The search for renewable energy supplies for today’s global energy demand, particularly ocean wave energy for coastal areas, has become undeniably widespread in the last two decades. The Caspian Sea represents an immense opportunity for using ocean renewable energy, especially considering its long shoreline. In this study, the locations with maximum potential wave energy were chosen in the central, eastern, and western zones of the Caspian Sea’s southern coasts. Accordingly, the wave and bathymetric data were used as the input to calculate the oscillating surge wave energy converter’s flap geometric dimensions based on previous studies. Then, the geometric model was designed, and then the wave energy converters were modeled in the Wave Energy Converter Simulator (WEC-Sim) module in the MATLAB software. Furthermore, eight models in each sea state were simulated to find the best value of the PTO damping coefficient, which led to the highest capture factor. Finally, all the external forces on the WEC’s flap and the converter’s power output results were compared, taking into account the effects of the flap height on the total power output. It was found that Nowshahr port has more potential than the Anzali and Amirabad ports, as the converter’s absorbed power proved to be 16.7 kW/m (Capture factor = 63%) at this site. Consequently, by conducting a comparative analysis between the selected sites, the excitation, radiation damping, and power take-off forces were scrutinized. The results show that the highest applied forces to the converter’s flap occurred at Nowshahr port, followed by the Anzali and Amirabad ports, due to the directional characteristics of the waves at the central coasts of the Caspian Sea.

One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC’s PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.

Ocean wave energy is one of the latest renewable energy resources, projected to be commercialized and competitive with other energy technologies in the near future. However, wave energy technologies are not fully developed, so various criteria must be optimized to enter the energy market. To optimize the performance of wave energy converters (WECs) components, three challenges are mostly considered: i)Power take-off systems settings (PTO), ii)Geometry parameters of WECs, iii)WECs' layout. As each of them plays a significant role in harnessing the maximum power output, this paper reviews applied optimization techniques in WECs. Furthermore, due to the importance of fidelity and computational cost in numerical methods, we discuss methods to analyze a WEC together with the basics and developments of WECs interactions. Moreover, the most popular optimization methods applied to optimize WEC parameters are categorized, and their key characteristics are briefly discussed. As a result, in terms of convergence rate, a combination of bio-inspired algorithms and local search can outperform the competition in layout optimization. A review of PTO coefficients and the geometry of WECs have emphasized the indispensability of optimizing PTO coefficients and

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