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Energies 2018, 11(3), 499; https://doi.org/10.3390/en11030499

Identifying the Optimal Offshore Areas for Wave Energy Converter Deployments in Taiwanese Waters Based on 12-Year Model Hindcasts

National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan
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Received: 8 January 2018 / Revised: 29 January 2018 / Accepted: 23 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Wave and Tidal Energy)
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Abstract

A 12-year sea-state hindcast for Taiwanese waters, covering the period from 2005 to 2016, was conducted using a fully coupled tide-surge-wave model. The hindcasts of significant wave height and peak period were employed to estimate the wave power resources in the waters surrounding Taiwan. Numerical simulations based on unstructured grids were converted to structured grids with a resolution of 25 × 25 km. The spatial distribution maps of offshore annual mean wave power were created for each year and for the 12-year period. Waters with higher wave power density were observed off the northern, northeastern, southeastern (south of Green Island and southeast of Lanyu) and southern coasts of Taiwan. Five energetic sea areas with spatial average annual total wave energy density of 60–90 MWh/m were selected for further analysis. The 25 × 25 km square grids were then downscaled to resolutions of 5 × 5 km, and five 5 × 5 km optimal areas were identified for wave energy converter deployments. The spatial average annual total wave energy yields at the five optimal areas (S1)–(S5) were estimated to be 64.3, 84.1, 84.5, 111.0 and 99.3 MWh/m, respectively. The prevailing wave directions for these five areas lie between east and northeast. View Full-Text
Keywords: tide-surge-wave model; Taiwanese waters; sea-state hindcast; wave power; wave energy tide-surge-wave model; Taiwanese waters; sea-state hindcast; wave power; wave energy
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Shih, H.-J.; Chang, C.-H.; Chen, W.-B.; Lin, L.-Y. Identifying the Optimal Offshore Areas for Wave Energy Converter Deployments in Taiwanese Waters Based on 12-Year Model Hindcasts. Energies 2018, 11, 499.

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