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Downscaling wave energy converters for optimum performance in low-energy seas

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  • Majidi, AjabGul
  • Bingölbali, Bilal
  • Akpınar, Adem
  • Iglesias, Gregorio
  • Jafali, Halid

Abstract

As wave energy converters (WECs) are typically designed and optimized for ocean wave conditions, they struggle to perform in low-energy seas or bays, where wave conditions are very different. This work investigates the hypothesis that downscaled versions of WECs may well be more suited for such conditions. More specifically, fifteen downscaled WECs are considered for deployment in the Black Sea. The resizing (downscaling) of the WECs is based on Froude scaling law. Ten values are considered for the scaling factor (λL = 1.0, 0.9, 0.8 … 0.1), and the value that yields the highest capacity factor is selected for downscaling the WEC. The downscaled WEC is then compared with the original (full-scale) WEC in terms of performance (capacity factor, full-load hours, and rated capacity). This analysis is carried out for fifteen WECs and 62 locations at different water depths (5, 25, 50, 75, and 100 m), distributed on 13 lines perpendicular to the shoreline along the south-western coast of the Black Sea. The highest capacity factor was obtained by Oyster, whereas the highest energy output was achieved by SSG and WaveDragon for the locations with 4–16 m depths. For deeper waters (25, 50, 75, and 100 m), the highest capacity factor was obtained by Oceantec. In terms of energy output, the best performers were WaveDragon (at 25 m water depth) and Pontoon (at 50, 75, and 100 m water depths). The interest of this approach, however, lies not only in that it enables a scaling factor to be determined for downscaling a WEC for a given site, but also – and more generally – in that it proves the initial hypothesis that downscaled WECs may provide a better alternative for low-energy seas than their full-scale counterparts.

Suggested Citation

  • Majidi, AjabGul & Bingölbali, Bilal & Akpınar, Adem & Iglesias, Gregorio & Jafali, Halid, 2021. "Downscaling wave energy converters for optimum performance in low-energy seas," Renewable Energy, Elsevier, vol. 168(C), pages 705-722.
  • Handle: RePEc:eee:renene:v:168:y:2021:i:c:p:705-722
    DOI: 10.1016/j.renene.2020.12.092
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    3. Liang, Hongjian & Qin, Hao & Su, Haowen & Wen, Zhixuan & Mu, Lin, 2024. "Environmental-Sensing and adaptive optimization of wave energy converter based on deep reinforcement learning and computational fluid dynamics," Energy, Elsevier, vol. 297(C).
    4. Shao, Zhuxiao & Gao, Huijun & Liang, Bingchen & Lee, Dongyoung, 2022. "Potential, trend and economic assessments of global wave power," Renewable Energy, Elsevier, vol. 195(C), pages 1087-1102.
    5. Foteinis, Spyros, 2022. "Wave energy converters in low energy seas: Current state and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. Majidi, Ajab Gul & Ramos, Victor & Amarouche, Khalid & Rosa Santos, Paulo & das Neves, Luciana & Taveira-Pinto, Francisco, 2023. "Assessing the impact of wave model calibration in the uncertainty of wave energy estimation," Renewable Energy, Elsevier, vol. 212(C), pages 415-429.

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