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removing old eurostat data reports as an option, cleaning up code

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This commit is contained in:
toniseibold 2024-02-27 12:04:07 +01:00
parent 3298572ced
commit d363aeb57d
7 changed files with 152 additions and 234 deletions
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1
config/config.default.yaml
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@ -316,7 +316,6 @@ pypsa_eur:
energy:
energy_totals_year: 2019
base_emissions_year: 1990
eurostat_report_year: 2023
emissions: CO2
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#biomass

4
rules/build_sector.smk
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@ -270,7 +270,7 @@ rule build_energy_totals:
swiss="data/switzerland-new_format-all_years.csv",
idees="data/bundle-sector/jrc-idees-2015",
district_heat_share="data/district_heat_share.csv",
eurostat=input_eurostat,
eurostat="data/bundle-sector/eurostat-energy_balances-april_2023_edition",
output:
energy_name=resources("energy_totals.csv"),
co2_name=resources("co2_totals.csv"),
@ -865,7 +865,7 @@ rule prepare_sector_network:
),
network=resources("networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"),
energy_totals_name=resources("energy_totals.csv"),
eurostat=input_eurostat,
eurostat="data/bundle-sector/eurostat-energy_balances-april_2023_edition",
pop_weighted_energy_totals=resources(
"pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
),

8
rules/common.smk
View File

@ -129,14 +129,6 @@ def has_internet_access(url="www.zenodo.org") -> bool:
finally:
conn.close()
def input_eurostat(w):
if config["energy"]["eurostat_report_year"] != 2023:
report_year = config["energy"]["eurostat_report_year"]
return f"data/bundle-sector/eurostat-energy_balances-june_{report_year}_edition"
else:
return "data/bundle-sector/eurostat-energy_balances-april_2023_edition"
def solved_previous_horizon(w):
planning_horizons = config_provider("scenario", "planning_horizons")(w)
i = planning_horizons.index(int(w.planning_horizons))

2
rules/postprocess.smk
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@ -247,7 +247,7 @@ rule plot_summary:
costs=RESULTS + "csvs/costs.csv",
energy=RESULTS + "csvs/energy.csv",
balances=RESULTS + "csvs/supply_energy.csv",
eurostat=input_eurostat,
eurostat="data/bundle-sector/eurostat-energy_balances-april_2023_edition",
co2="data/bundle-sector/eea/UNFCCC_v23.csv",
output:
costs=RESULTS + "graphs/costs.pdf",

3
rules/retrieve.smk
View File

@ -142,6 +142,9 @@ if config["enable"]["retrieve"] and config["enable"].get(
protected(
directory("data/bundle-sector/eurostat-energy_balances-may_2018_edition")
),
protected(
directory("data/bundle-sector/eurostat-energy_balances-april_2023_edition")
),
protected(directory("data/bundle-sector/jrc-idees-2015")),
]

354
scripts/build_energy_totals.py
View File

@ -117,97 +117,65 @@ to_ipcc = {
}
def build_eurostat(input_eurostat, countries, report_year, year):
def build_eurostat(input_eurostat, countries, year):
"""
Return multi-index for all countries' energy data in TWh/a.
"""
if report_year != 2023:
filenames = {
2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx",
}
with mute_print():
dfs = pd.read_excel(
input_eurostat + filenames[report_year],
sheet_name=None,
skiprows=1,
index_col=list(range(4)),
)
# sorted_index necessary for slicing
lookup = eurostat_codes
labelled_dfs = {
lookup[df.columns[0]]: df
for df in dfs.values()
if lookup[df.columns[0]] in countries
}
df = pd.concat(labelled_dfs, sort=True).sort_index()
# drop non-numeric and country columns
non_numeric_cols = df.columns[df.dtypes != float]
country_cols = df.columns.intersection(lookup.keys())
to_drop = non_numeric_cols.union(country_cols)
df.drop(to_drop, axis=1, inplace=True)
# convert ktoe/a to TWh/a
df *= 11.63 / 1e3
# read in every country file in countries
eurostat = pd.DataFrame()
countries = [country if country != 'GB' else 'UK' for country in countries]
countries = [country if country != 'GR' else 'EL' for country in countries]
for country in countries:
filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
if os.path.exists(input_eurostat + filename):
df = pd.read_excel(
input_eurostat + filename,
engine='pyxlsb',
sheet_name=str(year),
skiprows=4,
index_col=list(range(4)))
# replace entry 'Z' with 0
df.replace('Z', 0, inplace=True)
# write 'International aviation' to the 2nd level of the multiindex
index_number = (df.index.get_level_values(1) == 'International aviation').argmax()
new_index = ('-', 'International aviation', 'International aviation', 'ktoe')
modified_index = list(df.index)
modified_index[index_number] = new_index
df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names)
# drop the annoying subhead line
df.drop(df[df[year] == year].index, inplace=True)
# replace 'Z' with 0
df = df.replace('Z', 0)
# add country to the multiindex
new_tuple = [(country, *idx) for idx in df.index]
new_mindex = pd.MultiIndex.from_tuples(new_tuple, names=['country', None, 'name', None, 'unit'])
df.index = new_mindex
# make numeric values where possible
df = df.apply(pd.to_numeric, errors='coerce')
# drop non-numeric columns
non_numeric_cols = df.columns[df.dtypes != float]
df.drop(non_numeric_cols, axis=1, inplace=True)
# concatenate the dataframes
eurostat = pd.concat([eurostat, df], axis=0)
else:
# read in every country file in countries
eurostat = pd.DataFrame()
countries = [country if country != 'GB' else 'UK' for country in countries]
countries = [country if country != 'GR' else 'EL' for country in countries]
for country in countries:
filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
if os.path.exists(input_eurostat + filename):
df = pd.read_excel(
input_eurostat + filename,
engine='pyxlsb',
sheet_name=str(year),
skiprows=4,
index_col=list(range(4)))
# replace entry 'Z' with 0
df.replace('Z', 0, inplace=True)
# write 'International aviation' to the 2nd level of the multiindex
index_number = (df.index.get_level_values(1) == 'International aviation').argmax()
new_index = ('-', 'International aviation', 'International aviation', 'ktoe')
modified_index = list(df.index)
modified_index[index_number] = new_index
df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names)
# drop the annoying subhead line
df.drop(df[df[year] == year].index, inplace=True)
# replace 'Z' with 0
df = df.replace('Z', 0)
# add country to the multiindex
new_tuple = [(country, *idx) for idx in df.index]
new_mindex = pd.MultiIndex.from_tuples(new_tuple, names=['country', None, 'name', None, 'unit'])
df.index = new_mindex
# make numeric values where possible
df = df.apply(pd.to_numeric, errors='coerce')
# drop non-numeric columns
non_numeric_cols = df.columns[df.dtypes != float]
df.drop(non_numeric_cols, axis=1, inplace=True)
# concatenate the dataframes
eurostat = pd.concat([eurostat, df], axis=0)
eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True)
# Renaming some indices
rename = {
'Households': 'Residential',
'Commercial & public services': 'Services',
'Domestic navigation': 'Domestic Navigation'
}
for name, rename in rename.items():
eurostat.index = eurostat.index.set_levels(
eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename),
level=3)
new_index = eurostat.index.set_levels(eurostat.index.levels[2].where(eurostat.index.levels[2] != 'International maritime bunkers', 'Bunkers'), level=2)
eurostat.index = new_index
eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True)
# Renaming some indices
rename = {
'Households': 'Residential',
'Commercial & public services': 'Services',
'Domestic navigation': 'Domestic Navigation'
}
for name, rename in rename.items():
eurostat.index = eurostat.index.set_levels(
eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename),
level=3)
new_index = eurostat.index.set_levels(eurostat.index.levels[2].where(eurostat.index.levels[2] != 'International maritime bunkers', 'Bunkers'), level=2)
eurostat.index = new_index
eurostat.rename(columns={'Total': 'Total all products'}, inplace=True)
eurostat.index = eurostat.index.set_levels(eurostat.index.levels[0].where(eurostat.index.levels[0] != 'UK', 'GB'), level=0)
df = eurostat * 11.63 / 1e3
eurostat.rename(columns={'Total': 'Total all products'}, inplace=True)
eurostat.index = eurostat.index.set_levels(eurostat.index.levels[0].where(eurostat.index.levels[0] != 'UK', 'GB'), level=0)
df = eurostat * 11.63 / 1e3
return df
@ -709,8 +677,8 @@ def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
return emissions / 1e3
def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
eurostat = build_eurostat(input_eurostat, countries, report_year, year)
def build_eurostat_co2(input_eurostat, countries, year=1990):
eurostat = build_eurostat(input_eurostat, countries, year)
specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
@ -727,49 +695,26 @@ def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
return eurostat.multiply(specific_emissions).sum(axis=1)
def build_co2_totals(countries, eea_co2, eurostat_co2, report_year):
def build_co2_totals(countries, eea_co2, eurostat_co2):
co2 = eea_co2.reindex(countries)
for ct in pd.Index(countries).intersection(["BA", "RS", "AL", "ME", "MK"]):
if report_year != 2023:
mappings = {
"electricity": (
ct,
"+",
"Conventional Thermal Power Stations",
"of which From Coal",
),
"residential non-elec": (ct, "+", "+", "Residential"),
"services non-elec": (ct, "+", "+", "Services"),
"road non-elec": (ct, "+", "+", "Road"),
"rail non-elec": (ct, "+", "+", "Rail"),
"domestic navigation": (ct, "+", "+", "Domestic Navigation"),
"international navigation": (ct, "-", "Bunkers"),
"domestic aviation": (ct, "+", "+", "Domestic aviation"),
"international aviation": (ct, "+", "+", "International aviation"),
# does not include industrial process emissions or fuel processing/refining
"industrial non-elec": (ct, "+", "Industry"),
# does not include non-energy emissions
"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
& eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
}
else:
mappings = {
"electricity": (ct, "+", "Electricity & heat generation", np.nan),
"residential non-elec": (ct, "+", "+", "Residential"),
"services non-elec": (ct, "+", "+", "Services"),
"road non-elec": (ct, "+", "+", "Road"),
"rail non-elec": (ct, "+", "+", "Rail"),
"domestic navigation": (ct, "+", "+", "Domestic Navigation"),
"international navigation": (ct, "-", "Bunkers"),
"domestic aviation": (ct, "+", "+", "Domestic aviation"),
"international aviation": (ct, "-", "International aviation"),
# does not include industrial process emissions or fuel processing/refining
"industrial non-elec": (ct, "+", "Industry sector"),
# does not include non-energy emissions
"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
& eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
}
mappings = {
"electricity": (ct, "+", "Electricity & heat generation", np.nan),
"residential non-elec": (ct, "+", "+", "Residential"),
"services non-elec": (ct, "+", "+", "Services"),
"road non-elec": (ct, "+", "+", "Road"),
"rail non-elec": (ct, "+", "+", "Rail"),
"domestic navigation": (ct, "+", "+", "Domestic Navigation"),
"international navigation": (ct, "-", "Bunkers"),
"domestic aviation": (ct, "+", "+", "Domestic aviation"),
"international aviation": (ct, "-", "International aviation"),
# does not include industrial process emissions or fuel processing/refining
"industrial non-elec": (ct, "+", "Industry sector"),
# does not include non-energy emissions
"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
& eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
}
for i, mi in mappings.items():
co2.at[ct, i] = eurostat_co2.loc[mi].sum()
@ -820,83 +765,65 @@ def rescale(idees_countries, energy, eurostat):
'''
# read in the eurostat data for 2015
eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[["Total all products", "Electricity"]]
# eurostat_2015 = eurostat_2015.rename(index={'GB': 'UK'}, level=0)
eurostat_year = eurostat[["Total all products", "Electricity"]]
# calculate the ratio of the two data sets
ratio = eurostat_year / eurostat_2015
ratio = ratio.droplevel([1,4])
ratio.rename(columns={"Total all products": "total", "Electricity": "ele"}, inplace=True)
ratio = ratio.rename(index={"GB": "UK"}, level=0)
ratio = ratio.rename(index={"EL": "GR"}, level=0)
residential_total = [
"total residential space",
"total residential water",
"total residential cooking",
"total residential",
"derived heat residential",
"thermal uses residential",
]
residential_ele = [
"electricity residential space",
"electricity residential water",
"electricity residential cooking",
"electricity residential",
]
service_total = [
"total services space",
"total services water",
"total services cooking",
"total services",
"derived heat services",
"thermal uses services",
]
service_ele = [
"electricity services space",
"electricity services water",
"electricity services cooking",
"electricity services",
]
agri_total = [
"total agriculture heat",
"total agriculture machinery",
"total agriculture",
]
agri_ele = [
"total agriculture electricity",
]
road_total = [
"total road",
"total passenger cars",
"total other road passenger",
"total light duty road freight",
]
road_ele = [
"electricity road",
"electricity passenger cars",
"electricity other road passenger",
"electricity light duty road freight",
]
rail_total = [
"total rail",
"total rail passenger",
"total rail freight",
]
rail_ele = [
"electricity rail",
"electricity rail passenger",
"electricity rail freight",
]
mappings = {
"Residential": {
"total": ["total residential space",
"total residential water",
"total residential cooking",
"total residential",
"derived heat residential",
"thermal uses residential",],
"elec": ["electricity residential space",
"electricity residential water",
"electricity residential cooking",
"electricity residential",]},
"Services": {
"total": ["total services space",
"total services water",
"total services cooking",
"total services",
"derived heat services",
"thermal uses services",],
"elec": ["electricity services space",
"electricity services water",
"electricity services cooking",
"electricity services",]},
"Agriculture & forestry": {
"total": ["total agriculture heat",
"total agriculture machinery",
"total agriculture",],
"elec": ["total agriculture electricity",]},
"Road": {
"total": ["total road",
"total passenger cars",
"total other road passenger",
"total light duty road freight",],
"elec": ["electricity road",
"electricity passenger cars",
"electricity other road passenger",
"electricity light duty road freight",]},
"Rail": {
"total": ["total rail",
"total rail passenger",
"total rail freight",],
"elec": ["electricity rail",
"electricity rail passenger",
"electricity rail freight",]},
}
avia_inter = [
'total aviation passenger',
'total aviation freight',
'total international aviation passenger',
'total international aviation freight',
'total international aviation'
'total aviation passenger',
'total aviation freight',
'total international aviation passenger',
'total international aviation freight',
'total international aviation'
]
avia_domestic = [
'total domestic aviation passenger',
@ -906,30 +833,14 @@ def rescale(idees_countries, energy, eurostat):
navigation = [
"total domestic navigation",
]
idees_countries = idees_countries.repalce({'GB': 'UK', 'GR': 'EL'})
for country in idees_countries:
res = ratio.loc[(country, slice(None), 'Residential')]
energy.loc[country, residential_total] *= res[['total']].iloc[0,0]
energy.loc[country, residential_ele] *= res[['ele']].iloc[0,0]
ser = ratio.loc[(country, slice(None), 'Services')]
energy.loc[country, service_total] *= ser[['total']].iloc[0,0]
energy.loc[country, service_ele] *= ser[['ele']].iloc[0,0]
agri = ratio.loc[(country, slice(None), 'Agriculture & forestry')]
energy.loc[country, agri_total] *= agri[['total']].iloc[0,0]
energy.loc[country, agri_ele] *= agri[['ele']].iloc[0,0]
road = ratio.loc[(country, slice(None), 'Road')]
energy.loc[country, road_total] *= road[['total']].iloc[0,0]
energy.loc[country, road_ele] *= road[['ele']].iloc[0,0]
rail = ratio.loc[(country, slice(None), 'Rail')]
energy.loc[country, rail_total] *= rail[['total']].iloc[0,0]
energy.loc[country, rail_ele] *= rail[['ele']].iloc[0,0]
for sector, mapping in mappings.items():
sector_ratio = ratio.loc[(country, slice(None), sector)]
energy.loc[country, mapping["total"]] *= sector_ratio[['total']].iloc[0,0]
energy.loc[country, mapping["elec"]] *= sector_ratio[['ele']].iloc[0,0]
avi_d = ratio.loc[(country, slice(None), 'Domestic aviation')]
avi_i = ratio.loc[(country, 'International aviation', slice(None))]
energy.loc[country, avia_inter] *= avi_i[['total']].iloc[0,0]
@ -958,9 +869,8 @@ if __name__ == "__main__":
idees_countries = pd.Index(countries).intersection(eu28)
data_year = params["energy_totals_year"]
report_year = snakemake.params.energy["eurostat_report_year"]
input_eurostat = snakemake.input.eurostat
eurostat = build_eurostat(input_eurostat, countries, report_year, data_year)
eurostat = build_eurostat(input_eurostat, countries, data_year)
swiss = build_swiss(data_year)
# data from idees only exists for 2015
if data_year > 2015:
@ -984,10 +894,10 @@ if __name__ == "__main__":
emissions_scope = snakemake.params.energy["emissions"]
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
eurostat_co2 = build_eurostat_co2(
input_eurostat, countries, report_year, base_year_emissions
input_eurostat, countries, base_year_emissions
)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2, report_year)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
co2.to_csv(snakemake.output.co2_name)
transport = build_transport_data(countries, population, idees)

14
scripts/retrieve_sector_databundle.py
View File

@ -8,6 +8,7 @@ Retrieve and extract data bundle for sector-coupled studies.
import logging
import tarfile
import zipfile
from pathlib import Path
from _helpers import (
@ -47,3 +48,16 @@ if __name__ == "__main__":
tarball_fn.unlink()
logger.info(f"Databundle available in '{to_fn}'.")
url_eurostat = "https://ec.europa.eu/eurostat/documents/38154/4956218/Balances-December2022.zip/f7cf0d19-5c0f-60ad-4e48-098a5ddd6e48?t=1671184070589"
tarball_fn = Path(f"{rootpath}/data/bundle-sector/eurostat_2023.zip")
to_fn = Path(f"{rootpath}/data/bundle-sector/eurostat-energy_balances-april_2023_edition/")
logger.info(f"Downloading Eurostat data from '{url_eurostat}'.")
progress_retrieve(url_eurostat, tarball_fn, disable=disable_progress)
logger.info("Extracting Eurostat data.")
with zipfile.ZipFile(tarball_fn, 'r') as zip_ref:
zip_ref.extractall(to_fn)
logger.info(f"Eurostat data available in '{to_fn}'.")