Merge pull request PyPSA#1290 from PyPSA/annual-temperature-reduction

Add option to reduce central heating supply temperatures annually (defaults to 1%/a)

config/config.default.yaml

@@ -456,7 +456,7 @@ sector:
       2050: 1.0
     district_heating_loss: 0.15
     supply_temperature_approximation:
-      max_forward_temperature:
+      max_forward_temperature_baseyear:
         FR: 110
         DK: 75
         DE: 109
@@ -465,13 +465,14 @@ sector:
         PL: 130
         SE: 102
         IT: 90
-      min_forward_temperature:
+      min_forward_temperature_baseyear:
         DE: 82
-      return_temperature:
+      return_temperature_baseyear:
         DE: 58
       lower_threshold_ambient_temperature: 0
       upper_threshold_ambient_temperature: 10
       rolling_window_ambient_temperature: 72
+      relative_annual_temperature_reduction: 0.01
     heat_source_cooling: 6 #K
     heat_pump_cop_approximation:
       refrigerant: ammonia

doc/configtables/sector.csv

@@ -10,12 +10,13 @@ district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-
 -- 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
 -- supply_temperature_approximation,,,
--- -- max_forward_temperature,°C,Dictionary with country codes as keys. One key must be 'default'., Max. forward temperature in district heating (if ambient temperature lower-or-equal `lower_threshold_ambient_temperature`)
--- -- min_forward_temperature,°C,Dictionary with country codes as keys. One key must be 'default'., Min. forward temperature in district heating (if ambient temperature higher-or-equal `upper_threshold_ambient_temperature`)
--- -- 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
+-- -- max_forward_temperature_baseyear,°C,Dictionary with country codes as keys. One key must be 'default'., Max. forward temperature in district heating in baseyear (if ambient temperature lower-or-equal `lower_threshold_ambient_temperature`)
+-- -- min_forward_temperature_baseyear,°C,Dictionary with country codes as keys. One key must be 'default'., Min. forward temperature in district heating in baseyear (if ambient temperature higher-or-equal `upper_threshold_ambient_temperature`)
+-- -- return_temperature_baseyear,°C,Dictionary with country codes as keys. One key must be 'default'.,Return temperature in district heating in baseyear . Must be lower than forward temperature
 -- -- lower_threshold_ambient_temperature,°C,float, Assume `max_forward_temperature` if ambient temperature is below this threshold
 -- -- upper_threshold_ambient_temperature,°C,float, Assume `min_forward_temperature` if ambient temperature is above this threshold
 -- -- rolling_window_ambient_temperature, h, int, Rolling window size for averaging ambient temperature when approximating supply temperature
+-- -- relative_annual_temperature_reduction,, float, Relative annual reduction of district heating forward and return temperature - defaults to 0.01 (1%)
 -- 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

@@ -11,6 +11,7 @@ Release Notes
 Upcoming Release
 ================
 
+* Added option to reduce central heating forward temperatures by annual percentage (see rule :mod:`build_central_heating_temperature_profiles`). This makes COP profiles and heat pump efficiencies planning-horizon-dependent. Myopic and perfect foresight modes were adjusted accordingly to update COPs of existing heat pumps in preceding years to adjusted temperatures.
 
 * Rearranged workflow to cluster the electricity network before calculating
   renewable profiles and adding further electricity system components.

rules/build_sector.smk

@@ -214,23 +214,23 @@ rule build_temperature_profiles:
 
 rule build_central_heating_temperature_profiles:
     params:
-        max_forward_temperature_central_heating=config_provider(
+        max_forward_temperature_central_heating_baseyear=config_provider(
             "sector",
             "district_heating",
             "supply_temperature_approximation",
-            "max_forward_temperature",
+            "max_forward_temperature_baseyear",
         ),
-        min_forward_temperature_central_heating=config_provider(
+        min_forward_temperature_central_heating_baseyear=config_provider(
             "sector",
             "district_heating",
             "supply_temperature_approximation",
-            "min_forward_temperature",
+            "min_forward_temperature_baseyear",
         ),
-        return_temperature_central_heating=config_provider(
+        return_temperature_central_heating_baseyear=config_provider(
             "sector",
             "district_heating",
             "supply_temperature_approximation",
-            "return_temperature",
+            "return_temperature_baseyear",
         ),
         snapshots=config_provider("snapshots"),
         lower_threshold_ambient_temperature=config_provider(
@@ -251,22 +251,33 @@ rule build_central_heating_temperature_profiles:
             "supply_temperature_approximation",
             "rolling_window_ambient_temperature",
         ),
+        relative_annual_temperature_reduction=config_provider(
+            "sector",
+            "district_heating",
+            "supply_temperature_approximation",
+            "relative_annual_temperature_reduction",
+        ),
+        energy_totals_year=config_provider("energy", "energy_totals_year"),
     input:
         temp_air_total=resources("temp_air_total_base_s_{clusters}.nc"),
         regions_onshore=resources("regions_onshore_base_s_{clusters}.geojson"),
     output:
         central_heating_forward_temperature_profiles=resources(
-            "central_heating_forward_temperature_profiles_base_s_{clusters}.nc"
+            "central_heating_forward_temperature_profiles_base_s_{clusters}_{planning_horizons}.nc"
         ),
         central_heating_return_temperature_profiles=resources(
-            "central_heating_return_temperature_profiles_base_s_{clusters}.nc"
+            "central_heating_return_temperature_profiles_base_s_{clusters}_{planning_horizons}.nc"
         ),
     resources:
         mem_mb=20000,
     log:
-        logs("build_central_heating_temperature_profiles_s_{clusters}.log"),
+        logs(
+            "build_central_heating_temperature_profiles_s_{clusters}_{planning_horizons}.log"
+        ),
     benchmark:
-        benchmarks("build_central_heating_temperature_profiles/s_{clusters}")
+        benchmarks(
+            "build_central_heating_temperature_profiles/s_{clusters}_{planning_horizons}"
+        )
     conda:
         "../envs/environment.yaml"
     script:
@@ -288,22 +299,22 @@ rule build_cop_profiles:
         snapshots=config_provider("snapshots"),
     input:
         central_heating_forward_temperature_profiles=resources(
-            "central_heating_forward_temperature_profiles_base_s_{clusters}.nc"
+            "central_heating_forward_temperature_profiles_base_s_{clusters}_{planning_horizons}.nc"
         ),
         central_heating_return_temperature_profiles=resources(
-            "central_heating_return_temperature_profiles_base_s_{clusters}.nc"
+            "central_heating_return_temperature_profiles_base_s_{clusters}_{planning_horizons}.nc"
         ),
         temp_soil_total=resources("temp_soil_total_base_s_{clusters}.nc"),
         temp_air_total=resources("temp_air_total_base_s_{clusters}.nc"),
         regions_onshore=resources("regions_onshore_base_s_{clusters}.geojson"),
     output:
-        cop_profiles=resources("cop_profiles_base_s_{clusters}.nc"),
+        cop_profiles=resources("cop_profiles_base_s_{clusters}_{planning_horizons}.nc"),
     resources:
         mem_mb=20000,
     log:
-        logs("build_cop_profiles_s_{clusters}.log"),
+        logs("build_cop_profiles_s_{clusters}_{planning_horizons}.log"),
     benchmark:
-        benchmarks("build_cop_profiles/s_{clusters}")
+        benchmarks("build_cop_profiles/s_{clusters}_{planning_horizons}")
     conda:
         "../envs/environment.yaml"
     script:
@@ -1092,7 +1103,7 @@ rule prepare_sector_network:
         heating_efficiencies=resources("heating_efficiencies.csv"),
         temp_soil_total=resources("temp_soil_total_base_s_{clusters}.nc"),
         temp_air_total=resources("temp_air_total_base_s_{clusters}.nc"),
-        cop_profiles=resources("cop_profiles_base_s_{clusters}.nc"),
+        cop_profiles=resources("cop_profiles_base_s_{clusters}_{planning_horizons}.nc"),
         solar_thermal_total=lambda w: (
             resources("solar_thermal_total_base_s_{clusters}.nc")
             if config_provider("sector", "solar_thermal")(w)

rules/solve_myopic.smk

@@ -23,7 +23,7 @@ rule add_existing_baseyear:
                 config_provider("scenario", "planning_horizons", 0)(w)
             )
         ),
-        cop_profiles=resources("cop_profiles_base_s_{clusters}.nc"),
+        cop_profiles=resources("cop_profiles_base_s_{clusters}_{planning_horizons}.nc"),
         existing_heating_distribution=resources(
             "existing_heating_distribution_base_s_{clusters}_{planning_horizons}.csv"
         ),
@@ -80,7 +80,7 @@ rule add_brownfield:
         + "prenetworks/base_s_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
         network_p=solved_previous_horizon,  #solved network at previous time step
         costs=resources("costs_{planning_horizons}.csv"),
-        cop_profiles=resources("cop_profiles_base_s_{clusters}.nc"),
+        cop_profiles=resources("cop_profiles_base_s_{clusters}_{planning_horizons}.nc"),
     output:
         RESULTS
         + "prenetworks-brownfield/base_s_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",

rules/solve_perfect.smk

@@ -21,7 +21,7 @@ rule add_existing_baseyear:
                 config_provider("scenario", "planning_horizons", 0)(w)
             )
         ),
-        cop_profiles=resources("cop_profiles_base_s_{clusters}.nc"),
+        cop_profiles=resources("cop_profiles_base_s_{clusters}_{planning_horizons}.nc"),
         existing_heating_distribution=resources(
             "existing_heating_distribution_base_s_{clusters}_{planning_horizons}.csv"
         ),

scripts/add_brownfield.py

@@ -221,6 +221,40 @@ def adjust_renewable_profiles(n, input_profiles, params, year):
             n.generators_t.p_max_pu.loc[:, p_max_pu.columns] = p_max_pu
 
 
+def update_heat_pump_efficiency(n: pypsa.Network, n_p: pypsa.Network, year: int):
+    """
+    Update the efficiency of heat pumps from previous years to current year
+    (e.g. 2030 heat pumps receive 2040 heat pump COPs in 2030).
+
+    Parameters
+    ----------
+    n : pypsa.Network
+        The original network.
+    n_p : pypsa.Network
+        The network with the updated parameters.
+    year : int
+        The year for which the efficiency is being updated.
+
+    Returns
+    -------
+    None
+        This function updates the efficiency in place and does not return a value.
+    """
+
+    # get names of heat pumps in previous iteration
+    heat_pump_idx_previous_iteration = n_p.links.index[
+        n_p.links.index.str.contains("heat pump")
+    ]
+    # construct names of same-technology heat pumps in the current iteration
+    corresponding_idx_this_iteration = heat_pump_idx_previous_iteration.str[:-4] + str(
+        year
+    )
+    # update efficiency of heat pumps in previous iteration in-place to efficiency in this iteration
+    n_p.links_t["efficiency"].loc[:, heat_pump_idx_previous_iteration] = (
+        n.links_t["efficiency"].loc[:, corresponding_idx_this_iteration].values
+    )
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -251,6 +285,8 @@ def adjust_renewable_profiles(n, input_profiles, params, year):
 
     n_p = pypsa.Network(snakemake.input.network_p)
 
+    update_heat_pump_efficiency(n, n_p, year)
+
     add_brownfield(n, n_p, year)
 
     disable_grid_expansion_if_limit_hit(n)

scripts/build_central_heating_temperature_profiles/run.py

@@ -130,45 +130,128 @@ def map_temperature_dict_to_onshore_regions(
     )
 
 
+def scale_temperature_to_investment_year(
+    temperature_baseyear: dict,
+    relative_annual_temperature_reduction: float,
+    investment_year: int,
+    current_year: int,
+) -> dict:
+    """
+    Scale temperature for each country to investment year by constant
+    exponential factor.
+
+    Parameters
+    ----------
+    temperature_baseyear : dict
+        Dictionary with the current temperatures as values and country keys as keys.
+    relative_annual_temperature_reduction : float
+        The annual reduction rate of the temperature, must be between 0 and 1.
+    investment_year : int
+        The year in which the investment is planned.
+    current_year : int
+        The current year.
+
+    Returns
+    -------
+    dict
+        A dictionary with the temperature for each country in the investment year.
+
+    Raises
+    ------
+    ValueError
+        If the investment year is before the current year.
+        If the relative annual temperature reduction is not between 0 and 1.
+    """
+
+    if investment_year < current_year:
+        raise ValueError(
+            f"Error: Investment year {investment_year} is before current year {current_year}."
+        )
+    if (
+        relative_annual_temperature_reduction < 0
+        or relative_annual_temperature_reduction > 1
+    ):
+        raise ValueError(
+            f"Error: Relative annual temperature reduction must be between 0 and 1, but is {relative_annual_temperature_reduction}."
+        )
+
+    return {
+        key: temperature_baseyear[key]
+        * (
+            (1 - relative_annual_temperature_reduction)
+            ** (investment_year - current_year)
+        )
+        for key in temperature_baseyear.keys()
+    }
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
 
         snakemake = mock_snakemake(
             "build_cop_profiles",
             clusters=48,
+            planning_horizons="2050",
         )
 
     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,
+    # reduce temperatures to investment years by constant exponential factor
+    max_forward_temperature_investment_year = scale_temperature_to_investment_year(
+        temperature_baseyear=snakemake.params.max_forward_temperature_central_heating_baseyear,
+        relative_annual_temperature_reduction=snakemake.params.relative_annual_temperature_reduction,
+        investment_year=int(snakemake.wildcards.planning_horizons),
+        current_year=int(snakemake.params.energy_totals_year),
+    )
+
+    min_forward_temperature_investment_year = scale_temperature_to_investment_year(
+        temperature_baseyear=snakemake.params.min_forward_temperature_central_heating_baseyear,
+        relative_annual_temperature_reduction=snakemake.params.relative_annual_temperature_reduction,
+        investment_year=int(snakemake.wildcards.planning_horizons),
+        current_year=int(snakemake.params.energy_totals_year),
     )
-    return_temperature = extrapolate_missing_supply_temperatures_by_country(
-        extrapolate_from=max_forward_temperature,
-        extrapolate_to=snakemake.params.return_temperature_central_heating,
+
+    return_temperature_investment_year = scale_temperature_to_investment_year(
+        temperature_baseyear=snakemake.params.return_temperature_central_heating_baseyear,
+        relative_annual_temperature_reduction=snakemake.params.relative_annual_temperature_reduction,
+        investment_year=int(snakemake.wildcards.planning_horizons),
+        current_year=int(snakemake.params.energy_totals_year),
+    )
+
+    # min_forward_temperature and return_temperature contain only values for Germany by default
+    # extrapolate missing values based on ratio between max_forward_temperature and min_forward_temperature / return_temperature for Germany (or other countries provided in min_forward_temperature_baseyear and return_temperature_baseyear)
+    min_forward_temperature_investment_year = (
+        extrapolate_missing_supply_temperatures_by_country(
+            extrapolate_from=max_forward_temperature_investment_year,
+            extrapolate_to=min_forward_temperature_investment_year,
+        )
+    )
+    return_temperature_investment_year = (
+        extrapolate_missing_supply_temperatures_by_country(
+            extrapolate_from=max_forward_temperature_investment_year,
+            extrapolate_to=return_temperature_investment_year,
+        )
     )
 
     # 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=max_forward_temperature,
+            supply_temperature_by_country=max_forward_temperature_investment_year,
             regions_onshore=regions_onshore,
         )
     )
     min_forward_temperature_central_heating_by_node_and_time: xr.DataArray = (
         map_temperature_dict_to_onshore_regions(
-            supply_temperature_by_country=min_forward_temperature,
+            supply_temperature_by_country=min_forward_temperature_investment_year,
             regions_onshore=regions_onshore,
         )
     )
     return_temperature_central_heating_by_node_and_time: xr.DataArray = (
         map_temperature_dict_to_onshore_regions(
-            supply_temperature_by_country=return_temperature,
+            supply_temperature_by_country=return_temperature_investment_year,
             regions_onshore=regions_onshore,
         )
     )