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A CFD Model for Spatial Extrapolation of Wind Field over Complex Terrain—Wi.Sp.Ex

Author

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  • Dimitrios Michos

    (Laboratory of Atmospheric Physics, University of Patras, 26500 Patras, Greece)

  • Francky Catthoor

    (Interuniversity Microelectronics Centre (IMEC) vzw, Kapeldreef 75, 3001 Leuven, Belgium
    Department of Electrical Engineering (ESAT), KU Leuven, 3000 Leuven, Belgium)

  • Dimitris Foussekis

    (CRES Wind Farm, 19009 Lavrio, Greece)

  • Andreas Kazantzidis

    (Laboratory of Atmospheric Physics, University of Patras, 26500 Patras, Greece)

Abstract

High-resolution wind datasets are crucial for ultra-short-term wind forecasting. Penetration of WT installations near urban areas that are constantly changing will motivate researchers to understand how to adapt their models to terrain changes to reduce forecasting errors. Although CFD modelling is not widely used for ultra-short-term forecasting purposes, it can overcome such difficulties. In this research, we will spatially extrapolate vertical profile LIDAR wind measurements into a 3D wind velocity field over a large and relatively complex terrain with the use of stationary CFD simulations. The extrapolated field is validated with measurements at a hub height of three WTs located in the area. The accuracy of the model increases with height because of the terrain anomalies and turbulence effects. The maximum MAE of wind velocity at WT hub height is 0.81 m/s, and MAPE is 7.98%. Our model remains accurate even with great simplifications and scarce measurements for the complex terrain conditions of our case study. The models’ performance under such circumstances establishes it as a promising tool for the evolution of ultra-short-term forecasting as well as for the evaluation of new WT installations by providing valuable data for all models.

Suggested Citation

  • Dimitrios Michos & Francky Catthoor & Dimitris Foussekis & Andreas Kazantzidis, 2024. "A CFD Model for Spatial Extrapolation of Wind Field over Complex Terrain—Wi.Sp.Ex," Energies, MDPI, vol. 17(16), pages 1-15, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4139-:d:1459887
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    References listed on IDEAS

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    1. Wang, Jian & Yang, Zhongshan, 2021. "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, Elsevier, vol. 171(C), pages 1418-1435.
    2. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
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    Cited by:

    1. Arkadiusz Małek & Andrzej Marciniak & Tomasz Bednarczyk, 2024. "Probabilistic Analysis of Electricity Production from a Photovoltaic–Wind Energy Mix for Sustainable Transport Needs," Sustainability, MDPI, vol. 16(23), pages 1-23, November.
    2. Dimitrios Michos & Francky Catthoor & Dimitris Foussekis & Andreas Kazantzidis, 2024. "Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach," Energies, MDPI, vol. 17(21), pages 1-20, November.

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