Assessing the Applicability of Uncertainty Importance Measures for Power System Studies

Abstract

This paper critically evaluates a number of uncertainty importance measures for use in power system stability studies. Sensitivity analysis of uncertain system parameters is vital as new technologies proliferate and the total level of system uncertainty grows. Accurate assessment of the importance of different uncertainties can guide power system operators towards parameters which will require the greatest levels of mitigation or increased monitoring in order to reduce the uncertainty and its subsequent impact. Local and global sensitivity analysis techniques are described and evaluated within this paper, including nonparametric methods, variance-based approaches, and distribution-based techniques. The techniques are illustrated using a large 295-bus realistic network model of a generic distribution system. Numerical experiments on dynamic models are used in order to assess the impact of uncertainties on the mitigation of system frequency excursions using single-site and distributed energy storage devices. Robin Preece (GS'10-M'13) received the B.Eng. degree in electrical and electronic engineering and Ph.D. degree from the University of Manchester, Manchester, U.K., in 2009 and 2013, respectively. He is currently a Lecturer in power systems engineering with the University of Manchester, Manchester, U.K. Jovica V. Milanović (M'95-SM'98-F'10) received the Dipl.Ing. and M.Sc. degrees from the University of Belgrade, Belgrade, Yugoslavia, the Ph.D. degree from the University of Newcastle, Newcastle, Australia, and the D.Sc. degree from the University of Manchester, Manchester, U.K., all in electrical engineering.Currently, he is a Professor of electrical power engineering,

Influence of uncertainties and parameter structural dependencies in distribution system state estimation

“…3(b). The Pearson correlation coefficient [10] is used to rank the importance of variables for case 2 and compared with Morris method. With the same number of simulations as Morris method, the Pearson approach generates the ranking of 1>3>2>5>8>7>6>4, i.e., similar but not exactly the same as Morris method.…”

“…In [8], nine SA techniques including probabilistic approaches have been compared in terms of their performance and efficiency, and it has been demonstrated that for many applications, the Morris screening approach is most suitable, providing a good balance between accuracy and efficiency. The Morris screening method has also been successfully applied in different areas [9] including the power system studies, where the focus has been on generator ranking, load classification and frequency support [10]. Morris screening approach is selected to perform sensitivity analysis in the study.…”

Power sensitivity models with wide adaptability in active distribution networks considering loops and DC networks

“…Sensitivity models are obtained through the simplified power flow model, which are effective for determining changes in power losses, nodal voltages, and other system states because of variations of nodal power injections. For the advantages of simplicity and accepted accuracy, sensitivity models have been applied in security‐constrained unit commitment, under‐frequency load shedding, and power system stability assessment …”

Probabilistic Framework for Transient Stability Assessment of Power Systems With High Penetration of Renewable Generation

“…Due to spatial and temporal uncertainties associated with RES operation, in particular, it is neither practical nor feasible to perform TSA based on "worst case scenarios". Probabilistic methods are therefore used to assess various aspects of transient behaviour of power system with RES [12]- [18].…”