[论文解读] Investigating Wave Energy Potential in Southern Coasts of the Caspian Sea and Evaluating the Application of Gray Wolf Optimizer Algorithm.

Erfan Amini, Sayyed Taghi Omid Naeeni+1 more|arXiv (Cornell University)|Dec 31, 2019

Global Energy Security and Policy被引用 5

一句话总结

本研究利用不规则波理论和灰狼优化器（GWO）算法，评估了里海南部九个沿海港口的波浪能潜力，以确定最适合波浪能转换器部署的地点。基于GWO的适应度评估结合了相关函数和范数向量，确定波高是影响能量集中度的主导因素，最佳条件出现在Hs ≈ 3 m且Te介于Te_max的0.75至0.85倍之间。

ABSTRACT

There is a significantly accelerating trend in the application of the wave energy converters. As a result, it is imperative to adopt a suitable point for implementing these systems. Besides, the Caspian Sea, as one of the most important marine renewable energy sources in Asia, is capable of supplying the coastal areas with a large amount of energy. Therefore, areas around nine ports in the southern coasts of the Caspian Sea were selected to measure their wave energy potential. Initially, the amount of energy on these points was measured using the irregular energy theory. A new approach was developed to compare these points and measure their fitness in supplying the maximum energy using the Grey Wolf optimizer (GWO) algorithm and time history analysis. In this method, the optimal parameters were first extracted from the algorithm for assessing the points within the southern areas of the Caspian Sea. These values were regarded as the assessment indices. Then, the fitness of each point was obtained using the correlation function and the norm vector to present the most optimal point with maximum waver energy exploitation potential. Finally, the side-by-side comparison of the parameters affecting the wave energy showed that an increase or decrease in the wave energy along the southern areas of the Caspian Sea is influenced more by the wave height than the depth on that points. Moreover, the waver energy concentration occurs in the range of Hs = 3 and $T_e$ range is between $0.75 imes T_{e_{max}}$ and $0.85 imes T_{e_{max}}$.

研究动机与目标

• 评估里海南部沿海九个港口的波浪能潜力，以应用于可再生能源。
• 采用先进的优化方法，识别最适合波浪能转换器实施的地点。
• 确定影响该地区波浪能分布的主要环境因素。
• 开发并应用一种新型适应度评估框架，结合GWO算法、时序历史分析和基于向量的相关性分析。
• 量化研究区域内实现最大能量提取的最佳波浪条件。

提出的方法

• 使用不规则波理论估算能量通量，测量九个沿海点的波浪能潜力。
• 应用灰狼优化器（GWO）算法，以确定站点评估的最优参数。
• 利用相关函数和范数向量计算各站点的适应度得分，以对能量开发潜力进行排序。
• 整合时序历史分析，评估选定位置间波浪能的时间变化。
• 从GWO输出结果建立评估指标，以比较和优先排序基于能量产出潜力的站点。
• 通过对比参数分析，评估波高和水深对能量集中度的影响。

实验结果

研究问题

• RQ1 里海南部哪个沿海站点表现出最高的波浪能潜力，适合波浪能转换器的部署？
• RQ2 波高和波周期在多大程度上影响里海南部海岸波浪能分布的空间变化？
• RQ3 与波高相比，水深在该研究区域对波浪能集中度的影响程度如何？
• RQ4 在里海南部，实现最大波浪能提取的最佳波高和能量周期范围是什么？
• RQ5 灰狼优化器算法在此背景下识别最适合波浪能开发的站点的效率如何？

主要发现

• 在里海南部沿海地区，波浪能潜力主要受波高影响，而非水深。
• 显著波高（Hs）约为3米时，波浪能集中度达到最优。
• 当能量周期（Te）介于最大观测能量周期（Te_max）的0.75至0.85倍之间时，能量提取潜力最大。
• 基于GWO的适应度评估通过整合时序历史和基于向量的相关性，成功识别出最适合波浪能部署的站点。
• 影响参数的并列比较结果证实，波高变化主导了该地区能量波动的大部分变化。
• 所提出的方法通过元启发式优化与物理波浪建模相结合的混合方法，有效对沿海站点进行排序。