initial dsm implementation for the heating sector

config/config.default.yaml

@@ -487,6 +487,9 @@ sector:
     rural:
     - air
     - ground
+  residential_heat_dsm: false
+  residential_heat_restriction_value: 0.27
+  residential_heat_restriction_time: [10, 22] # 9am and 9pm
   cluster_heat_buses: true
   heat_demand_cutout: default
   bev_dsm_restriction_value: 0.75

doc/configtables/sector.csv

@@ -27,10 +27,13 @@ district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-
 -- -- urban central,--,List of heat sources for heat pumps in urban central heating,
 -- -- urban decentral,--,List of heat sources for heat pumps in urban decentral heating,
 -- -- rural,--,List of heat sources for heat pumps in rural heating,
+residential_heat_dsm,--,"{true, false}", Add the option for heat pumps (HPs) to participate in demand-side management (DSM),
+residential_heat_restriction_value,--,float,Adds an upper state of charge (SOC) limit for heat pumps (HPs) to manage its own energy demand (DSM). Located in `build_hourly_heat_demand.py <https://github.com/PyPSA/pypsa-eur/blob/master/scripts/build_hourly_heat_demand.py>`_. Set to 0 for full restriction on HP DSM, default value is 0.27,
+residential_heat_restriction_time,--,list of int, Time at which SOC of HPs has to be residential_heat_restriction_value. Defaults to [10, 22] corresponding to 9am and 9pm,
 cluster_heat_buses,--,"{true, false}",Cluster residential and service heat buses in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_  to one to save memory.
 ,,,
-bev_dsm_restriction _value,--,float,Adds a lower state of charge (SOC) limit for battery electric vehicles (BEV) to manage its own energy demand (DSM). Located in `build_transport_demand.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_transport_demand.py>`_. Set to 0 for no restriction on BEV DSM
-bev_dsm_restriction _time,--,float,Time at which SOC of BEV has to be dsm_restriction_value
+bev_dsm_restriction_value,--,float,Adds a lower state of charge (SOC) limit for battery electric vehicles (BEV) to manage its own energy demand (DSM). Located in `build_transport_demand.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_transport_demand.py>`_. Set to 0 for no restriction on BEV DSM,
+bev_dsm_restriction_time,--,float,Time at which SOC of BEV has to be bev_dsm_restriction_value,
 transport_heating _deadband_upper,°C,float,"The maximum temperature in the vehicle. At higher temperatures, the energy required for cooling in the vehicle increases."
 transport_heating _deadband_lower,°C,float,"The minimum temperature in the vehicle. At lower temperatures, the energy required for heating in the vehicle increases."
 ,,,

doc/release_notes.rst

@@ -83,6 +83,8 @@ Upcoming Release
 
 * Bugfix: Bug when multiple DC links are connected to the same DC bus and the DC bus is connected to an AC bus via converter. In this case, the DC links were wrongly simplified, completely dropping the shared DC bus. Bug fixed by adding preceding converter removal. Other functionalities are not impacted.
 
+* Add demand-side-response (DSR) for the heating sector.
+
 PyPSA-Eur 0.13.0 (13th September 2024)
 ======================================
 

rules/build_sector.smk

@@ -170,11 +170,15 @@ rule build_hourly_heat_demand:
     params:
         snapshots=config_provider("snapshots"),
         drop_leap_day=config_provider("enable", "drop_leap_day"),
+        sector=config_provider("sector"),
     input:
         heat_profile="data/heat_load_profile_BDEW.csv",
         heat_demand=resources("daily_heat_demand_total_base_s_{clusters}.nc"),
     output:
         heat_demand=resources("hourly_heat_demand_total_base_s_{clusters}.nc"),
+        heat_dsm_profile=resources(
+            "residential_heat_dsm_profile_total_base_s_{clusters}.csv"
+        ),
     resources:
         mem_mb=2000,
     threads: 8
@@ -1069,6 +1073,9 @@ rule prepare_sector_network:
         transport_data=resources("transport_data_s_{clusters}.csv"),
         avail_profile=resources("avail_profile_s_{clusters}.csv"),
         dsm_profile=resources("dsm_profile_s_{clusters}.csv"),
+        heat_dsm_profile=resources(
+            "residential_heat_dsm_profile_total_base_s_{clusters}.csv"
+        ),
         co2_totals_name=resources("co2_totals.csv"),
         co2="data/bundle/eea/UNFCCC_v23.csv",
         biomass_potentials=resources(

scripts/build_hourly_heat_demand.py

@@ -32,10 +32,27 @@
 
 from itertools import product
 
+import numpy as np
 import pandas as pd
 import xarray as xr
 from _helpers import generate_periodic_profiles, get_snapshots, set_scenario_config
 
+
+def heat_dsm_profile(nodes, options):
+
+    weekly_profile = np.ones((24 * 7))
+    for i in options["residential_heat_restriction_time"]:
+        weekly_profile[(np.arange(0, 7, 1) * 24 + int(i))] = 0
+
+    dsm_profile = generate_periodic_profiles(
+        dt_index=pd.date_range(freq="h", **snakemake.params.snapshots, tz="UTC"),
+        nodes=nodes,
+        weekly_profile=weekly_profile,
+    )
+
+    return dsm_profile
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -51,6 +68,8 @@
         snakemake.params.snapshots, snakemake.params.drop_leap_day
     )
 
+    options = snakemake.params.sector
+
     daily_space_heat_demand = (
         xr.open_dataarray(snakemake.input.heat_demand)
         .to_pandas()
@@ -63,6 +82,7 @@
     uses = ["water", "space"]
 
     heat_demand = {}
+    dsm_profile = {}
     for sector, use in product(sectors, uses):
         weekday = list(intraday_profiles[f"{sector} {use} weekday"])
         weekend = list(intraday_profiles[f"{sector} {use} weekend"])
@@ -77,13 +97,19 @@
             heat_demand[f"{sector} {use}"] = (
                 daily_space_heat_demand * intraday_year_profile
             )
+            if sector == "residential":
+                dsm_profile[f"{sector} {use}"] = heat_dsm_profile(
+                    daily_space_heat_demand.columns, options
+                )
         else:
             heat_demand[f"{sector} {use}"] = intraday_year_profile
 
     heat_demand = pd.concat(heat_demand, axis=1, names=["sector use", "node"])
+    dsm_profile = pd.concat(dsm_profile, axis=1, names=["sector use", "node"])
 
     heat_demand.index.name = "snapshots"
 
     ds = heat_demand.stack(future_stack=True).to_xarray()
 
     ds.to_netcdf(snakemake.output.heat_demand)
+    dsm_profile.to_csv(snakemake.output.heat_dsm_profile)

scripts/prepare_sector_network.py

@@ -2143,6 +2143,50 @@ def add_heat(n: pypsa.Network, costs: pd.DataFrame, cop: xr.DataArray):
             p_set=heat_load.loc[n.snapshots],
         )
 
+        if options["residential_heat_dsm"] and heat_system in [
+            HeatSystem.RESIDENTIAL_RURAL,
+            HeatSystem.RESIDENTIAL_URBAN_DECENTRAL,
+            HeatSystem.URBAN_CENTRAL,
+        ]:
+            factor = heat_system.heat_demand_weighting(
+                urban_fraction=urban_fraction[nodes], dist_fraction=dist_fraction[nodes]
+            )
+
+            heat_dsm_profile = pd.read_csv(
+                snakemake.input.heat_dsm_profile, header=[1], index_col=[0]
+            )[nodes]
+            heat_dsm_profile.index = n.snapshots
+
+            e_nom = (
+                heat_demand[["residential space"]]
+                .T.groupby(level=1)
+                .sum()
+                .T[nodes]
+                .multiply(factor)
+            )
+
+            heat_dsm_profile = (
+                heat_dsm_profile * options["residential_heat_restriction_value"]
+            )
+            e_nom = e_nom.max()
+
+            # Thermal (standing) losses of buildings assumed to be the same as decentralized water tanks
+            tes_time_constant_days = options["tes_tau"]["decentral"]
+
+            n.madd(
+                "Store",
+                nodes,
+                suffix=f" {heat_system} heat flexibility",
+                bus=nodes + f" {heat_system} heat",
+                carrier="residential heating flexibility",
+                standing_loss=1 - np.exp(-1 / 24 / tes_time_constant_days),
+                e_cyclic=True,
+                e_nom=e_nom,
+                e_max_pu=heat_dsm_profile,
+            )
+
+            logger.info(f"adding heat dsm in {heat_system} heating.")
+
         ## Add heat pumps
         for heat_source in snakemake.params.heat_pump_sources[
             heat_system.system_type.value