DOI QR코드

DOI QR Code

Refined numerical simulation in wind resource assessment

  • Cheng, Xue-Ling (State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences) ;
  • Li, Jun (State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences) ;
  • Hu, Fei (State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences) ;
  • Xu, Jingjing (International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences) ;
  • Zhu, Rong (Public Weather Service Center China Meteorological Administration)
  • 투고 : 2014.07.08
  • 심사 : 2014.11.27
  • 발행 : 2015.01.25

초록

A coupled model system for Wind Resource Assessment (WRA) was studied. Using a mesoscale meteorological model, the Weather Research and Forecasting (WRF) model, global-scale data were downscaled to the inner nested grid scale (typically a few kilometers), and then through the coupling Computational Fluid Dynamics (CFD) mode, FLUENT. High-resolution results (50 m in the horizontal direction; 10 m in the vertical direction below 150 m) of the wind speed distribution data and ultimately refined wind farm information, were obtained. The refined WRF/FLUENT system was then applied to assess the wind resource over complex terrain in the northern Poyang Lake region. The results showed that the approach is viable for the assessment of wind energy.

키워드

과제정보

연구 과제번호 : Refined Numerical Simulation of wind farm base on CFD downscaling method

연구 과제 주관 기관 : National Natural Science Foundation of China

참고문헌

  1. Bechmann, A., Sorensen, N.N., Berg, J., Mann, J. and Rethore, P.E. (2011), "The Bolund experiment, part II: blind comparison of microscale flow models", Bound. - Lay. Meteorol., 141(2), 245-271. https://doi.org/10.1007/s10546-011-9637-x
  2. Berg, J., Mann, J., Bechmann, A., Courtney, M. and Jørgensen, H. (2011), "The Bolund experiment, part I: flow over a steep, three-dimensional hill", Bound. - Lay. Meteorol., 141(2), 219-243. https://doi.org/10.1007/s10546-011-9636-y
  3. Boquet, M. (2010), "Innovative solutions for pulsed wind LiDAR accuracy in complex terrain", Proceedings of the 15th International Symposium for the Advancement of Boundary Layer Remote Sensing, Paris.
  4. Brower, M., Marcus, M., Taylor, M. et al. (2010), Wind Resource Assessment Handbook, New York State Energy Research and Development Authority, AWS Truepower, 203pp.
  5. Cattin, R., Schaffner, B. and Kunz, S. (2006), "Validation of CFD wind resource modeling in highly complex terrain", Proceedings of the Fourth European Wind Energy Conference, Athens, Greece, European Wind Energy Association.
  6. Landberg, L., Myllerup, L., Rathmann, O., Petersen, E.L., Jorgensen, B.H., Badger, J. and Mortensen, G. (2003), "Wind resource estimation - an overview", Wind Eng., 6(3), 261-271. https://doi.org/10.1002/we.94
  7. Li, L., Hu, F., Cheng, X.L. and Han, H.Y. (2004), "The application of computational fluid dynamics to pedestrian level wind safety problem induced by high-rise buildings", Chinese Phys., 13(7), 1070-1075. https://doi.org/10.1088/1009-1963/13/7/018
  8. Li, L., Hu, F., Cheng, X.L., Jiang, J.H. and Ma, X.G. (2006), "Numerical simulation of the flow within and over an intersection model with Reynolds-averaged Navier-Stokes method", Chinese Phys., 15(1), 149-155. https://doi.org/10.1088/1009-1963/15/1/024
  9. Li, L., Chan, P.W. (2012), "Numerical simulation study of the effect of buildings and complex terrain on the low-level winds at an airport in typhoon situation", Meteorologische Zeitschrift, 21(2), 183-192. https://doi.org/10.1127/0941-2948/2012/0252
  10. Li, L., Chan, P.W., Zhang, L.J. and Hu, F. (2013), "Numerical simulation of a lee wave case over three-dimensional mountainous terrain under strong wind condition", Adv. Meteorol. 2013, Article ID 304321.
  11. Montavon, C. (1998), "Validation of a non-hydrostatic numerical model to simulate stratified wind fields over complex topography", J. Wind Eng. Ind. Aerod., 74-76, 273-282. https://doi.org/10.1016/S0167-6105(98)00024-5
  12. Ozerdem, B., Turkeli, H.M. (2005), "Wind energy potential estimation and micrositting on Izmir Institute of Technology Campus, Turkey", Renew. Energ., 30(10), 1623-1633. https://doi.org/10.1016/j.renene.2004.11.010
  13. Rodrigo, J.S. (2010), State-of-the-Art of Wind Resource Assessment. Wind Resource Assessment Audit and Standardization Project.
  14. Stangroom, P. (2004), CFD Modelling of Wind Flow over Terrain, PhD Thesis, University of Nottingham, 298pp.
  15. Thogersen, M.L., Nielsen, P., Mads, V.S. et al. (2003), "Applying new computer-aided tools for wind farm planning and environmental impact analysis", Proceedings of the 2003 European Wind Energy Conference & Exhibition, CD 2, Madrid, Spain.
  16. Uchida, T. and Ohya Y. (1999), "Numerical simulation of atmospheric flow over complex terrain", J. Wind Eng. Ind. Aerod., 81, 283-293. https://doi.org/10.1016/S0167-6105(99)00024-0
  17. Uchida, T. and Ohya Y. (2003), "Large-eddy simulation of turbulent airflow over complex terrain", J. Wind Eng. Ind. Aerod., 91(1-2), 219-229. https://doi.org/10.1016/S0167-6105(02)00347-1
  18. Wang, Y., Smith, G.M. and Schlez, W. (2006), "Recent developments in precision wind farm modeling", Proceedings of the Asian Wind Energy Conference & Exhibition, 20060628-30, Beijing(CN).
  19. Wang, Z.Y., Shi, J.L., Zhao, Y.Q. et al. (2011), China Wind Energy Development Roadmap 2050, Energy Research Institute, National Development and Reform Commission (NDRC) of P. R. China.
  20. Zhang, D., Zhu, R., Luo, Y. et al. (2008), "Application of wind energy simulation toolkit (WEST) to wind energy numerical simulation of China", Plateau Meteorology, 27(1), 202-207.

피인용 문헌

  1. Spatial correlation-based WRF observation-nudging approach in simulating regional wind field vol.28, pp.2, 2015, https://doi.org/10.12989/was.2019.28.2.129
  2. Nonlinear Kalman filter bias correction for wind ramp event forecasts at wind turbine height vol.30, pp.4, 2015, https://doi.org/10.12989/was.2020.30.4.393
  3. Improving the Near-Surface Wind Forecast around the Turpan Basin of the Northwest China by Using the WRF_TopoWind Model vol.12, pp.12, 2015, https://doi.org/10.3390/atmos12121624