Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation, a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization (IGWO) is proposed. Firstly, building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system. Secondly, the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function, and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function. By iterating through the outer and inner layers, the system improves operational stability while achieving economic configuration. Then, using the energy-self-smoothness of the microgrid as the evaluation index, a double-layer optimizing configuration method of the microgrid is constructed. Finally, to improve the disadvantages of grey wolf optimization (GWO), such as slow convergence in the later period and easy falling into local optima, by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator, an IGWO with excellent global performance is proposed. After testing with the typical test functions, the superiority of IGWO is verified. Next, using IGWO to solve the double-layer model. The case analysis shows that compared to GWO and particle swarm optimization (PSO), the IGWO reduced the comprehensive cost by 15.6% and 18.8%, respectively. Therefore, the proposed double-layer optimization method of capacity configuration of microgrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.