Optimizing Wind farm layout using a one-by-one replacement mechanism-incorporated gradient-based optimizer

Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam and Adam Słowik

Energy, 2025, vol. 314, issue C

Abstract: Wind power generation is considered an important green renewable energy source due to its ability to meet the world's power requirements over time. However, optimizing the layout of a wind farm to alleviate the wake effect and maximize power generation is a challenging optimization problem due to being non-convex and NP-hard. Several optimization approaches have been recently proposed for tackling this problem; however, they still suffer from low quality of final results and slow convergence speed. Therefore, this study proposes a new, effective approach, namely GBOT, based on the gradient-based optimizer and the recently proposed encoding mechanism. Despite that, GBOT still suffers from a slow convergence rate as the number of wind turbines increases. Therefore, it is improved by replacing the updating scheme of GBO with a novel one to aid in improving the exploration and exploitation performance along the optimization process. This improved variant is known as IGBOT. Both GBOT and IGBOT are compared with several state-of-the-art methods based on two wind scenarios. This comparison is conducted in terms of several performance metrics, including best power output, average power output, worst power output, standard deviation, Wilcoxon rank sum test, Friedman mean rank, multiple comparison test, and convergence curve.

Keywords: Renewable energy; Wind farm layout; Wake effect; Wind power generation; Metaheuristic algorithms (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422403932X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:314:y:2025:i:c:s036054422403932x

DOI: 10.1016/j.energy.2024.134154

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().