Utilizing Metaheuristics to Estimate Wind Energy Integration in Smart Grids With A Comparative Analysis of Ten Distributions

Abstract

Abstract—Renewable energy presents the most favorable approach to address the escalating challenge of greenhouse gas emissions while simultaneously guaranteeing the safeguarding of the environment. This article utilizes ten different distributions to approximate the wind energy integration in smart grids. The employed distributions are Rayleigh, Poisson, Weibull, Normal, Gamma, Laplace, LogNormal, Nakagami, Birnbaum Saunders, and Burr. The parameters of each distribution are calculated based on metaheuristic methods such as particle swarm optimization and genetic algorithms. Six error criteria have been employed to evaluate the precision of introduced distributions and metaheuristic methods. The approximation is performed by utilizing the wind data collected over three years hourly in the Marmara region of Turkiye. The empirical findings indicate that Gamma, Burr, and Weibull distributions exhibit more significant superiority than the remaining distributions across all datasets.

Keywords:

• smart grids
• wind energy
• statistical distributions
• genetic algorithms
• particle swarm optimization

ACKNOWLEDGMENTS

We would like to express our sincere gratitude to Istanbul Sabahattin Zaim University for their valuable assistance and support during the research and writing of this article.

AUTHORS CONTRIBUTIONS

All authors actively contributed to developing the concept, ensuring the article represents a technically flawless outcome for the designated research endeavor.

DISCLOSURE STATEMENT

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

Additional information

Funding

This research received no dedicated funding from public, commercial, or nonprofit organizations or agencies.

Notes on contributors

Mohammed Wadi

Mohammed Wadi is a highly accomplished electrical engineer with a rich background spanning academia and industry. He holds a BSc and MSc in Electrical Engineering from The Islamic University of Gaza, Palestine (2006 and 2012), and a Ph.D. in power systems from Yildiz Technical University, Istanbul, Turkey (2017). Presently, Mohammed Wadi serves as an Assistant Professor at the Department of Electrical & Electronics Engineering at Istanbul Sabahattin Zaim University in Istanbul, Turkey. In this capacity, he instructs various courses such as Circuits I & II, Logic Design, Microcontrollers, Embedded Systems, Power Systems Analysis, Energy Transmission Systems, High Voltage Techniques, Protection in Power Systems, Renewable Energy and Power Systems Reliability. His primary research interests encompass the Reliability Assessment of Power Systems, Smart Grids, Renewable Energy, Fault Detection in Power Systems, Machine Learning, and Fuzzy Control.

Wisam Elmasry

Wisam Elmasry received his B.Sc. and M.Sc. degrees in computer engineering from The Islamic University of Gaza (IUG), Gaza, Palestine in 2004 and 2010 respectively. He earned his Ph.D. (2019) in computer engineering at Istanbul Commerce University in Turkey. He currently holds the position of assistant professor in the Computer Engineering Department at Istanbul Kultur University. Prior to the Istanbul Kultur University, he was a Postdoctoral Researcher in the Electronics and Electrical Engineering Department at Istanbul Sabahattain Zaim University between 2020 and 2021. His areas of research interest include Image Steganography, Cryptography, Cyber Security, Deep Learning, Evolutionary Algorithms, and MANET Routing Protocols Security.

Ilhami Colak

Ilhami Colak was born in Turkey, in 1962. He received the Diploma degree in electrical engineering and the M.Sc. degree in electrical engineering in the field of speed control of wound rotor induction machines using semiconductor devices from Gazi University, in 1985 and 1991, respectively, the M.Phil. degree from the University of Birmingham, U.K., in 1991, and the Ph.D. degree from Aston University, U.K., in 1994, with a focus on mixed frequency testing of induction machines using inverters. He became an Assistant Professor, an Associate Professor, and a Full Professor, in 1995, 1999, and 2005, respectively. He has published more than 108 journal articles, 239 conference papers, and seven books on different subjects, including electrical machines, drive systems, machine learning, reactive power compensation, inverters, converters, artificial neural networks, distance learning automation, and alternating energy sources.

Mohammed Jouda

Mohammed Jouda attained his Bachelor of Science (BSc) and Master of Science (MSc) degrees in Electrical Engineering from the Islamic University of Gaza (IUG), Palestine, in 2011 and 2015, respectively. Subsequently, he completed his Doctorate (PhD) at Yıldız Technical University (YTU) in Istanbul, Turkey, in 2022. Presently, he holds the position of assistant professor at Istanbul Sabahattin Zaim University (IZU). His professional journey began as a teaching assistant at IUG from 2011 to 2013, followed by a role at the University College of Applied Sciences from 2013 to 2015. Notably, he contributed to the design of various projects, such as a comprehensive control system for power electronics employed in renewable energy applications. He specializes in the field of power electronics, encompassing DC/AC and DC/DC converters, as well as analog and digital electronics design, digital control, and microgrids.

Ismail Kucuk

Ismail Kucuk is a seasoned academic with extensive experience in higher education, both as a professor and an administrator. He possesses expertise in various areas, including differential equations, optimal control, higher education research, lecturing, computational mathematics, and curriculum development. Prof. Kucuk holds a Ph.D. in Applied Mathematics from the University of Utah. Currently, he serves as the Dean of the Faculty of Engineering and Natural Sciences, as well as the Vice Rector at Istanbul Sabahattin Zaim University. Prof. Kucuk has made significant contributions to his field, having authored over 66 journal articles and conference papers. His primary areas of research encompass optimal control, computational methods, and mechanical systems.