A Novel Ameliorated Moth Swarm Algorithm for Optimization of Hybrid Power System Incorporating FACTS Controllers

Abstract

In power system engineering, a recent trend is to integrate conventional thermal units (CTUs) and alternative energy sources (AESs) for cost-effective and environmentally friendly operation. In the current work, the optimal power flow (OPF) problem that integrates CTUs with three highly intermittent energy sources wind energy (WE), photovoltaic energy (PVE), and electric vehicle (EV) as a vehicle to grid (V2G) source is being developed. Using various probability distribution functions (PDFs), an interpretable probabilistic strategy is devised to deal with the uncertainties of the aforementioned energy sources. To ensure stable operational conditions, a single static series compensator (SSSC) is optimally configured and placed in the combined power system. Further, to address the instability and generation uncertainty of an overloaded AESs-based power system, a single SSSC and a single unified power flow controller (UPFC) is optimally placed and configured simultaneously. The ameliorated moth swarm algorithm (AMSA) is proposed for the solution of the developed complex OPF model by combining a) chaotic mapping technique (CMT) and b) a novel mutation tactic with conventional MSA. The performance of the proposed AMSA is verified by utilizing ten standard benchmark functions. Furthermore, the proposed technique's performance is assessed using nine different power-related, uncertainty-induced, and single SSSC-included test setups, taking into account both IEEE 30 and 118-bus-based test power networks. For a more in-depth analysis of the ability of the proposed technique, it is further applied to optimise AESs-based IEEE 118-bus test network under enhanced load level and in the presence of optimally placed single SSSC and single UPFC. The efficacy of our proposed AMSA technique is compared against seven powerful metaheuristic techniques, both analytically and statistically. The results of the tests reveal that the proposed AMSA outperforms the other methods investigated in terms of quality and accuracy.