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

Hasan, Kazi N.; Preece, Robin

doi:10.1109/tpwrs.2017.2779887

Cited by 36 publications

(43 citation statements)

References 24 publications

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“…In order to consider the stochastic dependence structures between the modelled uncertainties in power systems, the copula methods can be applied to generate a correlated input dataset. In [26], six correlation modelling techniques have been compared in terms of their accuracy and efficiency. It has been demonstrated that the multivariate Gaussian (mvG) copula is the most accurate and efficient.…”

Section: Correlations Between Input Uncertaintiesmentioning

confidence: 99%

“…The research discussed above employs independent probability distributions for the modelling of uncertain parameters. The random sampled data set obtained in this way however, does not represent the correlations among uncertainties within the real system, hence the results of the analysis may not be accurate enough [20][21][22][23][24][25][26]. The modelling of correlations among system parameters can be done effectively using Copula theory [26].…”

mentioning

confidence: 99%

“…The performance of six different copula approaches, (i) Gaussian (ii) Student (iii) Clayton (iv) Frank (v) Gumbel and (vi) multivariate joint normal distribution, was compared in [26] and the application of different copulas to different dimensional systems in [27]. It was reported in [26] that the multivariate Gaussian copula is the most effective and accurate for representation of the correlations between uncertainties involved in modelling load demand, wind generation and PV generation for power system small-disturbance stability analysis. In [28], the Gaussian T copula has been employed for voltage stability analysis.…”

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confidence: 99%

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Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

Hasan

Milanović

2019

IEEE Trans. Power Syst.

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The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviours of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index (TSI) have been chosen as the relevant stability indices for voltage stability, small-disturbance stability and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behaviour due to its correlation with other influential parameters.

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Section: Correlations Between Input Uncertaintiesmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

Hasan

Milanović

2019

IEEE Trans. Power Syst.

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“…N. Hasan and R. Preece investigated the influence of stochastic dependence on the small-disturbance stability of the power system with high penetration of renewable energy [5]. Global sensitivity analysis (GSA) was applied to identify and rank the critical uncertainties that most affect the damping of the most critical oscillatory mode.…”

Section: A Stochastic Dependencies Modelingmentioning

confidence: 99%

Guest Editorial for the Special Section on Enabling Very High Penetration Renewable Energy Integration Into Future Power Systems

Chen

Zhang

et al. 2018

IEEE Trans. Power Syst.

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“…The output of the system is represented by a sparse chaotic polynomial with random variables, and the polynomial coefficients are determined by a small number of input and output samples. The chaotic polynomial is used as the proxy model to achieve fast calculation of GSA indicators [1][2].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Island Micro-Grid Based on Probabilistic Load Flow Model

Ye¹,

Zhang²,

Jingwei³

et al. 2019

Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturin

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In the global energy crisis and environmental pollution problems, vigorous development of clean energy is a common initiative of all countries in the world. As an important means of new energy consumption and reliability improvement of power supply, micro-grid has a very broad development prospect. In order to overcome the problem of low efficiency of traditional MCS calculation, an micro-grid GSA method based on sparse polynomial chaotic expansion in the paper is proposed, which is used to accurately and quickly identify key input random variables that affect the operating state of the system. Furthermore, the accuracy and efficiency of the proposed method are verified by the example analysis of the island micro-grid.

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

Cited by 36 publications

References 24 publications

Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

Guest Editorial for the Special Section on Enabling Very High Penetration Renewable Energy Integration Into Future Power Systems

Sensitivity Analysis of Island Micro-Grid Based on Probabilistic Load Flow Model

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