水平降尺度方法对WRF风电场参数化方案风速模拟精度提升的研究

Research on improvement of wind speed simulation accuracy of WRF wind power plant parameterization scheme by horizontal downscaling method

为了研究水平降尺度方法对于与中尺度天气预报模型WRF相耦合的风电场参数化模型模拟风电场内部风速的提升效果,以我国某大型海上风电场为研究对象,分别采用不同的水平分辨率进行48 h模拟实验,并利用单台风机的SCADA观测数据对水平降尺度方法降低模拟误差的效果进行验证分析。研究结果表明:水平降尺度方法对于风电场内部风速模拟准确度具有良好的提升效果,且在下风向风机处的效果更为明显,但在风电场上风向第一排风机处的效果可忽略;在风电场下风向最后一排风机处,水平分辨率由12 km降尺度至1 km可使风速模拟误差减小20%以上。另外,相比于位于风电场两侧边缘的风机,水平降尺度方法对于位于中间的风机风速模拟精度提升效果更好。该实验成果可为将WRF+WFP模型应用于实际风电场的功率预测提供理论基础。

In order to study the improvement effect of horizontal downscaling method on increasing the wind speed(inside the wind power plant)simulated by the wind power plant parameterization model which is coupled with the mesoscale weather research and forecast(WRF)model,a 48 h simulation experiment at different horizontal resolutions was carried out,and the effect of horizontal downscaling method on reducing simulation error was verified and analyzed by means of the SCADA(supervisory control and data acquisition)observation data of a single wind turbine.In this study,a certain large offshore wind farm in China is taken as the research object.The study results show that the horizontal downscaling method has a good improvement effect on simulation accuracy of wind speed inside the wind farm,and the effect is more obvious for the wind turbines at downwind,but the effect for the wind turbines located in the first row in the upwind direction of the wind farm is negligible.At the wind turbines located in the last row in the downwind direction of the wind farm,when the horizontal resolution is reduced from 12 km to 1 km,the simulation error of wind speed can be reduced by more than 20%.In addition,the horizontal downscaling method has a better effect on the improvement of wind speed simulation accuracy of the wind turbines located in the middle position in comparison with the turbines located on both sides of the wind farm.The results of experiments can provide a theoretical basis for applying WRF+WFP model to the power prediction of real wind power plant.