A Study on Optimal Layouts of Multiple Point-Absorber Wave Energy Converters Using Metaheuristic Algorithms

The design of ocean wave energy structures depends heavily on the geometry and layout of the structures. Particularly, when multiple structures are installed near the coast, their optimal placement based on incident wave conditions becomes a critical design factor for maximizing energy extraction. Thus, the optimization of the layout of multiple ocean wave energy structures involves determining the most efficient arrangement that maximizes energy production while considering interactions between the structures. However, as the number of design variables increases, the time and computational effort required for optimization also grows significantly. To address this, optimization algorithms can be employed to derive efficient and accurate optimal design variable values within a shorter timeframe. Specifically, metaheuristic algorithms are advantageous as they can avoid local maxima and achieve global maxima with fewer computations. In this study, the optimal layout of multiple point-absorber wave energy converters (WECs) installed in front of a vertical sea wall was determined using metaheuristic algorithms to maximize wave power generation efficiency. All WECs were assumed to have the same size and shape, and the design variables included the position of each WEC (x,y), the distance from the vertical wall, and the spacing between WECs. For the hydrodynamic analysis of the WECs, the commercial hydrodynamics program Ansys AQWA was utilized. A metaheuristic algorithm implemented in Python was integrated with the AQWA computational process to establish an optimization framework. The optimization was performed using metaheuristic algorithms such as Differential Evolution, Particle Swarm Optimization (PSO), and Advanced PSO, which were tested and validated in previous studies (Jeong and Koo, 2023). Finally, the optimal positions of the WECs for maximum power generation efficiency were calculated, and the resulting values and trends in power production were analyzed.