4.7 Article

Non-Cooperative and Cooperative Optimization of Scheduling With Vehicle-to-Grid Regulation Services

期刊

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 69, 期 1, 页码 114-130

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2952712

关键词

Electric vehicles (EVs); frequency regulation; game theory; vehicle-to-grid (V2G)

资金

  1. Research Grants Council of the Hong Kong Special Administrative Region, China [17261416]

向作者/读者索取更多资源

Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for the power grid, stimulates new ideas in current smart grid research. Since EVs are selfish individuals owned by different parties, how to motivate them to provide ancillary services becomes an issue. In this paper, game theoretic approaches using non-cooperative and cooperative game are proposed to motivate EVs to provide frequency regulation services for the power grid. In a non-cooperative V2G system, the interaction between the EV aggregator and EVs is formulated as a non-cooperative Stackelberg game. The EV aggregator as the leader decides the electricity trading price, and EVs as the followers determine their charging/discharging strategies. In a cooperative V2G system, a potential game is formulated to achieve the optimal social welfare of the V2G system. The existence and uniqueness of the Nash equilibrium of these two games are validated. Our simulation results show that the proposed game theoretic approaches can motivate EVs to smooth out the power fluctuations from the grid while EVs schedule their charging/discharging activities to maximize their utilities. This demonstrates the effectiveness of the use of the V2G game in providing regulation services to the grid. Through cooperation and extra information exchange, the social welfare of EVs and the EV aggregator can be improved to the global optimum and the V2G regulation services can also achieve near-optimal performance.

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