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Beyond the First Generation of Wind Modeling for Resource Assessment and Siting: From Meteorology to Uncertainty Quantification

Author

Listed:
  • Mark Kelly

    (Department of Wind and Energy Systems, Danish Technical University, Risø Campus, 4000 Roskilde, Denmark)

Abstract

Increasingly large turbines have led to a transition from surface-based ‘bottom–up’ wind flow modeling and meteorological understanding, to more complex modeling of wind resources, energy yields, and site assessment. More expensive turbines, larger windfarms, and maturing commercialization have meant that uncertainty quantification (UQ) of such modeling has become crucial for the wind industry. In this paper, we outline the meteorological roots of wind modeling and why it was initially possible, advancing to the more complex models needed for large wind turbines today, and the tradeoffs and implications of using such models. Statistical implications of how data are averaged and/or split in various resource assessment methodologies are also examined, and requirements for validation of classic and complex models are considered. Uncertainty quantification is outlined, and its current practice on the ‘wind’ side of the industry is discussed, including the emerging standard for such. Demonstrative examples are given for uncertainty propagation and multi-project performance versus uncertainty, with a final reminder about the distinction between UQ and risk.

Suggested Citation

  • Mark Kelly, 2025. "Beyond the First Generation of Wind Modeling for Resource Assessment and Siting: From Meteorology to Uncertainty Quantification," Energies, MDPI, vol. 18(7), pages 1-30, March.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1589-:d:1618054
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    References listed on IDEAS

    as
    1. Strickland, Jessica M.I. & Gadde, Srinidhi N. & Stevens, Richard J.A.M., 2022. "Wind farm blockage in a stable atmospheric boundary layer," Renewable Energy, Elsevier, vol. 197(C), pages 50-58.
    2. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    3. Hodgson, Emily Louise & Troldborg, Niels & Andersen, Søren Juhl, 2025. "Impact of freestream turbulence integral length scale on wind farm flows and power generation," Renewable Energy, Elsevier, vol. 238(C).
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