Lightweight Data-Driven Planning Method of Hybrid Energy Storage Systems in the New Power System

作者：Yangyang Huo, Jiahui Yang, J. L. Qu, Chao Zhang, Zhou Li, Weiran Zhao, Hua Jiang, Zhenhua Wu, Jianfeng Dai, Wei Duan, Jintao Jiang, Chengshan Wang · 发表于：IEEE Transactions on Industry Applications · 年份：2025 · DOI：10.1109/tia.2025.3542731 · 被引用次数：17 · 研究领域：Power Systems and Renewable Energy、Power Systems and Technologies

With the development of energy storage systems (ESS), the integration of a hybrid energy storage system (HESS) in the new power system is beneficial to alleviate the uncertainty and inflexibility caused by the high penetration of renewable energy sources (RES). However, the multi-time scale coupling characteristics of HESS pose challenges to conventional planning methods in the modeling process. To improve the applicability of the planning model, a lightweight data-driven planning method with decoupled operation and planning stage is proposed in this paper. First, the demand function of the new power system is quantified for HESS based on the production simulation. Second, a graphical model is established to describe the multi-time scale characteristic of HESS. Then, considering the investment cost, a lightweight data-driven planning model is proposed to optimize the capacities of HESS, including energy-energy ESS and power-energy ESS. Finally, the proposed method is verified using a regional test case. Case studies show that average electricity cost of the proposed method is the lowest. In addition, the maximum 5-minute fluctuation is reduced by more than 50% due to power-energy ESS. Therefore, the proposed lightweight data-driven planning method for HESS can effectively solve the planning problems of HESS.