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Feedback correction scheduling strategy for electric vehicles based on multi-regional agent master-slave and evolutionary hybrid game

Author

Listed:
  • Chen, Runxin
  • Song, Dongran
  • Liao, Liqing
  • Yang, Jian
  • Dong, Mi
  • Talaat, M.
  • Elkholy, M.H.

Abstract

The large-scale participation of electric vehicles in power grid scheduling will provide important flexibility resources for the new power system. When electric vehicles participate in scheduled dispatching, their changing proportion could affect the safe and stable operation of the system and the interests of various entities. To resolve this issue, this paper proposes a feedback correction scheduling strategy for electric vehicles based on a multi-regional agent master-slave and evolutionary hybrid game. Firstly, the demand response mechanism of real-time pricing in virtual power plants is studied based on a master-slave game model. Secondly, as followers of the virtual power plant operator, electric vehicle users adjust their charging and discharging amounts and regional selection probabilities based on evolutionary game models. Finally, a load feedback correction model is established to track the deviation between planned and actual response loads in real-time. The case study analysis shows that the proposed strategy has a relative deviation of only 2.12 % in actual load response, ensuring the safe and stable operation of the system. Additionally, the overall electric vehicle charging cost is reduced by 24.6 %, while the revenue of virtual power plant operator increases by 43.2 % and the revenue of distribution system operator increases by 44.6 %.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:319:y:2025:i:c:s0360544225006267
    DOI: 10.1016/j.energy.2025.134984
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