Document the new time_aggregation rule

doc/release_notes.rst

@@ -260,6 +260,8 @@ Upcoming Release
 
 * Fix gas network retrofitting in `add_brownfield`.
 
+* Time aggregation for sector-coupled networks have been split into its own rule. When using time step segmentation, time aggregation is constant over planning horizons of the same network.
+
 PyPSA-Eur 0.10.0 (19th February 2024)
 =====================================
 

doc/sector.rst

@@ -183,6 +183,11 @@ Rule ``cluster_gas_network``
 
 .. automodule:: cluster_gas_network
 
+Rule ``time_aggregation``
+==============================================================================
+
+.. automodule:: time_aggregation
+
 Rule ``prepare_sector_network``
 ==============================================================================
 

scripts/time_aggregation.py

@@ -1,4 +1,40 @@
 # -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Defines the time aggregation to be used for sector-coupled network.
+
+Relevant Settings
+-----------------
+
+.. code:: yaml
+
+    clustering:
+        temporal:
+            resolution_sector:
+
+    enable:
+        drop_leap_day:
+
+Inputs
+------
+
+- ``networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: the network whose snapshots are to be aggregated
+- ``resources/hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc``: the total hourly heat demand
+- ``resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc``: the total hourly solar thermal generation
+
+Outputs
+-------
+
+- ``snapshot_weightings_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.csv``
+
+Description
+-----------
+Computes a time aggregation scheme for the given network, in the form of a CSV file with the snapshot weightings, indexed by the new subset of snapshots. This rule only computes said aggregation scheme; aggregation of time-varying network data is done in ``prepare_sector_network.py``.
+"""
+
+
 import logging
 
 import numpy as np
@@ -69,16 +105,18 @@ if __name__ == "__main__":
             for attr, pnl in c.pnl.items()
             if not pnl.empty and attr != "e_min_pu"
         ]
-        dfs.append(
-            xr.open_dataset(snakemake.input.hourly_heat_demand_total)
-            .to_dataframe()
-            .unstack(level=1)
-        )
-        dfs.append(
-            xr.open_dataset(snakemake.input.solar_thermal_total)
-            .to_dataframe()
-            .unstack(level=1)
-        )
+        if snakemake.input.hourly_heat_demand_total:
+            dfs.append(
+                xr.open_dataset(snakemake.input.hourly_heat_demand_total)
+                .to_dataframe()
+                .unstack(level=1)
+            )
+        if snakemake.input.solar_thermal_total:
+            dfs.append(
+                xr.open_dataset(snakemake.input.solar_thermal_total)
+                .to_dataframe()
+                .unstack(level=1)
+            )
         df = pd.concat(dfs, axis=1)
 
         # Reset columns to flat index