Longitudinal Moment Markov Chain Model of Wind Power and Its Application on Ultra-short-term Prediction

被引：0

作者：

Sun, Jingwen ^{[1
]}

Yun, Zhihao ^{[1
]}

Liang, Jun ^{[1
]}

Yang, Xiaojuan ^{[2
]}

Yang, Libin ^{[3
]}

Wang, Xueli ^{[4
]}

机构：

[1] Shandong Univ, Key Lab Power Syst Intelligent Dispatch & Control, Jinan, Peoples R China

[2] Shandong Normal Univ, Sch Commun, Jinan, Peoples R China

[3] State Grid Qing Hai Elect Power Res Inst, Qinghai, Peoples R China

[4] State Grid Shandong Elect Power Maintenance Co, Jinan, Peoples R China

来源：

2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT 2015) | 2015年

关键词：

wind power; longitudinal moment; Markov chain model; wind power prediction;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, a longitudinal moment Markov chain model of wind power time series based on the longitudinal time concept is proposed. This model emphasizes the transition characteristics related to different moments by providing a set of transition probabilities matrices. This matrices set, describing the inherent transition information of moments, gives the necessary probabilistic conditions for optimization decision of power systems containing wind farm. Besides of rapid calculation as conventional Markov chain model has, the proposed model makes the transition information more detailed and accurate. To illustrate the effect of improvement, a wind power prediction (WPP) method on ultra-short-term horizon using the longitudinal moment Markov chain model is put forward. The case study based on actual wind power data under multiple time scales shows that the proposed method achieves a higher prediction precision.

引用

页码：1874 / 1878

页数：5

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