Uncertainty evaluation of wind power prediction based on monte-carlo method

被引：0

作者：

Wang, Bo ^{[1
]}

Liu, Chun ^{[1
]}

Zhang, Jun ^{[2
]}

Feng, Shuanglei ^{[1
]}

Li, Yingyi ^{[2
]}

Guo, Feng ^{[2
]}

机构：

[1] China Electric Power Research Institute, Beijing

[2] State Grid Zhejiang Electric Power Company, Hangzhou

来源：

Gaodianya Jishu/High Voltage Engineering | 2015年 / 41卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Error distribution; Monte-Carlo; Numerical weather prediction; Power prediction; Uncertainty evaluation; Wind farm;

D O I：

10.13336/j.1003-6520.hve.2015.10.027

中图分类号：

学科分类号：

摘要：

The uncertainty estimation of wind power prediction is the basis of wind farm optimal dispatching. Firstly, we proposed a qualitative analysis method of sensitive meteorological factors of wind power prediction, and quantitatively evaluated the prediction error distribution of sensitive factors using non-parametric regression and Gaussian fitting respectively. Then, we adopted Monte-Carlo random sampling to realize the uncertainty estimation of the wind farm power prediction. Finally, we verified the validity of the proposed algorithm by case study. The results show that there are significant differences between uncertainties of the forecast error corresponding to different forecast time, the uncertainty estimation of predicted power can be achieved through the Monte-Carlo method, and hourly sample estimation is superior to the overall sample estimation at the same confidence level. Compared with traditional uncertainty estimation methods of wind power forecasting, the proposed method has a low requirement for operating data, and does not need additional model. © 2015, High Voltage Engineering Editorial Department of CEPRI. All right reserved.

引用

页码：3385 / 3391

页数：6

共 23 条

[1]

National wind power operation of the first half of 2015, (2015)

[2]

Chi Y., Liu Y., Wang W., Et al., Study on impact of wind power integration on power system, Power System Technology, 31, 3, pp. 77-81, (2007)

[3]

Wu D., Wang Y., Guo C., Et al., An economic dispatching model considering wind power forecast error in electricity market environment, Automation of Electric Power Systems, 36, 6, pp. 23-28, (2012)

[4]

Sun Y., Wu J., Li G., Et al., Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming, Proceedings of the CSEE, 29, 4, pp. 41-47, (2009)

[5]

Fu Y., Wang Y., Zhang X., Et al., Coordinated control of wind power in multi-terminal VSC-HVDC transmission system, High Voltage Engineering, 40, 2, pp. 611-619, (2014)

[6]

Liang L., Li J., Hui D., Optimization configuration for capacity of energy storage system in large-scale wind farm, High Voltage Engineering, 37, 4, pp. 930-936, (2011)

[7]

Miao F., Tang X., Qi Z., Analysis of frequency characteristics of power system based on wind farm-energy storage combined frequency regulation, High Voltage Engineering, 41, 7, pp. 2209-2216, (2015)

[8]

Wang B., Feng S., Liu C., Uncertainty valuation of wind power prediction considering impacts of power curve and predicted wind speed, Power System Technology, 38, 2, pp. 463-468, (2014)

[9]

Wang Z., Wang W., Liu C., Et al., Uncertainty estimation of wind power prediction result based on wind process method, Power System Technology, 37, 1, pp. 242-247, (2013)

[10]

Shi J., Optimization for very short-term wind power forecasting algorithm, (2012)

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