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Merge branch 'master' into split-plotting-rules

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.git-blame-ignore-revs
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@ -6,3 +6,4 @@
5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0 5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0
92080b1cd2ca5f123158571481722767b99c2b27 92080b1cd2ca5f123158571481722767b99c2b27
13769f90af4500948b0376d57df4cceaa13e78b5 13769f90af4500948b0376d57df4cceaa13e78b5
9865a970893d9e515786f33c629b14f71645bf1e

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doc/release_notes.rst
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@ -28,6 +28,26 @@ Upcoming Release
* Cluster residential and services heat buses by default. Can be disabled with ``cluster_heat_buses: false``. * Cluster residential and services heat buses by default. Can be disabled with ``cluster_heat_buses: false``.
* Bugfix: Do not reduce district heat share when building population-weighted
energy statistics. Previously the district heating share was being multiplied
by the population weighting, reducing the DH share with multiple nodes.
* Move building of daily heat profile to its own rule
:mod:`build_hourly_heat_demand` from :mod:`prepare_sector_network`.
* In :mod:`build_energy_totals`, district heating shares are now reported in a
separate file.
* Move calculation of district heating share to its own rule
:mod:`build_district_heat_share`.
* Move building of distribution of existing heating to own rule
:mod:`build_existing_heating_distribution`. This makes the distribution of
existing heating to urban/rural, residential/services and spatially more
transparent.
* Bugfix: Correctly read out number of solver threads from configuration file.
* Air-sourced heat pumps can now also be built in rural areas. Previously, only * Air-sourced heat pumps can now also be built in rural areas. Previously, only
ground-sourced heat pumps were considered for this category. ground-sourced heat pumps were considered for this category.

20
doc/sector.rst
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@ -20,6 +20,12 @@ Rule ``add_existing_baseyear``
.. automodule:: add_existing_baseyear .. automodule:: add_existing_baseyear
Rule ``build_existing_heating_distribution``
==============================================================================
.. automodule:: build_existing_heating_distribution
Rule ``build_ammonia_production`` Rule ``build_ammonia_production``
============================================================================== ==============================================================================
@ -60,10 +66,20 @@ Rule ``build_gas_network``
.. automodule:: build_gas_network .. automodule:: build_gas_network
Rule ``build_heat_demand`` Rule ``build_daily_heat_demand``
============================================================================== ==============================================================================
.. automodule:: build_heat_demand .. automodule:: build_daily_heat_demand
Rule ``build_hourly_heat_demand``
==============================================================================
.. automodule:: build_hourly_heat_demand
Rule ``build_district_heat_share``
==============================================================================
.. automodule:: build_district_heat_share
Rule ``build_industrial_distribution_key`` Rule ``build_industrial_distribution_key``
============================================================================== ==============================================================================

60
rules/build_sector.smk
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@ -123,7 +123,7 @@ rule cluster_gas_network:
"../scripts/cluster_gas_network.py" "../scripts/cluster_gas_network.py"
rule build_heat_demands: rule build_daily_heat_demand:
params: params:
snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]}, snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
input: input:
@ -131,18 +131,39 @@ rule build_heat_demands:
regions_onshore=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson", regions_onshore=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
cutout="cutouts/" + CDIR + config["atlite"]["default_cutout"] + ".nc", cutout="cutouts/" + CDIR + config["atlite"]["default_cutout"] + ".nc",
output: output:
heat_demand=RESOURCES + "heat_demand_{scope}_elec_s{simpl}_{clusters}.nc", heat_demand=RESOURCES + "daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
resources: resources:
mem_mb=20000, mem_mb=20000,
threads: 8 threads: 8
log: log:
LOGS + "build_heat_demands_{scope}_{simpl}_{clusters}.loc", LOGS + "build_daily_heat_demand_{scope}_{simpl}_{clusters}.loc",
benchmark: benchmark:
BENCHMARKS + "build_heat_demands/{scope}_s{simpl}_{clusters}" BENCHMARKS + "build_daily_heat_demand/{scope}_s{simpl}_{clusters}"
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
"../scripts/build_heat_demand.py" "../scripts/build_daily_heat_demand.py"
rule build_hourly_heat_demand:
params:
snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
input:
heat_profile="data/heat_load_profile_BDEW.csv",
heat_demand=RESOURCES + "daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
output:
heat_demand=RESOURCES + "hourly_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc",
resources:
mem_mb=2000,
threads: 8
log:
LOGS + "build_hourly_heat_demand_{scope}_{simpl}_{clusters}.loc",
benchmark:
BENCHMARKS + "build_hourly_heat_demand/{scope}_s{simpl}_{clusters}"
conda:
"../envs/environment.yaml"
script:
"../scripts/build_hourly_heat_demand.py"
rule build_temperature_profiles: rule build_temperature_profiles:
@ -235,6 +256,7 @@ rule build_energy_totals:
energy_name=RESOURCES + "energy_totals.csv", energy_name=RESOURCES + "energy_totals.csv",
co2_name=RESOURCES + "co2_totals.csv", co2_name=RESOURCES + "co2_totals.csv",
transport_name=RESOURCES + "transport_data.csv", transport_name=RESOURCES + "transport_data.csv",
district_heat_share=RESOURCES + "district_heat_share.csv",
threads: 16 threads: 16
resources: resources:
mem_mb=10000, mem_mb=10000,
@ -688,6 +710,26 @@ rule build_transport_demand:
"../scripts/build_transport_demand.py" "../scripts/build_transport_demand.py"
rule build_district_heat_share:
params:
sector=config["sector"],
input:
district_heat_share=RESOURCES + "district_heat_share.csv",
clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
output:
district_heat_share=RESOURCES
+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
threads: 1
resources:
mem_mb=1000,
log:
LOGS + "build_district_heat_share_s{simpl}_{clusters}_{planning_horizons}.log",
conda:
"../envs/environment.yaml"
script:
"../scripts/build_district_heat_share.py"
rule prepare_sector_network: rule prepare_sector_network:
params: params:
co2_budget=config["co2_budget"], co2_budget=config["co2_budget"],
@ -727,7 +769,6 @@ rule prepare_sector_network:
if config["foresight"] == "overnight" if config["foresight"] == "overnight"
else RESOURCES else RESOURCES
+ "biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv", + "biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv",
heat_profile="data/heat_load_profile_BDEW.csv",
costs="data/costs_{}.csv".format(config["costs"]["year"]) costs="data/costs_{}.csv".format(config["costs"]["year"])
if config["foresight"] == "overnight" if config["foresight"] == "overnight"
else "data/costs_{planning_horizons}.csv", else "data/costs_{planning_horizons}.csv",
@ -740,9 +781,10 @@ rule prepare_sector_network:
simplified_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}.csv", simplified_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}.csv",
industrial_demand=RESOURCES industrial_demand=RESOURCES
+ "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv", + "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
heat_demand_urban=RESOURCES + "heat_demand_urban_elec_s{simpl}_{clusters}.nc", hourly_heat_demand_total=RESOURCES
heat_demand_rural=RESOURCES + "heat_demand_rural_elec_s{simpl}_{clusters}.nc", + "hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc",
heat_demand_total=RESOURCES + "heat_demand_total_elec_s{simpl}_{clusters}.nc", district_heat_share=RESOURCES
+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
temp_soil_total=RESOURCES + "temp_soil_total_elec_s{simpl}_{clusters}.nc", temp_soil_total=RESOURCES + "temp_soil_total_elec_s{simpl}_{clusters}.nc",
temp_soil_rural=RESOURCES + "temp_soil_rural_elec_s{simpl}_{clusters}.nc", temp_soil_rural=RESOURCES + "temp_soil_rural_elec_s{simpl}_{clusters}.nc",
temp_soil_urban=RESOURCES + "temp_soil_urban_elec_s{simpl}_{clusters}.nc", temp_soil_urban=RESOURCES + "temp_soil_urban_elec_s{simpl}_{clusters}.nc",

39
rules/solve_myopic.smk
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@ -1,8 +1,42 @@
# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors # SPDX-FileCopyrightText: : 2023-4 The PyPSA-Eur Authors
# #
# SPDX-License-Identifier: MIT # SPDX-License-Identifier: MIT
rule build_existing_heating_distribution:
params:
baseyear=config["scenario"]["planning_horizons"][0],
sector=config["sector"],
existing_capacities=config["existing_capacities"],
input:
existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
clustered_pop_energy_layout=RESOURCES
+ "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
district_heat_share=RESOURCES
+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
output:
existing_heating_distribution=RESOURCES
+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
wildcard_constraints:
planning_horizons=config["scenario"]["planning_horizons"][0], #only applies to baseyear
threads: 1
resources:
mem_mb=2000,
log:
LOGS
+ "build_existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.log",
benchmark:
(
BENCHMARKS
+ "build_existing_heating_distribution/elec_s{simpl}_{clusters}_{planning_horizons}"
)
conda:
"../envs/environment.yaml"
script:
"../scripts/build_existing_heating_distribution.py"
rule add_existing_baseyear: rule add_existing_baseyear:
params: params:
baseyear=config["scenario"]["planning_horizons"][0], baseyear=config["scenario"]["planning_horizons"][0],
@ -19,7 +53,8 @@ rule add_existing_baseyear:
costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]), costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc", cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc", cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc",
existing_heating="data/existing_infrastructure/existing_heating_raw.csv", existing_heating_distribution=RESOURCES
+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv", existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv", existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",
existing_offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv", existing_offwind="data/existing_infrastructure/offwind_capacity_IRENA.csv",

2
rules/solve_perfect.smk
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@ -17,6 +17,8 @@ rule add_existing_baseyear:
costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]), costs="data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc", cop_soil_total=RESOURCES + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc", cop_air_total=RESOURCES + "cop_air_total_elec_s{simpl}_{clusters}.nc",
existing_heating_distribution=RESOURCES
+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
existing_heating="data/existing_infrastructure/existing_heating_raw.csv", existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv", existing_solar="data/existing_infrastructure/solar_capacity_IRENA.csv",
existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv", existing_onwind="data/existing_infrastructure/onwind_capacity_IRENA.csv",

139
scripts/add_existing_baseyear.py
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@ -409,97 +409,18 @@ def add_heating_capacities_installed_before_baseyear(
# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv". # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
# TODO start from original file # TODO start from original file
# retrieve existing heating capacities existing_heating = pd.read_csv(
techs = [ snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
"gas boiler",
"oil boiler",
"resistive heater",
"air heat pump",
"ground heat pump",
]
df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
# data for Albania, Montenegro and Macedonia not included in database
df.loc["Albania"] = np.nan
df.loc["Montenegro"] = np.nan
df.loc["Macedonia"] = np.nan
df.fillna(0.0, inplace=True)
# convert GW to MW
df *= 1e3
df.index = cc.convert(df.index, to="iso2")
# coal and oil boilers are assimilated to oil boilers
df["oil boiler"] = df["oil boiler"] + df["coal boiler"]
df.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_df = df.loc[pop_layout.ct]
nodal_df.index = pop_layout.index
nodal_df = nodal_df.multiply(pop_layout.fraction, axis=0)
# split existing capacities between residential and services
# proportional to energy demand
p_set_sum = n.loads_t.p_set.sum()
ratio_residential = pd.Series(
[
(
p_set_sum[f"{node} residential rural heat"]
/ (
p_set_sum[f"{node} residential rural heat"]
+ p_set_sum[f"{node} services rural heat"]
)
)
# if rural heating demand for one of the nodes doesn't exist,
# then columns were dropped before and heating demand share should be 0.0
if all(
f"{node} {service} rural heat" in p_set_sum.index
for service in ["residential", "services"]
)
else 0.0
for node in nodal_df.index
],
index=nodal_df.index,
) )
for tech in techs: techs = existing_heating.columns.get_level_values(1).unique()
nodal_df["residential " + tech] = nodal_df[tech] * ratio_residential
nodal_df["services " + tech] = nodal_df[tech] * (1 - ratio_residential)
names = [ for name in existing_heating.columns.get_level_values(0).unique():
"residential rural",
"services rural",
"residential urban decentral",
"services urban decentral",
"urban central",
]
nodes = {}
p_nom = {}
for name in names:
name_type = "central" if name == "urban central" else "decentral" name_type = "central" if name == "urban central" else "decentral"
nodes[name] = pd.Index(
[
n.buses.at[index, "location"]
for index in n.buses.index[
n.buses.index.str.contains(name)
& n.buses.index.str.contains("heat")
]
]
)
heat_pump_type = "air" if "urban" in name else "ground"
heat_type = "residential" if "residential" in name else "services"
if name == "urban central": nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
p_nom[name] = nodal_df["air heat pump"][nodes[name]]
else: heat_pump_type = "air" if "urban" in name else "ground"
p_nom[name] = nodal_df[f"{heat_type} {heat_pump_type} heat pump"][
nodes[name]
]
# Add heat pumps # Add heat pumps
costs_name = f"decentral {heat_pump_type}-sourced heat pump" costs_name = f"decentral {heat_pump_type}-sourced heat pump"
@ -507,7 +428,7 @@ def add_heating_capacities_installed_before_baseyear(
cop = {"air": ashp_cop, "ground": gshp_cop} cop = {"air": ashp_cop, "ground": gshp_cop}
if time_dep_hp_cop: if time_dep_hp_cop:
efficiency = cop[heat_pump_type][nodes[name]] efficiency = cop[heat_pump_type][nodes]
else: else:
efficiency = costs.at[costs_name, "efficiency"] efficiency = costs.at[costs_name, "efficiency"]
@ -520,27 +441,28 @@ def add_heating_capacities_installed_before_baseyear(
n.madd( n.madd(
"Link", "Link",
nodes[name], nodes,
suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}", suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
bus0=nodes[name], bus0=nodes,
bus1=nodes[name] + " " + name + " heat", bus1=nodes + " " + name + " heat",
carrier=f"{name} {heat_pump_type} heat pump", carrier=f"{name} {heat_pump_type} heat pump",
efficiency=efficiency, efficiency=efficiency,
capital_cost=costs.at[costs_name, "efficiency"] capital_cost=costs.at[costs_name, "efficiency"]
* costs.at[costs_name, "fixed"], * 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")]
* ratio
/ costs.at[costs_name, "efficiency"],
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[costs_name, "lifetime"], lifetime=costs.at[costs_name, "lifetime"],
) )
# add resistive heater, gas boilers and oil boilers # add resistive heater, gas boilers and oil boilers
# (50% capacities to rural buses, 50% to urban buses)
n.madd( n.madd(
"Link", "Link",
nodes[name], nodes,
suffix=f" {name} resistive heater-{grouping_year}", suffix=f" {name} resistive heater-{grouping_year}",
bus0=nodes[name], bus0=nodes,
bus1=nodes[name] + " " + name + " heat", bus1=nodes + " " + name + " heat",
carrier=name + " resistive heater", carrier=name + " resistive heater",
efficiency=costs.at[f"{name_type} resistive heater", "efficiency"], efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
capital_cost=( capital_cost=(
@ -548,21 +470,20 @@ def add_heating_capacities_installed_before_baseyear(
* costs.at[f"{name_type} resistive heater", "fixed"] * costs.at[f"{name_type} resistive heater", "fixed"]
), ),
p_nom=( p_nom=(
0.5 existing_heating.loc[nodes, (name, "resistive heater")]
* nodal_df[f"{heat_type} resistive heater"][nodes[name]]
* ratio * ratio
/ costs.at[f"{name_type} resistive heater", "efficiency"] / costs.at[f"{name_type} resistive heater", "efficiency"]
), ),
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[costs_name, "lifetime"], lifetime=costs.at[f"{name_type} resistive heater", "lifetime"],
) )
n.madd( n.madd(
"Link", "Link",
nodes[name], nodes,
suffix=f" {name} gas boiler-{grouping_year}", suffix=f" {name} gas boiler-{grouping_year}",
bus0=spatial.gas.nodes, bus0=spatial.gas.nodes,
bus1=nodes[name] + " " + name + " heat", bus1=nodes + " " + name + " heat",
bus2="co2 atmosphere", bus2="co2 atmosphere",
carrier=name + " gas boiler", carrier=name + " gas boiler",
efficiency=costs.at[f"{name_type} gas boiler", "efficiency"], 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"] * costs.at[f"{name_type} gas boiler", "fixed"]
), ),
p_nom=( p_nom=(
0.5 existing_heating.loc[nodes, (name, "gas boiler")]
* nodal_df[f"{heat_type} gas boiler"][nodes[name]]
* ratio * ratio
/ costs.at[f"{name_type} gas boiler", "efficiency"] / costs.at[f"{name_type} gas boiler", "efficiency"]
), ),
@ -583,20 +503,21 @@ def add_heating_capacities_installed_before_baseyear(
n.madd( n.madd(
"Link", "Link",
nodes[name], nodes,
suffix=f" {name} oil boiler-{grouping_year}", suffix=f" {name} oil boiler-{grouping_year}",
bus0=spatial.oil.nodes, bus0=spatial.oil.nodes,
bus1=nodes[name] + " " + name + " heat", bus1=nodes + " " + name + " heat",
bus2="co2 atmosphere", bus2="co2 atmosphere",
carrier=name + " oil boiler", carrier=name + " oil boiler",
efficiency=costs.at["decentral oil boiler", "efficiency"], efficiency=costs.at["decentral oil boiler", "efficiency"],
efficiency2=costs.at["oil", "CO2 intensity"], efficiency2=costs.at["oil", "CO2 intensity"],
capital_cost=costs.at["decentral oil boiler", "efficiency"] capital_cost=costs.at["decentral oil boiler", "efficiency"]
* costs.at["decentral oil boiler", "fixed"], * costs.at["decentral oil boiler", "fixed"],
p_nom=0.5 p_nom=(
* nodal_df[f"{heat_type} oil boiler"][nodes[name]] existing_heating.loc[nodes, (name, "oil boiler")]
* ratio * ratio
/ costs.at["decentral oil boiler", "efficiency"], / costs.at["decentral oil boiler", "efficiency"]
),
build_year=int(grouping_year), build_year=int(grouping_year),
lifetime=costs.at[f"{name_type} gas boiler", "lifetime"], 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 # drop assets which are at the end of their lifetime
links_i = n.links[(n.links.build_year + n.links.lifetime <= baseyear)].index 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) n.mremove("Link", links_i)

3
scripts/build_heat_demand.py → scripts/build_daily_heat_demand.py
View File

@ -18,7 +18,8 @@ if __name__ == "__main__":
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake( snakemake = mock_snakemake(
"build_heat_demands", "build_daily_heat_demands",
scope="total",
simpl="", simpl="",
clusters=48, clusters=48,
) )

81
scripts/build_district_heat_share.py Normal file
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@ -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)

40
scripts/build_energy_totals.py
View File

@ -391,13 +391,6 @@ def build_idees(countries, year):
# convert TWh/100km to kWh/km # convert TWh/100km to kWh/km
totals.loc["passenger car efficiency"] *= 10 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 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"] ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
df.loc["BA", missing] = ratio * df.loc["RS", missing] 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 # Missing district heating share
dh_share = pd.read_csv( dh_share = (
snakemake.input.district_heat_share, index_col=0, usecols=[0, 1] 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 # make conservative assumption and take minimum from both data sets
df["district heat share"] = pd.concat( district_heat_share = pd.concat(
[df["district heat share"], dh_share.reindex(index=df.index) / 100], axis=1 [district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1
).min(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"): 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 = build_energy_totals(countries, eurostat, swiss, idees)
energy.to_csv(snakemake.output.energy_name) 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"] base_year_emissions = params["base_emissions_year"]
emissions_scope = snakemake.params.energy["emissions"] emissions_scope = snakemake.params.energy["emissions"]
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope) eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)

122
scripts/build_existing_heating_distribution.py Normal file
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@ -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
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@ -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)

198
scripts/prepare_sector_network.py
View File

@ -18,7 +18,7 @@ import numpy as np
import pandas as pd import pandas as pd
import pypsa import pypsa
import xarray as xr 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 add_electricity import calculate_annuity, sanitize_carriers
from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2 from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
from networkx.algorithms import complement from networkx.algorithms import complement
@ -1639,40 +1639,25 @@ def add_land_transport(n, costs):
def build_heat_demand(n): def build_heat_demand(n):
# copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile heat_demand_shape = (
daily_space_heat_demand = ( xr.open_dataset(snakemake.input.hourly_heat_demand_total)
xr.open_dataarray(snakemake.input.heat_demand_total) .to_dataframe()
.to_pandas() .unstack(level=1)
.reindex(index=n.snapshots, method="ffill")
) )
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
sectors = ["residential", "services"] sectors = ["residential", "services"]
uses = ["water", "space"] uses = ["water", "space"]
heat_demand = {} heat_demand = {}
electric_heat_supply = {} electric_heat_supply = {}
for sector, use in product(sectors, uses): for sector, use in product(sectors, uses):
weekday = list(intraday_profiles[f"{sector} {use} weekday"]) name = f"{sector} {use}"
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[name] = (
heat_demand_shape = daily_space_heat_demand * intraday_year_profile heat_demand_shape[name] / heat_demand_shape[name].sum()
else:
heat_demand_shape = intraday_year_profile
heat_demand[f"{sector} {use}"] = (
heat_demand_shape / heat_demand_shape.sum()
).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6 ).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
electric_heat_supply[f"{sector} {use}"] = ( electric_heat_supply[name] = (
heat_demand_shape / heat_demand_shape.sum() heat_demand_shape[name] / heat_demand_shape[name].sum()
).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6 ).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
heat_demand = pd.concat(heat_demand, axis=1) heat_demand = pd.concat(heat_demand, axis=1)
@ -1695,7 +1680,9 @@ def add_heat(n, costs):
heat_demand = build_heat_demand(n) 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) # 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: for name in heat_systems:
name_type = "central" if name == "urban central" else "decentral" 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.add("Carrier", name + " heat")
n.madd( n.madd(
"Bus", "Bus",
nodes[name] + f" {name} heat", nodes + f" {name} heat",
location=nodes[name], location=nodes,
carrier=name + " heat", carrier=name + " heat",
unit="MWh_th", unit="MWh_th",
) )
@ -1748,9 +1740,9 @@ def add_heat(n, costs):
if name == "urban central" and options.get("central_heat_vent"): if name == "urban central" and options.get("central_heat_vent"):
n.madd( n.madd(
"Generator", "Generator",
nodes[name] + f" {name} heat vent", nodes + f" {name} heat vent",
bus=nodes[name] + f" {name} heat", bus=nodes + f" {name} heat",
location=nodes[name], location=nodes,
carrier=name + " heat vent", carrier=name + " heat vent",
p_nom_extendable=True, p_nom_extendable=True,
p_max_pu=0, p_max_pu=0,
@ -1763,11 +1755,11 @@ def add_heat(n, costs):
for sector in sectors: for sector in sectors:
# heat demand weighting # heat demand weighting
if "rural" in name: if "rural" in name:
factor = 1 - urban_fraction[nodes[name]] factor = 1 - urban_fraction[nodes]
elif "urban central" in name: elif "urban central" in name:
factor = dist_fraction[nodes[name]] factor = dist_fraction[nodes]
elif "urban decentral" in name: elif "urban decentral" in name:
factor = urban_fraction[nodes[name]] - dist_fraction[nodes[name]] factor = urban_fraction[nodes] - dist_fraction[nodes]
else: else:
raise NotImplementedError( raise NotImplementedError(
f" {name} not in " f"heat systems: {heat_systems}" f" {name} not in " f"heat systems: {heat_systems}"
@ -1778,7 +1770,7 @@ def add_heat(n, costs):
heat_demand[[sector + " water", sector + " space"]] heat_demand[[sector + " water", sector + " space"]]
.T.groupby(level=1) .T.groupby(level=1)
.sum() .sum()
.T[nodes[name]] .T[nodes]
.multiply(factor) .multiply(factor)
) )
@ -1786,7 +1778,7 @@ def add_heat(n, costs):
heat_load = ( heat_load = (
heat_demand.T.groupby(level=1) heat_demand.T.groupby(level=1)
.sum() .sum()
.T[nodes[name]] .T[nodes]
.multiply( .multiply(
factor * (1 + options["district_heating"]["district_heating_loss"]) factor * (1 + options["district_heating"]["district_heating_loss"])
) )
@ -1794,9 +1786,9 @@ def add_heat(n, costs):
n.madd( n.madd(
"Load", "Load",
nodes[name], nodes,
suffix=f" {name} heat", suffix=f" {name} heat",
bus=nodes[name] + f" {name} heat", bus=nodes + f" {name} heat",
carrier=name + " heat", carrier=name + " heat",
p_set=heat_load, p_set=heat_load,
) )
@ -1808,17 +1800,17 @@ def add_heat(n, costs):
for heat_pump_type in heat_pump_types: for heat_pump_type in heat_pump_types:
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump" costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
efficiency = ( efficiency = (
cop[heat_pump_type][nodes[name]] cop[heat_pump_type][nodes]
if options["time_dep_hp_cop"] if options["time_dep_hp_cop"]
else costs.at[costs_name, "efficiency"] else costs.at[costs_name, "efficiency"]
) )
n.madd( n.madd(
"Link", "Link",
nodes[name], nodes,
suffix=f" {name} {heat_pump_type} heat pump", suffix=f" {name} {heat_pump_type} heat pump",
bus0=nodes[name], bus0=nodes,
bus1=nodes[name] + f" {name} heat", bus1=nodes + f" {name} heat",
carrier=f"{name} {heat_pump_type} heat pump", carrier=f"{name} {heat_pump_type} heat pump",
efficiency=efficiency, efficiency=efficiency,
capital_cost=costs.at[costs_name, "efficiency"] capital_cost=costs.at[costs_name, "efficiency"]
@ -1832,17 +1824,17 @@ def add_heat(n, costs):
n.madd( n.madd(
"Bus", "Bus",
nodes[name] + f" {name} water tanks", nodes + f" {name} water tanks",
location=nodes[name], location=nodes,
carrier=name + " water tanks", carrier=name + " water tanks",
unit="MWh_th", unit="MWh_th",
) )
n.madd( n.madd(
"Link", "Link",
nodes[name] + f" {name} water tanks charger", nodes + f" {name} water tanks charger",
bus0=nodes[name] + f" {name} heat", bus0=nodes + f" {name} heat",
bus1=nodes[name] + f" {name} water tanks", bus1=nodes + f" {name} water tanks",
efficiency=costs.at["water tank charger", "efficiency"], efficiency=costs.at["water tank charger", "efficiency"],
carrier=name + " water tanks charger", carrier=name + " water tanks charger",
p_nom_extendable=True, p_nom_extendable=True,
@ -1850,9 +1842,9 @@ def add_heat(n, costs):
n.madd( n.madd(
"Link", "Link",
nodes[name] + f" {name} water tanks discharger", nodes + f" {name} water tanks discharger",
bus0=nodes[name] + f" {name} water tanks", bus0=nodes + f" {name} water tanks",
bus1=nodes[name] + f" {name} heat", bus1=nodes + f" {name} heat",
carrier=name + " water tanks discharger", carrier=name + " water tanks discharger",
efficiency=costs.at["water tank discharger", "efficiency"], efficiency=costs.at["water tank discharger", "efficiency"],
p_nom_extendable=True, p_nom_extendable=True,
@ -1871,8 +1863,8 @@ def add_heat(n, costs):
n.madd( n.madd(
"Store", "Store",
nodes[name] + f" {name} water tanks", nodes + f" {name} water tanks",
bus=nodes[name] + f" {name} water tanks", bus=nodes + f" {name} water tanks",
e_cyclic=True, e_cyclic=True,
e_nom_extendable=True, e_nom_extendable=True,
carrier=name + " water tanks", carrier=name + " water tanks",
@ -1886,9 +1878,9 @@ def add_heat(n, costs):
n.madd( n.madd(
"Link", "Link",
nodes[name] + f" {name} resistive heater", nodes + f" {name} resistive heater",
bus0=nodes[name], bus0=nodes,
bus1=nodes[name] + f" {name} heat", bus1=nodes + f" {name} heat",
carrier=name + " resistive heater", carrier=name + " resistive heater",
efficiency=costs.at[key, "efficiency"], efficiency=costs.at[key, "efficiency"],
capital_cost=costs.at[key, "efficiency"] * costs.at[key, "fixed"], capital_cost=costs.at[key, "efficiency"] * costs.at[key, "fixed"],
@ -1901,10 +1893,10 @@ def add_heat(n, costs):
n.madd( n.madd(
"Link", "Link",
nodes[name] + f" {name} gas boiler", nodes + f" {name} gas boiler",
p_nom_extendable=True, p_nom_extendable=True,
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus0=spatial.gas.df.loc[nodes, "nodes"].values,
bus1=nodes[name] + f" {name} heat", bus1=nodes + f" {name} heat",
bus2="co2 atmosphere", bus2="co2 atmosphere",
carrier=name + " gas boiler", carrier=name + " gas boiler",
efficiency=costs.at[key, "efficiency"], efficiency=costs.at[key, "efficiency"],
@ -1918,13 +1910,13 @@ def add_heat(n, costs):
n.madd( n.madd(
"Generator", "Generator",
nodes[name], nodes,
suffix=f" {name} solar thermal collector", suffix=f" {name} solar thermal collector",
bus=nodes[name] + f" {name} heat", bus=nodes + f" {name} heat",
carrier=name + " solar thermal", carrier=name + " solar thermal",
p_nom_extendable=True, p_nom_extendable=True,
capital_cost=costs.at[name_type + " solar thermal", "fixed"], 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"], 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 # add gas CHP; biomass CHP is added in biomass section
n.madd( n.madd(
"Link", "Link",
nodes[name] + " urban central gas CHP", nodes + " urban central gas CHP",
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus0=spatial.gas.df.loc[nodes, "nodes"].values,
bus1=nodes[name], bus1=nodes,
bus2=nodes[name] + " urban central heat", bus2=nodes + " urban central heat",
bus3="co2 atmosphere", bus3="co2 atmosphere",
carrier="urban central gas CHP", carrier="urban central gas CHP",
p_nom_extendable=True, p_nom_extendable=True,
@ -1951,12 +1943,12 @@ def add_heat(n, costs):
n.madd( n.madd(
"Link", "Link",
nodes[name] + " urban central gas CHP CC", nodes + " urban central gas CHP CC",
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus0=spatial.gas.df.loc[nodes, "nodes"].values,
bus1=nodes[name], bus1=nodes,
bus2=nodes[name] + " urban central heat", bus2=nodes + " urban central heat",
bus3="co2 atmosphere", 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", carrier="urban central gas CHP CC",
p_nom_extendable=True, p_nom_extendable=True,
capital_cost=costs.at["central gas CHP", "fixed"] 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": if options["chp"] and options["micro_chp"] and name != "urban central":
n.madd( n.madd(
"Link", "Link",
nodes[name] + f" {name} micro gas CHP", nodes + f" {name} micro gas CHP",
p_nom_extendable=True, p_nom_extendable=True,
bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus0=spatial.gas.df.loc[nodes, "nodes"].values,
bus1=nodes[name], bus1=nodes,
bus2=nodes[name] + f" {name} heat", bus2=nodes + f" {name} heat",
bus3="co2 atmosphere", bus3="co2 atmosphere",
carrier=name + " micro gas CHP", carrier=name + " micro gas CHP",
efficiency=costs.at["micro CHP", "efficiency"], 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): def add_biomass(n, costs):
logger.info("Add biomass") logger.info("Add biomass")
@ -2384,7 +2332,7 @@ def add_biomass(n, costs):
if options["biomass_boiler"]: if options["biomass_boiler"]:
# TODO: Add surcharge for pellets # TODO: Add surcharge for pellets
nodes_heat = create_nodes_for_heat_sector()[0] nodes = pop_layout.index
for name in [ for name in [
"residential rural", "residential rural",
"services rural", "services rural",
@ -2393,10 +2341,10 @@ def add_biomass(n, costs):
]: ]:
n.madd( n.madd(
"Link", "Link",
nodes_heat[name] + f" {name} biomass boiler", nodes + f" {name} biomass boiler",
p_nom_extendable=True, p_nom_extendable=True,
bus0=spatial.biomass.df.loc[nodes_heat[name], "nodes"].values, bus0=spatial.biomass.df.loc[nodes, "nodes"].values,
bus1=nodes_heat[name] + f" {name} heat", bus1=nodes + f" {name} heat",
carrier=name + " biomass boiler", carrier=name + " biomass boiler",
efficiency=costs.at["biomass boiler", "efficiency"], efficiency=costs.at["biomass boiler", "efficiency"],
capital_cost=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"]: if options["oil_boilers"]:
nodes_heat = create_nodes_for_heat_sector()[0] nodes = pop_layout.index
for name in [ for name in [
"residential rural", "residential rural",
@ -2849,10 +2797,10 @@ def add_industry(n, costs):
]: ]:
n.madd( n.madd(
"Link", "Link",
nodes_heat[name] + f" {name} oil boiler", nodes + f" {name} oil boiler",
p_nom_extendable=True, p_nom_extendable=True,
bus0=spatial.oil.nodes, bus0=spatial.oil.nodes,
bus1=nodes_heat[name] + f" {name} heat", bus1=nodes + f" {name} heat",
bus2="co2 atmosphere", bus2="co2 atmosphere",
carrier=f"{name} oil boiler", carrier=f"{name} oil boiler",
efficiency=costs.at["decentral oil boiler", "efficiency"], efficiency=costs.at["decentral oil boiler", "efficiency"],