An Efficient Planning Method for High-Proportion Renewable Energy Systems Based on Holomorphic Embedding Method

Under the background of high-proportion grid integration of renewable energy, the source-storage collaborative planning model faces large-scale and time-consuming solution problems due to the uncertainty of distributed resources and multi-interaction scenarios. This paper constructs a two-layer model for source-storage collaborative planning, aiming to minimize equipment investment and operation costs while considering constraints such as power flow constraints. A high-efficiency solution method based on the Holomorphic Embedding Method (HEM) is proposed. The method covers massive scenarios by combining K-means clustering, and utilizes HEM integrated with a complex variable correction mechanism for fast power flow calculation and scheme optimization. Case studies show the significant advantages of this method in improving the solution efficiency of the planning model.