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<p>The wind farm parameterization (WFP) is an indispensable physical scheme for characterizing wind farm influences in mesoscale models. Most existing parameterizations fail to account for the sub-grid wake effects and take the mesoscale grid wind speed as the incoming wind speed of each wind turbine directly. Recently, a few coupled parameterizations inclusive of sub-grid wake effects have been proposed in the literature. However, these parameterizations take the mesoscale grid wind speed as the background wind speed and face the double-counting issue of grid-resolved wake effect and turbine-scale wake interactions within the wind farm especially under fine grid resolution. To this end, we propose a meso-microscale coupled WFP model by coupling the microscale wind-farm flow analytical model with the mesoscale model. The proposed WFP can effectively reconstruct the background wind speed free of the grid-resolved wake effect but inclusive of the upstream global blockage effect during mesoscale simulations, thereby fundamentally avoiding the double-counting issue in existing coupling strategies. Based on this reconstructed wind speed, the microscale analytical model is further used to explicitly resolve turbine-scale wake interferences within the wind farm, finally leading to improvements in the prediction of momentum sink, turbulent kinetic energy (TKE) source, and power output. The proposed WFP is verified and compared with the Fitch model and the XAM3-Fitch model under fine grid resolution (1&#xa0;km) based on large-eddy simulation (LES) datasets of ideal wind farms and SCADA datasets of operational wind farms. The results show that our model demonstrates the highest power prediction accuracy with the mean root mean square error of 8.93% and the mean correlation coefficient of 0.94 across all verification datasets. In addition, our model predicts a higher momentum sink and TKE source, which are closer to LES results. Therefore, the proposed parameterization can be better utilized to wind-energy planning and shed light on the characterization of wind-farm-flow effects and their environmental impacts.</p>

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A Meso-Microscale Coupled Wind Farm Parameterization

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Bowen Du,
  • Mingwei Ge,
  • Xintao Li,
  • Yongqian Liu

摘要

The wind farm parameterization (WFP) is an indispensable physical scheme for characterizing wind farm influences in mesoscale models. Most existing parameterizations fail to account for the sub-grid wake effects and take the mesoscale grid wind speed as the incoming wind speed of each wind turbine directly. Recently, a few coupled parameterizations inclusive of sub-grid wake effects have been proposed in the literature. However, these parameterizations take the mesoscale grid wind speed as the background wind speed and face the double-counting issue of grid-resolved wake effect and turbine-scale wake interactions within the wind farm especially under fine grid resolution. To this end, we propose a meso-microscale coupled WFP model by coupling the microscale wind-farm flow analytical model with the mesoscale model. The proposed WFP can effectively reconstruct the background wind speed free of the grid-resolved wake effect but inclusive of the upstream global blockage effect during mesoscale simulations, thereby fundamentally avoiding the double-counting issue in existing coupling strategies. Based on this reconstructed wind speed, the microscale analytical model is further used to explicitly resolve turbine-scale wake interferences within the wind farm, finally leading to improvements in the prediction of momentum sink, turbulent kinetic energy (TKE) source, and power output. The proposed WFP is verified and compared with the Fitch model and the XAM3-Fitch model under fine grid resolution (1 km) based on large-eddy simulation (LES) datasets of ideal wind farms and SCADA datasets of operational wind farms. The results show that our model demonstrates the highest power prediction accuracy with the mean root mean square error of 8.93% and the mean correlation coefficient of 0.94 across all verification datasets. In addition, our model predicts a higher momentum sink and TKE source, which are closer to LES results. Therefore, the proposed parameterization can be better utilized to wind-energy planning and shed light on the characterization of wind-farm-flow effects and their environmental impacts.