Correlation analysis and modeling of multiple wind power based on Pair Copula

被引：0

作者：

Wu, Wei ^{[1
,2
]}

Wang, Keyou ^{[1
,2
]}

Li, Guojie ^{[1
,2
]}

Wang, Zhilin ^{[3
]}

机构：

[1] Key Laboratory of Control of Power Transmission and Conversion (Shanghai Jiao Tong University), Ministry of Education, Shanghai

[2] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai

[3] Alstom Grid China Technology Center, Shanghai

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2015年 / 39卷 / 16期

基金：

中国国家自然科学基金;

关键词：

Multiple correlation; Pair Copula; Probabilistic load flow; Quasi Monte Carlo sampling;

D O I：

10.7500/AEPS20141031011

中图分类号：

学科分类号：

摘要：

Large-scale integration of wind farms leads to the complex dependence among wind power outputs. It is important to model the stochastic and dependent wind generation accurately to analyze the impact of wind generation on power system operation. Current methods, such as the Copula theory, are accurate enough for describing two dependent random variables. However, they are inadequate for modeling more random variables as accurately. Thus, a high-dimensional probabilistic model is proposed for dependent wind power outputs based on the canonical-vine Pair Copula theory. The corresponding sampling method is also introduced. Pair Copula can describe different patterns of dependence between pairs of wind power outputs. Hence high-dimensional wind power outputs with complex dependence can be modeled accurately. Moreover, The Pair Copula model can be easily constructed and has wide applicability as well as flexibility. The modeling and analysis of wind generation in a number of wind farms in Australia are implemented to prove the effectiveness of the proposed model. Finally, the probabilistic load flow of an IEEE 118-bus system is solved. Simulation results show that the operation characteristics of power systems incorporating wind farms can be analyzed more accurately if dependent wind power outputs are rationally described. ©2015 Automation of Electric Power Systems Press

引用

页码：37 / 42

页数：5

共 21 条

[1] Global wind report 2013-annual market update, (2014)
[2] Wan Y.H., Milligan M., Parsons B., Output power correlation between adjacent wind power plants, Journal of Solar Energy Engineering, 125, 4, pp. 551-555, (2003)
[3] Morales J.M., Baringo L., Conejo A.J., Et al., Probabilistic power flow with correlated wind sources, IET Generation, Transmission & Distribution, 4, 5, pp. 641-651, (2010)
[4] Yang H., Zou B., A three-point estimate method for solving probabilistic power flow problems with correlated random variables, Automation of Electric Power Systems, 36, 15, pp. 51-56, (2012)
[5] Zou B., Xiao Q., Solving probabilistic optimal power flow problem using quasi Monte Carlo method and ninth-order polynomial normal transformation, IEEE Trans on Power Systems, 29, 1, pp. 300-306, (2014)
[6] Xu X., Yan Z., Feng D., Et al., Probabilistic load flow calculation based on rank correlation coefficient of input random variables, Automation of Electric Power Systems, 38, 12, pp. 54-61, (2014)
[7] Cai D., Shi D., Chen J., Et al., Probabilistic load flow considering correlation between input random variables based on Copula theory, Power System Protection and Control, 41, 20, pp. 13-19, (2013)
[8] Li J., Wen J., Cheng S., Et al., A scene generation method considering Copula correlation relationship of multi-wind farms power, Proceedings of the CSEE, 33, 16, pp. 30-36, (2013)
[9] Xu Y., Zhang L., Modeling of wind speed correlation and analysis of probabilistically optimal power flow based on Copula function, Journal of North China Electric Power University, 40, 5, pp. 54-59, (2013)
[10] Zhang N., Kang C., Liu J., Et al., Mid-short-term risk assessment of power systems considering impact of external environment, Journal of Modern Power Systems and Clean Energy, 1, 2, pp. 118-126, (2013)

← 1 2 3 →