Copy data preparation scripts from pypsa-eur sector branch
This commit is contained in:
Tom Brown
commit
1b26c3c737
32
.gitignore
vendored
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32
.gitignore
vendored
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.snakemake*
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.ipynb_checkpoints
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__pycache__
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gurobi.log
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/bak
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/resources
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/results
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/networks
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/benchmarks
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/logs
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/notebooks
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/data
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/data/links_p_nom.csv
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/data/*totals.csv
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/data/emobility/
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/data/heating/
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/data/eurostat*
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/data/odyssee/
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/data/transport_data.csv
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/data/switzerland*
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/data/.nfs*
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*.org
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*.nc
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*~
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/scripts/old
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*.pyc
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/cutouts
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126
Snakefile
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126
Snakefile
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configfile: "config.yaml"
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subworkflow pypsaeur:
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workdir: "../pypsa-eur"
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snakefile: "../pypsa-eur/Snakefile"
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configfile: "../pypsa-eur/config.yaml"
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rule prepare_sector_networks:
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input:
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pypsaeur(expand(config['results_dir'] + config['run'] + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}.nc",
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**config['scenario']))
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rule build_population_layouts:
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input:
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nuts3_shapes='resources/nuts3_shapes.geojson',
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urban_percent="data/urban_percent.csv"
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output:
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pop_layout_total="resources/pop_layout_total.nc",
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pop_layout_urban="resources/pop_layout_urban.nc",
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pop_layout_rural="resources/pop_layout_rural.nc"
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script: "scripts/build_population_layouts.py"
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rule build_clustered_population_layouts:
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input:
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pop_layout_total="resources/pop_layout_total.nc",
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pop_layout_urban="resources/pop_layout_urban.nc",
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pop_layout_rural="resources/pop_layout_rural.nc",
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regions_onshore="resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson"
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output:
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv"
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script: "scripts/build_clustered_population_layouts.py"
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rule build_heat_demand:
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input:
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pop_layout_total="resources/pop_layout_total.nc",
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pop_layout_urban="resources/pop_layout_urban.nc",
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pop_layout_rural="resources/pop_layout_rural.nc",
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regions_onshore="resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson"
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output:
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heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
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heat_demand_rural="resources/heat_demand_rural_{network}_s{simpl}_{clusters}.nc",
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heat_demand_total="resources/heat_demand_total_{network}_s{simpl}_{clusters}.nc"
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script: "scripts/build_heat_demand.py"
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rule build_temperature_profiles:
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input:
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pop_layout_total="resources/pop_layout_total.nc",
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pop_layout_urban="resources/pop_layout_urban.nc",
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pop_layout_rural="resources/pop_layout_rural.nc",
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regions_onshore="resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson"
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output:
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temp_soil_total="resources/temp_soil_total_{network}_s{simpl}_{clusters}.nc",
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temp_soil_rural="resources/temp_soil_rural_{network}_s{simpl}_{clusters}.nc",
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temp_soil_urban="resources/temp_soil_urban_{network}_s{simpl}_{clusters}.nc",
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temp_air_total="resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
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temp_air_rural="resources/temp_air_rural_{network}_s{simpl}_{clusters}.nc",
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temp_air_urban="resources/temp_air_urban_{network}_s{simpl}_{clusters}.nc"
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script: "scripts/build_temperature_profiles.py"
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rule build_cop_profiles:
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input:
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temp_soil_total="resources/temp_soil_total_{network}_s{simpl}_{clusters}.nc",
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temp_soil_rural="resources/temp_soil_rural_{network}_s{simpl}_{clusters}.nc",
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temp_soil_urban="resources/temp_soil_urban_{network}_s{simpl}_{clusters}.nc",
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temp_air_total="resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
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temp_air_rural="resources/temp_air_rural_{network}_s{simpl}_{clusters}.nc",
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temp_air_urban="resources/temp_air_urban_{network}_s{simpl}_{clusters}.nc"
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output:
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cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
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cop_soil_rural="resources/cop_soil_rural_{network}_s{simpl}_{clusters}.nc",
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cop_soil_urban="resources/cop_soil_urban_{network}_s{simpl}_{clusters}.nc",
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cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
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cop_air_rural="resources/cop_air_rural_{network}_s{simpl}_{clusters}.nc",
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cop_air_urban="resources/cop_air_urban_{network}_s{simpl}_{clusters}.nc"
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script: "scripts/build_cop_profiles.py"
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rule build_solar_thermal_profiles:
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input:
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pop_layout_total="resources/pop_layout_total.nc",
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pop_layout_urban="resources/pop_layout_urban.nc",
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pop_layout_rural="resources/pop_layout_rural.nc",
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regions_onshore="resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson"
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output:
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solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc"
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script: "scripts/build_solar_thermal_profiles.py"
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rule build_energy_totals:
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input:
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nuts3_shapes='resources/nuts3_shapes.geojson'
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output:
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energy_name='data/energy_totals.csv',
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co2_name='data/co2_totals.csv',
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transport_name='data/transport_data.csv'
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threads: 1
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resources: mem_mb=10000
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script: 'scripts/build_energy_totals.py'
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rule build_biomass_potentials:
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input:
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jrc_potentials="data/biomass/JRC Biomass Potentials.xlsx"
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output:
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biomass_potentials='data/biomass_potentials.csv'
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threads: 1
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resources: mem_mb=1000
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script: 'scripts/build_biomass_potentials.py'
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rule build_industrial_demand:
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input:
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv"
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output:
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industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv"
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threads: 1
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resources: mem_mb=1000
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script: 'scripts/build_industrial_demand.py'
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12
config.yaml
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config.yaml
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results_dir: 'results/'
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summary_dir: results
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run: '190416-modular-test'
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scenario:
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sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
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simpl: ['']
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lv: [1.0]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
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clusters: [128] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
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opts: [Co2L0-3H-T-H-B-I,Co2L0-3H-T-H-B-I-onwind0,Co2L0-3H-T-H-B-I-onwind0p1]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H] #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
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# Co2L will give default (5%); Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
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53
scripts/build_biomass_potentials.py
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scripts/build_biomass_potentials.py
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import pandas as pd
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idx = pd.IndexSlice
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def build_biomass_potentials():
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#delete empty column C from this sheet first before reading it in
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df = pd.read_excel(snakemake.input.jrc_potentials,
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"Potentials (PJ)")
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df.rename(columns={"Unnamed: 16":"Municipal waste"},inplace=True)
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df.drop(columns="Total",inplace=True)
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df.replace("-",0.,inplace=True)
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df_dict = {}
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for i in range(36):
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df_dict[df.iloc[i*16,1]] = df.iloc[1+i*16:(i+1)*16].astype(float)
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df_new = pd.concat(df_dict)
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us_type = pd.Series(index=df_new.columns)
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us_type.iloc[0:7] = "not included"
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us_type.iloc[7:8] = "biogas"
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us_type.iloc[8:9] = "solid biomass"
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us_type.iloc[9:11] = "not included"
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us_type.iloc[11:16] = "solid biomass"
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us_type.iloc[16:17] = "biogas"
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#convert from PJ to MWh
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biomass_potentials = df_new.loc[idx[:,snakemake.config['biomass']['year'],snakemake.config['biomass']['scenario']],:].groupby(us_type,axis=1).sum().groupby(level=0).sum().rename({"UK" : "GB", "BH" : "BA"})/3.6*1e6
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biomass_potentials.to_csv(snakemake.output.biomass_potentials)
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if 'snakemake' not in globals():
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from vresutils import Dict
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import yaml
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snakemake = Dict()
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snakemake.input = Dict()
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snakemake.input['jrc_potentials'] = "data/biomass/JRC Biomass Potentials.xlsx"
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snakemake.output = Dict()
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snakemake.output['biomass_potentials'] = 'data/biomass_potentials.csv'
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with open('config.yaml') as f:
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snakemake.config = yaml.load(f)
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build_biomass_potentials()
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32
scripts/build_clustered_population_layouts.py
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scripts/build_clustered_population_layouts.py
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import geopandas as gpd
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import xarray as xr
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import pandas as pd
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import atlite
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cutout = atlite.Cutout(snakemake.config['renewable']['onwind']['cutout'],
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cutout_dir=snakemake.config['atlite']['cutout_dir'])
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clustered_busregions_as_geopd = gpd.read_file(snakemake.input.regions_onshore).set_index('name', drop=True)
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clustered_busregions = pd.Series(clustered_busregions_as_geopd.geometry, index=clustered_busregions_as_geopd.index)
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I = cutout.indicatormatrix(clustered_busregions)
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items = ["total","urban","rural"]
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pop = pd.DataFrame(columns=items,
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index=clustered_busregions.index)
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for item in items:
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pop_layout = xr.open_dataarray(snakemake.input['pop_layout_'+item])
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pop[item] = I.dot(pop_layout.stack(spatial=('y', 'x')))
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pop.to_csv(snakemake.output.clustered_pop_layout)
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25
scripts/build_cop_profiles.py
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scripts/build_cop_profiles.py
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import xarray as xr
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#quadratic regression based on Staffell et al. (2012)
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#https://doi.org/10.1039/C2EE22653G
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# COP is function of temp difference source to sink
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cop_f = {"air" : lambda d_t: 6.81 -0.121*d_t + 0.000630*d_t**2,
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"soil" : lambda d_t: 8.77 -0.150*d_t + 0.000734*d_t**2}
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sink_T = 55. # Based on DTU / large area radiators
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for area in ["total", "urban", "rural"]:
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for source in ["air", "soil"]:
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source_T = xr.open_dataarray(snakemake.input["temp_{}_{}".format(source,area)])
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delta_T = sink_T - source_T
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cop = cop_f[source](delta_T)
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cop.to_netcdf(snakemake.output["cop_{}_{}".format(source,area)])
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460
scripts/build_energy_totals.py
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scripts/build_energy_totals.py
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import pandas as pd
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import geopandas as gpd
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idx = pd.IndexSlice
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#translations for Eurostat
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country_to_code = {
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'EU28' : 'EU',
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'EA19' : 'EA',
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'Belgium' : 'BE',
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'Bulgaria' : 'BG',
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'Czech Republic' : 'CZ',
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'Denmark' : 'DK',
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'Germany' : 'DE',
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'Estonia' : 'EE',
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'Ireland' : 'IE',
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'Greece' : 'GR',
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'Spain' : 'ES',
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'France' : 'FR',
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'Croatia' : 'HR',
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'Italy' : 'IT',
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'Cyprus' : 'CY',
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'Latvia' : 'LV',
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'Lithuania' : 'LT',
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'Luxembourg' : 'LU',
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'Hungary' : 'HU',
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'Malta' : 'MA',
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'Netherlands' : 'NL',
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'Austria' : 'AT',
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'Poland' : 'PL',
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'Portugal' : 'PT',
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'Romania' : 'RO',
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'Slovenia' : 'SI',
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'Slovakia' : 'SK',
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'Finland' : 'FI',
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'Sweden' : 'SE',
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'United Kingdom' : 'GB',
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'Iceland' : 'IS',
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'Norway' : 'NO',
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'Montenegro' : 'ME',
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'FYR of Macedonia' : 'MK',
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'Albania' : 'AL',
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'Serbia' : 'RS',
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'Turkey' : 'TU',
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'Bosnia and Herzegovina' : 'BA',
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'Kosovo\n(UNSCR 1244/99)' : 'KO', #2017 version
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'Kosovo\n(under United Nations Security Council Resolution 1244/99)' : 'KO', #2016 version
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'Moldova' : 'MO',
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'Ukraine' : 'UK',
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'Switzerland' : 'CH',
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}
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non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
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rename = {"GR" : "EL",
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"GB" : "UK"}
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eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI', 'CZ',
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'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV',
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'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI'] + ['CY','MT']
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eu28_eea = eu28[:]
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eu28_eea.remove("GB")
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eu28_eea.append("UK")
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def build_eurostat(year):
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"""Return multi-index for all countries' energy data in TWh/a."""
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stats_from_year = 2016
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fns = {2016: "data/eurostat-energy_balances-june_2016_edition/{year}-Energy-Balances-June2016edition.xlsx",
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2017: "data/eurostat-energy_balances-june_2017_edition/{year}-ENERGY-BALANCES-June2017edition.xlsx"}
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#2016 includes BA, 2017 doesn't
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#with sheet as None, an ordered dictionary of all sheets is returned
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dfs = pd.read_excel(fns[stats_from_year].format(year=year),
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None,
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skiprows=1,
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index_col=list(range(4)))
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#sorted_index necessary for slicing
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df = pd.concat({country_to_code[df.columns[0]] : df for ct,df in dfs.items()},sort=True).sort_index()
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#drop non-numeric columns; convert ktoe/a to TWh/a
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return df.drop(df.columns[df.dtypes != float],axis=1)*11.63/1e3
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def build_swiss(year):
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fn = "data/switzerland-sfoe/switzerland-new_format.csv"
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#convert PJ/a to TWh/a
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return (pd.read_csv(fn,index_col=list(range(2)))/3.6).loc["CH",str(year)]
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def build_idees(year):
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base_dir = "data/jrc-idees-2015"
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totals = pd.DataFrame()
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#convert ktoe/a to TWh/a
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factor = 11.63/1e3
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for ct in population.index:
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if ct in non_EU:
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print("When reading IDEES, skipping non-EU28 country",ct)
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continue
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#RESIDENTIAL
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filename = "{}/JRC-IDEES-2015_Residential_{}.xlsx".format(base_dir,rename.get(ct,ct))
|
||||
df = pd.read_excel(filename,"RES_hh_fec")
|
||||
|
||||
assert df.iloc[2,0] == "Space heating"
|
||||
totals.loc[ct,"total residential space"] = df.loc[2,year]
|
||||
|
||||
assert df.iloc[10,0] == "Advanced electric heating"
|
||||
assert df.iloc[11,0] == "Conventional electric heating"
|
||||
totals.loc[ct,"electricity residential space"] = df.loc[[10,11],year].sum()
|
||||
|
||||
assert df.iloc[15,0] == "Water heating"
|
||||
totals.loc[ct,"total residential water"] = df.loc[15,year]
|
||||
assert df.iloc[23,0] == "Electricity"
|
||||
totals.loc[ct,"electricity residential water"] = df.loc[23,year]
|
||||
|
||||
assert df.iloc[25,0] == "Cooking"
|
||||
totals.loc[ct,"total residential cooking"] = df.loc[25,year]
|
||||
assert df.iloc[30,0] == "Electricity"
|
||||
totals.loc[ct,"electricity residential cooking"] = df.loc[30,year]
|
||||
|
||||
df = pd.read_excel(filename,"RES_summary")
|
||||
|
||||
assert df.iloc[34,0] == "Energy consumption by fuel - Eurostat structure (ktoe)"
|
||||
totals.loc[ct,"total residential"] = df.loc[34,year]
|
||||
|
||||
assert df.iloc[47,0] == "Electricity"
|
||||
totals.loc[ct,"electricity residential"] = df.loc[47,year]
|
||||
|
||||
|
||||
#SERVICES
|
||||
|
||||
filename = "{}/JRC-IDEES-2015_Tertiary_{}.xlsx".format(base_dir,rename.get(ct,ct))
|
||||
df = pd.read_excel(filename,"SER_hh_fec")
|
||||
|
||||
assert df.iloc[2,0] == "Space heating"
|
||||
totals.loc[ct,"total services space"] = df.loc[2,year]
|
||||
|
||||
assert df.iloc[11,0] == "Advanced electric heating"
|
||||
assert df.iloc[12,0] == "Conventional electric heating"
|
||||
totals.loc[ct,"electricity services space"] = df.loc[[11,12],year].sum()
|
||||
|
||||
assert df.iloc[17,0] == "Hot water"
|
||||
totals.loc[ct,"total services water"] = df.loc[17,year]
|
||||
assert df.iloc[24,0] == "Electricity"
|
||||
totals.loc[ct,"electricity services water"] = df.loc[24,year]
|
||||
|
||||
assert df.iloc[27,0] == "Catering"
|
||||
totals.loc[ct,"total services cooking"] = df.loc[27,year]
|
||||
assert df.iloc[31,0] == "Electricity"
|
||||
totals.loc[ct,"electricity services cooking"] = df.loc[31,year]
|
||||
|
||||
df = pd.read_excel(filename,"SER_summary")
|
||||
|
||||
assert df.iloc[37,0] == "Energy consumption by fuel - Eurostat structure (ktoe)"
|
||||
totals.loc[ct,"total services"] = df.loc[37,year]
|
||||
|
||||
assert df.iloc[50,0] == "Electricity"
|
||||
totals.loc[ct,"electricity services"] = df.loc[50,year]
|
||||
|
||||
|
||||
# TRANSPORT
|
||||
|
||||
filename = "{}/JRC-IDEES-2015_Transport_{}.xlsx".format(base_dir,rename.get(ct,ct))
|
||||
df = pd.read_excel(filename,"TrRoad_ene")
|
||||
|
||||
assert df.iloc[2,0] == "by fuel (EUROSTAT DATA)"
|
||||
totals.loc[ct,"total road"] = df.loc[2,year]
|
||||
assert df.iloc[13,0] == "Electricity"
|
||||
totals.loc[ct,"electricity road"] = df.loc[13,year]
|
||||
|
||||
assert df.iloc[61,0] == "Passenger cars"
|
||||
totals.loc[ct,"passenger car efficiency"] = df.loc[61,year]
|
||||
|
||||
df = pd.read_excel(filename,"TrRail_ene")
|
||||
|
||||
assert df.iloc[2,0] == "by fuel (EUROSTAT DATA)"
|
||||
totals.loc[ct,"total rail"] = df.loc[2,year]
|
||||
assert df.iloc[12,0] == "Electricity"
|
||||
totals.loc[ct,"electricity rail"] = df.loc[12,year]
|
||||
|
||||
df = pd.read_excel(filename,"TrRoad_act")
|
||||
|
||||
assert df.iloc[85,0] == "Passenger cars"
|
||||
totals.loc[ct,"passenger cars"] = df.loc[85,year]
|
||||
|
||||
totals = totals*factor
|
||||
|
||||
totals["passenger cars"] = totals["passenger cars"]/factor
|
||||
|
||||
#convert ktoe/100km to kWh per km
|
||||
totals["passenger car efficiency"] = 10*totals["passenger car efficiency"]
|
||||
|
||||
return totals
|
||||
|
||||
|
||||
def build_energy_totals():
|
||||
|
||||
clean_df = idees.reindex(population.index).drop(["passenger cars","passenger car efficiency"],axis=1)
|
||||
|
||||
clean_df.loc["CH"] = swiss
|
||||
|
||||
#get values for missing countries based on Eurostat EnergyBalances
|
||||
#divide cooking/space/water according to averages in EU28
|
||||
|
||||
missing = clean_df.index[clean_df["total residential"].isnull()]
|
||||
missing_in_eurostat = missing.intersection(eurostat.index.levels[0])
|
||||
uses = ["space","cooking","water"]
|
||||
|
||||
for sector,eurostat_sector in [("residential","Residential"),("services","Services"),
|
||||
("road","Road"),("rail","Rail")]:
|
||||
for fuel,eurostat_fuel in [("electricity","Electricity"),("total","Total all products")]:
|
||||
clean_df.loc[missing_in_eurostat,"{} {}".format(fuel,sector)] = eurostat.loc[idx[missing_in_eurostat,:,:,eurostat_sector],eurostat_fuel].groupby(level=0).sum()
|
||||
|
||||
if sector in ["road","rail"]:
|
||||
continue
|
||||
|
||||
fuel = "electricity"
|
||||
for use in uses:
|
||||
avg = (clean_df["{} {} {}".format(fuel,sector,use)]/clean_df["{} {}".format(fuel,sector)]).mean()
|
||||
print("{}: average fraction of {} for {} is {}".format(sector,fuel,use,avg))
|
||||
clean_df.loc[missing_in_eurostat,"{} {} {}".format(fuel,sector,use)] = avg*clean_df.loc[missing_in_eurostat,"{} {}".format(fuel,sector)]
|
||||
|
||||
fuel = "total"
|
||||
for use in uses:
|
||||
avg = ((clean_df["{} {} {}".format("total",sector,use)]-clean_df["{} {} {}".format("electricity",sector,use)])/
|
||||
(clean_df["{} {}".format("total",sector)]-clean_df["{} {}".format("electricity",sector)])).mean()
|
||||
print("{}: average fraction of non-electric for {} is {}".format(sector,use,avg))
|
||||
clean_df.loc[missing_in_eurostat,"{} {} {}".format(fuel,sector,use)] = \
|
||||
clean_df.loc[missing_in_eurostat,"{} {} {}".format("electricity",sector,use)] \
|
||||
+ avg*(clean_df.loc[missing_in_eurostat,"{} {}".format("total",sector)] - clean_df.loc[missing_in_eurostat,"{} {}".format("electricity",sector)])
|
||||
|
||||
|
||||
|
||||
#Fix Norway space and water heating fractions
|
||||
#http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14
|
||||
#The main heating source for about 73 per cent of the households is based on electricity
|
||||
#=> 26% is non-electric
|
||||
elec_fraction = 0.73
|
||||
|
||||
without_norway = clean_df.drop("NO")
|
||||
|
||||
for sector in ["residential","services"]:
|
||||
|
||||
#assume non-electric is heating
|
||||
total_heating = (clean_df.loc["NO","{} {}".format("total",sector)]-clean_df.loc["NO","{} {}".format("electricity",sector)])/(1-elec_fraction)
|
||||
|
||||
for use in uses:
|
||||
fraction = ((without_norway["{} {} {}".format("total",sector,use)]-without_norway["{} {} {}".format("electricity",sector,use)])/
|
||||
(without_norway["{} {}".format("total",sector)]-without_norway["{} {}".format("electricity",sector)])).mean()
|
||||
clean_df.loc["NO","{} {} {}".format("total",sector,use)] = total_heating*fraction
|
||||
clean_df.loc["NO","{} {} {}".format("electricity",sector,use)] = total_heating*fraction*elec_fraction
|
||||
|
||||
#fix missing data for BA (services and road energy data)
|
||||
missing = (clean_df.loc["BA"] == 0.)
|
||||
|
||||
#add back in proportional to RS with ratio of total residential demand
|
||||
clean_df.loc["BA",missing] = clean_df.loc["BA","total residential"]/clean_df.loc["RS","total residential"]*clean_df.loc["RS",missing]
|
||||
|
||||
clean_df.to_csv(snakemake.output.energy_name)
|
||||
|
||||
return clean_df
|
||||
|
||||
|
||||
def build_eea_co2():
|
||||
# see ../notebooks/compute_1990_Europe_emissions_for_targets.ipynb
|
||||
|
||||
#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-14
|
||||
#downloaded 190222 (modified by EEA last on 181130)
|
||||
fn = "data/eea/UNFCCC_v21.csv"
|
||||
df = pd.read_csv(fn, encoding="latin-1")
|
||||
df.loc[df["Year"] == "1985-1987","Year"] = 1986
|
||||
df["Year"] = df["Year"].astype(int)
|
||||
df = df.set_index(['Country_code', 'Pollutant_name', 'Year', 'Sector_name']).sort_index()
|
||||
|
||||
e = pd.Series()
|
||||
e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
|
||||
e['residential non-elec'] = '1.A.4.b - Residential'
|
||||
e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
|
||||
e['rail non-elec'] = "1.A.3.c - Railways"
|
||||
e["road non-elec"] = '1.A.3.b - Road Transportation'
|
||||
e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
|
||||
e['international navigation'] = '1.D.1.b - International Navigation'
|
||||
e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
|
||||
e["international aviation"] = '1.D.1.a - International Aviation'
|
||||
e['total energy'] = '1 - Energy'
|
||||
e['industrial processes'] = '2 - Industrial Processes and Product Use'
|
||||
e['agriculture'] = '3 - Agriculture'
|
||||
e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
|
||||
e['waste management'] = '5 - Waste management'
|
||||
e['other'] = '6 - Other Sector'
|
||||
e['indirect'] = 'ind_CO2 - Indirect CO2'
|
||||
e["total wL"] = "Total (with LULUCF, with indirect CO2)"
|
||||
e["total woL"] = "Total (without LULUCF, with indirect CO2)"
|
||||
|
||||
|
||||
pol = "CO2" #["All greenhouse gases - (CO2 equivalent)","CO2"]
|
||||
|
||||
cts = ["CH","EUA","NO"] + eu28_eea
|
||||
|
||||
year = 1990
|
||||
|
||||
emissions = df.loc[idx[cts,pol,year,e.values],"emissions"].unstack("Sector_name").rename(columns=pd.Series(e.index,e.values)).rename(index={"All greenhouse gases - (CO2 equivalent)" : "GHG"},level=1)
|
||||
|
||||
#only take level 0, since level 1 (pol) and level 2 (year) are trivial
|
||||
emissions = emissions.groupby(level=0,axis=0).sum()
|
||||
|
||||
emissions.rename(index={"EUA" : "EU28", "UK" : "GB"},inplace=True)
|
||||
|
||||
emissions['industrial non-elec'] = emissions['total energy'] - emissions[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
|
||||
'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
|
||||
'international navigation']].sum(axis=1)
|
||||
|
||||
emissions.drop(columns=["total energy", "total wL", "total woL"],inplace=True)
|
||||
|
||||
return emissions/1e3
|
||||
|
||||
|
||||
def build_eurostat_co2(year=1990):
|
||||
|
||||
eurostat_for_co2 = build_eurostat(year)
|
||||
|
||||
se = pd.Series(index=eurostat_for_co2.columns,dtype=float)
|
||||
|
||||
#emissions in tCO2_equiv per MWh_th
|
||||
se["Solid fuels"] = 0.36 #Approximates coal
|
||||
se["Oil (total)"] = 0.285 #Average of distillate and residue
|
||||
se["Gas"] = 0.2 #For natural gas
|
||||
|
||||
#oil values from https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11
|
||||
#Distillate oil (No. 2) 0.276
|
||||
#Residual oil (No. 6) 0.298
|
||||
#https://www.eia.gov/electricity/annual/html/epa_a_03.html
|
||||
|
||||
|
||||
|
||||
eurostat_co2 = eurostat_for_co2.multiply(se).sum(axis=1)
|
||||
|
||||
return eurostat_co2
|
||||
|
||||
|
||||
def build_co2_totals(year=1990):
|
||||
|
||||
co2 = eea_co2.reindex(["EU28","NO","CH","BA","RS","AL","ME","MK"] + eu28)
|
||||
|
||||
for ct in ["BA","RS","AL","ME","MK"]:
|
||||
co2.loc[ct,"electricity"] = eurostat_co2[ct,"+","Conventional Thermal Power Stations","of which From Coal"].sum()
|
||||
co2.loc[ct,"residential non-elec"] = eurostat_co2[ct,"+","+","Residential"].sum()
|
||||
co2.loc[ct,"services non-elec"] = eurostat_co2[ct,"+","+","Services"].sum()
|
||||
co2.loc[ct,"road non-elec"] = eurostat_co2[ct,"+","+","Road"].sum()
|
||||
co2.loc[ct,"rail non-elec"] = eurostat_co2[ct,"+","+","Rail"].sum()
|
||||
co2.loc[ct,"domestic navigation"] = eurostat_co2[ct,"+","+","Domestic Navigation"].sum()
|
||||
co2.loc[ct,'international navigation'] = eurostat_co2[ct,"-","Bunkers"].sum()
|
||||
co2.loc[ct,"domestic aviation"] = eurostat_co2[ct,"+","+","Domestic aviation"].sum()
|
||||
co2.loc[ct,"international aviation"] = eurostat_co2[ct,"+","+","International aviation"].sum()
|
||||
#doesn't include industrial process emissions or fuel processing/refining
|
||||
co2.loc[ct,'industrial non-elec'] = eurostat_co2[ct,"+","Industry"].sum()
|
||||
#doesn't include non-energy emissions
|
||||
co2.loc[ct,'agriculture'] = eurostat_co2[ct,"+","+","Agriculture / Forestry"].sum()
|
||||
|
||||
|
||||
|
||||
co2.to_csv(snakemake.output.co2_name)
|
||||
|
||||
return co2
|
||||
|
||||
|
||||
def build_transport_data():
|
||||
|
||||
transport_data = pd.DataFrame(columns=["number cars","average fuel efficiency"],
|
||||
index=population.index)
|
||||
|
||||
## collect number of cars
|
||||
|
||||
transport_data["number cars"] = idees["passenger cars"]
|
||||
|
||||
#CH from http://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EU#Luxembourg_has_the_highest_number_of_passenger_cars_per_inhabitant
|
||||
transport_data.loc["CH","number cars"] = 4.136e6
|
||||
|
||||
missing = transport_data.index[transport_data["number cars"].isnull()]
|
||||
|
||||
print("Missing data on cars from:")
|
||||
|
||||
print(missing)
|
||||
|
||||
cars_pp = transport_data["number cars"]/population
|
||||
|
||||
transport_data.loc[missing,"number cars"] = cars_pp.mean()*population
|
||||
|
||||
|
||||
## collect average fuel efficiency in kWh/km
|
||||
|
||||
transport_data["average fuel efficiency"] = idees["passenger car efficiency"]
|
||||
|
||||
missing = transport_data.index[transport_data["average fuel efficiency"].isnull()]
|
||||
|
||||
print("Missing data on fuel efficiency from:")
|
||||
|
||||
print(missing)
|
||||
|
||||
transport_data.loc[missing,"average fuel efficiency"] = transport_data["average fuel efficiency"].mean()
|
||||
|
||||
transport_data.to_csv(snakemake.output.transport_name)
|
||||
|
||||
return transport_data
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
|
||||
# Detect running outside of snakemake and mock snakemake for testing
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
snakemake = Dict()
|
||||
snakemake.output = Dict()
|
||||
snakemake.output['energy_name'] = "data/energy_totals.csv"
|
||||
snakemake.output['co2_name'] = "data/co2_totals.csv"
|
||||
snakemake.output['transport_name'] = "data/transport_data.csv"
|
||||
|
||||
snakemake.input = Dict()
|
||||
snakemake.input['nuts3_shapes'] = 'resources/nuts3_shapes.geojson'
|
||||
|
||||
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
|
||||
population = nuts3['pop'].groupby(nuts3.country).sum()
|
||||
|
||||
year = 2011
|
||||
|
||||
eurostat = build_eurostat(year)
|
||||
|
||||
swiss = build_swiss(year)
|
||||
|
||||
idees = build_idees(year)
|
||||
|
||||
build_energy_totals()
|
||||
|
||||
eea_co2 = build_eea_co2()
|
||||
|
||||
eurostat_co2 = build_eurostat_co2()
|
||||
|
||||
build_co2_totals()
|
||||
|
||||
build_transport_data()
|
||||
40
scripts/build_heat_demand.py
Normal file
40
scripts/build_heat_demand.py
Normal file
@ -0,0 +1,40 @@
|
||||
|
||||
import geopandas as gpd
|
||||
import atlite
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
import scipy as sp
|
||||
|
||||
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
time = pd.date_range(freq='m', **snakemake.config['snapshots'])
|
||||
params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
|
||||
|
||||
cutout = atlite.Cutout(snakemake.config['renewable']['onwind']['cutout'],
|
||||
cutout_dir=snakemake.config['atlite']['cutout_dir'],
|
||||
**params)
|
||||
|
||||
clustered_busregions_as_geopd = gpd.read_file(snakemake.input.regions_onshore).set_index('name', drop=True)
|
||||
|
||||
clustered_busregions = pd.Series(clustered_busregions_as_geopd.geometry, index=clustered_busregions_as_geopd.index)
|
||||
|
||||
I = cutout.indicatormatrix(clustered_busregions)
|
||||
|
||||
|
||||
for item in ["rural","urban","total"]:
|
||||
|
||||
pop_layout = xr.open_dataarray(snakemake.input['pop_layout_'+item])
|
||||
|
||||
M = I.T.dot(sp.diag(I.dot(pop_layout.stack(spatial=('y', 'x')))))
|
||||
|
||||
heat_demand = cutout.heat_demand(matrix=M.T,index=clustered_busregions.index)
|
||||
|
||||
heat_demand.to_netcdf(snakemake.output["heat_demand_"+item])
|
||||
35
scripts/build_industrial_demand.py
Normal file
35
scripts/build_industrial_demand.py
Normal file
@ -0,0 +1,35 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
idx = pd.IndexSlice
|
||||
|
||||
def build_industrial_demand():
|
||||
|
||||
population = pd.read_csv(snakemake.input.clustered_pop_layout,
|
||||
index_col=0)
|
||||
|
||||
totals = pd.Series(data=[1100.,1814.,586.,400.,580.,186.],
|
||||
index=["industry new electricity","industry process heat",
|
||||
"naptha feedstock","shipping H2","aviation kerosene","process emissions"])
|
||||
|
||||
industrial_demand = pd.DataFrame({i : population["total"]*totals[i]*1e6/population["total"].sum() for i in totals.index })
|
||||
|
||||
industrial_demand.to_csv(snakemake.output.industrial_demand)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# Detect running outside of snakemake and mock snakemake for testing
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
snakemake.input = Dict()
|
||||
snakemake.input['clustered_pop_layout'] = "resources/pop_layout_elec_s_128.csv"
|
||||
snakemake.output = Dict()
|
||||
snakemake.output['industrial_demand'] = "resources/industrial_demand_elec_s_128.csv"
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
|
||||
build_industrial_demand()
|
||||
104
scripts/build_population_layouts.py
Normal file
104
scripts/build_population_layouts.py
Normal file
@ -0,0 +1,104 @@
|
||||
|
||||
# Build mapping between grid cells and population (total, urban, rural)
|
||||
|
||||
import atlite
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
|
||||
from vresutils import shapes as vshapes
|
||||
|
||||
import geopandas as gpd
|
||||
|
||||
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
snakemake.input["urban_percent"] = "data/urban_percent.csv"
|
||||
|
||||
cutout = atlite.Cutout(snakemake.config['renewable']['onwind']['cutout'],
|
||||
cutout_dir=snakemake.config['atlite']['cutout_dir'])
|
||||
|
||||
grid_cells = cutout.grid_cells()
|
||||
|
||||
#nuts3 has columns country, gdp, pop, geometry
|
||||
#population is given in dimensions of 1e3=k
|
||||
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
|
||||
|
||||
|
||||
# Indicator matrix NUTS3 -> grid cells
|
||||
I = atlite.cutout.compute_indicatormatrix(nuts3.geometry, grid_cells)
|
||||
|
||||
# Indicator matrix grid_cells -> NUTS3; inprinciple Iinv*I is identity
|
||||
# but imprecisions mean not perfect
|
||||
Iinv = cutout.indicatormatrix(nuts3.geometry)
|
||||
|
||||
countries = nuts3.country.value_counts().index.sort_values()
|
||||
|
||||
urban_fraction = pd.read_csv(snakemake.input.urban_percent,
|
||||
header=None,index_col=0,squeeze=True)/100.
|
||||
|
||||
#fill missing Balkans values
|
||||
missing = ["AL","ME","MK"]
|
||||
reference = ["RS","BA"]
|
||||
urban_fraction = urban_fraction.reindex(urban_fraction.index|missing)
|
||||
urban_fraction.loc[missing] = urban_fraction[reference].mean()
|
||||
|
||||
|
||||
#population in each grid cell
|
||||
pop_cells = pd.Series(I.dot(nuts3['pop']))
|
||||
|
||||
#in km^2
|
||||
cell_areas = pd.Series(cutout.grid_cells()).map(vshapes.area)/1e6
|
||||
|
||||
#pop per km^2
|
||||
density_cells = pop_cells/cell_areas
|
||||
|
||||
|
||||
#rural or urban population in grid cell
|
||||
pop_rural = pd.Series(0.,density_cells.index)
|
||||
pop_urban = pd.Series(0.,density_cells.index)
|
||||
|
||||
for ct in countries:
|
||||
print(ct,urban_fraction[ct])
|
||||
|
||||
indicator_nuts3_ct = pd.Series(0.,nuts3.index)
|
||||
indicator_nuts3_ct[nuts3.index[nuts3.country==ct]] = 1.
|
||||
|
||||
indicator_cells_ct = pd.Series(Iinv.T.dot(indicator_nuts3_ct))
|
||||
|
||||
density_cells_ct = indicator_cells_ct*density_cells
|
||||
|
||||
pop_cells_ct = indicator_cells_ct*pop_cells
|
||||
|
||||
#correct for imprecision of Iinv*I
|
||||
pop_ct = nuts3['pop'][indicator_nuts3_ct.index[indicator_nuts3_ct == 1.]].sum()
|
||||
pop_cells_ct = pop_cells_ct*pop_ct/pop_cells_ct.sum()
|
||||
|
||||
# The first low density grid cells to reach rural fraction are rural
|
||||
index_from_low_d_to_high_d = density_cells_ct.sort_values().index
|
||||
pop_ct_rural_b = pop_cells_ct[index_from_low_d_to_high_d].cumsum()/pop_cells_ct.sum() < (1-urban_fraction[ct])
|
||||
pop_ct_urban_b = ~pop_ct_rural_b
|
||||
|
||||
pop_ct_rural_b[indicator_cells_ct==0.] = False
|
||||
pop_ct_urban_b[indicator_cells_ct==0.] = False
|
||||
|
||||
pop_rural += pop_cells_ct.where(pop_ct_rural_b,0.)
|
||||
pop_urban += pop_cells_ct.where(pop_ct_urban_b,0.)
|
||||
|
||||
pop_cells = {"total" : pop_cells}
|
||||
|
||||
pop_cells["rural"] = pop_rural
|
||||
pop_cells["urban"] = pop_urban
|
||||
|
||||
for key in pop_cells.keys():
|
||||
layout = xr.DataArray(pop_cells[key].values.reshape(cutout.shape),
|
||||
[('y', cutout.coords['y']), ('x', cutout.coords['x'])])
|
||||
|
||||
layout.to_netcdf(snakemake.output["pop_layout_"+key])
|
||||
|
||||
55
scripts/build_solar_thermal_profiles.py
Normal file
55
scripts/build_solar_thermal_profiles.py
Normal file
@ -0,0 +1,55 @@
|
||||
|
||||
import sys
|
||||
|
||||
#override atlite
|
||||
sys.path = ["/home/vres/data/tom/lib/atlite"] + sys.path
|
||||
|
||||
import geopandas as gpd
|
||||
import atlite
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
import scipy as sp
|
||||
|
||||
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
time = pd.date_range(freq='m', **snakemake.config['snapshots'])
|
||||
params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
|
||||
|
||||
|
||||
|
||||
cutout = atlite.Cutout(snakemake.config['renewable']['onwind']['cutout'],
|
||||
cutout_dir=snakemake.config['atlite']['cutout_dir'],
|
||||
**params)
|
||||
|
||||
clustered_busregions_as_geopd = gpd.read_file(snakemake.input.regions_onshore).set_index('name', drop=True)
|
||||
|
||||
clustered_busregions = pd.Series(clustered_busregions_as_geopd.geometry, index=clustered_busregions_as_geopd.index)
|
||||
|
||||
I = cutout.indicatormatrix(clustered_busregions)
|
||||
|
||||
|
||||
for item in ["total","rural","urban"]:
|
||||
|
||||
pop_layout = xr.open_dataarray(snakemake.input['pop_layout_'+item])
|
||||
|
||||
M = I.T.dot(sp.diag(I.dot(pop_layout.stack(spatial=('y', 'x')))))
|
||||
nonzero_sum = M.sum(axis=0, keepdims=True)
|
||||
nonzero_sum[nonzero_sum == 0.] = 1.
|
||||
M_tilde = M/nonzero_sum
|
||||
|
||||
solar_thermal_angle = 45.
|
||||
#should clearsky_model be "simple" or "enhanced"?
|
||||
solar_thermal = cutout.solar_thermal(clearsky_model="simple",
|
||||
orientation={'slope': solar_thermal_angle, 'azimuth': 180.},
|
||||
matrix = M_tilde.T,
|
||||
index=clustered_busregions.index)
|
||||
|
||||
solar_thermal.to_netcdf(snakemake.output["solar_thermal_"+item])
|
||||
48
scripts/build_temperature_profiles.py
Normal file
48
scripts/build_temperature_profiles.py
Normal file
@ -0,0 +1,48 @@
|
||||
|
||||
import geopandas as gpd
|
||||
import atlite
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
import scipy as sp
|
||||
|
||||
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
time = pd.date_range(freq='m', **snakemake.config['snapshots'])
|
||||
params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
|
||||
|
||||
|
||||
cutout = atlite.Cutout(snakemake.config['renewable']['onwind']['cutout'],
|
||||
cutout_dir=snakemake.config['atlite']['cutout_dir'],
|
||||
**params)
|
||||
|
||||
clustered_busregions_as_geopd = gpd.read_file(snakemake.input.regions_onshore).set_index('name', drop=True)
|
||||
|
||||
clustered_busregions = pd.Series(clustered_busregions_as_geopd.geometry, index=clustered_busregions_as_geopd.index)
|
||||
|
||||
I = cutout.indicatormatrix(clustered_busregions)
|
||||
|
||||
|
||||
for item in ["total","rural","urban"]:
|
||||
|
||||
pop_layout = xr.open_dataarray(snakemake.input['pop_layout_'+item])
|
||||
|
||||
M = I.T.dot(sp.diag(I.dot(pop_layout.stack(spatial=('y', 'x')))))
|
||||
nonzero_sum = M.sum(axis=0, keepdims=True)
|
||||
nonzero_sum[nonzero_sum == 0.] = 1.
|
||||
M_tilde = M/nonzero_sum
|
||||
|
||||
temp_air = cutout.temperature(matrix=M_tilde.T,index=clustered_busregions.index)
|
||||
|
||||
temp_air.to_netcdf(snakemake.output["temp_air_"+item])
|
||||
|
||||
temp_soil = cutout.soil_temperature(matrix=M_tilde.T,index=clustered_busregions.index)
|
||||
|
||||
temp_soil.to_netcdf(snakemake.output["temp_soil_"+item])
|
||||
Loading…
Reference in New Issue
Block a user