Enhanced electric eel foraging algorithm for integrated optimization of distributed generation and distribution static compensators with network reconfiguration

Abstract

This research introduces the Enhanced Electric Eel Foraging Optimization (EEEFO) algorithm, a hybrid optimization approach for simultaneously sizing and placing distributed generation (DG) units at optimal power factor, optimizing distribution static compensators (DSC), and performing optimal network reconfiguration (ONR) on large-scale electrical distribution networks. The EEEFO algorithm leverages the electric eel foraging optimizer's skills with genetic operators to address the challenging issues of optimum design of large electrical distribution networks. The primary objective of this study is to minimize power loss and enhance voltage stability of distribution networks. The effectiveness of the EEEFO algorithm is demonstrated through its application to several large distribution networks, including 70-bus, 85-bus, 118-bus, 136-bus, 141-bus, and 415-bus systems. Additionally, the EEEFO algorithm's efficiency is evaluated against other algorithms and previous research in the area. Simulation findings demonstrate the usefulness of optimizing DG at the optimum power factor while also optimizing DSC with ONR to improve power system performance. Specifically, the active power loss is reduced by 80.46 %, 97.00 %, 87.76 %, 88.71 %, 92.14 %, and 82.18 %, while reactive power loss is reduced by 73.62 %, 97.73 %, 87.41 %, 91.92 %, 92.58 %, and 81.22 % for the 70-bus, 85-bus, 118-bus, 136-bus, 141-bus, and 417-bus systems, respectively. Furthermore, this strategy considerably improves the system's voltage stability, demonstrating its usefulness and scalability across a wide range of network topologies.