Abstract
Abstract. A complex and varied terrain has a great impact on the
distribution of wind energy resources, resulting in uncertainty in
accurately assessing wind energy resources. In this study, three wind speed
distributions of kernel, Weibull, and Rayleigh type for estimating average
wind power density were first compared by using meteorological tower data
from 2018 to 2020 under varied desert steppe terrain contexts in northern
China. Then three key parameters of scale factor (c) and shape factor (k) from the Weibull model and surface roughness (z0) were investigated for estimating wind energy resource. The results show that the Weibull distribution is the most suitable wind speed distribution over that terrain. The scale factor (c) in the Weibull distribution model increases with an increase in height, exhibiting an obvious form of power function, while there were two different forms for the relationship between the shape factor (k) and height: i.e., the reciprocal of the quadratic function and the logarithmic function, respectively. The estimated roughness length
(z0) varied with the withering period, the growing period, and the lush period, which can be represented by the estimated median value in each
period. The maximum and minimum values of surface roughness length over the
whole period are 0.15 and 0.12 m, respectively. The power-law model and
the logarithmic model are used to estimate the average power density values
at six specific heights, which show greater differences in autumn and
winter, and smaller differences in spring and summer. The gradient of the
increase in average power density values with height is largest in autumn
and winter, and smallest in spring and summer. Our findings suggest that
dynamic changes in three key parameters (c, k, and z0) should be accurately considered for estimating wind energy resources under varied desert steppe terrain contexts.
Funder
National Natural Science Foundation of China
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献