Multi-Objective Energy Optimization for Photovoltaic Storage Charging Station

The large-scale integration of electric vehicles presents considerable challenges to power grid stability and security. To address these challenges, this study investigates the energy scheduling problem in photovoltaic storage charging stations. This study develops a vehicle charging model and a multi-objective optimization framework to minimize total operating costs and load fluctuations. Furthermore, to enhance efficiency, an improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed. Three scheduling strategies are thoroughly analyzed: a conventional passive approach (Strategy 1), traditional MOPSO optimization (Strategy 2), and improved MOPSO optimization (Strategy 3). The results show that Strategy 3 significantly outperforms the other strategies, reducing total operating costs by 37.92% and load fluctuations by 40.35% compared to Strategy 1. Simulation results confirm the improved IMOPSO algorithm's effectiveness in addressing energy scheduling challenges in photovoltaic storage charging stations. The algorithm not only enhances the economic efficiency of photovoltaic storage charging stations but also achieves peak shaving and valley filling for the power grid. These results demonstrate its broad potential for practical applications.