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

Bagheri Tookanlou, Mahsa, Pourmousavikani, Seyyed Ali and Marzband, Mousa (2021) An Optimal Day-Ahead Scheduling Framework for E-Mobility Ecosystem Operation with Drivers Preferences. IEEE Transactions on Power Systems, 36 (6). pp. 5245-5257. ISSN 0885-8950

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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 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.

Item Type: Article
Additional Information: Funding information: This work is funded by PGR scholarship at Northumbria University and supported by a project funded by the British Council under grant contract No. IND/CONT/GA/18-19/22.
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 31 Mar 2021 17:37
Last Modified: 01 Nov 2021 09:24

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