bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

[pre-commit.ci] auto fixes from pre-commit.com hooks

Browse Source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot] 2024-01-17 10:36:13 +00:00
parent ec4e9aa548
commit 446b0b3722
2 changed files with 14 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
scripts/build_transport_demand.py
View File

@ -13,9 +13,7 @@ import logging
import numpy as np
import pandas as pd
import xarray as xr
from _helpers import configure_logging
from _helpers import generate_periodic_profiles
from _helpers import configure_logging, generate_periodic_profiles
logger = logging.getLogger(__name__)
@ -137,8 +135,10 @@ def bev_availability_profile(fn, snapshots, nodes, options):
)
if not avail[avail < 0].empty:
logger.warning("The BEV availability weekly profile has negative values which can "
"lead to infeasibility.")
logger.warning(
"The BEV availability weekly profile has negative values which can "
"lead to infeasibility."
)
return generate_periodic_profiles(
dt_index=snapshots,

12
scripts/prepare_sector_network.py
View File

@ -1324,7 +1324,7 @@ def add_storage_and_grids(n, costs):
n, "H2 pipeline ", carriers=["DC", "gas pipeline"]
)
h2_pipes["p_nom"] = 0.
h2_pipes["p_nom"] = 0.0
if snakemake.input.get("custom_h2_pipelines"):
fn = snakemake.input.custom_h2_pipelines
@ -1333,8 +1333,14 @@ def add_storage_and_grids(n, costs):
h2_pipes = pd.concat([h2_pipes, wkn])
# drop duplicates according to buses (order can be different) and keep pipe with highest p_nom
h2_pipes['buses_sorted'] = h2_pipes.apply(lambda row: tuple(sorted([row['bus0'], row['bus1']])), axis=1)
h2_pipes = h2_pipes.sort_values('p_nom').drop_duplicates(subset=['buses_sorted'], keep='last').drop(columns = 'buses_sorted')
h2_pipes["buses_sorted"] = h2_pipes.apply(
lambda row: tuple(sorted([row["bus0"], row["bus1"]])), axis=1
)
h2_pipes = (
h2_pipes.sort_values("p_nom")
.drop_duplicates(subset=["buses_sorted"], keep="last")
.drop(columns="buses_sorted")
)
# TODO Add efficiency losses
n.madd(