move building of daily heat profile to its own script

Previously this was handled inside prepare_sector_network.py.

rules/build_sector.smk

@@ -123,7 +123,7 @@ rule cluster_gas_network:
         "../scripts/cluster_gas_network.py"
 
 
-rule build_heat_demands:
+rule build_daily_heat_demand:
     params:
         snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
     input:
@@ -131,18 +131,39 @@ rule build_heat_demands:
         regions_onshore=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
         cutout="cutouts/" + CDIR + config["atlite"]["default_cutout"] + ".nc",
     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:
         mem_mb=20000,
     threads: 8
     log:
-        LOGS + "build_heat_demands_{scope}_{simpl}_{clusters}.loc",
+        LOGS + "build_daily_heat_demand_{scope}_{simpl}_{clusters}.loc",
     benchmark:
-        BENCHMARKS + "build_heat_demands/{scope}_s{simpl}_{clusters}"
+        BENCHMARKS + "build_daily_heat_demand/{scope}_s{simpl}_{clusters}"
     conda:
         "../envs/environment.yaml"
     script:
-        "../scripts/build_heat_demand.py"
+        "../scripts/build_daily_heat_demand.py"
+
+
+rule build_hourly_heat_demand:
+    params:
+        snapshots=config["snapshots"],
+    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:
@@ -727,7 +748,6 @@ rule prepare_sector_network:
         if config["foresight"] == "overnight"
         else RESOURCES
         + "biomass_potentials_s{simpl}_{clusters}_{planning_horizons}.csv",
-        heat_profile="data/heat_load_profile_BDEW.csv",
         costs="data/costs_{}.csv".format(config["costs"]["year"])
         if config["foresight"] == "overnight"
         else "data/costs_{planning_horizons}.csv",
@@ -740,9 +760,7 @@ rule prepare_sector_network:
         simplified_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}.csv",
         industrial_demand=RESOURCES
         + "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
-        heat_demand_urban=RESOURCES + "heat_demand_urban_elec_s{simpl}_{clusters}.nc",
-        heat_demand_rural=RESOURCES + "heat_demand_rural_elec_s{simpl}_{clusters}.nc",
-        heat_demand_total=RESOURCES + "heat_demand_total_elec_s{simpl}_{clusters}.nc",
+        hourly_heat_demand_total=RESOURCES + "hourly_heat_demand_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_urban=RESOURCES + "temp_soil_urban_elec_s{simpl}_{clusters}.nc",

scripts/build_hourly_heat_demand.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Build hourly heat demand time series from daily ones.
+"""
+
+import pandas as pd
+import xarray as xr
+from _helpers import generate_periodic_profiles, update_config_with_sector_opts
+from itertools import product
+
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_heat_demands",
+            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"
+
+    print(heat_demand)
+
+    print(heat_demand.stack())
+
+    ds = heat_demand.stack().to_xarray()#xr.Dataset.from_dataframe(heat_demand)
+
+    print(ds)
+
+    ds.to_netcdf(snakemake.output.heat_demand)

scripts/prepare_sector_network.py

@@ -18,7 +18,7 @@ import numpy as np
 import pandas as pd
 import pypsa
 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 build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
 from networkx.algorithms import complement
@@ -1639,14 +1639,8 @@ def add_land_transport(n, costs):
 
 
 def build_heat_demand(n):
-    # copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile
-    daily_space_heat_demand = (
-        xr.open_dataarray(snakemake.input.heat_demand_total)
-        .to_pandas()
-        .reindex(index=n.snapshots, method="ffill")
-    )
 
-    intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
+    heat_demand_shape = xr.open_dataset(snakemake.input.hourly_heat_demand_total).to_dataframe().unstack(level=1)
 
     sectors = ["residential", "services"]
     uses = ["water", "space"]
@@ -1654,25 +1648,14 @@ def build_heat_demand(n):
     heat_demand = {}
     electric_heat_supply = {}
     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_shape = daily_space_heat_demand * intraday_year_profile
-        else:
-            heat_demand_shape = intraday_year_profile
+        name = f"{sector} {use}"
 
-        heat_demand[f"{sector} {use}"] = (
-            heat_demand_shape / heat_demand_shape.sum()
+        heat_demand[name] = (
+            heat_demand_shape[name] / heat_demand_shape[name].sum()
         ).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
         electric_heat_supply[f"{sector} {use}"] = (
-            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
 
     heat_demand = pd.concat(heat_demand, axis=1)