Stage-Wise Optimal Configuration of Energy Storage for Multi-Energy Complementary Systems in Qinghai-Based on a Bilevel Optimization Model

For power systems with a high penetration of renewable energy, energy storage allocation is important for enhancing system flexibility and supporting renewable energy integration. Existing planning methods cannot simultaneously reflect source-load uncertainty and the stage-wise evolution of system development. To address this issue, this paper proposes a stage-wise energy storage planning framework based on bilevel optimization. The proposed method employs an LSTM model to construct representative wind power, photovoltaic power, and load time series for the subsequent optimization analysis, and applies K-means clustering to extract representative operating scenarios. The Qinghai power system is selected as a case study for validation. The results show that the proposed method can reasonably capture the stage-wise characteristics of storage demand, with deviation rates of 4.6% for storage power and 3.2% for storage capacity. Under low-, medium-, and high-growth scenarios, storage demand increases significantly with renewable development scale. In the high-growth scenario, the required storage capacity increases from 277,836 MWh in 2030 to 926,120 MWh in 2035. Meanwhile, the role of storage shifts from short-term power balancing to peak shaving and inter-temporal energy shifting, while the optimal storage duration remains stable at 3–4 h. The proposed framework provides a basis for long-term energy storage planning in power systems with high renewable penetration.