基于大气边界层气象和气候学理论分析以及中尺度数值模拟,采用秒级探空气象资料和典型地形激光雷达观测资料,依据风能利用高度内总体风能资源开发潜力,划分出9个风环境区。年平均风能环境指数最高的风环境区是北方通风廊道,其次是东北平原,最低的是青藏高原下游地区。发现在稳定大气条件下,风能利用高度内的平均风速垂直变化呈两层分布形态,下层平均风速随高度的增速比上层大2~5倍。下层风速的垂直变化取决于地表特征,上层则受上游大地形造成的局地环流影响,由此形成不同风环境区风能资源特性的差异。最后给出构建不同地形条件下平均风廓线计算方法的建议。结论可为中国风能资源评估理论拓展与数值模拟、风电场选址和适用复杂地形条件的风电机组设计提供科学支撑。
Abstract
Based on the theoretical analysis of atmospheric boundary layer meteorology and climatology and mesoscale numerical simulation, this paper uses radiosondes data and lidar data on typical terrain, and 9 wind environment regions are divided according to the overall wind power potential within the height of wind energy utilization. The wind environment region with the highest annual mean wind environment index is the northern ventilation channel, followed by the Northeast Plain, and the lowest is the downstream areas of the Qinghai-Tibet Plateau. It is found that the vertical variation of the mean wind speed in stable condition within the height of wind energy utilization is in a two-layer distribution pattern. The average wind speed of the lower layer increases with height by 2-5 times bigger than that of the upper layer. The vertical variation of wind speed in the lower layer depends on the surface characteristics, and the upper layer is affected by the local circulation caused by the large terrain in the upstream, therefore led to the difference of wind energy resource characteristics in different wind environment areas. Finally, suggestions for the calculation method of average wind profile under different terrain conditions are given. The conclusion can provide scientific support for the expansion of wind energy resources assessment theory and numerical simulation, the wind farm siting and the design of wind turbines suitable for complex terrain conditions in China.
关键词
风能 /
风廓线 /
数值模式 /
无线电探空 /
激光雷达 /
风环境区划
Key words
wind energy /
wind profile /
numerical model /
radiosondes /
lidar /
wind environmental regionalization
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参考文献
[1] EMEIS S.Wind energy meteorology-atmospheric physics for wind power generation[M]. Berlin: Springer, 2013.
[2] KAIMAL J C, FINNIGAN J J.Atmospheric boundary layer flows[M]. New York: Oxford University Press, 1994.
[3] GRYNING S E, BATCHVAROVA E, BRUMMER B, et al.On the extension of the wind profile over homogeneous terrain beyond the surface layer[J]. Boundary-layer meteorology, 2007, 124: 251-268.
[4] EMEIS S.Review: current issues in wind energy meteorology[J]. Meteorological applications, 2014, 21: 803-819.
[5] FERNANDO H J S, MANN J, PALMA J M L M, et al. The Perdigao-Peering into microscale details of mountain winds[J]. BAMS, 2019, 100(5): 799-819.
[6] SERAFIN S, ADLER B, CUXART J, et al.Exchange processes in the atmospheric boundary layer over mountainous terrain[J]. Atmosphere, 2018, 9(3): 102.
[7] EMEIS S.Wind speed and shear associated with low-level jets over Northern Germany[J]. Meteorologische zeitschrift, 2014, 23: 295-304.
[8] WIMHURST J J, GREENE J S.Oklahoma’s future wind energy resources and their relationship with the Central Plains low-level jet[J]. Renewable and sustainable energy reviews, 2019, 115: 109374.
[9] GREENE J S, MCNABB K, ZWILLING R, et al.Analysis of vertical wind shear in the southern great Plains and potential impacts on estimation of wind energy production[J]. International journal of glob energy issues, 2009, 32: 191-211.
[10] LMPERT A, JIMENEZ B B, GROSS G, et al.One-year observations of the wind distribution and low-level jet occurrence at Braunschweig, North German Plain[J]. Wind energy, 2016, 19: 1807-1817.
[11] GUTIERREZ W, RULZ-COLUMBIE A, TUTKUN M, et al.Impacts of the low-level jet’s negative wind shear on the wind turbine[J]. Wind energy science, 2017, 2: 533-545.
[12] 丁一汇. 中国气候[M]. 北京: 科学出版社, 2013: 115-144, 403-406.
DING Y H.China climate[M]. Beijing: Science Press, 2013: 115-144, 403-406.
[13] TROEN I, ERIK L.European wind atlas[M]. Copenhagen: Risø National Laboratory, 1989.
[14] 中国气象局. 中国风能资源评价报告[M]. 北京: 气象出版社, 2006: 7.
China Meteorological Administration.China’s wind energy resource assessment report[M]. Beijing: China Meteorological Press, 2006: 7.
[15] 中国气象局. 全国风能资源详查和评价报告[M]. 北京: 气象出版社, 2014: 127.
China Meteorological Administration.The detail investigation and assessment report of wind energy resource in China[M]. Beijing: China Meteorological Press, 2014: 127.
[16] 朱蓉, 王阳, 向洋, 等. 中国风能资源气候特征和开发潜力研究[J]. 太阳能学报, 2021, 42(6): 409-418.
ZHU R, WANG Y, XIANG Y, et al.Study on climate characteristics and development potential of wind energy resources in China[J]. Acta energiae solaris sinica, 2021, 42(6): 409-418.
[17] LOPEZ A, ROBERTS B, HEIMILLER D, et al. U.S. renewable energy technical potentials: a GIS-based analysis[R]. NREL technical report2012, NREL/TP-6A20-51946.
[18] WAGNER R, COURTNEY M, GOTTSCHALL J, et al.Accounting for the speed shear in wind turbine power performance measurement[J]. Wind energy, 2011, 14(8): 993-1004.
[19] WAGNER R, CANADILLA B, CLIFTON A, et al.Rotor equivalent wind speed for power curve measurement-comparative exercise for IEA Wind Annex 32[C]//5th International conference on the science of Making Torque from wind 2014, Copenhagen, Den mark, 2014.
[20] SCHEURICH F, ENEVOLDSEN P B, PAULSEN H N, et al.Improving the accuracy of wind turbine power curve validation by the rotor equivalent wind speed concept[J]. Journal of physics: conference series, 2016, 753(7): 072029.
[21] 程维明, 周成虎, 李炳元, 等. 中国地貌区划理论与分区体系研究[J]. 地理学报, 2019, 74(5): 839-856.
CHENG W M, ZHOU C H, LI B Y, et al.Geomorphological regionalization theory system and division methodology of China[J]. Acta geographica sinica, 2019, 74(5): 839-856.
[22] EMEIS S, BAUMANN-STANZER K, PIRINGER M, et al.Wind and turbulence in the urban boundary layer-analysis from acoustic remote sensing data and fit to analytical relations[J]. Meteorologische zeitschrift, 2017, 16: 393-406.
基金
国家重点研发计划“可再生能源与氢能技术”专项“风力发电复杂风资源特性研究部及其应用与验证”项目（2018YFB1501100）
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