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Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

Author

Listed:
  • Dimitrios Michos

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

  • Francky Catthoor

    (Interuniversity Microelectronics Centre (IMEC) vzw, Kapeldreef 75, 3001 Leuven, Belgium
    ESAT-EE, Katholieke Universiteit (K.U.), 3000 Leuven, Belgium)

  • Dimitris Foussekis

    (CRES Wind Farm, 19009 Lavrio, Greece)

  • Andreas Kazantzidis

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

Abstract

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5493-:d:1512930
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    References listed on IDEAS

    as
    1. Yan, Shu & Shi, Shaoping & Chen, Xinming & Wang, Xiaodong & Mao, Linzhi & Liu, Xiaojie, 2018. "Numerical simulations of flow interactions between steep hill terrain and large scale wind turbine," Energy, Elsevier, vol. 151(C), pages 740-747.
    2. Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
    3. 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.
    4. 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.
    5. Yang, Mao & Wang, Da & Zhang, Wei, 2023. "A short-term wind power prediction method based on dynamic and static feature fusion mining," Energy, Elsevier, vol. 280(C).
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