# The datafev framework
# Copyright (C) 2022,
# Institute for Automation of Complex Power Systems (ACS),
# E.ON Energy Research Center (E.ON ERC),
# RWTH Aachen University
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import numpy as np
import pandas as pd
[docs]def reservation_routine(ts, tdelta, system, fleet, traffic_forecast):
"""
This routine is executed to reserve chargers for the EVs approaching a multi-cluster system.
The smart reservations specifies the cluster and charger the approaching EVs must connect to.
Parameters
----------
ts : datetime
Current time.
tdelta : timedelta
Resolution of scheduling.
system : data_handling.multi_cluster
Multi-cluster system object.
fleet : data_handling.fleet
EV fleet object.
traffic_forecast : dict of dict
Traffic forecast data.
Returns
-------
None.
"""
reserving_vehicles = fleet.reserving_vehicles_at(ts)
for ev in reserving_vehicles:
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# Step 1: Identify available chargers
available_chargers = system.query_availability(
ev.t_arr_est, ev.t_dep_est, tdelta, traffic_forecast
)
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if len(available_chargers) == 0:
ev.reserved = False
else:
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# Step 2: Apply a specific reservation management strategy
# In the simple resevation strategy, an available charger is selected randomly
selected_charger_id = np.random.choice(list(available_chargers.index))
selected_cluster_id = available_chargers.loc[selected_charger_id, "cluster"]
selected_cluster = system.clusters[selected_cluster_id]
selected_charger = selected_cluster.chargers[selected_charger_id]
# End: Reservation management strategy
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# Step 3: Reserve the selected charger for the EV and assign relevant reservation parameters
res_at = ts
res_from = ev.t_arr_est + traffic_forecast["arr_del"][selected_cluster_id]
res_until = ev.t_dep_est + traffic_forecast["dep_del"][selected_cluster_id]
selected_cluster.reserve(res_at, res_from, res_until, ev, selected_charger)
ev.reserved = True
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