Multi-objective optimization of co-located wave-wind farm layouts supported by genetic algorithms and numerical models

Felipe Teixeira-Duarte, Paulo Rosa-Santos and Francisco Taveira-Pinto

Renewable Energy, 2025, vol. 241, issue C

Abstract: This study introduces a novel methodology for optimizing Wave Energy Converter (WEC) positioning in an array using a continuous domain, surpassing the traditional fixed layout approaches. The Wave Energy Park Layout Assessment Index (WLA), which integrates the wave protection factor (HRA) and power absorption efficiency (q-factor), is employed to evaluate the performance of WEC farms. To enhance computational efficiency, unsupervised classification methods, such as k-means clustering, are used to reduce the number of sea states while accurately representing wave energy, preserving 90 % of incoming wave energy. Genetic algorithms, integrating the SNL-SWAN hydrodynamic model, are then used to optimize WEC layout by balancing exploration and computational cost, maintaining solution diversity, and avoiding premature convergence. Compared to the non-optimized designs, the proposed method increases absorbed wave power by 87 % and wave height reduction by 46 %. The study acknowledges trade-offs between objectives and area restrictions, and provides an open-source code for further research and development in WEC farm optimization. This integrated approach aims to enhance the efficiency and effectiveness of WEC farm designs, offering a robust framework for future advancements in wave energy extraction.

Keywords: WEC; Layout optimization; Genetic algorithm; SNL-SWAN; Offshore renewable energy; Co-located wave-wind energy farms; K-means (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148125000242
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:renene:v:241:y:2025:i:c:s0960148125000242

DOI: 10.1016/j.renene.2025.122362

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

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