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A game theory based scheduling approach for charging coordination of multiple electric vehicles aggregators in smart cities

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  • Modarresi, Javad
  • Ahmadian, Ali
  • Diabat, Ali
  • Elkamel, Ali

Abstract

In this paper, a game theory-based coordination approach is presented to find the minimum operation cost of multiple electric vehicle (EV) aggregators that are connected to the upstream network. In order to provide a comprehensive study, various EVs aggregators, including residential, commercial, official, university, and industry parking lots, have been considered and the proposed game theory is utilized to coordinate the charging power from/to different microgrids and/or upstream network. In addition of operation cost, a reliability index, service charge and power loss are included in the objective function. The proposed approach is applied on an electricity network with 7 connected microgrids, and different scenarios have been investigated. The simulation results show the overall operation cost in the coalition operation mode is globally minimized in comparison with the non-coalition operation mode. In addition, the reliability index, service charge and power loss force the aggregators to buy their necessary power from the nearby microgrids. The average network loss in the coalition and non-coalition modes are 2.88 % and 4.28 %, respectively. Moreover, the average reliability cost in coalition and non-coalition modes are $2.65 and $4.25, respectively.

Suggested Citation

  • Modarresi, Javad & Ahmadian, Ali & Diabat, Ali & Elkamel, Ali, 2024. "A game theory based scheduling approach for charging coordination of multiple electric vehicles aggregators in smart cities," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224034522
    DOI: 10.1016/j.energy.2024.133674
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    References listed on IDEAS

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    Cited by:

    1. Chen, Runxin & Song, Dongran & Liao, Liqing & Yang, Jian & Dong, Mi & Talaat, M. & Elkholy, M.H., 2025. "Feedback correction scheduling strategy for electric vehicles based on multi-regional agent master-slave and evolutionary hybrid game," Energy, Elsevier, vol. 319(C).

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