A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks

Abstract

This paper aims to apply an improved meta-heuristic method to optimize the allocation of distributed generation (DG) units in radial distribution networks (RDNs). The proposed method, namely the Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS) algorithm, is the improved version of the original SOS algorithm. QOCSOS incorporates the Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies into SOS to improve the global search capacity. In this study, the objective of the optimal DG allocation (OGDA) problem is to optimally reduce the real power loss, improve the voltage profile, and increase the voltage stability in RDNs. The proposed QOCSOS algorithm was applied to find the optimal locations and sizes of DG units with different DG power factors (unity and non-unity) in the RDNs including 33, 69, and 118-bus. It was found that the operation of DG units with optimal power factor significantly improved the performance of RDNs in terms of voltage deviation minimization, and voltage stability maximization, especially for power loss reduction. After the DG integration, for the case of DG units operating with unity power factor, the power loss reduction was reduced by 65.50%, 69.14%, and 60.23% for the 33, 69, and 118-bus RDNs, respectively. In addition, it should be emphasized that for the cases of DG units operating with optimal power factor, the power loss reduction was reduced up to 94.44%, 98.10%, and 90.28% for these RDNs, respectively. The obtained results from QOCSOS were evaluated by comparing to those from SOS and other optimization methods in the literature. The results showed that the proposed QOCSOS method performed greater than SOS, and offered better quality solutions than many other compared methods, suggesting the feasibility of QOCSOS in solving the ODGA problem, especially for a complex and large-scale system.