Wind speed probability distribution estimation and wind energy assessment. (July 2016)

• Record Type: Journal Article
• Authors: Wang, Jianzhou; Hu, Jianming; Ma, Kailiang
• Is Part Of: Renewable & sustainable energy reviews. Volume 60(2016:Jul.)
• Page Start: 881
• Page End: 899
• Publication Date: 2016-07
• Subjects: Stochastic heuristic optimization algorithm -- Non-parametric estimation -- Wind speed probability distribution -- Wind energy potential assessment; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.01.057
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The statistical characteristics of wind and the selection of suitable wind turbines are essential to effectively evaluate wind energy potential and design wind farms. Using four sites in central China as examples, this research reviews and compares the popular parametric and non-parametric models for wind speed probability distribution and the estimation methods for these models' parameters (the widely used methods and stochastic heuristic optimization algorithm). The simulations reveal that the non-parametric model outperforms all of the selected parametric models in terms of the fitting accuracy and the operational simplicity, and the stochastic heuristic optimization algorithm is superior to the widely used estimation methods. This study also reviews and discusses six power curves proposed by the literature and the power loss caused by the mutual wake effect between turbines in the wind energy potential assessment process. The evaluation results demonstrate that choice of power curves influences the selection of wind turbines and that consideration of the mutual wake effect may help to optimize wind farm design in wind energy assessment.