4.8 Article

Adaptive systematic optimization of a multi-axis ocean wave energy converter

期刊

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2023.113920

关键词

Ocean wave energy; Wave energy converter; BEM solver; EMnO; Systematic design

向作者/读者索取更多资源

Due to the increasing global energy needs, renewable energy systems, such as wave energy converters (WECs), have become a feasible solution to meet the current energy demand. This study proposes a multiscenario model using a boundary element method (BEM) solver, NEMOH, integrated with evolutionary manyobjective algorithms to evaluate the performance of a multi-axis point absorber WEC with different geometries and dimensions. The results show that the cylindrical and octagonal geometries produce more annual energy, and the arrow layout with thirty buoys performs the best in terms of energy production, levelized cost of energy, and net present value.
Due to the growing global energy needs, renewable energy systems, particularly wave energy converters (WECs), are a feasible solution to satisfy current energy demand. Recently, wave farms with diverse technologies, geometries, and layouts have been developed; however, evaluating the performance of these devices is complicated and requires precise hydrodynamic modeling to efficiently deploy wave farms. This study proposes a multiscenario model using boundary element method (BEM) solver, NEMOH, integrated with evolutionary manyobjective algorithms to evaluate the performance of a multi-axis point absorber WEC with respect to cylindrical, triangular, quadrilateral, and octagonal geometries and varying dimensions, that is, radius, draft, and height. To this end, six objective functions were considered to maximize the energy absorption and significant velocity and to minimize the separation distance, levelized cost of energy, net present value, and q-factor. Accordingly, three EMnO frameworks were utilized: the non-dominated sorings genetic algorithm (NSGA-III), reference pointbased NSGA-III (R-NSGA-III), and multi-objective evolutionary algorithm by decomposition (MOEA/D). The results of the three optimization algorithms indicate that R-NSGA-III converges faster than the other two and also found that the cylindrical and octagonal geometries produce more annual energy among other forms. Comparing the performances of the three different layouts for cylindrical and octagonal geometries reveals that the arrow layout with thirty buoys produced more energy and had a lower levelized cost of energy and net present value.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据