DocumentCode :
1126597
Title :
Analytical and Monte Carlo Approaches to Evaluate Probability Distributions of Interruption Duration
Author :
Silva, Armando M Leite da ; Schmitt, William Frederic ; Cassula, Agnelo Marotta ; Sacramento, Cleber Esteves
Author_Institution :
INESC, Porto, Portugal
Volume :
20
Issue :
3
fYear :
2005
Firstpage :
1341
Lastpage :
1348
Abstract :
Regulatory authorities in many countries, in order to maintain an acceptable balance between appropriate customer service qualities and costs, are introducing a performance-based regulation. These regulations impose penalties—and, in some cases, rewards—that introduce a component of financial risk to an electric power utility due to the uncertainty associated with preserving a specific level of system reliability. In Brazil, for instance, one of the reliability indices receiving special attention by the utilities is the maximum continuous interruption duration (MCID) per customer. This parameter is responsible for the majority of penalties in many electric distribution utilities. This paper describes analytical and Monte Carlo simulation approaches to evaluate probability distributions of interruption duration indices. More emphasis will be given to the development of an analytical method to assess the probability distribution associated with the parameter MCID and the corresponding penalties. Case studies on a simple distribution network and on a real Brazilian distribution system are presented and discussed.
Keywords :
Monte Carlo methods; customer services; electricity supply industry; power distribution economics; Brazilian distribution system; Monte Carlo simulation approach; customer service qualities; electric power distribution utility; financial risk; interruption duration; performance-based regulation; probability distribution; system reliability; Costs; Customer service; Frequency; Maintenance; Monte Carlo methods; Power engineering and energy; Power system planning; Power system reliability; Probability distribution; Uncertainty; Distribution reliability; Markov chains; Monte Carlo simulation; reliability network analysis;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/TPWRS.2005.851944
Filename :
1490585
Link To Document :
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