Lightweight Data-Driven Planning Method for Hybrid Energy Storage Systems

The integration of a hybrid energy storage system (HESS) in the new power system is beneficial for alleviating 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 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 multiple time-scale characteristic of HESS. Then considering the investment cost, a lightweight data-driven planning model is proposed to optimize the capacities of HESS. Finally, the proposed method is verified using a regional test case. Case studies illustrate that the proposed lightweight data-driven planning method for HESS can effectively solve the planning problems of HESS.