Coordinated Optimization and Configuration Optimization of Wind, Photovoltaics and Energy Storage based on Particle Swarm Optimization Algorithm

Amidst the growing global emphasis on renewable energy utilization, microgrids in industrial parks have emerged as crucial carriers for advancing energy structure transformation, with the coordinated optimization of wind, photovoltaics, and storage resources becoming a research hotspot. This study, tailored to the specific needs of industrial park microgrids, establishes an optimization configuration model that balances economic efficiency and stability using linear programming methods. The model aims to minimize investment and operational costs while maximizing the benefits of energy storage systems, taking into account multiple constraints such as the output power of distributed energy sources, the capacity of energy storage systems, load demands, and the safe operation of the grid. To address this complex optimization problem, the study employs a Particle Swarm Optimization (PSO) algorithm, which mimics the intelligent behavior of swarms to iteratively refine the solution quality until the optimal configuration is found. The research findings indicate that the coordinated optimization of wind, photovoltaics, and storage resources not only significantly enhances the economic efficiency of industrial park microgrids by reducing overall investment and operational costs but also effectively mitigates the volatility of renewable energy generation, improving power supply reliability and stability. Furthermore, the optimized configuration promotes the integration of renewable energy, enhances the utilization rate of clean energy, and provides robust support for the low-carbon and sustainable development of industrial parks. This study not only offers a scientific basis for the planning and construction of industrial park microgrids but also provides reference ideas and methods for the optimization of similar energy systems.