A Stochastic Planning Model for Improving Resilience of Distribution System Considering Master-Slave Distributed Generators and Network Reconfiguration

The recent experiences of extreme weather events highlight the significance boosting resilience distribution systems. In this situation, systems planning leads to an efficient solution for protecting system from these via line hardening and installation distributed generators (DGs). For aim, study presents a new two-stage stochastic mixed-integer linear programming model (SMILP) hedge against natural disaster uncertainty. first stage involves making investment decisions about DG installation. Then, in second stage, dynamic microgrids are created according master-slave concept with ability integrating minimize cost loss load each uncertain outage scenario. particular, paper approach select damage scenarios SMILP. addition, operational strategies such as control capability, microgrid formation network reconfiguration integrated into plans improvement both emergency response steps. simulation results IEEE 33-bus test demonstrate effectiveness proposed improving disaster-induced