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Evaluation of an Offshore Wind Farm by Using Data from the Weather Station, Floating LiDAR, Mast, and MERRA

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Listed:
  • Cheng-Dar Yue

    (Department of Landscape Architecture, National Chiayi University, No. 300, Syuefu Rd., Chiayi 600, Taiwan)

  • Yi-Shegn Chiu

    (Department of Landscape Architecture, National Chiayi University, No. 300, Syuefu Rd., Chiayi 600, Taiwan)

  • Chien-Cheng Tu

    (Research Center for Energy Technology and Strategy, National Cheng Kung University, No.25, Xiaodong Rd., North Dist., Tainan City 704, Taiwan)

  • Ta-Hui Lin

    (Department of Mechanical Engineering, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan)

Abstract

Offshore wind energy is regarded as a key alternative to fossil fuels in many parts of the world. Its exploitation is based on the sound evaluation of wind resources. This study used data from a meteorological mast, a floating light detection and ranging (LiDAR) device, and the Modern-Era Retrospective Analysis for Research and Applications, a reanalysis data set established by the NASA Center for Climate Simulation, to evaluate wind resources of the Changhua-South Offshore Wind Farm. The average wind speeds evaluated at a height of 105 m in the studied wind farm were 7.97 and 8.02 m/s according to the data obtained from the floating LiDAR device and a mast, respectively. The full-load hours were 3320.5 and 3296.5 h per year when data from the LiDAR device and mast were used, respectively. The estimated annual energy production (AEP) with a probability of 50% ( P 50 ) reached 314 GWh/y, whereas the AEPs with a probability of 75% ( P 75 ) and with a probability of 90% ( P 90 ) were 283 GWh/y and 255 GWh/y, respectively. The estimated AEP of P 75 was 90% of the AEP of P 50 , whereas the estimated AEP of P 90 was 81% of the AEP of P 50 . This difference might need to be considered when assessing the risk of financing a wind project.

Suggested Citation

  • Cheng-Dar Yue & Yi-Shegn Chiu & Chien-Cheng Tu & Ta-Hui Lin, 2020. "Evaluation of an Offshore Wind Farm by Using Data from the Weather Station, Floating LiDAR, Mast, and MERRA," Energies, MDPI, vol. 13(1), pages 1-20, January.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:185-:d:304004
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    References listed on IDEAS

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    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. Bansal, Jagdish Chand & Farswan, Pushpa, 2017. "Wind farm layout using biogeography based optimization," Renewable Energy, Elsevier, vol. 107(C), pages 386-402.
    3. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    4. Park, Jinkyoo & Law, Kincho H., 2015. "Layout optimization for maximizing wind farm power production using sequential convex programming," Applied Energy, Elsevier, vol. 151(C), pages 320-334.
    5. Kim, Ji-Young & Oh, Ki-Yong & Kim, Min-Suek & Kim, Kwang-Yul, 2019. "Evaluation and characterization of offshore wind resources with long-term met mast data corrected by wind lidar," Renewable Energy, Elsevier, vol. 144(C), pages 41-55.
    6. Kwon, Soon-Duck, 2010. "Uncertainty analysis of wind energy potential assessment," Applied Energy, Elsevier, vol. 87(3), pages 856-865, March.
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