Optimizing nearshore wave energy Farms: A multi-objective genetic algorithm framework for power capture and coastal protection

This study presents a multi-objective genetic algorithm (GA) framework for optimizing nearshore wave energy converter (WEC) farm layouts, balancing renewable energy production and coastal protection. Applied to the Ofir–Cávado River mouth in Esposende, Portugal, the framework integrates the SNL-SWAN spectral wave model to simulate wave propagation and energy extraction. Representative sea states are derived through k-means clustering of historical wave data, reducing computational costs while preserving key metocean variability. The GA simultaneously optimizes two metrics: the q-factor (power capture efficiency) and the HRA (reduction in significant wave height in the protected area), combined into a Wave Energy Park Layout Assessment index. Two optimization scenarios are tested: prioritizing coastal protection (maximizing HRA) and minimizing environmental impact (minimizing HRA). The coastal protection layout achieved a 26% increase in q-factor and a 40% improvement in HRA compared to a fixed benchmark, while the minimal-impact layout had a 24% q-factor gain with a 7% HRA reduction. Results confirm GA’s effectiveness in designing WEC farms that enhance energy capture and shoreline resilience. The adaptable framework supports sustainable coastal energy planning and can integrate future wave modeling tools. • Nearshore WEC farm optimization for coastal management using a GA framework. • The GA framework balances energy production and wave attenuation. • K-means clustering and SNL-SWAN are integrated for efficiency and reliability. • Coastal protection layout optimization boosts q-factor by 26% and shielding by 40%. • The minimal impact layout increases the q-factor by 24% and reduces impact by 7%.