diff --git a/Snakefile b/Snakefile index 08381d9e0..35bdf5d10 100644 --- a/Snakefile +++ b/Snakefile @@ -24,6 +24,7 @@ wildcard_constraints: opts="[-+a-zA-Z0-9]*", sopts="[-+a-zA-Z0-9\.\s]*", discountrate="[-+a-zA-Z0-9\.\s]*", + demand="[-+a-zA-Z0-9\.\s]*", subworkflow pypsaearth: @@ -59,7 +60,7 @@ rule solve_all_networks: input: expand( RDIR - + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}.nc", + + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", **config["scenario"], **config["costs"] ), @@ -68,37 +69,41 @@ rule solve_all_networks: rule prepare_sector_network: input: network=RDIR - + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_presec.nc", + + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}_presec.nc", costs=CDIR + "costs_{planning_horizons}.csv", h2_cavern="data/hydrogen_salt_cavern_potentials.csv", - nodal_energy_totals="resources/nodal_energy_heat_totals_s{simpl}_{clusters}.csv", - transport="resources/transport_s{simpl}_{clusters}.csv", - avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv", - dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv", - nodal_transport_data="resources/nodal_transport_data_s{simpl}_{clusters}.csv", + nodal_energy_totals="resources/nodal_energy_heat_totals_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + transport="resources/transport_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + avail_profile="resources/avail_profile_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + dsm_profile="resources/dsm_profile_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + nodal_transport_data="resources/nodal_transport_data_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", overrides="data/override_component_attrs", clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv", - industrial_demand="resources/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}.csv", - energy_totals="resources/energy_totals.csv", + industrial_demand="resources/industry/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}_{demand}.csv", + energy_totals="data/energy_totals_{demand}_{planning_horizons}.csv", airports="data/airports.csv", ports="data/ports.csv", - heat_demand="resources/heat/heat_demand_s{simpl}_{clusters}.csv", - ashp_cop="resources/heat/ashp_cop_s{simpl}_{clusters}.csv", - gshp_cop="resources/heat/gshp_cop_s{simpl}_{clusters}.csv", - solar_thermal="resources/heat/solar_thermal_s{simpl}_{clusters}.csv", - district_heat_share="resources/heat/district_heat_share_s{simpl}_{clusters}.csv", + heat_demand="resources/heat/heat_demand_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + ashp_cop="resources/heat/ashp_cop_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + gshp_cop="resources/heat/gshp_cop_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + solar_thermal="resources/heat/solar_thermal_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + district_heat_share="resources/heat/district_heat_share_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", biomass_potentials="data/temp_hard_coded/biomass_potentials_s_37.csv", biomass_transport_costs="data/temp_hard_coded/biomass_transport_costs.csv", + shapes_path=pypsaearth( + "resources/bus_regions/regions_onshore_elec_s{simpl}_{clusters}.geojson" + ), + #shapes_path="../pypsa-earth/resources/shapes/MAR2.geojson", output: RDIR - + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}.nc", + + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", threads: 1 resources: mem_mb=2000, benchmark: ( RDIR - + "/benchmarks/prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}" + + "/benchmarks/prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}" ) script: "scripts/prepare_sector_network.py" @@ -120,9 +125,10 @@ rule override_respot: }, overrides="data/override_component_attrs", network=pypsaearth("networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"), + energy_totals="data/energy_totals_{demand}_{planning_horizons}.csv", output: RDIR - + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_presec.nc", + + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}_presec.nc", script: "scripts/override_respot.py" @@ -130,53 +136,22 @@ rule override_respot: rule prepare_transport_data: input: network=pypsaearth("networks/elec_s{simpl}_{clusters}.nc"), - energy_totals_name="resources/energy_totals.csv", + energy_totals_name="data/energy_totals_{demand}_{planning_horizons}.csv", traffic_data_KFZ="data/emobility/KFZ__count", traffic_data_Pkw="data/emobility/Pkw__count", transport_name="resources/transport_data.csv", clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv", temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc", output: - nodal_energy_totals="resources/nodal_energy_totals_s{simpl}_{clusters}.csv", - transport="resources/transport_s{simpl}_{clusters}.csv", - avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv", - dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv", - nodal_transport_data="resources/nodal_transport_data_s{simpl}_{clusters}.csv", + # nodal_energy_totals="resources/nodal_energy_totals_s{simpl}_{clusters}.csv", + transport="resources/transport_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + avail_profile="resources/avail_profile_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + dsm_profile="resources/dsm_profile_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + nodal_transport_data="resources/nodal_transport_data_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", script: "scripts/prepare_transport_data.py" -rule build_industrial_production_per_country_tomorrow: #YES - input: - industrial_production_per_country="data/industrial_production_per_country.csv", - output: - industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv", - threads: 1 - resources: - mem_mb=1000, - benchmark: - "benchmarks/build_industrial_production_per_country_tomorrow_{planning_horizons}" - script: - "scripts/build_industrial_production_tomorrow.py" - - -rule build_industry_demand: #Yes - input: - industry_sector_ratios="data/industry_sector_ratios.csv", - industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", - industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv", - industrial_production_per_country="data/industrial_production_per_country.csv", - output: - industrial_energy_demand_per_node="resources/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}.csv", - threads: 1 - resources: - mem_mb=1000, - benchmark: - "benchmarks/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}.csv" - script: - "scripts/build_industry_demand.py" - - rule build_cop_profiles: input: temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc", @@ -203,7 +178,7 @@ rule build_cop_profiles: rule prepare_heat_data: input: network=pypsaearth("networks/elec_s{simpl}_{clusters}.nc"), - energy_totals_name="resources/energy_totals.csv", + energy_totals_name="data/energy_totals_{demand}_{planning_horizons}.csv", clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv", temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc", cop_soil_total="resources/cop_soil_total_elec_s{simpl}_{clusters}.nc", @@ -212,12 +187,12 @@ rule prepare_heat_data: heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc", heat_profile="data/heat_load_profile_BDEW.csv", output: - nodal_energy_totals="resources/nodal_energy_heat_totals_s{simpl}_{clusters}.csv", - heat_demand="resources/heat/heat_demand_s{simpl}_{clusters}.csv", - ashp_cop="resources/heat/ashp_cop_s{simpl}_{clusters}.csv", - gshp_cop="resources/heat/gshp_cop_s{simpl}_{clusters}.csv", - solar_thermal="resources/heat/solar_thermal_s{simpl}_{clusters}.csv", - district_heat_share="resources/heat/district_heat_share_s{simpl}_{clusters}.csv", + nodal_energy_totals="resources/nodal_energy_heat_totals_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + heat_demand="resources/heat/heat_demand_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + ashp_cop="resources/heat/ashp_cop_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + gshp_cop="resources/heat/gshp_cop_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + solar_thermal="resources/heat/solar_thermal_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", + district_heat_share="resources/heat/district_heat_share_{demand}_s{simpl}_{clusters}_{planning_horizons}.csv", script: "scripts/prepare_heat_data.py" @@ -261,56 +236,6 @@ rule build_population_layouts: "scripts/build_population_layouts.py" -rule build_industrial_distribution_key: #YES - input: - regions_onshore=pypsaearth( - "resources/bus_regions/regions_onshore_elec_s{simpl}_{clusters}.geojson" - ), - clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv", - industrial_database="data/morocco_cement_industry.csv", - output: - industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", - threads: 1 - resources: - mem_mb=1000, - benchmark: - "benchmarks/build_industrial_distribution_key/s{simpl}_{clusters}" - script: - "scripts/build_industrial_distribution_key.py" - - -# rule build_industrial_energy_demand_per_node: -# input: -# industry_sector_ratios="data/industry_sector_ratios.csv", -# industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv", -# industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", -# industrial_production_per_country_tomorrow="data/industrial_production_per_country_tomorrow_{planning_horizons}.csv", -# output: -# industrial_energy_demand_per_node="resources/industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv", -# threads: 1 -# resources: -# mem_mb=1000, -# benchmark: -# "benchmarks/build_industrial_energy_demand_per_node/s{simpl}_{clusters}_{planning_horizons}" -# script: -# "scripts/build_industrial_energy_demand_per_node.py" - - -# rule build_industrial_energy_demand_per_node_today: -# input: -# industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", -# industrial_energy_demand_per_country_today="data/industrial_energy_demand_per_country_today.csv", -# output: -# industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv", -# threads: 1 -# resources: -# mem_mb=1000, -# benchmark: -# "benchmarks/build_industrial_energy_demand_per_node_today/s{simpl}_{clusters}" -# script: -# "scripts/build_industrial_energy_demand_per_node_today.py" - - rule move_hardcoded_files_temp: input: "data/temp_hard_coded/energy_totals.csv", @@ -402,27 +327,27 @@ rule solve_network: input: overrides="data/override_component_attrs", network=RDIR - + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}.nc", + + "/prenetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", costs=CDIR + "costs_{planning_horizons}.csv", output: RDIR - + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}.nc", + + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", shadow: "shallow" log: solver=RDIR - + "/logs/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_solver.log", + + "/logs/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}_solver.log", python=RDIR - + "/logs/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_python.log", + + "/logs/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}_python.log", memory=RDIR - + "/logs/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_memory.log", + + "/logs/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}_memory.log", threads: 25 resources: mem_mb=config["solving"]["mem"], benchmark: ( RDIR - + "/benchmarks/solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}" + + "/benchmarks/solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}" ) script: "scripts/solve_network.py" @@ -433,14 +358,14 @@ rule make_summary: overrides="data/override_component_attrs", networks=expand( RDIR - + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}.nc", + + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", **config["scenario"], **config["costs"] ), costs=CDIR + "costs_{}.csv".format(config["scenario"]["planning_horizons"][0]), plots=expand( RDIR - + "/maps/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}-costs-all_{planning_horizons}_{discountrate}.pdf", + + "/maps/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}-costs-all_{planning_horizons}_{discountrate}_{demand}.pdf", **config["scenario"], **config["costs"] ), @@ -473,17 +398,17 @@ rule plot_network: input: overrides="data/override_component_attrs", network=RDIR - + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}.nc", + + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", output: map=RDIR - + "/maps/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}-costs-all_{planning_horizons}_{discountrate}.pdf", + + "/maps/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}-costs-all_{planning_horizons}_{discountrate}_{demand}.pdf", threads: 2 resources: mem_mb=10000, benchmark: ( RDIR - + "/benchmarks/plot_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}" + + "/benchmarks/plot_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}" ) script: "scripts/plot_network.py" @@ -510,17 +435,17 @@ rule plot_summary: rule prepare_db: input: network=RDIR - + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}.nc", + + "/postnetworks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}.nc", output: db=RDIR - + "/summaries/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}-costs-all_{planning_horizons}.csv", + + "/summaries/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}-costs-all_{planning_horizons}_{discountrate}_{demand}.csv", threads: 2 resources: mem_mb=10000, benchmark: ( RDIR - + "/benchmarks/prepare_db/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}" + + "/benchmarks/prepare_db/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{sopts}_{planning_horizons}_{discountrate}_{demand}" ) script: "scripts/prepare_db.py" @@ -548,3 +473,96 @@ rule clean: run: shell("rm -r ../pypsa-earth/resources") shell("rm -r ../pypsa-earth/networks") + + +if config["custom_data"].get("industry_demand", False) == True: + + rule build_industrial_distribution_key: #custom data + input: + regions_onshore=pypsaearth( + "resources/bus_regions/regions_onshore_elec_s{simpl}_{clusters}.geojson" + ), + clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv", + industrial_database="resources/custom_data/industrial_database.csv", + #shapes_path=pypsaearth("resources/bus_regions/regions_onshore_elec_s{simpl}_{clusters}.geojson") + shapes_path="../pypsa-earth/resources/shapes/MAR2.geojson", + output: + industrial_distribution_key="resources/industry/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", + threads: 1 + resources: + mem_mb=1000, + benchmark: + "benchmarks/build_industrial_distribution_key/s{simpl}_{clusters}" + script: + "scripts/build_industrial_distribution_key.py" + + rule build_industry_demand: #custom data + input: + industry_sector_ratios="data/industry_sector_ratios_{demand}_{planning_horizons}.csv", + industrial_distribution_key="resources/industry/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", + industrial_production_per_country_tomorrow="resources/custom_data/industrial_production_per_country_tomorrow_{planning_horizons}_{demand}.csv", + costs=CDIR + + "costs_{}.csv".format(config["scenario"]["planning_horizons"][0]), + output: + industrial_energy_demand_per_node="resources/industry/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}_{demand}.csv", + threads: 1 + resources: + mem_mb=1000, + benchmark: + "benchmarks/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}_{demand}.csv" + script: + "scripts/build_industry_demand.py" + + +if config["custom_data"].get("industry_demand", False) == False: + + rule build_industrial_distribution_key: #default data + input: + regions_onshore=pypsaearth( + "resources/bus_regions/regions_onshore_elec_s{simpl}_{clusters}.geojson" + ), + clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv", + industrial_database="data/industrial_database.csv", + shapes_path=pypsaearth( + "resources/bus_regions/regions_onshore_elec_s{simpl}_{clusters}.geojson" + ), + output: + industrial_distribution_key="resources/industry/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", + threads: 1 + resources: + mem_mb=1000, + benchmark: + "benchmarks/build_industrial_distribution_key_elec_s{simpl}_{clusters}" + script: + "scripts/build_industrial_distribution_key.py" + + rule build_industrial_production_per_country_tomorrow: #default data + input: + industrial_production_per_country="data/industrial_production_per_country.csv", + output: + industrial_production_per_country_tomorrow="resources/industry/industrial_production_per_country_tomorrow_{planning_horizons}_{demand}.csv", + threads: 1 + resources: + mem_mb=1000, + benchmark: + "benchmarks/build_industrial_production_per_country_tomorrow_{planning_horizons}_{demand}" + script: + "scripts/build_industrial_production_tomorrow.py" + + rule build_industry_demand: #default data + input: + industry_sector_ratios="data/industry_sector_ratios_{demand}_{planning_horizons}.csv", + industrial_distribution_key="resources/industry/industrial_distribution_key_elec_s{simpl}_{clusters}.csv", + industrial_production_per_country_tomorrow="resources/industry/industrial_production_per_country_tomorrow_{planning_horizons}_{demand}.csv", + industrial_production_per_country="data/industrial_production_per_country.csv", + costs=CDIR + + "costs_{}.csv".format(config["scenario"]["planning_horizons"][0]), + output: + industrial_energy_demand_per_node="resources/industry/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}_{demand}.csv", + threads: 1 + resources: + mem_mb=1000, + benchmark: + "benchmarks/industrial_energy_demand_per_node_elec_s{simpl}_{clusters}_{planning_horizons}_{demand}.csv" + script: + "scripts/build_industry_demand.py" diff --git a/config.pypsa-earth.yaml b/config.pypsa-earth.yaml index d538bdcec..25589f877 100644 --- a/config.pypsa-earth.yaml +++ b/config.pypsa-earth.yaml @@ -17,7 +17,7 @@ scenario: opts: [Co2L1-144H] countries: ["MA"] -# Can be replaced by country ["NG", "BJ"] or user specific region, more at https://pypsa-meets-africa.readthedocs.io/en/latest/configuration.html#top-level-configuration +# Can be replaced by country ["NG", "BJ"] or user specific region, more at https://pypsa-meets-earth.readthedocs.io/en/latest/configuration.html#top-level-configuration snapshots: start: "2013-01-01" @@ -96,7 +96,7 @@ load_options: electricity: voltages: [220., 300., 380.] - co2limit: 4.0e+6 # 0.05 * 3.1e9*0.5 + co2limit: 4.0e+3 # 0.05 * 3.1e9*0.5 co2base: 1.487e+8 agg_p_nom_limits: data/agg_p_nom_minmax.csv hvdc_as_lines: true # should HVDC lines be modeled as `Line` or as `Link` component? @@ -291,6 +291,20 @@ costs: emission_prices: # in currency per tonne emission, only used with the option Ep co2: 0. +monte_carlo: + options: + add_to_snakefile: false + samples: 7 # number of optimizations + sampling_strategy: "chaospy" # "pydoe2", "chaospy", "scipy", packages that are supported + pypsa_standard: + # User can add here flexibly more features for the Monte-Carlo sampling. + # Given as "key: value" format + # Key: add below the pypsa object for the monte_carlo sampling, "network" is only allowed for filtering! + # Value: currently supported format [l_bound, u_bound] or empty [], represent multiplication factors for the object + loads_t.p_set: [0.9, 1.1] + generators_t.p_max_pu.loc[:, n.generators.carrier == "wind"]: [0.9, 1.1] + generators_t.p_max_pu.loc[:, n.generators.carrier == "solar"]: [0.9, 1.1] + solving: options: diff --git a/config.yaml b/config.yaml index 1fa8d77e8..303ef1ead 100644 --- a/config.yaml +++ b/config.yaml @@ -5,7 +5,7 @@ results_dir: results/ summary_dir: results/ costs_dir: data/ #TODO change to the equivalent of technology data -run: runname_test +run: runname_test_new_gadm7 foresight: overnight @@ -13,7 +13,7 @@ scenario: simpl: # only relevant for PyPSA-Eur - "" clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred - - 20 + - 117 planning_horizons: # investment years for myopic and perfect; or costs year for overnight - 2030 ll: @@ -22,12 +22,19 @@ scenario: - "Co2L" sopts: - "730H" + demand: + - "NZ" +clustering_options: + alternative_clustering: false countries: ['MA'] custom_data: - renewables: [] #['csp', 'rooftop-solar', 'solar', 'onwind', 'onwind-2', 'offwind', 'offwind-2'] + renewables: ['csp', 'rooftop-solar', 'solar', 'onwind', 'onwind2', 'offwind', 'offwind2'] + elec_demand: true + industry_demand: true + costs: lifetime: 25 #default lifetime # From a Lion Hirth paper, also reflects average of Noothout et al 2016 @@ -53,7 +60,7 @@ costs: length_factor: 1.25 #to estimate offwind connection costs industry: - St_primary_fraction: 0.9 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6 + St_primary_fraction: 0.90 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6 # 2020: 0.6 # 2025: 0.55 # 2030: 0.5 @@ -172,16 +179,25 @@ sector: biomass_transport: true # biomass transport between nodes - land_transport_fuel_cell_share: 0.05 # 1 means all FCEVs - # 2020: 0 - # 2030: 0.05 - # 2040: 0.1 - # 2050: 0.15 - land_transport_electric_share: 0.15 # 1 means all EVs # This leads to problems when non-zero - # 2020: 0 - # 2030: 0.25 - # 2040: 0.6 - # 2050: 0.85 + land_transport_fuel_cell_share: # 1 means all FCEVs HERE + BU_2030: 0.00 + AP_2030: 0.004 + NZ_2030: 0.02 + DF_2030: 0.01 + BU_2050: 0.00 + AP_2050: 0.06 + NZ_2050: 0.28 + DF_2050: 0.05 + + land_transport_electric_share: # 1 means all EVs # This leads to problems when non-zero HERE + BU_2030: 0.00 + AP_2030: 0.075 + NZ_2030: 0.13 + DF_2030: 0.01 + BU_2050: 0.00 + AP_2050: 0.42 + NZ_2050: 0.68 + DF_2050: 0.15 co2_network: true co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe @@ -189,14 +205,23 @@ sector: hydrogen_underground_storage: true shipping_hydrogen_liquefaction: false shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011 - shipping_hydrogen_share: 1.0 + shipping_hydrogen_share: + BU_2030: 0.00 + AP_2030: 0.00 + NZ_2030: 0.10 + DF_2030: 0.10 + BU_2050: 0.00 + AP_2050: 0.25 + NZ_2050: 0.36 + DF_2050: 0.36 + gadm_level: 2 h2_cavern: true marginal_cost_storage: 0 methanation: true helmeth: true dac: true - SMR: true + SMR: false cc_fraction: 0.9 conventional_generation: # generator : carrier diff --git a/data/airports.csv b/data/airports.csv index 962d124fa..ac45de2d1 100644 --- a/data/airports.csv +++ b/data/airports.csv @@ -1,9 +1,13 @@ -name,country,fraction,y,x -Casablanca-Mohammed V,MA,0.428393181,33.373831,-7.579726 -Marrakesh,MA,0.265873634,31.60416425,-8.035166526 -Agadir,MA,0.083484208,30.325,-9.413056 -Fez,MA,0.05893589,33.927222,-4.978056 -Tangiers,MA,0.056274782,35.179557,-3.839891 -Rabat,MA,0.045757958,34.051389,-6.751389 -Nador,MA,0.032104508,34.988889,-3.028333 -Oujda,MA,0.029175839,34.787222,-1.923889 +name,country,fraction,y,x +Casablanca-Mohammed V,MA,0.428393181,33.373831,-7.579726 +Marrakesh,MA,0.265873634,31.60416425,-8.035166526 +Agadir,MA,0.083484208,30.325,-9.413056 +Fez,MA,0.05893589,33.927222,-4.978056 +Tangiers,MA,0.056274782,35.179557,-3.839891 +Rabat,MA,0.045757958,34.051389,-6.751389 +Nador,MA,0.032104508,34.988889,-3.028333 +Oujda,MA,0.029175839,34.787222,-1.923889 +Dummy Airport1,NG,0.7,11.423,6.032 +Dummy Airport2,NG,0.3,8.34321,9.143 +Dummy Airport1,BJ,0.7,10.23,2.32 +Dummy Airport2,BJ,0.3,7.3,2.24 diff --git a/data/energy_totals_DF_2030.csv b/data/energy_totals_DF_2030.csv new file mode 100644 index 000000000..50aeb6f61 --- /dev/null +++ b/data/energy_totals_DF_2030.csv @@ -0,0 +1,4 @@ +country,total residential space,electricity residential space,total residential water,electricity residential water,total residential cooking,electricity residential cooking,total residential,electricity residential,derived heat residential,thermal uses residential,total services space,electricity services space,total services water,electricity services water,total services cooking,electricity services cooking,total services,electricity services,derived heat services,thermal uses services,total agriculture electricity,total agriculture heat,total agriculture machinery,total agriculture,total road,electricity road,total two-wheel,total passenger cars,electricity passenger cars,total other road passenger,electricity other road passenger,total light duty road freight,electricity light duty road freight,total heavy duty road freight,total rail,electricity rail,total rail passenger,electricity rail passenger,total rail freight,electricity rail freight,total aviation passenger,total aviation freight,total domestic aviation passenger,total international aviation passenger,total domestic aviation freight,total international aviation freight,total domestic aviation,total international aviation,total domestic navigation,district heat share,total international navigation +MA,4.2,0.28,7.3,0.23,4.9,3.3,23.31,8.03,,,,,,,,,15.89,9.13,,,4.91,0,,12.03,64.23,,,,,,,,,,,,,,,,,,,,,,0.39,8.93,10.32,,1.32 +NG,4.2,0.28,7.3,0.23,4.9,3.3,23.31,8.03,,,,,,,,,15.89,9.13,,,4.91,0,,12.03,64.23,,,,,,,,,,,,,,,,,,,,,,0.39,8.93,10.32,,1.32 +BJ,4.2,0.28,7.3,0.23,4.9,3.3,23.31,8.03,,,,,,,,,15.89,9.13,,,4.91,0,,12.03,64.23,,,,,,,,,,,,,,,,,,,,,,0.39,8.93,10.32,,1.32 diff --git a/data/energy_totals_NZ_2030.csv b/data/energy_totals_NZ_2030.csv new file mode 100644 index 000000000..278bb748e --- /dev/null +++ b/data/energy_totals_NZ_2030.csv @@ -0,0 +1,2 @@ +country,total residential space,electricity residential space,total residential water,electricity residential water,total residential cooking,electricity residential cooking,total residential,electricity residential,derived heat residential,thermal uses residential,total services space,electricity services space,total services water,electricity services water,total services cooking,electricity services cooking,total services,electricity services,derived heat services,thermal uses services,total agriculture electricity,total agriculture heat,total agriculture machinery,total agriculture,total road,electricity road,total two-wheel,total passenger cars,electricity passenger cars,total other road passenger,electricity other road passenger,total light duty road freight,electricity light duty road freight,total heavy duty road freight,total rail,electricity rail,total rail passenger,electricity rail passenger,total rail freight,electricity rail freight,total aviation passenger,total aviation freight,total domestic aviation passenger,total international aviation passenger,total domestic aviation freight,total international aviation freight,total domestic aviation,total international aviation,total domestic navigation,district heat share,total international navigation,Unnamed: 52 +MA,5.766916119,0.342850196,8.711837404,0.517929358,6.345549244,3.965968278,26.79420503,22.8,,,,,,,,,18.12963843,10.87778306,,,5.893436668,0,,14.73359167,71.52182725,8.960706721,0.246818419,44.53787907,5.11363105,1.090110939,0.178445734,2.433799832,0.280386662,23.21321899,3.378036398,1.194635708,1.818410082,0.799396967,1.559626316,0.395238741,,,0.507593252,,,,0.507593252,11.2666949,11.83967678,,1.82134562, diff --git a/data/industrial_database.csv b/data/industrial_database.csv new file mode 100644 index 000000000..c17b7fcc4 --- /dev/null +++ b/data/industrial_database.csv @@ -0,0 +1,23 @@ +country,y,x,technology,capacity,unit,quality,location,Source +MA,33.592793,-7.475613,Industry Steel Secondary EAF,1000,Kt/yr,actual,"Ahl Moughlam, Tit Mellil",Tracker Map - Global Energy Monitor +MA,33.136529,-8.603555,Industry Steel Secondary EAF,800,Kt/yr,actual,"El Jadida, Jorf Lasfar",Tracker Map - Global Energy Monitor +MA,33.603593,-7.640174,Industry Steel Secondary EAF,800,Kt/yr,actual,Casablanca,Tracker Map - Global Energy Monitor +MA,30.218836,-9.157324,Industry NMM Cement,2200,Kt/yr,actual,Chtouka-Aït Baha,Cement Plants located in Morocco (cemnet.com) +MA,31.585034,-8.502796,Industry NMM Cement,1400,Kt/yr,actual,Mzoudia,"Our History| Cements from Morocco (cimentsdumaroc.com), https://www.cemnet.com/global-cement-report/country/morocco" +MA,32.550023,-9.10011,Industry NMM Cement,1000,Kt/yr,actual,Safi,Marrakech factory| Cements from Morocco (cimentsdumaroc.com) +MA,32.584623,-6.148034,Industry NMM Cement,1600,Kt/yr,actual,Beni Mellal,Cimat - NOS SITES +MA,32.990162,-7.145939,Industry NMM Cement,1600,Kt/yr,actual,Ben Ahmed,Cimat - NOS SITES +MA,33.05424,-7.533158,Industry NMM Cement,1700,Kt/yr,actual,Settat,"Morocco’s Settat cement plant ranked as second most efficient in LafargeHolcim Group - Cement industry news from Global Cement, https://www.archyde.com/lafargeholcim-morocco-settat-cement-plant-stands-out-on-a-global-scale/" +MA,33.36865,-7.748368,Industry NMM Cement,3000,Kt/yr,actual,Bouskoura,Communiqué de presse Lafarge / Lafarge press release (holcim.com) +MA,33.891323,-6.946778,Industry NMM Cement,1200,Kt/yr,actual,Temara,Company Votorantim Cimentos Morocco History +MA,33.909977,-5.495519,Industry NMM Cement,1800,Kt/yr,actual,Meknes,GID cement database +MA,33.944215,-5.117161,Industry NMM Cement,1900,Kt/yr,actual,Fes,GID cement database +MA,34.59326,-2.349346,Industry NMM Cement,1300,Kt/yr,actual,Oujda,GID cement database +MA,36.602102,-5.43119,Industry NMM Cement,2500,Kt/yr,actual,Tétouan,GID cement database +MA,27.093082,-13.133753,Industry NMM Cement,500,Kt/yr,actual,Laâyoune,Our History| Cements from Morocco (cimentsdumaroc.com) +MA,27.180683,-13.394904,Industry NMM Cement,500,Kt/yr,actual,Laâyoune,anouar-undated.pdf (wsrw.org) +MA,27.896842,-12.893876,Industry NMM Cement,300,Kt/yr,actual,Tarfaya,CEMOS CIMENT S.A | Society +MA,33.157731,-8.605763,Industry NMM Cement,450,Kt/yr,actual,El Jadida,Jorf Lasfar shredding centre | Cements from Morocco (cimentsdumaroc.com) +MA,35.656333,-5.915386,Industry NMM Cement,500,Kt/yr,actual,Tangier, +NG,9.164421,-9.191386,Industry NMM Cement,500,Kt/yr,actual,test, +BJ,9.164421,-2.032412,Industry NMM Cement,400,Kt/yr,actual,test, diff --git a/data/industry_sector_ratios_DF_2030.csv b/data/industry_sector_ratios_DF_2030.csv new file mode 100644 index 000000000..f0f8f980c --- /dev/null +++ b/data/industry_sector_ratios_DF_2030.csv @@ -0,0 +1,11 @@ +MWh/tMaterial,Electric arc,DRI + Electric arc,Integrated steelworks,HVC,HVC (mechanical recycling),HVC (chemical recycling),Ammonia,Chlorine,Methanol,Other chemicals,Pharmaceutical products etc.,Cement,Ceramics & other NMM,Glass production,Pulp production,Paper production,Printing and media reproduction,"Food, beverages and tobacco",Alumina production,Aluminium - primary production,Aluminium - secondary production,Other non-ferrous metals,Transport Equipment,Machinery Equipment,Textiles and leather,Wood and wood products,Other Industrial Sectors +elec,1.08131011263201,1.40331011263201,1.02081290065712,2.85171802237564,0.547,6.9,1.17,3.6,0.167,4.59413467649299,2.74571454900933,0.061927560760266,0.444131182274648,2.07129313682578,1.18836563342834,0.671816951975397,2.58433845085736,0.609579312924535,1.7148611860778,13.6682779635768,1.70679863625316,3.21870208268845,0.26360808307069,0.302321103903853,0.465709368129419,0.959299391661276,1.28392230762155 +coal,0,0,1.37700428586814,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +coke,0,0,1.31179002640128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +biomass,0,0,0,0,0,0,0,0,0,0,0,0.197014429694347,0,0,2.11770263341691,2.3556399768381,0,0.639097011494721,0,0,0,0,0.063791711155947,0.046328443575653,0.25434164393245,1.81917612747947,0.385739912368899 +gas,0.22647645142597,0.22647645142597,0.223726256567725,4.07019298065976,0,0,0,0,10.25,0,0,0.517919514482129,0,0,0,0,0,0,2.33849373100086,0,0,0,0,0,0,0,0 +hydrogen,0,1.7,0,0.000172661076923,0,0,6.5,-0.9372,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +heat,0.003017860956765,0.003017860956765,0.005250701955935,0.246660062683918,0,0,0,0,0,0.14504371624202,0.093834952759942,0.002137249780593,0.002499487519859,0.017944882377285,0.039954020725194,0.03386441536949,0.726758205777018,0.026536577121055,0.00315480532373,0.010242405894815,0.002131812723607,0.003474096021933,0.010264108163452,0.010450031028829,0.060237643860528,0.058567765114422,0.042513616053224 +oil,0,0,0,14.0078364473941,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +process emission,0.027663169349199,0.027663169349199,0.216388525166488,0.024489586623069,0,0,0,0,0,6.86721429642212E-05,0,0.542433217279835,0.031681531903839,0.102551470416709,0,0,0,0,0,1.49134585796386,0,0.169966661912688,0,0,0,0,0 +process emission from feedstock,0,0,0,0.490384615384615,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/data/industry_sector_ratios_NZ_2030.csv b/data/industry_sector_ratios_NZ_2030.csv new file mode 100644 index 000000000..0b30589d2 --- /dev/null +++ b/data/industry_sector_ratios_NZ_2030.csv @@ -0,0 +1,9 @@ +,Industry Steel Primary Blast Furnace Open Hearth Furnace,Industry Steel Primary Blast Furnace Basic Oxygen Furnace,Industry Steel Primary DRI,Industry Steel Secondary EAF,Industry Steel Casting Rolling Finishing,Industry Steel Other,Industry Chemical Ammonia SMR,Industry Chemical Ammonia Other conventional,Industry Chemical Ammonia Renewable,Industry Chemical HVC Naphtha,Industry Chemical HVC LPG,Industry Chemical HVC Methanol,Industry Chemical Other,Industry NMM Cement,Industry NMM Other,Industry Food and tobacco,Industry Construction,Industry Mining,Industry Non ferrous metals Aluminium Primary,Industry Non ferrous metals Aluminium Secondary,Industry Non ferrous metals Other,Industry Paper and pulp Pulp Primary,Industry Paper and pulp Pulp Secondary,Industry Paper and pulp Paper,Industry Paper and pulp Other,Industry Transport equipment,Industry Textiles and leather,Industry Wood,Industry Miscellaneous +oil,0,0,0,0.16,0,2,0,3.52,0,4.55,5.2,0,310143,1.18,17968,1600257,541532,3220424,5.57,2.07,600620,0.58,0.12,0.78,1366,38486,346145,0,204340 +gas,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0.02,49910,0,0,0,0,0,0,1.85,0,0.07,3375,27490,0,0,0 +electricity,0.27,0.23,0.51,0.57,0,0,0.23,1.17,1.8,0.29,0.34,0.97,1329186,0.33,79856,2046841,370279,3252954,16.7,0.7,1449772,1.65,0.37,1.31,3214,206173,862283,35612,255426 +coal,4.15,3.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +heat,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +biomass,0,0,0,0,0,0,0,0,0,0,0,0,97474,0.1,49910,37215,9257,0,0,0,0,0,0,0,0,0,11086,1874,0 +hydrogen,1.1,0.92,2.35,0,0,0,0,0,0,0.05,0.06,0,35445,0.02,1996,37215,4628,32530,0,0.03,20711,0.04,0,0.02,80,2749,12318,0,4644 +process emission,0.216388525,0.216388525,0.027663169349199,0.027663169349199,0,0,0,0,0,0.024489586623069,0.024489586623069,0.024489586623069,6.86721429642211E-05,0.542433217279835,0.031681531903839,0,0,0,1.49134585796386,0,0.169966661912688,0,0,0,0,0,0,0,0 diff --git a/data/ports.csv b/data/ports.csv index a96b1acb8..e0b56eaf3 100644 --- a/data/ports.csv +++ b/data/ports.csv @@ -1 +1,8 @@ -name,country,fraction,y,x Port of Nador,MA,0.2,35.2748795,-2.92229843 Port of Tanger Med,MA,0.2,35.5324,-5.3036 Port of Kenitra,MA,0.1,34.26101,-6.5802 Port of Casablanca,MA,0.4,33.5999976,-7.6166642 Port of Kenitra,MA,0.1,33.1267,-8.62028 \ No newline at end of file +name,country,fraction,y,x +Port of Nador,MA,0.2,35.2748795,-2.92229843 +Port of Tanger Med,MA,0.2,35.5324,-5.3036 +Port of Kenitra,MA,0.1,34.26101,-6.5802 +Port of Casablanca,MA,0.4,33.5999976,-7.6166642 +Port of Kenitra,MA,0.1,33.1267,-8.62028 +dummy port 1,NG,1,6.455,4.234 +dummy port 1,BJ,1,6.47,2.63 diff --git a/energy_totals_NZ_2030_ready.csv b/energy_totals_NZ_2030_ready.csv new file mode 100644 index 000000000..c3ecc7863 --- /dev/null +++ b/energy_totals_NZ_2030_ready.csv @@ -0,0 +1,2 @@ +country,total residential space,electricity residential space,total residential water,electricity residential water,total residential cooking,electricity residential cooking,total residential,electricity residential,derived heat residential,thermal uses residential,total services space,electricity services space,total services water,electricity services water,total services cooking,electricity services cooking,total services,electricity services,derived heat services,thermal uses services,total agriculture electricity,total agriculture heat,total agriculture machinery,total agriculture,total road,electricity road,total two-wheel,total passenger cars,electricity passenger cars,total other road passenger,electricity other road passenger,total light duty road freight,electricity light duty road freight,total heavy duty road freight,total rail,electricity rail,total rail passenger,electricity rail passenger,total rail freight,electricity rail freight,total aviation passenger,total aviation freight,total domestic aviation passenger,total international aviation passenger,total domestic aviation freight,total international aviation freight,total domestic aviation,total international aviation,total domestic navigation,district heat share,total international navigation,agriculture electricity, agriculture oil,residential biomass,residential gas,services electricity,services biomass,services oil,services gas,residential oil 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import Point gpd_version = StrictVersion(gpd.__version__) -# def locate_missing_industrial_sites(df): -# """ -# Locate industrial sites without valid locations based on -# city and countries. Should only be used if the model's -# spatial resolution is coarser than individual cities. -# """ - -# try: -# from geopy.extra.rate_limiter import RateLimiter -# from geopy.geocoders import Nominatim -# except: -# raise ModuleNotFoundError( -# "Optional dependency 'geopy' not found." -# "Install via 'conda install -c conda-forge geopy'" -# "or set 'industry: hotmaps_locate_missing: false'." -# ) - -# locator = Nominatim(user_agent=str(uuid.uuid4())) -# geocode = RateLimiter(locator.geocode, min_delay_seconds=2) - -# def locate_missing(s): - -# if pd.isna(s.City) or s.City == "CONFIDENTIAL": -# return None - -# loc = geocode([s.City, s.Country], geometry="wkt") -# if loc is not None: -# print(f"Found:\t{loc}\nFor:\t{s['City']}, {s['Country']}\n") -# return f"POINT({loc.longitude} {loc.latitude})" -# else: -# return None - -# missing = df.index[df.geom.isna()] -# df.loc[missing, "coordinates"] = df.loc[missing].apply(locate_missing, axis=1) - -# # report stats -# num_still_missing = df.coordinates.isna().sum() -# num_found = len(missing) - num_still_missing -# share_missing = len(missing) / len(df) * 100 -# share_still_missing = num_still_missing / len(df) * 100 -# print( -# f"Found {num_found} missing locations.", -# f"Share of missing locations reduced from {share_missing:.2f}% to {share_still_missing:.2f}%.", -# ) - -# return df - - -def map_industry_to_buses(regions): +def map_industry_to_buses(df): """ Load hotmaps database of industrial sites and map onto bus regions. Build industrial demand... Change name and add other functions. @@ -68,19 +21,19 @@ def map_industry_to_buses(regions): Only cement not steel - proof of concept. Change hotmaps to more descriptive name, etc. """ - + df = df[df.country.isin(snakemake.config["countries"])] df["gadm_{}".format(gadm_level)] = df[["x", "y", "country"]].apply( lambda site: locate_bus( - site[["x", "y"]].astype("float"), site["country"], gadm_level + site[["x", "y"]].astype("float"), + site["country"], + gadm_level, + shapes_path, + gadm_clustering, ), axis=1, ) - # df["gadm_{}".format(gadm_level)] = df["gadm_{}".format(gadm_level)].apply( - # lambda cocode: three_2_two_digits_country(cocode[:3]) + cocode[3:] - # ) - - return df + return df.set_index("gadm_2") def build_nodal_distribution_key( @@ -88,44 +41,38 @@ def build_nodal_distribution_key( ): # returns percentage of co2 emissions """Build nodal distribution keys for each sector.""" - sectors = ( - industrial_database.Sector.unique() - ) # TODO add more than just cement to data - - countries = regions.index.str[:2].unique() + countries = regions["name"].str[:2].unique() - keys = pd.DataFrame(index=regions.index, columns=sectors, dtype=float) + keys = pd.DataFrame(index=regions.name, columns=technology, dtype=float) pop = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0) pop["country"] = pop.index.str[:2] - keys["population"] = pop["total"].values + keys["population"] = pop["total"].values / pop["total"].sum() - for sector, country in product(sectors, countries): + for tech, country in product(technology, countries): - regions_ct = regions.index[regions.index.str.contains(country)] + regions_ct = regions.name[regions.name.str.contains(country)] facilities = industrial_database.query( - "country == @country and Sector == @sector" + "country == @country and technology == @tech" ) # TODO adapt for facilities with production values not emissions if not facilities.empty: - emissions = facilities["Total CO2 emission"] - if emissions.sum() == 0: + indicator = facilities["capacity"] + if indicator.sum() == 0: key = pd.Series(1 / len(facilities), facilities.index) else: # TODO BEWARE: this is a strong assumption - emissions = emissions.fillna(0) - key = emissions / emissions.sum() + # indicator = indicator.fillna(0) + key = indicator / indicator.sum() key = ( - key.groupby(facilities["gadm_{}".format(gadm_level)]) - .sum() - .reindex(regions_ct, fill_value=0.0) + key.groupby(facilities.index).sum().reindex(regions_ct, fill_value=0.0) ) else: key = keys.loc[regions_ct, "population"] - keys.loc[regions_ct, sector] = key + keys.loc[regions_ct, tech] = key return keys @@ -137,7 +84,9 @@ def build_nodal_distribution_key( snakemake = mock_snakemake( "build_industrial_distribution_key", simpl="", - clusters=1006, + clusters=4, + demand="NZ", + planning_horizons=2030, ) options = snakemake.config["sector"] @@ -152,13 +101,21 @@ def build_nodal_distribution_key( lambda name: three_2_two_digits_country(name[:3]) + name[3:] ) - regions = regions.set_index("name") - - df = pd.read_csv( - snakemake.input.industrial_database, sep=",", header=0, encoding=("latin1") + geo_locs = pd.read_csv( + snakemake.input.industrial_database, sep=",", header=0 # , index_col=0 ) - industrial_database = map_industry_to_buses(regions) + gadm_clustering = snakemake.config["clustering_options"]["alternative_clustering"] + + geo_locs = geo_locs[geo_locs.quality != "nonexistent"] + + technology = geo_locs.technology.unique() + + shapes_path = snakemake.input.shapes_path + + industrial_database = map_industry_to_buses( + geo_locs[geo_locs.quality != "unavailable"] + ) keys = build_nodal_distribution_key(industrial_database, regions) diff --git a/scripts/build_industrial_energy_demand_per_node.py b/scripts/build_industrial_energy_demand_per_node.py deleted file mode 100644 index 9ebdb3607..000000000 --- a/scripts/build_industrial_energy_demand_per_node.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -"""Build industrial energy demand per node.""" - -import os -from itertools import product - -import pandas as pd - -sector_mapping = {"Cement": "Cement"} - - -def build_nodal_industrial_production(): - - fn = ( - snakemake.input.industrial_production_per_country_tomorrow - ) # TODO implement HyPAT data - industrial_production = pd.read_csv(fn, index_col=0) - - fn = snakemake.input.industrial_distribution_key - keys = pd.read_csv(fn, index_col=0) - keys["country"] = keys.index.str[:2] - - nodal_production = pd.DataFrame( - index=keys.index, - columns=industrial_production.columns, # TODO need hypat - dtype=float, - ) - - countries = keys.country.unique() - sectors = industrial_production.columns # TODO need hypat - - for country, sector in product(countries, sectors): - - buses = keys.index[keys.country == country] - mapping = sector_mapping.get(sector, "population") - - key = keys.loc[buses, mapping] - nodal_production.loc[buses, sector] = ( - industrial_production.at[country, sector] * key - ) # TODO need hypat - - # nodal_production.to_csv(snakemake.output.industrial_production_per_node) - return nodal_production - - -if __name__ == "__main__": - if "snakemake" not in globals(): - from helpers import mock_snakemake - - os.chdir(os.path.dirname(os.path.abspath(__file__))) - - snakemake = mock_snakemake( - "build_industrial_energy_demand_per_node", - simpl="", - clusters=48, - planning_horizons=2030, - ) - - # build_nodal_industrial_production() - - # import EU ratios df as csv - fn = snakemake.input.industry_sector_ratios - industry_sector_ratios = pd.read_csv(fn, index_col=0) - - # material demand per node and industry (kton/a) - # fn = snakemake.input.industrial_production_per_node - # nodal_production = pd.read_csv(fn, index_col=0) - fn = build_nodal_industrial_production() - nodal_production = pd.DataFrame(fn) - - # energy demand today to get current electricity - fn = snakemake.input.industrial_energy_demand_per_node_today - nodal_today = pd.read_csv(fn, index_col=0) - - # final energy consumption per node and industry (TWh/a) - nodal_df = nodal_production.dot(industry_sector_ratios.T) - - # convert GWh to TWh and ktCO2 to MtCO2 - nodal_df *= 0.001 - - rename_sectors = { - "elec": "electricity", - "biomass": "solid biomass", - "heat": "low-temperature heat", - } - nodal_df.rename(columns=rename_sectors, inplace=True) - - nodal_df["current electricity"] = nodal_today["electricity"] - - nodal_df.index.name = "TWh/a (MtCO2/a)" - - fn = snakemake.output.industrial_energy_demand_per_node - nodal_df.to_csv(fn, float_format="%.2f") diff --git a/scripts/build_industrial_energy_demand_per_node_today.py b/scripts/build_industrial_energy_demand_per_node_today.py deleted file mode 100644 index 473054bf8..000000000 --- a/scripts/build_industrial_energy_demand_per_node_today.py +++ /dev/null @@ -1,63 +0,0 @@ -# -*- coding: utf-8 -*- -"""Build industrial energy demand per node.""" - -import os -from itertools import product - -import numpy as np -import pandas as pd - -# map JRC/our sectors to hotmaps sector, where mapping exist -sector_mapping = { - "Cement": "Cement", -} - - -def build_nodal_industrial_energy_demand(): - - fn = snakemake.input.industrial_energy_demand_per_country_today - industrial_demand = pd.read_csv(fn, header=[0, 1], index_col=0) - - fn = snakemake.input.industrial_distribution_key - keys = pd.read_csv(fn, index_col=0) - keys["country"] = keys.index.str[:2] - - nodal_demand = pd.DataFrame( - 0.0, dtype=float, index=keys.index, columns=industrial_demand.index - ) - - countries = keys.country.unique() - sectors = industrial_demand.columns.levels[1] - - for country, sector in product(countries, sectors): - - buses = keys.index[keys.country == country] - mapping = sector_mapping.get(sector, "population") - - key = keys.loc[buses, mapping] - demand = industrial_demand[country, sector] - - outer = pd.DataFrame( - np.outer(key, demand), index=key.index, columns=demand.index - ) - - nodal_demand.loc[buses] += outer - - nodal_demand.index.name = "TWh/a" - - nodal_demand.to_csv(snakemake.output.industrial_energy_demand_per_node_today) - - -if __name__ == "__main__": - if "snakemake" not in globals(): - from helpers import mock_snakemake - - os.chdir(os.path.dirname(os.path.abspath(__file__))) - - snakemake = mock_snakemake( - "build_industrial_energy_demand_per_node_today", - simpl="", - clusters=4, - ) - - build_nodal_industrial_energy_demand() diff --git a/scripts/build_industrial_production_tomorrow.py b/scripts/build_industrial_production_tomorrow.py index 91fa00db2..9cd654c12 100644 --- a/scripts/build_industrial_production_tomorrow.py +++ b/scripts/build_industrial_production_tomorrow.py @@ -103,6 +103,7 @@ def industry_prod_tomorrow(production): config_ind = snakemake.config["industry"] investment_year = int(snakemake.wildcards.planning_horizons) + demand_sc = snakemake.wildcards.demand production = pd.read_csv( snakemake.input.industrial_production_per_country, index_col=0 diff --git a/scripts/build_industry_demand.py b/scripts/build_industry_demand.py index 1d4ae7cb2..42f83d0bc 100644 --- a/scripts/build_industry_demand.py +++ b/scripts/build_industry_demand.py @@ -11,31 +11,6 @@ import numpy as np import pandas as pd -# # map JRC/our sectors to hotmaps sector, where mapping exist -# sector_mapping = { -# 'Electric arc': 'Iron and steel', -# 'Integrated steelworks': 'Iron and steel', -# 'DRI + Electric arc': 'Iron and steel', -# 'Ammonia': 'Chemical industry', -# 'HVC': 'Chemical industry', -# 'HVC (mechanical recycling)': 'Chemical industry', -# 'HVC (chemical recycling)': 'Chemical industry', -# 'Methanol': 'Chemical industry', -# 'Chlorine': 'Chemical industry', -# 'Other chemicals': 'Chemical industry', -# 'Pharmaceutical products etc.': 'Chemical industry', -# 'Cement': 'Cement', -# 'Ceramics & other NMM': 'Non-metallic mineral products', -# 'Glass production': 'Glass', -# 'Pulp production': 'Paper and printing', -# 'Paper production': 'Paper and printing', -# 'Printing and media reproduction': 'Paper and printing', -# 'Alumina production': 'Non-ferrous metals', -# 'Aluminium - primary production': 'Non-ferrous metals', -# 'Aluminium - secondary production': 'Non-ferrous metals', -# 'Other non-ferrous metals': 'Non-ferrous metals', -# } - def country_to_nodal(industrial_production, keys): @@ -70,13 +45,23 @@ def country_to_nodal(industrial_production, keys): from helpers import mock_snakemake snakemake = mock_snakemake( - "build_industry_demand", simpl="", clusters=9077, planning_horizons="2030" + "build_industry_demand", + simpl="", + clusters=112, + planning_horizons="2030", + demand="NZ", ) # Load production per country tomorrow prod_tom_path = snakemake.input.industrial_production_per_country_tomorrow production_tom = pd.read_csv(prod_tom_path, header=0, index_col=0) + if snakemake.config["custom_data"]["industry_demand"]: + production_tom.drop("Industry Machinery", axis=1, inplace=True) + + # to_drop=production_tom.sum()[production_tom.sum()==0].index + # production_tom.drop(to_drop, axis=1, inplace=True) + # Load distribution keys keys_path = snakemake.input.industrial_distribution_key dist_keys = pd.read_csv(keys_path, index_col=0) @@ -88,6 +73,10 @@ def country_to_nodal(industrial_production, keys): industry_sector_ratios = pd.read_csv( snakemake.input.industry_sector_ratios, index_col=0 ) + if snakemake.config["custom_data"]["industry_demand"]: + industry_sector_ratios.drop( + "Industry Steel Casting Rolling Finishing", axis=1, inplace=True + ) # final energy consumption per node and industry (TWh/a) nodal_df = nodal_production.dot(industry_sector_ratios.T) @@ -102,21 +91,26 @@ def country_to_nodal(industrial_production, keys): } nodal_df.rename(columns=rename_sectors, inplace=True) - # energy demand today to get current electricity #TODO - prod_tod_path = snakemake.input.industrial_production_per_country - production_tod = pd.read_csv(prod_tod_path, header=0, index_col=0).filter( - snakemake.config["countries"], axis=0 - ) + if not snakemake.config["custom_data"]["industry_demand"]: - nodal_production_tod = country_to_nodal(production_tod, dist_keys) - nodal_production_tod = nodal_production_tod.reindex( - columns=list(industry_sector_ratios.columns), fill_value=0 - ) - nodal_energy_today = nodal_production_tod.dot(industry_sector_ratios.T) - nodal_energy_today.rename(columns=rename_sectors, inplace=True) - nodal_energy_today *= 0.001 + # energy demand today to get current electricity #TODO + prod_tod_path = snakemake.input.industrial_production_per_country + production_tod = pd.read_csv(prod_tod_path, header=0, index_col=0).filter( + snakemake.config["countries"], axis=0 + ) + # if electricity demand is provided by pypsa-earth, the electricty used + # in industry is included, and need to be removed from the default elec + # demand that's why it isolated to be used in prepare_sector_network + + nodal_production_tod = country_to_nodal(production_tod, dist_keys) + nodal_production_tod = nodal_production_tod.reindex( + columns=list(industry_sector_ratios.columns), fill_value=0 + ) + nodal_energy_today = nodal_production_tod.dot(industry_sector_ratios.T) + nodal_energy_today.rename(columns=rename_sectors, inplace=True) + nodal_energy_today *= 0.001 # convert GWh to TWh and ktCO2 to MtCO2 - nodal_df["current electricity"] = nodal_energy_today["electricity"] + nodal_df["current electricity"] = nodal_energy_today["electricity"] nodal_df.index.name = "TWh/a (MtCO2/a)" diff --git a/scripts/helpers.py b/scripts/helpers.py index 3610deb9d..6932ccf66 100644 --- a/scripts/helpers.py +++ b/scripts/helpers.py @@ -512,7 +512,14 @@ def get_GADM_layer(country_list, layer_id, update=False, outlogging=False): return geodf_GADM -def locate_bus(coords, co, gadm_level, path_to_gadm=None): +def locate_bus( + coords, + co, + gadm_level, + path_to_gadm=None, + gadm_clustering=False, + col="name", +): """ Function to locate the right node for a coordinate set input coords of point @@ -525,28 +532,39 @@ def locate_bus(coords, co, gadm_level, path_to_gadm=None): code of the countries where the coordinates are """ - country_list = ["MA"] # TODO connect with entire list of countries - if not path_to_gadm: - gdf = get_GADM_layer(country_list, gadm_level) - + col = "name" + if not gadm_clustering: + gdf = gpd.read_file(path_to_gadm) else: - gdf = gpd.read_file(path_to_gadm).set_index("GADM_ID")["geometry"] - - gdf_co = gdf[ - gdf["GID_{}".format(gadm_level)].str.contains(co) - ] # geodataframe of entire continent - output of prev function {} are placeholders - # in strings - conditional formatting - # insert any variable into that place using .format - extract string and filter for those containing co (MA) - point = Point(coords["x"], coords["y"]) # point object - - try: - return gdf_co[gdf_co.contains(point)][ - "GID_{}".format(gadm_level) - ].item() # filter gdf_co which contains point and returns the bus - - except ValueError: - return gdf_co[ - gdf_co.geometry == min(gdf_co.geometry, key=(point.distance)) - ][ - "GID_{}".format(gadm_level) - ].item() # looks for closest one shape=node + if path_to_gadm: + gdf = gpd.read_file(path_to_gadm) + if "GADM_ID" in gdf.columns: + col = "GADM_ID" + + if gdf[col][0][ + :3 + ].isalpha(): # TODO clean later by changing all codes to 2 letters + gdf[col] = gdf[col].apply( + lambda name: three_2_two_digits_country(name[:3]) + name[3:] + ) + else: + gdf = get_GADM_layer(co, gadm_level) + col = "GID_{}".format(gadm_level) + + # gdf.set_index("GADM_ID", inplace=True) + gdf_co = gdf[ + gdf[col].str.contains(co) + ] # geodataframe of entire continent - output of prev function {} are placeholders + # in strings - conditional formatting + # insert any variable into that place using .format - extract string and filter for those containing co (MA) + point = Point(coords["x"], coords["y"]) # point object + + try: + return gdf_co[gdf_co.contains(point)][ + col + ].item() # filter gdf_co which contains point and returns the bus + + except ValueError: + return gdf_co[gdf_co.geometry == min(gdf_co.geometry, key=(point.distance))][ + col + ].item() # looks for closest one shape=node diff --git a/scripts/override_respot.py b/scripts/override_respot.py index 36de8e5d8..a9ed2b813 100644 --- a/scripts/override_respot.py +++ b/scripts/override_respot.py @@ -13,7 +13,6 @@ def override_values(tech, year, dr): - buses = list(n.buses[n.buses.carrier == "AC"].index) custom_res_t = pd.read_csv( snakemake.input["custom_res_pot_{0}_{1}_{2}".format(tech, year, dr)], @@ -51,7 +50,7 @@ def override_values(tech, year, dr): p_nom_extendable=True, p_nom_max=custom_res["p_nom_max"].values, # weight=ds["weight"].to_pandas(), - marginal_cost=custom_res["fixedomEuroPKW"].values * 1000, + # marginal_cost=custom_res["fixedomEuroPKW"].values * 1000, capital_cost=custom_res["annualcostEuroPMW"].values, efficiency=1.0, p_max_pu=custom_res_t, @@ -67,11 +66,12 @@ def override_values(tech, year, dr): snakemake = mock_snakemake( "override_respot", simpl="", - clusters="931", + clusters="113", ll="c1.0", opts="Co2L", planning_horizons="2030", - sopts="73H", + sopts="730H", + demand="NZ", discountrate=0.069, ) sets_path_to_root("pypsa-earth-sec") @@ -79,6 +79,9 @@ def override_values(tech, year, dr): overrides = override_component_attrs(snakemake.input.overrides) n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides) m = n.copy() + buses = list(n.buses[n.buses.carrier == "AC"].index) + energy_totals = pd.read_csv(snakemake.input.energy_totals, index_col=0) + countries = snakemake.config["countries"] if snakemake.config["custom_data"]["renewables"]: techs = snakemake.config["custom_data"]["renewables"] year = snakemake.wildcards["planning_horizons"] @@ -89,7 +92,16 @@ def override_values(tech, year, dr): for tech in techs: override_values(tech, year, dr) - n.export_to_netcdf(snakemake.output[0]) else: print("No RES potential techs to override...") - n.export_to_netcdf(snakemake.output[0]) + + if snakemake.config["custom_data"]["elec_demand"]: + + for country in countries: + + n.loads_t.p_set.filter(like=country)[buses] = ( + n.loads_t.p_set.filter(like=country)[buses] + / n.loads_t.p_set.filter(like=country)[buses].sum().sum() + ) * energy_totals.loc[country, "electricity residential"] + + n.export_to_netcdf(snakemake.output[0]) diff --git a/scripts/plot_network.py b/scripts/plot_network.py index 601470192..013409c6b 100644 --- a/scripts/plot_network.py +++ b/scripts/plot_network.py @@ -786,5 +786,6 @@ def plot_map( tech_colors = snakemake.config["plotting"]["tech_colors"] plot_map(n, transmission=False) plot_transmission_topology(n) - plot_smr(n) + if snakemake.config["sector"]["SMR"]: + plot_smr(n) plot_h2_infra(n) diff --git a/scripts/prepare_heat_data.py b/scripts/prepare_heat_data.py index ce611e37c..83bb09b07 100644 --- a/scripts/prepare_heat_data.py +++ b/scripts/prepare_heat_data.py @@ -128,7 +128,13 @@ def prepare_heat_data(n): os.chdir(os.path.dirname(os.path.abspath(__file__))) - snakemake = mock_snakemake("prepare_heat_data", simpl="", clusters="15") + snakemake = mock_snakemake( + "prepare_heat_data", + simpl="", + clusters="105", + planning_horizons=2030, + demand="NZ", + ) sets_path_to_root("pypsa-earth-sec") n = pypsa.Network(snakemake.input.network) diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py index f46fb6a7e..666348d3e 100644 --- a/scripts/prepare_sector_network.py +++ b/scripts/prepare_sector_network.py @@ -664,11 +664,18 @@ def add_aviation(n, cost): ) airports = pd.read_csv(snakemake.input.airports) + airports = airports[airports.country.isin(countries)] gadm_level = options["gadm_level"] airports["gadm_{}".format(gadm_level)] = airports[["x", "y", "country"]].apply( - lambda airport: locate_bus(airport[["x", "y"]], airport["country"], gadm_level), + lambda airport: locate_bus( + airport[["x", "y"]], + airport["country"], + gadm_level, + snakemake.input.shapes_path, + snakemake.config["clustering_options"]["alternative_clustering"], + ), axis=1, ) @@ -846,6 +853,7 @@ def h2_hc_conversions(n, costs): def add_shipping(n, costs): ports = pd.read_csv(snakemake.input.ports, index_col=None, squeeze=True) + ports = ports[ports.country.isin(countries)] gadm_level = options["gadm_level"] @@ -860,10 +868,19 @@ def add_shipping(n, costs): ) # check whether item depends on investment year - shipping_hydrogen_share = get(options["shipping_hydrogen_share"], investment_year) + shipping_hydrogen_share = get( + options["shipping_hydrogen_share"], demand_sc + "_" + str(investment_year) + ) ports["gadm_{}".format(gadm_level)] = ports[["x", "y", "country"]].apply( - lambda port: locate_bus(port[["x", "y"]], port["country"], gadm_level), axis=1 + lambda port: locate_bus( + port[["x", "y"]], + port["country"], + gadm_level, + snakemake.input["shapes_path"], + snakemake.config["clustering_options"]["alternative_clustering"], + ), + axis=1, ) ports = ports.set_index("gadm_{}".format(gadm_level)) @@ -993,59 +1010,59 @@ def add_industry(n, costs): # Add carrier Biomass - n.madd( - "Bus", - spatial.biomass.industry, - location=spatial.biomass.locations, - carrier="solid biomass for industry", - ) - - if options["biomass_transport"]: - p_set = ( - industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename( - index=lambda x: x + " solid biomass for industry" - ) - / 8760 - ) - else: - p_set = industrial_demand["solid biomass"].sum() / 8760 + # n.madd( + # "Bus", + # spatial.biomass.industry, + # location=spatial.biomass.locations, + # carrier="solid biomass for industry", + # ) - n.madd( - "Load", - spatial.biomass.industry, - bus=spatial.biomass.industry, - carrier="solid biomass for industry", - p_set=p_set, - ) + # if options["biomass_transport"]: + # p_set = ( + # industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename( + # index=lambda x: x + " solid biomass for industry" + # ) + # / 8760 + # ) + # else: + # p_set = industrial_demand["solid biomass"].sum() / 8760 + + # n.madd( + # "Load", + # spatial.biomass.industry, + # bus=spatial.biomass.industry, + # carrier="solid biomass for industry", + # p_set=p_set, + # ) - n.madd( - "Link", - spatial.biomass.industry, - bus0=spatial.biomass.nodes, - bus1=spatial.biomass.industry, - carrier="solid biomass for industry", - p_nom_extendable=True, - efficiency=1.0, - ) + # n.madd( + # "Link", + # spatial.biomass.industry, + # bus0=spatial.biomass.nodes, + # bus1=spatial.biomass.industry, + # carrier="solid biomass for industry", + # p_nom_extendable=True, + # efficiency=1.0, + # ) - n.madd( - "Link", - spatial.biomass.industry_cc, - bus0=spatial.biomass.nodes, - bus1=spatial.biomass.industry, - bus2="co2 atmosphere", - bus3=spatial.co2.nodes, - carrier="solid biomass for industry CC", - p_nom_extendable=True, - capital_cost=costs.at["cement capture", "fixed"] - * costs.at["solid biomass", "CO2 intensity"], - efficiency=0.9, # TODO: make config option - efficiency2=-costs.at["solid biomass", "CO2 intensity"] - * costs.at["cement capture", "capture_rate"], - efficiency3=costs.at["solid biomass", "CO2 intensity"] - * costs.at["cement capture", "capture_rate"], - lifetime=costs.at["cement capture", "lifetime"], - ) + # n.madd( + # "Link", + # spatial.biomass.industry_cc, + # bus0=spatial.biomass.nodes, + # bus1=spatial.biomass.industry, + # bus2="co2 atmosphere", + # bus3=spatial.co2.nodes, + # carrier="solid biomass for industry CC", + # p_nom_extendable=True, + # capital_cost=costs.at["cement capture", "fixed"] + # * costs.at["solid biomass", "CO2 intensity"], + # efficiency=0.9, # TODO: make config option + # efficiency2=-costs.at["solid biomass", "CO2 intensity"] + # * costs.at["cement capture", "capture_rate"], + # efficiency3=costs.at["solid biomass", "CO2 intensity"] + # * costs.at["cement capture", "capture_rate"], + # lifetime=costs.at["cement capture", "lifetime"], + # ) # CARRIER = FOSSIL GAS @@ -1066,7 +1083,7 @@ def add_industry(n, costs): carrier="gas for industry", ) - gas_demand = industrial_demand.loc[nodes, "methane"] / 8760.0 + gas_demand = industrial_demand.loc[nodes, "gas"] / 8760.0 if options["gas_network"]: spatial_gas_demand = gas_demand.rename(index=lambda x: x + " gas for industry") @@ -1138,7 +1155,7 @@ def add_industry(n, costs): suffix=" naphtha for industry", bus=spatial.oil.nodes, carrier="naphtha for industry", - p_set=industrial_demand["naphtha"].apply( + p_set=industrial_demand["oil"].apply( lambda frac: frac / 8760 ) # *n.snapshot_weightings.objective[0]), #TODO change the way pset is sampled here # the current way leads to inaccuracies in the last timestep in case @@ -1155,7 +1172,7 @@ def add_industry(n, costs): co2 = ( n.loads.loc[nodes + co2_release, "p_set"].sum() * costs.at["oil", "CO2 intensity"] - - industrial_demand["process emission from feedstock"].sum() + # - industrial_demand["process emission from feedstock"].sum() / 8760 # *n.snapshot_weightings.objective[0] #TODO change the way pset is sampled here # the current way leads to inaccuracies in the last timestep in case # the timestep if 8760 is not divisble by it), @@ -1195,16 +1212,29 @@ def add_industry(n, costs): # TODO map onto n.bus.country # TODO make sure to check this one, should AC have carrier pf "electricity"? loads_i = n.loads.index[ - (n.loads.index.str[:2] == ct) & (n.loads.carrier == "electricity") + (n.loads.index.str[:2] == ct) & (n.loads.carrier == "AC") ] if n.loads_t.p_set[loads_i].empty: continue - factor = ( - 1 - - industrial_demand.loc[loads_i, "current electricity"].sum() - / n.loads_t.p_set[loads_i].sum().sum() - ) - n.loads_t.p_set[loads_i] *= factor + + if not snakemake.config["custom_data"]["elec_demand"]: + # if electricity demand is provided by pypsa-earth, the electricty used + # in industry is included, and need to be removed from the default elec + # demand here, and added as "industry electricity" + factor = ( + 1 + - industrial_demand.loc[loads_i, "current electricity"].sum() + / n.loads_t.p_set[loads_i].sum().sum() + ) + n.loads_t.p_set[loads_i] *= factor + industrial_elec = industrial_demand["current electricity"].apply( + lambda frac: frac / 8760 + ) + + else: + industrial_elec = industrial_demand["electricity"].apply( + lambda frac: frac / 8760 + ) n.madd( "Load", @@ -1212,12 +1242,10 @@ def add_industry(n, costs): suffix=" industry electricity", bus=nodes, carrier="industry electricity", - p_set=industrial_demand["current electricity"].apply( - lambda frac: frac - / 8760 # *n.snapshot_weightings.objective[0] #TODO change the way pset is sampled here - # the current way leads to inaccuracies in the last timestep in case - # the timestep if 8760 is not divisble by it), - ), # TODO Multiply by demand and find true fraction + p_set=industrial_elec # *n.snapshot_weightings.objective[0] #TODO change the way pset is sampled here + # the current way leads to inaccuracies in the last timestep in case + # the timestep if 8760 is not divisble by it), + , # TODO Multiply by demand and find true fraction ) n.add("Bus", "process emissions", location="Africa", carrier="process emissions") @@ -1231,8 +1259,8 @@ def add_industry(n, costs): bus="process emissions", carrier="process emissions", p_set=-( - industrial_demand["process emission from feedstock"] - + industrial_demand["process emission"] + # industrial_demand["process emission from feedstock"]+ + industrial_demand["process emission"] ) / 8760 # *n.snapshot_weightings.objective[0] #TODO change the way pset is sampled here # the current way leads to inaccuracies in the last timestep in case @@ -1293,8 +1321,13 @@ def add_land_transport(n, costs): print("adding land transport") - fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year) - electric_share = get(options["land_transport_electric_share"], investment_year) + fuel_cell_share = get( + options["land_transport_fuel_cell_share"], + demand_sc + "_" + str(investment_year), + ) + electric_share = get( + options["land_transport_electric_share"], demand_sc + "_" + str(investment_year) + ) ice_share = 1 - fuel_cell_share - electric_share print("FCEV share", fuel_cell_share) @@ -1939,12 +1972,13 @@ def add_dac(n, costs): snakemake = mock_snakemake( "prepare_sector_network", simpl="", - clusters="30", + clusters="114", ll="c1.0", opts="Co2L", planning_horizons="2030", sopts="730H", - discountrate="0.071", + discountrate="0.069", + demand="NZ", ) # TODO fetch from config @@ -1961,12 +1995,14 @@ def add_dac(n, costs): ].index # TODO if you take nodes from the index of buses of n it's more than pop_layout # clustering of regions must be double checked.. refer to regions onshore + n.loads.loc[nodes, "carrier"] = "AC" + Nyears = n.snapshot_weightings.generators.sum() / 8760 # TODO fetch investment year from config investment_year = int(snakemake.wildcards.planning_horizons[-4:]) - + demand_sc = snakemake.wildcards.demand # loading the demand scenrario wildcard pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0) costs = prepare_costs( diff --git a/scripts/prepare_transport_data.py b/scripts/prepare_transport_data.py index 457eba415..c11bd57b4 100644 --- a/scripts/prepare_transport_data.py +++ b/scripts/prepare_transport_data.py @@ -62,7 +62,8 @@ def prepare_transport_data(n): """ energy_totals = pd.read_csv( - snakemake.input.energy_totals_name, index_col=0 + snakemake.input.energy_totals_name, + index_col=0, ) # TODO change with real numbers nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.0) @@ -191,12 +192,19 @@ def prepare_transport_data(n): os.chdir(os.path.dirname(os.path.abspath(__file__))) - snakemake = mock_snakemake("prepare_transport_data", simpl="", clusters="111") + snakemake = mock_snakemake( + "prepare_transport_data", + simpl="", + clusters="105", + demand="NZ", + planning_horizons=2030, + ) + sets_path_to_root("pypsa-earth-sec") n = pypsa.Network(snakemake.input.network) - # Get pop_layout + # Get population layout pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0) # Add options @@ -215,8 +223,15 @@ def prepare_transport_data(n): ) = prepare_transport_data(n) # Save the generated output files to snakemake paths - nodal_energy_totals.to_csv(snakemake.output.nodal_energy_totals) + + # Transport demand per node per timestep transport.to_csv(snakemake.output.transport) + + # Available share of the battery to be used by the grid avail_profile.to_csv(snakemake.output.avail_profile) + + # Restrictions on state of charge of EVs dsm_profile.to_csv(snakemake.output.dsm_profile) + + # Nodal data on number of cars nodal_transport_data.to_csv(snakemake.output.nodal_transport_data) diff --git a/test/config.test1.yaml b/test/config.test1.yaml index f89dd0d51..b442e4f87 100644 --- a/test/config.test1.yaml +++ b/test/config.test1.yaml @@ -19,15 +19,45 @@ scenario: ll: - "c1" opts: - - "Co2L-144H" + - "Co2L" sopts: - - "7H" + - "144H" + demand: + - "DF" +clustering_options: + alternative_clustering: false countries: ['NG', 'BJ'] custom_data: renewables: [] # ['csp', 'rooftop-solar', 'solar'] + elec_demand: false + industry_demand: false + +costs: + lifetime: 25 #default lifetime + # From a Lion Hirth paper, also reflects average of Noothout et al 2016 + discountrate: [0.071] #, 0.086, 0.111] + # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501 + USD2013_to_EUR2013: 0.7532 + + # Marginal and capital costs can be overwritten + # capital_cost: + # onwind: 500 + marginal_cost: + solar: 0.01 + onwind: 0.015 + offwind: 0.015 + hydro: 0. + H2: 0. + battery: 0. + + emission_prices: # only used with the option Ep (emission prices) + co2: 0. + + lines: + length_factor: 1.25 #to estimate offwind connection costs industry: St_primary_fraction: 0.9 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6 @@ -77,30 +107,6 @@ industry: hotmaps_locate_missing: false reference_year: 2015 -costs: - lifetime: 25 #default lifetime - # From a Lion Hirth paper, also reflects average of Noothout et al 2016 - discountrate: [0.071, 0.086, 0.111] - # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501 - USD2013_to_EUR2013: 0.7532 - - # Marginal and capital costs can be overwritten - # capital_cost: - # onwind: 500 - marginal_cost: - solar: 0.01 - onwind: 0.015 - offwind: 0.015 - hydro: 0. - H2: 0. - battery: 0. - - emission_prices: # only used with the option Ep (emission prices) - co2: 0. - - lines: - length_factor: 1.25 #to estimate offwind connection costs - solar_thermal: clearsky_model: simple orientation: @@ -173,25 +179,43 @@ sector: biomass_transport: true # biomass transport between nodes - land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs - # 2020: 0 - # 2030: 0.05 - # 2040: 0.1 - # 2050: 0.15 - land_transport_electric_share: 0. # 1 means all EVs # This leads to problems when non-zero - # 2020: 0 - # 2030: 0.25 - # 2040: 0.6 - # 2050: 0.85 + land_transport_fuel_cell_share: # 1 means all FCEVs HERE + BU_2030: 0.00 + AP_2030: 0.004 + NZ_2030: 0.02 + DF_2030: 0.01 + BU_2050: 0.00 + AP_2050: 0.06 + NZ_2050: 0.28 + DF_2050: 0.08 + + land_transport_electric_share: # 1 means all EVs # This leads to problems when non-zero HERE + BU_2030: 0.00 + AP_2030: 0.075 + NZ_2030: 0.13 + DF_2030: 0.01 + BU_2050: 0.00 + AP_2050: 0.42 + NZ_2050: 0.68 + DF_2050: 0.011 co2_network: true co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2 hydrogen_underground_storage: true - shipping_hydrogen_liquefaction: true + shipping_hydrogen_liquefaction: false shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011 - shipping_hydrogen_share: 1.0 - gadm_level: 1 + shipping_hydrogen_share: #1.0 + BU_2030: 0.00 + AP_2030: 0.00 + NZ_2030: 0.10 + DF_2030: 0.05 + BU_2050: 0.00 + AP_2050: 0.25 + NZ_2050: 0.36 + DF_2050: 0.12 + + gadm_level: 2 h2_cavern: true marginal_cost_storage: 0 methanation: true @@ -206,10 +230,9 @@ sector: # snapshots are originally set in PyPSA-Earth/config.yaml but used again by PyPSA-Earth-Sec snapshots: - # arguments to pd.date_range start: "2013-03-1" - end: "2013-03-8" - closed: "left" # end is not inclusive + end: "2013-03-7" + inclusive: "left" # end is not inclusive # atlite: # cutout: ./cutouts/africa-2013-era5.nc