Optimal Scheduling of Highway Service Area PV-Wind-BESS Based on Hybrid Genetic Algorithm

The rapid growing electric vehicle (EV) charging load in highway service areas bringing pressure to the power grid. To solve this problem, this paper proposes a joint optimization and scheduling method that combines energy of photovoltaic (PV) power, wind power, and battery energy storage systems (BESS). A multi-objective model is developed with three goals: minimizing voltage fluctuations, maximizing renewable energy utilization, and restoring the state of charge (SOC) of the BESS. A Memetic Genetic Algorithm (GA) is used for optimization, incorporating simulated binary crossover, polynomial mutation, and local search. The simulation results demonstrate that the Memetic GA algorithm outperforms the Classic GA in terms of voltage control, scheduling stability, and renewable energy utilization optimization. Compared to the classic GA algorithm, the Memetic GA algorithm reduces voltage variance by 74%, increases renewable energy usage to 100%, and decreases final SOC deviation by 67%.