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Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling

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  • Liu, Yichao
  • Chen, Daoyi
  • Li, Sunwei
  • Chan, P.W.

Abstract

An improved dynamic downscaling method is introduced in the present study to discern the spatial variations in offshore wind resources over the China coastal waters. In the improved method, the authors develop a novel pre-processing technique to provide the lateral boundary and initial conditions for the main dynamic downscaling process. In detail, the multivariate orthogonal decomposition is employed, at first, to extract the time-independent componential wind fields from the 30-year regional ECMWF meteorology data, which are then used to run the Weather Research and Forecast (WRF) model to produce the offshore wind field with high spatial resolutions from dynamic downscaling. Given the contribution of each componential wind field estimated in the decomposition, the WRF simulation results are subsequently recomposed into the final wind field showing wind resources along the coast of China. It has been found that the offshore wind resources are abundant over the South and East China Sea, especially in the Taiwan Strait where the maximum annual wind power density ∼ 800W/m2 is observed at the 90m height. Via the improved dynamic downscaling method, the small-scale features of the localized offshore wind fields are improved by ∼13% after comparing to the raw ERA-Interim data, which is used to facilitate the siting of offshore wind farms.

Suggested Citation

  • Liu, Yichao & Chen, Daoyi & Li, Sunwei & Chan, P.W., 2018. "Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling," Energy, Elsevier, vol. 160(C), pages 582-596.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:582-596
    DOI: 10.1016/j.energy.2018.06.205
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    Cited by:

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    2. Zhang, Shijie & Wei, Jing & Chen, Xi & Zhao, Yuhao, 2020. "China in global wind power development: Role, status and impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    3. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    4. Tuchtenhagen, Patrícia & Carvalho, Gilvani Gomes de & Martins, Guilherme & Silva, Pollyanne Evangelista da & Oliveira, Cristiano Prestrelo de & de Melo Barbosa Andrade, Lara & Araújo, João Medeiros de, 2020. "WRF model assessment for wind intensity and power density simulation in the southern coast of Brazil," Energy, Elsevier, vol. 190(C).
    5. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(C).
    6. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2019. "Improved maintenance optimization of offshore wind systems considering effects of government subsidies, lost production and discounted cost model," Energy, Elsevier, vol. 187(C).
    7. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).

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