Co-optimized Scheduling of New Energy Charging Stations and Electric Vehicles in Parks Based on a Two-layer Optimization Model

To address the challenges of renewable energy generation and grid security under the 'dual carbon' goal, this paper proposes a coordinated scheduling strategy for park-based renewable energy charging stations and electric vehicles (EVs) based on a bi-level optimization model. To mitigate the uncertainty of distributed wind and solar power output and the load peak-valley difference caused by uncoordinated EV charging, a dynamic real-time pricing mechanism is introduced to guide EV charging and discharging behavior, forming a 'time-space' coordinated optimization framework. First, mathematical models of EVs and wind-solar generation are established. Second, a dynamic time-of-use pricing strategy is designed to enable real-time interaction between electricity prices and grid conditions. On this basis, a bi-level optimization model is developed: the upper level aims to maximize charging station revenue by optimizing time-of-use pricing and renewable energy utilization, while the lower level seeks to minimize user charging costs by adjusting charging and discharging plans in response to price changes. The bi-level model is transformed into a single-level mixed-integer programming problem using the Karush-Kuhn-Tucker (KKT) conditions and is efficiently solved using the branch-and-bound method combined with the CPLEX solver. Case simulations demonstrate that the proposed strategy significantly reduces the load peak-valley difference, increases EV charging during peak renewable energy output periods, and lowers EV user charging costs by 52.6% compared to uncoordinated charging, validating the model's effectiveness in enhancing grid economy, security, and user satisfaction. This study provides theoretical support for the coordinated scheduling of renewable energy and EVs, contributing to the low-carbon transition of modern power systems.