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New reliability and control concepts
Published in Fred I. Denny, David E. Dismukes, Power System Operations and Electricity Markets, 2017
Fred I. Denny, David E. Dismukes
At the power distribution system level, a number of indices have been defined for assessing reliability. The system average interruption duration index, or SAIDI, provides a measure of the average length of time that customers are without power. SAIDI is normally expressed in minutes per year. The system average interruption frequency index (SAIFI) is closely related to SAIDI. SAIFI provides a measure of the number of times when customers lose power. SAIFI generally is expressed in terms of the average number of outages per year. The customer average interruption duration index (CAIDI) is still another index that may be calculated by dividing SAIDI by SAIFI. Alternatively, CAIDI may be directly calculated as the average length of time when customers are without power each time there is a power supply interruption. CAIDI is usually expressed in minutes.
Reliability and Reliability Evaluation
Published in H. Lee Willis, Walter G. Scott, Distributed Power Generation, 2018
H. Lee Willis, Walter G. Scott
System Average Interruption Frequency Index (SAIFI) is the average number of interruptions per utility customer during the period of analysis. () SAIFI=number of customer interruptionstotal customers in system
Performance of Distribution Systems
Published in James Northcote-Green, Robert Wilson, Control and Automation of Electrical Power Distribution Systems, 2017
James Northcote-Green, Robert Wilson
System average interruption frequency index (SAIFI) is the average number of interruptions (sustained) per utility customer during the period of analysis. Simply, this is the number of customer interruptions per year, divided by the total customers on the system.
Reliability and Sensitivity Analysis for Closed-Ring Distribution Power Systems
Published in Electric Power Components and Systems, 2022
Mohammed Wadi, Mustafa Baysal, Abdulfetah Shobole
Once again, based on LPs indices, the overall system indices can be computed. The SAIFI is defined as the ratio of total number of customer interruptions to the total number of customers served. The SAIDI is defined as the ratio of total customer interruption duration to the total number of customers served. The CAIDI is defined as the ratio of the total customer interruption duration to the total number of customer interruptions. The ASAI is defined as the ratio of the customer available service to the customer demand. The computation formulas are given in (4), and (5) [29]. The Expected Energy not Supplied (ENS) is defined as the sum of product of average LP demand and the average outage time (U) per year, and AENS is defined as the expected ENS divided by the total number of customers served, computation formulas as given in (6) [30]: where is the number of customers at 8760: is the total number of hours per year, is the average load at
Electric utility valuations of investments to reduce the risks of long-duration, widespread power interruptions, part II: Case studies
Published in Sustainable and Resilient Infrastructure, 2023
Benjamin D. Leibowicz, A. H. Sanstad, Q. Zhu, P. H. Larsen, J. H. Eto
Resilience is the overarching rubric for efforts at the level of state government to reduce the state power system’s vulnerability to high winds, severe storms, and other threats. However, the details of the record show that neither the regulatory authorities nor the BGE utility recognizes a sharp distinction between reliability and resilience in practice, other than a five-minute threshold for the duration of interruptions. Most of the detailed analysis refers to reliability and uses conventional metrics such as the System Average Interruption Frequency Index (SAIFI).
Fault diagnosis, service restoration, and data loss mitigation through multi-agent system in a smart power distribution grid
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Ishan Srivastava, Sunil Bhat, Arvind R. Singh
The primary objective of any fault detection, isolation, and SR algorithm is to reduce the outage time and improve the overall reliability of the system. The key feature of a smart distribution system is reliability and resilience (Jiang, Liu, and Xu 2016). There are different indices defined to estimate the reliability of the system like System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI) and Customer Average Interruption Duration Index (CAIDI). For the sustained outages lasting for 5 minutes or more, these indices are used for evaluation of distribution automation performance (“IEEE Guide for Electric Power Distribution Reliability Indices”, 2012). It is possible to operate a distribution system through a centralized control station or de-centralized controls. In recent research work, it has been reported that a de-centralized control strategy is more advantageous in terms of resource allocation. A multi-agent system can provide a software-based platform for the system to operate in a distributed manner. To achieve a common goal, different intelligent agents are defined for a multi-agent system. These agents are coupled and perform a defined set of tasks independently. In the IEEE power engineering society general meeting held in June 2005, a task force was created to explore applications of the multi-agent framework to the power system. For the distribution automation problem, different multi-agent-based approaches have been reported in the literature. In (Nagata et al. 2003), the authors proposed a multi-agent-based SR plan. The authors in (Solanki, Khushalani, and Schulz 2007) applied the multi-agent framework to solve the SR problem for full de-centralized distribution network. Also, a multi-agent-based technique is proposed for the detection of fault location and its isolation in (Habib et al. 2017).