Variability of the Wind Turbine Power Curve

被引:22
作者
Bandi, Mahesh M. [1 ]
Apt, Jay [2 ,3 ]
机构
[1] Okinawa Inst Sci & Technol, Collect Interact Unit, Okinawa 9040495, Japan
[2] Carnegie Mellon Univ, Dept Engn & Publ Policy, 5000 Forbes Ave, Pittsburgh, PA 15213 USA
[3] Carnegie Mellon Univ, Tepper Sch Business, 5000 Forbes Ave, Pittsburgh, PA 15213 USA
来源
APPLIED SCIENCES-BASEL | 2016年 / 6卷 / 09期
基金
美国国家科学基金会; 美国安德鲁·梅隆基金会;
关键词
wind power; power curve; variability; FARM; WAKES;
D O I
10.3390/app6090262
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Wind turbine power curves are calibrated by turbine manufacturers under requirements stipulated by the International Electrotechnical Commission to provide a functional mapping between the mean wind speed and the mean turbine power output . Wind plant operators employ these power curves to estimate or forecast wind power generation under given wind conditions. However, it is general knowledge that wide variability exists in these mean calibration values. We first analyse how the standard deviation in wind speed and the standard deviation of wind power. We find that the magnitude of wind power fluctuations scales as the square of the mean wind speed. Using data from three planetary locations, we find that the wind speed standard deviation systematically varies with mean wind speed , and in some instances, follows a scaling of the form ; C being a constant and a fractional power. We show that, when applicable, this scaling form provides a minimal parameter description of the power curve in terms of alone. Wind data from different locations establishes that (in instances when this scaling exists) the exponent varies with location, owing to the influence of local environmental conditions on wind speed variability. Since manufacturer-calibrated power curves cannot account for variability influenced by local conditions, this variability translates to forecast uncertainty in power generation. We close with a proposal for operators to perform post-installation recalibration of their turbine power curves to account for the influence of local environmental factors on wind speed variability in order to reduce the uncertainty of wind power forecasts. Understanding the relationship between wind's speed and its variability is likely to lead to lower costs for the integration of wind power into the electric grid.
引用
收藏
页数:9
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