Abstract
Ocean wave energy stands as a crucial component in the quest for sustainable and renewable energy sources, essential in the global effort to mitigate climate change. However, a significant challenge in this field is optimizing the efficiency of Wave Energy Converters (WECs) on a regional scale, particularly Oscillating Surge Wave Energy Converters (OSWECs). This challenge stems from the complex, nonlinear interactions between ocean waves and these devices, necessitating precise tuning of Power Take-Off (PTO) system settings and optimal placement for the highest possible performance and stability. To address this challenge, our study introduces the Hill Climb - Explorative Grey Wolf Optimizer (HC-EGWO), a novel algorithm combining local search and swarm-based global optimization strategies. This hybrid approach effectively balances exploration and exploitation in the solution space, leading to more optimal PTO settings for OSWECs. Alongside this algorithmic development, we conduct a thorough feasibility analysis based on the constraints of the flap’s maximum angular motion. This ensures the optimized OSWEC operates within safe and efficient limits. In a comparative analysis with the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the artificial Gorilla Troops Optimizer (GTO), and different implementations of the GWO, our results show an improvement in power output, with the HC-EGWO method achieving up to a 3.31% increase over other variations of the GWO and 45% increase compared to all the methods. The findings of this study not only demonstrate the effectiveness of the HC-EGWO method but also provide strategic insights for the deployment of OSWECs in areas like the South Caspian Sea, where unique environmental factors imply careful consideration in the site selection process.