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

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. • Genetic algorithms used to optimize wave farm layouts by balancing exploration and computational cost • A flexible continuous domain used for WEC positioning, improving upon previous grid-based layout methodologies • K-means clustering effectively increases computational efficiency while preserving 90% of incoming wave energy • Increase in absorbed wave power (87%) and wave height reduction (46%) compared to previous studies without optimization • Open-source code and processing tools made available on GitHub to accelerate development of efficient wave farms • Inherent trade-offs between optimization objectives (wave protection vs. power capture) and the limitations caused by area restrictions

Authors

Institutions

Publication Details

Journal
Renewable Energy
Published
2025-01-09
DOI
https://doi.org/10.1016/j.renene.2025.122362
Citations
5
Primary Topic
Wave and Wind Energy Systems
Type
article
Field-Weighted Citation Impact
5.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Francisco Taveira-Pinto, Felipe Teixeira-Duarte, Paulo Rosa-Santos
5 citations
Renewable Energy
Wave and Wind Energy Systems
5.73
article

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

Francisco Taveira-Pinto, Felipe Teixeira-Duarte, Paulo Rosa-Santos
article en
5 citations

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. • Genetic algorithms used to optimize wave farm layouts by balancing exploration and computational cost • A flexible continuous domain used for WEC positioning, improving upon previous grid-based layout methodologies • K-means clustering effectively increases computational efficiency while preserving 90% of incoming wave energy • Increase in absorbed wave power (87%) and wave height reduction (46%) compared to previous studies without optimization • Open-source code and processing tools made available on GitHub to accelerate development of efficient wave farms • Inherent trade-offs between optimization objectives (wave protection vs. power capture) and the limitations caused by area restrictions

Renewable EnergyVol. 241
Universidade do Porto (PT)
Affordable and clean energy
Openalex Percentile: Top 5%
Wave and Wind Energy Systems
5.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.