Assessment of onshore wind energy potential under different geographical climate conditions in China

Highlights: • Assess wind energy potential under different geographical climate conditions. • Weibull distribution is verified to be reliable for wind speed prediction. • Explore variation of wind speed, Weibull parameters and wind power density. • Predict annual energy outputs under different geographical climate conditions. • Provide an effective approach for wind energy potential assessment. Wind resource in China is abundant due to its vast land mass and long coastline. Based on wind speed and direction records from wind measurement towers at six onshore sites with different geographical climate conditions in China, statistical assessment of wind characteristics and wind energy potential at height of 70 m corresponding to the hub heights of multi-megawatt wind turbines is presented and discussed in this paper. First of all, the Weibull distribution function is verified to be a reliable model for wind speed prediction and the moment method is proved to be an accurate approach for estimation of the Weibull parameters at all the sites. Moreover, the variations of mean wind speed, the Weibull parameters and wind power density at the six sites are investigated in terms of seasonal, monthly and diurnal time scales. Finally, annual energy outputs at the six sites are determined by taking a commercial wind turbine as an example to evaluate the economic feasibility of wind energy development. The results of this study indicate that the six sites in North China, Northeast China, Central China, coastal regions in East China and Southeast China are in rich wind energy areas which are suitable for wind energy utilization. In particular, among the six sites, Tongliao in North China is the most promising location for wind energy development with the maximum annual energy output of 10.33 GWh. The objective of this study is to provide useful information for wind energy development and an effective approach for wind energy potential assessment.