基于数值模拟的雅砻江流域风能资源初步评估

杨明祥, 王浩, 蒋云钟, 雷晓辉, 宋健蛟

清华大学学报(自然科学版) ›› 2018, Vol. 58 ›› Issue (1) : 101-107.

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清华大学学报(自然科学版) ›› 2018, Vol. 58 ›› Issue (1) : 101-107. DOI: 10.16511/j.cnki.qhdxxb.2018.22.013
水利水电工程

基于数值模拟的雅砻江流域风能资源初步评估

  • 杨明祥1, 王浩1, 蒋云钟1, 雷晓辉1, 宋健蛟2
作者信息 +

Numerical simulations of the wind energy resources in the Yalong River area

  • YANG Mingxiang1, WANG Hao1, JIANG Yunzhong1, LEI Xiaohui1, SONG Jianjiao2
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摘要

针对传统评估手段在大范围风能资源评估中的不足,基于数值天气模式,从2000至2016年再分析资料中获得了雅砻江流域近17年的高分辨率风速模拟资料,并对雅砻江全流域风能资源进行了初步评估。评估内容主要包括平均风速空间分布、风功率密度空间分布、风速年内分布以及年风能可利用小时空间分布等。初步掌握了雅砻江流域风能资源的时空特征,分析结果表明:流域内存在一定比例的风能资源富集区,如宁蒗河与敢鱼河之间、甘孜所在经度线两侧、安宁河谷等成片区域。该研究为进一步的风能资源详查与实地观测提供了依据。

Abstract

Large scale wind energy resources are difficult to accurately assess. This study uses a numerical weather prediction model and wind data from 2000 to 2016 to calculate the wind speeds in the Yalong River area for about 17 years. The wind energy resources in the Yalong River area are assessed in terms of the spatial distribution of the average wind speed, the spatial distribution of the wind energy density, the annual distribution of the wind speed, and the temporal and spatial distribution of the annual exploitable wind energy. The analyses give the temporal and spatial features of the wind energy resources in the Yalong River area. There are several wind resource rich regions in the Yalong River area, such as the region near the longitude line of Ganzi, the region between the Ninglang River and the Ganyu River, and the Anning River valley. These results can be used for site selection for wind energy resources.

关键词

数值模拟 / 风能资源评估 / 雅砻江流域

Key words

numerical simulation / wind resource assessment / Yalong River area

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导出引用
杨明祥, 王浩, 蒋云钟, 雷晓辉, 宋健蛟. 基于数值模拟的雅砻江流域风能资源初步评估[J]. 清华大学学报(自然科学版). 2018, 58(1): 101-107 https://doi.org/10.16511/j.cnki.qhdxxb.2018.22.013
YANG Mingxiang, WANG Hao, JIANG Yunzhong, LEI Xiaohui, SONG Jianjiao. Numerical simulations of the wind energy resources in the Yalong River area[J]. Journal of Tsinghua University(Science and Technology). 2018, 58(1): 101-107 https://doi.org/10.16511/j.cnki.qhdxxb.2018.22.013
中图分类号: TK81   

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