Add warning when negative bev availability profile values
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@ -12,7 +12,8 @@ Upcoming Release
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* New configuration option ``everywhere_powerplants`` to build conventional powerplants everywhere, irrespective of existing powerplants locations, in the network (https://github.com/PyPSA/pypsa-eur/pull/850).
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* New configuration option ``everywhere_powerplants`` to build conventional powerplants everywhere, irrespective of existing powerplants locations, in the network (https://github.com/PyPSA/pypsa-eur/pull/850).
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* Remove option for wave energy as technology data is not maintained.
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* Remove option for wave energy as technology data is not maintained.
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* Add warning when BEV availability weekly profile has negative values in `build_transport_demand`.
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PyPSA-Eur 0.9.0 (5th January 2024)
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PyPSA-Eur 0.9.0 (5th January 2024)
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@ -8,11 +8,17 @@ improvements due to drivetrain changes, time series for electric vehicle
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availability and demand-side management constraints.
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availability and demand-side management constraints.
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"""
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"""
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import logging
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import numpy as np
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import numpy as np
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import pandas as pd
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import pandas as pd
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import xarray as xr
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import xarray as xr
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from _helpers import configure_logging
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from _helpers import generate_periodic_profiles
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from _helpers import generate_periodic_profiles
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logger = logging.getLogger(__name__)
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def build_nodal_transport_data(fn, pop_layout):
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def build_nodal_transport_data(fn, pop_layout):
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transport_data = pd.read_csv(fn, index_col=0)
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transport_data = pd.read_csv(fn, index_col=0)
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@ -130,6 +136,10 @@ def bev_availability_profile(fn, snapshots, nodes, options):
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traffic.mean() - traffic.min()
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traffic.mean() - traffic.min()
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)
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)
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if not avail[avail < 0].empty:
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logger.warning("The BEV availability weekly profile has negative values which can "
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"lead to infeasibility.")
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return generate_periodic_profiles(
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return generate_periodic_profiles(
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dt_index=snapshots,
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dt_index=snapshots,
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nodes=nodes,
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nodes=nodes,
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@ -160,6 +170,7 @@ if __name__ == "__main__":
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simpl="",
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simpl="",
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clusters=48,
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clusters=48,
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)
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)
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configure_logging(snakemake)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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