Merge branch 'master' into split-plotting-rules
This commit is contained in:
Fabian Neumann
GitHub
commit
b43088ea49
@ -6,3 +6,4 @@
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5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0
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92080b1cd2ca5f123158571481722767b99c2b27
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13769f90af4500948b0376d57df4cceaa13e78b5
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9865a970893d9e515786f33c629b14f71645bf1e
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@ -28,6 +28,26 @@ Upcoming Release
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* Cluster residential and services heat buses by default. Can be disabled with ``cluster_heat_buses: false``.
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* Bugfix: Do not reduce district heat share when building population-weighted
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energy statistics. Previously the district heating share was being multiplied
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by the population weighting, reducing the DH share with multiple nodes.
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* Move building of daily heat profile to its own rule
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:mod:`build_hourly_heat_demand` from :mod:`prepare_sector_network`.
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* In :mod:`build_energy_totals`, district heating shares are now reported in a
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separate file.
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* Move calculation of district heating share to its own rule
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:mod:`build_district_heat_share`.
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* Move building of distribution of existing heating to own rule
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:mod:`build_existing_heating_distribution`. This makes the distribution of
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existing heating to urban/rural, residential/services and spatially more
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transparent.
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* Bugfix: Correctly read out number of solver threads from configuration file.
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* Air-sourced heat pumps can now also be built in rural areas. Previously, only
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ground-sourced heat pumps were considered for this category.
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@ -20,6 +20,12 @@ Rule ``add_existing_baseyear``
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.. automodule:: add_existing_baseyear
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Rule ``build_existing_heating_distribution``
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==============================================================================
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.. automodule:: build_existing_heating_distribution
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Rule ``build_ammonia_production``
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==============================================================================
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@ -60,10 +66,20 @@ Rule ``build_gas_network``
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.. automodule:: build_gas_network
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Rule ``build_heat_demand``
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Rule ``build_daily_heat_demand``
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==============================================================================
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.. automodule:: build_heat_demand
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.. automodule:: build_daily_heat_demand
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Rule ``build_hourly_heat_demand``
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==============================================================================
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.. automodule:: build_hourly_heat_demand
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Rule ``build_district_heat_share``
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==============================================================================
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.. automodule:: build_district_heat_share
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Rule ``build_industrial_distribution_key``
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==============================================================================
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@ -123,7 +123,7 @@ rule cluster_gas_network:
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"../scripts/cluster_gas_network.py"
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rule build_heat_demands:
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rule build_daily_heat_demand:
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params:
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snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
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input:
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@ -131,18 +131,39 @@ rule build_heat_demands:
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regions_onshore=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
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cutout="cutouts/" + CDIR + config["atlite"]["default_cutout"] + ".nc",
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output:
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heat_demand=RESOURCES + "heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
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heat_demand=RESOURCES + "daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
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resources:
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mem_mb=20000,
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threads: 8
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log:
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LOGS + "build_heat_demands_{scope}_{simpl}_{clusters}.loc",
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LOGS + "build_daily_heat_demand_{scope}_{simpl}_{clusters}.loc",
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benchmark:
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BENCHMARKS + "build_heat_demands/{scope}_s{simpl}_{clusters}"
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BENCHMARKS + "build_daily_heat_demand/{scope}_s{simpl}_{clusters}"
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_heat_demand.py"
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"../scripts/build_daily_heat_demand.py"
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rule build_hourly_heat_demand:
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params:
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snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
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input:
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heat_profile="data/heat_load_profile_BDEW.csv",
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heat_demand=RESOURCES + "daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
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output:
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heat_demand=RESOURCES + "hourly_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
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resources:
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mem_mb=2000,
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threads: 8
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log:
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LOGS + "build_hourly_heat_demand_{scope}_{simpl}_{clusters}.loc",
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benchmark:
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BENCHMARKS + "build_hourly_heat_demand/{scope}_s{simpl}_{clusters}"
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_hourly_heat_demand.py"
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rule build_temperature_profiles:
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@ -235,6 +256,7 @@ rule build_energy_totals:
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energy_name=RESOURCES + "energy_totals.csv",
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co2_name=RESOURCES + "co2_totals.csv",
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transport_name=RESOURCES + "transport_data.csv",
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district_heat_share=RESOURCES + "district_heat_share.csv",
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threads: 16
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resources:
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mem_mb=10000,
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@ -688,6 +710,26 @@ rule build_transport_demand:
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"../scripts/build_transport_demand.py"
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rule build_district_heat_share:
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params:
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sector=config["sector"],
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input:
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district_heat_share=RESOURCES + "district_heat_share.csv",
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clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
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output:
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district_heat_share=RESOURCES
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+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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threads: 1
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resources:
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mem_mb=1000,
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log:
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LOGS + "build_district_heat_share_s{simpl}_{clusters}_{planning_horizons}.log",
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_district_heat_share.py"
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rule prepare_sector_network:
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params:
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co2_budget=config["co2_budget"],
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@ -727,7 +769,6 @@ rule prepare_sector_network:
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if config["foresight"] == "overnight"
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else RESOURCES
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+ "biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv",
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heat_profile="data/heat_load_profile_BDEW.csv",
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costs="data/costs_{}.csv".format(config["costs"]["year"])
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if config["foresight"] == "overnight"
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else "data/costs_{planning_horizons}.csv",
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@ -740,9 +781,10 @@ rule prepare_sector_network:
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simplified_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}.csv",
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industrial_demand=RESOURCES
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+ "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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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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hourly_heat_demand_total=RESOURCES
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+ "hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc",
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district_heat_share=RESOURCES
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+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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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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@ -1,8 +1,42 @@
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# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
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# SPDX-FileCopyrightText: : 2023-4 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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rule build_existing_heating_distribution:
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params:
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baseyear=config["scenario"]["planning_horizons"][0],
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sector=config["sector"],
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existing_capacities=config["existing_capacities"],
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input:
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existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
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clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
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clustered_pop_energy_layout=RESOURCES
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+ "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
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district_heat_share=RESOURCES
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+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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output:
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existing_heating_distribution=RESOURCES
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+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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wildcard_constraints:
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planning_horizons=config["scenario"]["planning_horizons"][0], #only applies to baseyear
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threads: 1
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resources:
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mem_mb=2000,
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log:
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LOGS
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+ "build_existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.log",
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benchmark:
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(
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BENCHMARKS
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+ "build_existing_heating_distribution/elec_s{simpl}_{clusters}_{planning_horizons}"
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)
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_existing_heating_distribution.py"
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rule add_existing_baseyear:
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params:
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baseyear=config["scenario"]["planning_horizons"][0],
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@ -19,7 +53,8 @@ rule add_existing_baseyear:
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costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
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cop_soil_total=RESOURCES + "cop_soil_total_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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existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
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existing_heating_distribution=RESOURCES
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+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
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existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
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existing_offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv",
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@ -17,6 +17,8 @@ rule add_existing_baseyear:
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costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
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cop_soil_total=RESOURCES + "cop_soil_total_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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existing_heating_distribution=RESOURCES
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+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
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existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
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existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
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existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
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@ -409,97 +409,18 @@ def add_heating_capacities_installed_before_baseyear(
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# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
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# TODO start from original file
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# retrieve existing heating capacities
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techs = [
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"gas boiler",
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"oil boiler",
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"resistive heater",
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"air heat pump",
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"ground heat pump",
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]
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df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
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# data for Albania, Montenegro and Macedonia not included in database
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df.loc["Albania"] = np.nan
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df.loc["Montenegro"] = np.nan
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df.loc["Macedonia"] = np.nan
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df.fillna(0.0, inplace=True)
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# convert GW to MW
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df *= 1e3
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df.index = cc.convert(df.index, to="iso2")
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# coal and oil boilers are assimilated to oil boilers
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df["oil boiler"] = df["oil boiler"] + df["coal boiler"]
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df.drop(["coal boiler"], axis=1, inplace=True)
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# distribute technologies to nodes by population
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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nodal_df = df.loc[pop_layout.ct]
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nodal_df.index = pop_layout.index
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nodal_df = nodal_df.multiply(pop_layout.fraction, axis=0)
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# split existing capacities between residential and services
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# proportional to energy demand
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p_set_sum = n.loads_t.p_set.sum()
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ratio_residential = pd.Series(
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[
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(
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p_set_sum[f"{node} residential rural heat"]
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/ (
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p_set_sum[f"{node} residential rural heat"]
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+ p_set_sum[f"{node} services rural heat"]
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)
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)
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# if rural heating demand for one of the nodes doesn't exist,
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# then columns were dropped before and heating demand share should be 0.0
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if all(
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f"{node} {service} rural heat" in p_set_sum.index
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for service in ["residential", "services"]
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)
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else 0.0
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for node in nodal_df.index
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],
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index=nodal_df.index,
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existing_heating = pd.read_csv(
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snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
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)
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for tech in techs:
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nodal_df["residential " + tech] = nodal_df[tech] * ratio_residential
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nodal_df["services " + tech] = nodal_df[tech] * (1 - ratio_residential)
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techs = existing_heating.columns.get_level_values(1).unique()
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names = [
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"residential rural",
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"services rural",
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"residential urban decentral",
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"services urban decentral",
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"urban central",
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]
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nodes = {}
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p_nom = {}
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for name in names:
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for name in existing_heating.columns.get_level_values(0).unique():
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name_type = "central" if name == "urban central" else "decentral"
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nodes[name] = pd.Index(
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[
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n.buses.at[index, "location"]
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for index in n.buses.index[
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n.buses.index.str.contains(name)
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& n.buses.index.str.contains("heat")
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]
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]
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)
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heat_pump_type = "air" if "urban" in name else "ground"
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heat_type = "residential" if "residential" in name else "services"
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if name == "urban central":
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p_nom[name] = nodal_df["air heat pump"][nodes[name]]
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else:
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p_nom[name] = nodal_df[f"{heat_type} {heat_pump_type} heat pump"][
|
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nodes[name]
|
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]
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nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
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heat_pump_type = "air" if "urban" in name else "ground"
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# Add heat pumps
|
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costs_name = f"decentral {heat_pump_type}-sourced heat pump"
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@ -507,7 +428,7 @@ def add_heating_capacities_installed_before_baseyear(
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cop = {"air": ashp_cop, "ground": gshp_cop}
|
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|
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if time_dep_hp_cop:
|
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efficiency = cop[heat_pump_type][nodes[name]]
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efficiency = cop[heat_pump_type][nodes]
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else:
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efficiency = costs.at[costs_name, "efficiency"]
|
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|
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@ -520,27 +441,28 @@ def add_heating_capacities_installed_before_baseyear(
|
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|
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n.madd(
|
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"Link",
|
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nodes[name],
|
||||
nodes,
|
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suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
|
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bus0=nodes[name],
|
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bus1=nodes[name] + " " + name + " heat",
|
||||
bus0=nodes,
|
||||
bus1=nodes + " " + name + " heat",
|
||||
carrier=f"{name} {heat_pump_type} heat pump",
|
||||
efficiency=efficiency,
|
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capital_cost=costs.at[costs_name, "efficiency"]
|
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* costs.at[costs_name, "fixed"],
|
||||
p_nom=p_nom[name] * ratio / costs.at[costs_name, "efficiency"],
|
||||
p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")]
|
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* ratio
|
||||
/ costs.at[costs_name, "efficiency"],
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[costs_name, "lifetime"],
|
||||
)
|
||||
|
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# add resistive heater, gas boilers and oil boilers
|
||||
# (50% capacities to rural buses, 50% to urban buses)
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name],
|
||||
nodes,
|
||||
suffix=f" {name} resistive heater-{grouping_year}",
|
||||
bus0=nodes[name],
|
||||
bus1=nodes[name] + " " + name + " heat",
|
||||
bus0=nodes,
|
||||
bus1=nodes + " " + name + " heat",
|
||||
carrier=name + " resistive heater",
|
||||
efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
|
||||
capital_cost=(
|
||||
@ -548,21 +470,20 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
* costs.at[f"{name_type} resistive heater", "fixed"]
|
||||
),
|
||||
p_nom=(
|
||||
0.5
|
||||
* nodal_df[f"{heat_type} resistive heater"][nodes[name]]
|
||||
existing_heating.loc[nodes, (name, "resistive heater")]
|
||||
* ratio
|
||||
/ costs.at[f"{name_type} resistive heater", "efficiency"]
|
||||
),
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[costs_name, "lifetime"],
|
||||
lifetime=costs.at[f"{name_type} resistive heater", "lifetime"],
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name],
|
||||
nodes,
|
||||
suffix=f" {name} gas boiler-{grouping_year}",
|
||||
bus0=spatial.gas.nodes,
|
||||
bus1=nodes[name] + " " + name + " heat",
|
||||
bus1=nodes + " " + name + " heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=name + " gas boiler",
|
||||
efficiency=costs.at[f"{name_type} gas boiler", "efficiency"],
|
||||
@ -572,8 +493,7 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
* costs.at[f"{name_type} gas boiler", "fixed"]
|
||||
),
|
||||
p_nom=(
|
||||
0.5
|
||||
* nodal_df[f"{heat_type} gas boiler"][nodes[name]]
|
||||
existing_heating.loc[nodes, (name, "gas boiler")]
|
||||
* ratio
|
||||
/ costs.at[f"{name_type} gas boiler", "efficiency"]
|
||||
),
|
||||
@ -583,20 +503,21 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name],
|
||||
nodes,
|
||||
suffix=f" {name} oil boiler-{grouping_year}",
|
||||
bus0=spatial.oil.nodes,
|
||||
bus1=nodes[name] + " " + name + " heat",
|
||||
bus1=nodes + " " + name + " heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=name + " oil boiler",
|
||||
efficiency=costs.at["decentral oil boiler", "efficiency"],
|
||||
efficiency2=costs.at["oil", "CO2 intensity"],
|
||||
capital_cost=costs.at["decentral oil boiler", "efficiency"]
|
||||
* costs.at["decentral oil boiler", "fixed"],
|
||||
p_nom=0.5
|
||||
* nodal_df[f"{heat_type} oil boiler"][nodes[name]]
|
||||
* ratio
|
||||
/ costs.at["decentral oil boiler", "efficiency"],
|
||||
p_nom=(
|
||||
existing_heating.loc[nodes, (name, "oil boiler")]
|
||||
* ratio
|
||||
/ costs.at["decentral oil boiler", "efficiency"]
|
||||
),
|
||||
build_year=int(grouping_year),
|
||||
lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
|
||||
)
|
||||
@ -624,6 +545,8 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
|
||||
# drop assets which are at the end of their lifetime
|
||||
links_i = n.links[(n.links.build_year + n.links.lifetime <= baseyear)].index
|
||||
logger.info("Removing following links because at end of their lifetime:")
|
||||
logger.info(links_i)
|
||||
n.mremove("Link", links_i)
|
||||
|
||||
|
||||
|
||||
@ -18,7 +18,8 @@ if __name__ == "__main__":
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_heat_demands",
|
||||
"build_daily_heat_demands",
|
||||
scope="total",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
)
|
||||
81
scripts/build_district_heat_share.py
Normal file
81
scripts/build_district_heat_share.py
Normal file
@ -0,0 +1,81 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
"""
|
||||
Build district heat shares at each node, depending on investment year.
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
import pandas as pd
|
||||
from prepare_sector_network import get
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_district_heat_share",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
planning_horizons="2050",
|
||||
)
|
||||
|
||||
investment_year = int(snakemake.wildcards.planning_horizons[-4:])
|
||||
|
||||
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
|
||||
|
||||
district_heat_share = pd.read_csv(snakemake.input.district_heat_share, index_col=0)[
|
||||
"district heat share"
|
||||
]
|
||||
|
||||
# make ct-based share nodal
|
||||
district_heat_share = district_heat_share.loc[pop_layout.ct]
|
||||
district_heat_share.index = pop_layout.index
|
||||
|
||||
# total urban population per country
|
||||
ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
|
||||
|
||||
# distribution of urban population within a country
|
||||
pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
|
||||
|
||||
# fraction of node that is urban
|
||||
urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
|
||||
|
||||
# maximum potential of urban demand covered by district heating
|
||||
central_fraction = snakemake.config["sector"]["district_heating"]["potential"]
|
||||
|
||||
# district heating share at each node
|
||||
dist_fraction_node = (
|
||||
district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
|
||||
)
|
||||
|
||||
# if district heating share larger than urban fraction -> set urban
|
||||
# fraction to district heating share
|
||||
urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1)
|
||||
|
||||
# difference of max potential and today's share of district heating
|
||||
diff = (urban_fraction * central_fraction) - dist_fraction_node
|
||||
progress = get(
|
||||
snakemake.config["sector"]["district_heating"]["progress"], investment_year
|
||||
)
|
||||
dist_fraction_node += diff * progress
|
||||
logger.info(
|
||||
f"Increase district heating share by a progress factor of {progress:.2%} "
|
||||
f"resulting in new average share of {dist_fraction_node.mean():.2%}"
|
||||
)
|
||||
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"original district heat share": district_heat_share,
|
||||
"district fraction of node": dist_fraction_node,
|
||||
"urban fraction": urban_fraction,
|
||||
},
|
||||
dtype=float,
|
||||
)
|
||||
|
||||
df.to_csv(snakemake.output.district_heat_share)
|
||||
@ -391,13 +391,6 @@ def build_idees(countries, year):
|
||||
# convert TWh/100km to kWh/km
|
||||
totals.loc["passenger car efficiency"] *= 10
|
||||
|
||||
# district heating share
|
||||
district_heat = totals.loc[
|
||||
["derived heat residential", "derived heat services"]
|
||||
].sum()
|
||||
total_heat = totals.loc[["thermal uses residential", "thermal uses services"]].sum()
|
||||
totals.loc["district heat share"] = district_heat.div(total_heat)
|
||||
|
||||
return totals.T
|
||||
|
||||
|
||||
@ -572,16 +565,36 @@ def build_energy_totals(countries, eurostat, swiss, idees):
|
||||
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
|
||||
df.loc["BA", missing] = ratio * df.loc["RS", missing]
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def build_district_heat_share(countries, idees):
|
||||
# district heating share
|
||||
district_heat = idees[["derived heat residential", "derived heat services"]].sum(
|
||||
axis=1
|
||||
)
|
||||
total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
|
||||
axis=1
|
||||
)
|
||||
|
||||
district_heat_share = district_heat / total_heat
|
||||
|
||||
district_heat_share = district_heat_share.reindex(countries)
|
||||
|
||||
# Missing district heating share
|
||||
dh_share = pd.read_csv(
|
||||
snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]
|
||||
dh_share = (
|
||||
pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1])
|
||||
.div(100)
|
||||
.squeeze()
|
||||
)
|
||||
# make conservative assumption and take minimum from both data sets
|
||||
df["district heat share"] = pd.concat(
|
||||
[df["district heat share"], dh_share.reindex(index=df.index) / 100], axis=1
|
||||
district_heat_share = pd.concat(
|
||||
[district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1
|
||||
).min(axis=1)
|
||||
|
||||
return df
|
||||
district_heat_share.name = "district heat share"
|
||||
|
||||
return district_heat_share
|
||||
|
||||
|
||||
def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
|
||||
@ -750,6 +763,9 @@ if __name__ == "__main__":
|
||||
energy = build_energy_totals(countries, eurostat, swiss, idees)
|
||||
energy.to_csv(snakemake.output.energy_name)
|
||||
|
||||
district_heat_share = build_district_heat_share(countries, idees)
|
||||
district_heat_share.to_csv(snakemake.output.district_heat_share)
|
||||
|
||||
base_year_emissions = params["base_emissions_year"]
|
||||
emissions_scope = snakemake.params.energy["emissions"]
|
||||
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
|
||||
|
||||
122
scripts/build_existing_heating_distribution.py
Normal file
122
scripts/build_existing_heating_distribution.py
Normal file
@ -0,0 +1,122 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
"""
|
||||
Builds table of existing heat generation capacities for initial planning
|
||||
horizon.
|
||||
"""
|
||||
import country_converter as coco
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
cc = coco.CountryConverter()
|
||||
|
||||
|
||||
def build_existing_heating():
|
||||
# retrieve existing heating capacities
|
||||
|
||||
existing_heating = pd.read_csv(
|
||||
snakemake.input.existing_heating, index_col=0, header=0
|
||||
)
|
||||
|
||||
# data for Albania, Montenegro and Macedonia not included in database
|
||||
existing_heating.loc["Albania"] = np.nan
|
||||
existing_heating.loc["Montenegro"] = np.nan
|
||||
existing_heating.loc["Macedonia"] = np.nan
|
||||
|
||||
existing_heating.fillna(0.0, inplace=True)
|
||||
|
||||
# convert GW to MW
|
||||
existing_heating *= 1e3
|
||||
|
||||
existing_heating.index = cc.convert(existing_heating.index, to="iso2")
|
||||
|
||||
# coal and oil boilers are assimilated to oil boilers
|
||||
existing_heating["oil boiler"] = (
|
||||
existing_heating["oil boiler"] + existing_heating["coal boiler"]
|
||||
)
|
||||
existing_heating.drop(["coal boiler"], axis=1, inplace=True)
|
||||
|
||||
# distribute technologies to nodes by population
|
||||
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
|
||||
|
||||
nodal_heating = existing_heating.loc[pop_layout.ct]
|
||||
nodal_heating.index = pop_layout.index
|
||||
nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)
|
||||
|
||||
district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
|
||||
dist_fraction = district_heat_info["district fraction of node"]
|
||||
urban_fraction = district_heat_info["urban fraction"]
|
||||
|
||||
energy_layout = pd.read_csv(
|
||||
snakemake.input.clustered_pop_energy_layout, index_col=0
|
||||
)
|
||||
|
||||
uses = ["space", "water"]
|
||||
sectors = ["residential", "services"]
|
||||
|
||||
nodal_sectoral_totals = pd.DataFrame(dtype=float)
|
||||
|
||||
for sector in sectors:
|
||||
nodal_sectoral_totals[sector] = energy_layout[
|
||||
[f"total {sector} {use}" for use in uses]
|
||||
].sum(axis=1)
|
||||
|
||||
nodal_sectoral_fraction = nodal_sectoral_totals.div(
|
||||
nodal_sectoral_totals.sum(axis=1), axis=0
|
||||
)
|
||||
|
||||
nodal_heat_name_fraction = pd.DataFrame(dtype=float)
|
||||
|
||||
nodal_heat_name_fraction["urban central"] = dist_fraction
|
||||
|
||||
for sector in sectors:
|
||||
nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[
|
||||
sector
|
||||
] * (1 - urban_fraction)
|
||||
nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[
|
||||
sector
|
||||
] * (urban_fraction - dist_fraction)
|
||||
|
||||
nodal_heat_name_tech = pd.concat(
|
||||
{
|
||||
name: nodal_heating.multiply(nodal_heat_name_fraction[name], axis=0)
|
||||
for name in nodal_heat_name_fraction.columns
|
||||
},
|
||||
axis=1,
|
||||
names=["heat name", "technology"],
|
||||
)
|
||||
|
||||
# move all ground HPs to rural, all air to urban
|
||||
|
||||
for sector in sectors:
|
||||
nodal_heat_name_tech[(f"{sector} rural", "ground heat pump")] += (
|
||||
nodal_heat_name_tech[("urban central", "ground heat pump")]
|
||||
* nodal_sectoral_fraction[sector]
|
||||
+ nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")]
|
||||
)
|
||||
nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] = 0.0
|
||||
|
||||
nodal_heat_name_tech[
|
||||
(f"{sector} urban decentral", "air heat pump")
|
||||
] += nodal_heat_name_tech[(f"{sector} rural", "air heat pump")]
|
||||
nodal_heat_name_tech[(f"{sector} rural", "air heat pump")] = 0.0
|
||||
|
||||
nodal_heat_name_tech[("urban central", "ground heat pump")] = 0.0
|
||||
|
||||
nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_existing_heating_distribution",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
planning_horizons=2050,
|
||||
)
|
||||
|
||||
build_existing_heating()
|
||||
63
scripts/build_hourly_heat_demand.py
Normal file
63
scripts/build_hourly_heat_demand.py
Normal file
@ -0,0 +1,63 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
"""
|
||||
Build hourly heat demand time series from daily ones.
|
||||
"""
|
||||
|
||||
from itertools import product
|
||||
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
from _helpers import generate_periodic_profiles
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_hourly_heat_demands",
|
||||
scope="total",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
)
|
||||
|
||||
snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
|
||||
|
||||
daily_space_heat_demand = (
|
||||
xr.open_dataarray(snakemake.input.heat_demand)
|
||||
.to_pandas()
|
||||
.reindex(index=snapshots, method="ffill")
|
||||
)
|
||||
|
||||
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
|
||||
|
||||
sectors = ["residential", "services"]
|
||||
uses = ["water", "space"]
|
||||
|
||||
heat_demand = {}
|
||||
for sector, use in product(sectors, uses):
|
||||
weekday = list(intraday_profiles[f"{sector} {use} weekday"])
|
||||
weekend = list(intraday_profiles[f"{sector} {use} weekend"])
|
||||
weekly_profile = weekday * 5 + weekend * 2
|
||||
intraday_year_profile = generate_periodic_profiles(
|
||||
daily_space_heat_demand.index.tz_localize("UTC"),
|
||||
nodes=daily_space_heat_demand.columns,
|
||||
weekly_profile=weekly_profile,
|
||||
)
|
||||
|
||||
if use == "space":
|
||||
heat_demand[f"{sector} {use}"] = (
|
||||
daily_space_heat_demand * intraday_year_profile
|
||||
)
|
||||
else:
|
||||
heat_demand[f"{sector} {use}"] = intraday_year_profile
|
||||
|
||||
heat_demand = pd.concat(heat_demand, axis=1, names=["sector use", "node"])
|
||||
|
||||
heat_demand.index.name = "snapshots"
|
||||
|
||||
ds = heat_demand.stack().to_xarray()
|
||||
|
||||
ds.to_netcdf(snakemake.output.heat_demand)
|
||||
@ -18,7 +18,7 @@ import numpy as np
|
||||
import pandas as pd
|
||||
import pypsa
|
||||
import xarray as xr
|
||||
from _helpers import generate_periodic_profiles, update_config_with_sector_opts
|
||||
from _helpers import update_config_with_sector_opts
|
||||
from add_electricity import calculate_annuity, sanitize_carriers
|
||||
from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
|
||||
from networkx.algorithms import complement
|
||||
@ -1639,40 +1639,25 @@ def add_land_transport(n, costs):
|
||||
|
||||
|
||||
def build_heat_demand(n):
|
||||
# copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile
|
||||
daily_space_heat_demand = (
|
||||
xr.open_dataarray(snakemake.input.heat_demand_total)
|
||||
.to_pandas()
|
||||
.reindex(index=n.snapshots, method="ffill")
|
||||
heat_demand_shape = (
|
||||
xr.open_dataset(snakemake.input.hourly_heat_demand_total)
|
||||
.to_dataframe()
|
||||
.unstack(level=1)
|
||||
)
|
||||
|
||||
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
|
||||
|
||||
sectors = ["residential", "services"]
|
||||
uses = ["water", "space"]
|
||||
|
||||
heat_demand = {}
|
||||
electric_heat_supply = {}
|
||||
for sector, use in product(sectors, uses):
|
||||
weekday = list(intraday_profiles[f"{sector} {use} weekday"])
|
||||
weekend = list(intraday_profiles[f"{sector} {use} weekend"])
|
||||
weekly_profile = weekday * 5 + weekend * 2
|
||||
intraday_year_profile = generate_periodic_profiles(
|
||||
daily_space_heat_demand.index.tz_localize("UTC"),
|
||||
nodes=daily_space_heat_demand.columns,
|
||||
weekly_profile=weekly_profile,
|
||||
)
|
||||
name = f"{sector} {use}"
|
||||
|
||||
if use == "space":
|
||||
heat_demand_shape = daily_space_heat_demand * intraday_year_profile
|
||||
else:
|
||||
heat_demand_shape = intraday_year_profile
|
||||
|
||||
heat_demand[f"{sector} {use}"] = (
|
||||
heat_demand_shape / heat_demand_shape.sum()
|
||||
heat_demand[name] = (
|
||||
heat_demand_shape[name] / heat_demand_shape[name].sum()
|
||||
).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
|
||||
electric_heat_supply[f"{sector} {use}"] = (
|
||||
heat_demand_shape / heat_demand_shape.sum()
|
||||
electric_heat_supply[name] = (
|
||||
heat_demand_shape[name] / heat_demand_shape[name].sum()
|
||||
).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
|
||||
|
||||
heat_demand = pd.concat(heat_demand, axis=1)
|
||||
@ -1695,7 +1680,9 @@ def add_heat(n, costs):
|
||||
|
||||
heat_demand = build_heat_demand(n)
|
||||
|
||||
nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
|
||||
district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
|
||||
dist_fraction = district_heat_info["district fraction of node"]
|
||||
urban_fraction = district_heat_info["urban fraction"]
|
||||
|
||||
# NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)
|
||||
|
||||
@ -1735,12 +1722,17 @@ def add_heat(n, costs):
|
||||
for name in heat_systems:
|
||||
name_type = "central" if name == "urban central" else "decentral"
|
||||
|
||||
if name == "urban central":
|
||||
nodes = dist_fraction.index[dist_fraction > 0]
|
||||
else:
|
||||
nodes = pop_layout.index
|
||||
|
||||
n.add("Carrier", name + " heat")
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
nodes[name] + f" {name} heat",
|
||||
location=nodes[name],
|
||||
nodes + f" {name} heat",
|
||||
location=nodes,
|
||||
carrier=name + " heat",
|
||||
unit="MWh_th",
|
||||
)
|
||||
@ -1748,9 +1740,9 @@ def add_heat(n, costs):
|
||||
if name == "urban central" and options.get("central_heat_vent"):
|
||||
n.madd(
|
||||
"Generator",
|
||||
nodes[name] + f" {name} heat vent",
|
||||
bus=nodes[name] + f" {name} heat",
|
||||
location=nodes[name],
|
||||
nodes + f" {name} heat vent",
|
||||
bus=nodes + f" {name} heat",
|
||||
location=nodes,
|
||||
carrier=name + " heat vent",
|
||||
p_nom_extendable=True,
|
||||
p_max_pu=0,
|
||||
@ -1763,11 +1755,11 @@ def add_heat(n, costs):
|
||||
for sector in sectors:
|
||||
# heat demand weighting
|
||||
if "rural" in name:
|
||||
factor = 1 - urban_fraction[nodes[name]]
|
||||
factor = 1 - urban_fraction[nodes]
|
||||
elif "urban central" in name:
|
||||
factor = dist_fraction[nodes[name]]
|
||||
factor = dist_fraction[nodes]
|
||||
elif "urban decentral" in name:
|
||||
factor = urban_fraction[nodes[name]] - dist_fraction[nodes[name]]
|
||||
factor = urban_fraction[nodes] - dist_fraction[nodes]
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f" {name} not in " f"heat systems: {heat_systems}"
|
||||
@ -1778,7 +1770,7 @@ def add_heat(n, costs):
|
||||
heat_demand[[sector + " water", sector + " space"]]
|
||||
.T.groupby(level=1)
|
||||
.sum()
|
||||
.T[nodes[name]]
|
||||
.T[nodes]
|
||||
.multiply(factor)
|
||||
)
|
||||
|
||||
@ -1786,7 +1778,7 @@ def add_heat(n, costs):
|
||||
heat_load = (
|
||||
heat_demand.T.groupby(level=1)
|
||||
.sum()
|
||||
.T[nodes[name]]
|
||||
.T[nodes]
|
||||
.multiply(
|
||||
factor * (1 + options["district_heating"]["district_heating_loss"])
|
||||
)
|
||||
@ -1794,9 +1786,9 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Load",
|
||||
nodes[name],
|
||||
nodes,
|
||||
suffix=f" {name} heat",
|
||||
bus=nodes[name] + f" {name} heat",
|
||||
bus=nodes + f" {name} heat",
|
||||
carrier=name + " heat",
|
||||
p_set=heat_load,
|
||||
)
|
||||
@ -1808,17 +1800,17 @@ def add_heat(n, costs):
|
||||
for heat_pump_type in heat_pump_types:
|
||||
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
|
||||
efficiency = (
|
||||
cop[heat_pump_type][nodes[name]]
|
||||
cop[heat_pump_type][nodes]
|
||||
if options["time_dep_hp_cop"]
|
||||
else costs.at[costs_name, "efficiency"]
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name],
|
||||
nodes,
|
||||
suffix=f" {name} {heat_pump_type} heat pump",
|
||||
bus0=nodes[name],
|
||||
bus1=nodes[name] + f" {name} heat",
|
||||
bus0=nodes,
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=f"{name} {heat_pump_type} heat pump",
|
||||
efficiency=efficiency,
|
||||
capital_cost=costs.at[costs_name, "efficiency"]
|
||||
@ -1832,17 +1824,17 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
nodes[name] + f" {name} water tanks",
|
||||
location=nodes[name],
|
||||
nodes + f" {name} water tanks",
|
||||
location=nodes,
|
||||
carrier=name + " water tanks",
|
||||
unit="MWh_th",
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + f" {name} water tanks charger",
|
||||
bus0=nodes[name] + f" {name} heat",
|
||||
bus1=nodes[name] + f" {name} water tanks",
|
||||
nodes + f" {name} water tanks charger",
|
||||
bus0=nodes + f" {name} heat",
|
||||
bus1=nodes + f" {name} water tanks",
|
||||
efficiency=costs.at["water tank charger", "efficiency"],
|
||||
carrier=name + " water tanks charger",
|
||||
p_nom_extendable=True,
|
||||
@ -1850,9 +1842,9 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + f" {name} water tanks discharger",
|
||||
bus0=nodes[name] + f" {name} water tanks",
|
||||
bus1=nodes[name] + f" {name} heat",
|
||||
nodes + f" {name} water tanks discharger",
|
||||
bus0=nodes + f" {name} water tanks",
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=name + " water tanks discharger",
|
||||
efficiency=costs.at["water tank discharger", "efficiency"],
|
||||
p_nom_extendable=True,
|
||||
@ -1871,8 +1863,8 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Store",
|
||||
nodes[name] + f" {name} water tanks",
|
||||
bus=nodes[name] + f" {name} water tanks",
|
||||
nodes + f" {name} water tanks",
|
||||
bus=nodes + f" {name} water tanks",
|
||||
e_cyclic=True,
|
||||
e_nom_extendable=True,
|
||||
carrier=name + " water tanks",
|
||||
@ -1886,9 +1878,9 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + f" {name} resistive heater",
|
||||
bus0=nodes[name],
|
||||
bus1=nodes[name] + f" {name} heat",
|
||||
nodes + f" {name} resistive heater",
|
||||
bus0=nodes,
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=name + " resistive heater",
|
||||
efficiency=costs.at[key, "efficiency"],
|
||||
capital_cost=costs.at[key, "efficiency"] * costs.at[key, "fixed"],
|
||||
@ -1901,10 +1893,10 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + f" {name} gas boiler",
|
||||
nodes + f" {name} gas boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values,
|
||||
bus1=nodes[name] + f" {name} heat",
|
||||
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes + f" {name} heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=name + " gas boiler",
|
||||
efficiency=costs.at[key, "efficiency"],
|
||||
@ -1918,13 +1910,13 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Generator",
|
||||
nodes[name],
|
||||
nodes,
|
||||
suffix=f" {name} solar thermal collector",
|
||||
bus=nodes[name] + f" {name} heat",
|
||||
bus=nodes + f" {name} heat",
|
||||
carrier=name + " solar thermal",
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at[name_type + " solar thermal", "fixed"],
|
||||
p_max_pu=solar_thermal[nodes[name]],
|
||||
p_max_pu=solar_thermal[nodes],
|
||||
lifetime=costs.at[name_type + " solar thermal", "lifetime"],
|
||||
)
|
||||
|
||||
@ -1932,10 +1924,10 @@ def add_heat(n, costs):
|
||||
# add gas CHP; biomass CHP is added in biomass section
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + " urban central gas CHP",
|
||||
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values,
|
||||
bus1=nodes[name],
|
||||
bus2=nodes[name] + " urban central heat",
|
||||
nodes + " urban central gas CHP",
|
||||
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes,
|
||||
bus2=nodes + " urban central heat",
|
||||
bus3="co2 atmosphere",
|
||||
carrier="urban central gas CHP",
|
||||
p_nom_extendable=True,
|
||||
@ -1951,12 +1943,12 @@ def add_heat(n, costs):
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + " urban central gas CHP CC",
|
||||
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values,
|
||||
bus1=nodes[name],
|
||||
bus2=nodes[name] + " urban central heat",
|
||||
nodes + " urban central gas CHP CC",
|
||||
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes,
|
||||
bus2=nodes + " urban central heat",
|
||||
bus3="co2 atmosphere",
|
||||
bus4=spatial.co2.df.loc[nodes[name], "nodes"].values,
|
||||
bus4=spatial.co2.df.loc[nodes, "nodes"].values,
|
||||
carrier="urban central gas CHP CC",
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at["central gas CHP", "fixed"]
|
||||
@ -1988,11 +1980,11 @@ def add_heat(n, costs):
|
||||
if options["chp"] and options["micro_chp"] and name != "urban central":
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes[name] + f" {name} micro gas CHP",
|
||||
nodes + f" {name} micro gas CHP",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values,
|
||||
bus1=nodes[name],
|
||||
bus2=nodes[name] + f" {name} heat",
|
||||
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes,
|
||||
bus2=nodes + f" {name} heat",
|
||||
bus3="co2 atmosphere",
|
||||
carrier=name + " micro gas CHP",
|
||||
efficiency=costs.at["micro CHP", "efficiency"],
|
||||
@ -2123,50 +2115,6 @@ def add_heat(n, costs):
|
||||
)
|
||||
|
||||
|
||||
def create_nodes_for_heat_sector():
|
||||
# TODO pop_layout
|
||||
|
||||
# rural are areas with low heating density and individual heating
|
||||
# urban are areas with high heating density
|
||||
# urban can be split into district heating (central) and individual heating (decentral)
|
||||
|
||||
ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
|
||||
# distribution of urban population within a country
|
||||
pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
|
||||
|
||||
sectors = ["residential", "services"]
|
||||
|
||||
nodes = {}
|
||||
urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
|
||||
|
||||
for sector in sectors:
|
||||
nodes[sector + " rural"] = pop_layout.index
|
||||
nodes[sector + " urban decentral"] = pop_layout.index
|
||||
|
||||
district_heat_share = pop_weighted_energy_totals["district heat share"]
|
||||
|
||||
# maximum potential of urban demand covered by district heating
|
||||
central_fraction = options["district_heating"]["potential"]
|
||||
# district heating share at each node
|
||||
dist_fraction_node = (
|
||||
district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
|
||||
)
|
||||
nodes["urban central"] = dist_fraction_node.index
|
||||
# if district heating share larger than urban fraction -> set urban
|
||||
# fraction to district heating share
|
||||
urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1)
|
||||
# difference of max potential and today's share of district heating
|
||||
diff = (urban_fraction * central_fraction) - dist_fraction_node
|
||||
progress = get(options["district_heating"]["progress"], investment_year)
|
||||
dist_fraction_node += diff * progress
|
||||
logger.info(
|
||||
f"Increase district heating share by a progress factor of {progress:.2%} "
|
||||
f"resulting in new average share of {dist_fraction_node.mean():.2%}"
|
||||
)
|
||||
|
||||
return nodes, dist_fraction_node, urban_fraction
|
||||
|
||||
|
||||
def add_biomass(n, costs):
|
||||
logger.info("Add biomass")
|
||||
|
||||
@ -2384,7 +2332,7 @@ def add_biomass(n, costs):
|
||||
|
||||
if options["biomass_boiler"]:
|
||||
# TODO: Add surcharge for pellets
|
||||
nodes_heat = create_nodes_for_heat_sector()[0]
|
||||
nodes = pop_layout.index
|
||||
for name in [
|
||||
"residential rural",
|
||||
"services rural",
|
||||
@ -2393,10 +2341,10 @@ def add_biomass(n, costs):
|
||||
]:
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes_heat[name] + f" {name} biomass boiler",
|
||||
nodes + f" {name} biomass boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.biomass.df.loc[nodes_heat[name], "nodes"].values,
|
||||
bus1=nodes_heat[name] + f" {name} heat",
|
||||
bus0=spatial.biomass.df.loc[nodes, "nodes"].values,
|
||||
bus1=nodes + f" {name} heat",
|
||||
carrier=name + " biomass boiler",
|
||||
efficiency=costs.at["biomass boiler", "efficiency"],
|
||||
capital_cost=costs.at["biomass boiler", "efficiency"]
|
||||
@ -2839,7 +2787,7 @@ def add_industry(n, costs):
|
||||
)
|
||||
|
||||
if options["oil_boilers"]:
|
||||
nodes_heat = create_nodes_for_heat_sector()[0]
|
||||
nodes = pop_layout.index
|
||||
|
||||
for name in [
|
||||
"residential rural",
|
||||
@ -2849,10 +2797,10 @@ def add_industry(n, costs):
|
||||
]:
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes_heat[name] + f" {name} oil boiler",
|
||||
nodes + f" {name} oil boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.oil.nodes,
|
||||
bus1=nodes_heat[name] + f" {name} heat",
|
||||
bus1=nodes + f" {name} heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=f"{name} oil boiler",
|
||||
efficiency=costs.at["decentral oil boiler", "efficiency"],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user