clear up units
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@ -430,8 +430,8 @@ sector:
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2040: 0.3
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2045: 0.15
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2050: 0
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transport_fuel_cell_efficiency: 0.5
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transport_internal_combustion_efficiency: 0.3
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transport_fuel_cell_efficiency: 30.003 # MWh_H2 per 100 km
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transport_internal_combustion_efficiency: 16.0712 # MWh_oil per 100 km
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agriculture_machinery_electric_share: 0
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agriculture_machinery_oil_share: 1
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agriculture_machinery_fuel_efficiency: 0.7
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@ -45,7 +45,7 @@ def build_nodal_transport_data(fn, pop_layout, year):
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def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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"""
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Returns transport demand per bus in unit kinetic energy.
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Returns transport demand per bus in unit km driven [100 km].
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"""
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# averaged weekly counts from the year 2010-2015
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traffic = pd.read_csv(traffic_fn, skiprows=2, usecols=["count"]).squeeze("columns")
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@ -78,9 +78,12 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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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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# convert average fuel efficiency from kW/100 km -> MW/100km
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eff = nodal_transport_data["average fuel efficiency"] * 1e3
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return (transport_shape.multiply(energy_totals_transport) * 1e6 * nyears).divide(
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nodal_transport_data["average fuel efficiency"] * ice_correction
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eff * ice_correction
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)
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@ -1562,7 +1562,7 @@ def add_EVs(
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unit="MWh_el",
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)
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car_efficiency = costs.at["Battery electric (passenger cars)", "efficiency"]
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car_efficiency = options["transport_electric_efficiency"]
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# temperature corrected efficiency
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efficiency = get_temp_efficency(
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@ -1730,7 +1730,7 @@ def add_land_transport(n, costs):
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logger.info("Add land transport")
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# read in transport demand in units kinetic energy
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# read in transport demand in units driven km [100 km]
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transport = pd.read_csv(
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snakemake.input.transport_demand, index_col=0, parse_dates=True
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)
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@ -1764,7 +1764,7 @@ def add_land_transport(n, costs):
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location=nodes,
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suffix=" land transport",
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carrier="land transport demand",
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unit="MWh_kinetic",
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unit="100 km",
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)
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p_set = transport[nodes]
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