Research on optimal energy storage capacity allocation for new power systems

HUANG Rong; ZHOU Yanke; LIANG Tian; XU Xiaoshuang; LUO Qian

doi:10.3969/j.issn.1002-9605.2025.11.007

Abstract：For new power systems with high proportions of renewable energy integration,the optimal allocation of energy storage capacity is key to improving system economy and renewable energy consumption capability.A dual-objective energy storage capacity optimization model was established,aiming at minimizing the full lifecycle economic cost and maximizing the renewable energy consumption rate,comprehensively considering the operational characteristics of pumped storage and electrochemical energy storage as well as various practical constraints.Considering the high-dimensional,nonlinear,and multi-objective characteristics of the model,a Multi-Objective Grey Wolf Optimization(MOGWO)algorithm was introduced for solving it.Utilizing its hierarchical leadership mechanism and adaptive encircling strategy,a well-distributed Pareto optimal solution set can be efficiently searched in complex solution spaces.A case study was conducted based on 8 760-hour time series data of actual load,wind power,and photovoltaic generation in a certain province.The results show that,under the premise of meeting the total energy storage demand of 24 800 MW,the optimal allocation ratio between pumped storage and electrochemical energy storage is 1∶1.17,leading to the lowest total cost over the full lifecycle,approximately 2 654.5 billion yuan,and the SOC operation curves of both storage types exhibit good peak shaving,valley filling,and rapid power response coordination characteristics.The study validates the effectiveness and economic benefits of the proposed model and algorithm in multi-objective energy storage optimization allocation,providing theoretical basis and decision support for the scientific planning of multiple types of energy storage in new power systems.