Stochastic Power Flow Using a New Adaptive Sparse Pseudospectral Approximation Method

被引：0

作者：

Lin J. ^{[1
]}

Shen D. ^{[1
]}

Liu Y. ^{[1
]}

机构：

[1] College of Electronics and Information Engineering, Tongji University, Yangpu District, Shanghai

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2019年 / 39卷 / 10期

关键词：

Kronrod-Patterson quadrature rules; Nataf-Margin transformation; New adaptive sparse pseudospectral approximation method (NA-SPAM); Stochastic power flow calculation;

D O I：

10.13334/j.0258-8013.pcsee.172750

中图分类号：

学科分类号：

摘要：

A new method for power flow calculation using a new adaptive sparse pseudospectral approximation method was proposed. The basic procedures of the proposed method were as follows. First, the Nataf-Margin transformation for transforming the correlated random variables to independent ones was proposed and proved to be effective and most efficiency-maintaining for NA-SPAM bya proposition and a corollary. Next, the nested NA-SPAM was proposed by replacing traditional Gaussian quadrature rules with nested Kronrod-Patterson quadrature rules in NA-SPAM, which reduced the number of integral points and corresponding computational effort of NA-SPAM. Finally, the integrated NA-SPAM that combined the Nataf-Margin transformation and the nested NA-SPAM was proposed and applied in stochastic power flow calculation, by which the expectations, variances and probability density functions of state variables can be calculated quickly and accurately. The effectiveness of NA-SPAM and its advantage over classic pseudospectral SCM, LHS and MCM were validated by several cases on IEEE-9 system and IEEE-118 system. © 2019 Chin. Soc. for Elec. Eng.

引用

页码：2875 / 2884

页数：9

共 28 条

[1]

Borkowska B., Probabilistic load flow, IEEE Transactions on Power Apparatus and Systems, 3, pp. 752-759, (1974)

[2]

Mackay David J.C., Introduction to Monte Carlo methods, Proceedings of the NATO Advanced Study Institute on Learning in Graphical Models, pp. 175-204, (1998)

[3]

Xu Q., Huang Y., Liu J., Et al., Probabilistic load flow method using montecarlo simulation based on gaussian mixture model and marginal transformation, Automation of Electric Power Systems, 40, 16, pp. 23-30, (2016)

[4]

Wang X., Zhang Z., Hou Y., Stochastic load flow calculation based on quasi-Monte Carlo method for active distribution network, Electric Power Automation Equipment, 37, 3, pp. 7-12, (2017)

[5]

Cai D., Shi D., Chen J., Probabilistic load flow computation using Copula and Latin hypercube sampling, IET Generation, Transmission & Distribution, 8, 9, pp. 1539-1549, (2014)

[6]

Ruiz-Rodriguez F.J., Hernandez J.C., Jurado F., Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion, Electric Power Systems Research, 89, pp. 129-138, (2012)

[7]

Zhu X., Liu W., Zhang J., Probabilistic load flow method considering large-scale wind power integration, Proceedings of the CSEE, 33, 7, pp. 77-85, (2013)

[8]

Liu J., Hao X., Cheng P., Et al., Probabilistic load flow method combining M-copula theory and cumulants, Power System Technology, 42, 2, pp. 578-584, (2018)

[9]

Zhang L., Cheng H., Zeng P., Et al., A three-point estimate method for solving probabilistic load flow based on inverse Nataftransformation, Transactions of China Electrotechnical Society, 31, 6, pp. 187-194, (2016)

[10]

Wu W., Wang K., Han B., Et al., Pair copula based three-point estimate method for probabilistic load flow calculation, Transactions of China Electrotechnical Society, 30, 9, pp. 121-128, (2015)

← 1 2 3 →