A Novel Hybrid Algorithm for Optimized Solutions in Ocean Renewable
Energy Industry: Enhancing Power Take-Off Parameters and Site Selection
Procedure of Wave Energy Converters
Ocean renewable energy, particularly wave energy, has emerged as a pivotal
component for diversifying the global energy portfolio, reducing dependence on
fossil fuels, and mitigating climate change impacts. This study delves into the
optimization of power take-off (PTO) parameters and the site selection process
for an offshore oscillating surge wave energy converter (OSWEC). However, the
intrinsic dynamics of these interactions, coupled with the multi-modal nature
of the optimization landscape, make this a daunting challenge. Addressing this,
we introduce the novel Hill Climb - Explorative Gray Wolf Optimizer (HC-EGWO).
This new methodology blends a local search method with a global optimizer,
incorporating dynamic control over exploration and exploitation rates. This
balance paves the way for an enhanced exploration of the solution space,
ensuring the identification of superior-quality solutions. Further anchoring
our approach, a feasibility landscape analysis based on linear water wave
theory assumptions and the flap's maximum angular motion is conducted. This
ensures the optimized OSWEC consistently operates within safety and efficiency
parameters. Our findings hold significant promise for the development of more
streamlined OSWEC power take-off systems. They provide insights for selecting
the prime offshore site, optimizing power output, and bolstering the overall
adoption of ocean renewable energy sources. Impressively, by employing the
HC-EGWO method, we achieved an upswing of up to 3.31% in power output compared
to other methods. This substantial increment underscores the efficacy of our
proposed optimization approach. Conclusively, the outcomes offer invaluable
knowledge for deploying OSWECs in the South Caspian Sea, where unique
environmental conditions intersect with considerable energy potential.Comment: 35 pages, 22 Figures, 7 Table