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include code review

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This commit is contained in:
lisazeyen 2022-09-14 16:16:50 +02:00
parent 4accfff1bb
commit ed8f783096
1 changed files with 17 additions and 13 deletions
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30
scripts/prepare_sector_network.py
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@ -2415,7 +2415,7 @@ def limit_individual_line_extension(n, maxext):
def apply_time_segmentation(n, segments, solver_name="cbc", def apply_time_segmentation(n, segments, solver_name="cbc",
overwrite_time_dependent=False): overwrite_time_dependent=True):
"""Aggregating time series to segments with different lengths """Aggregating time series to segments with different lengths
Input: Input:
@ -2435,12 +2435,12 @@ def apply_time_segmentation(n, segments, solver_name="cbc",
# get all time-dependent data # get all time-dependent data
columns = pd.MultiIndex.from_tuples([],names=['component', 'key', 'asset']) columns = pd.MultiIndex.from_tuples([],names=['component', 'key', 'asset'])
raw = pd.DataFrame(index=n.snapshots,columns=columns) raw = pd.DataFrame(index=n.snapshots,columns=columns)
for component in n.all_components: for c in n.iterate_components():
pnl = n.pnl(component) for attr, pnl in c.pnl.items():
for key in pnl.keys(): # exclude e_min_pu which is used for SOC of EVs in the morning
if not pnl[key].empty: if not pnl.empty and attr != 'e_min_pu':
df = pnl[key].copy() df = pnl.copy()
df.columns = pd.MultiIndex.from_product([[component], [key], df.columns]) df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
raw = pd.concat([raw, df], axis=1) raw = pd.concat([raw, df], axis=1)
# normalise all time-dependent data # normalise all time-dependent data
@ -2478,18 +2478,22 @@ def set_temporal_aggregation(n, opts, solver_name):
m = re.match(r"^\d+h$", o, re.IGNORECASE) m = re.match(r"^\d+h$", o, re.IGNORECASE)
if m is not None: if m is not None:
n = average_every_nhours(n, m.group(0)) n = average_every_nhours(n, m.group(0))
break
# representive snapshots # representive snapshots
m = re.match(r"^\d+sn$", o, re.IGNORECASE) m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
if m is not None: if m is not None:
sn = int(m.group(0).split("sn")[0]) sn = int(m[1])
logger.info("use every {} snapshot as representative".format(sn)) logger.info(f"use every {sn} snapshot as representative")
n.set_snapshots(n.snapshots[::sn]) n.set_snapshots(n.snapshots[::sn])
n.snapshot_weightings *= sn n.snapshot_weightings *= sn
break
# segments with package tsam # segments with package tsam
if "SEG" in o: m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
segments = int(o.replace("SEG","")) if m is not None:
logger.info("use temporal segmentation with {} segments".format(segments)) segments = int(m[1])
logger.info(f"use temporal segmentation with {segments} segments")
n = apply_time_segmentation(n, segments, solver_name=solver_name) n = apply_time_segmentation(n, segments, solver_name=solver_name)
break
return n return n
#%% #%%
if __name__ == "__main__": if __name__ == "__main__":