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Conference Paper: Chemical reaction optimization for the optimal power flow problem
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TitleChemical reaction optimization for the optimal power flow problem
 
AuthorsSun, Y1
Lam, AYS2
Li, VOK1
Xu, J1
Yu, JJQ1
 
KeywordsChemical reaction optimization
Metaheuristic
Optimal power flow
Power grid
Smart grid
 
Issue Date2012
 
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
 
CitationThe 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings, 2012, p. 1-8 [How to Cite?]
 
AbstractThis paper presents an implementation of the Chemical Reaction Optimization (CRO) algorithm to solve the optimal power flow (OPF) problem in power systems with the objective of minimizing generation costs. Multiple constraints, such as the balance of the power, bus voltage magnitude limits, transmission line flow limits, transformer tap settings, etc., are considered. We adapt the CRO framework to the OPF problem by redesigning the elementary reaction operators. We perform simulations on the standard IEEE-14, -30, and -57 bus benchmark systems. We compare the perform of CRO with other reported evolutionary algorithms in the IEEE-30 test case. Simulation results show that CRO can obtain a solution with the lowest cost, when compared with other algorithms. To be more complete, we also give the average result for the IEEE-30 case, and the best and average results for the IEEE-14 and -57 test cases. The results given in this paper suggest that CRO is a better alternative for solving the OPF problem, as well as its variants for the future smart grid. © 2012 IEEE.
 
DescriptionIEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)
 
ISBN978-1-4673-1509-8
 
DC FieldValue
dc.contributor.authorSun, Y
 
dc.contributor.authorLam, AYS
 
dc.contributor.authorLi, VOK
 
dc.contributor.authorXu, J
 
dc.contributor.authorYu, JJQ
 
dc.date.accessioned2012-09-20T08:16:53Z
 
dc.date.available2012-09-20T08:16:53Z
 
dc.date.issued2012
 
dc.description.abstractThis paper presents an implementation of the Chemical Reaction Optimization (CRO) algorithm to solve the optimal power flow (OPF) problem in power systems with the objective of minimizing generation costs. Multiple constraints, such as the balance of the power, bus voltage magnitude limits, transmission line flow limits, transformer tap settings, etc., are considered. We adapt the CRO framework to the OPF problem by redesigning the elementary reaction operators. We perform simulations on the standard IEEE-14, -30, and -57 bus benchmark systems. We compare the perform of CRO with other reported evolutionary algorithms in the IEEE-30 test case. Simulation results show that CRO can obtain a solution with the lowest cost, when compared with other algorithms. To be more complete, we also give the average result for the IEEE-30 case, and the best and average results for the IEEE-14 and -57 test cases. The results given in this paper suggest that CRO is a better alternative for solving the OPF problem, as well as its variants for the future smart grid. © 2012 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.descriptionIEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)
 
dc.identifier.citationThe 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings, 2012, p. 1-8 [How to Cite?]
 
dc.identifier.epage8
 
dc.identifier.hkuros210469
 
dc.identifier.isbn978-1-4673-1509-8
 
dc.identifier.scopuseid_2-s2.0-84866850786
 
dc.identifier.spage1
 
dc.identifier.urihttp://hdl.handle.net/10722/165307
 
dc.languageeng
 
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
 
dc.publisher.placeUnited States
 
dc.relation.ispartofCongress on Evolutionary Computation Proceedings
 
dc.rightsCongress on Evolutionary Computation Proceedings. Copyright © IEEE.
 
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectChemical reaction optimization
 
dc.subjectMetaheuristic
 
dc.subjectOptimal power flow
 
dc.subjectPower grid
 
dc.subjectSmart grid
 
dc.titleChemical reaction optimization for the optimal power flow problem
 
dc.typeConference_Paper
 
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<item><contributor.author>Sun, Y</contributor.author>
<contributor.author>Lam, AYS</contributor.author>
<contributor.author>Li, VOK</contributor.author>
<contributor.author>Xu, J</contributor.author>
<contributor.author>Yu, JJQ</contributor.author>
<date.accessioned>2012-09-20T08:16:53Z</date.accessioned>
<date.available>2012-09-20T08:16:53Z</date.available>
<date.issued>2012</date.issued>
<identifier.citation>The 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings, 2012, p. 1-8</identifier.citation>
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<description>IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012  hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)</description>
<description.abstract>This paper presents an implementation of the Chemical Reaction Optimization (CRO) algorithm to solve the optimal power flow (OPF) problem in power systems with the objective of minimizing generation costs. Multiple constraints, such as the balance of the power, bus voltage magnitude limits, transmission line flow limits, transformer tap settings, etc., are considered. We adapt the CRO framework to the OPF problem by redesigning the elementary reaction operators. We perform simulations on the standard IEEE-14, -30, and -57 bus benchmark systems. We compare the perform of CRO with other reported evolutionary algorithms in the IEEE-30 test case. Simulation results show that CRO can obtain a solution with the lowest cost, when compared with other algorithms. To be more complete, we also give the average result for the IEEE-30 case, and the best and average results for the IEEE-14 and -57 test cases. The results given in this paper suggest that CRO is a better alternative for solving the OPF problem, as well as its variants for the future smart grid. &#169; 2012 IEEE.</description.abstract>
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<rights>Creative Commons: Attribution 3.0 Hong Kong License</rights>
<subject>Chemical reaction optimization</subject>
<subject>Metaheuristic</subject>
<subject>Optimal power flow</subject>
<subject>Power grid</subject>
<subject>Smart grid</subject>
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Author Affiliations
  1. The University of Hong Kong
  2. UC Berkeley