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.

Keywords:

• Ameliorated moth swarm algorithm (AMSA)
• electric vehicle (EV)
• optimal power flow (OPF)
• photovoltaic energy (PVE)
• static series synchronous compensator (SSSC)
• unified power flow controller (UPFC)
• wind energy (WE)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Dhiman Banerjee

Dhiman Banerjee was born in 1985 in Jalpaiguri, West Bengal, India. He received his Btech degree in Electrical Engineering from M.A.K.A.U.T (formerly known as W.B.U.T), West Bengal, India, in 2008; Mtech degree in Power Electronics and Electric Drives from Jalpaiguri Government Engineering College, West Bengal, India, in 2011. He is currently pursuing PhD from M.A.K.A.U.T. Presently, he is working as an assistant professor in the Department of Electrical Engineering at Surendra Institute of Engineering and Management, West Bengal, India. His field of research interest consists of optimal power flow, renewable energy, economic load dispatch, FACTS, soft-computing.

Provas Kumar Roy

Provas Kumar Roy obtained PhD degree in Electrical Engineering from NIT Durgapur in 2011. He received his Master degree in Electrical Machine in 2001 from Jadavpur University. He finished his Engineering studies in Electrical Engineering from RE College (Presently Known as NIT) Durgapur. Presently, he is working as a professor in Electrical Engineering Department at Kalyani Government Engineering College, West Bengal, India. He has published more than 238 research papers in National/International Journals and conferences. Twelve research scholars have obtained their PhD degree under his guidance. His research interest includes economic load dispatch, optimal power flow, FACTS, automatic generation control, radial distribution network, power system stabiliser, image processing, machine learning etc.

Goutam Kumar Panda

Goutam Kumar Panda received the BE degree in electrical engineering from Jalpaiguri Government Engineering College, Jalpaiguri, India, in 1990, the ME degree in electrical engineering from Jadavpur University, Kolkata, India, in 1992, and the PhD degree in engineering from the University of North Bengal, Darjeeling, India, in 2007. He is currently a professor in the Department of Electrical Engineering, Jalpaiguri Government Engineering College. His research interests include nonlinear dynamics in power electronics and electric drives.