Generating Capacity Reliability Evaluation Based on Monte Carlo Simulation and Cross-Entropy Methods
This paper presents a new Monte Carlo simulation (MCS) approach based on cross-entropy (CE) method to evaluate generating capacity reliability (GCR) indices. The basic idea is to use an auxiliary importance sampling density function, whose parameters are obtained from an optimization process that mi...
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| Published in: | IEEE transactions on power systems Vol. 25; no. 1; pp. 129 - 137 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01-02-2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | This paper presents a new Monte Carlo simulation (MCS) approach based on cross-entropy (CE) method to evaluate generating capacity reliability (GCR) indices. The basic idea is to use an auxiliary importance sampling density function, whose parameters are obtained from an optimization process that minimizes the computational effort of the MCS estimation approach. In order to improve the performance of the CE-based method as applied to the GCR assessment, various aspects are considered: system size, rarity of the failure event, number of different units, unit capacity sizes, and load shape. The IEEE Reliability Test System is used to test the proposed methodology, and also various modifications of this system are created to fully verify the ability of the proposed approach against both, a crude MCS and an extremely efficient analytical technique based on discrete convolution. A configuration of the Brazilian South-Southeastern generating system is also used to demonstrate the capability of the proposed CE-based MCS method in real applications. |
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| AbstractList | This paper presents a new Monte Carlo simulation (MCS) approach based on cross-entropy (CE) method to evaluate generating capacity reliability (GCR) indices. The basic idea is to use an auxiliary importance sampling density function, whose parameters are obtained from an optimization process that minimizes the computational effort of the MCS estimation approach. In order to improve the performance of the CE-based method as applied to the GCR assessment, various aspects are considered: system size, rarity of the failure event, number of different units, unit capacity sizes, and load shape. The IEEE Reliability Test System is used to test the proposed methodology, and also various modifications of this system are created to fully verify the ability of the proposed approach against both, a crude MCS and an extremely efficient analytical technique based on discrete convolution. A configuration of the Brazilian South-Southeastern generating system is also used to demonstrate the capability of the proposed CE-based MCS method in real applications. |
| Author | Singh, Chanan Fernandez, Reinaldo A. G. Leite da Silva, Armando M. |
| Author_xml | – sequence: 1 givenname: Armando M. surname: Leite da Silva fullname: Leite da Silva, Armando M. organization: Institute of Electric Systems and Energy, Federal University of ItajubÁ UNIFEI, Brazil – sequence: 2 givenname: Reinaldo A. G. surname: Fernandez fullname: Fernandez, Reinaldo A. G. organization: Institute of Electric Systems and Energy, Federal University of ItajubÁ UNIFEI, Brazil – sequence: 3 givenname: Chanan surname: Singh fullname: Singh, Chanan organization: Department of Electrical and Computer Engineering, Texas A&M University, USA |
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| References | ref12 ref15 ref14 ref2 ref1 ref17 rubinstein (ref13) 1991 ref16 ref19 ref18 fernndez (ref32) 2009 leite da silva (ref11) 1992; 139 billinton (ref6) 1994 singh (ref5) 1977 ref24 belmudes (ref30) 2008 ref23 leite da silva (ref10) 1991; 138 ref25 ref20 anders (ref22) 1990 rubinstein (ref26) 2004 biernat (ref4) 1991 ref21 ref28 ref27 (ref31) 1979; pas 99 ref29 ref8 ref7 ref9 endrenyi (ref3) 1978 |
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| SubjectTerms | Assessments Brazil Brazil Council Capacity planning Computational efficiency Computer simulation Conferences Convolution Cross-entropy (CE) method Density Density functional theory Frequency generating capacity reliability (GCR) Importance sampling importance sampling (IS) Mathematical analysis Monte Carlo methods Monte Carlo simulation Monte Carlo simulation (MCS) Optimization Power system reliability Power system simulation rare events risk analysis Studies System testing |
| Title | Generating Capacity Reliability Evaluation Based on Monte Carlo Simulation and Cross-Entropy Methods |
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