prepare: separate code for transport demand and nodal energy totals
This commit is contained in:
Fabian Neumann
parent
b6cfcf6364
commit
b112da0565
36
Snakefile
36
Snakefile
@ -427,15 +427,45 @@ else:
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build_retro_cost_output = {}
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rule build_population_weighted_energy_totals:
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input:
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energy_totals='resources/energy_totals.csv',
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clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv"
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output: "resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
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threads: 1
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resources: mem_mb=2000
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script: "scripts/build_population_weighted_energy_totals.py"
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rule build_transport_demand:
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input:
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clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
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pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
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transport_data='resources/transport_data.csv',
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traffic_data_KFZ="data/emobility/KFZ__count",
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traffic_data_Pkw="data/emobility/Pkw__count",
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temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
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output:
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transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
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transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
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avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
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dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv"
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threads: 1
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resources: mem_mb=2000
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script: "scripts/build_transport_demand.py"
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rule prepare_sector_network:
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input:
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overrides="data/override_component_attrs",
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network=pypsaeur('networks/elec_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
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energy_totals_name='resources/energy_totals.csv',
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pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
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transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
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transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
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avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
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dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv",
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co2_totals_name='resources/co2_totals.csv',
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transport_name='resources/transport_data.csv',
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traffic_data_KFZ="data/emobility/KFZ__count",
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traffic_data_Pkw="data/emobility/Pkw__count",
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biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
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heat_profile="data/heat_load_profile_BDEW.csv",
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costs=CDIR + "costs_{planning_horizons}.csv",
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22
scripts/build_population_weighted_energy_totals.py
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22
scripts/build_population_weighted_energy_totals.py
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@ -0,0 +1,22 @@
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"""Build population-weighted energy totals."""
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import pandas as pd
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if __name__ == '__main__':
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if 'snakemake' not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake(
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'build_transport_demand',
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simpl='',
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clusters=48,
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)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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energy_totals = pd.read_csv(snakemake.input.energy_totals, index_col=0)
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nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
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nodal_energy_totals.index = pop_layout.index
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nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
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nodal_energy_totals.to_csv(snakemake.output[0])
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201
scripts/build_transport_demand.py
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201
scripts/build_transport_demand.py
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@ -0,0 +1,201 @@
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"""Build transport demand."""
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import pandas as pd
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import numpy as np
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import xarray as xr
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from helper import generate_periodic_profiles
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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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nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
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nodal_transport_data.index = pop_layout.index
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nodal_transport_data["number cars"] = (
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pop_layout["fraction"] * nodal_transport_data["number cars"]
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)
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nodal_transport_data.loc[
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nodal_transport_data["average fuel efficiency"] == 0.0,
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"average fuel efficiency",
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] = transport_data["average fuel efficiency"].mean()
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return nodal_transport_data
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def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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## Get overall demand curve for all vehicles
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traffic = pd.read_csv(
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traffic_fn, skiprows=2, usecols=["count"], squeeze=True
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)
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transport_shape = generate_periodic_profiles(
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dt_index=snapshots,
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nodes=nodes,
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weekly_profile=traffic.values,
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)
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transport_shape = transport_shape / transport_shape.sum()
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# electric motors are more efficient, so alter transport demand
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plug_to_wheels_eta = options["bev_plug_to_wheel_efficiency"]
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battery_to_wheels_eta = plug_to_wheels_eta * options["bev_charge_efficiency"]
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efficiency_gain = (
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nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
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)
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# get heating demand for correction to demand time series
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temperature = xr.open_dataarray(airtemp_fn).to_pandas()
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# correction factors for vehicle heating
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dd_ICE = transport_degree_factor(
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temperature,
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options["transport_heating_deadband_lower"],
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options["transport_heating_deadband_upper"],
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options["ICE_lower_degree_factor"],
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options["ICE_upper_degree_factor"],
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)
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dd_EV = transport_degree_factor(
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temperature,
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options["transport_heating_deadband_lower"],
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options["transport_heating_deadband_upper"],
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options["EV_lower_degree_factor"],
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options["EV_upper_degree_factor"],
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)
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# divide out the heating/cooling demand from ICE totals
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# and multiply back in the heating/cooling demand for EVs
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ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
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energy_totals_transport = (
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pop_weighted_energy_totals["total road"]
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+ pop_weighted_energy_totals["total rail"]
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- pop_weighted_energy_totals["electricity rail"]
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)
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transport = (
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(transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears)
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.divide(efficiency_gain * ice_correction)
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.multiply(1 + dd_EV)
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)
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return transport
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def transport_degree_factor(
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temperature,
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deadband_lower=15,
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deadband_upper=20,
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lower_degree_factor=0.5,
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upper_degree_factor=1.6,
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):
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"""
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Work out how much energy demand in vehicles increases due to heating and cooling.
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There is a deadband where there is no increase.
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Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
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Returns per unit increase in demand for each place and time
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"""
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dd = temperature.copy()
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dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.0
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dT_lower = deadband_lower - temperature[temperature < deadband_lower]
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dd[temperature < deadband_lower] = lower_degree_factor / 100 * dT_lower
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dT_upper = temperature[temperature > deadband_upper] - deadband_upper
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dd[temperature > deadband_upper] = upper_degree_factor / 100 * dT_upper
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return dd
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def bev_availability_profile(fn, snapshots, nodes, options):
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"""
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Derive plugged-in availability for passenger electric vehicles.
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"""
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traffic = pd.read_csv(fn, skiprows=2, usecols=["count"], squeeze=True)
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avail_max = options["bev_avail_max"]
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avail_mean = options["bev_avail_mean"]
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avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (
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traffic.mean() - traffic.min()
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)
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avail_profile = generate_periodic_profiles(
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dt_index=snapshots,
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nodes=nodes,
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weekly_profile=avail.values,
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)
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return avail_profile
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def bev_dsm_profile(snapshots, nodes, options):
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dsm_week = np.zeros((24 * 7,))
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dsm_week[(np.arange(0, 7, 1) * 24 + options["bev_dsm_restriction_time"])] = options[
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"bev_dsm_restriction_value"
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]
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dsm_profile = generate_periodic_profiles(
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dt_index=snapshots,
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nodes=nodes,
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weekly_profile=dsm_week,
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)
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return dsm_profile
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake(
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"build_transport_demand",
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simpl="",
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clusters=48,
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)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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nodes = pop_layout.index
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pop_weighted_energy_totals = pd.read_csv(
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snakemake.input.pop_weighted_energy_totals, index_col=0
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)
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options = snakemake.config["sector"]
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snapshots = pd.date_range(freq='h', **snakemake.config["snapshots"], tz="UTC")
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Nyears = 1
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nodal_transport_data = build_nodal_transport_data(
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snakemake.input.transport_data,
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pop_layout
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)
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transport_demand = build_transport_demand(
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snakemake.input.traffic_data_KFZ,
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snakemake.input.temp_air_total,
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nodes, nodal_transport_data
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)
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avail_profile = bev_availability_profile(
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snakemake.input.traffic_data_Pkw,
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snapshots, nodes, options
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)
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dsm_profile = bev_dsm_profile(snapshots, nodes, options)
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nodal_transport_data.to_csv(snakemake.output.transport_data)
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transport_demand.to_csv(snakemake.output.transport_demand)
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avail_profile.to_csv(snakemake.output.avail_profile)
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dsm_profile.to_csv(snakemake.output.dsm_profile)
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@ -1,4 +1,5 @@
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import os
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import pytz
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import pandas as pd
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from pathlib import Path
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from pypsa.descriptors import Dict
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@ -101,3 +102,24 @@ def progress_retrieve(url, file):
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pbar.update( int(count * blockSize * 100 / totalSize) )
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urllib.request.urlretrieve(url, file, reporthook=dlProgress)
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def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
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"""
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Give a 24*7 long list of weekly hourly profiles, generate this for each
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country for the period dt_index, taking account of time zones and summer time.
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"""
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weekly_profile = pd.Series(weekly_profile, range(24*7))
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week_df = pd.DataFrame(index=dt_index, columns=nodes)
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for node in nodes:
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timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
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tz_dt_index = dt_index.tz_convert(timezone)
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week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
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week_df[node] = week_df[node].map(weekly_profile)
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week_df = week_df.tz_localize(localize)
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return week_df
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@ -3,7 +3,6 @@
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import pypsa
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import re
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import os
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import pytz
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import pandas as pd
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import numpy as np
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@ -15,7 +14,7 @@ from scipy.stats import beta
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from vresutils.costdata import annuity
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from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
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from helper import override_component_attrs
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from helper import override_component_attrs, generate_periodic_profiles
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from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
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from networkx.algorithms import complement
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@ -565,27 +564,6 @@ def average_every_nhours(n, offset):
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return m
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def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
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"""
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Give a 24*7 long list of weekly hourly profiles, generate this for each
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country for the period dt_index, taking account of time zones and summer time.
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"""
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weekly_profile = pd.Series(weekly_profile, range(24*7))
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week_df = pd.DataFrame(index=dt_index, columns=nodes)
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for node in nodes:
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timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
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tz_dt_index = dt_index.tz_convert(timezone)
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week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
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week_df[node] = week_df[node].map(weekly_profile)
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week_df = week_df.tz_localize(localize)
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return week_df
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def cycling_shift(df, steps=1):
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"""Cyclic shift on index of pd.Series|pd.DataFrame by number of steps"""
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df = df.copy()
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@ -594,55 +572,10 @@ def cycling_shift(df, steps=1):
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return df
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def transport_degree_factor(
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temperature,
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deadband_lower=15,
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deadband_upper=20,
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lower_degree_factor=0.5,
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upper_degree_factor=1.6):
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"""
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Work out how much energy demand in vehicles increases due to heating and cooling.
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There is a deadband where there is no increase.
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Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
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Returns per unit increase in demand for each place and time
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"""
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dd = temperature.copy()
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dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.
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dT_lower = deadband_lower - temperature[temperature < deadband_lower]
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dd[temperature < deadband_lower] = lower_degree_factor / 100 * dT_lower
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dT_upper = temperature[temperature > deadband_upper] - deadband_upper
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dd[temperature > deadband_upper] = upper_degree_factor / 100 * dT_upper
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return dd
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def build_heat_demand(n):
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# TODO separate sectors and move into own rules
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def prepare_data(n):
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##############
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#Heating
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##############
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ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
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gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
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solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
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# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
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solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
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energy_totals = pd.read_csv(snakemake.input.energy_totals_name, index_col=0)
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nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
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nodal_energy_totals.index = pop_layout.index
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# district heat share not weighted by population
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district_heat_share = nodal_energy_totals["district heat share"].round(2)
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nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
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# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
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daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
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@ -669,8 +602,8 @@ def prepare_data(n):
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else:
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heat_demand_shape = intraday_year_profile
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heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"total {sector} {use}"]) * 1e6
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electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"electricity {sector} {use}"]) * 1e6
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heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
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electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
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heat_demand = pd.concat(heat_demand, axis=1)
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electric_heat_supply = pd.concat(electric_heat_supply, axis=1)
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@ -679,92 +612,7 @@ def prepare_data(n):
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electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
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n.loads_t.p_set[electric_nodes] = n.loads_t.p_set[electric_nodes] - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]
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##############
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#Transport
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##############
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## Get overall demand curve for all vehicles
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traffic = pd.read_csv(snakemake.input.traffic_data_KFZ, skiprows=2, usecols=["count"], squeeze=True)
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#Generate profiles
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transport_shape = generate_periodic_profiles(
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dt_index=n.snapshots.tz_localize("UTC"),
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nodes=pop_layout.index,
|
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weekly_profile=traffic.values
|
||||
)
|
||||
transport_shape = transport_shape / transport_shape.sum()
|
||||
|
||||
transport_data = pd.read_csv(snakemake.input.transport_name, index_col=0)
|
||||
|
||||
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.)
|
||||
nodal_transport_data.index = pop_layout.index
|
||||
nodal_transport_data["number cars"] = pop_layout["fraction"] * nodal_transport_data["number cars"]
|
||||
nodal_transport_data.loc[nodal_transport_data["average fuel efficiency"] == 0., "average fuel efficiency"] = transport_data["average fuel efficiency"].mean()
|
||||
|
||||
|
||||
# electric motors are more efficient, so alter transport demand
|
||||
|
||||
plug_to_wheels_eta = options.get("bev_plug_to_wheel_efficiency", 0.2)
|
||||
battery_to_wheels_eta = plug_to_wheels_eta * options.get("bev_charge_efficiency", 0.9)
|
||||
|
||||
efficiency_gain = nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
|
||||
|
||||
#get heating demand for correction to demand time series
|
||||
temperature = xr.open_dataarray(snakemake.input.temp_air_total).to_pandas()
|
||||
|
||||
# correction factors for vehicle heating
|
||||
dd_ICE = transport_degree_factor(
|
||||
temperature,
|
||||
options['transport_heating_deadband_lower'],
|
||||
options['transport_heating_deadband_upper'],
|
||||
options['ICE_lower_degree_factor'],
|
||||
options['ICE_upper_degree_factor']
|
||||
)
|
||||
|
||||
dd_EV = transport_degree_factor(
|
||||
temperature,
|
||||
options['transport_heating_deadband_lower'],
|
||||
options['transport_heating_deadband_upper'],
|
||||
options['EV_lower_degree_factor'],
|
||||
options['EV_upper_degree_factor']
|
||||
)
|
||||
|
||||
# divide out the heating/cooling demand from ICE totals
|
||||
# and multiply back in the heating/cooling demand for EVs
|
||||
ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
|
||||
|
||||
energy_totals_transport = nodal_energy_totals["total road"] + nodal_energy_totals["total rail"] - nodal_energy_totals["electricity rail"]
|
||||
|
||||
transport = (transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears).divide(efficiency_gain * ice_correction).multiply(1 + dd_EV)
|
||||
|
||||
## derive plugged-in availability for PKW's (cars)
|
||||
|
||||
traffic = pd.read_csv(snakemake.input.traffic_data_Pkw, skiprows=2, usecols=["count"], squeeze=True)
|
||||
|
||||
avail_max = options.get("bev_avail_max", 0.95)
|
||||
avail_mean = options.get("bev_avail_mean", 0.8)
|
||||
|
||||
avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (traffic.mean() - traffic.min())
|
||||
|
||||
avail_profile = generate_periodic_profiles(
|
||||
dt_index=n.snapshots.tz_localize("UTC"),
|
||||
nodes=pop_layout.index,
|
||||
weekly_profile=avail.values
|
||||
)
|
||||
|
||||
dsm_week = np.zeros((24*7,))
|
||||
|
||||
dsm_week[(np.arange(0,7,1) * 24 + options['bev_dsm_restriction_time'])] = options['bev_dsm_restriction_value']
|
||||
|
||||
dsm_profile = generate_periodic_profiles(
|
||||
dt_index=n.snapshots.tz_localize("UTC"),
|
||||
nodes=pop_layout.index,
|
||||
weekly_profile=dsm_week
|
||||
)
|
||||
|
||||
|
||||
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share
|
||||
return heat_demand
|
||||
|
||||
|
||||
# TODO checkout PyPSA-Eur script
|
||||
@ -1324,6 +1172,11 @@ def add_land_transport(n, costs):
|
||||
|
||||
logger.info("Add land transport")
|
||||
|
||||
transport = pd.read_csv(snakemake.input.transport_demand, index_col=0, parse_dates=True)
|
||||
number_cars = pd.read_csv(snakemake.input.transport_data, index_col=0)["number cars"]
|
||||
avail_profile = pd.read_csv(snakemake.input.avail_profile, index_col=0, parse_dates=True)
|
||||
dsm_profile = pd.read_csv(snakemake.input.dsm_profile, index_col=0, parse_dates=True)
|
||||
|
||||
fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
|
||||
electric_share = get(options["land_transport_electric_share"], investment_year)
|
||||
ice_share = 1 - fuel_cell_share - electric_share
|
||||
@ -1357,8 +1210,7 @@ def add_land_transport(n, costs):
|
||||
p_set=p_set
|
||||
)
|
||||
|
||||
|
||||
p_nom = nodal_transport_data["number cars"] * options.get("bev_charge_rate", 0.011) * electric_share
|
||||
p_nom = number_cars * options.get("bev_charge_rate", 0.011) * electric_share
|
||||
|
||||
n.madd("Link",
|
||||
nodes,
|
||||
@ -1390,7 +1242,7 @@ def add_land_transport(n, costs):
|
||||
|
||||
if electric_share > 0 and options["bev_dsm"]:
|
||||
|
||||
e_nom = nodal_transport_data["number cars"] * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
|
||||
e_nom = number_cars * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
|
||||
|
||||
n.madd("Store",
|
||||
nodes,
|
||||
@ -1468,6 +1320,15 @@ def add_heat(n, costs):
|
||||
"urban central"
|
||||
]
|
||||
|
||||
cop = {
|
||||
"air": xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots),
|
||||
"ground": xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
|
||||
}
|
||||
|
||||
solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
|
||||
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
|
||||
solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
|
||||
|
||||
for name in heat_systems:
|
||||
|
||||
name_type = "central" if name == "urban central" else "decentral"
|
||||
@ -1513,7 +1374,6 @@ def add_heat(n, costs):
|
||||
heat_pump_type = "air" if "urban" in name else "ground"
|
||||
|
||||
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
|
||||
cop = {"air" : ashp_cop, "ground" : gshp_cop}
|
||||
efficiency = cop[heat_pump_type][nodes[name]] if options["time_dep_hp_cop"] else costs.at[costs_name, 'efficiency']
|
||||
|
||||
n.madd("Link",
|
||||
@ -1792,6 +1652,8 @@ def create_nodes_for_heat_sector():
|
||||
nodes[sector + " rural"] = pop_layout.index
|
||||
nodes[sector + " urban decentral"] = pop_layout.index
|
||||
|
||||
district_heat_share = pop_weighted_energy_totals["district heat share"]
|
||||
|
||||
# maximum potential of urban demand covered by district heating
|
||||
central_fraction = options['district_heating']["potential"]
|
||||
# district heating share at each node
|
||||
@ -2075,7 +1937,7 @@ def add_industry(n, costs):
|
||||
all_navigation = ["total international navigation", "total domestic navigation"]
|
||||
efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
|
||||
shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
|
||||
p_set = shipping_hydrogen_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
|
||||
p_set = shipping_hydrogen_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
|
||||
|
||||
n.madd("Load",
|
||||
nodes,
|
||||
@ -2089,7 +1951,7 @@ def add_industry(n, costs):
|
||||
|
||||
shipping_oil_share = 1 - shipping_hydrogen_share
|
||||
|
||||
p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
|
||||
p_set = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
|
||||
|
||||
n.madd("Load",
|
||||
nodes,
|
||||
@ -2099,7 +1961,7 @@ def add_industry(n, costs):
|
||||
p_set=p_set
|
||||
)
|
||||
|
||||
co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
|
||||
co2 = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
|
||||
|
||||
n.add("Load",
|
||||
"shipping oil emissions",
|
||||
@ -2177,7 +2039,7 @@ def add_industry(n, costs):
|
||||
)
|
||||
|
||||
all_aviation = ["total international aviation", "total domestic aviation"]
|
||||
p_set = nodal_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
|
||||
p_set = pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
|
||||
|
||||
n.add("Load",
|
||||
"kerosene for aviation",
|
||||
@ -2297,7 +2159,7 @@ def add_agriculture(n, costs):
|
||||
suffix=" agriculture electricity",
|
||||
bus=nodes,
|
||||
carrier='agriculture electricity',
|
||||
p_set=nodal_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
|
||||
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
|
||||
)
|
||||
|
||||
# heat
|
||||
@ -2307,7 +2169,7 @@ def add_agriculture(n, costs):
|
||||
suffix=" agriculture heat",
|
||||
bus=nodes + " services rural heat",
|
||||
carrier="agriculture heat",
|
||||
p_set=nodal_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
|
||||
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
|
||||
)
|
||||
|
||||
# machinery
|
||||
@ -2316,7 +2178,7 @@ def add_agriculture(n, costs):
|
||||
assert electric_share <= 1.
|
||||
ice_share = 1 - electric_share
|
||||
|
||||
machinery_nodal_energy = nodal_energy_totals.loc[nodes, "total agriculture machinery"]
|
||||
machinery_nodal_energy = pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"]
|
||||
|
||||
if electric_share > 0:
|
||||
|
||||
@ -2436,6 +2298,8 @@ if __name__ == "__main__":
|
||||
Nyears,
|
||||
snakemake.config['costs']['lifetime'])
|
||||
|
||||
pop_weighted_energy_totals = pd.read_csv(snakemake.input.pop_weighted_energy_totals, index_col=0)
|
||||
|
||||
patch_electricity_network(n)
|
||||
|
||||
define_spatial(pop_layout.index)
|
||||
@ -2466,7 +2330,7 @@ if __name__ == "__main__":
|
||||
if o == "biomasstransport":
|
||||
options["biomass_transport"] = True
|
||||
|
||||
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)
|
||||
heat_demand = build_heat_demand(n)
|
||||
|
||||
if "nodistrict" in opts:
|
||||
options["district_heating"]["progress"] = 0.0
|
||||
|
||||
Loading…
Reference in New Issue
Block a user