Active distribution network reconfiguration considering Adjustable load and using two-track coordination algorithm

At present, most of the existing optimal reconfiguration methods for power system active distribution network (ADN) aim to change the network topology without considering the Adjustable load. In ADN, the efficiency of the original reconstruction method can't meet the requirements when the proportion of the Adjustable load is increasing. Based on the consideration of the Adjustable load, this paper establishes the fast non dominated sorting genetic algorithm (NSGA-II) based on the coevolution and the dual track system coevolution group evolution model based on the coevolution total learning particle swarm optimization (CE-MOCLPSO), and solves the problem of the active distribution network reconfiguration and the simultaneous optimization of the Adjustable load. In this paper, based on the IEEE33 node distribution system design, several simulation examples are analyzed. The results show that the active distribution network reconfiguration considers the scheduling of Adjustable load, effectively improves the operation quality of the distribution network, and verifies the efficiency of the reconfiguration scheme is higher than the original two-stage reconfiguration method.