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The impact of model physics on numerical wind forecasts

Author

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  • Cheng, William Y.Y.
  • Liu, Yubao
  • Liu, Yuewei
  • Zhang, Yongxin
  • Mahoney, William P.
  • Warner, Thomas T.

Abstract

Fine scale numerical weather prediction (NWP) models are now widely applied to predict power production at wind farms. Given the fact that demand for specialized forecasts for wind farms is growing, it is important to understand the strengths and limitations of NWP models for producing wind forecasts. This paper seeks to partially fulfill this goal by exploring the sensitivity of NWP-based wind forecasts to the choice of model physics schemes. The authors used two distinct case studies to explore these sensitivities with a NWP model used in realtime wind power forecast, where the underlying meteorology in both cases had a profound impact on the wind ramp-up of a wind farm in Northern Colorado. The first case was a strong cold frontal system moving through the wind farm during winter, and the second case was for a line of strong thunderstorms passing through the wind farm during summer. The model results were compared with observed hub-height wind.

Suggested Citation

  • Cheng, William Y.Y. & Liu, Yubao & Liu, Yuewei & Zhang, Yongxin & Mahoney, William P. & Warner, Thomas T., 2013. "The impact of model physics on numerical wind forecasts," Renewable Energy, Elsevier, vol. 55(C), pages 347-356.
    • Handle: RePEc:eee:renene:v:55:y:2013:i:c:p:347-356
    DOI: 10.1016/j.renene.2012.12.041
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    References listed on IDEAS

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    1. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    2. Lazić, Lazar & Pejanović, Goran & Živković, Momčilo, 2010. "Wind forecasts for wind power generation using the Eta model," Renewable Energy, Elsevier, vol. 35(6), pages 1236-1243.
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    1. Lazić, Lazar & Pejanović, Goran & Živković, Momčilo & Ilić, Luka, 2014. "Improved wind forecasts for wind power generation using the Eta model and MOS (Model Output Statistics) method," Energy, Elsevier, vol. 73(C), pages 567-574.
    2. 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.
    3. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    4. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    5. Mylonas, M.P. & Barbouchi, S. & Herrmann, H. & Nastos, P.T., 2018. "Sensitivity analysis of observational nudging methodology to reduce error in wind resource assessment (WRA) in the North Sea," Renewable Energy, Elsevier, vol. 120(C), pages 446-456.
    6. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    7. Xu, Weifeng & Liu, Pan & Cheng, Lei & Zhou, Yong & Xia, Qian & Gong, Yu & Liu, Yini, 2021. "Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy," Renewable Energy, Elsevier, vol. 163(C), pages 772-782.
    8. Perini de Souza, Noele Bissoli & Sperandio Nascimento, Erick Giovani & Bandeira Santos, Alex Alisson & Moreira, Davidson Martins, 2022. "Wind mapping using the mesoscale WRF model in a tropical region of Brazil," Energy, Elsevier, vol. 240(C).
    9. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.

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