Interactive optimization scheduling method for distribution network and charging stations based on fuzzy logic and multi-strategy serial implementation mechanism

• Establishes a user response model linking incentive price and response rate in V2G. • Uses fuzzy logic for real-time updates of user models based on dynamic factors. • Proposes a multi-strategy dispatch mechanism for real-time electric vehicle scheduling. • PSO optimizes charging power and incentive price, reducing load control costs. • Simulations confirm the effectiveness of the fuzzy logic and dispatch mechanism. In the vehicle-to-grid (V2G) interaction scheduling process, the incentive price is a crucial factor affecting the user-side response. Therefore, how to reasonably set the incentive price is a key issue in V2G technology. Based on this, this paper first establishes an incentive-based user response model to determine the mathematical relationship between the incentive price and the response rate. Then, considering that changes in user states lead to the time-varying nature of user-side model parameters, fuzzy inputs such as charging price, remaining dwell time, and current state of charge (SOC) are used to update the parameters of different user response models in real-time through fuzzy logic, achieving user-specific modeling. Secondly, to avoid the shortage of response volume and ensure effective response volume, three scheduling strategies are designed, and a multi-strategy serial implementation mechanism is established, enabling the electric vehicle aggregator to dispatch electric vehicles according to the scheduling tasks of each period and adjust the scheduling strategy in real-time based on the response completion. Next, the PSO (Particle Swarm Optimization) algorithm is used to minimize the difference between the optimized total load and peak load control targets and the aggregator’s incentive cost, resulting in the optimized charging power of each electric vehicle and the corresponding incentive price. Finally, simulation results verify the effectiveness of the proposed multi-strategy serial implementation mechanism and the fuzzy logic method for determining response model parameters.