data.datasets.renewable_feedin.add_metadata() - Code Metrics - Inspection of "Features/#450 add metadata kh" - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — dev (#1122)

unknown

created 2023-04-14 13:43 UTC

data.datasets.renewable_feedin.add_metadata() B

↳ Parent: data.datasets.renewable_feedin

Complexity

Conditions

Size

Total Lines	97
Code Lines	60

Duplication

Lines	97
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	60
nop	0
dl	97
loc	97
rs	8.309
c	0
b	0
f	0

How to fix Long Method

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

import datetime
import json
import time
from sqlalchemy import Column, ForeignKey, Integer
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 import Dataset
from egon.data.datasets.era5 import EgonEra5Cells, EgonRenewableFeedIn, import_cutout
from egon.data.datasets.scenario_parameters import get_sector_parameters
from egon.data.metadata import (
    context,
    generate_resource_fields_from_sqla_model,
    license_ccby,
    meta_metadata,
    sources,
)
from egon.data.datasets.zensus_vg250 import DestatisZensusPopulationPerHa
import egon.data.config


class RenewableFeedin(Dataset):
    def __init__(self, dependencies):
        super().__init__(
            name="RenewableFeedin",
            version="0.0.8",
            dependencies=dependencies,
            tasks={
                wind,
                pv,
                solar_thermal,
                heat_pump_cop,
                wind_offshore,
                mapping_zensus_weather,
            },
        )


Base = declarative_base()
engine = db.engine()


class MapZensusWeatherCell(Base):
    __tablename__ = "egon_map_zensus_weather_cell"
    __table_args__ = {"schema": "boundaries"}

    zensus_population_id = Column(
        Integer,
        ForeignKey(DestatisZensusPopulationPerHa.id),
        primary_key=True,
        index=True,
    )
    w_id = Column(Integer, ForeignKey(EgonEra5Cells.w_id), index=True)


def 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

    """

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

    return db.select_geodataframe(
        f"""SELECT w_id, geom_point, geom
        FROM {cfg['weather_cells']['schema']}.
        {cfg['weather_cells']['table']}
        WHERE ST_Intersects('SRID=4326;
        POLYGON((5 56, 15.5 56, 15.5 47, 5 47, 5 56))', geom)""",
        geom_col=geom_column,
        index_col="w_id",
    )


def offshore_weather_cells(geom_column="geom"):
    """Get weather cells which intersect with Germany

    Returns
    -------
    GeoPandas.GeoDataFrame
        Index and points of weather cells inside Germany

    """

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

    return db.select_geodataframe(
        f"""SELECT w_id, geom_point, geom
        FROM {cfg['weather_cells']['schema']}.
        {cfg['weather_cells']['table']}
        WHERE ST_Intersects('SRID=4326;
        POLYGON((5.5 55.5, 14.5 55.5, 14.5 53.5, 5.5 53.5, 5.5 55.5))',
         geom)""",
        geom_col=geom_column,
        index_col="w_id",
    )


def 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

    """

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

    # Select weather cells and ferear states from database
    weather_cells = weather_cells_in_germany(geom_column="geom_point")

    federal_states = db.select_geodataframe(
        f"""SELECT gen, geometry
        FROM {cfg['vg250_lan_union']['schema']}.
        {cfg['vg250_lan_union']['table']}""",
        geom_col="geometry",
        index_col="gen",
    )

    # Map federal state and onshore wind turbine to weather cells
    weather_cells["federal_state"] = gpd.sjoin(
        weather_cells, federal_states
    ).index_right

    # Assign a federal state to each cell inside Germany
    buffer = 1000

    while (buffer < 30000) & (
        len(weather_cells[weather_cells["federal_state"].isnull()]) > 0
    ):

        cells = weather_cells[weather_cells["federal_state"].isnull()]

        cells.loc[:, "geom_point"] = cells.geom_point.buffer(buffer)

        weather_cells.loc[cells.index, "federal_state"] = gpd.sjoin(
            cells, federal_states
        ).index_right

        buffer += 200

        weather_cells = (
            weather_cells.reset_index()
            .drop_duplicates(subset="w_id", keep="first")
            .set_index("w_id")
        )

    weather_cells = weather_cells.dropna(axis=0, subset=["federal_state"])

    return weather_cells.to_crs(4326)


def turbine_per_weather_cell():
    """Assign wind onshore turbine types to weather cells

    Returns
    -------
    weather_cells : GeoPandas.GeoDataFrame
        Weather cells in Germany including turbine type

    """

    # Select representative onshore wind turbines per federal state
    map_federal_states_turbines = {
        "Schleswig-Holstein": "E-126",
        "Bremen": "E-126",
        "Hamburg": "E-126",
        "Mecklenburg-Vorpommern": "E-126",
        "Niedersachsen": "E-126",
        "Berlin": "E-141",
        "Brandenburg": "E-141",
        "Hessen": "E-141",
        "Nordrhein-Westfalen": "E-141",
        "Sachsen": "E-141",
        "Sachsen-Anhalt": "E-141",
        "Thüringen": "E-141",
        "Baden-Württemberg": "E-141",
        "Bayern": "E-141",
        "Rheinland-Pfalz": "E-141",
        "Saarland": "E-141",
    }

    # Select weather cells and federal states
    weather_cells = federal_states_per_weather_cell()

    # Assign turbine type per federal state
    weather_cells["wind_turbine"] = weather_cells["federal_state"].map(
        map_federal_states_turbines
    )

    return weather_cells


def 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

    """

    # Select weather data for Germany
    cutout = import_cutout(boundary="Germany")

    gdf = gpd.GeoDataFrame(geometry=cutout.grid_cells(), crs=4326)

    # Calculate feedin-timeseries for E-141
    # source:
    # https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library
    turbine_e141 = {
        "name": "E141 4200 kW",
        "hub_height": 129,
        "P": 4.200,
        "V": np.arange(1, 26, dtype=float),
        "POW": np.array(
            [
                0.0,
                0.022,
                0.104,
                0.26,
                0.523,
                0.92,
                1.471,
                2.151,
                2.867,
                3.481,
                3.903,
                4.119,
                4.196,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
            ]
        ),
    }
    ts_e141 = cutout.wind(
        turbine_e141, per_unit=True, shapes=cutout.grid_cells()
    )

    gdf["E-141"] = ts_e141.to_pandas().transpose().values.tolist()

    # Calculate feedin-timeseries for E-126
    # source:
    # https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library
    turbine_e126 = {
        "name": "E126 4200 kW",
        "hub_height": 159,
        "P": 4.200,
        "V": np.arange(1, 26, dtype=float),
        "POW": np.array(
            [
                0.0,
                0.0,
                0.058,
                0.185,
                0.4,
                0.745,
                1.2,
                1.79,
                2.45,
                3.12,
                3.66,
                4.0,
                4.15,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
                4.2,
            ]
        ),
    }
    ts_e126 = cutout.wind(
        turbine_e126, per_unit=True, shapes=cutout.grid_cells()
    )

    gdf["E-126"] = ts_e126.to_pandas().transpose().values.tolist()

    return gdf


def wind():
    """Insert feed-in timeseries for wind onshore turbines to database

    Returns
    -------
    None.

    """

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

    # Get weather cells with turbine type
    weather_cells = turbine_per_weather_cell()
    weather_cells = weather_cells[weather_cells.wind_turbine.notnull()]

    # Calculate feedin timeseries per turbine and weather cell
    timeseries_per_turbine = feedin_per_turbine()

    # Join weather cells and feedin-timeseries
    timeseries = gpd.sjoin(weather_cells, timeseries_per_turbine)[
        ["E-141", "E-126"]
    ]

    weather_year = get_sector_parameters("global", "eGon2035")["weather_year"]

    df = pd.DataFrame(
        index=weather_cells.index,
        columns=["weather_year", "carrier", "feedin"],
        data={"weather_year": weather_year, "carrier": "wind_onshore"},
    )

    # Insert feedin for selected turbine per weather cell
    for turbine in ["E-126", "E-141"]:
        idx = weather_cells.index[
            (weather_cells.wind_turbine == turbine)
            & (weather_cells.index.isin(timeseries.index))
        ]
        df.loc[idx, "feedin"] = timeseries.loc[idx, turbine].values

    db.execute_sql(
        f"""
                   DELETE FROM {cfg['feedin_table']['schema']}.
                   {cfg['feedin_table']['table']}
                   WHERE carrier = 'wind_onshore'"""
    )

    # Insert values into database
    df.to_sql(
        cfg["feedin_table"]["table"],
        schema=cfg["feedin_table"]["schema"],
        con=db.engine(),
        if_exists="append",
    )


def wind_offshore():
    """Insert feed-in timeseries for wind offshore turbines to database

    Returns
    -------
    None.

    """

    # Get offshore weather cells arround Germany
    weather_cells = offshore_weather_cells()

    # Select weather data for German coast
    cutout = import_cutout(boundary="Germany-offshore")

    # Select weather year from cutout
    weather_year = cutout.name.split("-")[2]

    # Calculate feedin timeseries
    ts_wind_offshore = cutout.wind(
        "Vestas_V164_7MW_offshore",
        per_unit=True,
        shapes=weather_cells.to_crs(4326).geom,
    )

    # Create dataframe and insert to database
    insert_feedin(ts_wind_offshore, "wind_offshore", weather_year)


def pv():
    """Insert feed-in timeseries for pv plants to database

    Returns
    -------
    None.

    """

    # Get weather cells in Germany
    weather_cells = weather_cells_in_germany()

    # Select weather data for Germany
    cutout = import_cutout(boundary="Germany")

    # Select weather year from cutout
    weather_year = cutout.name.split("-")[1]

    # Calculate feedin timeseries
    ts_pv = cutout.pv(
        "CSi",
        orientation={"slope": 35.0, "azimuth": 180.0},
        per_unit=True,
        shapes=weather_cells.to_crs(4326).geom,
    )

    # Create dataframe and insert to database
    insert_feedin(ts_pv, "pv", weather_year)


def solar_thermal():
    """Insert feed-in timeseries for pv plants to database

    Returns
    -------
    None.

    """

    # Get weather cells in Germany
    weather_cells = weather_cells_in_germany()

    # Select weather data for Germany
    cutout = import_cutout(boundary="Germany")

    # Select weather year from cutout
    weather_year = cutout.name.split("-")[1]

    # Calculate feedin timeseries
    ts_solar_thermal = cutout.solar_thermal(
        clearsky_model="simple",
        orientation={"slope": 45.0, "azimuth": 180.0},
        per_unit=True,
        shapes=weather_cells.to_crs(4326).geom,
        capacity_factor=False,
    )

    # Create dataframe and insert to database
    insert_feedin(ts_solar_thermal, "solar_thermal", weather_year)


def 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.

    """
    # Assume temperature of heating system to 55°C according to Brown et. al
    t_sink = 55

    carrier = "heat_pump_cop"

    # Load configuration
    cfg = egon.data.config.datasets()["renewable_feedin"]

    # Get weather cells in Germany
    weather_cells = weather_cells_in_germany()

    # Select weather data for Germany
    cutout = import_cutout(boundary="Germany")

    # Select weather year from cutout
    weather_year = cutout.name.split("-")[1]

    # Calculate feedin timeseries
    temperature = cutout.temperature(
        shapes=weather_cells.to_crs(4326).geom
    ).transpose()

    t_source = temperature.to_pandas()

    delta_t = t_sink - t_source

    # Calculate coefficient of performance for air sourced heat pumps
    # according to Brown et. al
    cop = 6.81 - 0.121 * delta_t + 0.00063 * delta_t**2

    df = pd.DataFrame(
        index=temperature.to_pandas().index,
        columns=["weather_year", "carrier", "feedin"],
        data={"weather_year": weather_year, "carrier": carrier},
    )

    df.feedin = cop.values.tolist()

    # Delete existing rows for carrier
    db.execute_sql(
        f"""
                   DELETE FROM {cfg['targets']['feedin_table']['schema']}.
                   {cfg['targets']['feedin_table']['table']}
                   WHERE carrier = '{carrier}'"""
    )

    # Insert values into database
    df.to_sql(
        cfg["targets"]["feedin_table"]["table"],
        schema=cfg["targets"]["feedin_table"]["schema"],
        con=db.engine(),
        if_exists="append",
    )


def 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.

    """
    # Transpose DataFrame
    data = data.transpose().to_pandas()

    # Load configuration
    cfg = egon.data.config.datasets()["renewable_feedin"]

    # Initialize DataFrame
    df = pd.DataFrame(
        index=data.index,
        columns=["weather_year", "carrier", "feedin"],
        data={"weather_year": weather_year, "carrier": carrier},
    )

    # Convert solar thermal data from W/m^2 to MW/(1000m^2) = kW/m^2
    if carrier == "solar_thermal":
        data *= 1e-3

    # Insert feedin into DataFrame
    df.feedin = data.values.tolist()

    # Delete existing rows for carrier
    db.execute_sql(
        f"""
                   DELETE FROM {cfg['targets']['feedin_table']['schema']}.
                   {cfg['targets']['feedin_table']['table']}
                   WHERE carrier = '{carrier}'"""
    )

    # Insert values into database
    df.to_sql(
        cfg["targets"]["feedin_table"]["table"],
        schema=cfg["targets"]["feedin_table"]["schema"],
        con=db.engine(),
        if_exists="append",
    )


def mapping_zensus_weather():
    """Perform mapping between era5 weather cell and zensus grid"""

    with db.session_scope() as session:
        cells_query = session.query(
            DestatisZensusPopulationPerHa.id.label("zensus_population_id"),
            DestatisZensusPopulationPerHa.geom_point,
        )

    gdf_zensus_population = gpd.read_postgis(
        cells_query.statement,
        cells_query.session.bind,
        index_col=None,
        geom_col="geom_point",
    )

    with db.session_scope() as session:
        cells_query = session.query(EgonEra5Cells.w_id, EgonEra5Cells.geom)

    gdf_weather_cell = gpd.read_postgis(
        cells_query.statement,
        cells_query.session.bind,
        index_col=None,
        geom_col="geom",
    )
    # CRS is 4326
    gdf_weather_cell = gdf_weather_cell.to_crs(epsg=3035)

    gdf_zensus_weather = gdf_zensus_population.sjoin(
        gdf_weather_cell, how="left", predicate="within"
    )

    MapZensusWeatherCell.__table__.drop(bind=engine, checkfirst=True)
    MapZensusWeatherCell.__table__.create(bind=engine, checkfirst=True)

    # Write mapping into db
    with db.session_scope() as session:
        session.bulk_insert_mappings(
            MapZensusWeatherCell,
            gdf_zensus_weather[["zensus_population_id", "w_id"]].to_dict(
                orient="records"
            ),
        )


def add_metadata():

    """Add metdata to supply.egon_era5_renewable_feedin

    Returns
    -------
    None.

    """

    # Import column names and datatypes
    fields = [
        {
            "description": "Weather cell index",
            "name": "w_id",
            "type": "integer",
            "unit": "none",
        },
        {
            "description": "Weather year",
            "name": "weather_year",
            "type": "integer",
            "unit": "none",
        },
        {
            "description": "Energy carrier",
            "name": "carrier",
            "type": "string",
            "unit": "none",
        },
        {
            "description": "Weather-dependent feedin timeseries",
            "name": "feedin",
            "type": "array",
            "unit": "p.u.",
        },
    ]

    meta = {
        "name": "supply.egon_era5_renewable_feedin",
        "title": "eGon feedin timeseries for RES",
        "id": "WILL_BE_SET_AT_PUBLICATION",
        "description": "Weather-dependent feedin timeseries for RES",
        "language": ["EN"],
        "publicationDate": datetime.date.today().isoformat(),
        "context": context(),
        "spatial": {
            "location": None,
            "extent": "Germany",
            "resolution": None,
        },
        "sources": [
            sources()["era5"],
            sources()["vg250"],
            sources()["egon-data"],
        ],
        "licenses": [
            license_ccby(
                "© Bundesamt für Kartographie und Geodäsie 2020 (Daten verändert); "
                "© Copernicus Climate Change Service (C3S) Climate Data Store "
                "© Jonathan Amme, Clara Büttner, Ilka Cußmann, Julian Endres, Carlos Epia, Stephan Günther, Ulf Müller, Amélia Nadal, Guido Pleßmann, Francesco Witte",
            )
        ],
        "contributors": [
            {
                "title": "Clara Büttner",
                "email": "http://github.com/ClaraBuettner",
                "date": time.strftime("%Y-%m-%d"),
                "object": None,
                "comment": "Imported data",
            },
        ],
        "resources": [
            {
                "profile": "tabular-data-resource",
                "name": "supply.egon_scenario_capacities",
                "path": None,
                "format": "PostgreSQL",
                "encoding": "UTF-8",
                "schema": {
                    "fields": fields,
                    "primaryKey": ["index"],
                    "foreignKeys": [],
                },
                "dialect": {"delimiter": None, "decimalSeparator": "."},
            }
        ],
        "metaMetadata": meta_metadata(),
    }

    # Create json dump
    meta_json = "'" + json.dumps(meta) + "'"

    # Add metadata as a comment to the table
    db.submit_comment(
        meta_json,
        EgonRenewableFeedIn.__table__.schema,
        EgonRenewableFeedIn.__table__.name,
    )


1		"""
2		Central module containing all code dealing with processing era5 weather data.
3		"""
4
5		import datetime
6		import json
7		import time
8		from sqlalchemy import Column, ForeignKey, Integer
9		from sqlalchemy.ext.declarative import declarative_base
10		import geopandas as gpd
11		import numpy as np
12		import pandas as pd
13
14		from egon.data import db
15		from egon.data.datasets import Dataset
16		from egon.data.datasets.era5 import EgonEra5Cells, EgonRenewableFeedIn, import_cutout
17		from egon.data.datasets.scenario_parameters import get_sector_parameters
18		from egon.data.metadata import (
19		context,
20		generate_resource_fields_from_sqla_model,
21		license_ccby,
22		meta_metadata,
23		sources,
24		)
25		from egon.data.datasets.zensus_vg250 import DestatisZensusPopulationPerHa
26		import egon.data.config
27
28
29		class RenewableFeedin(Dataset):
30		def __init__(self, dependencies):
31		super().__init__(
32		name="RenewableFeedin",
33		version="0.0.8",
34		dependencies=dependencies,
35		tasks={
36		wind,
37		pv,
38		solar_thermal,
39		heat_pump_cop,
40		wind_offshore,
41		mapping_zensus_weather,
42		},
43		)
44
45
46		Base = declarative_base()
47		engine = db.engine()
48
49
50		class MapZensusWeatherCell(Base):
51		__tablename__ = "egon_map_zensus_weather_cell"
52		__table_args__ = {"schema": "boundaries"}
53
54		zensus_population_id = Column(
55		Integer,
56		ForeignKey(DestatisZensusPopulationPerHa.id),
57		primary_key=True,
58		index=True,
59		)
60		w_id = Column(Integer, ForeignKey(EgonEra5Cells.w_id), index=True)
61
62
63		def weather_cells_in_germany(geom_column="geom"):
64		"""Get weather cells which intersect with Germany
65
66		Returns
67		-------
68		GeoPandas.GeoDataFrame
69		Index and points of weather cells inside Germany
70
71		"""
72
73		cfg = egon.data.config.datasets()["renewable_feedin"]["sources"]
74
75		return db.select_geodataframe(
76		f"""SELECT w_id, geom_point, geom
77		FROM {cfg['weather_cells']['schema']}.
78		{cfg['weather_cells']['table']}
79		WHERE ST_Intersects('SRID=4326;
80		POLYGON((5 56, 15.5 56, 15.5 47, 5 47, 5 56))', geom)""",
81		geom_col=geom_column,
82		index_col="w_id",
83		)
84
85
86		def offshore_weather_cells(geom_column="geom"):
87		"""Get weather cells which intersect with Germany
88
89		Returns
90		-------
91		GeoPandas.GeoDataFrame
92		Index and points of weather cells inside Germany
93
94		"""
95
96		cfg = egon.data.config.datasets()["renewable_feedin"]["sources"]
97
98		return db.select_geodataframe(
99		f"""SELECT w_id, geom_point, geom
100		FROM {cfg['weather_cells']['schema']}.
101		{cfg['weather_cells']['table']}
102		WHERE ST_Intersects('SRID=4326;
103		POLYGON((5.5 55.5, 14.5 55.5, 14.5 53.5, 5.5 53.5, 5.5 55.5))',
104		geom)""",
105		geom_col=geom_column,
106		index_col="w_id",
107		)
108
109
110		def federal_states_per_weather_cell():
111		"""Assings a federal state to each weather cell in Germany.
112
113		Sets the federal state to the weather celss using the centroid.
114		Weather cells at the borders whoes centroid is not inside Germany
115		are assinged to the closest federal state.
116
117		Returns
118		-------
119		GeoPandas.GeoDataFrame
120		Index, points and federal state of weather cells inside Germany
121
122		"""
123
124		cfg = egon.data.config.datasets()["renewable_feedin"]["sources"]
125
126		# Select weather cells and ferear states from database
127		weather_cells = weather_cells_in_germany(geom_column="geom_point")
128
129		federal_states = db.select_geodataframe(
130		f"""SELECT gen, geometry
131		FROM {cfg['vg250_lan_union']['schema']}.
132		{cfg['vg250_lan_union']['table']}""",
133		geom_col="geometry",
134		index_col="gen",
135		)
136
137		# Map federal state and onshore wind turbine to weather cells
138		weather_cells["federal_state"] = gpd.sjoin(
139		weather_cells, federal_states
140		).index_right
141
142		# Assign a federal state to each cell inside Germany
143		buffer = 1000
144
145		while (buffer < 30000) & (
146		len(weather_cells[weather_cells["federal_state"].isnull()]) > 0
147		):
148
149		cells = weather_cells[weather_cells["federal_state"].isnull()]
150
151		cells.loc[:, "geom_point"] = cells.geom_point.buffer(buffer)
152
153		weather_cells.loc[cells.index, "federal_state"] = gpd.sjoin(
154		cells, federal_states
155		).index_right
156
157		buffer += 200
158
159		weather_cells = (
160		weather_cells.reset_index()
161		.drop_duplicates(subset="w_id", keep="first")
162		.set_index("w_id")
163		)
164
165		weather_cells = weather_cells.dropna(axis=0, subset=["federal_state"])
166
167		return weather_cells.to_crs(4326)
168
169
170		def turbine_per_weather_cell():
171		"""Assign wind onshore turbine types to weather cells
172
173		Returns
174		-------
175		weather_cells : GeoPandas.GeoDataFrame
176		Weather cells in Germany including turbine type
177
178		"""
179
180		# Select representative onshore wind turbines per federal state
181		map_federal_states_turbines = {
182		"Schleswig-Holstein": "E-126",
183		"Bremen": "E-126",
184		"Hamburg": "E-126",
185		"Mecklenburg-Vorpommern": "E-126",
186		"Niedersachsen": "E-126",
187		"Berlin": "E-141",
188		"Brandenburg": "E-141",
189		"Hessen": "E-141",
190		"Nordrhein-Westfalen": "E-141",
191		"Sachsen": "E-141",
192		"Sachsen-Anhalt": "E-141",
193		"Thüringen": "E-141",
194		"Baden-Württemberg": "E-141",
195		"Bayern": "E-141",
196		"Rheinland-Pfalz": "E-141",
197		"Saarland": "E-141",
198		}
199
200		# Select weather cells and federal states
201		weather_cells = federal_states_per_weather_cell()
202
203		# Assign turbine type per federal state
204		weather_cells["wind_turbine"] = weather_cells["federal_state"].map(
205		map_federal_states_turbines
206		)
207
208		return weather_cells
209
210
211		def feedin_per_turbine():
212		"""Calculate feedin timeseries per turbine type and weather cell
213
214		Returns
215		-------
216		gdf : GeoPandas.GeoDataFrame
217		Feed-in timeseries per turbine type and weather cell
218
219		"""
220
221		# Select weather data for Germany
222		cutout = import_cutout(boundary="Germany")
223
224		gdf = gpd.GeoDataFrame(geometry=cutout.grid_cells(), crs=4326)
225
226		# Calculate feedin-timeseries for E-141
227		# source:
228		# https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library
229		turbine_e141 = {
230		"name": "E141 4200 kW",
231		"hub_height": 129,
232		"P": 4.200,
233		"V": np.arange(1, 26, dtype=float),
234		"POW": np.array(
235		[
236		0.0,
237		0.022,
238		0.104,
239		0.26,
240		0.523,
241		0.92,
242		1.471,
243		2.151,
244		2.867,
245		3.481,
246		3.903,
247		4.119,
248		4.196,
249		4.2,
250		4.2,
251		4.2,
252		4.2,
253		4.2,
254		4.2,
255		4.2,
256		4.2,
257		4.2,
258		4.2,
259		4.2,
260		4.2,
261		]
262		),
263		}
264		ts_e141 = cutout.wind(
265		turbine_e141, per_unit=True, shapes=cutout.grid_cells()
266		)
267
268		gdf["E-141"] = ts_e141.to_pandas().transpose().values.tolist()
269
270		# Calculate feedin-timeseries for E-126
271		# source:
272		# https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library
273		turbine_e126 = {
274		"name": "E126 4200 kW",
275		"hub_height": 159,
276		"P": 4.200,
277		"V": np.arange(1, 26, dtype=float),
278		"POW": np.array(
279		[
280		0.0,
281		0.0,
282		0.058,
283		0.185,
284		0.4,
285		0.745,
286		1.2,
287		1.79,
288		2.45,
289		3.12,
290		3.66,
291		4.0,
292		4.15,
293		4.2,
294		4.2,
295		4.2,
296		4.2,
297		4.2,
298		4.2,
299		4.2,
300		4.2,
301		4.2,
302		4.2,
303		4.2,
304		4.2,
305		]
306		),
307		}
308		ts_e126 = cutout.wind(
309		turbine_e126, per_unit=True, shapes=cutout.grid_cells()
310		)
311
312		gdf["E-126"] = ts_e126.to_pandas().transpose().values.tolist()
313
314		return gdf
315
316
317		def wind():
318		"""Insert feed-in timeseries for wind onshore turbines to database
319
320		Returns
321		-------
322		None.
323
324		"""
325
326		cfg = egon.data.config.datasets()["renewable_feedin"]["targets"]
327
328		# Get weather cells with turbine type
329		weather_cells = turbine_per_weather_cell()
330		weather_cells = weather_cells[weather_cells.wind_turbine.notnull()]
331
332		# Calculate feedin timeseries per turbine and weather cell
333		timeseries_per_turbine = feedin_per_turbine()
334
335		# Join weather cells and feedin-timeseries
336		timeseries = gpd.sjoin(weather_cells, timeseries_per_turbine)[
337		["E-141", "E-126"]
338		]
339
340		weather_year = get_sector_parameters("global", "eGon2035")["weather_year"]
341
342		df = pd.DataFrame(
343		index=weather_cells.index,
344		columns=["weather_year", "carrier", "feedin"],
345		data={"weather_year": weather_year, "carrier": "wind_onshore"},
346		)
347
348		# Insert feedin for selected turbine per weather cell
349		for turbine in ["E-126", "E-141"]:
350		idx = weather_cells.index[
351		(weather_cells.wind_turbine == turbine)
352		& (weather_cells.index.isin(timeseries.index))
353		]
354		df.loc[idx, "feedin"] = timeseries.loc[idx, turbine].values
355
356		db.execute_sql(
357		f"""
358		DELETE FROM {cfg['feedin_table']['schema']}.
359		{cfg['feedin_table']['table']}
360		WHERE carrier = 'wind_onshore'"""
361		)
362
363		# Insert values into database
364		df.to_sql(
365		cfg["feedin_table"]["table"],
366		schema=cfg["feedin_table"]["schema"],
367		con=db.engine(),
368		if_exists="append",
369		)
370
371
372		def wind_offshore():
373		"""Insert feed-in timeseries for wind offshore turbines to database
374
375		Returns
376		-------
377		None.
378
379		"""
380
381		# Get offshore weather cells arround Germany
382		weather_cells = offshore_weather_cells()
383
384		# Select weather data for German coast
385		cutout = import_cutout(boundary="Germany-offshore")
386
387		# Select weather year from cutout
388		weather_year = cutout.name.split("-")[2]
389
390		# Calculate feedin timeseries
391		ts_wind_offshore = cutout.wind(
392		"Vestas_V164_7MW_offshore",
393		per_unit=True,
394		shapes=weather_cells.to_crs(4326).geom,
395		)
396
397		# Create dataframe and insert to database
398		insert_feedin(ts_wind_offshore, "wind_offshore", weather_year)
399
400
401		def pv():
402		"""Insert feed-in timeseries for pv plants to database
403
404		Returns
405		-------
406		None.
407
408		"""
409
410		# Get weather cells in Germany
411		weather_cells = weather_cells_in_germany()
412
413		# Select weather data for Germany
414		cutout = import_cutout(boundary="Germany")
415
416		# Select weather year from cutout
417		weather_year = cutout.name.split("-")[1]
418
419		# Calculate feedin timeseries
420		ts_pv = cutout.pv(
421		"CSi",
422		orientation={"slope": 35.0, "azimuth": 180.0},
423		per_unit=True,
424		shapes=weather_cells.to_crs(4326).geom,
425		)
426
427		# Create dataframe and insert to database
428		insert_feedin(ts_pv, "pv", weather_year)
429
430
431		def solar_thermal():
432		"""Insert feed-in timeseries for pv plants to database
433
434		Returns
435		-------
436		None.
437
438		"""
439
440		# Get weather cells in Germany
441		weather_cells = weather_cells_in_germany()
442
443		# Select weather data for Germany
444		cutout = import_cutout(boundary="Germany")
445
446		# Select weather year from cutout
447		weather_year = cutout.name.split("-")[1]
448
449		# Calculate feedin timeseries
450		ts_solar_thermal = cutout.solar_thermal(
451		clearsky_model="simple",
452		orientation={"slope": 45.0, "azimuth": 180.0},
453		per_unit=True,
454		shapes=weather_cells.to_crs(4326).geom,
455		capacity_factor=False,
456		)
457
458		# Create dataframe and insert to database
459		insert_feedin(ts_solar_thermal, "solar_thermal", weather_year)
460
461
462		def heat_pump_cop():
463		"""
464		Calculate coefficient of performance for heat pumps according to
465		T. Brown et al: "Synergies of sector coupling and transmission
466		reinforcement in a cost-optimised, highlyrenewable European energy system",
467		2018, p. 8
468
469		Returns
470		-------
471		None.
472
473		"""
474		# Assume temperature of heating system to 55°C according to Brown et. al
475		t_sink = 55
476
477		carrier = "heat_pump_cop"
478
479		# Load configuration
480		cfg = egon.data.config.datasets()["renewable_feedin"]
481
482		# Get weather cells in Germany
483		weather_cells = weather_cells_in_germany()
484
485		# Select weather data for Germany
486		cutout = import_cutout(boundary="Germany")
487
488		# Select weather year from cutout
489		weather_year = cutout.name.split("-")[1]
490
491		# Calculate feedin timeseries
492		temperature = cutout.temperature(
493		shapes=weather_cells.to_crs(4326).geom
494		).transpose()
495
496		t_source = temperature.to_pandas()
497
498		delta_t = t_sink - t_source
499
500		# Calculate coefficient of performance for air sourced heat pumps
501		# according to Brown et. al
502		cop = 6.81 - 0.121 * delta_t + 0.00063 * delta_t**2
503
504		df = pd.DataFrame(
505		index=temperature.to_pandas().index,
506		columns=["weather_year", "carrier", "feedin"],
507		data={"weather_year": weather_year, "carrier": carrier},
508		)
509
510		df.feedin = cop.values.tolist()
511
512		# Delete existing rows for carrier
513		db.execute_sql(
514		f"""
515		DELETE FROM {cfg['targets']['feedin_table']['schema']}.
516		{cfg['targets']['feedin_table']['table']}
517		WHERE carrier = '{carrier}'"""
518		)
519
520		# Insert values into database
521		df.to_sql(
522		cfg["targets"]["feedin_table"]["table"],
523		schema=cfg["targets"]["feedin_table"]["schema"],
524		con=db.engine(),
525		if_exists="append",
526		)
527
528
529		def insert_feedin(data, carrier, weather_year):
530		"""Insert feedin data into database
531
532		Parameters
533		----------
534		data : xarray.core.dataarray.DataArray
535		Feedin timeseries data
536		carrier : str
537		Name of energy carrier
538		weather_year : int
539		Selected weather year
540
541		Returns
542		-------
543		None.
544
545		"""
546		# Transpose DataFrame
547		data = data.transpose().to_pandas()
548
549		# Load configuration
550		cfg = egon.data.config.datasets()["renewable_feedin"]
551
552		# Initialize DataFrame
553		df = pd.DataFrame(
554		index=data.index,
555		columns=["weather_year", "carrier", "feedin"],
556		data={"weather_year": weather_year, "carrier": carrier},
557		)
558
559		# Convert solar thermal data from W/m^2 to MW/(1000m^2) = kW/m^2
560		if carrier == "solar_thermal":
561		data *= 1e-3
562
563		# Insert feedin into DataFrame
564		df.feedin = data.values.tolist()
565
566		# Delete existing rows for carrier
567		db.execute_sql(
568		f"""
569		DELETE FROM {cfg['targets']['feedin_table']['schema']}.
570		{cfg['targets']['feedin_table']['table']}
571		WHERE carrier = '{carrier}'"""
572		)
573
574		# Insert values into database
575		df.to_sql(
576		cfg["targets"]["feedin_table"]["table"],
577		schema=cfg["targets"]["feedin_table"]["schema"],
578		con=db.engine(),
579		if_exists="append",
580		)
581
582
583		def mapping_zensus_weather():
584		"""Perform mapping between era5 weather cell and zensus grid"""
585
586		with db.session_scope() as session:
587		cells_query = session.query(
588		DestatisZensusPopulationPerHa.id.label("zensus_population_id"),
589		DestatisZensusPopulationPerHa.geom_point,
590		)
591
592		gdf_zensus_population = gpd.read_postgis(
593		cells_query.statement,
594		cells_query.session.bind,
595		index_col=None,
596		geom_col="geom_point",
597		)
598
599		with db.session_scope() as session:
600		cells_query = session.query(EgonEra5Cells.w_id, EgonEra5Cells.geom)
601
602		gdf_weather_cell = gpd.read_postgis(
603		cells_query.statement,
604		cells_query.session.bind,
605		index_col=None,
606		geom_col="geom",
607		)
608		# CRS is 4326
609		gdf_weather_cell = gdf_weather_cell.to_crs(epsg=3035)
610
611		gdf_zensus_weather = gdf_zensus_population.sjoin(
612		gdf_weather_cell, how="left", predicate="within"
613		)
614
615		MapZensusWeatherCell.__table__.drop(bind=engine, checkfirst=True)
616		MapZensusWeatherCell.__table__.create(bind=engine, checkfirst=True)
617
618		# Write mapping into db
619		with db.session_scope() as session:
620		session.bulk_insert_mappings(
621		MapZensusWeatherCell,
622		gdf_zensus_weather[["zensus_population_id", "w_id"]].to_dict(
623		orient="records"
624		),
625		)
626
627
628	View Code Duplication	def add_metadata():
		0 ignored issues – show Duplication introduced 2021-12-13 16:43 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
629		"""Add metdata to supply.egon_era5_renewable_feedin
630
631		Returns
632		-------
633		None.
634
635		"""
636
637		# Import column names and datatypes
638		fields = [
639		{
640		"description": "Weather cell index",
641		"name": "w_id",
642		"type": "integer",
643		"unit": "none",
644		},
645		{
646		"description": "Weather year",
647		"name": "weather_year",
648		"type": "integer",
649		"unit": "none",
650		},
651		{
652		"description": "Energy carrier",
653		"name": "carrier",
654		"type": "string",
655		"unit": "none",
656		},
657		{
658		"description": "Weather-dependent feedin timeseries",
659		"name": "feedin",
660		"type": "array",
661		"unit": "p.u.",
662		},
663		]
664
665		meta = {
666		"name": "supply.egon_era5_renewable_feedin",
667		"title": "eGon feedin timeseries for RES",
668		"id": "WILL_BE_SET_AT_PUBLICATION",
669		"description": "Weather-dependent feedin timeseries for RES",
670		"language": ["EN"],
671		"publicationDate": datetime.date.today().isoformat(),
672		"context": context(),
673		"spatial": {
674		"location": None,
675		"extent": "Germany",
676		"resolution": None,
677		},
678		"sources": [
679		sources()["era5"],
680		sources()["vg250"],
681		sources()["egon-data"],
682		],
683		"licenses": [
684		license_ccby(
685		"© Bundesamt für Kartographie und Geodäsie 2020 (Daten verändert); "
686		"© Copernicus Climate Change Service (C3S) Climate Data Store "
687		"© Jonathan Amme, Clara Büttner, Ilka Cußmann, Julian Endres, Carlos Epia, Stephan Günther, Ulf Müller, Amélia Nadal, Guido Pleßmann, Francesco Witte",
688		)
689		],
690		"contributors": [
691		{
692		"title": "Clara Büttner",
693		"email": "http://github.com/ClaraBuettner",
694		"date": time.strftime("%Y-%m-%d"),
695		"object": None,
696		"comment": "Imported data",
697		},
698		],
699		"resources": [
700		{
701		"profile": "tabular-data-resource",
702		"name": "supply.egon_scenario_capacities",
703		"path": None,
704		"format": "PostgreSQL",
705		"encoding": "UTF-8",
706		"schema": {
707		"fields": fields,
708		"primaryKey": ["index"],
709		"foreignKeys": [],
710		},
711		"dialect": {"delimiter": None, "decimalSeparator": "."},
712		}
713		],
714		"metaMetadata": meta_metadata(),
715		}
716
717		# Create json dump
718		meta_json = "'" + json.dumps(meta) + "'"
719
720		# Add metadata as a comment to the table
721		db.submit_comment(
722		meta_json,
723		EgonRenewableFeedIn.__table__.schema,
724		EgonRenewableFeedIn.__table__.name,
725		)
726

openego / eGon-data

Pull Request — dev (#1122)

data.datasets.renewable_feedin.add_metadata() B

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