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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. [ABSTRACT FROM AUTHOR]

With the advancement of policy initiatives and technological developments, vehicle-to-grid (V2G) interactions have emerged as a critical focus within power system research. Despite numerous current studies on V2G, the exploration of incentive mechanisms to encourage user participation in these systems remains comparatively underdeveloped. This study proposes an innovative demand response (DR) incentive mechanism for electric vehicles (EVs) grounded in the regulation of fast charging powers via strategically assigning and allocating virtual points defined as fast charging right (FCR). The primary objective of this mechanism is to meet the power regulation requirements of DR while simultaneously balancing the grid's response costs and accommodating the charging demands of EV users. Through simulation and comparative analysis with the existing pilot DR incentive mechanisms, the results demonstrate that the proposed mechanism outperforms the current model in three critical aspects: power regulation efficacy, cost-effectiveness, and user experience. In addition, this paper investigates the performance of the incentive mechanisms in both online and offline scheduling environments. The findings reveal that the FCR mechanism maintains robust power regulation performance even under online scheduling conditions. • Proposed an FCR-based DR mechanisms for EVs, enhancing economic efficiency and regulation. • Validated a two-layer optimization model under both offline and online dispatch conditions. • Simulation shows the mechanisms balances DR costs and EV user experience effectively. [ABSTRACT FROM AUTHOR]