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Quantifying Power System Operational Reliability

Power Systems Research Group, Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
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Appl. Sci. 2019, 9(14), 2777; https://doi.org/10.3390/app9142777
Received: 28 April 2019 / Revised: 6 July 2019 / Accepted: 8 July 2019 / Published: 10 July 2019
(This article belongs to the Special Issue Power System Reliability)
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Abstract

Rising uncertainty in power systems due to ongoing system and operational changes has led to increasing risks in operating a system in a reliable and economic manner. There are growing reliability concerns with the widely used methods, such as the N-1 criterion, to determine operating reserve requirement during unit commitment, and the economic load dispatch method to allocate regulating margin to respond to disturbances. These deterministic methods do not consider the uncertainty or stochastic nature of power systems and are often inadequate to maintain the required operating reliability of modern power systems. This paper introduces a reliability index, designated as the committed generators’ response risk (CGRR) that can be used to maintain a specified level of operating reliability. A probabilistic method to evaluate the CGRR is presented and validated using a Monte Carlo simulation technique. An application of the new index and the methodology is illustrated using the IEEE Reliability Test System. The CGRR provides a comprehensive operating risk measurement of the committed generation until further assistance is available to support the system, and therefore helps operators in decision making for unit commitment and dispatch of the generating units to meet the projected load in the short future time. View Full-Text
Keywords: power system reliability; operating risk; regulating margin; unit commitment; load dispatch; Monte Carlo simulation power system reliability; operating risk; regulating margin; unit commitment; load dispatch; Monte Carlo simulation
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Nepal, N.; Karki, R. Quantifying Power System Operational Reliability. Appl. Sci. 2019, 9, 2777.

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