Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

作者：Z. J. Wang, Tao Wang, Qunfeng Niu, Jianfeng Wu, Mingwei Li, Shuaiqi Zhu · 发表于：Energy Science & Engineering · 年份：2024 · DOI：10.1002/ese3.1727 · 被引用次数：17 · 研究领域：Microgrid Control and Optimization、Smart Grid Energy Management、Hybrid Renewable Energy Systems

Abstract How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics o...