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.
Highlights
Wind energy is a renewable, environmentally friendly, and popular alternative source of clean energy (Islam et al, 2013; Gabbasa et al, 2013), and as a source of power it has great potential (Chaurasiya et al, 2019)
The wind speed probability density function (PDF) is of great significance in selecting wind tur
The scale factor c increases with an increase in height, showing an obvious form of power function
Summary
Wind energy is a renewable, environmentally friendly, and popular alternative source of clean energy (Islam et al, 2013; Gabbasa et al, 2013), and as a source of power it has great potential (Chaurasiya et al, 2019). China and the United States remain the world’s largest markets for new onshore installations (Joyce and Feng, 2021). To use this kind of nonpolluting energy, a lot of research has been conducted through a variety of different methods to develop an accurate and reliable wind energy evaluation model. The probability density function (PDF) of wind speed can effectively characterize wind speed. The wind speed PDF is of great significance in selecting wind tur-
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