Merge branch 'master' into retrofit-gas-pipelines
This commit is contained in:
lisazeyen
commit
33f7f5026e
1
.gitignore
vendored
1
.gitignore
vendored
@ -26,6 +26,7 @@ gurobi.log
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/data/switzerland*
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/data/.nfs*
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/data/Industrial_Database.csv
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/data/retro/tabula-calculator-calcsetbuilding.csv
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*.org
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201
Snakefile
201
Snakefile
@ -51,9 +51,9 @@ 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=pypsaeur('resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson')
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regions_onshore=pypsaeur('resources/regions_onshore_elec_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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clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv"
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resources: mem_mb=10000
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script: "scripts/build_clustered_population_layouts.py"
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@ -63,9 +63,9 @@ rule build_simplified_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=pypsaeur('resources/regions_onshore_{network}_s{simpl}.geojson')
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regions_onshore=pypsaeur('resources/regions_onshore_elec_s{simpl}.geojson')
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output:
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}.csv"
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clustered_pop_layout="resources/pop_layout_elec_s{simpl}.csv"
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resources: mem_mb=10000
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script: "scripts/build_clustered_population_layouts.py"
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@ -84,11 +84,11 @@ rule build_heat_demands:
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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=pypsaeur("resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson")
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regions_onshore=pypsaeur("resources/regions_onshore_elec_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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heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
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heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
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heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc"
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resources: mem_mb=20000
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script: "scripts/build_heat_demand.py"
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@ -97,33 +97,33 @@ 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=pypsaeur("resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson")
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regions_onshore=pypsaeur("resources/regions_onshore_elec_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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temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc",
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temp_soil_rural="resources/temp_soil_rural_elec_s{simpl}_{clusters}.nc",
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temp_soil_urban="resources/temp_soil_urban_elec_s{simpl}_{clusters}.nc",
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temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
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temp_air_rural="resources/temp_air_rural_elec_s{simpl}_{clusters}.nc",
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temp_air_urban="resources/temp_air_urban_elec_s{simpl}_{clusters}.nc"
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resources: mem_mb=20000
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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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temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc",
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temp_soil_rural="resources/temp_soil_rural_elec_s{simpl}_{clusters}.nc",
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temp_soil_urban="resources/temp_soil_urban_elec_s{simpl}_{clusters}.nc",
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temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
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temp_air_rural="resources/temp_air_rural_elec_s{simpl}_{clusters}.nc",
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temp_air_urban="resources/temp_air_urban_elec_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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cop_soil_total="resources/cop_soil_total_elec_s{simpl}_{clusters}.nc",
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cop_soil_rural="resources/cop_soil_rural_elec_s{simpl}_{clusters}.nc",
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cop_soil_urban="resources/cop_soil_urban_elec_s{simpl}_{clusters}.nc",
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cop_air_total="resources/cop_air_total_elec_s{simpl}_{clusters}.nc",
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cop_air_rural="resources/cop_air_rural_elec_s{simpl}_{clusters}.nc",
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cop_air_urban="resources/cop_air_urban_elec_s{simpl}_{clusters}.nc"
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resources: mem_mb=20000
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script: "scripts/build_cop_profiles.py"
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@ -133,11 +133,11 @@ 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=pypsaeur("resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson")
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regions_onshore=pypsaeur("resources/regions_onshore_elec_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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solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
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solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
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solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc"
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resources: mem_mb=20000
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script: "scripts/build_solar_thermal_profiles.py"
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@ -208,12 +208,12 @@ rule build_industrial_production_per_country_tomorrow:
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rule build_industrial_distribution_key:
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input:
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
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europe_shape=pypsaeur('resources/europe_shape.geojson'),
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hotmaps_industrial_database="data/Industrial_Database.csv",
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network=pypsaeur('networks/{network}_s{simpl}_{clusters}.nc')
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network=pypsaeur('networks/elec_s{simpl}_{clusters}.nc')
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output:
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industrial_distribution_key="resources/industrial_distribution_key_{network}_s{simpl}_{clusters}.csv"
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industrial_distribution_key="resources/industrial_distribution_key_elec_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_distribution_key.py'
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@ -222,10 +222,10 @@ rule build_industrial_distribution_key:
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rule build_industrial_production_per_node:
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input:
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industrial_distribution_key="resources/industrial_distribution_key_{network}_s{simpl}_{clusters}.csv",
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industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
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industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow.csv"
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output:
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industrial_production_per_node="resources/industrial_production_{network}_s{simpl}_{clusters}.csv"
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industrial_production_per_node="resources/industrial_production_elec_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_production_per_node.py'
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@ -234,10 +234,10 @@ rule build_industrial_production_per_node:
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rule build_industrial_energy_demand_per_node:
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input:
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industry_sector_ratios="resources/industry_sector_ratios.csv",
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industrial_production_per_node="resources/industrial_production_{network}_s{simpl}_{clusters}.csv",
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industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_{network}_s{simpl}_{clusters}.csv"
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industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}.csv",
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industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv"
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output:
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industrial_energy_demand_per_node="resources/industrial_energy_demand_{network}_s{simpl}_{clusters}.csv"
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industrial_energy_demand_per_node="resources/industrial_energy_demand_elec_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_energy_demand_per_node.py'
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@ -256,10 +256,10 @@ rule build_industrial_energy_demand_per_country_today:
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rule build_industrial_energy_demand_per_node_today:
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input:
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industrial_distribution_key="resources/industrial_distribution_key_{network}_s{simpl}_{clusters}.csv",
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industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
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industrial_energy_demand_per_country_today="resources/industrial_energy_demand_per_country_today.csv"
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output:
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industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_{network}_s{simpl}_{clusters}.csv"
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industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_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_energy_demand_per_node_today.py'
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@ -279,10 +279,10 @@ rule build_industrial_energy_demand_per_country:
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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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clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
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industrial_demand_per_country="resources/industrial_energy_demand_per_country.csv"
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output:
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industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv"
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industrial_demand="resources/industrial_demand_elec_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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@ -290,24 +290,25 @@ rule build_industrial_demand:
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rule build_retro_cost:
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input:
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building_stock="data/retro/data_building_stock.csv",
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data_tabula="data/retro/tabula-calculator-calcsetbuilding.csv",
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air_temperature = "resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
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u_values_PL="data/retro/u_values_poland.csv",
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tax_w="data/retro/electricity_taxes_eu.csv",
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construction_index="data/retro/comparative_level_investment.csv",
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average_surface="data/retro/average_surface_components.csv",
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floor_area_missing="data/retro/floor_area_missing.csv",
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
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cost_germany="data/retro/retro_cost_germany.csv",
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window_assumptions="data/retro/window_assumptions.csv"
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window_assumptions="data/retro/window_assumptions.csv",
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output:
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retro_cost="resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
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floor_area="resources/floor_area_{network}_s{simpl}_{clusters}.csv"
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retro_cost="resources/retro_cost_elec_s{simpl}_{clusters}.csv",
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floor_area="resources/floor_area_elec_s{simpl}_{clusters}.csv"
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resources: mem_mb=1000
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script: "scripts/build_retro_cost.py"
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rule prepare_sector_network:
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input:
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network=pypsaeur('networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
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network=pypsaeur('networks/elec_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
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energy_totals_name='resources/energy_totals.csv',
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co2_totals_name='resources/co2_totals.csv',
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transport_name='resources/transport_data.csv',
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@ -319,35 +320,35 @@ rule prepare_sector_network:
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h2_cavern = "data/hydrogen_salt_cavern_potentials.csv",
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profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
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profile_offwind_dc=pypsaeur("resources/profile_offwind-dc.nc"),
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busmap_s=pypsaeur("resources/busmap_{network}_s{simpl}.csv"),
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busmap=pypsaeur("resources/busmap_{network}_s{simpl}_{clusters}.csv"),
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||||
clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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||||
simplified_pop_layout="resources/pop_layout_{network}_s{simpl}.csv",
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||||
industrial_demand="resources/industrial_energy_demand_{network}_s{simpl}_{clusters}.csv",
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||||
heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
heat_demand_rural="resources/heat_demand_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
heat_demand_total="resources/heat_demand_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_total="resources/temp_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_rural="resources/temp_soil_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_urban="resources/temp_soil_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_total="resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_rural="resources/temp_air_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_urban="resources/temp_air_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_rural="resources/cop_soil_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_urban="resources/cop_soil_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_rural="resources/cop_air_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_urban="resources/cop_air_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
|
||||
output: config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
|
||||
busmap_s=pypsaeur("resources/busmap_elec_s{simpl}.csv"),
|
||||
busmap=pypsaeur("resources/busmap_elec_s{simpl}_{clusters}.csv"),
|
||||
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
|
||||
simplified_pop_layout="resources/pop_layout_elec_s{simpl}.csv",
|
||||
industrial_demand="resources/industrial_energy_demand_elec_s{simpl}_{clusters}.csv",
|
||||
heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
|
||||
heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
|
||||
heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc",
|
||||
temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
temp_soil_rural="resources/temp_soil_rural_elec_s{simpl}_{clusters}.nc",
|
||||
temp_soil_urban="resources/temp_soil_urban_elec_s{simpl}_{clusters}.nc",
|
||||
temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
|
||||
temp_air_rural="resources/temp_air_rural_elec_s{simpl}_{clusters}.nc",
|
||||
temp_air_urban="resources/temp_air_urban_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="resources/cop_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_rural="resources/cop_soil_rural_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_urban="resources/cop_soil_urban_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_rural="resources/cop_air_rural_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_urban="resources/cop_air_urban_elec_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
|
||||
retro_cost_energy = "resources/retro_cost_elec_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_elec_s{simpl}_{clusters}.csv"
|
||||
output: config['results_dir'] + config['run'] + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
|
||||
threads: 1
|
||||
resources: mem_mb=2000
|
||||
benchmark: config['results_dir'] + config['run'] + "/benchmarks/prepare_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}"
|
||||
benchmark: config['results_dir'] + config['run'] + "/benchmarks/prepare_network/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}"
|
||||
script: "scripts/prepare_sector_network.py"
|
||||
|
||||
|
||||
@ -420,16 +421,16 @@ if config["foresight"] == "overnight":
|
||||
|
||||
rule solve_network:
|
||||
input:
|
||||
network=config['results_dir'] + config['run'] + "/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
network=config['results_dir'] + config['run'] + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
costs=config['costs_dir'] + "costs_{planning_horizons}.csv",
|
||||
config=config['summary_dir'] + '/' + config['run'] + '/configs/config.yaml'
|
||||
output: config['results_dir'] + config['run'] + "/postnetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
output: config['results_dir'] + config['run'] + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
shadow: "shallow"
|
||||
log:
|
||||
solver=config['results_dir'] + config['run'] + "/logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
|
||||
python=config['results_dir'] + config['run'] + "/logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_python.log",
|
||||
memory=config['results_dir'] + config['run'] + "/logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_memory.log"
|
||||
benchmark: config['results_dir'] + config['run'] + "/benchmarks/solve_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}"
|
||||
solver=config['results_dir'] + config['run'] + "/logs/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
|
||||
python=config['results_dir'] + config['run'] + "/logs/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_python.log",
|
||||
memory=config['results_dir'] + config['run'] + "/logs/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_memory.log"
|
||||
benchmark: config['results_dir'] + config['run'] + "/benchmarks/solve_network/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}"
|
||||
threads: 4
|
||||
resources: mem_mb=config['solving']['mem']
|
||||
# group: "solve" # with group, threads is ignored https://bitbucket.org/snakemake/snakemake/issues/971/group-job-description-does-not-contain
|
||||
@ -440,15 +441,15 @@ if config["foresight"] == "myopic":
|
||||
|
||||
rule add_existing_baseyear:
|
||||
input:
|
||||
network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc',
|
||||
network=config['results_dir'] + config['run'] + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc',
|
||||
powerplants=pypsaeur('resources/powerplants.csv'),
|
||||
busmap_s=pypsaeur("resources/busmap_{network}_s{simpl}.csv"),
|
||||
busmap=pypsaeur("resources/busmap_{network}_s{simpl}_{clusters}.csv"),
|
||||
clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
|
||||
busmap_s=pypsaeur("resources/busmap_elec_s{simpl}.csv"),
|
||||
busmap=pypsaeur("resources/busmap_elec_s{simpl}_{clusters}.csv"),
|
||||
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
|
||||
costs=config['costs_dir'] + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
|
||||
cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc"
|
||||
output: config['results_dir'] + config['run'] + '/prenetworks-brownfield/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
|
||||
cop_soil_total="resources/cop_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_elec_s{simpl}_{clusters}.nc"
|
||||
output: config['results_dir'] + config['run'] + '/prenetworks-brownfield/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
|
||||
wildcard_constraints:
|
||||
planning_horizons=config['scenario']['planning_horizons'][0] #only applies to baseyear
|
||||
threads: 1
|
||||
@ -457,18 +458,18 @@ if config["foresight"] == "myopic":
|
||||
|
||||
def process_input(wildcards):
|
||||
i = config["scenario"]["planning_horizons"].index(int(wildcards.planning_horizons))
|
||||
return config['results_dir'] + config['run'] + "/postnetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_" + str(config["scenario"]["planning_horizons"][i-1]) + ".nc"
|
||||
return config['results_dir'] + config['run'] + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_" + str(config["scenario"]["planning_horizons"][i-1]) + ".nc"
|
||||
|
||||
|
||||
rule add_brownfield:
|
||||
input:
|
||||
network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc',
|
||||
network=config['results_dir'] + config['run'] + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc',
|
||||
network_p=process_input, #solved network at previous time step
|
||||
costs=config['costs_dir'] + "costs_{planning_horizons}.csv",
|
||||
cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc"
|
||||
cop_soil_total="resources/cop_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_elec_s{simpl}_{clusters}.nc"
|
||||
|
||||
output: config['results_dir'] + config['run'] + "/prenetworks-brownfield/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
output: config['results_dir'] + config['run'] + "/prenetworks-brownfield/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
threads: 4
|
||||
resources: mem_mb=10000
|
||||
script: "scripts/add_brownfield.py"
|
||||
@ -477,16 +478,16 @@ if config["foresight"] == "myopic":
|
||||
|
||||
rule solve_network_myopic:
|
||||
input:
|
||||
network=config['results_dir'] + config['run'] + "/prenetworks-brownfield/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
network=config['results_dir'] + config['run'] + "/prenetworks-brownfield/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
costs=config['costs_dir'] + "costs_{planning_horizons}.csv",
|
||||
config=config['summary_dir'] + '/' + config['run'] + '/configs/config.yaml'
|
||||
output: config['results_dir'] + config['run'] + "/postnetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
output: config['results_dir'] + config['run'] + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
shadow: "shallow"
|
||||
log:
|
||||
solver=config['results_dir'] + config['run'] + "/logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
|
||||
python=config['results_dir'] + config['run'] + "/logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_python.log",
|
||||
memory=config['results_dir'] + config['run'] + "/logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_memory.log"
|
||||
benchmark: config['results_dir'] + config['run'] + "/benchmarks/solve_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}"
|
||||
solver=config['results_dir'] + config['run'] + "/logs/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
|
||||
python=config['results_dir'] + config['run'] + "/logs/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_python.log",
|
||||
memory=config['results_dir'] + config['run'] + "/logs/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}_memory.log"
|
||||
benchmark: config['results_dir'] + config['run'] + "/benchmarks/solve_network/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}"
|
||||
threads: 4
|
||||
resources: mem_mb=config['solving']['mem']
|
||||
script: "scripts/solve_network.py"
|
||||
|
||||
@ -119,20 +119,25 @@ sector:
|
||||
'time_dep_hp_cop' : True #time dependent heat pump coefficient of performance
|
||||
'heat_pump_sink_T' : 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
|
||||
# conservatively high to cover hot water and space heating in poorly-insulated buildings
|
||||
'retrofitting' :
|
||||
'retro_exogen': True # space heat demand savings exogenously
|
||||
'dE': # reduction of space heat demand (applied before losses in DH)
|
||||
2020 : 0.
|
||||
2030 : 0.15
|
||||
2040 : 0.3
|
||||
2050 : 0.4
|
||||
'reduce_space_heat_exogenously': True # reduces space heat demand by a given factor (applied before losses in DH)
|
||||
# this can represent e.g. building renovation, building demolition, or if
|
||||
# the factor is negative: increasing floor area, increased thermal comfort, population growth
|
||||
'reduce_space_heat_exogenously_factor': # per unit reduction in space heat demand
|
||||
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
|
||||
2020: 0.10 # this results in a space heat demand reduction of 10%
|
||||
2025: 0.09 # first heat demand increases compared to 2020 because of larger floor area per capita
|
||||
2030: 0.09
|
||||
2035: 0.11
|
||||
2040: 0.16
|
||||
2045: 0.21
|
||||
2050: 0.29
|
||||
'retrofitting' : # co-optimises building renovation to reduce space heat demand
|
||||
'retro_endogen': False # co-optimise space heat savings
|
||||
'cost_factor' : 1.0
|
||||
'cost_factor' : 1.0 # weight costs for building renovation
|
||||
'interest_rate': 0.04 # for investment in building components
|
||||
'annualise_cost': True # annualise the investment costs
|
||||
'tax_weighting': False # weight costs depending on taxes in countries
|
||||
'construction_index': True # weight costs depending on labour/material costs per ct
|
||||
'l_strength': ["0.076", "0.197"] # additional insulation thickness[m], determines number of retro steps(=generators per bus) and maximum possible savings
|
||||
'construction_index': True # weight costs depending on labour/material costs per country
|
||||
'tes' : True
|
||||
'tes_tau' : 3.
|
||||
'boilers' : True
|
||||
|
||||
@ -1,7 +0,0 @@
|
||||
,Dwelling,Ceilling,Standard component surfaces (m2),component,surfaces,(m2),,
|
||||
Building type,Space(m²),Height(m),Roof,Facade,Floor,Windows,,
|
||||
Single/two family house,120,2.5,90,166,63,29,,
|
||||
Large apartment house,1457,2.5,354,1189,354,380,,
|
||||
Apartment house,5276,,598.337,2992.1,598.337,756,tabula ,http://webtool.building-typology.eu/#pdfes
|
||||
,,,,,,,,
|
||||
"Source: https://link.springer.com/article/10.1007/s12053-010-9090-6 ,p.4",,,,,,,,
|
||||
|
@ -22,5 +22,5 @@ U-values Poland,u_values_poland.csv,unknown,https://data.europa.eu/euodp/de/data
|
||||
Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown,https://data.europa.eu/euodp/de/data/dataset/building-stock-observatory
|
||||
Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
|
||||
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
|
||||
Average surface components,average_surface_components.csv,unknown,http://webtool.building-typology.eu/#bm
|
||||
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
|
||||
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
|
||||
|
||||
|
@ -73,8 +73,8 @@ To download and extract the data bundle on the command line:
|
||||
|
||||
.. code:: bash
|
||||
|
||||
projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-210125.tar.gz"
|
||||
projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-210125.tar.gz
|
||||
projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-210418.tar.gz"
|
||||
projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-210418.tar.gz
|
||||
|
||||
|
||||
The data licences and sources are given in the following table.
|
||||
|
||||
@ -6,6 +6,7 @@ Future release
|
||||
===================
|
||||
|
||||
* For the myopic investment option, a carbon budget and a type of decay (exponential or beta) can be selected in the ``config.yaml`` file to distribute the budget across the ``planning_horizons``. For example, ``cb40ex0`` in the ``{sector_opts}`` wildcard will distribute a carbon budget of 40 GtCO2 following an exponential decay with initial growth rate 0.
|
||||
* The cost database for retrofitting of the thermal envelope of buildings has been updated. Now, for calculating the space heat savings of a building, losses by thermal bridges and ventilation are included as well as heat gains (internal and by solar radiation). See the section :ref:`retro` for more details on the retrofitting module.
|
||||
* Added an option to alter the capital cost or maximum capacity of carriers by a factor via ``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for exploring uncertain cost parameters. Example: ``solar+c0.5`` reduces the ``capital_cost`` of solar to 50\% of original values. Similarly ``solar+p3`` multiplies the ``p_nom_max`` by 3.
|
||||
* Rename the bus for European liquid hydrocarbons from ``Fischer-Tropsch`` to ``EU oil``, since it can be supplied not just with the Fischer-Tropsch process, but also with fossil oil.
|
||||
* Bugfix: The new separation of land transport by carrier in Version 0.4.0 failed to account for the carbon dioxide emissions from internal combustion engines. This is now treated as a negative load on the atmospheric carbon dioxide bus, just like aviation emissions.
|
||||
@ -137,4 +138,4 @@ To make a new release of the data bundle, make an archive of the files in ``data
|
||||
|
||||
.. code:: bash
|
||||
|
||||
data % tar pczf pypsa-eur-sec-data-bundle-YYMMDD.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv
|
||||
data % tar pczf pypsa-eur-sec-data-bundle-YYMMDD.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv
|
||||
|
||||
@ -108,6 +108,43 @@ Small for decentral applications.
|
||||
|
||||
Big water pit storage for district heating.
|
||||
|
||||
.. _retro:
|
||||
|
||||
Retrofitting of the thermal envelope of buildings
|
||||
===================================================
|
||||
Co-optimising building renovation is only enabled if in the ``config.yaml`` the
|
||||
option :mod:`retro_endogen: True`. To reduce the computational burden
|
||||
default setting is
|
||||
|
||||
.. literalinclude:: ../config.default.yaml
|
||||
:language: yaml
|
||||
:lines: 134-135
|
||||
|
||||
Renovation of the thermal envelope reduces the space heating demand and is
|
||||
optimised at each node for every heat bus. Renovation measures through additional
|
||||
insulation material and replacement of energy inefficient windows are considered.
|
||||
|
||||
In a first step, costs per energy savings are estimated in :mod:`build_retro_cost.py`.
|
||||
They depend on the insulation condition of the building stock and costs for
|
||||
renovation of the building elements.
|
||||
In a second step, for those cost per energy savings two possible renovation
|
||||
strengths are determined: a moderate renovation with lower costs and lower
|
||||
maximum possible space heat savings, and an ambitious renovation with associated
|
||||
higher costs and higher efficiency gains. They are added by step-wise
|
||||
linearisation in form of two additional generations in
|
||||
:mod:`prepare_sector_network.py`.
|
||||
|
||||
Settings in the config.yaml concerning the endogenously optimisation of building
|
||||
renovation
|
||||
|
||||
.. literalinclude:: ../config.default.yaml
|
||||
:language: yaml
|
||||
:lines: 136-140
|
||||
|
||||
Further information are given in the publication
|
||||
|
||||
`Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
|
||||
|
||||
|
||||
Hydrogen demand
|
||||
==================
|
||||
|
||||
@ -90,12 +90,12 @@ if __name__ == "__main__":
|
||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
||||
co2_budget_name='go',
|
||||
planning_horizons='2030'),
|
||||
input=dict(network='pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
|
||||
network_p='pypsa-eur-sec/results/test/postnetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_2020.nc',
|
||||
input=dict(network='pypsa-eur-sec/results/test/prenetworks/elec_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
|
||||
network_p='pypsa-eur-sec/results/test/postnetworks/elec_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_2020.nc',
|
||||
costs='pypsa-eur-sec/data/costs/costs_{planning_horizons}.csv',
|
||||
cop_air_total="pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc"),
|
||||
output=['pypsa-eur-sec/results/test/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
|
||||
cop_air_total="pypsa-eur-sec/resources/cop_air_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
output=['pypsa-eur-sec/results/test/prenetworks_brownfield/elec_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
|
||||
)
|
||||
import yaml
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
|
||||
@ -411,15 +411,15 @@ if __name__ == "__main__":
|
||||
wildcards=dict(network='elec', simpl='', clusters='45', lv='1.0',
|
||||
sector_opts='Co2L0-3H-T-H-B-I-solar3-dist1',
|
||||
planning_horizons='2020'),
|
||||
input=dict(network='pypsa-eur-sec/results/version-2/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc',
|
||||
input=dict(network='pypsa-eur-sec/results/version-2/prenetworks/elec_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc',
|
||||
powerplants='pypsa-eur/resources/powerplants.csv',
|
||||
busmap_s='pypsa-eur/resources/busmap_{network}_s{simpl}.csv',
|
||||
busmap='pypsa-eur/resources/busmap_{network}_s{simpl}_{clusters}.csv',
|
||||
busmap_s='pypsa-eur/resources/busmap_elec_s{simpl}.csv',
|
||||
busmap='pypsa-eur/resources/busmap_elec_s{simpl}_{clusters}.csv',
|
||||
costs='technology_data/outputs/costs_{planning_horizons}.csv',
|
||||
cop_air_total="pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
clustered_pop_layout="pypsa-eur-sec/resources/pop_layout_{network}_s{simpl}_{clusters}.csv",),
|
||||
output=['pypsa-eur-sec/results/version-2/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc'],
|
||||
cop_air_total="pypsa-eur-sec/resources/cop_air_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
clustered_pop_layout="pypsa-eur-sec/resources/pop_layout_elec_s{simpl}_{clusters}.csv",),
|
||||
output=['pypsa-eur-sec/results/version-2/prenetworks_brownfield/elec_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc'],
|
||||
)
|
||||
import yaml
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
|
||||
import pandas as pd
|
||||
import geopandas as gpd
|
||||
|
||||
@ -51,7 +50,6 @@ country_to_code = {
|
||||
'Switzerland' : 'CH',
|
||||
}
|
||||
|
||||
|
||||
non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
|
||||
|
||||
rename = {"GR" : "EL",
|
||||
@ -73,7 +71,6 @@ def build_eurostat(year):
|
||||
|
||||
fns = {2016: "data/eurostat-energy_balances-june_2016_edition/{year}-Energy-Balances-June2016edition.xlsx",
|
||||
2017: "data/eurostat-energy_balances-june_2017_edition/{year}-ENERGY-BALANCES-June2017edition.xlsx"}
|
||||
|
||||
#2016 includes BA, 2017 doesn't
|
||||
|
||||
#with sheet as None, an ordered dictionary of all sheets is returned
|
||||
@ -82,7 +79,6 @@ def build_eurostat(year):
|
||||
skiprows=1,
|
||||
index_col=list(range(4)))
|
||||
|
||||
|
||||
#sorted_index necessary for slicing
|
||||
df = pd.concat({country_to_code[df.columns[0]] : df for ct,df in dfs.items()},sort=True).sort_index()
|
||||
|
||||
@ -91,15 +87,12 @@ def build_eurostat(year):
|
||||
|
||||
|
||||
def build_swiss(year):
|
||||
|
||||
fn = "data/switzerland-sfoe/switzerland-new_format.csv"
|
||||
|
||||
#convert PJ/a to TWh/a
|
||||
return (pd.read_csv(fn,index_col=list(range(2)))/3.6).loc["CH",str(year)]
|
||||
|
||||
|
||||
|
||||
|
||||
def build_idees(year):
|
||||
base_dir = "data/jrc-idees-2015"
|
||||
|
||||
@ -275,7 +268,7 @@ def build_idees(year):
|
||||
return totals
|
||||
|
||||
|
||||
def build_energy_totals():
|
||||
def build_energy_totals(eurostat, swiss, idees):
|
||||
|
||||
clean_df = idees.reindex(population.index).drop(["passenger cars","passenger car efficiency"],axis=1)
|
||||
|
||||
@ -316,7 +309,6 @@ def build_energy_totals():
|
||||
+ 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
|
||||
@ -458,14 +450,12 @@ def build_eurostat_co2(year=1990):
|
||||
#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(eea_co2, eurostat_co2, year=1990):
|
||||
def build_co2_totals(eea_co2, eurostat_co2):
|
||||
|
||||
co2 = eea_co2.reindex(["EU28","NO","CH","BA","RS","AL","ME","MK"] + eu28)
|
||||
|
||||
@ -530,7 +520,6 @@ def build_transport_data():
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
|
||||
# Detect running outside of snakemake and mock snakemake for testing
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
@ -546,21 +535,19 @@ if __name__ == "__main__":
|
||||
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
|
||||
population = nuts3['pop'].groupby(nuts3.country).sum()
|
||||
|
||||
year = 2011
|
||||
data_year = 2011
|
||||
eurostat = build_eurostat(data_year)
|
||||
swiss = build_swiss(data_year)
|
||||
idees = build_idees(data_year)
|
||||
|
||||
eurostat = build_eurostat(year)
|
||||
build_energy_totals(eurostat, swiss, idees)
|
||||
|
||||
swiss = build_swiss(year)
|
||||
|
||||
idees = build_idees(year)
|
||||
|
||||
build_energy_totals()
|
||||
|
||||
eea_co2 = build_eea_co2()
|
||||
|
||||
eurostat_co2 = build_eurostat_co2()
|
||||
|
||||
co2=build_co2_totals(eea_co2, eurostat_co2, year)
|
||||
base_year_emissions = 1990
|
||||
eea_co2 = build_eea_co2(base_year_emissions)
|
||||
eurostat_co2 = build_eurostat_co2(base_year_emissions)
|
||||
|
||||
co2 = build_co2_totals(eea_co2, eurostat_co2)
|
||||
co2.to_csv(snakemake.output.co2_name)
|
||||
|
||||
build_transport_data()
|
||||
|
||||
@ -11,7 +11,7 @@ if 'snakemake' not in globals():
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.config = yaml.safe_load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
|
||||
@ -98,7 +98,7 @@ for ct in eu28:
|
||||
|
||||
for fuel in fuels:
|
||||
summary.at[fuel,sub] = s[fuels[fuel]].sum()
|
||||
summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index^['all','other'],sub].sum()
|
||||
summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index.symmetric_difference(['all','other']),sub].sum()
|
||||
|
||||
summary['Other Industrial Sectors'] = summary[ois_subs].sum(axis=1)
|
||||
summary.drop(columns=ois_subs,inplace=True)
|
||||
@ -128,7 +128,7 @@ output = pd.read_csv(snakemake.input.industrial_production_per_country,
|
||||
|
||||
eu28_averages = final_summary.groupby(level=1,axis=1).sum().divide(output.loc[eu28].sum(),axis=1)
|
||||
|
||||
non_eu28 = output.index^eu28
|
||||
non_eu28 = output.index.symmetric_difference(eu28)
|
||||
|
||||
for ct in non_eu28:
|
||||
print(ct)
|
||||
|
||||
@ -196,7 +196,7 @@ ammonia = pd.read_csv(snakemake.input.ammonia_production,
|
||||
index_col=0)
|
||||
|
||||
there = ammonia.index.intersection(countries_demand.index)
|
||||
missing = countries_demand.index^there
|
||||
missing = countries_demand.index.symmetric_difference(there)
|
||||
|
||||
print("Following countries have no ammonia demand:", missing)
|
||||
|
||||
|
||||
@ -15,7 +15,7 @@ if 'snakemake' not in globals():
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.config = yaml.safe_load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
@ -46,7 +46,7 @@ urban_fraction = pd.read_csv(snakemake.input.urban_percent,
|
||||
#fill missing Balkans values
|
||||
missing = ["AL","ME","MK"]
|
||||
reference = ["RS","BA"]
|
||||
urban_fraction = urban_fraction.reindex(urban_fraction.index|missing)
|
||||
urban_fraction = urban_fraction.reindex(urban_fraction.index.union(missing))
|
||||
urban_fraction.loc[missing] = urban_fraction[reference].mean()
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -11,7 +11,7 @@ if 'snakemake' not in globals():
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.config = yaml.safe_load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ if 'snakemake' not in globals():
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.config = yaml.safe_load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
|
||||
@ -79,7 +79,7 @@ def calculate_nodal_cfs(n,label,nodal_cfs):
|
||||
cf_c = p_c/capacities_c
|
||||
|
||||
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in cf_c.index.to_list()])
|
||||
nodal_cfs = nodal_cfs.reindex(index|nodal_cfs.index)
|
||||
nodal_cfs = nodal_cfs.reindex(index.union(nodal_cfs.index))
|
||||
nodal_cfs.loc[index,label] = cf_c.values
|
||||
|
||||
return nodal_cfs
|
||||
@ -106,7 +106,7 @@ def calculate_cfs(n,label,cfs):
|
||||
|
||||
cf_c = pd.concat([cf_c], keys=[c.list_name])
|
||||
|
||||
cfs = cfs.reindex(cf_c.index|cfs.index)
|
||||
cfs = cfs.reindex(cf_c.index.union(cfs.index))
|
||||
|
||||
cfs.loc[cf_c.index,label] = cf_c
|
||||
|
||||
@ -121,7 +121,7 @@ def calculate_nodal_costs(n,label,nodal_costs):
|
||||
c.df["capital_costs"] = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
|
||||
capital_costs = c.df.groupby(["location","carrier"])["capital_costs"].sum()
|
||||
index = pd.MultiIndex.from_tuples([(c.list_name,"capital") + t for t in capital_costs.index.to_list()])
|
||||
nodal_costs = nodal_costs.reindex(index|nodal_costs.index)
|
||||
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
|
||||
nodal_costs.loc[index,label] = capital_costs.values
|
||||
|
||||
if c.name == "Link":
|
||||
@ -143,7 +143,7 @@ def calculate_nodal_costs(n,label,nodal_costs):
|
||||
c.df["marginal_costs"] = p*c.df.marginal_cost
|
||||
marginal_costs = c.df.groupby(["location","carrier"])["marginal_costs"].sum()
|
||||
index = pd.MultiIndex.from_tuples([(c.list_name,"marginal") + t for t in marginal_costs.index.to_list()])
|
||||
nodal_costs = nodal_costs.reindex(index|nodal_costs.index)
|
||||
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
|
||||
nodal_costs.loc[index,label] = marginal_costs.values
|
||||
|
||||
return nodal_costs
|
||||
@ -158,7 +158,7 @@ def calculate_costs(n,label,costs):
|
||||
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
|
||||
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
|
||||
|
||||
costs = costs.reindex(capital_costs_grouped.index|costs.index)
|
||||
costs = costs.reindex(capital_costs_grouped.index.union(costs.index))
|
||||
|
||||
costs.loc[capital_costs_grouped.index,label] = capital_costs_grouped
|
||||
|
||||
@ -185,7 +185,7 @@ def calculate_costs(n,label,costs):
|
||||
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
|
||||
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
|
||||
|
||||
costs = costs.reindex(marginal_costs_grouped.index|costs.index)
|
||||
costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
|
||||
|
||||
costs.loc[marginal_costs_grouped.index,label] = marginal_costs_grouped
|
||||
|
||||
@ -196,31 +196,31 @@ def calculate_costs(n,label,costs):
|
||||
|
||||
return costs
|
||||
|
||||
def calculate_cumulative_cost():
|
||||
def calculate_cumulative_cost():
|
||||
planning_horizons = snakemake.config['scenario']['planning_horizons']
|
||||
|
||||
cumulative_cost = pd.DataFrame(index = df["costs"].sum().index,
|
||||
columns=pd.Series(data=np.arange(0,0.1, 0.01), name='social discount rate'))
|
||||
|
||||
|
||||
#discount cost and express them in money value of planning_horizons[0]
|
||||
for r in cumulative_cost.columns:
|
||||
cumulative_cost[r]=[df["costs"].sum()[index]/((1+r)**(index[-1]-planning_horizons[0])) for index in cumulative_cost.index]
|
||||
|
||||
|
||||
#integrate cost throughout the transition path
|
||||
for r in cumulative_cost.columns:
|
||||
for r in cumulative_cost.columns:
|
||||
for cluster in cumulative_cost.index.get_level_values(level=0).unique():
|
||||
for lv in cumulative_cost.index.get_level_values(level=1).unique():
|
||||
for sector_opts in cumulative_cost.index.get_level_values(level=2).unique():
|
||||
cumulative_cost.loc[(cluster, lv, sector_opts,'cumulative cost'),r] = np.trapz(cumulative_cost.loc[idx[cluster, lv, sector_opts,planning_horizons],r].values, x=planning_horizons)
|
||||
|
||||
return cumulative_cost
|
||||
|
||||
return cumulative_cost
|
||||
|
||||
def calculate_nodal_capacities(n,label,nodal_capacities):
|
||||
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
|
||||
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
|
||||
nodal_capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
|
||||
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in nodal_capacities_c.index.to_list()])
|
||||
nodal_capacities = nodal_capacities.reindex(index|nodal_capacities.index)
|
||||
nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
|
||||
nodal_capacities.loc[index,label] = nodal_capacities_c.values
|
||||
|
||||
return nodal_capacities
|
||||
@ -234,7 +234,7 @@ def calculate_capacities(n,label,capacities):
|
||||
capacities_grouped = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
|
||||
capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
|
||||
|
||||
capacities = capacities.reindex(capacities_grouped.index|capacities.index)
|
||||
capacities = capacities.reindex(capacities_grouped.index.union(capacities.index))
|
||||
|
||||
capacities.loc[capacities_grouped.index,label] = capacities_grouped
|
||||
|
||||
@ -267,7 +267,7 @@ def calculate_energy(n,label,energy):
|
||||
|
||||
c_energies = pd.concat([c_energies], keys=[c.list_name])
|
||||
|
||||
energy = energy.reindex(c_energies.index|energy.index)
|
||||
energy = energy.reindex(c_energies.index.union(energy.index))
|
||||
|
||||
energy.loc[c_energies.index,label] = c_energies
|
||||
|
||||
@ -285,7 +285,7 @@ def calculate_supply(n,label,supply):
|
||||
|
||||
for c in n.iterate_components(n.one_port_components):
|
||||
|
||||
items = c.df.index[c.df.bus.map(bus_map)]
|
||||
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
|
||||
|
||||
if len(items) == 0:
|
||||
continue
|
||||
@ -294,7 +294,7 @@ def calculate_supply(n,label,supply):
|
||||
s = pd.concat([s], keys=[c.list_name])
|
||||
s = pd.concat([s], keys=[i])
|
||||
|
||||
supply = supply.reindex(s.index|supply.index)
|
||||
supply = supply.reindex(s.index.union(supply.index))
|
||||
supply.loc[s.index,label] = s
|
||||
|
||||
|
||||
@ -313,7 +313,7 @@ def calculate_supply(n,label,supply):
|
||||
s = pd.concat([s], keys=[c.list_name])
|
||||
s = pd.concat([s], keys=[i])
|
||||
|
||||
supply = supply.reindex(s.index|supply.index)
|
||||
supply = supply.reindex(s.index.union(supply.index))
|
||||
supply.loc[s.index,label] = s
|
||||
|
||||
return supply
|
||||
@ -330,7 +330,7 @@ def calculate_supply_energy(n,label,supply_energy):
|
||||
|
||||
for c in n.iterate_components(n.one_port_components):
|
||||
|
||||
items = c.df.index[c.df.bus.map(bus_map)]
|
||||
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
|
||||
|
||||
if len(items) == 0:
|
||||
continue
|
||||
@ -339,7 +339,7 @@ def calculate_supply_energy(n,label,supply_energy):
|
||||
s = pd.concat([s], keys=[c.list_name])
|
||||
s = pd.concat([s], keys=[i])
|
||||
|
||||
supply_energy = supply_energy.reindex(s.index|supply_energy.index)
|
||||
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
|
||||
supply_energy.loc[s.index,label] = s
|
||||
|
||||
|
||||
@ -357,7 +357,7 @@ def calculate_supply_energy(n,label,supply_energy):
|
||||
s = pd.concat([s], keys=[c.list_name])
|
||||
s = pd.concat([s], keys=[i])
|
||||
|
||||
supply_energy = supply_energy.reindex(s.index|supply_energy.index)
|
||||
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
|
||||
|
||||
supply_energy.loc[s.index,label] = s
|
||||
|
||||
@ -366,7 +366,7 @@ def calculate_supply_energy(n,label,supply_energy):
|
||||
|
||||
def calculate_metrics(n,label,metrics):
|
||||
|
||||
metrics = metrics.reindex(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])|metrics.index)
|
||||
metrics = metrics.reindex(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"]).union(metrics.index))
|
||||
|
||||
metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
|
||||
metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum()
|
||||
@ -384,7 +384,7 @@ def calculate_metrics(n,label,metrics):
|
||||
|
||||
def calculate_prices(n,label,prices):
|
||||
|
||||
prices = prices.reindex(prices.index|n.buses.carrier.unique())
|
||||
prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
|
||||
|
||||
#WARNING: this is time-averaged, see weighted_prices for load-weighted average
|
||||
prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
|
||||
@ -467,7 +467,7 @@ def calculate_market_values(n, label, market_values):
|
||||
|
||||
techs = n.generators.loc[generators,"carrier"].value_counts().index
|
||||
|
||||
market_values = market_values.reindex(market_values.index | techs)
|
||||
market_values = market_values.reindex(market_values.index.union(techs))
|
||||
|
||||
|
||||
for tech in techs:
|
||||
@ -488,7 +488,7 @@ def calculate_market_values(n, label, market_values):
|
||||
|
||||
techs = n.links.loc[all_links,"carrier"].value_counts().index
|
||||
|
||||
market_values = market_values.reindex(market_values.index | techs)
|
||||
market_values = market_values.reindex(market_values.index.union(techs))
|
||||
|
||||
for tech in techs:
|
||||
links = all_links[n.links.loc[all_links,"carrier"] == tech]
|
||||
@ -505,7 +505,7 @@ def calculate_market_values(n, label, market_values):
|
||||
def calculate_price_statistics(n, label, price_statistics):
|
||||
|
||||
|
||||
price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
|
||||
price_statistics = price_statistics.reindex(price_statistics.index.union(pd.Index(["zero_hours","mean","standard_deviation"])))
|
||||
|
||||
buses = n.buses.index[n.buses.carrier == "AC"]
|
||||
|
||||
@ -611,7 +611,7 @@ if __name__ == "__main__":
|
||||
print(networks_dict)
|
||||
|
||||
Nyears = 1
|
||||
|
||||
|
||||
costs_db = prepare_costs(snakemake.input.costs,
|
||||
snakemake.config['costs']['USD2013_to_EUR2013'],
|
||||
snakemake.config['costs']['discountrate'],
|
||||
@ -623,10 +623,9 @@ if __name__ == "__main__":
|
||||
df["metrics"].loc["total costs"] = df["costs"].sum()
|
||||
|
||||
to_csv(df)
|
||||
|
||||
|
||||
if snakemake.config["foresight"]=='myopic':
|
||||
cumulative_cost=calculate_cumulative_cost()
|
||||
cumulative_cost.to_csv(snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/csvs/cumulative_cost.csv')
|
||||
|
||||
|
||||
|
||||
@ -130,7 +130,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
|
||||
|
||||
costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
|
||||
|
||||
new_columns = ((preferred_order & costs.columns)
|
||||
new_columns = (preferred_order.intersection(costs.columns)
|
||||
.append(costs.columns.difference(preferred_order)))
|
||||
costs = costs[new_columns]
|
||||
|
||||
@ -147,7 +147,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
|
||||
n.links.carrier != "B2B")], inplace=True)
|
||||
|
||||
# drop non-bus
|
||||
to_drop = costs.index.levels[0] ^ n.buses.index
|
||||
to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
|
||||
if len(to_drop) != 0:
|
||||
print("dropping non-buses", to_drop)
|
||||
costs.drop(to_drop, level=0, inplace=True, axis=0)
|
||||
@ -463,7 +463,7 @@ def plot_series(network, carrier="AC", name="test"):
|
||||
"battery storage",
|
||||
"hot water storage"])
|
||||
|
||||
new_columns = ((preferred_order & supply.columns)
|
||||
new_columns = (preferred_order.intersection(supply.columns)
|
||||
.append(supply.columns.difference(preferred_order)))
|
||||
|
||||
supply = supply.groupby(supply.columns, axis=1).sum()
|
||||
|
||||
@ -82,7 +82,7 @@ def plot_costs():
|
||||
|
||||
print(df.sum())
|
||||
|
||||
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
|
||||
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
|
||||
|
||||
new_columns = df.sum().sort_values().index
|
||||
|
||||
@ -136,7 +136,7 @@ def plot_energy():
|
||||
|
||||
print(df)
|
||||
|
||||
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
|
||||
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
|
||||
|
||||
new_columns = df.columns.sort_values()
|
||||
#new_columns = df.sum().sort_values().index
|
||||
@ -177,7 +177,7 @@ def plot_balances():
|
||||
balances_df = pd.read_csv(snakemake.input.balances,index_col=list(range(3)),header=list(range(n_header)))
|
||||
|
||||
balances = {i.replace(" ","_") : [i] for i in balances_df.index.levels[0]}
|
||||
balances["energy"] = balances_df.index.levels[0]^co2_carriers
|
||||
balances["energy"] = balances_df.index.levels[0].symmetric_difference(co2_carriers)
|
||||
|
||||
for k,v in balances.items():
|
||||
|
||||
@ -205,7 +205,7 @@ def plot_balances():
|
||||
if df.empty:
|
||||
continue
|
||||
|
||||
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
|
||||
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
|
||||
|
||||
new_columns = df.columns.sort_values()
|
||||
|
||||
|
||||
@ -6,9 +6,8 @@ import pandas as pd
|
||||
idx = pd.IndexSlice
|
||||
|
||||
import numpy as np
|
||||
import scipy as sp
|
||||
import xarray as xr
|
||||
import re, os
|
||||
import re, os, sys
|
||||
|
||||
from six import iteritems, string_types
|
||||
|
||||
@ -50,22 +49,19 @@ override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"lif
|
||||
|
||||
|
||||
def co2_emissions_year(cts, opts, year):
|
||||
|
||||
"""
|
||||
calculate co2 emissions in one specific year (e.g. 1990 or 2018).
|
||||
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
|
||||
"""
|
||||
eea_co2 = build_eea_co2(year)
|
||||
|
||||
#TODO: read Eurostat data from year>2014, this only affects the estimation of
|
||||
|
||||
# TODO: read Eurostat data from year>2014, this only affects the estimation of
|
||||
# CO2 emissions for "BA","RS","AL","ME","MK"
|
||||
if year > 2014:
|
||||
eurostat_co2 = build_eurostat_co2(year=2014)
|
||||
else:
|
||||
eurostat_co2 = build_eurostat_co2(year)
|
||||
|
||||
co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)
|
||||
|
||||
co2_totals = build_co2_totals(eea_co2, eurostat_co2)
|
||||
|
||||
co2_emissions = co2_totals.loc[cts, "electricity"].sum()
|
||||
|
||||
@ -77,11 +73,11 @@ def co2_emissions_year(cts, opts, year):
|
||||
co2_emissions += co2_totals.loc[cts, ["industrial non-elec","industrial processes",
|
||||
"domestic aviation","international aviation",
|
||||
"domestic navigation","international navigation"]].sum().sum()
|
||||
co2_emissions *=0.001 #MtCO2 to GtCO2
|
||||
|
||||
co2_emissions *= 0.001 # Convert MtCO2 to GtCO2
|
||||
return co2_emissions
|
||||
|
||||
|
||||
|
||||
def build_carbon_budget(o):
|
||||
#distribute carbon budget following beta or exponential transition path
|
||||
if "be" in o:
|
||||
@ -244,7 +240,7 @@ def remove_elec_base_techs(n):
|
||||
|
||||
for c in n.iterate_components(snakemake.config["pypsa_eur"]):
|
||||
to_keep = snakemake.config["pypsa_eur"][c.name]
|
||||
to_remove = pd.Index(c.df.carrier.unique())^to_keep
|
||||
to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
|
||||
print("Removing",c.list_name,"with carrier",to_remove)
|
||||
names = c.df.index[c.df.carrier.isin(to_remove)]
|
||||
print(names)
|
||||
@ -252,6 +248,14 @@ def remove_elec_base_techs(n):
|
||||
n.carriers.drop(to_remove, inplace=True, errors="ignore")
|
||||
|
||||
|
||||
def remove_non_electric_buses(n):
|
||||
"""
|
||||
remove buses from pypsa-eur with carriers which are not AC buses
|
||||
"""
|
||||
print("drop buses from PyPSA-Eur with carrier: ", n.buses[~n.buses.carrier.isin(["AC", "DC"])].carrier.unique())
|
||||
n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
|
||||
|
||||
|
||||
def add_co2_tracking(n):
|
||||
|
||||
|
||||
@ -925,7 +929,7 @@ def add_storage(network):
|
||||
|
||||
# hydrogen stored overground
|
||||
h2_capital_cost = costs.at["hydrogen storage tank", "fixed"]
|
||||
nodes_overground = nodes ^ cavern_nodes.index
|
||||
nodes_overground = nodes.symmetric_difference(cavern_nodes.index)
|
||||
|
||||
network.madd("Store",
|
||||
nodes_overground + " H2 Store",
|
||||
@ -1178,11 +1182,10 @@ def add_heat(network):
|
||||
|
||||
urban_fraction = options['central_fraction']*pop_layout["urban"]/(pop_layout[["urban","rural"]].sum(axis=1))
|
||||
|
||||
# building retrofitting, exogenously reduce space heat demand
|
||||
if options["retrofitting"]["retro_exogen"]:
|
||||
dE = get_parameter(options["retrofitting"]["dE"])
|
||||
print("retrofitting exogenously, assumed space heat reduction of ",
|
||||
dE)
|
||||
# exogenously reduce space heat demand
|
||||
if options["reduce_space_heat_exogenously"]:
|
||||
dE = get_parameter(options["reduce_space_heat_exogenously_factor"])
|
||||
print("assumed space heat reduction of {} %".format(dE*100))
|
||||
for sector in sectors:
|
||||
heat_demand[sector + " space"] = (1-dE)*heat_demand[sector + " space"]
|
||||
|
||||
@ -1488,7 +1491,7 @@ def create_nodes_for_heat_sector():
|
||||
else:
|
||||
nodes[sector + " urban decentral"] = pop_layout.index
|
||||
# for central nodes, residential and services are aggregated
|
||||
nodes["urban central"] = pop_layout.index ^ nodes["residential urban decentral"]
|
||||
nodes["urban central"] = pop_layout.index.symmetric_difference(nodes["residential urban decentral"])
|
||||
return nodes
|
||||
|
||||
|
||||
@ -1874,7 +1877,7 @@ if __name__ == "__main__":
|
||||
opts='', planning_horizons='2020',
|
||||
sector_opts='120H-T-H-B-I-onwind+p3-dist1-cb48be3'),
|
||||
|
||||
input=dict( network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
|
||||
input=dict( network='../pypsa-eur/networks/elec_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
|
||||
energy_totals_name='resources/energy_totals.csv',
|
||||
co2_totals_name='resources/co2_totals.csv',
|
||||
transport_name='resources/transport_data.csv',
|
||||
@ -1886,34 +1889,33 @@ if __name__ == "__main__":
|
||||
h2_cavern = "data/hydrogen_salt_cavern_potentials.csv",
|
||||
profile_offwind_ac="../pypsa-eur/resources/profile_offwind-ac.nc",
|
||||
profile_offwind_dc="../pypsa-eur/resources/profile_offwind-dc.nc",
|
||||
busmap_s="../pypsa-eur/resources/busmap_{network}_s{simpl}.csv",
|
||||
busmap="../pypsa-eur/resources/busmap_{network}_s{simpl}_{clusters}.csv",
|
||||
clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
|
||||
simplified_pop_layout="resources/pop_layout_{network}_s{simpl}.csv",
|
||||
industrial_demand="resources/industrial_energy_demand_{network}_s{simpl}_{clusters}.csv",
|
||||
heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
heat_demand_rural="resources/heat_demand_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
heat_demand_total="resources/heat_demand_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_total="resources/temp_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_rural="resources/temp_soil_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_urban="resources/temp_soil_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_total="resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_rural="resources/temp_air_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_urban="resources/temp_air_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_rural="resources/cop_soil_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_urban="resources/cop_soil_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_rural="resources/cop_air_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_urban="resources/cop_air_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
|
||||
busmap_s="../pypsa-eur/resources/busmap_elec_s{simpl}.csv",
|
||||
busmap="../pypsa-eur/resources/busmap_elec_s{simpl}_{clusters}.csv",
|
||||
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
|
||||
simplified_pop_layout="resources/pop_layout_elec_s{simpl}.csv",
|
||||
industrial_demand="resources/industrial_energy_demand_elec_s{simpl}_{clusters}.csv",
|
||||
heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
|
||||
heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
|
||||
heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc",
|
||||
temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
temp_soil_rural="resources/temp_soil_rural_elec_s{simpl}_{clusters}.nc",
|
||||
temp_soil_urban="resources/temp_soil_urban_elec_s{simpl}_{clusters}.nc",
|
||||
temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
|
||||
temp_air_rural="resources/temp_air_rural_elec_s{simpl}_{clusters}.nc",
|
||||
temp_air_urban="resources/temp_air_urban_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="resources/cop_soil_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_rural="resources/cop_soil_rural_elec_s{simpl}_{clusters}.nc",
|
||||
cop_soil_urban="resources/cop_soil_urban_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_rural="resources/cop_air_rural_elec_s{simpl}_{clusters}.nc",
|
||||
cop_air_urban="resources/cop_air_urban_elec_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
|
||||
retro_cost_energy = "resources/retro_cost_elec_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_elec_s{simpl}_{clusters}.csv"
|
||||
),
|
||||
output=['results/version-cb48be3/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
|
||||
|
||||
)
|
||||
import yaml
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
@ -1953,6 +1955,8 @@ if __name__ == "__main__":
|
||||
|
||||
n.loads["carrier"] = "electricity"
|
||||
|
||||
remove_non_electric_buses(n)
|
||||
|
||||
n.buses["location"] = n.buses.index
|
||||
|
||||
update_wind_solar_costs(n, costs)
|
||||
|
||||
@ -376,10 +376,10 @@ if __name__ == "__main__":
|
||||
wildcards=dict(network='elec', simpl='', clusters='39', lv='1.0',
|
||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
||||
co2_budget_name='b30b3', planning_horizons='2050'),
|
||||
input=dict(network="pypsa-eur-sec/results/test/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc"),
|
||||
input=dict(network="pypsa-eur-sec/results/test/prenetworks_brownfield/elec_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc"),
|
||||
output=["results/networks/s{simpl}_{clusters}_lv{lv}_{sector_opts}_{co2_budget_name}_{planning_horizons}-test.nc"],
|
||||
log=dict(gurobi="logs/{network}_s{simpl}_{clusters}_lv{lv}_{sector_opts}_{co2_budget_name}_{planning_horizons}_gurobi-test.log",
|
||||
python="logs/{network}_s{simpl}_{clusters}_lv{lv}_{sector_opts}_{co2_budget_name}_{planning_horizons}_python-test.log")
|
||||
log=dict(gurobi="logs/elec_s{simpl}_{clusters}_lv{lv}_{sector_opts}_{co2_budget_name}_{planning_horizons}_gurobi-test.log",
|
||||
python="logs/elec_s{simpl}_{clusters}_lv{lv}_{sector_opts}_{co2_budget_name}_{planning_horizons}_python-test.log")
|
||||
)
|
||||
import yaml
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user