Wang Shuxin, Yue Yinggao, Cai Shaotang, Li Xiaojuan, Chen Changzu, Zhao Hongliang, Li Tiejun
School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, China.
School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou, 325035, China.
Sci Rep. 2024 Aug 2;14(1):17958. doi: 10.1038/s41598-024-68964-w.
With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimization algorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.
随着可再生能源的快速发展,光伏储能系统(PV-ESS)在提高能源效率、确保电网稳定性和促进能源转型方面发挥着重要作用。作为微电网系统的重要组成部分,储能系统可通过光伏储能系统的设计优化和调度优化实现微电网系统的稳定运行。总结了光伏储能系统的结构和特点。从光伏储能系统的角度出发,讨论了优化目标和约束条件,并对当前储能系统的主要优化算法进行了比较和评估。简要描述了储能系统面临的挑战和未来发展方向,并总结了储能系统优化方法的研究成果。本文综述了群体智能优化算法在光伏储能系统中的应用,包括算法原理、优化目标、实际应用案例、挑战和未来发展方向,为更好地推广和应用新能源光伏储能系统提供了新思路和有价值的参考。