data.datasets.industry.temporal.insert_sites_ind_load() - Code Metrics - Inspection of "Features/#1048 desagg dsm time series" - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — dev (#1052)

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created 2022-11-29 12:12 UTC

insert_sites_ind_load() A

↳ Parent: data.datasets.industry.temporal

Complexity

Conditions

Size

Total Lines	62
Code Lines	29

Duplication

Lines	58
Ratio	93.55 %

Importance

Changes

Metric	Value
eloc	29
dl	58
loc	62
rs	9.184
c	0
b	0
f	0
cc	2
nop	0

How to fix Long Method

"""The central module containing all code dealing with processing
timeseries data using demandregio

"""

from sqlalchemy import ARRAY, Column, Float, Integer, String
from sqlalchemy.ext.declarative import declarative_base
import geopandas as gpd
import numpy as np
import pandas as pd

from egon.data import db
from egon.data.datasets.electricity_demand.temporal import calc_load_curve
import egon.data.config

Base = declarative_base()


def identify_voltage_level(df):

    """Identify the voltage_level of a grid component based on its peak load and
    defined thresholds.


    Parameters
    ----------
    df : pandas.DataFrame
        Data frame containing information about peak loads


    Returns
    -------
    pandas.DataFrame
        Data frame with an additional column with voltage level

    """

    df["voltage_level"] = np.nan

    # Identify voltage_level for every demand area taking thresholds into account which were defined in the eGon project
    df.loc[df["peak_load"] <= 0.1, "voltage_level"] = 7
    df.loc[df["peak_load"] > 0.1, "voltage_level"] = 6
    df.loc[df["peak_load"] > 0.2, "voltage_level"] = 5
    df.loc[df["peak_load"] > 5.5, "voltage_level"] = 4
    df.loc[df["peak_load"] > 20, "voltage_level"] = 3
    df.loc[df["peak_load"] > 120, "voltage_level"] = 1

    return df


def identify_bus(load_curves, demand_area):
    """Identify the grid connection point for a consumer by determining its grid level
    based on the time series' peak load and the spatial intersection to mv
    grid districts or ehv voronoi cells.


    Parameters
    ----------
    load_curves : pandas.DataFrame
        Demand timeseries per demand area (e.g. osm landuse area, industrial site)

    demand_area: pandas.DataFrame
        Dataframe with id and geometry of areas where an industrial demand
        is assigned to, such as osm landuse areas or industrial sites.

    Returns
    -------
    pandas.DataFrame
        Aggregated industrial demand timeseries per bus

    """

    sources = egon.data.config.datasets()["electrical_load_curves_industry"][
        "sources"
    ]

    # Select mv griddistrict
    griddistrict = db.select_geodataframe(
        f"""SELECT bus_id, geom FROM
                {sources['egon_mv_grid_district']['schema']}.
                {sources['egon_mv_grid_district']['table']}""",
        geom_col="geom",
        epsg=3035,
    )

    # Initialize dataframe to identify peak load per demand area (e.g. osm landuse area or industrial site)
    peak = pd.DataFrame(columns=["id", "peak_load"])
    peak["id"] = load_curves.max(axis=0).index
    peak["peak_load"] = load_curves.max(axis=0).values

    peak = identify_voltage_level(peak)

    # Assign bus_id to demand area by merging landuse and peak df
    peak = pd.merge(demand_area, peak, right_on="id", left_index=True)

    # Identify all demand areas connected to HVMV buses
    peak_hv = peak[peak["voltage_level"] > 1]

    # Perform a spatial join between the centroid of the demand area and mv grid districts to identify grid connection point
    peak_hv["centroid"] = peak_hv["geom"].centroid
    peak_hv = peak_hv.set_geometry("centroid")
    peak_hv_c = gpd.sjoin(peak_hv, griddistrict, how="inner", op="intersects")

    # Perform a spatial join between the polygon of the demand area  and mv grid districts to ensure every area got assign to a bus
    peak_hv_p = peak_hv[~peak_hv.isin(peak_hv_c)].dropna().set_geometry("geom")
    peak_hv_p = gpd.sjoin(
        peak_hv_p, griddistrict, how="inner", op="intersects"
    ).drop_duplicates(subset=["id"])

    # Bring both dataframes together
    peak_bus = peak_hv_c.append(peak_hv_p, ignore_index=True)

    # Select ehv voronoi
    ehv_voronoi = db.select_geodataframe(
        f"""SELECT bus_id, geom FROM
                {sources['egon_mv_grid_district']['schema']}.
                {sources['egon_mv_grid_district']['table']}""",
        geom_col="geom",
        epsg=3035,
    )

    # Identify all demand areas connected to EHV buses
    peak_ehv = peak[peak["voltage_level"] == 1]

    # Perform a spatial join between the centroid of the demand area and ehv voronoi to identify grid connection point
    peak_ehv["centroid"] = peak_ehv["geom"].centroid
    peak_ehv = peak_ehv.set_geometry("centroid")
    peak_ehv = gpd.sjoin(peak_ehv, ehv_voronoi, how="inner", op="intersects")

    # Bring both dataframes together
    peak_bus = peak_bus.append(peak_ehv, ignore_index=True)

    # Combine dataframes to bring loadcurves and bus id together
    curves_da = pd.merge(
        load_curves.T,
        peak_bus[["bus_id", "id", "geom"]],
        left_index=True,
        right_on="id",
    )

    return curves_da


def calc_load_curves_ind_osm(scenario):
    """Temporal disaggregate electrical demand per osm industrial landuse area.


    Parameters
    ----------
    scenario : str
        Scenario name.

    Returns
    -------
    pandas.DataFrame
        Demand timeseries of industry allocated to osm landuse areas and aggregated
        per substation id

    """

    sources = egon.data.config.datasets()["electrical_load_curves_industry"][
        "sources"
    ]

    # Select demands per industrial branch and osm landuse area
    demands_osm_area = db.select_dataframe(
        f"""SELECT osm_id, wz, demand
            FROM {sources['osm']['schema']}.
            {sources['osm']['table']}
            WHERE scenario = '{scenario}'
            AND demand > 0
            """
    ).set_index(["osm_id", "wz"])

    # Select industrial landuse polygons as demand area
    demand_area = db.select_geodataframe(
        f"""SELECT id, geom FROM
                {sources['osm_landuse']['schema']}.
                {sources['osm_landuse']['table']}
                WHERE sector = 3 """,
        index_col="id",
        geom_col="geom",
        epsg=3035,
    )

    # Calculate shares of industrial branches per osm area
    osm_share_wz = demands_osm_area.groupby("osm_id").apply(
        lambda grp: grp / grp.sum()
    )

    osm_share_wz.reset_index(inplace=True)

    share_wz_transpose = pd.DataFrame(
        index=osm_share_wz.osm_id.unique(), columns=osm_share_wz.wz.unique()
    )
    share_wz_transpose.index.rename("osm_id", inplace=True)

    for wz in share_wz_transpose.columns:
        share_wz_transpose[wz] = (
            osm_share_wz[osm_share_wz.wz == wz].set_index("osm_id").demand
        )

    # Rename columns to bring it in line with demandregio data
    share_wz_transpose.rename(columns={1718: 17}, inplace=True)

    # Calculate industrial annual demand per osm area
    annual_demand_osm = demands_osm_area.groupby("osm_id").demand.sum()

    # Return electrical load curves per osm industrial landuse area
    load_curves = calc_load_curve(share_wz_transpose, annual_demand_osm)

    curves_da = identify_bus(load_curves, demand_area)

    # Group all load curves per bus
    curves_bus = (
        curves_da.drop(["id"], axis=1).fillna(0).groupby("bus_id").sum()
    )

    # Initalize pandas.DataFrame for export to database
    load_ts_df = pd.DataFrame(index=curves_bus.index, columns=["p_set"])

    # Insert time series data to df as an array
    load_ts_df.p_set = curves_bus.values.tolist()

    # Create Dataframe to store time series individually
    curves_individual_interim = (
        curves_da.drop(["bus_id", "geom"], axis=1).fillna(0)
    ).set_index("id")
    curves_individual = curves_da[["id", "bus_id"]]
    curves_individual["p_set"] = curves_individual_interim.values.tolist()
    curves_individual["scn_name"] = scenario
    curves_individual = curves_individual.rename(
        columns={"id": "osm_id"}
    ).set_index(["osm_id", "scn_name"])

    return load_ts_df, curves_individual


def insert_osm_ind_load():

    """Inserts electrical industry loads assigned to osm landuse areas to the database

    Returns
    -------
    None.

    """

    targets = egon.data.config.datasets()["electrical_load_curves_industry"][
        "targets"
    ]

    for scenario in ["eGon2035", "eGon100RE"]:

        # Delete existing data from database
        db.execute_sql(
            f"""
            DELETE FROM
            {targets['osm_load']['schema']}.{targets['osm_load']['table']}
            WHERE scn_name = '{scenario}'
            """
        )

        db.execute_sql(
            f"""
            DELETE FROM
            {targets['osm_load_individual']['schema']}.{targets['osm_load_individual']['table']}
            WHERE scn_name = '{scenario}'
            """
        )

        # Calculate cts load curves per mv substation (hvmv bus)
        data, curves_individual = calc_load_curves_ind_osm(scenario)
        data.index = data.index.rename("bus")
        data["scn_name"] = scenario

        data.set_index(["scn_name"], inplace=True, append=True)

        # Insert into database
        data.to_sql(
            targets["osm_load"]["table"],
            schema=targets["osm_load"]["schema"],
            con=db.engine(),
            if_exists="append",
        )

        curves_individual["peak_load"] = np.array(
            curves_individual["p_set"].values.tolist()
        ).max(axis=1)
        curves_individual["demand"] = np.array(
            curves_individual["p_set"].values.tolist()
        ).sum(axis=1)
        curves_individual = identify_voltage_level(curves_individual)

        curves_individual.to_sql(
            targets["osm_load_individual"]["table"],
            schema=targets["osm_load_individual"]["schema"],
            con=db.engine(),
            if_exists="append",
        )


def calc_load_curves_ind_sites(scenario):
    """Temporal disaggregation of load curves per industrial site and industrial subsector.


    Parameters
    ----------
    scenario : str
        Scenario name.

    Returns
    -------
    pandas.DataFrame
        Demand timeseries of industry allocated to industrial sites and aggregated
        per substation id and industrial subsector

    """
    sources = egon.data.config.datasets()["electrical_load_curves_industry"][
        "sources"
    ]

    # Select demands per industrial site including the subsector information
    demands_ind_sites = db.select_dataframe(
        f"""SELECT industrial_sites_id, wz, demand
            FROM {sources['sites']['schema']}.
            {sources['sites']['table']}
            WHERE scenario = '{scenario}'
            AND demand > 0
            """
    ).set_index(["industrial_sites_id"])

    # Select industrial sites as demand_areas from database

    demand_area = db.select_geodataframe(
        f"""SELECT id, geom FROM
                {sources['sites_geom']['schema']}.
                {sources['sites_geom']['table']}""",
        index_col="id",
        geom_col="geom",
        epsg=3035,
    )

    # Replace entries to bring it in line with demandregio's subsector definitions
    demands_ind_sites.replace(1718, 17, inplace=True)
    share_wz_sites = demands_ind_sites.copy()

    # Create additional df on wz_share per industrial site, which is always set to one
    # as the industrial demand per site is subsector specific

    share_wz_sites.demand = 1
    share_wz_sites.reset_index(inplace=True)

    share_transpose = pd.DataFrame(
        index=share_wz_sites.industrial_sites_id.unique(),
        columns=share_wz_sites.wz.unique(),
    )
    share_transpose.index.rename("industrial_sites_id", inplace=True)
    for wz in share_transpose.columns:
        share_transpose[wz] = (
            share_wz_sites[share_wz_sites.wz == wz]
            .set_index("industrial_sites_id")
            .demand
        )

    load_curves = calc_load_curve(share_transpose, demands_ind_sites["demand"])

    curves_da = identify_bus(load_curves, demand_area)

    curves_da = pd.merge(
        curves_da, demands_ind_sites.wz, left_on="id", right_index=True
    )

    # Group all load curves per bus and wz
    curves_bus = (
        curves_da.fillna(0)
        .groupby(["bus_id", "wz"])
        .sum()
        .drop(["id"], axis=1)
    )

    # Initalize pandas.DataFrame for pf table load timeseries
    load_ts_df = pd.DataFrame(index=curves_bus.index, columns=["p_set"])

    # Insert data for pf load timeseries table
    load_ts_df.p_set = curves_bus.values.tolist()

    # Create Dataframe to store time series individually
    curves_individual_interim = (
        curves_da.drop(["bus_id", "geom", "wz"], axis=1).fillna(0)
    ).set_index("id")
    curves_individual = curves_da[["id", "bus_id"]]
    curves_individual["p_set"] = curves_individual_interim.values.tolist()
    curves_individual["scn_name"] = scenario
    curves_individual = curves_individual.merge(
        curves_da[["wz", "id"]], left_on="id", right_on="id"
    )
    curves_individual = curves_individual.rename(
        columns={"id": "site_id"}
    ).set_index(["site_id", "scn_name"])

    return load_ts_df, curves_individual


def insert_sites_ind_load():

    """Inserts electrical industry loads assigned to osm landuse areas to the database

    Returns
    -------
    None.

    """

    targets = egon.data.config.datasets()["electrical_load_curves_industry"][
        "targets"
    ]

    for scenario in ["eGon2035", "eGon100RE"]:

        # Delete existing data from database
        db.execute_sql(
            f"""
            DELETE FROM
            {targets['sites_load']['schema']}.{targets['sites_load']['table']}
            WHERE scn_name = '{scenario}'
            """
        )

        # Delete existing data from database
        db.execute_sql(
            f"""
            DELETE FROM
            {targets['sites_load_individual']['schema']}.
            {targets['sites_load_individual']['table']}
            WHERE scn_name = '{scenario}'
            """
        )

        # Calculate industrial load curves per bus
        data, curves_individual = calc_load_curves_ind_sites(scenario)
        data.index = data.index.rename(["bus", "wz"])
        data["scn_name"] = scenario

        data.set_index(["scn_name"], inplace=True, append=True)

        # Insert into database
        data.to_sql(
            targets["sites_load"]["table"],
            schema=targets["sites_load"]["schema"],
            con=db.engine(),
            if_exists="append",
        )

        curves_individual["peak_load"] = np.array(
            curves_individual["p_set"].values.tolist()
        ).max(axis=1)
        curves_individual["demand"] = np.array(
            curves_individual["p_set"].values.tolist()
        ).sum(axis=1)
        curves_individual = identify_voltage_level(curves_individual)

        curves_individual.to_sql(
            targets["sites_load_individual"]["table"],
            schema=targets["sites_load_individual"]["schema"],
            con=db.engine(),
            if_exists="append",
        )


1		"""The central module containing all code dealing with processing
2		timeseries data using demandregio
3
4		"""
5
6		from sqlalchemy import ARRAY, Column, Float, Integer, String
7		from sqlalchemy.ext.declarative import declarative_base
8		import geopandas as gpd
9		import numpy as np
10		import pandas as pd
11
12		from egon.data import db
13		from egon.data.datasets.electricity_demand.temporal import calc_load_curve
14		import egon.data.config
15
16		Base = declarative_base()
17
18
19		def identify_voltage_level(df):
20
21		"""Identify the voltage_level of a grid component based on its peak load and
22		defined thresholds.
23
24
25		Parameters
26		----------
27		df : pandas.DataFrame
28		Data frame containing information about peak loads
29
30
31		Returns
32		-------
33		pandas.DataFrame
34		Data frame with an additional column with voltage level
35
36		"""
37
38		df["voltage_level"] = np.nan
39
40		# Identify voltage_level for every demand area taking thresholds into account which were defined in the eGon project
41		df.loc[df["peak_load"] <= 0.1, "voltage_level"] = 7
42		df.loc[df["peak_load"] > 0.1, "voltage_level"] = 6
43		df.loc[df["peak_load"] > 0.2, "voltage_level"] = 5
44		df.loc[df["peak_load"] > 5.5, "voltage_level"] = 4
45		df.loc[df["peak_load"] > 20, "voltage_level"] = 3
46		df.loc[df["peak_load"] > 120, "voltage_level"] = 1
47
48		return df
49
50
51		def identify_bus(load_curves, demand_area):
52		"""Identify the grid connection point for a consumer by determining its grid level
53		based on the time series' peak load and the spatial intersection to mv
54		grid districts or ehv voronoi cells.
55
56
57		Parameters
58		----------
59		load_curves : pandas.DataFrame
60		Demand timeseries per demand area (e.g. osm landuse area, industrial site)
61
62		demand_area: pandas.DataFrame
63		Dataframe with id and geometry of areas where an industrial demand
64		is assigned to, such as osm landuse areas or industrial sites.
65
66		Returns
67		-------
68		pandas.DataFrame
69		Aggregated industrial demand timeseries per bus
70
71		"""
72
73		sources = egon.data.config.datasets()["electrical_load_curves_industry"][
74		"sources"
75		]
76
77		# Select mv griddistrict
78		griddistrict = db.select_geodataframe(
79		f"""SELECT bus_id, geom FROM
80		{sources['egon_mv_grid_district']['schema']}.
81		{sources['egon_mv_grid_district']['table']}""",
82		geom_col="geom",
83		epsg=3035,
84		)
85
86		# Initialize dataframe to identify peak load per demand area (e.g. osm landuse area or industrial site)
87		peak = pd.DataFrame(columns=["id", "peak_load"])
88		peak["id"] = load_curves.max(axis=0).index
89		peak["peak_load"] = load_curves.max(axis=0).values
90
91		peak = identify_voltage_level(peak)
92
93		# Assign bus_id to demand area by merging landuse and peak df
94		peak = pd.merge(demand_area, peak, right_on="id", left_index=True)
95
96		# Identify all demand areas connected to HVMV buses
97		peak_hv = peak[peak["voltage_level"] > 1]
98
99		# Perform a spatial join between the centroid of the demand area and mv grid districts to identify grid connection point
100		peak_hv["centroid"] = peak_hv["geom"].centroid
101		peak_hv = peak_hv.set_geometry("centroid")
102		peak_hv_c = gpd.sjoin(peak_hv, griddistrict, how="inner", op="intersects")
103
104		# Perform a spatial join between the polygon of the demand area and mv grid districts to ensure every area got assign to a bus
105		peak_hv_p = peak_hv[~peak_hv.isin(peak_hv_c)].dropna().set_geometry("geom")
106		peak_hv_p = gpd.sjoin(
107		peak_hv_p, griddistrict, how="inner", op="intersects"
108		).drop_duplicates(subset=["id"])
109
110		# Bring both dataframes together
111		peak_bus = peak_hv_c.append(peak_hv_p, ignore_index=True)
112
113		# Select ehv voronoi
114		ehv_voronoi = db.select_geodataframe(
115		f"""SELECT bus_id, geom FROM
116		{sources['egon_mv_grid_district']['schema']}.
117		{sources['egon_mv_grid_district']['table']}""",
118		geom_col="geom",
119		epsg=3035,
120		)
121
122		# Identify all demand areas connected to EHV buses
123		peak_ehv = peak[peak["voltage_level"] == 1]
124
125		# Perform a spatial join between the centroid of the demand area and ehv voronoi to identify grid connection point
126		peak_ehv["centroid"] = peak_ehv["geom"].centroid
127		peak_ehv = peak_ehv.set_geometry("centroid")
128		peak_ehv = gpd.sjoin(peak_ehv, ehv_voronoi, how="inner", op="intersects")
129
130		# Bring both dataframes together
131		peak_bus = peak_bus.append(peak_ehv, ignore_index=True)
132
133		# Combine dataframes to bring loadcurves and bus id together
134		curves_da = pd.merge(
135		load_curves.T,
136		peak_bus[["bus_id", "id", "geom"]],
137		left_index=True,
138		right_on="id",
139		)
140
141		return curves_da
142
143
144		def calc_load_curves_ind_osm(scenario):
145		"""Temporal disaggregate electrical demand per osm industrial landuse area.
146
147
148		Parameters
149		----------
150		scenario : str
151		Scenario name.
152
153		Returns
154		-------
155		pandas.DataFrame
156		Demand timeseries of industry allocated to osm landuse areas and aggregated
157		per substation id
158
159		"""
160
161		sources = egon.data.config.datasets()["electrical_load_curves_industry"][
162		"sources"
163		]
164
165		# Select demands per industrial branch and osm landuse area
166		demands_osm_area = db.select_dataframe(
167		f"""SELECT osm_id, wz, demand
168		FROM {sources['osm']['schema']}.
169		{sources['osm']['table']}
170		WHERE scenario = '{scenario}'
171		AND demand > 0
172		"""
173		).set_index(["osm_id", "wz"])
174
175		# Select industrial landuse polygons as demand area
176		demand_area = db.select_geodataframe(
177		f"""SELECT id, geom FROM
178		{sources['osm_landuse']['schema']}.
179		{sources['osm_landuse']['table']}
180		WHERE sector = 3 """,
181		index_col="id",
182		geom_col="geom",
183		epsg=3035,
184		)
185
186		# Calculate shares of industrial branches per osm area
187		osm_share_wz = demands_osm_area.groupby("osm_id").apply(
188		lambda grp: grp / grp.sum()
189		)
190
191		osm_share_wz.reset_index(inplace=True)
192
193		share_wz_transpose = pd.DataFrame(
194		index=osm_share_wz.osm_id.unique(), columns=osm_share_wz.wz.unique()
195		)
196		share_wz_transpose.index.rename("osm_id", inplace=True)
197
198		for wz in share_wz_transpose.columns:
199		share_wz_transpose[wz] = (
200		osm_share_wz[osm_share_wz.wz == wz].set_index("osm_id").demand
201		)
202
203		# Rename columns to bring it in line with demandregio data
204		share_wz_transpose.rename(columns={1718: 17}, inplace=True)
205
206		# Calculate industrial annual demand per osm area
207		annual_demand_osm = demands_osm_area.groupby("osm_id").demand.sum()
208
209		# Return electrical load curves per osm industrial landuse area
210		load_curves = calc_load_curve(share_wz_transpose, annual_demand_osm)
211
212		curves_da = identify_bus(load_curves, demand_area)
213
214		# Group all load curves per bus
215		curves_bus = (
216		curves_da.drop(["id"], axis=1).fillna(0).groupby("bus_id").sum()
217		)
218
219		# Initalize pandas.DataFrame for export to database
220		load_ts_df = pd.DataFrame(index=curves_bus.index, columns=["p_set"])
221
222		# Insert time series data to df as an array
223		load_ts_df.p_set = curves_bus.values.tolist()
224
225		# Create Dataframe to store time series individually
226		curves_individual_interim = (
227		curves_da.drop(["bus_id", "geom"], axis=1).fillna(0)
228		).set_index("id")
229		curves_individual = curves_da[["id", "bus_id"]]
230		curves_individual["p_set"] = curves_individual_interim.values.tolist()
231		curves_individual["scn_name"] = scenario
232		curves_individual = curves_individual.rename(
233		columns={"id": "osm_id"}
234		).set_index(["osm_id", "scn_name"])
235
236		return load_ts_df, curves_individual
237
238
239	View Code Duplication	def insert_osm_ind_load():
		0 ignored issues – show Duplication introduced 2021-07-28 12:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
240		"""Inserts electrical industry loads assigned to osm landuse areas to the database
241
242		Returns
243		-------
244		None.
245
246		"""
247
248		targets = egon.data.config.datasets()["electrical_load_curves_industry"][
249		"targets"
250		]
251
252		for scenario in ["eGon2035", "eGon100RE"]:
253
254		# Delete existing data from database
255		db.execute_sql(
256		f"""
257		DELETE FROM
258		{targets['osm_load']['schema']}.{targets['osm_load']['table']}
259		WHERE scn_name = '{scenario}'
260		"""
261		)
262
263		db.execute_sql(
264		f"""
265		DELETE FROM
266		{targets['osm_load_individual']['schema']}.{targets['osm_load_individual']['table']}
267		WHERE scn_name = '{scenario}'
268		"""
269		)
270
271		# Calculate cts load curves per mv substation (hvmv bus)
272		data, curves_individual = calc_load_curves_ind_osm(scenario)
273		data.index = data.index.rename("bus")
274		data["scn_name"] = scenario
275
276		data.set_index(["scn_name"], inplace=True, append=True)
277
278		# Insert into database
279		data.to_sql(
280		targets["osm_load"]["table"],
281		schema=targets["osm_load"]["schema"],
282		con=db.engine(),
283		if_exists="append",
284		)
285
286		curves_individual["peak_load"] = np.array(
287		curves_individual["p_set"].values.tolist()
288		).max(axis=1)
289		curves_individual["demand"] = np.array(
290		curves_individual["p_set"].values.tolist()
291		).sum(axis=1)
292		curves_individual = identify_voltage_level(curves_individual)
293
294		curves_individual.to_sql(
295		targets["osm_load_individual"]["table"],
296		schema=targets["osm_load_individual"]["schema"],
297		con=db.engine(),
298		if_exists="append",
299		)
300
301
302		def calc_load_curves_ind_sites(scenario):
303		"""Temporal disaggregation of load curves per industrial site and industrial subsector.
304
305
306		Parameters
307		----------
308		scenario : str
309		Scenario name.
310
311		Returns
312		-------
313		pandas.DataFrame
314		Demand timeseries of industry allocated to industrial sites and aggregated
315		per substation id and industrial subsector
316
317		"""
318		sources = egon.data.config.datasets()["electrical_load_curves_industry"][
319		"sources"
320		]
321
322		# Select demands per industrial site including the subsector information
323		demands_ind_sites = db.select_dataframe(
324		f"""SELECT industrial_sites_id, wz, demand
325		FROM {sources['sites']['schema']}.
326		{sources['sites']['table']}
327		WHERE scenario = '{scenario}'
328		AND demand > 0
329		"""
330		).set_index(["industrial_sites_id"])
331
332		# Select industrial sites as demand_areas from database
333
334		demand_area = db.select_geodataframe(
335		f"""SELECT id, geom FROM
336		{sources['sites_geom']['schema']}.
337		{sources['sites_geom']['table']}""",
338		index_col="id",
339		geom_col="geom",
340		epsg=3035,
341		)
342
343		# Replace entries to bring it in line with demandregio's subsector definitions
344		demands_ind_sites.replace(1718, 17, inplace=True)
345		share_wz_sites = demands_ind_sites.copy()
346
347		# Create additional df on wz_share per industrial site, which is always set to one
348		# as the industrial demand per site is subsector specific
349
350		share_wz_sites.demand = 1
351		share_wz_sites.reset_index(inplace=True)
352
353		share_transpose = pd.DataFrame(
354		index=share_wz_sites.industrial_sites_id.unique(),
355		columns=share_wz_sites.wz.unique(),
356		)
357		share_transpose.index.rename("industrial_sites_id", inplace=True)
358		for wz in share_transpose.columns:
359		share_transpose[wz] = (
360		share_wz_sites[share_wz_sites.wz == wz]
361		.set_index("industrial_sites_id")
362		.demand
363		)
364
365		load_curves = calc_load_curve(share_transpose, demands_ind_sites["demand"])
366
367		curves_da = identify_bus(load_curves, demand_area)
368
369		curves_da = pd.merge(
370		curves_da, demands_ind_sites.wz, left_on="id", right_index=True
371		)
372
373		# Group all load curves per bus and wz
374		curves_bus = (
375		curves_da.fillna(0)
376		.groupby(["bus_id", "wz"])
377		.sum()
378		.drop(["id"], axis=1)
379		)
380
381		# Initalize pandas.DataFrame for pf table load timeseries
382		load_ts_df = pd.DataFrame(index=curves_bus.index, columns=["p_set"])
383
384		# Insert data for pf load timeseries table
385		load_ts_df.p_set = curves_bus.values.tolist()
386
387		# Create Dataframe to store time series individually
388		curves_individual_interim = (
389		curves_da.drop(["bus_id", "geom", "wz"], axis=1).fillna(0)
390		).set_index("id")
391		curves_individual = curves_da[["id", "bus_id"]]
392		curves_individual["p_set"] = curves_individual_interim.values.tolist()
393		curves_individual["scn_name"] = scenario
394		curves_individual = curves_individual.merge(
395		curves_da[["wz", "id"]], left_on="id", right_on="id"
396		)
397		curves_individual = curves_individual.rename(
398		columns={"id": "site_id"}
399		).set_index(["site_id", "scn_name"])
400
401		return load_ts_df, curves_individual
402
403
404	View Code Duplication	def insert_sites_ind_load():
		0 ignored issues – show Duplication introduced 2021-07-28 12:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
405		"""Inserts electrical industry loads assigned to osm landuse areas to the database
406
407		Returns
408		-------
409		None.
410
411		"""
412
413		targets = egon.data.config.datasets()["electrical_load_curves_industry"][
414		"targets"
415		]
416
417		for scenario in ["eGon2035", "eGon100RE"]:
418
419		# Delete existing data from database
420		db.execute_sql(
421		f"""
422		DELETE FROM
423		{targets['sites_load']['schema']}.{targets['sites_load']['table']}
424		WHERE scn_name = '{scenario}'
425		"""
426		)
427
428		# Delete existing data from database
429		db.execute_sql(
430		f"""
431		DELETE FROM
432		{targets['sites_load_individual']['schema']}.
433		{targets['sites_load_individual']['table']}
434		WHERE scn_name = '{scenario}'
435		"""
436		)
437
438		# Calculate industrial load curves per bus
439		data, curves_individual = calc_load_curves_ind_sites(scenario)
440		data.index = data.index.rename(["bus", "wz"])
441		data["scn_name"] = scenario
442
443		data.set_index(["scn_name"], inplace=True, append=True)
444
445		# Insert into database
446		data.to_sql(
447		targets["sites_load"]["table"],
448		schema=targets["sites_load"]["schema"],
449		con=db.engine(),
450		if_exists="append",
451		)
452
453		curves_individual["peak_load"] = np.array(
454		curves_individual["p_set"].values.tolist()
455		).max(axis=1)
456		curves_individual["demand"] = np.array(
457		curves_individual["p_set"].values.tolist()
458		).sum(axis=1)
459		curves_individual = identify_voltage_level(curves_individual)
460
461		curves_individual.to_sql(
462		targets["sites_load_individual"]["table"],
463		schema=targets["sites_load_individual"]["schema"],
464		con=db.engine(),
465		if_exists="append",
466		)
467

openego / eGon-data

Pull Request — dev (#1052)

insert_sites_ind_load() A

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