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高分辨率数值模式在风能资源评估中的应用初探
引用本文:穆海振,徐家良,柯晓新,唐琳,陈德亮.高分辨率数值模式在风能资源评估中的应用初探[J].应用气象学报,2006,17(2):152-159.
作者姓名:穆海振  徐家良  柯晓新  唐琳  陈德亮
作者单位:1.上海气候中心, 上海 200030
摘    要:针对现有气象测站分布数量有限, 尤其是沿江沿海地带测站稀少的现状, 对数值模式在风能资源评估中的应用进行了尝试。首先利用TAPM数值模式对上海地区的风场作了数值模拟计算; 然后利用同步的气象站观测资料对风速模拟结果进行统计释用订正处理, 提高了模式计算结果的准确性和可靠性; 最后得到了分辨率为3 km的上海全年平均风速和风功率密度分布信息。这些结果为上海地区风能资源分析评估及风电场规划选址工作提供了科学依据, 同时也说明将统计释用的数值模拟结果应用到风能资源评估工作中是可行的。

关 键 词:风能资源    数值模式    风速    风功率密度
收稿时间:2005-10-08
修稿时间:2006-02-05

Application of High Resolution Numerical Model to Wind Energy Potential Assessment
Mu Haizhen,Xu Jialiang,Ke Xiaoxin,Tang Lin,Chen Deliang.Application of High Resolution Numerical Model to Wind Energy Potential Assessment[J].Quarterly Journal of Applied Meteorology,2006,17(2):152-159.
Authors:Mu Haizhen  Xu Jialiang  Ke Xiaoxin  Tang Lin  Chen Deliang
Affiliation:1.Shanghai Climate Center, Shanghai 2000302.Earth Sciences Center, Gothenburg University, Sweden3.National Climate Center, Beijing 100081
Abstract:In view of the sparse distribution of weather observation stations, particularly in coastal and riverbank zones by now, the meteorological component of TAPM (The Air Pollution Model) is utilized to assess wind energy resources, which is an incompressible, non-hydrostatic, primitive equation model with a terrain-following vertical coordinate for three-dimensional simulations. In the simulation scheme, Xujiahui station (31°12′N, 121°26′E) is selected as the center of the modeling region, the number of horizontal grids is 50 by 50, the number of vertical levels is 25. The model nests with outer grid resolution of 10 km and inner grid resolution of 3 km, the inner region ranges from 30°33′N to 31°55′N and from 120°43′E to 122°13′E, the acreage of inner region is 150 km by 150 km which covers Shanghai and parts of Jiangsu and Zhejiang Province.The model is integrated month-long for January, April, July and October in selected years, the initial data and boundary conditions needed for driving the model are obtained from attached synoptic scale analysis dataset. At first the model input parameters such as surface vegetation type, soil moisture and sea surface temperature are altered according to actual conditions of selected modeling region, then the wind field data is obtained from model output results, which agrees well with the observation data although there are slightly differences between them. The causes of model results error are discussed. In order to minimize the error, using the method of linear regression and considering impact of changes of environment around observation site on observation data quality, the reexplanation of wind speed and the third power of wind speed over different types of surface (land or water) are conducted by making use of synchronous observation data, the verification results show that these equations can be used in other years and the application range of these correction equations is wide, which will lead to improvement of accuracy and reliability of wind energy assessment result. Finally the distribution pattern of wind and wind energy density of Shanghai are obtained at the resolution of 3 km in 2002, this information, especially for detailed wind energy distribution information over coastal and riverbank regions, is impossible to be obtained by analysis of observational network data.The results of this study provide scientific basis for the work of wind energy resources assessment, planning and location-selecting of wind farm, as well as indicate that the numerical model results can be utilized in the evaluation of wind energy resources. At the same time the work of this study is primary, improvement is needed to obtain more precise results as the method of reexplanation is quite simple; the model needs to be run for long period to get more universal conclusion.
Keywords:wind energy resources  numerical model  wind speed  wind energy density  
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