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data.py
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# Copyright 2016-2020 Fabian Hofmann (FIAS), Jonas Hoersch (KIT, IAI) and
# Fabian Gotzens (FZJ, IEK-STE)
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3 of the
# License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
Collection of power plant data bases and statistical data
"""
import logging
import os
from zipfile import ZipFile
import entsoe
import numpy as np
import pandas as pd
import pycountry
import requests
from deprecation import deprecated
from .cleaning import (
clean_name,
gather_fueltype_info,
gather_set_info,
gather_specifications,
gather_technology_info,
)
from .core import _package_data, get_config
from .heuristics import scale_to_net_capacities
from .utils import (
config_filter,
convert_to_short_name,
correct_manually,
get_raw_file,
set_column_name,
)
logger = logging.getLogger(__name__)
cget = pycountry.countries.get
net_caps = get_config()["display_net_caps"]
def BEYONDCOAL(raw=False, update=False, config=None):
"""
Importer for the BEYOND COAL database.
Parameters
----------
raw : boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
fn = get_raw_file("BEYONDCOAL", update=update, config=config)
df = pd.read_excel(fn, sheet_name="Plant", header=[0, 1, 2], skiprows=[3])
if raw:
return df
RENAME_COLUMNS = {
"Plant name": "Name",
"Fuel type": "Fueltype",
"Latitude": "lat",
"Longitude": "lon",
"Commissioning year of first unit": "DateIn",
"(Announced) Retirement year of last unit": "DateOut",
"Coal capacity open": "Capacity",
"Plant status\n(gross)": "status",
"EBC plant ID": "projectID",
}
phaseout_col = "Covered by country phase-out? [if yes: country phase-out year]"
df = (
df["Plant Data"]
.droplevel(1, axis=1)
.rename(columns=RENAME_COLUMNS)
.query('status != "Cancelled"')
.assign(
DateOut=lambda df: df.DateOut.fillna(df[phaseout_col]).where(
lambda ds: ds <= 8000
),
projectID=lambda df: "BEYOND-" + df.projectID,
Fueltype=lambda df: df.Fueltype.str.title().replace("Unknown", "Other"),
Set="PP",
Technology=np.nan,
)
.pipe(scale_to_net_capacities)
.pipe(clean_name)
.pipe(set_column_name, "BEYONDCOAL")
.pipe(config_filter, config)
)
return df
def OPSD(
raw=False,
update=False,
statusDE=None,
config=None,
**fill_geoposition_kwargs,
):
"""
Importer for the OPSD (Open Power Systems Data) database.
Parameters
----------
raw : Boolean, default False
Whether to return a dictionary of the raw databases.
update: bool, default False
Whether to update the data from the url.
statusDE : list, default ['operating', 'reserve', 'special_case']
Filter DE entries by operational status ['operating', 'shutdown',
'reserve', etc.]
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
fill_geoposition_kwargs:
Keyword arguments for fill_geoposition.
"""
config = get_config() if config is None else config
opsd_DE = pd.read_csv(get_raw_file("OPSD_DE", update, config), na_values=" ")
opsd_EU = pd.read_csv(get_raw_file("OPSD_EU", update, config), na_values=" ")
if raw:
return {"EU": opsd_EU, "DE": opsd_DE}
EU_RENAME_COLUMNS = {
"Lat": "lat",
"Lon": "lon",
"Energy_Source": "Fueltype",
"Commissioned": "DateIn",
"Retrofit": "DateRetrofit",
"Shutdown": "DateOut",
"Efficiency_Estimate": "Efficiency",
"Eic_Code": "EIC",
"Chp": "Set",
}
DE_RENAME_COLUMNS = {
"Lat": "lat",
"Lon": "lon",
"Energy_Source": "Fueltype",
# "Type": "Set",
"Country_Code": "Country",
"Capacity_Net_Bnetza": "Capacity",
"Commissioned": "DateIn",
"Retrofit": "DateRetrofit",
"Shutdown": "DateOut",
"Efficiency_Estimate": "Efficiency",
"Eic_Code_Plant": "EIC",
"Chp": "Set",
"Id": "projectID",
}
opsd_EU = (
opsd_EU.rename(columns=str.title)
.rename(columns=EU_RENAME_COLUMNS)
.eval("DateRetrofit = DateIn")
.query("Country != 'DE'")
.assign(
Name=lambda df: df.Name.str.replace("\x96", " "), # for geoparsing
projectID=lambda s: "OEU-" + s.index.astype(str),
Fueltype=lambda d: d.Fueltype.fillna(d.Energy_Source_Level_1),
# We don't want to include this as this is overestimating CHP capacities
# Set=lambda df: np.where(df.Set.isin(["yes", "Yes"]), "CHP", "PP"),
)
.reindex(columns=config["target_columns"])
)
opsd_DE = (
opsd_DE.rename(columns=str.title)
.rename(columns=DE_RENAME_COLUMNS)
.assign(
Name=lambda d: d.Name_Bnetza.fillna(d.Name_Uba),
Fueltype=lambda d: d.Fueltype.fillna(d.Energy_Source_Level_1),
DateRetrofit=lambda d: d.DateRetrofit.fillna(d.DateIn),
# We don't want to include this as this is overestimating CHP capacities
# Set=lambda df: np.where(df.Set.isin(["yes", "Yes"]), "CHP", "PP"),
)
)
if statusDE is not None:
opsd_DE = opsd_DE.loc[opsd_DE.Status.isin(statusDE)]
opsd_DE = opsd_DE.reindex(columns=config["target_columns"])
return (
pd.concat([opsd_EU, opsd_DE], ignore_index=True)
.replace(
{
"Country": {"UK": "GB", "[ \t]+|[ \t]+$.": ""},
"Capacity": {0.0: np.nan},
},
regex=True,
)
.pipe(gather_specifications, config=config)
.pipe(clean_name)
.dropna(subset=["Capacity"])
.powerplant.convert_alpha2_to_country()
# .pipe(fill_geoposition, **fill_geoposition_kwargs)
.pipe(set_column_name, "OPSD")
.pipe(config_filter, config)
)
def GEO(raw=False, update=False, config=None):
"""
Importer for the GEO database.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
UNITS_RENAME_COLS = {
"GEO_Assigned_Identification_Number": "projectID",
"Capacity_MWe_nbr": "Capacity",
"Date_Commissioned_dt": "DateIn",
"Decommission_Date_dt": "DateOut",
"Unit_Efficiency_Percent": "Efficiency",
}
PPL_RENAME_COLS = {
"GEO_Assigned_Identification_Number": "projectID",
"Name": "Name",
"Type": "Fueltype",
"Type_of_Plant_rng1": "Technology",
"Type_of_Fuel_rng1_Primary": "FuelClassification1",
"Type_of_Fuel_rng2_Secondary": "FuelClassification2",
"Country": "Country",
"Design_Capacity_MWe_nbr": "Capacity",
"Year_Project_Commissioned": "DateIn",
"Year_rng1_yr1": "DateRetrofit",
"Longitude_Start": "lon",
"Latitude_Start": "lat",
}
def to_year(ds):
years = pd.to_numeric(ds.dropna().astype(str).str[:4], errors="coerce")
year = years[lambda x: x > 1900] # noqa
return years.reindex_like(ds)
fn = get_raw_file("GEO_units", update=update, config=config)
units = pd.read_csv(fn, low_memory=False)
fn = get_raw_file("GEO", update=update, config=config)
ppl = pd.read_csv(fn, low_memory=False)
if raw:
return {"Units": units, "Plants": ppl}
date_cols = ["Date_Commissioned_dt", "Decommission_Date_dt"]
units[date_cols] = units[date_cols].apply(to_year)
units = units.rename(columns=UNITS_RENAME_COLS)
units.Efficiency = units.Efficiency.str.replace("%", "").astype(float) / 100
date_cols = ["Year_Project_Commissioned", "Year_rng1_yr1"]
ppl[date_cols] = ppl[date_cols].apply(to_year)
ppl = ppl.rename(columns=PPL_RENAME_COLS)
cols = [
"Name",
"Fueltype",
"Technology",
"FuelClassification1",
"FuelClassification2",
]
ppl = gather_specifications(ppl, parse_columns=cols)
ppl = clean_name(ppl)
res = units.join(ppl.set_index("projectID"), "projectID", rsuffix="_ppl")
res["DateIn"] = res.DateIn.fillna(res.DateIn_ppl)
not_included_ppl = ppl.query("projectID not in @res.projectID")
res = pd.concat([res, not_included_ppl]).pipe(set_column_name, "GEO")
res = scale_to_net_capacities(res)
res = convert_to_short_name(res)
res = set_column_name(res, "GEO")
res = config_filter(res, config)
res["projectID"] = "GEO-" + res.projectID.astype(str)
return res
@deprecated(
deprecated_in="0.5.0",
removed_in="0.6.0",
details="Removed since data is not publicly available anymore",
)
def CARMA(raw=False, update=False, config=None):
"""
Importer for the Carma database.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
carma = pd.read_csv(get_raw_file("CARMA", update, config), low_memory=False)
if raw:
return carma
return (
carma.rename(
columns={
"Geoposition": "Geoposition",
"cap": "Capacity",
"city": "location",
"country": "Country",
"fuel1": "Fueltype",
"lat": "lat",
"lon": "lon",
"plant": "Name",
"plant.id": "projectID",
}
)
.assign(projectID=lambda df: "CARMA-" + df.projectID.astype(str))
.loc[lambda df: df.Country.isin(config["target_countries"])]
.replace(
dict(
Fueltype={
"COAL": "Hard Coal",
"WAT": "Hydro",
"FGAS": "Natural Gas",
"NUC": "Nuclear",
"FLIQ": "Oil",
"WIND": "Wind",
"EMIT": "Other",
"GEO": "Geothermal",
"WSTH": "Waste",
"SUN": "Solar",
"BLIQ": "Bioenergy",
"BGAS": "Bioenergy",
"BSOL": "Bioenergy",
"OTH": "Other",
}
)
)
.pipe(gather_specifications, config=config)
.pipe(clean_name)
.pipe(set_column_name, "CARMA")
.drop_duplicates()
.pipe(config_filter, config)
.pipe(scale_to_net_capacities, not config["CARMA"]["net_capacity"])
)
def JRC(raw=False, update=False, config=None):
"""
Importer for the JRC Hydro-power plants database retrieves from
https://github.com/energy-modelling-toolkit/hydro-power-database.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
fn = get_raw_file("JRC", update, config)
df = pd.read_csv(fn)
if raw:
return df
RENAME_COLUMNS = {
"id": "projectID",
"name": "Name",
"installed_capacity_MW": "Capacity",
"country_code": "Country",
"type": "Technology",
"dam_height_m": "DamHeight_m",
"volume_Mm3": "Volume_Mm3",
"storage_capacity_MWh": "StorageCapacity_MWh",
}
def set_large_spanish_stores_to_reservoirs(df):
"""
Set large Spanish stores which are larger than 50GWh to reservoirs.
These should will all assumed to be reservoirs.
Used to fix https://github.com/PyPSA/powerplantmatching/issues/72"
"""
where = (df.Country == "Spain") & (df.StorageCapacity_MWh > 50e3)
technology = np.where(where, "Reservoir", df.Technology)
return df.assign(Technology=technology)
df = (
df.rename(columns=RENAME_COLUMNS)
.assign(projectID=lambda df: "JRC-" + df.projectID.astype(str))
.eval("Duration = StorageCapacity_MWh / Capacity")
.replace(
dict(
Technology={
"HDAM": "Reservoir",
"HPHS": "Pumped Storage",
"HROR": "Run-Of-River",
}
)
)
.assign(
Set=lambda df: np.where(df.Technology == "Run-Of-River", "PP", "Store"),
Fueltype="Hydro",
)
.drop(columns=["pypsa_id", "GEO"])
.powerplant.convert_alpha2_to_country()
.pipe(clean_name)
.pipe(set_large_spanish_stores_to_reservoirs)
.pipe(set_column_name, "JRC")
.pipe(config_filter, config)
)
return df
@deprecated(
deprecated_in="0.5.0",
removed_in="0.6.0",
details="Use the JRC data instead",
)
def IWPDCY(config=None):
"""
This data is not yet available. Was extracted manually from
the 'International Water Power & Dam Country Yearbook'.
Parameters
----------
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
return (
pd.read_csv(config["IWPDCY"]["fn"], encoding="utf-8", index_col="id")
.assign(
File="IWPDCY.csv",
projectID=lambda df: "IWPDCY-" + df.index.astype(str),
)
.dropna(subset=["Capacity"])
.pipe(set_column_name, "IWPDCY")
.pipe(config_filter, config)
.pipe(gather_set_info)
.pipe(correct_manually, "IWPDCY", config=config)
)
def Capacity_stats(
raw=False,
config=None,
update=False,
source="ENTSO-E SOAF",
year=2015,
):
"""
Standardize the aggregated capacity statistics provided by the ENTSO-E.
Parameters
----------
year : int
Year of the data (range usually 2013-2017)
(defaults to 2016)
source : str
Which statistics source from
{'ENTSO-E Transparency Platform', 'EUROSTAT', ...}
(defaults to 'ENTSO-E Transparency Platform')
Returns
-------
df : pd.DataFrame
Capacity statistics per country and fuel-type
"""
if config is None:
config = get_config()
df = pd.read_csv(get_raw_file("Capacity_stats", update, config), index_col=0)
if raw:
return df
if source:
df = df.query("source == @source")
else:
source = "Capacity statistics"
fueltypes = config["target_fueltypes"] # noqa
df = (
df.query("year == @year")
.rename(columns={"technology": "Fueltype"})
.rename(columns=str.title)
.powerplant.convert_alpha2_to_country()
.pipe(gather_fueltype_info, config=config, search_col=["Fueltype"])
.query("Fueltype in @fueltypes and Fueltype != 'Other'")
.pipe(set_column_name, source.title())
)
return df
def GPD(raw=False, update=False, config=None, filter_other_dbs=True):
"""
Importer for the `Global Power Plant Database`.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
fn = get_raw_file("GPD", update, config)
key = "global_power_plant_database.csv"
with ZipFile(fn, "r") as file:
df = pd.read_csv(file.open(key), low_memory=False)
if raw:
return df
RENAME_COLS = {
"Gppd_Idnr": "projectID",
"Country_Long": "Country",
"Primary_Fuel": "Fueltype",
"Latitude": "lat",
"Longitude": "lon",
"Capacity_Mw": "Capacity",
"Commissioning_Year": "DateIn",
}
other_dbs = []
if filter_other_dbs:
other_dbs = ["GEODB", "Open Power System Data", "ENTSOE"] # noqa
countries = config["target_countries"] # noqa
return (
df.rename(columns=lambda x: x.title())
.drop(columns="Country")
.rename(columns=RENAME_COLS)
.pipe(convert_to_short_name)
.query("Country in @countries &" " Source not in @other_dbs")
.pipe(
gather_specifications,
parse_columns=["Name", "Fueltype"],
config=config,
)
.pipe(clean_name)
.pipe(set_column_name, "GPD")
.pipe(config_filter, config)
.pipe(gather_technology_info, config=config)
)
def WIKIPEDIA(raw=False, update=False, config=None):
"""
Importer for the WIKIPEDIA nuclear power plant database.
Parameters
----------
raw : boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
fn = get_raw_file("WIKIPEDIA", update=update, config=config)
df = pd.read_csv(fn, index_col=0)
if raw:
return df
RENAME_COLUMNS = {
"Net performance MW": "Capacity",
"country": "Country",
"decommission_year": "DateOut",
"commission_year": "DateIn",
}
df = (
df.rename(columns=RENAME_COLUMNS)
.pipe(clean_name)
.assign(
Fueltype="Nuclear",
Set="PP",
projectID=lambda df: "WIKIPEDIA-" + df.index.astype(str),
# plants which are not yet built are set to 2027
DateIn=lambda df: df.DateIn.where(~df.Status.str.contains("In Bau"), 2027),
)
.pipe(set_column_name, "WIKIPEDIA")
.pipe(config_filter, config)
)
return df
def ENTSOE(
raw=False,
update=False,
config=None,
entsoe_token=None,
**fill_geoposition_kwargs,
):
"""
Importer for the list of installed generators provided by the ENTSO-E
Transparency Project. Geographical information is not given.
If update=True, the dataset is parsed through a request to
'https://transparency.entsoe.eu/generation/r2/\
installedCapacityPerProductionUnit/show',
Internet connection required. If raw=True, the same request is done, but
the unprocessed data is returned.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
entsoe_token: String
Security token of the ENTSO-E Transparency platform
fill_geoposition_kwargs:
Keyword arguments passed to `fill_geoposition`.
Note: For obtaining a security token refer to section 2 of the
RESTful API documentation of the ENTSOE-E Transparency platform
https://transparency.entsoe.eu/content/static_content/Static%20content/
web%20api/Guide.html#_authentication_and_authorisation. Please save the
token in your config.yaml file (key 'entsoe_token').
"""
config = get_config() if config is None else config
def retrieve_data(token):
client = entsoe.EntsoePandasClient(api_key=token)
start = pd.Timestamp("20190101", tz="Europe/Brussels")
end = pd.Timestamp("20200101", tz="Europe/Brussels")
not_retrieved = []
dfs = []
for area in entsoe.mappings.Area:
kwargs = dict(start=start, end=end)
try:
dfs.append(
client.query_installed_generation_capacity_per_unit(
area.name, **kwargs
)
)
except (entsoe.exceptions.NoMatchingDataError, requests.HTTPError):
not_retrieved.append(area.name)
pass
if not_retrieved:
logger.warning(
f"Data for area(s) {', '.join(not_retrieved)} could not be retrieved."
)
return pd.concat(dfs)
path = get_raw_file("ENTSOE", config=config, skip_retrieve=True)
if os.path.exists(path) and not update:
df = pd.read_csv(path, index_col=0)
else:
token = config.get("entsoe_token")
if token is not None:
df = retrieve_data(token)
df.to_csv(path)
else:
logger.info(
"No entsoe_token in config.yaml given, "
"falling back to stored version."
)
df = pd.read_csv(get_raw_file("ENTSOE", update, config), index_col=0)
if raw:
return df
RENAME_COLUMNS = {
"Production Type": "Fueltype",
"Installed Capacity [MW]": "Capacity",
}
fn = _package_data("entsoe_country_codes.csv")
COUNTRY_MAP = pd.read_csv(fn, index_col=0).rename(index=str).Country
return (
df.rename_axis(index="projectID")
.reset_index()
.rename(columns=RENAME_COLUMNS)
.drop_duplicates("projectID")
.assign(
Name=lambda df: df.Name.str.replace("_", " "), # for geoparsing
EIC=lambda df: df.projectID,
Country=lambda df: df.projectID.str[:2].map(COUNTRY_MAP),
Capacity=lambda df: pd.to_numeric(df.Capacity),
Technology=np.nan,
Set=np.nan,
lat=np.nan,
lon=np.nan,
)
.powerplant.convert_alpha2_to_country()
# .pipe(fill_geoposition, **fill_geoposition_kwargs)
.query("Capacity > 0")
.pipe(gather_specifications, config=config)
.pipe(clean_name)
.pipe(set_column_name, "ENTSOE")
.pipe(config_filter, config)
)
def ENTSOE_EIC(raw=False, update=False, config=None, entsoe_token=None):
"""
Importer for the meta data given for each ENTSOE entry.
This data serves to fill up geographical information.
If update=True an internet connection is required.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
update: bool, default False
Whether to update the data from the url.
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
entsoe_token: String
Security token of the ENTSO-E Transparency platform
Note: For obtaining a security token refer to section 2 of the
RESTful API documentation of the ENTSOE-E Transparency platform
https://transparency.entsoe.eu/content/static_content/Static%20content/
web%20api/Guide.html#_authentication_and_authorisation. Please save the
token in your config.yaml file (key 'entsoe_token').
"""
config = get_config() if config is None else config
RENAME_COLS = {
"EicCode": "projectID",
"EicLongName": "Name",
"MarketParticipantPostalCode": "postalcode",
"MarketParticipantIsoCountryCode": "Country",
}
fn = get_raw_file("ENTSOE-EIC", update=update, config=config)
df = pd.read_csv(fn, sep=";", on_bad_lines="skip")
if raw:
return df
# Maybe use this to get additional data on ENTSOE power plants, like postal codes
# entsoe_eic = ENTSOE_EIC(update=update).set_index("projectID").drop_duplicates()
# index = df.index.drop_duplicates()
# postalcode = entsoe_eic.postalcode.reindex(index, fill_value="")
return (
df.rename(columns=RENAME_COLS)
.powerplant.convert_to_short_name()
.pipe(set_column_name, "ENTSOE-EIC")
.assign(lat=np.nan, lon=np.nan)
)
# def OSM():
# """
# Parser and Importer for Open Street Map power plant data.
# """
# import requests
# overpass_url = "http://overpass-api.de/api/interpreter"
# overpass_query = """
# [out:json][timeout:210];
# area["name"="Luxembourg"]->.boundaryarea;
# (
# // query part for: “power=plant”
# node["power"="plant"](area.boundaryarea);
# way["power"="plant"](area.boundaryarea);
# relation["power"="plant"](area.boundaryarea);
# node["power"="generator"](area.boundaryarea);
# way["power"="generator"](area.boundaryarea);
# relation["power"="generator"](area.boundaryarea);
# );
# out body;
# """
# response = requests.get(overpass_url,
# params={'data': overpass_query})
# data = response.json()
# df = pd.DataFrame(data['elements'])
# df = pd.concat([df.drop(columns='tags'), df.tags.apply(pd.Series)], axis=1)
#
@deprecated(
deprecated_in="0.5.0",
removed_in="0.6.0",
details="This function is not maintained anymore.",
)
def WEPP(raw=False, config=None):
"""
Importer for the standardized WEPP (Platts, World Elecrtric Power
Plants Database). This database is not provided by this repository because
of its restrictive licence.
Parameters
----------
raw : Boolean, default False
Whether to return the original dataset
config : dict, default None
Add custom specific configuration,
e.g. powerplantmatching.config.get_config(target_countries='Italy'),
defaults to powerplantmatching.config.get_config()
"""
config = get_config() if config is None else config
# Define the appropriate datatype for each column (some columns e.g.
# 'YEAR' cannot be integers, as there are N/A values, which np.int
# does not yet(?) support.)
datatypes = {
"UNIT": str,
"PLANT": str,
"COMPANY": str,
"MW": np.float64,
"STATUS": str,
"YEAR": np.float64,
"UTYPE": str,
"FUEL": str,
"FUELTYPE": str,
"ALTFUEL": str,
"SSSMFR": str,
"BOILTYPE": str,
"TURBMFR": str,
"TURBTYPE": str,
"GENMFR": str,
"GENTYPE": str,
"SFLOW": np.float64,
"SPRESS": np.float64,
"STYPE": str,
"STEMP": np.float64,
"REHEAT1": np.float64,
"REHEAT2": np.float64,
"PARTCTL": str,
"PARTMFR": str,
"SO2CTL": str,
"FGDMFR": str,
"NOXCTL": str,
"NOXMFR": str,
"AE": str,
"CONstr, UCT": str,
"COOL": str,
"RETIRE": np.float64,
"CITY": str,
"STATE": str,
"COUNTRY": str,
"AREA": str,
"SUBREGION": str,
"POSTCODE": str,
"PARENT": str,
"ELECTYPE": str,
"BUSTYPE": str,
"COMPID": str,
"LOCATIONID": str,
"UNITID": str,
}
# Now read the Platts WEPP Database
wepp = pd.read_csv(config["WEPP"]["source_file"], dtype=datatypes, encoding="utf-8")
if raw:
return wepp
# Fit WEPP-column names to our specifications
wepp.columns = wepp.columns.str.title()
wepp.rename(
columns={
"Unit": "Name",
"Fuel": "Fueltype",
"Fueltype": "Technology",
"Mw": "Capacity",
"Year": "DateIn",
"Retire": "DateOut",
"Lat": "lat",
"Lon": "lon",
"Unitid": "projectID",
},
inplace=True,
)
wepp["DateRetrofit"] = wepp.DateIn
# Do country transformations and drop those which are not in defined scope
c = {
"ENGLAND & WALES": "UNITED KINGDOM",
"GIBRALTAR": "SPAIN",
"SCOTLAND": "UNITED KINGDOM",
}
wepp.Country = wepp.Country.replace(c).str.title()
wepp = (
wepp.loc[lambda df: df.Country.isin(config["target_countries"])]
.loc[lambda df: df.Status.isin(["OPR", "CON"])]
.assign(File=config["WEPP"]["source_file"])
)
# Replace fueltypes
d = {
"AGAS": "Bioenergy", # Syngas from gasified agricultural waste
"BFG": "Other", # blast furnance gas -> "Hochofengas"
"BGAS": "Bioenergy",
"BIOMASS": "Bioenergy",
"BL": "Bioenergy",
"CGAS": "Hard Coal",
"COAL": "Hard Coal",
"COG": "Other", # coke oven gas -> deutsch: "Hochofengas"
"COKE": "Hard Coal",
"CSGAS": "Hard Coal", # Coal-seam-gas
"CWM": "Hard Coal", # Coal-water mixture (aka coal-water slurry)
"DGAS": "Other", # sewage digester gas -> deutsch: "Klaergas"
"FGAS": "Other", # Flare gas or wellhead gas or associated gas
"GAS": "Natural Gas",
"GEO": "Geothermal",
"H2": "Other", # Hydrogen gas
"HZDWST": "Waste", # Hazardous waste
"INDWST": "Waste", # Industrial waste or refinery waste
"JET": "Oil", # Jet fuels
"KERO": "Oil", # Kerosene
"LGAS": "Other", # landfill gas -> deutsch: "Deponiegas"
"LIGNIN": "Bioenergy",
"LIQ": "Other", # (black) liqour -> deutsch: "Schwarzlauge",
# die bei Papierherstellung anfaellt
"LNG": "Natural Gas", # Liquified natural gas
"LPG": "Natural Gas", # Liquified petroleum gas (u. butane/propane)
"MBM": "Bioenergy", # Meat and bonemeal
"MEDWST": "Bioenergy", # Medical waste
"MGAS": "Other", # mine gas -> deutsch: "Grubengas"
"NAP": "Oil", # naphta
"OGAS": "Oil", # Gasified crude oil/refinery bottoms/bitumen
"PEAT": "Other",
"REF": "Waste",
"REFGAS": "Other", # Syngas from gasified refuse
"RPF": "Waste", # Waste paper and/or waste plastic
"PWST": "Other", # paper mill waste
"RGAS": "Other", # refinery off-gas -> deutsch: "Raffineriegas"
"SHALE": "Oil",
"SUN": "Solar",
"TGAS": "Other", # top gas -> deutsch: "Hochofengas"
"TIRES": "Other", # Scrap tires
"UNK": "Other",
"UR": "Nuclear",
"WAT": "Hydro",
"WOOD": "Bioenergy",
"WOODGAS": "Bioenergy",
"WSTGAS": "Other", # waste gas -> deutsch: "Industrieabgas"
"WSTWSL": "Waste", # Wastewater sludge