A Novel hybrid Optimization-based Algorithm for the Single and Multi-Objective Achievement with Optimal DG Allocations in Radial Distribution Networks /

Abstract

This paper presents an improved metaheuristic method for the optimal sitting and sizing of the distributed generators (DG) in radial distribution networks (RDNs). The proposed technique has based on novel algorithm called improved grey wolf optimization with particle swarm optimization (I-GWOPSO). The novel aspect incorporates dimension learning based hunting (DLH), which has used in I-GWO that reduces the gap between local and global search to keep the balance. The main objectives aim at reduction of active power loss, voltage deviation and improve voltage stability in RDNs. Thus, roposed I-GWOPSO technique has applied at 33-bus and 69-bus test RDNs, for optimal allocation of DG units across various power factors. The achieved numerical results show the performance in favor of aimed objectives i.e. reduction of voltage deviation and power losses, and improvement of voltage stability index. The DGs operating at unity power have observed a reduction in real power losses by 66.369% and 69.4009% for both for 33-bus and 69-bus test RDNs, respectively. Likewise, DGs operating at optimal power factor shows a decrease of active power losses by 94.40% and 98.1% for both test RDNs, respectively. In addition, a comparative analysis of IGWOPSO and I-GWO with other techniques indicates that proposed technique is more efficient on techno-economic basis as compared to reported methods in the literature. Hence, evaluation of proposed I-GWOPSO approach as an efficient method validates its usage for large and complex distribution systems.