Optimization of Multi-scenario Distributed Generation Considering Voltage Sag

Considering the fluctuation and uncertainty of distributed generation (DG) output and its impact on the distribution network, how to reasonably plan the access scheme of DG is one of the current research focuses. The combination of K-means clustering algorithm (K-mean) and Adaptive and Fast Density-Based Spatial Clustering of Applications with Noise (AF -DBSCAN) is employed to realize more efficient scenario reduction. The voltage sag economic loss evaluation index of sensitive loads considering multi-scenario is proposed to further reflect the impact of DG output uncertainty on voltage sag of sensitive loads. A mathematical model for capacity optimal configuration of DG is established by taking voltage stability, active power loss, and voltage sag economic loss of sensitive loads as the optimization objectives. The sparrow search algorithm is used to solve the mathematical model, and the fuzzy matter-element evaluation method is adopted to evaluate the Pareto optimal set and finally obtain the optimal access scheme. The simulation results of the IEEE 33-bus distribution system show that the reasonable access of DG helps to improve the overall system voltage stability as well as reduce the system network loss and economic loss, which verifies the rationality and effectiveness of the proposed method.