Probabilistic Assessment of Power System Performance Quality

Abstract

In a recently published work by the authors, a novel framework was developed and applied for assessment of reliability and quality performance levels in real-life power systems with practical large-scale sizes. The new assessment methodology is based on three metaphors (dimensions) representing the relationship between available generation capacities and required demand levels. The developed reliability and performance quality indices were deterministic in nature. That is, they represent one operating state (a snapshot of the system conditions) in which the required demand as well as the generation and transmission capacities are known with 100% certainty. In real life, however, load variations occur randomly so as the contingencies which cause some generation and/or transmission capacities to be lost (become unavailable). In other words, neither the load levels nor the generation or transmission capacities are known with absolute certainty. They are rather subject to random variations and, consequently, the calculated reliability and performance quality indices are all subject to random variations where only expected values of these indices can be evaluated. This paper presents a major extension to the previously published work by developing a theory and formulas for computing the expected values of different system reliability and performance quality indices. In this context, a "contingency scenario" or a system "demand level" are regarded, in a more general sense, as a "state", which occurs with certain probability and represents a given demand value and availability pattern of various capacities in the system. The work of this paper provides a practical and meaningful methodology for real-life assessment of power system reliability and performance quality levels. Practical applications are also presented, for demonstration purposes, to the Saudi electricity power grid.

Novel assessment of power system reserve-based on the reliability and quality levels

“…In the computational scheme of El-Kady and Alshammari (2011), the integrated system quality assessment is performed via solving a master linear programming problem (El-Kady and Alshammari and El-Kady, 2012) in which a feasible power flow is established which minimizes the total system non-served load subject to capacity limits and flow equations. The master linear program, which utilizes the network bus incidence matrix A, is formulated as:…”

Probabilistic Evaluation of a Power System’s Reliability and Quality Measures

“…As has happened with many power system disciplines, the prime interest in system security, adequacy, and reliability has gradually shifted from completing and refining the theoretical basis, through developing suitable computational tools for demonstrating the capability and practicality of the methodologies, to upgrading the computational tools to handle the large-scale nature of present power systems and, finally, to relate various security, quality and reliability indices to the practical concerns of utility engineers and executives regarding supply and/or transmission deficiencies as well as the risk associated with ignoring such deficiencies [7][8][9][10]. Methods for computing probabilistic contingency-based reliability and performance quality indices have previously been published in [11][12][13][14][15][16][17]. These methods are based on a combined contingency analysis and reliability evaluation scheme which integrates both the contingency effect and its probability of occurrence into one routine of analysis [18][19].…”

Evaluation of Power System Reliability and Quality Levels for (N-2) Outage Contingency

“…A secure system is able to tolerate the outage of components without interrupting the demand supply. Given an electric power system on N components, the N−k criterion is used to evaluate the outage of k components [12][13][14]. Reliability indices for a power system are calculable from either its performance history or from component data utilizing mathematical models which express the system reliability indices in terms of the component indices included in the IEEE Committee reports [15][16][17][18].…”