Influence of Stochastic Dependence on Small-Disturbance Stability and Ranking Uncertainties

被引:33
作者
Hasan, Kazi N. [1 ]
Preece, Robin [1 ]
机构
[1] Univ Manchester, Sch Elect & Elect Engn, Manchester M60 1QD, Lancs, England
基金
英国工程与自然科学研究理事会;
关键词
Copula; correlation; probabilistic assessment; small-disturbance stability; sensitivity analysis; stochastic dependence; uncertainty; POWER-SYSTEMS; EFFICIENT ESTIMATION; WIND POWER; GENERATION; LOAD; PROBABILITY; COPULA; MODEL;
D O I
10.1109/TPWRS.2017.2779887
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
A high level of stochastic dependence (or correlation) exists between different uncertainties (i.e., loads and renewable generation), which is nonlinear and non-Gaussian and it affects power system stability. Accurate modeling of stochastic dependence becomesmore important and influential as the penetration of correlated uncertainties (such as renewable generation) increases in the network. The stochastic dependence between uncertainties can be modeled using 1) copula theory and 2) joint probability distributions. These methods have been implemented in this paper and their performances have been compared in assessing the small-disturbance stability of a power system. The value of modeling stochastic dependence with increased renewables has been assessed. Subsequently, the critical uncertainties that most affect the damping of the most critical oscillatory mode have been identified and ranked in terms of their influence using advanced global sensitivity analysis techniques. This has enabled the quantification and identification of the impact of modeling stochastic dependence on the raking of critical uncertainties. The results suggest that multivariate Gaussian copula is the most suitable approach for modeling correlation as it shows consistently low error even at higher levels of renewable energy penetration into the power system.
引用
收藏
页码:3227 / 3235
页数:9
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