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Wind resource assessment at mountainous wind farm: Fusion of RANS and vertical multi-point on-site measured wind field data

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  • Cheng, Xu
  • Yan, Bowen
  • Zhou, Xuhong
  • Yang, Qingshan
  • Huang, Guoqing
  • Su, Yanwen
  • Yang, Wei
  • Jiang, Yan

Abstract

RANS (Reynolds-averaged Navier-Stokes) has become one of the mainstream methods to determine the wind field in the wind resource assessment of the complex terrain. However, before the simulation of wind field by RANS, defining the inlet boundary condition often depends on user's experience, and lacks of theoretical guidance, which brings great uncertainties to the accuracy of simulation results. In this paper, based on the vertical multi-point wind speed data (including wind direction) obtained from the on-site measurement and the wind speed data simulated by RANS, a data fusion method called the RANS-VMM (Vertical Multi-point on-site Measurement) method is proposed. In this method, through the polar coordinate diagram (PCD) that can express the wind direction and wind profile index at the same time, the vertical multi-point measured wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations, which can reconstruct the wind field distribution with spatial variability in complex mountainous areas, thus improving the accuracy of wind resource assessment in the complex terrain. The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain, and the results indicate that the RANS-VMM method can improves the accuracy of wind resource assessment in the complex terrain.

Suggested Citation

  • Cheng, Xu & Yan, Bowen & Zhou, Xuhong & Yang, Qingshan & Huang, Guoqing & Su, Yanwen & Yang, Wei & Jiang, Yan, 2024. "Wind resource assessment at mountainous wind farm: Fusion of RANS and vertical multi-point on-site measured wind field data," Applied Energy, Elsevier, vol. 363(C).
    • Handle: RePEc:eee:appene:v:363:y:2024:i:c:s0306261924004999
    DOI: 10.1016/j.apenergy.2024.123116
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    References listed on IDEAS

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    1. Yang, Xiaolei & Pakula, Maggie & Sotiropoulos, Fotis, 2018. "Large-eddy simulation of a utility-scale wind farm in complex terrain," Applied Energy, Elsevier, vol. 229(C), pages 767-777.
    2. Kim, Kyeongseok & Kim, Byungil & Kim, Hyoungkwan, 2018. "A decision-making model for the analysis of offshore wind farm projects under climate uncertainties: A case study of South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 853-860.
    3. Durán, Pablo & Meiβner, Cathérine & Casso, Pau, 2020. "A new meso-microscale coupled modelling framework for wind resource assessment: A validation study," Renewable Energy, Elsevier, vol. 160(C), pages 538-554.
    4. Miller, Aaron & Chang, Byungik & Issa, Roy & Chen, Gerald, 2013. "Review of computer-aided numerical simulation in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 122-134.
    5. Hanslian, David & Hošek, Jiří, 2015. "Combining the VAS 3D interpolation method and Wind Atlas methodology to produce a high-resolution wind resource map for the Czech Republic," Renewable Energy, Elsevier, vol. 77(C), pages 291-299.
    6. José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, vol. 7(11), pages 1-87, October.
    7. Solaun, Kepa & Cerdá, Emilio, 2020. "Impacts of climate change on wind energy power – Four wind farms in Spain," Renewable Energy, Elsevier, vol. 145(C), pages 1306-1316.
    8. Dayal, Kunal K. & Bellon, Gilles & Cater, John E. & Kingan, Michael J. & Sharma, Rajnish N., 2021. "High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model," Energy, Elsevier, vol. 232(C).
    9. Tang, Xiao-Yu & Zhao, Shumian & Fan, Bo & Peinke, Joachim & Stoevesandt, Bernhard, 2019. "Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts," Applied Energy, Elsevier, vol. 238(C), pages 806-815.
    10. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
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    3. Zhao, Ning & Su, Yi & Dai, Xianxing & Jia, Shaomin & Wang, Xuewei, 2024. "A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction," Applied Energy, Elsevier, vol. 369(C).
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