An Optimal Day-Ahead Scheduling Framework for E-Mobility Ecosystem Operation With Drivers' Preferences

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (...

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Bibliographic Details
Published in:IEEE transactions on power systems 2021-11, Vol.36 (6), p.5245-5257
Main Authors: Bagheri Tookanlou, Mahsa, Pourmousavi Kani, S. Ali, Marzband, Mousa
Format: Article
Language:English
Subjects:
Batteries
Congestion
E-mobility ecosystem
Ecosystems
Electric vehicle charging
EV drivers’ preferences
G2V and V2G operation
Iterative methods
Optimization
optional trips
Revenue
Route selection
Scheduling
Simulation
three-layer optimization problem
Vehicle-to-grid
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Summary:The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers' preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers' preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3068689