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

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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: finally:
conn.close() conn.close()
def solved_previous_horizon(w): def solved_previous_horizon(w):
planning_horizons = config_provider("scenario", "planning_horizons")(w) planning_horizons = config_provider("scenario", "planning_horizons")(w)
i = planning_horizons.index(int(w.planning_horizons)) i = planning_horizons.index(int(w.planning_horizons))

151
scripts/build_energy_totals.py
View File

@ -123,35 +123,45 @@ def build_eurostat(input_eurostat, countries, year):
""" """
# read in every country file in countries # read in every country file in countries
eurostat = pd.DataFrame() eurostat = pd.DataFrame()
countries = [country if country != 'GB' else 'UK' 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] countries = [country if country != "GR" else "EL" for country in countries]
for country in countries: for country in countries:
filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb" filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
if os.path.exists(input_eurostat + filename): if os.path.exists(input_eurostat + filename):
df = pd.read_excel( df = pd.read_excel(
input_eurostat + filename, input_eurostat + filename,
engine='pyxlsb', engine="pyxlsb",
sheet_name=str(year), sheet_name=str(year),
skiprows=4, skiprows=4,
index_col=list(range(4))) index_col=list(range(4)),
)
# replace entry 'Z' with 0 # 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 # write 'International aviation' to the 2nd level of the multiindex
index_number = (df.index.get_level_values(1) == 'International aviation').argmax() index_number = (
new_index = ('-', 'International aviation', 'International aviation', 'ktoe') df.index.get_level_values(1) == "International aviation"
).argmax()
new_index = (
"-",
"International aviation",
"International aviation",
"ktoe",
)
modified_index = list(df.index) modified_index = list(df.index)
modified_index[index_number] = new_index modified_index[index_number] = new_index
df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names) df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names)
# drop the annoying subhead line # drop the annoying subhead line
df.drop(df[df[year] == year].index, inplace=True) df.drop(df[df[year] == year].index, inplace=True)
# replace 'Z' with 0 # replace 'Z' with 0
df = df.replace('Z', 0) df = df.replace("Z", 0)
# add country to the multiindex # add country to the multiindex
new_tuple = [(country, *idx) for idx in df.index] 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 df.index = new_mindex
# make numeric values where possible # 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 # drop non-numeric columns
non_numeric_cols = df.columns[df.dtypes != float] non_numeric_cols = df.columns[df.dtypes != float]
df.drop(non_numeric_cols, axis=1, inplace=True) df.drop(non_numeric_cols, axis=1, inplace=True)
@ -161,19 +171,27 @@ def build_eurostat(input_eurostat, countries, year):
eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True) eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True)
# Renaming some indices # Renaming some indices
rename = { rename = {
'Households': 'Residential', "Households": "Residential",
'Commercial & public services': 'Services', "Commercial & public services": "Services",
'Domestic navigation': 'Domestic Navigation' "Domestic navigation": "Domestic Navigation",
} }
for name, rename in rename.items(): for name, rename in rename.items():
eurostat.index = eurostat.index.set_levels( eurostat.index = eurostat.index.set_levels(
eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename), eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename),
level=3) level=3,
new_index = eurostat.index.set_levels(eurostat.index.levels[2].where(eurostat.index.levels[2] != 'International maritime bunkers', 'Bunkers'), level=2) )
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.index = new_index
eurostat.rename(columns={'Total': 'Total all products'}, inplace=True) 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.index = eurostat.index.set_levels(
eurostat.index.levels[0].where(eurostat.index.levels[0] != "UK", "GB"), level=0
)
df = eurostat * 11.63 / 1e3 df = eurostat * 11.63 / 1e3
@ -766,66 +784,95 @@ def rescale(idees_countries, energy, eurostat):
missing data: ['passenger car efficiency', 'passenger cars'] missing data: ['passenger car efficiency', 'passenger cars']
""" """
# read in the eurostat data for 2015 # 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"]] eurostat_year = eurostat[["Total all products", "Electricity"]]
# calculate the ratio of the two data sets # calculate the ratio of the two data sets
ratio = eurostat_year / eurostat_2015 ratio = eurostat_year / eurostat_2015
ratio = ratio.droplevel([1,4]) ratio = ratio.droplevel([1, 4])
ratio.rename(columns={"Total all products": "total", "Electricity": "ele"}, inplace=True) ratio.rename(
columns={"Total all products": "total", "Electricity": "ele"}, inplace=True
)
ratio = ratio.rename(index={"EL": "GR"}, level=0) ratio = ratio.rename(index={"EL": "GR"}, level=0)
mappings = { mappings = {
"Residential": { "Residential": {
"total": ["total residential space", "total": [
"total residential space",
"total residential water", "total residential water",
"total residential cooking", "total residential cooking",
"total residential", "total residential",
"derived heat residential", "derived heat residential",
"thermal uses residential",], "thermal uses residential",
"elec": ["electricity residential space", ],
"elec": [
"electricity residential space",
"electricity residential water", "electricity residential water",
"electricity residential cooking", "electricity residential cooking",
"electricity residential",]}, "electricity residential",
],
},
"Services": { "Services": {
"total": ["total services space", "total": [
"total services space",
"total services water", "total services water",
"total services cooking", "total services cooking",
"total services", "total services",
"derived heat services", "derived heat services",
"thermal uses services",], "thermal uses services",
"elec": ["electricity services space", ],
"elec": [
"electricity services space",
"electricity services water", "electricity services water",
"electricity services cooking", "electricity services cooking",
"electricity services",]}, "electricity services",
],
},
"Agriculture & forestry": { "Agriculture & forestry": {
"total": ["total agriculture heat", "total": [
"total agriculture heat",
"total agriculture machinery", "total agriculture machinery",
"total agriculture",], "total agriculture",
"elec": ["total agriculture electricity",]}, ],
"elec": [
"total agriculture electricity",
],
},
"Road": { "Road": {
"total": ["total road", "total": [
"total road",
"total passenger cars", "total passenger cars",
"total other road passenger", "total other road passenger",
"total light duty road freight",], "total light duty road freight",
"elec": ["electricity road", ],
"elec": [
"electricity road",
"electricity passenger cars", "electricity passenger cars",
"electricity other road passenger", "electricity other road passenger",
"electricity light duty road freight",]}, "electricity light duty road freight",
],
},
"Rail": { "Rail": {
"total": ["total rail", "total": [
"total rail",
"total rail passenger", "total rail passenger",
"total rail freight",], "total rail freight",
"elec": ["electricity rail", ],
"elec": [
"electricity rail",
"electricity rail passenger", "electricity rail passenger",
"electricity rail freight",]}, "electricity rail freight",
],
},
} }
avia_inter = [ avia_inter = [
'total aviation passenger', "total aviation passenger",
'total aviation freight', "total aviation freight",
'total international aviation passenger', "total international aviation passenger",
'total international aviation freight', "total international aviation freight",
'total international aviation' "total international aviation",
] ]
avia_domestic = [ avia_domestic = [
"total domestic aviation passenger", "total domestic aviation passenger",
@ -840,13 +887,13 @@ def rescale(idees_countries, energy, eurostat):
for sector, mapping in mappings.items(): for sector, mapping in mappings.items():
sector_ratio = ratio.loc[(country, slice(None), sector)] sector_ratio = ratio.loc[(country, slice(None), sector)]
energy.loc[country, mapping["total"]] *= sector_ratio[['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] energy.loc[country, mapping["elec"]] *= sector_ratio[["ele"]].iloc[0, 0]
avi_d = ratio.loc[(country, slice(None), 'Domestic aviation')] avi_d = ratio.loc[(country, slice(None), "Domestic aviation")]
avi_i = ratio.loc[(country, 'International aviation', slice(None))] avi_i = ratio.loc[(country, "International aviation", slice(None))]
energy.loc[country, avia_inter] *= avi_i[['total']].iloc[0,0] 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, avia_domestic] *= avi_d[["total"]].iloc[0, 0]
nav = ratio.loc[(country, slice(None), "Domestic Navigation")] nav = ratio.loc[(country, slice(None), "Domestic Navigation")]
energy.loc[country, navigation] *= nav[["total"]].iloc[0, 0] energy.loc[country, navigation] *= nav[["total"]].iloc[0, 0]
@ -898,9 +945,7 @@ if __name__ == "__main__":
base_year_emissions = params["base_emissions_year"] base_year_emissions = params["base_emissions_year"]
emissions_scope = snakemake.params.energy["emissions"] emissions_scope = snakemake.params.energy["emissions"]
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope) eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
eurostat_co2 = build_eurostat_co2( eurostat_co2 = build_eurostat_co2(input_eurostat, countries, base_year_emissions)
input_eurostat, countries, base_year_emissions
)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2) co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
co2.to_csv(snakemake.output.co2_name) co2.to_csv(snakemake.output.co2_name)