Dual-Layer Real-Time Scheduling Strategy for Electric Vehicle Charging and Discharging in a Microgrid Park Based on the “Dual Electricity Price Reservation—Surplus Refund Without Additional Charges Mechanism”

Abstract

To enhance the utilization efficiency of wind and solar renewable energy in industrial parks, reduce operational costs, and optimize the charging experience for electric vehicle (EV) users, this paper proposes a real-time scheduling strategy based on the “Dual Electricity Price Reservation—Surplus Refund Without Additional Charges Mechanism” (DPRSRWAC). The strategy employs a Gaussian Mixture Model (GMM) to analyze EV users’ charging and discharging behaviors within the park, constructing a behavior prediction model. It introduces reservation, penalty, and ticket-grabbing mechanisms, combined with the Interval Optimization Method (IOM) and Particle Swarm Optimization (PSO), to dynamically solve the optimal reservation electricity price at each time step, thereby guiding user behavior effectively. Furthermore, linear programming (LP) is used to optimize the real-time charging and discharging schedules of EVs, incorporating reservation data into the generation-side model. The generation-side optimal charging and discharging behavior, along with real-time electricity prices, is determined using Dynamic Programming (DP). In addition, this study explicitly considers the battery aging cost associated with V2G operations and proposes a benefit model for EV owners in V2G mode, thereby incentivizing user participation and enhancing acceptance. A simulation analysis demonstrates that the proposed strategy effectively reduces park operation costs and user charging costs by 8.0% and 33.1%, respectively, while increasing the utilization efficiency of wind and solar energy by 19.3%. Key performance indicators are significantly improved, indicating the strategy’s economic viability and feasibility. This work provides an effective solution for energy management in smart industrial parks.