A Cloud Based Model Symbiotic Organism Search Algorithm for Placement of Distributed Energy Resources in the Electrical Secondary Distribution Networks

The increased penetration of distributed energy resources (DERs) technologies to residential users has fostered the need for DERs integration and control methods in the secondary distribution networks (SDN). In order to reap the potential advantages of DERs and achieve their inclusion in the electrical power system while avoiding their negative impacts, the DERs should be optimally placed and sized. Considering the nature of electrical networks and DER operations, the DERs placement is a nondeterministic polynomial hard (NP-hard) optimization problem. Metaheuristic algorithms are efficient for solving DER placement problems. Metaheuristic algorithms for DER placement in SDN involve high computational effort, theoretical convergence assumptions that cannot be satisfied in the real world and dependence on parameter settings. Therefore, this study proposes a DER placement algorithm that employs a cloud-based model symbiotic organism search algorithm (CMSOS). The CMSOS is attributed to simple implementation and computation, good convergence, and parameter independence. The electrical network segment taken for Tanzania’s electrical distribution network was used for testing the algorithms, considering power loss and voltage deviations. Results show that using DERs in the proposed locations reduces power loss by 89.3%. The convergence profile shows that the proposed CMSOS-based algorithm converges faster than the conventional symbiotic organism search algorithm (SOS). Keywords:    Metaheuristic Algorithms, Symbiotic Organism Search, DER Placements, Radial Distribution Network, Cloud-based model