Researchers from the University of Science and Technology of China, led by Bowen Du and Qi Li, have developed a new method to improve the accuracy of wind farm simulations in mesoscale weather models. Their work, published in the journal Renewable Energy, focuses on enhancing the way wind turbines are represented in these models, which is crucial for understanding and predicting the interaction between wind farms and the atmosphere.
Currently, wind farms are typically modeled as elevated momentum sinks and sources of turbulence kinetic energy (TKE) in a single column of the mesoscale grid. However, this single-column method can lead to inaccuracies in the spatial distribution of these effects, impacting the overall accuracy of the simulations. To address this issue, the researchers proposed a multi-column spatial distribution method based on the Gaussian function.
The new method distributes the momentum sink and TKE source across multiple vertical grid columns, using grid weights that are analytically determined by integrating a two-dimensional Gaussian function over the mesoscale grid. The researchers applied this method to the classic Fitch model, creating an improved Fitch-Gaussian model, and integrated it into the Weather Research and Forecasting (WRF) model, a widely used mesoscale weather prediction system.
To validate their approach, the researchers compared the performance of the new method with the traditional single-column method using high-fidelity large eddy simulation as a benchmark. The results showed that the proposed method more accurately captured the spatial distribution of the sink and source, with a higher correlation coefficient and lower normalized root mean square error. Additionally, the Fitch-Gaussian model better represented the overall spatial distribution patterns of velocity deficit and added TKE.
The practical implications of this research for the energy sector are significant. More accurate wind farm simulations can lead to better wind resource assessments, improved wind farm planning and design, and enhanced predictions of wind farm impacts on local and regional weather patterns. This, in turn, can contribute to more efficient and sustainable wind energy development.
In conclusion, the researchers recommend the use of their proposed method for future mesoscale wind farm simulations, especially when the influence of the wind turbine rotor spans multiple mesoscale grid columns. This advancement in wind farm parameterization can support the continued growth and optimization of wind energy as a key component of the global renewable energy mix.
This article is based on research available at arXiv.