Probability Distributions Of Reliability Indices Research Articles

Predicting Bulk Electricity System Reliability Performance Indices Using Sequential Monte Carlo Simulation

System reliability performance is usually based on average customer interruption information. These average values are valuable information, but provide only a single customer risk dimension without underlying probability distributions. The average annual indices give no insight on how reliability may vary from year to year as a result of the random behavior of bulk electric systems (BES). A significant advantage when utilizing sequential Monte Carlo simulation in bulk electric system reliability analysis is the ability to provide reliability index probability distributions in addition to the expected values of their indices. Parameter distribution analysis and its potential utilization are relatively new concepts in composite power system reliability analysis and decision making. This paper presents a technique to predict future reliability performance indices of the BES using a sequential simulation approach. The results obtained using the sequential software show that the system performance index probability distributions have unique characteristics that are basically dependent on the system topology and the operating philosophy. Operating policies involving load shedding procedures have a considerable impact on the system performance indices and their associated distributions. Two test systems are used to illustrate the concepts associated with the determination of system performance indices and their associated probability distributions. The results obtained using two different load shedding policies are presented and compared.

Delivery Point Reliability Indices of a Bulk Electric System Using Sequential Monte Carlo Simulation

A significant advantage when utilizing sequential Monte Carlo simulation in bulk electric system reliability analysis is the ability to provide reliability index probability distributions in addition to the expected values of their indices. Reliability index probability distributions provide a pictorial representation of the annual variability of these parameters around their mean values. Parameter distribution analysis and its potential utilization are relatively new concepts in composite power system reliability analysis and decision making. Computer software has been developed to produce reliability index probability distributions for the individual delivery points (DPs) in a bulk electric system. The results obtained using the sequential software show that the DP reliability index probability distributions have unique characteristics that are basically dependent on the system topology and the system operating philosophy. Operating policies such as load shedding procedures have a significant impact on the DP reliability indices and their associated distributions. The IEEE Reliability Test System is used to illustrate the concepts associated with the determination of DP and system indices and their associated probability distributions. The results obtained using two different load shedding policies are also presented and compared.

Incorporating Reliability Index Probability Distributions in Financial Risk Assessment with Performance Based Regulation

Regulatory authorities are increasingly adopting performance based regulations (PBR) in the new electric power utility environment. A PBR regime is intended to provide distribution utilities with economic incentives. It could also introduce potential financial risk due to the integration of a reward/penalty structure into the PBR plan. In this new environment, distribution utilities will need to adjust their reliability performance strategy to avoid possible financial risk, and could decide to invest in new capital projects to improve system reliability and be financially rewarded. This article illustrates the utilization of Monte Carlo simulation to develop the relevant reliability indices and their distributions due to reliability improvements in an electric distribution system. Quantitative consideration of the effect of system reliability improvements on the financial risk under imposed reward/penalty structures is presented. This work should be useful for electric power utilities working to reduce their risk and raise their profit in the new regulatory environment.

Reliability-performance-index probability distribution analysis of bulk electricity systems

System reliability performance is usually based on average customer-interruption indices. The average values are valuable information, but provide only a single customer risk dimension without the underlying probability distributions. The average annual indices give no insight as to how reliability may vary from year to year as a result of the random behaviour of a bulk electric system. Reliability-index probability distributions, therefore, provide additional valuable information and a more complete understanding of composite power system behaviour. A significant advantage when utilizing sequential Monte Carlo simulation in bulk electric system reliability analysis is the ability to provide reliability-index probability distributions in addition to the expected values of their indices. This paper illustrates the development of probability distributions for bulk electric system reliability performance indices using sequential simulation. The results obtained using the developed software show that the system performance-index probability distributions have unique characteristics that are basically dependent on the system topology, operating philosophy and conditions. System conditions such as the peak load level and system reinforcement options have significant impacts on the performance-index probability distribution characteristics. The basic concepts and their application in composite power system reliability evaluation are illustrated by application to a small practical test system.

Historic Performance-Based Distribution System Risk Assessment

Regulatory authorities are increasingly adopting performance-based regulation (PBR) in the deregulated electricity industry. A PBR regime is intended to provide distribution utilities with incentives for economic efficiency gains. In order to discourage distribution utilities from sacrificing service reliability while pursuing economic incentives, historic utility performance is utilized as the specified service reliability standard in some places. A reward/penalty structure based on the historic reliability record could be integrated into a PBR plan. Historic reliability data are therefore, extremely important for distribution system risk assessment and remedial work in this new regime. This paper presents actual reliability data taken from the Canadian Electricity Association (CEA) Service Continuity Reports. The financial risk analyzes associated with the historic reliability data are conducted by incorporating reliability index probability distributions in imposed reward/penalty policies. The major cause contributions to the service continuity indices utilizing the CEA cause code categories are analyzed and illustrated. This work should prove useful for those utilities facing the emerging application of PBR.

Evaluation of the Probability Density Functions of Distribution System Reliability Indices With a Characteristic Functions-Based Approach

In reliability analysis of distribution systems, random events like the occurrence of a fault or the time to restore the service after a fault are represented by using random variables (RVs), so that the reliability indices built on the basis of these RVs also become RVs. Existing techniques for the evaluation of the probability distributions of reliability indices are typically based on Monte Carlo and analytical simulations. This paper presents a new method for computing the probability distribution of reliability indices. The random sums introduced by the randomness of the number of fault occurrences in the time interval of analysis are handled by using a characteristic functions-based approach. The direct convolution of the probability density functions is avoided by resorting to the properties of the compound Poisson process. In addition, the direct and inverse discrete Fourier transforms are used to allow for handling any type of probability distribution. The proposed method is an effective alternative to the existing methods, providing a fast and simple computation of probability distributions and moments for local and global reliability indices. Results obtained for large real urban distribution systems are presented.

Quantitative reliability considerations in the determination of performance-based rates and customer service disruption payments

The application of reliability index probability distributions to establish an appropriate balance between reliability improvement and cost saving strategies is discribed. Contracts between an administrative board and power utilities in the form of performance-based rates and contracts between a utility and a customer in the form of customer service disruption payments can be analysed using the appropriate index probability distributions. The developed concepts are illustrated using a test distribution system.

Approximate distributions of the number and duration of failures of parallel redundant systems

Methods are provided for obtaining approximate probability distributions of reliability indices for a two-component parallel redundant system. The indices include the number of system failures in a period, the duration of system failure, and the total duration of system failures in a period. Component uptimes are exponentially distributed. whereas downtimes are arbitrarily distributed. Component mean uptimes are assumed to be much longer than mean downtimes. The methods can be extended to handle multi-component parallel redundant systems.