Wind speed probability distribution estimation and wind energy assessment

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 fo...

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Veröffentlicht in:Renewable & sustainable energy reviews Jg. 60; S. 881 - 899
Hauptverfasser: Wang, Jianzhou, Hu, Jianming, Ma, Kailiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2016
Schlagworte:
Algorithms
Assessments
Design analysis
Heuristic
Non-parametric estimation
Stochastic heuristic optimization algorithm
Wakes
Wind energy
Wind energy potential assessment
Wind power
Wind speed probability distribution
Wind turbines
China, People's Rep
Wind energy potential assessment
Wind speed probability distribution
Stochastic heuristic optimization algorithm
Non-parametric estimation
ISSN:1364-0321, 1879-0690
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1364-0321
1879-0690
DOI:10.1016/j.rser.2016.01.057