Multi-objective evolutionary programming for solving economic dispatch problem

Economic dispatch (ED) is an optimisation strategy to ensure power systems operate in an economic manner. This paper proposes a multi-objective optimisation method to minimise the total generation cost and total system loss simultaneously and find the best adjustment for this economic dispatch probl...

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Main Authors: Adnan, N.A., Mansor, M.H., Roslan, N., Musirin, I., Khader, P.S.A., Kamil, K., Jelani, S., Zuhdi, A.W.M.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-133012020-07-03T05:32:58Z Multi-objective evolutionary programming for solving economic dispatch problem Adnan, N.A. Mansor, M.H. Roslan, N. Musirin, I. Khader, P.S.A. Kamil, K. Jelani, S. Zuhdi, A.W.M. Economic dispatch (ED) is an optimisation strategy to ensure power systems operate in an economic manner. This paper proposes a multi-objective optimisation method to minimise the total generation cost and total system loss simultaneously and find the best adjustment for this economic dispatch problem. This study focused on solving the multi-objective economic dispatch problem using a Heuristic Optimisation (HO) method, namely Multi-Objective Evolutionary Programming (MOEP). The Weighted Sum Method (WSM) is integrated with EP to find a trade-off solution between two objectives: total generation cost minimisation and total system loss minimisation. The practicable proposed method was tested on the IEEE 30-Bus Reliability Test System (RTS) for three different scenarios. MATLAB programming language was used to run the designated algorithm of MOEP. The performance of MOEP to solve the multi-objective ED problem was then compared with another method; the Multi-Objective Artificial Immune System (MOAIS). The experimental results show that MOEP dominates in all cases that have been tested, proving that MOEP is superior than MOAIS in providing high-quality solution to economic dispatch problem with multiple objectives in terms of cheap total generation cost and low total system loss. © 2019, World Academy of Research in Science and Engineering. All rights reserved. 2020-02-03T03:31:40Z 2020-02-03T03:31:40Z 2019 Article 10.30534/ijatcse/2019/4481.62019 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
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language English
description Economic dispatch (ED) is an optimisation strategy to ensure power systems operate in an economic manner. This paper proposes a multi-objective optimisation method to minimise the total generation cost and total system loss simultaneously and find the best adjustment for this economic dispatch problem. This study focused on solving the multi-objective economic dispatch problem using a Heuristic Optimisation (HO) method, namely Multi-Objective Evolutionary Programming (MOEP). The Weighted Sum Method (WSM) is integrated with EP to find a trade-off solution between two objectives: total generation cost minimisation and total system loss minimisation. The practicable proposed method was tested on the IEEE 30-Bus Reliability Test System (RTS) for three different scenarios. MATLAB programming language was used to run the designated algorithm of MOEP. The performance of MOEP to solve the multi-objective ED problem was then compared with another method; the Multi-Objective Artificial Immune System (MOAIS). The experimental results show that MOEP dominates in all cases that have been tested, proving that MOEP is superior than MOAIS in providing high-quality solution to economic dispatch problem with multiple objectives in terms of cheap total generation cost and low total system loss. © 2019, World Academy of Research in Science and Engineering. All rights reserved.
format Article
author Adnan, N.A.
Mansor, M.H.
Roslan, N.
Musirin, I.
Khader, P.S.A.
Kamil, K.
Jelani, S.
Zuhdi, A.W.M.
spellingShingle Adnan, N.A.
Mansor, M.H.
Roslan, N.
Musirin, I.
Khader, P.S.A.
Kamil, K.
Jelani, S.
Zuhdi, A.W.M.
Multi-objective evolutionary programming for solving economic dispatch problem
author_facet Adnan, N.A.
Mansor, M.H.
Roslan, N.
Musirin, I.
Khader, P.S.A.
Kamil, K.
Jelani, S.
Zuhdi, A.W.M.
author_sort Adnan, N.A.
title Multi-objective evolutionary programming for solving economic dispatch problem
title_short Multi-objective evolutionary programming for solving economic dispatch problem
title_full Multi-objective evolutionary programming for solving economic dispatch problem
title_fullStr Multi-objective evolutionary programming for solving economic dispatch problem
title_full_unstemmed Multi-objective evolutionary programming for solving economic dispatch problem
title_sort multi-objective evolutionary programming for solving economic dispatch problem
publishDate 2020
_version_ 1672614221225918464
score 13.222552