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Renewable energy will play a pivotal role in meeting future global energy demand. Of current renewable sources, wave energy offers enormous potential for growth. This research investigates the optimisation of the placement of oscillating buoy-type wave energy converters (WECs). This work explores the design of a wave farm consisting of an array of fully submerged three-tether buoys. In a wave farm, buoy positions strongly determine the farm's output. Optimising the buoy positions is a challenging research problem due to complex and extensive interactions (constructive and destructive) between buoys. This research focuses on maximising the power output of the farm through the placement of buoys in a size-constrained environment, and we propose a new hybrid approach mixing local search, using a surrogate power model, and numerical optimisation methods. The proposed hybrid method is compared with other state-of-the-art search methods in five different wave scenarios - one simplified irregular wave model and four real wave regimes. The new hybrid methods outperform well-known previous heuristic methods in terms of both quality of achieved solutions and the convergence-rate of search in all tested wave regimes. The best performing method in real-wave scenarios uses the active set non-linear optimisation method to tune final placements. The effectiveness of this method seems to stem for its capacity to search over a larger area than other compared tuning methods. Refereed/Peer-reviewed

Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work, we explore the optimisation of WEC arrays consisting of a three-tether buoy model called CETO. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each parameter setting is computationally expensive -- limiting the number of full model evaluations that can be made. To handle this problem, we propose a new hybrid cooperative co-evolution algorithm (HCCA). HCCA consists of a symmetric local search plus Nelder-Mead and a cooperative co-evolution algorithm (CC) with a backtracking strategy for optimising the positions and PTO settings of WECs, respectively. Moreover, a new adaptive scenario is proposed for tuning grey wolf optimiser (AGWO) hyper-parameter. AGWO participates notably with other applied optimisers in HCCA. For assessing the effectiveness of the proposed approach five popular Evolutionary Algorithms (EAs), four alternating optimisation methods and two modern hybrid ideas (LS-NM and SLS-NM-B) are carefully compared in four real wave situations (Adelaide, Tasmania, Sydney and Perth) with two wave farm sizes (4 and 16). According to the experimental outcomes, the hybrid cooperative framework exhibits better performance in terms of both runtime and quality of obtained solutions., Information Sciences (2020)