风能资源数值模拟评估的分型方法研究

Study of classification method for the wind energy resource numerical simulation assessment

中文关键词:  风能资源  分型  风速  风向  日最大混合层高度

英文关键词:Wind energy resource  Classification  Wind speed  Wind direction  Daily maximum mixing layer height

中文摘要:

      为了实现时间跨度较长(20—30年)的风能资源数值模拟评估,建立了一种新的分型方案,采用地面和探空观测资料,以风速、风向和最大混合层高度作为分型因子,针对每种类型随机抽取5%的日数进行真实算例的数值模拟。根据中国所有地区典型日的平均风能参数和30年观测平均结果的对比分析,得出以下结论:(1)中国所有分型站点挑选典型日的平均风速与30年平均风速的绝对误差均小于0.10 m/s,相对误差均低于6.50%;挑选典型日各风向频率值与30年结果的绝对误差平均值为0.28%—0.48%,风速频率绝对误差的平均值为0.09%—0.54%。(2)通过模拟区域分型站点以外探空气象站的风向频率和风速频率的对比检验发现:沿海地区风向频率绝对误差为0.27%—0.63%,风速频率绝对误差为0.14%—0.49%;内陆复杂山地风向频率绝对误差基本在0.57%以下,风速频率绝对误差为0.22%—0.60%;结果表明选取一个分型站点能够代表整个模拟区域内的风能资源特性。(3)根据沿海和内陆山地模拟区域重合范围内的探空站分型结果对比分析发现:对于模拟区域重合范围内的探空站,采用所有模拟区域分型站典型日结果加权平均后的风能参数对比误差大大低于各自模拟区域分型站点的对比误差。

英文摘要:

      In order to assess wind energy resource during a long time (20-30 years), a new weather classification method was built. The classification method was based on surface and sounding observational data with the three classification factors as wind speed, wind direction and daily maximum mixing layer height, and 5% days of each type were stochastically selected as typical days for simulation. The following conclusions were drawn by comparison analysis of the wind energy parameters of typical days with those averaged over the 30 years in whole country: (1) The absolute error of the average wind speed was less than 0.1 m/s between selected typical days and 30 years-mean for all the classification stations, with the absolute error all less than 6.5%. The average value of the wind direction frequency absolute error at all the 16 wind directions was 0.28%-0.48%, while that of the wind speed frequency absolute error from 0 to 14 m/s wind speed was 0.09%-0.54%. (2) It was indicated by comparison of the wind direction frequency and the wind speed frequency for the other sounding weather stations in the classification station area: the wind direction frequency error was 0.27%-0.63% in the coastal area, and the wind speed frequency error was 0.14%-0.49%; while in the inland mountain area that it were less than 0.57% of the the wind direction frequency and 0.22%-0.60% of the wind speed frequency. The result shows that one sounding station was able to be selected as the classification station that could represent the wind character in the whole simulation area. And, (3) it was shown through comparison of the classification results from the sounding weather stations in the overlap area that the wind parameter error of weighted average results over the typical days of all the classification stations was much less that of the average results of the respective simulative area.