cluster_heat_buses: performance boost and tidy code
This commit is contained in:
parent
cf5f3cbd88
commit
9530d63e55
@ -3332,24 +3332,24 @@ def limit_individual_line_extension(n, maxext):
|
|||||||
|
|
||||||
|
|
||||||
aggregate_dict = {
|
aggregate_dict = {
|
||||||
"p_nom": "sum",
|
"p_nom": pd.Series.sum,
|
||||||
"s_nom": "sum",
|
"s_nom": pd.Series.sum,
|
||||||
"v_nom": "max",
|
"v_nom": "max",
|
||||||
"v_mag_pu_max": "min",
|
"v_mag_pu_max": "min",
|
||||||
"v_mag_pu_min": "max",
|
"v_mag_pu_min": "max",
|
||||||
"p_nom_max": "sum",
|
"p_nom_max": pd.Series.sum,
|
||||||
"s_nom_max": "sum",
|
"s_nom_max": pd.Series.sum,
|
||||||
"p_nom_min": "sum",
|
"p_nom_min": pd.Series.sum,
|
||||||
"s_nom_min": "sum",
|
"s_nom_min": pd.Series.sum,
|
||||||
"v_ang_min": "max",
|
"v_ang_min": "max",
|
||||||
"v_ang_max": "min",
|
"v_ang_max": "min",
|
||||||
"terrain_factor": "mean",
|
"terrain_factor": "mean",
|
||||||
"num_parallel": "sum",
|
"num_parallel": "sum",
|
||||||
"p_set": "sum",
|
"p_set": "sum",
|
||||||
"e_initial": "sum",
|
"e_initial": "sum",
|
||||||
"e_nom": "sum",
|
"e_nom": pd.Series.sum,
|
||||||
"e_nom_max": "sum",
|
"e_nom_max": pd.Series.sum,
|
||||||
"e_nom_min": "sum",
|
"e_nom_min": pd.Series.sum,
|
||||||
"state_of_charge_initial": "sum",
|
"state_of_charge_initial": "sum",
|
||||||
"state_of_charge_set": "sum",
|
"state_of_charge_set": "sum",
|
||||||
"inflow": "sum",
|
"inflow": "sum",
|
||||||
@ -3411,13 +3411,10 @@ def cluster_heat_buses(n):
|
|||||||
pnl = c.pnl
|
pnl = c.pnl
|
||||||
agg = define_clustering(pd.Index(pnl.keys()), aggregate_dict)
|
agg = define_clustering(pd.Index(pnl.keys()), aggregate_dict)
|
||||||
for k in pnl.keys():
|
for k in pnl.keys():
|
||||||
pnl[k].rename(
|
def renamer(s):
|
||||||
columns=lambda x: x.replace("residential ", "").replace(
|
return s.replace("residential ", "").replace("services ", "")
|
||||||
"services ", ""
|
|
||||||
),
|
pnl[k] = pnl[k].groupby(renamer, axis=1).agg(agg[k], **agg_group_kwargs)
|
||||||
inplace=True,
|
|
||||||
)
|
|
||||||
pnl[k] = pnl[k].groupby(level=0, axis=1).agg(agg[k], **agg_group_kwargs)
|
|
||||||
|
|
||||||
# remove unclustered assets of service/residential
|
# remove unclustered assets of service/residential
|
||||||
to_drop = c.df.index.difference(df.index)
|
to_drop = c.df.index.difference(df.index)
|
||||||
|
Loading…
Reference in New Issue
Block a user