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

Dimitrios Michos (), Francky Catthoor, Dimitris Foussekis and Andreas Kazantzidis
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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
Dimitris Foussekis: CRES Wind Farm, 19009 Lavrio, Greece
Andreas Kazantzidis: Laboratory of Atmospheric Physics, University of Patras, 26500 Patras, Greece

Energies, 2024, vol. 17, issue 21, 1-20

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.

Keywords: wind energy; forecasting; turbulence; fluid dynamics; wind farm; complex terrain (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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