Available transfer capability determination using hybrid evolutionary algorithm

This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal po...

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Main Authors: Peeraool Jirapong, Weerakorn Ongsakul
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/60725
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-607252018-09-10T03:48:06Z Available transfer capability determination using hybrid evolutionary algorithm Peeraool Jirapong Weerakorn Ongsakul Physics and Astronomy This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system. © American Institute of Physics. 2018-09-10T03:48:06Z 2018-09-10T03:48:06Z 2008-11-12 Conference Proceeding 15517616 0094243X 2-s2.0-55549105104 10.1063/1.3008680 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=55549105104&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60725
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Physics and Astronomy
spellingShingle Physics and Astronomy
Peeraool Jirapong
Weerakorn Ongsakul
Available transfer capability determination using hybrid evolutionary algorithm
description This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system. © American Institute of Physics.
format Conference Proceeding
author Peeraool Jirapong
Weerakorn Ongsakul
author_facet Peeraool Jirapong
Weerakorn Ongsakul
author_sort Peeraool Jirapong
title Available transfer capability determination using hybrid evolutionary algorithm
title_short Available transfer capability determination using hybrid evolutionary algorithm
title_full Available transfer capability determination using hybrid evolutionary algorithm
title_fullStr Available transfer capability determination using hybrid evolutionary algorithm
title_full_unstemmed Available transfer capability determination using hybrid evolutionary algorithm
title_sort available transfer capability determination using hybrid evolutionary algorithm
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=55549105104&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/60725
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