A wind speed modelling method for multiple wind farms considering correlation and statistical characteristics

被引：0

作者：

Wang, Songyan ^{[1
]}

Yu, Jilai ^{[1
]}

Li, Haifeng ^{[2
]}

Luo, Jianyu ^{[2
]}

机构：

[1] School of Electrical Engineering and Automation, Harbin Institute of Technology

[2] Jiangsu Electric Power Company

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2013年 / 37卷 / 06期

关键词：

Correlation; Random data generation; Weibull distribution; Wind farm; Wind speed;

D O I：

10.7500/AEPS201205251

中图分类号：

学科分类号：

摘要：

By using wind speed probability distribution, wind disturbance histogram and correlation parameter as constraints, a wind modelling method considering correlation and statistical characteristics is proposed. Considering monthly 10-min averaged wind speeds usually follow a mixed Weibull distribution, the single month wind speed probability distribution is firstly divided into several single Weibull distribution with different number of samples. Then, several time series with different number of samples, special correlation parameters and single Weibull distribution characteristics are generated. Final time series can be obtained by combining generated time-series and adjusting the orders of samples. Simulation results show that the simulated wind speeds can both reflect its own and nearby wind farms statictical laws. © 2013 State Grid Electric Power Research Institute Press.

引用

页码：18 / 23+83

共 12 条

[1]

Zou B., Li D., Power system probabilistic production simulation with wind generation based on available capacity distribution, Proceedings of the CSEE, 32, 7, pp. 23-31, (2012)

[2]

Billinton R., Bai G., Generating capacity adequacy associated with wind energy, IEEE Trans on Energy Convertion, 19, 3, pp. 641-646, (2004)

[3]

Fan R., Chen J., Duan X., Et al., Impact of wind speed correlation on probabilistic load flow, Automation of Electric Power Systems, 35, 4, pp. 18-22, (2011)

[4]

Lojowska A., Kurowicka D., Papaefthymon G., Et al., Advantages of ARMA-GARCH wind speed time series modeling, Proceedings of IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, pp. 83-88, (2010)

[5]

Philippopoulos K., Deligiorgi D., Statistical simulation of wind speed in Athens, Greece based on Weibull and ARMA models, International Journal of Energy and Environment, 3, 4, pp. 151-158, (2009)

[6]

Doquet M., Use of a stochastic process to sample wind power curves in planning studies, Proceedings of IEEE Power Tech, pp. 663-670, (2007)

[7]

Wangdee W., Billinton R., Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment, IEEE Trans on Energy Convertion, 21, 3, pp. 734-741, (2006)

[8]

Xie K., Billinton R., Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique, Electric Power Systems Research, 79, 4, pp. 687-693, (2009)

[9]

Bechrakis D.A., Sparis P.D., Correlation of wind speed between neighboring measuring stations, IEEE Trans on Energy Conversion, 19, 2, pp. 400-406, (2004)

[10]

Wang S., Yu J., Approximation of two-peak wind speed probability density function with mixed Weibull distribution, Automation of Electric Power Systems, 34, 6, pp. 89-93, (2010)

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