].sum() |
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# match with clusters anyway (for weights) |
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region_points, region_poi = match_existing_points(in_region, poi_in_region) |
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region_points["exists"] = True |
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if num_public_real < num_public: |
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additional_public = num_public - num_public_real |
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# distribute additional public points via POI |
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add_points = distribute_by_poi(region_poi, additional_public) |
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region_points = pd.concat([region_points, add_points]) |
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region_points["energy"] = ( |
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region_points["potential"] |
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/ region_points["potential"].sum() |
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* energy_sum |
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) |
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# outputs |
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logger.debug( |
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f"{round(energy_sum, 1)} kWh got charged in region {uc_dict['key']}." |
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) |
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return gpd.GeoDataFrame(region_points, crs=public_points.crs) |
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def distribute_by_poi(region_poi: gpd.GeoDataFrame, num_points: int | float): |
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# sort clusters without existing points by weight, then choose highest |
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region_poi = region_poi.copy() |
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region_poi.sort_values("potential", inplace=True, ascending=False) |
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num_points = int(min(num_points, len(region_poi.index))) |
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# choose point in cluster that is closest to big street |
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return region_poi.iloc[:num_points] |
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def match_existing_points( |
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region_points: gpd.GeoDataFrame, region_poi: gpd.GeoDataFrame |
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): |
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region_poi = region_poi.assign(exists=False) |
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poi_buffer = region_poi.buffer(region_poi["radius"].astype(int)) |
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region_points = region_points.assign(potential=0) |
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for i in region_points.index: |
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lis_point = region_points.at[i, "geometry"] |
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cluster = poi_buffer.contains(lis_point) |
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clusters = region_poi.loc[cluster] |
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num_clusters = len(clusters.index) |
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if num_clusters == 0: |
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# decent average as fallback |
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region_points.at[i, "potential"] = 5 |
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elif num_clusters == 1: |
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region_points.at[i, "potential"] = clusters["potential"] |
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region_poi.loc[cluster, "exists"] = True |
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elif num_clusters > 1: |
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# choose cluster with closest Point |
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dist = clusters.distance(lis_point) |
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idx = dist.idxmin() |
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region_poi.at[idx, "exists"] = True |
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region_points.at[i, "potential"] = clusters.at[idx, "potential"] |
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# delete all clusters with exists = True |
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region_poi = region_poi.loc[~region_poi["exists"]] |
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return region_points, region_poi |
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def home( |
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home_data: gpd.GeoDataFrame, |
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uc_dict: dict, |
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) -> gpd.GeoDataFrame: |
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""" |
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Calculate placements and energy distribution for use case hpc. |
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191
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:param home_data: gpd.GeoDataFrame |
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info about house types |
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:param uc_dict: dict |
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contains basic run info like region boundary and save directory |
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""" |
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uc_id = "home" |
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logger.debug(f"Use case: {uc_id}") |
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num_home = 1000000 |
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energy_sum = 1 |
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# filter houses by region |
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in_region_bool = home_data["geometry"].within(uc_dict["region"].iat[0]) |
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in_region = home_data.loc[in_region_bool] |
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if in_region.empty: |
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return in_region |
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in_region = in_region.assign( |
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num=in_region["num"].fillna(value=0), |
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num_mfh=in_region["num_mfh"].fillna(value=0), |
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) |
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potential = apportion_home(in_region, num_home, uc_dict) |
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in_region["charge_spots"] = potential |
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in_region = in_region.loc[in_region["charge_spots"] > 0] |
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in_region["energy"] = energy_sum * in_region["charge_spots"] / num_home |
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in_region = in_region.sort_values(by="energy", ascending=False) |
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logger.debug( |
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f"{round(energy_sum, 1)} kWh got charged in region {uc_dict['key']}." |
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) |
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return gpd.GeoDataFrame(in_region, crs=home_data.crs) |
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227
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228
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def apportion_home(home_df: pd.DataFrame, num_spots: int, config: dict): |
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# use parameters to set number of possible charge spots per row |
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home_df["num_available"] = home_df[["num", "num_mfh"]].apply( |
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home_charge_spots, axis=1, raw=True, args=(config,) |
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) |
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# if too many spots need to be placed, every house gets a spot |
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if num_spots >= home_df["num_available"].sum(): |
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logger.debug( |
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f"All private home spots have been filled. Leftover: " |
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f"{num_spots - home_df['num_available'].sum()}" |
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) |
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return home_df.loc[:, "num_available"] |
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# distribute charge points based on houses per square |
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samples = home_df.sample( |
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num_spots, weights="num_available", random_state=1, replace=True |
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) |
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result = pd.Series([0] * len(home_df.index), index=home_df.index) |
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for i in samples.index: |
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result.at[i] += 1 |
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return result |
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249
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250
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def home_charge_spots(house_array: pd.Series | np.array, config: dict): |
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251
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# take number of houses, random seed, average spots per house and share of houses |
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252
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# with possible spots |
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253
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sfh = ( |
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house_array[0] |
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* config["sfh_avg_spots"] |
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256
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* max(config["random_seed"].normal(config["sfh_available"], 0.1), 0) |
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257
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) |
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258
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mfh = ( |
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259
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house_array[1] |
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260
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* config["mfh_avg_spots"] |
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* max(config["random_seed"].normal(config["mfh_available"], 0.1), 0) |
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262
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) |
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263
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return round(sfh + mfh) |
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264
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265
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266
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def work( |
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267
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landuse: gpd.GeoDataFrame, |
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268
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weights_dict: dict, |
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269
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uc_dict: dict, |
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270
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) -> gpd.GeoDataFrame: |
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271
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""" |
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272
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Calculate placements and energy distribution for use case hpc. |
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273
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274
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:param landuse: gpd.GeoDataFrame |
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275
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work areas by land use |
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276
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:param weights_dict: dict |
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277
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weights for different land use types |
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278
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:param uc_dict: dict |
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279
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|
|
contains basic run info like region boundary and save directory |
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280
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""" |
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281
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uc_id = "work" |
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282
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logger.debug(f"Use case: {uc_id}") |
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283
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284
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energy_sum = 1 |
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285
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286
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in_region_bool = landuse.within(uc_dict["region"].iat[0]) |
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287
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in_region = landuse[in_region_bool] |
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288
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289
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# calculating the area of polygons |
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290
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in_region = in_region.assign(area=in_region["geometry"].area / 10**6) |
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291
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292
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groups = in_region.groupby("landuse") |
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293
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group_labels = ["retail", "commercial", "industrial"] |
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294
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|
295
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srid = DATASET_CFG["original_data"]["sources"]["tracbev"]["srid"] |
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296
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|
297
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result = gpd.GeoDataFrame( |
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298
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columns=["geometry", "landuse", "potential"], crs=f"EPSG:{srid}" |
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299
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) |
|
300
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301
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for g in group_labels: |
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302
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if g in groups.groups: |
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303
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group = groups.get_group(g) |
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304
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group = group.assign( |
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305
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potential=group["geometry"].area * weights_dict[g] |
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306
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) |
|
307
|
|
|
group.to_crs(srid) |
|
308
|
|
|
result = gpd.GeoDataFrame( |
|
309
|
|
|
pd.concat([result, group]), crs=f"EPSG:{srid}" |
|
310
|
|
|
) |
|
311
|
|
|
|
|
312
|
|
|
result["energy"] = ( |
|
313
|
|
|
result["potential"] * energy_sum / result["potential"].sum() |
|
314
|
|
|
) |
|
315
|
|
|
# outputs |
|
316
|
|
|
logger.debug( |
|
317
|
|
|
f"{round(energy_sum, 1)} kWh got charged in region {uc_dict['key']}." |
|
318
|
|
|
) |
|
319
|
|
|
|
|
320
|
|
|
return gpd.GeoDataFrame(result, crs=landuse.crs) |
|
321
|
|
|
|
openego /
eGon-data
