考虑次/超同步振荡稳定约束的风电并网容量分析

The emerging sub/super-synchronous oscillation (SSO) issue in wind power integrated power systems is posing a great threat to system stability. To avoid SSO issues from the beginning, it is of great significance to determine the maximum admissible wind power integration capacity at the system planning stage. To achieve this purpose, the paper first proposes a model-data hybrid-driven method for constructing the security region (SR) of SSO, which is of high precision and suitable for large-scale systems. Then based on the SR, the SSO probability stability index is developed to evaluate SSO risk under variable system operating conditions. Next, with the SSO probability index as a stability constraint, the chance-constraint planning model is established to obtain the maximum wind power integration capacity and the improved particle swarm optimization algorithm is proposed to solve the model. Finally, the proposed method is applied on a single-wind-farm system and a practical multiple-wind-farms system. The SR of SSO is built with the classification accuracy improved by more than 5% compared to existing data-driven methods. The global optimal solution for the maximum wind power integration capacity is obtained, and the total consumed time is reduced by over 40 times compared to the existing method. Thus the effectiveness of the proposed method is fully demonstrated.