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A Parametric Study of Wave Energy Converter Layouts in Real Wave Models

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  • Erfan Amini

    (Coastal and Offshore Structures Engineering Group, School of Civil Engineering, University of Tehran, Tehran 13145-1384, Iran)

  • Danial Golbaz

    (Coastal and Offshore Structures Engineering Group, School of Civil Engineering, University of Tehran, Tehran 13145-1384, Iran)

  • Fereidoun Amini

    (School of Civil Engineering, Iran University of Science and Technology, Tehran 13114-16864, Iran)

  • Meysam Majidi Nezhad

    (Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Rome, Italy)

  • Mehdi Neshat

    (Optimization and Logistics Group, School of Computer Science, The University of Adelaide, Adelaide 5005, Australia)

  • Davide Astiaso Garcia

    (Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy)

Abstract

Ocean wave energy is a broadly accessible renewable energy source; however, it is not fully developed. Further studies on wave energy converter (WEC) technologies are required in order to achieve more commercial developments. In this study, four CETO6 spherical WEC arrangements have been investigated, in which a fully submerged spherical converter is modelled. The numerical model is applied using linear potential theory, frequency-domain analysis, and irregular wave scenario. We investigate a parametric study of the distance influence between WECs and the effect of rotation regarding significant wave direction in each arrangement compared to the pre-defined layout. Moreover, we perform a numerical landscape analysis using a grid search technique to validate the best-found power output of the layout in real wave models of four locations on the southern Australian coast. The results specify the prominent role of the distance between WECs, along with the relative angle of the layout to dominant wave direction, in harnessing more power from the waves. Furthermore, it is observed that a rise in the number of WECs contributed to an increase in the optimum distance between converters. Consequently, the maximum exploited power from each buoy array has been found, indicating the optimum values of the distance between buoys in different real wave scenarios and the relative angle of the designed layout with respect to the dominant in-site wave direction.

Suggested Citation

  • Erfan Amini & Danial Golbaz & Fereidoun Amini & Meysam Majidi Nezhad & Mehdi Neshat & Davide Astiaso Garcia, 2020. "A Parametric Study of Wave Energy Converter Layouts in Real Wave Models," Energies, MDPI, vol. 13(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6095-:d:448626
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    References listed on IDEAS

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    5. Mehdi Neshat & Nataliia Y. Sergiienko & Erfan Amini & Meysam Majidi Nezhad & Davide Astiaso Garcia & Bradley Alexander & Markus Wagner, 2020. "A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea," Energies, MDPI, vol. 13(20), pages 1-23, October.
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    Cited by:

    1. Han, Meng & Cao, Feifei & Shi, Hongda & Zhu, Kai & Dong, Xiaochen & Li, Demin, 2023. "Layout optimisation of the two-body heaving wave energy converter array," Renewable Energy, Elsevier, vol. 205(C), pages 410-431.
    2. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    3. Erfan Amini & Rojin Asadi & Danial Golbaz & Mahdieh Nasiri & Seyed Taghi Omid Naeeni & Meysam Majidi Nezhad & Giuseppe Piras & Mehdi Neshat, 2021. "Comparative Study of Oscillating Surge Wave Energy Converter Performance: A Case Study for Southern Coasts of the Caspian Sea," Sustainability, MDPI, vol. 13(19), pages 1-21, October.
    4. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.
    5. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2021. "Open Data and Models for Energy and Environment," Energies, MDPI, vol. 14(15), pages 1-2, July.

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