Wild Geese Algorithm for The Combination Problem of Network Reconfiguration and Distributed Generation Placement

Abstract

Due to operating at low voltage and high current level, the power loss caused by the distribution network (DN) is usually higher than that of other parts of the power system. Thus, power loss reduction is one of the important mission in operation the DN. This paper presents a method of simultaneous execution of network reconfiguration (REC) and distributed generation placement (DGP) based on a new swarm intelligent (SI) namely wild geese algorithm (WGA) to reduce power loss considering the improvement of voltage and current profiles as well as satisfy the constraints including radial topology, distributed generation capacity limit and power balance. The efficiency of the proposed WGA is evaluated on the 33-node and 69-node systems at two cases of REC and REC-DGP. The performance of WGA is contrasted with two SI-based methods including well-known particle swarm optimization (PSO) and recent developed pathfinder algorithm (PFA). The obtained results demonstrate that REC and REC-DGP are effective solutions to reduce power loss and improve voltage and current profiles of the DN, wherein REC-DGP achieves higher efficiency than REC. Furthermore, the statistical results show that WGA outperforms PSO and PFA for both problems in indexes of worst, average, standard deviation values of the fitness function and the computation time. The contrasted results with the previous performed methods also point that WGA can reach the better results than other ones for the REC and REC-DGP problems. Thus, WGA can be a potential method for the RECDGP problem.