Fix all data preparation steps up to prepare_network
This commit is contained in:
Tom Brown
parent
1b26c3c737
commit
ea22a792b7
69
Snakefile
69
Snakefile
@ -1,22 +1,35 @@
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configfile: "config.yaml"
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wildcard_constraints:
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lv="[a-z0-9\.]+",
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simpl="[a-zA-Z0-9]*",
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clusters="[0-9]+m?",
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sectors="[+a-zA-Z0-9]+",
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opts="[-+a-zA-Z0-9]*"
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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 test_script:
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input:
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expand("resources/heat_demand_urban_elec_s_{clusters}.nc",
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**config['scenario'])
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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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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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nuts3_shapes=pypsaeur('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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@ -30,18 +43,18 @@ rule build_clustered_population_layouts:
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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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regions_onshore=pypsaeur('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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rule build_heat_demands:
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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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regions_onshore=pypsaeur("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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@ -53,7 +66,7 @@ rule build_temperature_profiles:
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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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regions_onshore=pypsaeur("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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@ -87,7 +100,7 @@ rule build_solar_thermal_profiles:
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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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regions_onshore=pypsaeur("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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@ -98,7 +111,7 @@ rule build_solar_thermal_profiles:
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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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nuts3_shapes=pypsaeur('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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@ -124,3 +137,39 @@ rule build_industrial_demand:
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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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rule prepare_network:
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input:
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network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}.nc',
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energy_totals_name='data/energy_totals.csv',
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co2_totals_name='data/co2_totals.csv',
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transport_name='data/transport_data.csv',
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biomass_potentials='data/biomass_potentials.csv',
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv",
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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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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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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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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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output: config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc'
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threads: 1
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resources: mem=1000
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benchmark: "benchmarks/prepare_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}"
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script: "scripts/prepare_network.py"
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80
config.yaml
80
config.yaml
@ -10,3 +10,83 @@ scenario:
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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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snapshots:
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# arguments to pd.date_range
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start: "2013-01-01"
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end: "2014-01-01"
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closed: 'left' # end is not inclusive
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atlite:
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nprocesses: 4
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cutout_dir: '../pypsa-eur/cutouts'
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cutouts:
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europe-2013-era5:
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module: era5
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xs: [-12., 35.]
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ys: [72., 33.]
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years: [2013, 2013]
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europe-2013-sarah:
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module: sarah
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resolution: 0.2
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xs: [-12., 42.]
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ys: [65., 33.]
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years: [2013, 2013]
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renewable:
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onwind:
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cutout: europe-2013-era5
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resource:
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method: wind
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turbine: Vestas_V112_3MW
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# ScholzPhd Tab 4.3.1: 10MW/km^2
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capacity_per_sqm: 3
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# correction_factor: 0.93
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corine:
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#The selection of CORINE Land Cover [1] types that are allowed for wind and solar are based on [2] p.42 / p.28
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#
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#[1] https://www.eea.europa.eu/ds_resolveuid/C9RK15EA06
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#
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#[2] Scholz, Y. (2012). Renewable energy based electricity supply at low costs: development of the REMix model and application for Europe.
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grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
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24, 25, 26, 27, 28, 29, 31, 32]
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distance: 1000
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distance_grid_codes: [1, 2, 3, 4, 5, 6]
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natura: true
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offwind:
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cutout: europe-2013-era5
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resource:
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method: wind
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turbine: NREL_ReferenceTurbine_5MW_offshore
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# ScholzPhd Tab 4.3.1: 10MW/km^2
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capacity_per_sqm: 3
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height_cutoff: 50
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# correction_factor: 0.93
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corine:
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grid_codes: [44, 255]
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natura: true
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solar:
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cutout: europe-2013-sarah
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resource:
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method: pv
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panel: CSi
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orientation:
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slope: 35.
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azimuth: 180.
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# ScholzPhd Tab 4.3.1: 170 MW/km^2
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capacity_per_sqm: 1.7
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correction_factor: 0.877
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corine:
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grid_codes: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
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18, 19, 20, 26, 31, 32]
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natura: true
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hydro:
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cutout: europe-2013-era5
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carriers: [ror, PHS, hydro]
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PHS_max_hours: 6
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biomass:
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year: 2030
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scenario: "Med"
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@ -7,9 +7,10 @@ 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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"Potentials (PJ)",
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index_col=[0,1])
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df.rename(columns={"Unnamed: 16":"Municipal waste"},inplace=True)
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df.rename(columns={"Unnamed: 18":"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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