A comprehensive day-ahead scheduling strategy for electric vehicles operation

Mahsa Bagheri Tookanlou*, S. Ali Pourmousavi Kani, Mousa Marzband

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Distribution networks are envisaged to host significant number of electric vehicles and potentially many charging stations in the future to provide charging as well as vehicle-2-grid services to the electric vehicle owners. The main goal of this study is to develop a comprehensive day-ahead scheduling framework to achieve an economically rewarding operation for the ecosystem of electric vehicles, charging stations and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, an equilibrium problem is solved using a three-layer iterative optimisation problem for all stakeholders in the ecosystem. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To evaluate the effectiveness of the proposed framework, a simulation study, including three retailers, one aggregator, nine charging stations and 600 electric vehicles, is designed based on real data from San Francisco, the USA. The simulation results show that the total cost of electric vehicles decreased by 17.6%, and the total revenue of charging stations and retailers increased by 21.1% and 22.6%, respectively, in comparison with a base case strategy.
Original languageEnglish
Article number106912
Number of pages17
JournalInternational Journal of Electrical Power & Energy Systems
Volume131
Early online date15 Apr 2021
DOIs
Publication statusPublished - 1 Oct 2021

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