From 2a6dcc4c1236b3b1ce1bf0d65d93896ccfead14b Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Tue, 21 May 2024 15:44:04 +0000 Subject: [PATCH] Minor bugfixes for new time aggregation implementation --- scripts/prepare_sector_network.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py index fb290130b..3f8dd0225 100755 --- a/scripts/prepare_sector_network.py +++ b/scripts/prepare_sector_network.py @@ -3634,15 +3634,13 @@ def set_temporal_aggregation(n, resolution, snapshot_weightings): logger.info("Use every %s snapshot as representative", sn) n.set_snapshots(n.snapshots[::sn]) n.snapshot_weightings *= sn + return n else: # Otherwise, use the provided snapshots snapshot_weightings = pd.read_csv( snapshot_weightings, index_col=0, parse_dates=True ) - n.set_snapshots(snapshot_weightings.index) - n.snapshot_weightings = snapshot_weightings - # Define a series used for aggregation, mapping each hour in # n.snapshots to the closest previous timestep in # snapshot_weightings.index @@ -3656,16 +3654,23 @@ def set_temporal_aggregation(n, resolution, snapshot_weightings): .map(lambda i: snapshot_weightings.index[i]) ) + m = n.copy(with_time=False) + m.set_snapshots(snapshot_weightings.index) + m.snapshot_weightings = snapshot_weightings + # Aggregation all time-varying data. for c in n.iterate_components(): + pnl = getattr(m, c.list_name + "_t") for k, df in c.pnl.items(): if not df.empty: if c.list_name == "stores" and k == "e_max_pu": - c.pnl[k] = df.groupby(aggregation_map).min() + pnl[k] = df.groupby(aggregation_map).min() elif c.list_name == "stores" and k == "e_min_pu": - c.pnl[k] = df.groupby(aggregation_map).max() + pnl[k] = df.groupby(aggregation_map).max() else: - c.pnl[k] = df.groupby(aggregation_map).mean() + pnl[k] = df.groupby(aggregation_map).mean() + + return m def lossy_bidirectional_links(n, carrier, efficiencies={}): @@ -3818,7 +3823,7 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}): if options["allam_cycle"]: add_allam(n, costs) - set_temporal_aggregation( + n = set_temporal_aggregation( n, snakemake.params.time_resolution, snakemake.input.snapshot_weightings )