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
Hossein Mehdipour, Erfan Amini, Seyed Taghi Naeeni, Mehdi Neshat

TL;DR
This paper introduces HC-EGWO, a hybrid optimization algorithm that improves power take-off parameters and site selection for wave energy converters, leading to up to 3.31% increased power output and better operational safety.
Contribution
The study presents a novel hybrid optimizer combining local and global search techniques specifically tailored for wave energy converter optimization.
Findings
Up to 3.31% increase in power output using HC-EGWO.
Enhanced exploration of solution space with dynamic control of search rates.
Feasibility analysis ensures safe and efficient operation within safety parameters.
Abstract
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…
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Taxonomy
TopicsWave and Wind Energy Systems · Tropical and Extratropical Cyclones Research
