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

Abstract

This paper implements an efficient sensitivity analysis (SA) technique to identify and rank critically important uncertain parameters that affect the small-disturbance stability of a power system. Identification and ranking of uncertain parameters are vital in modern power system operation due to the adoption of deregulated market structure and integration of intermittent energy resources and new types of loads. Ranking of critical uncertain parameters will facilitate better operation and control with less monitoring (targeted only on the parameters of interest) by system operators and stakeholders. The Morris screening method of sensitivity analysis has been described and implemented in this paper as the most suitable for this study based on comparison with various local and global techniques which highlighted the their comparative computational complexities and simulation time requirements. All methods have been illustrated using a modified version of the 68 bus NETS-NYPS test system. Illustrative results are provided considering varying levels of parameter uncertainties in order to establish not only the impact of system variability on parameter ranking, but also the robustness of the presented technique.

Impact of Demand Side Management on Angular Stability of Power Systems with Renewable Generation

“…Four loads (load 17, 18, 41 and 42) have been selected to apply the DSM. These loads have been identified as the four most critical and influential loads with respect to their impact on the system small-disturbance stability [27,28]. In Cases 5 and 6, the relevant hours for the implementation of DSM are different.…”

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

“…All methods have a very good agreement in ranking the most influential parameters, where the top four are same for all methods (with some positional differences). 17 L is the most influential parameter by all SA techniques (except OAT).…”

“…A recent work [17] by the authors presented the applicability of the Morris screening method in identifying important parameters with varying levels of uncertainty at different system operating conditions. This paper presents a significant extension of [17] and compares the performance of a wide range of SA techniques applied to multiple test systems.…”

“…A recent work [17] by the authors presented the applicability of the Morris screening method in identifying important parameters with varying levels of uncertainty at different system operating conditions. This paper presents a significant extension of [17] and compares the performance of a wide range of SA techniques applied to multiple test systems. It describes and implements the local and global SA methods presented in [2] in addition to the computationally efficient Morris screening method [3,4,18] and a multiattribute decision making (MADM) approach [19][20][21][22].…”

The Influence of Load on Risk-Based Small-Disturbance Security Profile of a Power System

“…Load characteristics also have a major effect on the stability of the interarea modes [16]. Earlier works identified system loads as the most influential elements within a power system network with respect to their contribution to the small-disturbance stability [17,18]. The risk profile of a power system can be largely influenced by system load attributes.…”