A Bi-level Optimization Model for V2G Dynamic Tariffs Considering Coordinated Regulation and Incentive Compatibility

Traditional methods for regulating peak loads in air conditioners are costly and inefficient, and the peak-shaving capacity through flexible regulation is limited. To address this problem, coordinated regulation between air conditioners and electric vehicles (EVs) can offer a low-cost yet effective solution. However, existing researches on Vehicle to Grid (V2G) tariffs fail to explore coordinated regulation strategies between these two resources. To address the above issues, this paper proposes a bi-level optimization model for V2G dynamic tariffs considering coordinated regulation and incentive compatibility. First, this paper constructs a multi-agent value transfer framework that integrates demand response with the upper- and lower- level incentive compatibility constraints, and designs a bi-level optimization structure that includes coordinated regulation. Second, based on the bi-level optimization structure, establishing a bi-level optimization model for dynamic tariffs aimed at maximizing both societal and individual EV owners benefits. This model utilizes price signals to guide EV owners in participating in the coordinated regulation of air conditioners and EVs. Finally, based on the IEEE 14-node system, the genetic algorithm is used to verify the effectiveness of the model. Simulation results indicate that the proposed model outperform the fixed time-of-use (TOU) in balancing the interests of all stakeholders and enhancing peak-shaving capabilities. This model provides both theoretical foundations and practical references for the coordinated optimization of demand-side resources in the new electricity system.