Abstract: With the rapid development of hydrogen production and storage technology, the development of hydrogen energy storage systems（HESSs） will bring fundamental changes to energy and power system structure. The coordinated optimization of HESS and battery energy storage system（BESS） can solve the imbalance between supply and demand of various energy sources and improve energy efficiency. In order to ensure the effectiveness of BESS and HESS planning, minimum life cycle cost（LCC）, system network loss, switching power deviation, load fluctuation, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, a non-dominated sorting genetic algorithm-Ⅱ（NSGA2） with elite strategy is used to solve energy storage system（ESS） Pareto non-dominated solution set of site-constant volume planning scheme. The grey target decision based on entropy weight method（EWM） is used to select the best compromise solution in Pareto non-dominated solution set. Additionally, typical operation scenarios of source load are obtained by fuzzy kernel C-means（FKCM） clustering algorithm, and the simulation analysis is carried out on the basis of the extended IEEE 33-node system. Simulation results show that NSGA2 algorithm not only achieves the minimum LCC of the electricity-hydrogen hybrid energy storage system, but also improves voltage quality, power stability, network loss and load fluctuation compared to that of other algorithms.