风能资源评估中地表粗糙度的研究

On Estimation of Surface Roughness for Wind Energy Resources Assessment

利用内蒙东部草原接近1年4层风速廓线资料,采用最常用的两类方法(拟合对数风廓线法和Davenport土地类型系统划分法),结合风能资源利用的特点,来估算该地区地表粗糙度长度。选取10分钟平均风速50m高度大于6m/s时刻的数据,因为50m高度平均风速6m/s以下的时,轮毂高度为80m的风机只能产生少量不稳定的风能。这样挑选数据能够有效地减少粗糙度长度评估中的离散范围。拟合对数风廓线,发现该地区地表粗糙度长度有明显月份和季节变化,夏秋植被茂盛期和冬春植被枯萎期粗糙度长度分别为0.138m和0.088m;细微的地形起伏也会对粗糙度的评估造成影响。两类方法估算的粗糙度长度大致相当,且估算的风能密度只相差2%左右。Davenport法对比较平坦的地形进行粗糙度评估,具有一定的应用参考价值。

There are two commonly used techniques, i.e., fitting to logarithmic wind profile and the Davenport roughness classification system, in wind energy resources assessment, which were used for evaluation of surface aerodynamics roughness length of a steppe in Inner Mongolia. It was expected that the grassland location would have roughness lengths between the Davenport classes of the Fifth （0.25 m） to the Third （0.03 m）. Data, collected from May 21, 2009 through May 14, 2010 and measured by cup anemometers and wind vanes （1Hz） deployed at four levels （10, 30, 50 and 70 meters above the ground）, were analyzed. If one of the levels had missing data for a time period, all records from that time period were removed from the analysis. Then, original data were averaged over 10 minute intervals. Time periods with wind speed at 50 m height or above 6 m/s, were chosen for further analysis, which was because if wind speed at 50 m height is under 6 m/s, most large turbines would generate little usable power. This approach is useful for wind resource assessment in the absence of stability calculation and can largely reduce errors of calculation of roughness length. Ten-minute averaged wind profiles were classified into categories of twelve directions. The liner least-squares method was used to fit the averaged logarithmic wind profiles. It was found that the roughness length had monthly and seasonal variations. In summer-fall months when grass was lush, the average roughness length was measured to be 0.138 m and in winter-spring months when grass was shriveled, it was measured to be 0.088 m. The minimum value of the monthly-average roughness length was 0.041 m appearing in January of 2010, which may have reflected that the land was covered by snow. The hourly-average roughness lengths showed a diurnal variation, with the maximum value occurring at midnight and the minimum value at noon. The roughness lengths computed based on southwest and west wind measurements were also shown to be larger than these computed based on northeast and north wind measurements in all time periods. Minor topography changes could influence the roughness lengths. The higher topography, despite being several kilometers far away from tower, was shown to impact the roughness lengths. In general, the magnitude of roughness lengths calculated from the two techniques seems to be consistent. To estimate wind power densities, we extrapolated a 10 m/s wind speed at 50 m to an 80 m hub height and used the estimated roughness lengths based on above two techniques. Difference in estimated roughness length between the two techniques is about 2%, which means that the Davenport roughness classification system appears to be a practical method to estimate roughness length for flat terrain with moderated roughness.