山东高分辨率风能资源分布特征的数值模拟研究
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董旭光,男,硕士,高级工程师,主要从事气候变化和气候应用研究等工作,dongxugg@sina.com。

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环渤海区域科技协同创新基金项目(QYXM201611);公益性行业(气象)科研专项(GYHY201306034)


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Numerical simulation of distribution characteristics of high resolution wind energy resources in Shandong
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    摘要:

    基于WRF3.8.1数值模式,利用FNL 1°×1°再分析资料,对山东边界层10 m、70 m、100 m等高度2017年风场进行了逐日动力降尺度模拟,使用山东122个气象站逐日平均风速,对模拟结果进行了客观评估。结果表明:WRF模式可以较好地模拟出山东逐日平均风速变化特征,但模拟值普遍大于实测值,山东不同区域平均风速模拟效果差异较大,四季误差分析结果与全年略不同;山东沿海、半岛北部丘陵、崂山、日照中部五莲山、鲁中山区各山脉等区域以及微山湖、东平湖等大型湖泊区域年和四季10 m、70 m、100 m高度平均风速、平均风功率密度较大,大汶河、大清河、泗水河、沂河及其支流、淄河、潍河等流域中上游的山区间低矮平原地带较小,但各地风能资源的差异随高度增加而明显减小。平均风速、平均风功率密度时空分布结果可为山东内陆地区分散式低风速风电场的选址、风能资源开发利用提供参考。

    Abstract:

    Based on WRF3.8.1 and NCEP FNL data (1°×1°), a day-by-day dynamic downscaling simulation of the land boundary layer wind field at 10 m, 70 m, and 100 m in Shandong in 2017 is carried out. According to the daily mean wind speed of 122 weather stations in Shandong during the same period, the simulation results are evaluated. The results show that WRF model can soundly simulate the daily mean wind speed variations in Shandong, while the simulation value is generally larger than the measured value. The accuracy of simulation results of mean wind speed in different regions of Shandong shows obvious differences, and the characteristics of simulation error of each season are slightly different from those of the whole year. The yearly and seasonal mean wind speed and the annual mean wind power density at various heights are larger in the coastal areas of Shandong, the hills to the north of the peninsula, Mount Lao, Wulian Mountain in the central part of Rizhao, the central mountain area of Shandong, and large lakes such as Weishan Lake and Dongping Lake. The annual mean wind speed is relatively small in the low-lying plain areas of the middle and upper reaches of Dawen River, Daqing River, Si River, Yi River and its tributaries, Zi River, and Wei River. The differences of wind energy resources in different regions decrease with the increase in height. The spatial and temporal distribution of annual and seasonal wind power density at different heights can provide reference for the site selection of the decentralized wind farm of low wind speed and the exploitation of wind energy resources in inland regions of Shandong.

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董旭光,孟祥新,伯忠凯,邱粲,李娟.山东高分辨率风能资源分布特征的数值模拟研究[J].海洋气象学报,2019,39(2):117-125.

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  • 收稿日期:2018-01-31
  • 最后修改日期:2018-10-12
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  • 在线发布日期: 2019-05-22
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