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A Stackelberg-based competition model for optimal participation of electric vehicle load aggregators in demand response programs

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  • Zheng, Yanchong
  • Chen, Yuanyi
  • Yang, Qiang

Abstract

Plug-in electric vehicles are considered a flexible resource to participate in demand response programs via electric vehicle load aggregators. In practice, the aggregator often faces competition from other independent aggregators when the number of aggregated electric vehicles is limited. In this paper, a multi-leader multi-follower Stackelberg game is formulated to investigate the interactive behaviors between multiple aggregators and massive electric vehicles. In the game, aggregators act as leaders, setting the incentive price to aggregate demand response resources from electric vehicles for payoff maximization. Electric vehicle users act as followers, reducing the charging loads to achieve welfare maximization based on the aggregators' incentives. It is demonstrated that a unique Nash equilibrium solution always exists among multiple aggregators by utilizing the potential game. Moreover, for an exact potential game, the equilibrium solution is equivalent to maximizing the potential function of the game on its strategy set. The probabilistic models are adopted to capture a range of possible charging scenarios to address the charging uncertainty of electric vehicles. The proposed approach is assessed through simulation experiments and the numerical results indicate that aggregators can achieve total profit maximization by adopting the proposed equilibrium strategy. In addition, the factors affecting the Nash equilibrium, e.g., the expected demand response capacity of aggregators, and the available demand response capacity from electric vehicles, are also examined.

Suggested Citation

  • Zheng, Yanchong & Chen, Yuanyi & Yang, Qiang, 2025. "A Stackelberg-based competition model for optimal participation of electric vehicle load aggregators in demand response programs," Energy, Elsevier, vol. 315(C).
  • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s0360544225000568
    DOI: 10.1016/j.energy.2025.134414
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    References listed on IDEAS

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    1. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach," Energy, Elsevier, vol. 283(C).
    2. Wang, Yubin & Yang, Qiang & Zhou, Yue & Zheng, Yanchong, 2024. "A risk-averse day-ahead bidding strategy of transactive energy sharing microgrids with data-driven chance constraints," Applied Energy, Elsevier, vol. 353(PB).
    3. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    4. Voorneveld, Mark, 2000. "Best-response potential games," Economics Letters, Elsevier, vol. 66(3), pages 289-295, March.
    5. Sharma, S. & Jain, Prerna, 2023. "Risk-averse integrated DR and dynamic V2G scheduling of parking lot operator for enhanced market efficiency," Energy, Elsevier, vol. 275(C).
    6. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Nash bargaining based collaborative energy management for regional integrated energy systems in uncertain electricity markets," Energy, Elsevier, vol. 269(C).
    7. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    8. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    9. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators’ perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    10. Park, Sung-Won & Cho, Kyu-Sang & Hoefter, Gregor & Son, Sung-Yong, 2022. "Electric vehicle charging management using location-based incentives for reducing renewable energy curtailment considering the distribution system," Applied Energy, Elsevier, vol. 305(C).
    11. Sousa, Joana & Soares, Isabel, 2023. "Benefits and barriers concerning demand response stakeholder value chain: A systematic literature review," Energy, Elsevier, vol. 280(C).
    12. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    13. Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
    14. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).
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