Distribution Network Reconfiguration Method with Distributed Generators Based on an Improved Shuffled Frog Leaping Algorithm

Abstract

Reconstruction of a distribution network is a complex nonlinear combinatorial optimization problem. A reconfiguration algorithm of a distribution network based on an improved shuffled frog leaping algorithm using molecular dynamics theory and cloud simulation theory was proposed to solve the problems of slow convergence for current distribution network reconfiguration method. The reconstruction optimization process of the distribution network was as follows. First, the integer coding strategy based on the independent loop was adopted, and the initial data of distribution network was input, followed by random generation of the effective initial swarm of corresponding scale; Second, the distribution network was simplified, and the effective solution was obtained based on the reconfiguration network simplification principle; Third, the back forward sweep algorithm method for was used for the power flow calculation of effective solutions; Finally, iterative swarm optimization was conducted based on the improved shuffled frog leaping algorithm(SFLA). The simulation results based on the IEEE33 node distribution system showed that the active network loss value of the system was decreased obviously, reaching 16.57%. The active power P increased significantly compared with that before the reconfiguration in which the distributed generators (DGs) were accessed. The proposed algorithm has good optimization performance in solving the problem of distribution network reconfiguration compared with the traditional particle swarm optimization (PSO) and the differential evolution algorithm (DE).