Adaptive bi-level whale optimization algorithm for maximizing the power output of hybrid wave-wind energy site

Over the last fifty years, the world has been facing a global energy crisis, and due to industrial recovery from the COVID-19 pandemic, fossil fuel prices reached a new record in 2021-2022. The long-term solution to address this series of cyclical energy shortages is energy transition. Developing renewable energy systems play a significant role in encountering the global energy crisis and reducing carbon emissions. Hybrid wind-wave systems merge offshore wind turbines with wave energy converters in a shared venue. These techniques have attempted to maximize the average power output at a single site by trapping both the waves and wind energy simultaneously. In this work, we develop a fast and effective framework to adjust the geometry and power take-off (PTO) parameters using an adaptive bi-level whale optimization algorithm (AWOA). Combining geometry and PTO parameters makes a complex and Heterogeneous search space; thus, we propose to optimize this hybrid model into two levels: upper and lower. The outer optimization task involves tuning the geometry parameters, and the inner section tries to find the optimal PTO parameters. The proposed method is compared with six state-of-the-art meta-heuristic algorithms. The optimization results confirm the superiority of the AWOA considerably in terms of the quality of solutions and convergence speed.