Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems

Sichen Tao (), Yifei Yang, Ruihan Zhao, Hiroyoshi Todo and Zheng Tang ()
Additional contact information
Sichen Tao: Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
Yifei Yang: Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan
Ruihan Zhao: School of Mechanical Engineering, Tongji University, Shanghai 200082, China
Hiroyoshi Todo: Wicresoft Co., Ltd., Tokyo 163-0445, Japan
Zheng Tang: Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan

Mathematics, 2024, vol. 12, issue 23, 1-24

Abstract: The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-performance continuous optimization methods, such as differential evolution (DE) variants, exhibit limited performance when directly applied due to WFLOP’s discrete nature. Therefore, metaheuristic algorithms with inherent discrete characteristics like genetic algorithms (GAs) and particle swarm optimization (PSO) have been extensively developed into current state-of-the-art WFLOP optimizers. In this paper, we propose a novel DE optimizer based on a genetic learning-guided competitive elimination mechanism called CEDE. By designing specialized genetic learning and competitive elimination mechanisms, we effectively address the issue of DE variants failing in the WFLOP due to a lack of discrete optimization characteristics. This method retains the adaptive parameter adjustment capability of advanced DE variants and actively enhances population diversity during convergence through the proposed mechanism, preventing premature convergence caused by non-adaptiveness. Experimental results show that under 10 complex wind field conditions, CEDE significantly outperforms six state-of-the-art WFLOP optimizers, improving the upper limit of power generation efficiency while demonstrating robustness and effectiveness. Additionally, our experiments introduce more realistic wind condition data to enhance WFLOP modeling.

Keywords: sustainable energy; wind farm layout optimization; differential evolution; genetic learning competitive elimination strategy; genetic algorithm; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3762/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3762/ (text/html)

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:gam:jmathe:v:12:y:2024:i:23:p:3762-:d:1532482

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().