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Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts

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  • Tang, Xiao-Yu
  • Zhao, Shumian
  • Fan, Bo
  • Peinke, Joachim
  • Stoevesandt, Bernhard

Abstract

To date, the micro-scale wind resource assessment for complex terrain has always been a challenging mission, due to the flow field complexity caused by the local topography. In this paper, a novel method, combining on-site measurement from multiple masts and computational fluid dynamics (CFD) simulations, is proposed for complex terrain site assessment. It is designed to accomplish the spatial variability reproduction of wind energy distribution, as well as the dynamic wind velocity estimation of any desired positions within the concerned region. CFD simulations are carried out to provide detailed wind fields, which implicitly carry the correlations of physical properties with the concerned space. The on-site measurement data from multiple masts are integrated into the assessment process, to provide dynamical corrections based on the reference solutions obtained from the CFD simulations. A high-resolutional wind resource distribution and accurate wind velocity estimations are achieved. A detailed case study on micro-scale wind resource assessment for a wind farm with complex terrain, located in China is presented for validation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:806-815
    DOI: 10.1016/j.apenergy.2019.01.129
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    1. Fagbenle, R.O. & Katende, J. & Ajayi, O.O. & Okeniyi, J.O., 2011. "Assessment of wind energy potential of two sites in North-East, Nigeria," Renewable Energy, Elsevier, vol. 36(4), pages 1277-1283.
    2. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    3. 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.
    4. 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.
    5. Giannaros, Theodore M. & Melas, Dimitrios & Ziomas, Ioannis, 2017. "Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece," Renewable Energy, Elsevier, vol. 102(PA), pages 190-198.
    6. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    7. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: A case study in the Canary Islands," Applied Energy, Elsevier, vol. 88(11), pages 3869-3881.
    8. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    9. 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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    4. Takanori Uchida & Yasushi Kawashima, 2019. "New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence," Energies, MDPI, vol. 12(13), pages 1-27, July.
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    7. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    8. Rivarolo, M. & Freda, A. & Traverso, A., 2020. "Test campaign and application of a small-scale ducted wind turbine with analysis of yaw angle influence," Applied Energy, Elsevier, vol. 279(C).
    9. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements," Applied Energy, Elsevier, vol. 288(C).
    10. Navarro Diaz, Gonzalo P. & Saulo, A. Celeste & Otero, Alejandro D., 2021. "Full wind rose wind farm simulation including wake and terrain effects for energy yield assessment," Energy, Elsevier, vol. 237(C).
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