move SEG aggregation later
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lisazeyen
parent
0c42ac4bdc
commit
c35391d594
@ -10,13 +10,13 @@ import logging
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import re
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import pandas as pd
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import numpy as np
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import pypsa
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from _helpers import update_config_with_sector_opts
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from add_existing_baseyear import add_build_year_to_new_assets
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from pypsa.descriptors import expand_series
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from pypsa.io import import_components_from_dataframe
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from six import iterkeys
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logger = logging.getLogger(__name__)
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@ -328,7 +328,90 @@ def add_H2_boilers(n):
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# add H2 boilers to network
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import_components_from_dataframe(n, df, c)
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def apply_time_segmentation_perfect(
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n, segments, solver_name="cbc", overwrite_time_dependent=True
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):
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"""
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Aggregating time series to segments with different lengths.
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Input:
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n: pypsa Network
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segments: (int) number of segments in which the typical period should be
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subdivided
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solver_name: (str) name of solver
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overwrite_time_dependent: (bool) overwrite time dependent data of pypsa network
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with typical time series created by tsam
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"""
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try:
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import tsam.timeseriesaggregation as tsam
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except:
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raise ModuleNotFoundError(
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"Optional dependency 'tsam' not found." "Install via 'pip install tsam'"
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)
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# get all time-dependent data
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columns = pd.MultiIndex.from_tuples([], names=["component", "key", "asset"])
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raw = pd.DataFrame(index=n.snapshots, columns=columns)
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for c in n.iterate_components():
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for attr, pnl in c.pnl.items():
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# exclude e_min_pu which is used for SOC of EVs in the morning
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if not pnl.empty and attr != "e_min_pu":
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df = pnl.copy()
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df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
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raw = pd.concat([raw, df], axis=1)
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raw = raw.dropna(axis=1)
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sn_weightings = {}
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for year in raw.index.levels[0]:
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logger.info(f"Find representative snapshots for {year}.")
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raw_t = raw.loc[year]
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# normalise all time-dependent data
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annual_max = raw_t.max().replace(0, 1)
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raw_t = raw_t.div(annual_max, level=0)
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# get representative segments
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agg = tsam.TimeSeriesAggregation(
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raw_t,
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hoursPerPeriod=len(raw_t),
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noTypicalPeriods=1,
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noSegments=int(segments),
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segmentation=True,
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solver=solver_name,
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)
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segmented = agg.createTypicalPeriods()
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weightings = segmented.index.get_level_values("Segment Duration")
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offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
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timesteps = [raw_t.index[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
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snapshots = pd.DatetimeIndex(timesteps)
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sn_weightings[year] = pd.Series(
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weightings, index=snapshots, name="weightings", dtype="float64"
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)
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sn_weightings = pd.concat(sn_weightings)
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n.set_snapshots(sn_weightings.index)
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n.snapshot_weightings = n.snapshot_weightings.mul(sn_weightings, axis=0)
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# overwrite time-dependent data with timeseries created by tsam
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# if overwrite_time_dependent:
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# values_t = segmented.mul(annual_max).set_index(snapshots)
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# for component, key in values_t.columns.droplevel(2).unique():
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# n.pnl(component)[key] = values_t[component, key]
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return n
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def set_temporal_aggregation_perfect(n, opts, solver_name):
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"""
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Aggregate network temporally with tsam.
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"""
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for o in opts:
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# segments with package tsam
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m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
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if m is not None:
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segments = int(m[1])
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logger.info(f"Use temporal segmentation with {segments} segments")
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n = apply_time_segmentation_perfect(n, segments, solver_name=solver_name)
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break
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return n
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# %%
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if __name__ == "__main__":
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if "snakemake" not in globals():
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@ -340,7 +423,7 @@ if __name__ == "__main__":
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opts="",
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clusters="37",
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ll="v1.0",
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sector_opts="2p0-4380H-T-H-B-I-A-solar+p3-dist1",
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sector_opts="1p7-4380H-T-H-B-I-A-solar+p3-dist1",
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)
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update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
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@ -361,6 +444,11 @@ if __name__ == "__main__":
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# concat prenetworks of planning horizon to single network ------------
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n = concat_networks(years)
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# temporal aggregate
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opts = snakemake.wildcards.sector_opts.split("-")
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solver_name = snakemake.config["solving"]["solver"]["name"]
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n = set_temporal_aggregation_perfect(n, opts, solver_name)
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# adjust global constraints lv limit if the same for all years
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n = adjust_lvlimit(n)
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# adjust global constraints CO2 limit
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@ -716,7 +716,6 @@ def average_every_nhours(n, offset):
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return m
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def cycling_shift(df, steps=1):
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"""
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Cyclic shift on index of pd.Series|pd.DataFrame by number of steps.
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@ -3269,10 +3268,13 @@ def set_temporal_aggregation(n, opts, solver_name):
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# segments with package tsam
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m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
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if m is not None:
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segments = int(m[1])
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logger.info(f"Use temporal segmentation with {segments} segments")
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n = apply_time_segmentation(n, segments, solver_name=solver_name)
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break
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if snakemake.params.foresight!="perfect":
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segments = int(m[1])
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logger.info(f"Use temporal segmentation with {segments} segments")
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n = apply_time_segmentation(n, segments, solver_name=solver_name)
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break
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else:
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logger.info("Apply temporal segmentation at prepare_perfect_foresight.")
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return n
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#%%
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@ -3287,7 +3289,7 @@ if __name__ == "__main__":
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clusters="37",
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ll="v1.0",
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sector_opts="60SEG-T-H-B-I-A-solar+p3-dist1",
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planning_horizons="2020",
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planning_horizons="2050",
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)
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logging.basicConfig(level=snakemake.config["logging"]["level"])
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@ -3389,6 +3391,7 @@ if __name__ == "__main__":
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add_allam(n, costs)
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solver_name = snakemake.config["solving"]["solver"]["name"]
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n = set_temporal_aggregation(n, opts, solver_name)
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limit_type = "config"
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