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Vehicle-to-grid management for multi-time scale grid power balancing

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  • Li, Shuangqi
  • Gu, Chenghong
  • Zeng, Xianwu
  • Zhao, Pengfei
  • Pei, Xiaoze
  • Cheng, Shuang

Abstract

The mitigation of peak-valley difference and transient power fluctuation are both of great significance to the economy and stability of the power grid. This paper proposes a novel vehicle-to-grid behavior management method that can provide peak-shaving and fast power balancing service to the grid simultaneously. Firstly, a multi-time scale vehicle-to-grid behavior management framework is designed to enable large-scale optimization and real-time control at the same time in vehicle-to-grid scheduling. Then, the grid peak-shaving requirement is modeled as an optimization problem in a centralized V2G state coordinator, where the charging behavior of all grid-connected electric vehicles can be synergistically scheduled. The optimization variable is designed as a group of vehicle-to-grid state control signals that can respond to grid peak-shaving requirements. Further, a V2G power controller is designed to manage the vehicle charging power in real time based on the sampled grid frequency state and discrete state control signals. In the developed scheduling method, the charging power of grid-connected electric vehicles is scheduled by the cooperation between the V2G state coordinator and the power controller. The effectiveness of the proposed methodologies is verified on a microgrid system, and results indicate that the V2G scheduling can achieve multi-time scale grid power balancing. This work can bring dual benefits, enabling system operators to use cheap solutions to manage energy networks and allowing vehicle owners to gain profits from providing V2G services to the grid.

Suggested Citation

  • Li, Shuangqi & Gu, Chenghong & Zeng, Xianwu & Zhao, Pengfei & Pei, Xiaoze & Cheng, Shuang, 2021. "Vehicle-to-grid management for multi-time scale grid power balancing," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014493
    DOI: 10.1016/j.energy.2021.121201
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    References listed on IDEAS

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    2. Güven, Aykut Fatih, 2024. "Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management," Energy, Elsevier, vol. 303(C).
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    4. Rahman, Md Mustafizur & Gemechu, Eskinder & Oni, Abayomi Olufemi & Kumar, Amit, 2023. "The development of a techno-economic model for assessment of cost of energy storage for vehicle-to-grid applications in a cold climate," Energy, Elsevier, vol. 262(PA).

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