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

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Pull Request — dev (#568)

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created 2023-03-17 13:09 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,
    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		license_ccby,
21		meta_metadata,
22		sources,
23		)
24		from egon.data.datasets.zensus_vg250 import DestatisZensusPopulationPerHa
25		import egon.data.config
26
27
28		class RenewableFeedin(Dataset):
29		def __init__(self, dependencies):
30		super().__init__(
31		name="RenewableFeedin",
32		version="0.0.8",
33		dependencies=dependencies,
34		tasks={
35		wind,
36		pv,
37		solar_thermal,
38		heat_pump_cop,
39		wind_offshore,
40		mapping_zensus_weather,
41		},
42		)
43
44
45		Base = declarative_base()
46		engine = db.engine()
47
48
49		class MapZensusWeatherCell(Base):
50		__tablename__ = "egon_map_zensus_weather_cell"
51		__table_args__ = {"schema": "boundaries"}
52
53		zensus_population_id = Column(
54		Integer,
55		ForeignKey(DestatisZensusPopulationPerHa.id),
56		primary_key=True,
57		index=True,
58		)
59		w_id = Column(Integer, ForeignKey(EgonEra5Cells.w_id), index=True)
60
61
62		def weather_cells_in_germany(geom_column="geom"):
63		"""Get weather cells which intersect with Germany
64
65		Returns
66		-------
67		GeoPandas.GeoDataFrame
68		Index and points of weather cells inside Germany
69
70		"""
71
72		cfg = egon.data.config.datasets()["renewable_feedin"]["sources"]
73
74		return db.select_geodataframe(
75		f"""SELECT w_id, geom_point, geom
76		FROM {cfg['weather_cells']['schema']}.
77		{cfg['weather_cells']['table']}
78		WHERE ST_Intersects('SRID=4326;
79		POLYGON((5 56, 15.5 56, 15.5 47, 5 47, 5 56))', geom)""",
80		geom_col=geom_column,
81		index_col="w_id",
82		)
83
84
85		def offshore_weather_cells(geom_column="geom"):
86		"""Get weather cells which intersect with Germany
87
88		Returns
89		-------
90		GeoPandas.GeoDataFrame
91		Index and points of weather cells inside Germany
92
93		"""
94
95		cfg = egon.data.config.datasets()["renewable_feedin"]["sources"]
96
97		return db.select_geodataframe(
98		f"""SELECT w_id, geom_point, geom
99		FROM {cfg['weather_cells']['schema']}.
100		{cfg['weather_cells']['table']}
101		WHERE ST_Intersects('SRID=4326;
102		POLYGON((5.5 55.5, 14.5 55.5, 14.5 53.5, 5.5 53.5, 5.5 55.5))',
103		geom)""",
104		geom_col=geom_column,
105		index_col="w_id",
106		)
107
108
109		def federal_states_per_weather_cell():
110		"""Assings a federal state to each weather cell in Germany.
111
112		Sets the federal state to the weather celss using the centroid.
113		Weather cells at the borders whoes centroid is not inside Germany
114		are assinged to the closest federal state.
115
116		Returns
117		-------
118		GeoPandas.GeoDataFrame
119		Index, points and federal state of weather cells inside Germany
120
121		"""
122
123		cfg = egon.data.config.datasets()["renewable_feedin"]["sources"]
124
125		# Select weather cells and ferear states from database
126		weather_cells = weather_cells_in_germany(geom_column="geom_point")
127
128		federal_states = db.select_geodataframe(
129		f"""SELECT gen, geometry
130		FROM {cfg['vg250_lan_union']['schema']}.
131		{cfg['vg250_lan_union']['table']}""",
132		geom_col="geometry",
133		index_col="gen",
134		)
135
136		# Map federal state and onshore wind turbine to weather cells
137		weather_cells["federal_state"] = gpd.sjoin(
138		weather_cells, federal_states
139		).index_right
140
141		# Assign a federal state to each cell inside Germany
142		buffer = 1000
143
144		while (buffer < 30000) & (
145		len(weather_cells[weather_cells["federal_state"].isnull()]) > 0
146		):
147
148		cells = weather_cells[weather_cells["federal_state"].isnull()]
149
150		cells.loc[:, "geom_point"] = cells.geom_point.buffer(buffer)
151
152		weather_cells.loc[cells.index, "federal_state"] = gpd.sjoin(
153		cells, federal_states
154		).index_right
155
156		buffer += 200
157
158		weather_cells = (
159		weather_cells.reset_index()
160		.drop_duplicates(subset="w_id", keep="first")
161		.set_index("w_id")
162		)
163
164		weather_cells = weather_cells.dropna(axis=0, subset=["federal_state"])
165
166		return weather_cells.to_crs(4326)
167
168
169		def turbine_per_weather_cell():
170		"""Assign wind onshore turbine types to weather cells
171
172		Returns
173		-------
174		weather_cells : GeoPandas.GeoDataFrame
175		Weather cells in Germany including turbine type
176
177		"""
178
179		# Select representative onshore wind turbines per federal state
180		map_federal_states_turbines = {
181		"Schleswig-Holstein": "E-126",
182		"Bremen": "E-126",
183		"Hamburg": "E-126",
184		"Mecklenburg-Vorpommern": "E-126",
185		"Niedersachsen": "E-126",
186		"Berlin": "E-141",
187		"Brandenburg": "E-141",
188		"Hessen": "E-141",
189		"Nordrhein-Westfalen": "E-141",
190		"Sachsen": "E-141",
191		"Sachsen-Anhalt": "E-141",
192		"Thüringen": "E-141",
193		"Baden-Württemberg": "E-141",
194		"Bayern": "E-141",
195		"Rheinland-Pfalz": "E-141",
196		"Saarland": "E-141",
197		}
198
199		# Select weather cells and federal states
200		weather_cells = federal_states_per_weather_cell()
201
202		# Assign turbine type per federal state
203		weather_cells["wind_turbine"] = weather_cells["federal_state"].map(
204		map_federal_states_turbines
205		)
206
207		return weather_cells
208
209
210		def feedin_per_turbine():
211		"""Calculate feedin timeseries per turbine type and weather cell
212
213		Returns
214		-------
215		gdf : GeoPandas.GeoDataFrame
216		Feed-in timeseries per turbine type and weather cell
217
218		"""
219
220		# Select weather data for Germany
221		cutout = import_cutout(boundary="Germany")
222
223		gdf = gpd.GeoDataFrame(geometry=cutout.grid_cells(), crs=4326)
224
225		# Calculate feedin-timeseries for E-141
226		# source:
227		# https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library
228		turbine_e141 = {
229		"name": "E141 4200 kW",
230		"hub_height": 129,
231		"P": 4.200,
232		"V": np.arange(1, 26, dtype=float),
233		"POW": np.array(
234		[
235		0.0,
236		0.022,
237		0.104,
238		0.26,
239		0.523,
240		0.92,
241		1.471,
242		2.151,
243		2.867,
244		3.481,
245		3.903,
246		4.119,
247		4.196,
248		4.2,
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		]
261		),
262		}
263		ts_e141 = cutout.wind(
264		turbine_e141, per_unit=True, shapes=cutout.grid_cells()
265		)
266
267		gdf["E-141"] = ts_e141.to_pandas().transpose().values.tolist()
268
269		# Calculate feedin-timeseries for E-126
270		# source:
271		# https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library
272		turbine_e126 = {
273		"name": "E126 4200 kW",
274		"hub_height": 159,
275		"P": 4.200,
276		"V": np.arange(1, 26, dtype=float),
277		"POW": np.array(
278		[
279		0.0,
280		0.0,
281		0.058,
282		0.185,
283		0.4,
284		0.745,
285		1.2,
286		1.79,
287		2.45,
288		3.12,
289		3.66,
290		4.0,
291		4.15,
292		4.2,
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		]
305		),
306		}
307		ts_e126 = cutout.wind(
308		turbine_e126, per_unit=True, shapes=cutout.grid_cells()
309		)
310
311		gdf["E-126"] = ts_e126.to_pandas().transpose().values.tolist()
312
313		return gdf
314
315
316		def wind():
317		"""Insert feed-in timeseries for wind onshore turbines to database
318
319		Returns
320		-------
321		None.
322
323		"""
324
325		cfg = egon.data.config.datasets()["renewable_feedin"]["targets"]
326
327		# Get weather cells with turbine type
328		weather_cells = turbine_per_weather_cell()
329		weather_cells = weather_cells[weather_cells.wind_turbine.notnull()]
330
331		# Calculate feedin timeseries per turbine and weather cell
332		timeseries_per_turbine = feedin_per_turbine()
333
334		# Join weather cells and feedin-timeseries
335		timeseries = gpd.sjoin(weather_cells, timeseries_per_turbine)[
336		["E-141", "E-126"]
337		]
338
339		weather_year = get_sector_parameters("global", "eGon2035")["weather_year"]
340
341		df = pd.DataFrame(
342		index=weather_cells.index,
343		columns=["weather_year", "carrier", "feedin"],
344		data={"weather_year": weather_year, "carrier": "wind_onshore"},
345		)
346
347		# Insert feedin for selected turbine per weather cell
348		for turbine in ["E-126", "E-141"]:
349		idx = weather_cells.index[
350		(weather_cells.wind_turbine == turbine)
351		& (weather_cells.index.isin(timeseries.index))
352		]
353		df.loc[idx, "feedin"] = timeseries.loc[idx, turbine].values
354
355		db.execute_sql(
356		f"""
357		DELETE FROM {cfg['feedin_table']['schema']}.
358		{cfg['feedin_table']['table']}
359		WHERE carrier = 'wind_onshore'"""
360		)
361
362		# Insert values into database
363		df.to_sql(
364		cfg["feedin_table"]["table"],
365		schema=cfg["feedin_table"]["schema"],
366		con=db.engine(),
367		if_exists="append",
368		)
369
370
371		def wind_offshore():
372		"""Insert feed-in timeseries for wind offshore turbines to database
373
374		Returns
375		-------
376		None.
377
378		"""
379
380		# Get offshore weather cells arround Germany
381		weather_cells = offshore_weather_cells()
382
383		# Select weather data for German coast
384		cutout = import_cutout(boundary="Germany-offshore")
385
386		# Select weather year from cutout
387		weather_year = cutout.name.split("-")[2]
388
389		# Calculate feedin timeseries
390		ts_wind_offshore = cutout.wind(
391		"Vestas_V164_7MW_offshore",
392		per_unit=True,
393		shapes=weather_cells.to_crs(4326).geom,
394		)
395
396		# Create dataframe and insert to database
397		insert_feedin(ts_wind_offshore, "wind_offshore", weather_year)
398
399
400		def pv():
401		"""Insert feed-in timeseries for pv plants to database
402
403		Returns
404		-------
405		None.
406
407		"""
408
409		# Get weather cells in Germany
410		weather_cells = weather_cells_in_germany()
411
412		# Select weather data for Germany
413		cutout = import_cutout(boundary="Germany")
414
415		# Select weather year from cutout
416		weather_year = cutout.name.split("-")[1]
417
418		# Calculate feedin timeseries
419		ts_pv = cutout.pv(
420		"CSi",
421		orientation={"slope": 35.0, "azimuth": 180.0},
422		per_unit=True,
423		shapes=weather_cells.to_crs(4326).geom,
424		)
425
426		# Create dataframe and insert to database
427		insert_feedin(ts_pv, "pv", weather_year)
428
429
430		def solar_thermal():
431		"""Insert feed-in timeseries for pv plants to database
432
433		Returns
434		-------
435		None.
436
437		"""
438
439		# Get weather cells in Germany
440		weather_cells = weather_cells_in_germany()
441
442		# Select weather data for Germany
443		cutout = import_cutout(boundary="Germany")
444
445		# Select weather year from cutout
446		weather_year = cutout.name.split("-")[1]
447
448		# Calculate feedin timeseries
449		ts_solar_thermal = cutout.solar_thermal(
450		clearsky_model="simple",
451		orientation={"slope": 45.0, "azimuth": 180.0},
452		per_unit=True,
453		shapes=weather_cells.to_crs(4326).geom,
454		capacity_factor=False,
455		)
456
457		# Create dataframe and insert to database
458		insert_feedin(ts_solar_thermal, "solar_thermal", weather_year)
459
460
461		def heat_pump_cop():
462		"""
463		Calculate coefficient of performance for heat pumps according to
464		T. Brown et al: "Synergies of sector coupling and transmission
465		reinforcement in a cost-optimised, highlyrenewable European energy system",
466		2018, p. 8
467
468		Returns
469		-------
470		None.
471
472		"""
473		# Assume temperature of heating system to 55°C according to Brown et. al
474		t_sink = 55
475
476		carrier = "heat_pump_cop"
477
478		# Load configuration
479		cfg = egon.data.config.datasets()["renewable_feedin"]
480
481		# Get weather cells in Germany
482		weather_cells = weather_cells_in_germany()
483
484		# Select weather data for Germany
485		cutout = import_cutout(boundary="Germany")
486
487		# Select weather year from cutout
488		weather_year = cutout.name.split("-")[1]
489
490		# Calculate feedin timeseries
491		temperature = cutout.temperature(
492		shapes=weather_cells.to_crs(4326).geom
493		).transpose()
494
495		t_source = temperature.to_pandas()
496
497		delta_t = t_sink - t_source
498
499		# Calculate coefficient of performance for air sourced heat pumps
500		# according to Brown et. al
501		cop = 6.81 - 0.121 * delta_t + 0.00063 * delta_t**2
502
503		df = pd.DataFrame(
504		index=temperature.to_pandas().index,
505		columns=["weather_year", "carrier", "feedin"],
506		data={"weather_year": weather_year, "carrier": carrier},
507		)
508
509		df.feedin = cop.values.tolist()
510
511		# Delete existing rows for carrier
512		db.execute_sql(
513		f"""
514		DELETE FROM {cfg['targets']['feedin_table']['schema']}.
515		{cfg['targets']['feedin_table']['table']}
516		WHERE carrier = '{carrier}'"""
517		)
518
519		# Insert values into database
520		df.to_sql(
521		cfg["targets"]["feedin_table"]["table"],
522		schema=cfg["targets"]["feedin_table"]["schema"],
523		con=db.engine(),
524		if_exists="append",
525		)
526
527
528		def insert_feedin(data, carrier, weather_year):
529		"""Insert feedin data into database
530
531		Parameters
532		----------
533		data : xarray.core.dataarray.DataArray
534		Feedin timeseries data
535		carrier : str
536		Name of energy carrier
537		weather_year : int
538		Selected weather year
539
540		Returns
541		-------
542		None.
543
544		"""
545		# Transpose DataFrame
546		data = data.transpose().to_pandas()
547
548		# Load configuration
549		cfg = egon.data.config.datasets()["renewable_feedin"]
550
551		# Initialize DataFrame
552		df = pd.DataFrame(
553		index=data.index,
554		columns=["weather_year", "carrier", "feedin"],
555		data={"weather_year": weather_year, "carrier": carrier},
556		)
557
558		# Convert solar thermal data from W/m^2 to MW/(1000m^2) = kW/m^2
559		if carrier == "solar_thermal":
560		data *= 1e-3
561
562		# Insert feedin into DataFrame
563		df.feedin = data.values.tolist()
564
565		# Delete existing rows for carrier
566		db.execute_sql(
567		f"""
568		DELETE FROM {cfg['targets']['feedin_table']['schema']}.
569		{cfg['targets']['feedin_table']['table']}
570		WHERE carrier = '{carrier}'"""
571		)
572
573		# Insert values into database
574		df.to_sql(
575		cfg["targets"]["feedin_table"]["table"],
576		schema=cfg["targets"]["feedin_table"]["schema"],
577		con=db.engine(),
578		if_exists="append",
579		)
580
581
582		def mapping_zensus_weather():
583		"""Perform mapping between era5 weather cell and zensus grid"""
584
585		with db.session_scope() as session:
586		cells_query = session.query(
587		DestatisZensusPopulationPerHa.id.label("zensus_population_id"),
588		DestatisZensusPopulationPerHa.geom_point,
589		)
590
591		gdf_zensus_population = gpd.read_postgis(
592		cells_query.statement,
593		cells_query.session.bind,
594		index_col=None,
595		geom_col="geom_point",
596		)
597
598		with db.session_scope() as session:
599		cells_query = session.query(EgonEra5Cells.w_id, EgonEra5Cells.geom)
600
601		gdf_weather_cell = gpd.read_postgis(
602		cells_query.statement,
603		cells_query.session.bind,
604		index_col=None,
605		geom_col="geom",
606		)
607		# CRS is 4326
608		gdf_weather_cell = gdf_weather_cell.to_crs(epsg=3035)
609
610		gdf_zensus_weather = gdf_zensus_population.sjoin(
611		gdf_weather_cell, how="left", predicate="within"
612		)
613
614		MapZensusWeatherCell.__table__.drop(bind=engine, checkfirst=True)
615		MapZensusWeatherCell.__table__.create(bind=engine, checkfirst=True)
616
617		# Write mapping into db
618		with db.session_scope() as session:
619		session.bulk_insert_mappings(
620		MapZensusWeatherCell,
621		gdf_zensus_weather[["zensus_population_id", "w_id"]].to_dict(
622		orient="records"
623		),
624		)
625
626
627	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...
628		"""Add metdata to supply.egon_era5_renewable_feedin
629
630		Returns
631		-------
632		None.
633
634		"""
635
636		# Import column names and datatypes
637		fields = [
638		{
639		"description": "Weather cell index",
640		"name": "w_id",
641		"type": "integer",
642		"unit": "none",
643		},
644		{
645		"description": "Weather year",
646		"name": "weather_year",
647		"type": "integer",
648		"unit": "none",
649		},
650		{
651		"description": "Energy carrier",
652		"name": "carrier",
653		"type": "string",
654		"unit": "none",
655		},
656		{
657		"description": "Weather-dependent feedin timeseries",
658		"name": "feedin",
659		"type": "array",
660		"unit": "p.u.",
661		},
662		]
663
664		meta = {
665		"name": "supply.egon_era5_renewable_feedin",
666		"title": "eGon feedin timeseries for RES",
667		"id": "WILL_BE_SET_AT_PUBLICATION",
668		"description": "Weather-dependent feedin timeseries for RES",
669		"language": ["EN"],
670		"publicationDate": datetime.date.today().isoformat(),
671		"context": context(),
672		"spatial": {
673		"location": None,
674		"extent": "Germany",
675		"resolution": None,
676		},
677		"sources": [
678		sources()["era5"],
679		sources()["vg250"],
680		sources()["egon-data"],
681		],
682		"licenses": [
683		license_ccby(
684		"© Bundesamt für Kartographie und Geodäsie 2020 (Daten verändert); "
685		"© Copernicus Climate Change Service (C3S) Climate Data Store "
686		"© Jonathan Amme, Clara Büttner, Ilka Cußmann, Julian Endres, Carlos Epia, Stephan Günther, Ulf Müller, Amélia Nadal, Guido Pleßmann, Francesco Witte",
687		)
688		],
689		"contributors": [
690		{
691		"title": "Clara Büttner",
692		"email": "http://github.com/ClaraBuettner",
693		"date": time.strftime("%Y-%m-%d"),
694		"object": None,
695		"comment": "Imported data",
696		},
697		],
698		"resources": [
699		{
700		"profile": "tabular-data-resource",
701		"name": "supply.egon_scenario_capacities",
702		"path": None,
703		"format": "PostgreSQL",
704		"encoding": "UTF-8",
705		"schema": {
706		"fields": fields,
707		"primaryKey": ["index"],
708		"foreignKeys": [],
709		},
710		"dialect": {"delimiter": None, "decimalSeparator": "."},
711		}
712		],
713		"metaMetadata": meta_metadata(),
714		}
715
716		# Create json dump
717		meta_json = "'" + json.dumps(meta) + "'"
718
719		# Add metadata as a comment to the table
720		db.submit_comment(
721		meta_json,
722		EgonRenewableFeedIn.__table__.schema,
723		EgonRenewableFeedIn.__table__.name,
724		)
725

openego / eGon-data

Pull Request — dev (#568)

data.datasets.renewable_feedin.add_metadata() B

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