Probabilistic Sequential Service Restoration of Power Systems Based on Multimaster Microgrids Considering Dynamic Limitations

The service restoration of system after the occurrence of high-impact events is one of the aspects of power system resiliency. Microgrids (MGs) and distributed generations (DGs) make opportunities for the fast restoration. The sequential service restoration (SSR) problem based on MGs has been defined to determine the switching sequence and restorable load in each time step. In this article, a probabilistic SSR focusing on multimaster MGs is proposed in a mixed-integer linear programming format incorporating system dynamic constraints. The restoration time is minimized in addition to the maximization of the restored load in the weighted sum framework, where the optimal weights are obtained by the fuzzy decision-making method. The spherical simplex unscented transformation is used to handle system uncertainties. The proposed method is examined on the modified IEEE 123-node test feeder. The results show that the restoration based on multimaster MGs improves the ratio of the restored load amount to the restoration time by more than 35% in comparison with the existing restoration methods, which are based on single-master MGs. Furthermore, the adopted control scheme for master DGs is validated through a dynamic simulation of the SSR process obtained from the optimization problem.