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Merge branch 'energy_totals' of https://github.com/PyPSA/pypsa-eur into energy_totals

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This commit is contained in:
toniseibold 2024-02-27 13:33:08 +01:00
commit 7616ae31fe
2 changed files with 122 additions and 76 deletions
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1
rules/common.smk
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@ -129,6 +129,7 @@ def has_internet_access(url="www.zenodo.org") -> bool:
finally:
conn.close()
def solved_previous_horizon(w):
planning_horizons = config_provider("scenario", "planning_horizons")(w)
i = planning_horizons.index(int(w.planning_horizons))

197
scripts/build_energy_totals.py
View File

@ -123,58 +123,76 @@ def build_eurostat(input_eurostat, countries, year):
"""
# 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]
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',
engine="pyxlsb",
sheet_name=str(year),
skiprows=4,
index_col=list(range(4)))
index_col=list(range(4)),
)
# replace entry 'Z' with 0
df.replace('Z', 0, inplace=True)
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')
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)
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'])
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')
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'
"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.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)
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
@ -766,66 +784,95 @@ def rescale(idees_countries, energy, eurostat):
missing data: ['passenger car efficiency', 'passenger cars']
"""
# read in the eurostat data for 2015
eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[["Total all products", "Electricity"]]
eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[
["Total all products", "Electricity"]
]
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.droplevel([1, 4])
ratio.rename(
columns={"Total all products": "total", "Electricity": "ele"}, inplace=True
)
ratio = ratio.rename(index={"EL": "GR"}, level=0)
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",]},
"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",]},
"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",]},
"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",]},
"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",]},
"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",
@ -840,13 +887,13 @@ def rescale(idees_countries, energy, eurostat):
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]
energy.loc[country, avia_domestic] *= avi_d[['total']].iloc[0,0]
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]
energy.loc[country, avia_domestic] *= avi_d[["total"]].iloc[0, 0]
nav = ratio.loc[(country, slice(None), "Domestic Navigation")]
energy.loc[country, navigation] *= nav[["total"]].iloc[0, 0]
@ -898,9 +945,7 @@ if __name__ == "__main__":
base_year_emissions = params["base_emissions_year"]
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, base_year_emissions
)
eurostat_co2 = build_eurostat_co2(input_eurostat, countries, base_year_emissions)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
co2.to_csv(snakemake.output.co2_name)