This paper proposes a bi-level game-theoretic framework for coordinating large-scale electric vehicles (EVs) in regional power grid deep peak regulation under a demand response mechanism. The upper-level model formulates a non-cooperative game among the deep peak regulation market operator (DPRMO), electric vehicle aggregators (EVAs), and distributed generation units to optimize electricity pricing and scheduling strategies. The lower-level model adopts an evolutionary game based on the logit protocol to describe the bounded rational decision-making process of EV users in charging and discharging strategy selection. Through iterative interaction between the two layers, a Nash equilibrium of the overall system is achieved. Case studies demonstrate that the proposed method effectively improves system performance and economic efficiency. The total operating cost is reduced to 278,642.53 CNY, while the renewable energy utilization rate reaches 96.73%. The peak–valley load difference is reduced to 12,480.52 kW, indicating significant load-smoothing capability. In addition, EV participation generates 12,684.37 CNY in net benefit, while the profits of the distributed energy supplier and EV aggregator reach 94,836.71 CNY and 30,118.52 CNY, respectively. Furthermore, the consumer surplus of flexible loads increases to 417,863.29 CNY, reflecting enhanced demand-side participation. Comparative results show that the proposed bi-level game framework outperforms benchmark strategies in terms of economic cost reduction, renewable energy accommodation, and peak regulation capability. The results verify the effectiveness of the proposed coordinated optimization approach for multi-stakeholder energy systems with large-scale EV integration.
Sun et al. (Wed,) studied this question.
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