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Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm

Author

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  • Yuan, Renyu
  • Ji, Wenju
  • Luo, Kun
  • Wang, Jianwen
  • Zhang, Sanxia
  • Wang, Qiang
  • Fan, Jianren
  • Ni, MingJiang
  • Cen, Kefa

Abstract

The mesoscale Weather Research and Forecasting (WRF) model coupled with wind farm parameterization is newly developed to simulate the wake flow and power production of a real onshore wind farm. First, wind farm flow field simulations are conducted with 1000m, 500m and 200m horizontal resolutions, and the simulation results capture the wind farm observed data well. In addition, wind farm flow characteristics, power output, and influence on the atmosphere boundary layer (ABL) are resolved at a horizontal resolution of 200m. The wake interactions, wind speed, and power output deficit in the wind farm are analyzed. The power comparison results prove that the proposed method can be applied to simulate the power output of a real onshore wind farm with high accuracy in real time. The influence of the wind farm on the ABL is also discussed. The results show that wind farm effects on the ABL occur mainly within the turbine rotor-spanned heights and the downstream regions behind the wind farm within 10km, within which the speed deficit ratio can exceed 10%. For the region that is 18km downstream of the wind farm, the average speed deficit ratio is only about 2%. This study is the first attempt to reproduce the wake flow and power output of a real onshore wind farm by the WRF model at such high resolution.

Suggested Citation

  • Yuan, Renyu & Ji, Wenju & Luo, Kun & Wang, Jianwen & Zhang, Sanxia & Wang, Qiang & Fan, Jianren & Ni, MingJiang & Cen, Kefa, 2017. "Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm," Applied Energy, Elsevier, vol. 206(C), pages 113-125.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:113-125
    DOI: 10.1016/j.apenergy.2017.08.018
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    1. Fan, Xiao-chao & Wang, Wei-qing & Shi, Rui-jing & Li, Feng-ting, 2015. "Analysis and countermeasures of wind power curtailment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1429-1436.
    2. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    3. Yip, Chak Man Andrew & Gunturu, Udaya Bhaskar & Stenchikov, Georgiy L., 2016. "Wind resource characterization in the Arabian Peninsula," Applied Energy, Elsevier, vol. 164(C), pages 826-836.
    4. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    5. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    6. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    7. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    8. Ulazia, Alain & Saenz, Jon & Ibarra-Berastegui, Gabriel, 2016. "Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline," Applied Energy, Elsevier, vol. 180(C), pages 617-627.
    9. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula," Applied Energy, Elsevier, vol. 134(C), pages 57-64.
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    4. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    5. Cuevas-Figueroa, Gabriel & Stansby, Peter K. & Stallard, Timothy, 2022. "Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production," Energy, Elsevier, vol. 254(PB).
    6. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
    7. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    8. Fu, Xiaopeng & Wang, Chengshan & Li, Peng & Wang, Liwei, 2019. "Exponential integration algorithm for large-scale wind farm simulation with Krylov subspace acceleration," Applied Energy, Elsevier, vol. 254(C).
    9. Wang, Qiang & Luo, Kun & Yuan, Renyu & Zhang, Sanxia & Fan, Jianren, 2019. "Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations," Energy, Elsevier, vol. 166(C), pages 1168-1180.
    10. Zhang, Huan & Ge, Mingwei & Liu, Yongqian & Yang, Xiang I.A., 2021. "A new coupled model for the equivalent roughness heights of wind farms," Renewable Energy, Elsevier, vol. 171(C), pages 34-46.
    11. Syed, Abdul Haseeb & Javed, Adeel & Asim Feroz, Raja M. & Calhoun, Ronald, 2020. "Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations," Applied Energy, Elsevier, vol. 268(C).

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