A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and...

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Main Authors: Mahdi, F.P., Vasant, P., Abdullah-Al-Wadud, M., Watada, J., Kallimani, V.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21095 /
Published: John Wiley and Sons Ltd 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038096044&doi=10.1002%2fetep.2497&partnerID=40&md5=754855294593cc8472ddac49189adfe9
http://eprints.utp.edu.my/21095/
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spelling utp-eprints.210952019-02-26T03:17:22Z A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function Mahdi, F.P. Vasant, P. Abdullah-Al-Wadud, M. Watada, J. Kallimani, V. In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and NOx from the power generation system. A quantum-inspired particle swarm optimization (QPSO) technique is presented in this paper to solve many-objective environmental economic dispatch (EED) problems. Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4-objective problem considering ED. Many-objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well-known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum-behaved bat algorithm. Quantum-inspired particle swarm optimization is implemented on a 6-unit system to solve many-objective EED problems, and at the same time, to show the effectiveness of QPSO in large systems, it is also implemented in a 26-unit power generation system for ED problem. The obtained results demonstrate and verify the effectiveness and robustness of QPSO to solve many-objective EED problems. This also shows that QPSO can effectively be implemented in such power dispatch problems. Copyright © 2017 John Wiley & Sons, Ltd. John Wiley and Sons Ltd 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038096044&doi=10.1002%2fetep.2497&partnerID=40&md5=754855294593cc8472ddac49189adfe9 Mahdi, F.P. and Vasant, P. and Abdullah-Al-Wadud, M. and Watada, J. and Kallimani, V. (2018) A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function. International Transactions on Electrical Energy Systems, 28 (3). http://eprints.utp.edu.my/21095/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and NOx from the power generation system. A quantum-inspired particle swarm optimization (QPSO) technique is presented in this paper to solve many-objective environmental economic dispatch (EED) problems. Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4-objective problem considering ED. Many-objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well-known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum-behaved bat algorithm. Quantum-inspired particle swarm optimization is implemented on a 6-unit system to solve many-objective EED problems, and at the same time, to show the effectiveness of QPSO in large systems, it is also implemented in a 26-unit power generation system for ED problem. The obtained results demonstrate and verify the effectiveness and robustness of QPSO to solve many-objective EED problems. This also shows that QPSO can effectively be implemented in such power dispatch problems. Copyright © 2017 John Wiley & Sons, Ltd.
format Article
author Mahdi, F.P.
Vasant, P.
Abdullah-Al-Wadud, M.
Watada, J.
Kallimani, V.
spellingShingle Mahdi, F.P.
Vasant, P.
Abdullah-Al-Wadud, M.
Watada, J.
Kallimani, V.
A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
author_sort Mahdi, F.P.
title A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
title_short A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
title_full A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
title_fullStr A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
title_full_unstemmed A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
title_sort quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
publisher John Wiley and Sons Ltd
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038096044&doi=10.1002%2fetep.2497&partnerID=40&md5=754855294593cc8472ddac49189adfe9
http://eprints.utp.edu.my/21095/
_version_ 1741196424216838144
score 11.62408

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