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 non-parametric 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.

Probabilistic Framework for Online Identification of Dynamic Behavior of Power Systems With Renewable Generation

“…The inclusion of uncertainties associated with the presence and operation of RESs is essential for understanding dynamic behavior of the system, as their pre-fault operating conditions and participation in the overall generation mix can change significantly depending on weather conditions, i.e., their contribution to overall system dynamics may vary temporally and spatially. This might lead to system operating scenarios that have not been previously identified and might be critical for system stability [13].…”

Priority Ranking of Critical Uncertainties Affecting Small-Disturbance Stability Using Sensitivity Analysis Techniques

“…As the number of system parameters increases with the size of the network and due to the proliferation of new technologies and operational structure, it is computationally intensive and inefficient to address all uncertain parameters present in a realistic system. Moreover, it may not be necessary to model all uncertain parameters as some may have very little or no impact on the system phenomenon of interest [2]. Hence, identification of the most influential parameters of the system will facilitate better operation and control with less monitoring (only of the parameters of interest) by system operators and stakeholders.…”

Efficient identification of critical parameters affecting the small-disturbance stability of power systems with variable uncertainty

“…Identification of the most important uncertainties will facilitate an appropriate resource allocation guideline for system operators and stakeholders to monitor the most important parameters systematically or introduce suitable mitigation strategies where appropriate. Identification and ranking of important parameters is a vital research in power systems which has been implemented in generator ranking [1], load classification [2], voltage stability [3], PSS design [4], PMU placement [5], and frequency support [6]. Previous works [1][2][3][4][5] have applied 'local' linear search algorithms such as singular value decomposition, whereas a recent work [6] implemented computationally expensive 'global' methods.…”

“…Identification and ranking of important parameters is a vital research in power systems which has been implemented in generator ranking [1], load classification [2], voltage stability [3], PSS design [4], PMU placement [5], and frequency support [6]. Previous works [1][2][3][4][5] have applied 'local' linear search algorithms such as singular value decomposition, whereas a recent work [6] implemented computationally expensive 'global' methods. Screening methodswhich combine to a large extent the efficiency of 'local' and accuracy of 'global' methodsare an efficient way to identify influential parameters [7][8][9][10].…”