Merge pull request #1180 from PyPSA/country-specific-dh-forward-tempe…

…ratures

Add option of country-specific district heating supply temperatures

config/config.default.yaml

@@ -448,8 +448,19 @@ sector:
       2045: 0.8
       2050: 1.0
     district_heating_loss: 0.15
-    forward_temperature: 90 #C
-    return_temperature: 50 #C
+    # check these numbers!
+    forward_temperature:
+      default: 90
+      DK: 70
+      SE: 70
+      NO: 70
+      FI: 70
+    return_temperature:
+      default: 50
+      DK: 40
+      SE: 40
+      NO: 40
+      FI: 40
     heat_source_cooling: 6 #K
     heat_pump_cop_approximation:
       refrigerant: ammonia

doc/configtables/sector.csv

@@ -9,8 +9,8 @@ district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-
 -- potential,--,float,maximum fraction of urban demand which can be supplied by district heating
 -- progress,--,Dictionary with planning horizons as keys., Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating
 -- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses
--- forward_temperature,°C,float,Forward temperature in district heating
--- return_temperature,°C,float,Return temperature in district heating. Must be lower than forward temperature
+-- forward_temperature,°C,Dictionary with country codes as keys. One key must be 'default'.,Forward temperature in district heating
+-- return_temperature,°C,Dictionary with country codes as keys. One key must be 'default'.,Return temperature in district heating. Must be lower than forward temperature
 -- heat_source_cooling,K,float,Cooling of heat source for heat pumps
 -- heat_pump_cop_approximation,,,
 -- -- refrigerant,--,"{ammonia, isobutane}",Heat pump refrigerant assumed for COP approximation

doc/release_notes.rst

@@ -20,6 +20,8 @@ Upcoming Release
 
 * Update JRC-IDEES-2015 to `JRC-IDEES-2021 <https://publications.jrc.ec.europa.eu/repository/handle/JRC137809>`__. The reference year is changed from 2015 to 2019.
 
+* Added option to use country-specific district heating forward and return temperatures. Defaults to lower temperatures in Scandinavia.
+
 * Added unsustainable biomass potentials for solid, gaseous, and liquid biomass. The potentials can be phased-out and/or
   substituted by the phase-in of sustainable biomass types using the config parameters
   ``biomass: share_unsustainable_use_retained`` and ``biomass: share_sustainable_potential_available``.

rules/build_sector.smk

@@ -233,9 +233,11 @@ rule build_cop_profiles:
             "sector", "district_heating", "heat_pump_cop_approximation"
         ),
         heat_pump_sources=config_provider("sector", "heat_pump_sources"),
+        snapshots=config_provider("snapshots"),
     input:
         temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
         temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
+        regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
     output:
         cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
     resources:

scripts/add_existing_baseyear.py

@@ -22,12 +22,11 @@
     update_config_from_wildcards,
 )
 from add_electricity import sanitize_carriers
+from definitions.heat_sector import HeatSector
+from definitions.heat_system import HeatSystem
+from definitions.heat_system_type import HeatSystemType
 from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
 
-from scripts.definitions.heat_sector import HeatSector
-from scripts.definitions.heat_system import HeatSystem
-from scripts.definitions.heat_system_type import HeatSystemType
-
 logger = logging.getLogger(__name__)
 cc = coco.CountryConverter()
 idx = pd.IndexSlice

scripts/build_cop_profiles/run.py

@@ -2,9 +2,47 @@
 # SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
 #
 # SPDX-License-Identifier: MIT
+"""
+Approximate heat pump coefficient-of-performance (COP) profiles for different
+heat sources and systems.
+
+For central heating, this is based on Jensen et al. (2018) (c.f. `CentralHeatingCopApproximator <CentralHeatingCopApproximator.py>`_) and for decentral heating, the approximation is based on Staffell et al. (2012) (c.f. `DecentralHeatingCopApproximator <DecentralHeatingCopApproximator.py>`_).
+
+Relevant Settings
+-----------------
+
+.. code:: yaml
+    sector:
+        heat_pump_sink_T_decentral_heating:
+        district_heating:
+            forward_temperature:
+            return_temperature:
+            heat_source_cooling:
+            heat_pump_cop_approximation:
+                refrigerant:
+                heat_exchanger_pinch_point_temperature_difference
+                isentropic_compressor_efficiency:
+                heat_loss:
+            heat_pump_sources:
+                urban central:
+                urban decentral:
+                rural:
+    snapshots:
+
+Inputs
+------
+- `resources/<run_name>/regions_onshore.geojson`: Onshore regions
+- `resources/<run_name>/temp_soil_total`: Ground temperature
+- `resources/<run_name>/temp_air_total`: Air temperature
+
+Outputs
+-------
+- `resources/<run_name>/cop_profiles.nc`: Heat pump coefficient-of-performance (COP) profiles
+"""
 
 import sys
 
+import geopandas as gpd
 import numpy as np
 import pandas as pd
 import xarray as xr
@@ -14,13 +52,54 @@
 
 from scripts.definitions.heat_system_type import HeatSystemType
 
-sys.path.append("..")
+
+def map_temperature_dict_to_onshore_regions(
+    supply_temperature_by_country: dict,
+    regions_onshore: pd.Index,
+    snapshots: pd.DatetimeIndex,
+) -> xr.DataArray:
+    """
+    Map dictionary of temperatures to onshore regions.
+
+    Parameters:
+    ----------
+    supply_temperature_by_country : dictionary
+        Dictionary with temperatures as values and country keys as keys. One key must be named "default"
+    regions_onshore : pd.Index
+        Names of onshore regions
+    snapshots : pd.DatetimeIndex
+        Time stamps
+
+    Returns:
+    -------
+    xr.DataArray
+        The dictionary values mapped to onshore regions with onshore regions as coordinates.
+    """
+    return xr.DataArray(
+        [
+            [
+                (
+                    supply_temperature_by_country[get_country_from_node_name(node_name)]
+                    if get_country_from_node_name(node_name)
+                    in supply_temperature_by_country.keys()
+                    else supply_temperature_by_country["default"]
+                )
+                for node_name in regions_onshore.values
+            ]
+            # pass both nodes and snapshots as dimensions to preserve correct data structure
+            for _ in snapshots
+        ],
+        dims=["time", "name"],
+        coords={"time": snapshots, "name": regions_onshore},
+    )
 
 
 def get_cop(
     heat_system_type: str,
     heat_source: str,
     source_inlet_temperature_celsius: xr.DataArray,
+    forward_temperature_by_node_and_time: xr.DataArray = None,
+    return_temperature_by_node_and_time: xr.DataArray = None,
 ) -> xr.DataArray:
     """
     Calculate the coefficient of performance (COP) for a heating system.
@@ -41,8 +120,8 @@ def get_cop(
     """
     if HeatSystemType(heat_system_type).is_central:
         return CentralHeatingCopApproximator(
-            forward_temperature_celsius=snakemake.params.forward_temperature_central_heating,
-            return_temperature_celsius=snakemake.params.return_temperature_central_heating,
+            forward_temperature_celsius=forward_temperature_by_node_and_time,
+            return_temperature_celsius=return_temperature_by_node_and_time,
             source_inlet_temperature_celsius=source_inlet_temperature_celsius,
             source_outlet_temperature_celsius=source_inlet_temperature_celsius
             - snakemake.params.heat_source_cooling_central_heating,
@@ -56,6 +135,10 @@ def get_cop(
         ).approximate_cop()
 
 
+def get_country_from_node_name(node_name: str) -> str:
+    return node_name[:2]
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -68,6 +151,23 @@ def get_cop(
 
     set_scenario_config(snakemake)
 
+    # map forward and return temperatures specified on country-level to onshore regions
+    regions_onshore = gpd.read_file(snakemake.input.regions_onshore)["name"]
+    snapshots = pd.date_range(freq="h", **snakemake.params.snapshots)
+    forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
+        map_temperature_dict_to_onshore_regions(
+            supply_temperature_by_country=snakemake.params.forward_temperature_central_heating,
+            regions_onshore=regions_onshore,
+            snapshots=snapshots,
+        )
+    )
+    return_temperature_central_heating_by_node_and_time: xr.DataArray = (
+        map_temperature_dict_to_onshore_regions(
+            supply_temperature_by_country=snakemake.params.return_temperature_central_heating,
+            regions_onshore=regions_onshore,
+            snapshots=snapshots,
+        )
+    )
     cop_all_system_types = []
     for heat_system_type, heat_sources in snakemake.params.heat_pump_sources.items():
         cop_this_system_type = []
@@ -79,6 +179,8 @@ def get_cop(
                 heat_system_type=heat_system_type,
                 heat_source=heat_source,
                 source_inlet_temperature_celsius=source_inlet_temperature_celsius,
+                forward_temperature_by_node_and_time=forward_temperature_central_heating_by_node_and_time,
+                return_temperature_by_node_and_time=return_temperature_central_heating_by_node_and_time,
             )
             cop_this_system_type.append(cop_da)
         cop_all_system_types.append(

scripts/prepare_sector_network.py

@@ -30,17 +30,16 @@
     build_eurostat_co2,
 )
 from build_transport_demand import transport_degree_factor
+from definitions.heat_sector import HeatSector
+from definitions.heat_system import HeatSystem
+from definitions.heat_system_type import HeatSystemType
 from networkx.algorithms import complement
 from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
 from prepare_network import maybe_adjust_costs_and_potentials
 from pypsa.geo import haversine_pts
 from pypsa.io import import_components_from_dataframe
 from scipy.stats import beta
 
-from scripts.definitions.heat_sector import HeatSector
-from scripts.definitions.heat_system import HeatSystem
-from scripts.definitions.heat_system_type import HeatSystemType
-
 spatial = SimpleNamespace()
 logger = logging.getLogger(__name__)
 
@@ -2831,8 +2830,11 @@ def add_biomass(n, costs):
         )
 
     if options["bioH2"]:
-        name = (pd.Index(spatial.biomass.nodes) + " "
-                + pd.Index(spatial.h2.nodes.str.replace(" H2", "")))
+        name = (
+            pd.Index(spatial.biomass.nodes)
+            + " "
+            + pd.Index(spatial.h2.nodes.str.replace(" H2", ""))
+        )
         n.madd(
             "Link",
             name,
@@ -2842,16 +2844,22 @@ def add_biomass(n, costs):
             bus2=spatial.co2.nodes,
             bus3="co2 atmosphere",
             carrier="solid biomass to hydrogen",
-            efficiency=costs.at['solid biomass to hydrogen', 'efficiency'],
-            efficiency2=costs.at['solid biomass', 'CO2 intensity'] * options["cc_fraction"],
-            efficiency3=-costs.at['solid biomass', 'CO2 intensity'] * options["cc_fraction"],
+            efficiency=costs.at["solid biomass to hydrogen", "efficiency"],
+            efficiency2=costs.at["solid biomass", "CO2 intensity"]
+            * options["cc_fraction"],
+            efficiency3=-costs.at["solid biomass", "CO2 intensity"]
+            * options["cc_fraction"],
             p_nom_extendable=True,
-            capital_cost=costs.at['solid biomass to hydrogen', 'fixed'] * costs.at['solid biomass to hydrogen', 'efficiency']
-                         + costs.at['biomass CHP capture', 'fixed'] * costs.at['solid biomass', 'CO2 intensity'],
-            overnight_cost=costs.at['solid biomass to hydrogen', 'investment'] * costs.at['solid biomass to hydrogen', 'efficiency']
-                         + costs.at['biomass CHP capture', 'investment'] * costs.at['solid biomass', 'CO2 intensity'],
-            marginal_cost=0.,
-            )
+            capital_cost=costs.at["solid biomass to hydrogen", "fixed"]
+            * costs.at["solid biomass to hydrogen", "efficiency"]
+            + costs.at["biomass CHP capture", "fixed"]
+            * costs.at["solid biomass", "CO2 intensity"],
+            overnight_cost=costs.at["solid biomass to hydrogen", "investment"]
+            * costs.at["solid biomass to hydrogen", "efficiency"]
+            + costs.at["biomass CHP capture", "investment"]
+            * costs.at["solid biomass", "CO2 intensity"],
+            marginal_cost=0.0,
+        )
 
 
 def add_industry(n, costs):