A Network Reconfiguration Approach for Service Restoration Based on a Novel and Multiobjective Optimization Method

Restoring load following partial outages or local faults in a section of the distribution system, as addressed in this study, is crucial to minimizing service interruptions and financial damages. Reconfiguring the network is a crucial first step in load restoration. The presence of distributed generation (DG) units, in addition to the reconfiguration of the distribution network, can be very effective in the load recovery process. Therefore, in this study, the problem of reconfiguring the distribution network in the presence of DG units and charging stations of electric vehicles with the goals of reducing energy not supplied (ENS) and losses has been solved. The suggested problem is resolved by introducing and putting into practice the hybrid particle swarm optimization and shuffled frog leaping (HPSO‐SFL) algorithm, which is based on hybrid swarm intelligence. The effectiveness and accuracy of the proposed method are validated using a 33‐bus test system under multiple scenarios. To assess its performance, the results are compared against those reported in previous studies. Following network reconfiguration, the power losses were reduced by 45% without DG and by 77% with DG, relative to the initial system state. Furthermore, when electric vehicle charging stations (EVCSs), modeled as active loads, were included in the optimization process, power losses decreased by approximately 23% compared to the pre‐reconfiguration condition.