In a significant advancement for the wind energy sector, researchers have developed a physics-based model for ultra-short-term wind power forecasting that promises to enhance the efficiency and reliability of wind energy integration into power grids. Led by Dimitrios Michos from the Laboratory of Atmospheric Physics at the University of Patras, Greece, the study introduces a novel approach that leverages Computational Fluid Dynamics (CFD) to predict wind energy production over complex terrains.
As the demand for renewable energy sources continues to rise, accurate wind forecasting becomes essential. Michos emphasized the importance of this research, stating, “Real-time ultra-short-term forecasting is crucial for energy management decisions. It allows operators to respond swiftly to changing wind conditions, optimizing energy production and improving grid stability.”
The proposed model, known as the Wind Spatial Extrapolation model (WiSpEx), utilizes measured vertical wind profile data to reconstruct the wind flow around wind turbines (WTs). This reconstruction enables operators to estimate wind speeds and available energy at the hub height of turbines, which is vital for predicting energy output. The model’s performance has shown impressive results, achieving a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for one turbine model and 9.26% for another, significantly outperforming traditional persistence models.
This breakthrough is particularly relevant for wind farms situated in complex terrains, where obstacles like trees and buildings can dramatically alter wind patterns. The ability to deliver accurate forecasts in such challenging environments is not only a technical achievement but also a commercial boon for energy operators. By minimizing power losses and enhancing the reliability of wind energy, this model can contribute to reduced electricity price volatility, making wind energy more attractive and economically viable.
The computational efficiency of the model is another standout feature, requiring less than two minutes of processing time on a low-cost commercial platform. This speed makes it feasible for real-time applications, a significant improvement over traditional methods that often demand extensive computational resources. “In an industry where time is money, our approach combines accuracy with rapid processing, which is essential for real-time decision-making,” Michos noted.
The implications of this research extend beyond immediate operational benefits. As the energy sector continues to evolve, the integration of advanced forecasting models like WiSpEx can facilitate the broader adoption of renewable sources, supporting global efforts to transition to more sustainable energy systems. The study encourages further exploration into CFD applications for operational wind energy forecasting, potentially leading to hybrid models that can harness the strengths of both statistical and physics-based approaches.
Published in the journal ‘Energies’, this research represents a promising step forward in wind energy forecasting, paving the way for enhanced decision-making and improved energy management strategies. The potential for such models to adapt to various terrains and conditions could revolutionize the way wind energy is harnessed and integrated into the power grid, ultimately driving the sector toward greater efficiency and sustainability.
For more information, you can visit Laboratory of Atmospheric Physics, University of Patras.
