Update central heating temperatures based on Euroheat data and AGFW-Hauptbericht

config/config.default.yaml

@@ -451,21 +451,18 @@ sector:
     district_heating_loss: 0.15
     supply_temperature_approximation:
       max_forward_temperature:
-        default: 90
-        DK: 70
-        SE: 70
-        NO: 70
+        FR: 110
+        DK: 75
+        DE: 109
+        CZ: 130
+        FI: 115
+        PL: 130
+        SE: 102
+        IT: 90
       min_forward_temperature:
-        default: 68
-        DK: 54
-        SE: 54
-        NO: 54
+        DE: 82
       return_temperature:
-        default: 50
-        DK: 40
-        SE: 40
-        NO: 40
-        FI: 40
+        DE: 58
       lower_threshold_ambient_temperature: 0
       upper_threshold_ambient_temperature: 10
       rolling_window_ambient_temperature: 72

doc/release_notes.rst

@@ -9,8 +9,8 @@ Release Notes
 ##########################################
 
 .. Upcoming Release
-.. ================
 
+* Updated district heating supply temperatures based on `Euroheat's DHC Market Outlook 2024<https://api.euroheat.org/uploads/Market_Outlook_2024_beeecd62d4.pdf>`__ and `AGFW-Hauptbericht 2022 <https://www.agfw.de/securedl/sdl-eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE3MjU2MjI2MTUsImV4cCI6MTcyNTcxMjYxNSwidXNlciI6MCwiZ3JvdXBzIjpbMCwtMV0sImZpbGUiOiJmaWxlYWRtaW4vdXNlcl91cGxvYWQvWmFobGVuX3VuZF9TdGF0aXN0aWtlbi9IYXVwdGJlcmljaHRfMjAyMi9BR0ZXX0hhdXB0YmVyaWNodF8yMDIyLnBkZiIsInBhZ2UiOjQzNn0.Bhma3PKg9uJnC57Ixi2p9STW5-II9VXPTDXS544M208/AGFW_Hauptbericht_2022.pdf>`__. `min_forward_temperature` and `return_temperature` (not given by Euroheat) are extrapolated based on German values.
 * Made the overdimensioning factor for heating systems specific for central/decentral heating, defaults to no overdimensionining for central heating and no changes to decentral heating compared to previous version.
 
 * bugfix: The carrier of stores was silently overwritten by their bus_carrier as a side effect when building the co2 constraints

scripts/build_central_heating_temperature_profiles/central_heating_temperature_approximator.py

@@ -58,6 +58,15 @@ def __init__(
         rolling_window_ambient_temperature : int
             Rolling window size for averaging ambient temperature.
         """
+
+        if any(max_forward_temperature < min_forward_temperature):
+            raise ValueError(
+                "max_forward_temperature must be greater than min_forward_temperature"
+            )
+        if any(min_forward_temperature < fixed_return_temperature):
+            raise ValueError(
+                "min_forward_temperature must be greater than fixed_return_temperature"
+            )
         self._ambient_temperature = ambient_temperature
         self.max_forward_temperature = max_forward_temperature
         self.min_forward_temperature = min_forward_temperature

scripts/build_central_heating_temperature_profiles/run.py

@@ -9,8 +9,8 @@
 temperature is assumed and vice versa for temperatures above 10C. Between these
 threshold levels, forward temperatures are linearly interpolated.
 
-By default, temperature levels are increased for non-Scandinavian countries.
-The default ratios between min. and max. forward temperatures is based on AGFW-Hauptbericht 2022.
+By default, `max_forward_temperature` from Euroheat DHC Market Outlook 2024 is used; `min_forward_temperature` and `return_temperature` for Germany is used from AGFW-Hauptbericht 2022.
+`min_forward_temperature` and `return_temperature` for other countries are extrapolated based on the ratio between `max_forward_temperature` and `min_forward_temperature` and `return_temperature` for those countries not missing (by default only Germany).
 
 Relevant Settings
 -----------------
@@ -47,26 +47,68 @@
 )
 
 
+def extrapolate_missing_supply_temperatures_by_country(
+    extrapolate_from: dict, extrapolate_to: dict
+) -> xr.DataArray:
+    """
+    Extrapolates missing supply temperatures by country.
+
+    Parameters:
+        extrapolate_from (dict): A dictionary containing supply temperatures to extrapolate from. Should contain all countries.
+        extrapolate_to (dict): A dictionary containing supply temperatures to extrapolate to. Where `country` is present, average ratio between `extrapolate_to[country]` and `extrapolate_from[country]` is applied to all countries for which `country` is not present in `extrapolate_from.keys()`  to infer ratio for extrapolation.
+
+    Returns:
+        xr.DataArray: A DataArray containing the extrapolated supply temperatures.
+    """
+
+    if not all([key in extrapolate_from.keys() for key in extrapolate_to.keys()]):
+        raise ValueError(
+            "Not all countries in extrapolate_to are present in extrapolate_from."
+        )
+    # average ratio between extrapolate_from and extrapolate_to for those countries that are in both dictionaries
+    extrapolation_ratio = np.mean(
+        [extrapolate_to[key] / extrapolate_from[key] for key in extrapolate_to.keys()]
+    )
+
+    # apply extrapolation ratio to all keys missing in extrapolate_to
+    return {
+        key: (
+            extrapolate_to[key]
+            if key in extrapolate_to.keys()
+            else extrapolate_from[key] * extrapolation_ratio
+        )
+        for key in extrapolate_from.keys()
+    }
+
+
 def get_country_from_node_name(node_name: str) -> str:
+    """
+    Extracts the country code from a given node name.
+
+    Parameters:
+        node_name (str): The name of the node.
+
+    Returns:
+        str: The country code extracted from the node name.
+    """
     return node_name[:2]
 
 
 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.
 
+    Missing values are replaced by the mean of all values.
+
     Parameters:
     ----------
     supply_temperature_by_country : dictionary
-        Dictionary with temperatures as values and country keys as keys. One key must be named "default"
+        Dictionary with temperatures as values and country keys as keys.
     regions_onshore : pd.Index
         Names of onshore regions
-    snapshots : pd.DatetimeIndex
-        Time stamps
 
     Returns:
     -------
@@ -75,20 +117,16 @@ def map_temperature_dict_to_onshore_regions(
     """
     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
+            (
+                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 np.mean(list(supply_temperature_by_country.values()))
+            )
+            for node_name in regions_onshore.values
         ],
-        dims=["time", "name"],
-        coords={"time": snapshots, "name": regions_onshore},
+        dims=["name"],
+        coords={"name": regions_onshore},
     )
 
 
@@ -104,28 +142,35 @@ def map_temperature_dict_to_onshore_regions(
 
     set_scenario_config(snakemake)
 
+    max_forward_temperature = snakemake.params.max_forward_temperature_central_heating
+    min_forward_temperature = extrapolate_missing_supply_temperatures_by_country(
+        extrapolate_from=max_forward_temperature,
+        extrapolate_to=snakemake.params.min_forward_temperature_central_heating,
+    )
+    return_temperature = extrapolate_missing_supply_temperatures_by_country(
+        extrapolate_from=max_forward_temperature,
+        extrapolate_to=snakemake.params.return_temperature_central_heating,
+    )
+
     # 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)
     max_forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
         map_temperature_dict_to_onshore_regions(
-            supply_temperature_by_country=snakemake.params.max_forward_temperature_central_heating,
+            supply_temperature_by_country=max_forward_temperature,
             regions_onshore=regions_onshore,
-            snapshots=snapshots,
         )
     )
     min_forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
         map_temperature_dict_to_onshore_regions(
-            supply_temperature_by_country=snakemake.params.min_forward_temperature_central_heating,
+            supply_temperature_by_country=min_forward_temperature,
             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,
+            supply_temperature_by_country=return_temperature,
             regions_onshore=regions_onshore,
-            snapshots=snapshots,
         )
     )