Hybrid wave–wind energy site power output augmentation using effective ensemble covariance matrix adaptation evolutionary algorithm

Floating hybrid wind–wave systems combine offshore wind platforms and WECs to create cost-effective, reliable energy solutions. WECs that are properly designed and tuned are required to avoid unwanted loads that can interfere with turbine motion while efficiently extracting energy from waves. The systems diversify energy sources, enhance energy security, and reduce supply risks while delivering a smoother power output through the minimisation of energy production variability. However, optimisation of these systems is hindered by physical and hydrodynamic component–component interactions, which cause a challenging optimisation space. A 5-MW OC4-DeepCwind semi-submersible platform and three spherical WECs are taken into consideration in this paper in order to explore such synergies. To address these challenges, we propose an effective ensemble optimisation (EEA) technique that combines covariance matrix adaptation, novelty search, and discretisation techniques. To evaluate the EEA performance, we used four sea sites located along Australia's southern coast. In this framework, geometry and power take-off (PTO) parameters are simultaneously optimised to maximise the average power output of the hybrid wind–wave system. Ensemble optimisation methods enhance performance, flexibility, and robustness by identifying the best algorithm or combination of algorithms for a given problem, addressing issues like premature convergence, stagnation, and poor search space exploration. The EEA was benchmarked against 14 advanced optimisation methods, demonstrating superior solution quality and convergence rates. EEA improved total power output by 111%, 95%, and 52% compared to Whale Optimisation Algorithm (WOA), Equilibrium Optimiser (EO), and Artificial Hummingbird Algorithm (AHA), respectively. Additionally, in comparisons with advanced methods, Ensemble Sinusoidal Differential Covariance Matrix Adaptation (LSHADE), Self-adaptive Differential Evolution (SaNSDE), and Social Learning Particle Swarm Optimisation (SLPSO), EEA achieved absorbed power enhancements of 498%, 638%, and 349% at the Sydney sea site, showcasing its effectiveness in optimising hybrid energy systems.