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

move SEG aggregation later

Browse Source
This commit is contained in:
lisazeyen 2023-08-29 15:46:58 +02:00
parent 0c42ac4bdc
commit c35391d594
2 changed files with 100 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

94
scripts/prepare_perfect_foresight.py
View File

@ -10,13 +10,13 @@ import logging
import re
import pandas as pd
import numpy as np
import pypsa
from _helpers import update_config_with_sector_opts
from add_existing_baseyear import add_build_year_to_new_assets
from pypsa.descriptors import expand_series
from pypsa.io import import_components_from_dataframe
from six import iterkeys
logger = logging.getLogger(__name__)
@ -328,7 +328,90 @@ def add_H2_boilers(n):
# add H2 boilers to network
import_components_from_dataframe(n, df, c)
def apply_time_segmentation_perfect(
n, segments, solver_name="cbc", overwrite_time_dependent=True
):
"""
Aggregating time series to segments with different lengths.
Input:
n: pypsa Network
segments: (int) number of segments in which the typical period should be
subdivided
solver_name: (str) name of solver
overwrite_time_dependent: (bool) overwrite time dependent data of pypsa network
with typical time series created by tsam
"""
try:
import tsam.timeseriesaggregation as tsam
except:
raise ModuleNotFoundError(
"Optional dependency 'tsam' not found." "Install via 'pip install tsam'"
)
# get all time-dependent data
columns = pd.MultiIndex.from_tuples([], names=["component", "key", "asset"])
raw = pd.DataFrame(index=n.snapshots, columns=columns)
for c in n.iterate_components():
for attr, pnl in c.pnl.items():
# exclude e_min_pu which is used for SOC of EVs in the morning
if not pnl.empty and attr != "e_min_pu":
df = pnl.copy()
df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
raw = pd.concat([raw, df], axis=1)
raw = raw.dropna(axis=1)
sn_weightings = {}
for year in raw.index.levels[0]:
logger.info(f"Find representative snapshots for {year}.")
raw_t = raw.loc[year]
# normalise all time-dependent data
annual_max = raw_t.max().replace(0, 1)
raw_t = raw_t.div(annual_max, level=0)
# get representative segments
agg = tsam.TimeSeriesAggregation(
raw_t,
hoursPerPeriod=len(raw_t),
noTypicalPeriods=1,
noSegments=int(segments),
segmentation=True,
solver=solver_name,
)
segmented = agg.createTypicalPeriods()
weightings = segmented.index.get_level_values("Segment Duration")
offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
timesteps = [raw_t.index[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
snapshots = pd.DatetimeIndex(timesteps)
sn_weightings[year] = pd.Series(
weightings, index=snapshots, name="weightings", dtype="float64"
)
sn_weightings = pd.concat(sn_weightings)
n.set_snapshots(sn_weightings.index)
n.snapshot_weightings = n.snapshot_weightings.mul(sn_weightings, axis=0)
# overwrite time-dependent data with timeseries created by tsam
# if overwrite_time_dependent:
# values_t = segmented.mul(annual_max).set_index(snapshots)
# for component, key in values_t.columns.droplevel(2).unique():
# n.pnl(component)[key] = values_t[component, key]
return n
def set_temporal_aggregation_perfect(n, opts, solver_name):
"""
Aggregate network temporally with tsam.
"""
for o in opts:
# segments with package tsam
m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
if m is not None:
segments = int(m[1])
logger.info(f"Use temporal segmentation with {segments} segments")
n = apply_time_segmentation_perfect(n, segments, solver_name=solver_name)
break
return n
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
@ -340,7 +423,7 @@ if __name__ == "__main__":
opts="",
clusters="37",
ll="v1.0",
sector_opts="2p0-4380H-T-H-B-I-A-solar+p3-dist1",
sector_opts="1p7-4380H-T-H-B-I-A-solar+p3-dist1",
)
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
@ -361,6 +444,11 @@ if __name__ == "__main__":
# concat prenetworks of planning horizon to single network ------------
n = concat_networks(years)
# temporal aggregate
opts = snakemake.wildcards.sector_opts.split("-")
solver_name = snakemake.config["solving"]["solver"]["name"]
n = set_temporal_aggregation_perfect(n, opts, solver_name)
# adjust global constraints lv limit if the same for all years
n = adjust_lvlimit(n)
# adjust global constraints CO2 limit

15
scripts/prepare_sector_network.py
View File

@ -716,7 +716,6 @@ def average_every_nhours(n, offset):
return m
def cycling_shift(df, steps=1):
"""
Cyclic shift on index of pd.Series|pd.DataFrame by number of steps.
@ -3269,10 +3268,13 @@ def set_temporal_aggregation(n, opts, solver_name):
# segments with package tsam
m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
if m is not None:
segments = int(m[1])
logger.info(f"Use temporal segmentation with {segments} segments")
n = apply_time_segmentation(n, segments, solver_name=solver_name)
break
if snakemake.params.foresight!="perfect":
segments = int(m[1])
logger.info(f"Use temporal segmentation with {segments} segments")
n = apply_time_segmentation(n, segments, solver_name=solver_name)
break
else:
logger.info("Apply temporal segmentation at prepare_perfect_foresight.")
return n
#%%
@ -3287,7 +3289,7 @@ if __name__ == "__main__":
clusters="37",
ll="v1.0",
sector_opts="60SEG-T-H-B-I-A-solar+p3-dist1",
planning_horizons="2020",
planning_horizons="2050",
)
logging.basicConfig(level=snakemake.config["logging"]["level"])
@ -3389,6 +3391,7 @@ if __name__ == "__main__":
add_allam(n, costs)
solver_name = snakemake.config["solving"]["solver"]["name"]
n = set_temporal_aggregation(n, opts, solver_name)
limit_type = "config"