Wave Energy Converters (WECs) have been increasingly installed in various coastal regions due to the higher energy density of waves than other renewable sources. Regarding coastal regions’ high potential, it is remarkably better to emplace multiple devices concerning a layout of the arrays with different configurations. Although WECs can capture the highest energy in hotspots, the location for installing these devices must be optimized. Purposefully, the wave propagation model, SWAN, was first employed respected to stochastic wind data to assess the wave energy potential and compute the annual energy production (AEP). The Latin Hypercube Sampling (LHS) technique was used to generate samples. Finally, the optimal location and layout of the arrays were determined through the multi-objective optimization (MOOP) algorithm, NSGA-III, based on SWAN’s sequential outputs. The optimized layouts contained arrays with 4-, 8-, and 16-devices with regard to devices’ initial state. Almost 20 hotspots were located by solving the Pareto-front. It was found that the best arrangement for the 4-device arrays is linear. However, the optimal arrangement of the 8- and 16-device arrays widely varies and depends on the AEP of the region. Nevertheless, it seems best to position the 16-device array in a diagonal layout with one to three rows.
Keywords
Wave generation
Wave energy
SWAN
Latin hypercube sampling
MOOP
NSGA-III
