Two stage coordination planning method of wind power and storage considering uncertainty of distributed source-load

Introduction With the widespread integration of distributed power sources, the power grid is facing challenges such as increased losses, rising costs, voltage fluctuations, and overload, resulting in greater operational complexity. Traditional scheduling methods are no longer adequate, making reasonable planning of distributed power generation and energy storage configurations particularly crucial. Methods This article proposes a two-stage wind-storage coordination planning method that considers source-load uncertainty. The approach is based on an improved antlion algorithm and incorporates distributed energy storage charging and discharging strategies. The first stage focuses on wind power site selection and capacity determination, using voltage offset, network loss, and comprehensive system cost as evaluation indicators. A multi-objective function model is established to balance grid stability and economic efficiency. The second stage introduces distributed energy storage devices to reduce power fluctuations while minimizing the sum of operation, maintenance, and storage investment costs, thereby optimizing the energy storage charging and discharging strategy. The improved antlion algorithm, enhanced with adaptive Lévy flight and golden sine theory, is used to solve the two-stage planning model. Results The proposed method effectively improved system-level voltage distribution, reduced network losses, and lowered overall system costs. Specifically, it achieved a 27.95% increase in total capacity, a reduction of 32.14 kW in active power loss, and a total cost decrease of 221,200 yuan. The improved antlion algorithm demonstrated strong search capability, fast convergence speed, and high computational accuracy. Discussion The results indicate that the proposed method is better aligned with practical requirements compared to traditional approaches. The improvements in system performance and cost efficiency highlight the effectiveness of the two-stage planning framework and the enhanced optimization algorithm. The method offers a viable solution for the integrated planning of wind power and energy storage systems under uncertainty.