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Merge pull request #257 from PyPSA/cluster_heat_nodes

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Cluster heat nodes
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lisazeyen 2023-01-30 14:32:43 +01:00 committed by GitHub
commit bb61bf0d7f
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3 changed files with 111 additions and 1 deletions
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1
config.default.yaml
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@ -160,6 +160,7 @@ sector:
2040: 0.6
2050: 1.0
district_heating_loss: 0.15
cluster_heat_buses: false # cluster residential and service heat buses to one to save memory
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
transport_heating_deadband_upper: 20.

6
scripts/add_existing_baseyear.py
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@ -12,7 +12,7 @@ import xarray as xr
import pypsa
import yaml
from prepare_sector_network import prepare_costs, define_spatial
from prepare_sector_network import prepare_costs, define_spatial, cluster_heat_buses
from helper import override_component_attrs, update_config_with_sector_opts
from types import SimpleNamespace
@ -563,5 +563,9 @@ if __name__ == "__main__":
add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years_heat,
ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
if options.get("cluster_heat_buses", False):
cluster_heat_buses(n)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

105
scripts/prepare_sector_network.py
View File

@ -19,6 +19,7 @@ from helper import override_component_attrs, generate_periodic_profiles, update_
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
from networkx.algorithms import complement
from pypsa.geo import haversine_pts
from pypsa.io import import_components_from_dataframe
import logging
logger = logging.getLogger(__name__)
@ -26,6 +27,9 @@ logger = logging.getLogger(__name__)
from types import SimpleNamespace
spatial = SimpleNamespace()
from packaging.version import Version, parse
pd_version = parse(pd.__version__)
agg_group_kwargs = dict(numeric_only=False) if pd_version >= Version("1.3") else {}
def define_spatial(nodes, options):
"""
@ -2593,6 +2597,98 @@ def limit_individual_line_extension(n, maxext):
n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
aggregate_dict = {
"p_nom": "sum",
"s_nom": "sum",
"v_nom": "max",
"v_mag_pu_max": "min",
"v_mag_pu_min": "max",
"p_nom_max": "sum",
"s_nom_max": "sum",
"p_nom_min": "sum",
"s_nom_min": "sum",
'v_ang_min': "max",
"v_ang_max":"min",
"terrain_factor":"mean",
"num_parallel": "sum",
"p_set": "sum",
"e_initial": "sum",
"e_nom": "sum",
"e_nom_max": "sum",
"e_nom_min": "sum",
"state_of_charge_initial": "sum",
"state_of_charge_set": "sum",
"inflow": "sum",
"p_max_pu": "first",
"x": "mean",
"y": "mean"
}
def cluster_heat_buses(n):
"""Cluster residential and service heat buses to one representative bus.
This can be done to save memory and speed up optimisation
"""
def define_clustering(attributes, aggregate_dict):
"""Define how attributes should be clustered.
Input:
attributes : pd.Index()
aggregate_dict: dictionary (key: name of attribute, value
clustering method)
Returns:
agg : clustering dictionary
"""
keys = attributes.intersection(aggregate_dict.keys())
agg = dict(
zip(
attributes.difference(keys),
["first"] * len(df.columns.difference(keys)),
)
)
for key in keys:
agg[key] = aggregate_dict[key]
return agg
logger.info("Cluster residential and service heat buses.")
components = ["Bus", "Carrier", "Generator", "Link", "Load", "Store"]
for c in n.iterate_components(components):
df = c.df
cols = df.columns[df.columns.str.contains("bus") | (df.columns=="carrier")]
# rename columns and index
df[cols] = (df[cols]
.apply(lambda x: x.str.replace("residential ","")
.str.replace("services ", ""), axis=1))
df = df.rename(index=lambda x: x.replace("residential ","")
.replace("services ", ""))
# cluster heat nodes
# static dataframe
agg = define_clustering(df.columns, aggregate_dict)
df = df.groupby(level=0).agg(agg, **agg_group_kwargs)
# time-varying data
pnl = c.pnl
agg = define_clustering(pd.Index(pnl.keys()), aggregate_dict)
for k in pnl.keys():
pnl[k].rename(columns=lambda x: x.replace("residential ","")
.replace("services ", ""), inplace=True)
pnl[k] = (
pnl[k]
.groupby(level=0, axis=1)
.agg(agg[k], **agg_group_kwargs)
)
# remove unclustered assets of service/residential
to_drop = c.df.index.difference(df.index)
n.mremove(c.name, to_drop)
# add clustered assets
to_add = df.index.difference(c.df.index)
import_components_from_dataframe(n, df.loc[to_add], c.name)
def apply_time_segmentation(n, segments, solver_name="cbc",
overwrite_time_dependent=True):
"""Aggregating time series to segments with different lengths
@ -2674,6 +2770,7 @@ def set_temporal_aggregation(n, opts, solver_name):
n = apply_time_segmentation(n, segments, solver_name=solver_name)
break
return n
#%%
if __name__ == "__main__":
if 'snakemake' not in globals():
@ -2820,5 +2917,13 @@ if __name__ == "__main__":
if options['electricity_grid_connection']:
add_electricity_grid_connection(n, costs)
first_year_myopic = ((snakemake.config["foresight"] == 'myopic') and
(snakemake.config["scenario"]["planning_horizons"][0]==investment_year))
if options.get("cluster_heat_buses", False) and not first_year_myopic:
cluster_heat_buses(n)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])