Research on the Dispatching of Electric Vehicles Participating in Vehicle-to-Grid Interaction: Considering Grid Stability and User Benefits

Gang Zhang (), Hong Liu, Tuo Xie, Hua Li, Kaoshe Zhang and Ruogu Wang
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Gang Zhang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Hong Liu: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Tuo Xie: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Hua Li: State Grid Shaanxi Electric Power Co., Ltd., Electric Power Research Institute, Xi’an 710048, China
Kaoshe Zhang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Ruogu Wang: State Grid Shaanxi Electric Power Co., Ltd., Electric Power Research Institute, Xi’an 710048, China

Energies, 2024, vol. 17, issue 4, 1-24

Abstract: As the prevalence of electric vehicles (EVs) continues to grow, their charging and discharging behaviors pose a challenge to the stable operation of power systems. Therefore, this paper analyzes the charging demand of EV users through GPS trajectory data and proposes an EV-discharging-optimization model based on vehicle-to-grid interaction (V2G). Firstly, the spatial–temporal distribution of EV-charging demand is obtained by cleaning and mining the big data of traveling vehicles, considering dynamic energy consumption theory and users’ willingness; secondly, a probabilistic model of EV users’ participation in V2G-demand response is constructed based on expected utility theory, which both considers the heterogeneity of users and reflects the interactive influence of users’ decisions; finally, a scheduling model of EV discharging in the regional grid is established. The results show that the proposed model can explore the potential of user participation in V2G in the study area, and the V2G response resources can reduce the grid fluctuation and enable users to obtain certain benefits, which achieves a win–win situation between the grid side and the user side.

Keywords: electric vehicles (EVs); spatial–temporal distribution of charging demand; vehicle-to-grid interaction (V2G); behavioral decision making; discharge scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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