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A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems.

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Title: A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems.
Authors: Ghasemi, Mojtaba1 mojtaba.ghasemi1365@yahoo.com, Aghaei, Jamshid1, Akbari, Ebrahim2, Ghavidel, Sahand3, Li, Li3
Source: Energy. Jul2016, Vol. 107, p182-195. 14p.
Subjects: Differential evolution, Particle swarm optimization, Heuristic algorithms, Electric power systems, Cost functions
Abstract: This paper proposes a new, efficient and powerful heuristic-hybrid algorithm using hybrid DE (differential evolution) and PSO (particle swarm optimization) techniques DEPSO (differential evolution particle swarm optimization) designed to solve eight optimization problems with benchmark functions and the MAED (multi-area economic dispatch), RCMAED (reserve constrained MAED) and RCMAEED (reserve constrained multi area environmental/economic dispatch) problems with reserve sharing in power system operations. The proposed hybridizing sum-local search optimizer, entitled HSLSO, is a relatively simple but powerful technique. The HSLSO algorithm is used in this study for solving different MAED problems with non-smooth cost function. The effectiveness and efficiency of the HSLSO algorithm is first tested on a number of benchmark test functions. Experimental results showe the HSLSO has a better quality solution with the ability to converge for most of the tested functions. [ABSTRACT FROM AUTHOR]
Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 115978897
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems.
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  Data: <searchLink fieldCode="AR" term="%22Ghasemi%2C+Mojtaba%22">Ghasemi, Mojtaba</searchLink><relatesTo>1</relatesTo><i> mojtaba.ghasemi1365@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Aghaei%2C+Jamshid%22">Aghaei, Jamshid</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Akbari%2C+Ebrahim%22">Akbari, Ebrahim</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ghavidel%2C+Sahand%22">Ghavidel, Sahand</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Li%22">Li, Li</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Jul2016, Vol. 107, p182-195. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper proposes a new, efficient and powerful heuristic-hybrid algorithm using hybrid DE (differential evolution) and PSO (particle swarm optimization) techniques DEPSO (differential evolution particle swarm optimization) designed to solve eight optimization problems with benchmark functions and the MAED (multi-area economic dispatch), RCMAED (reserve constrained MAED) and RCMAEED (reserve constrained multi area environmental/economic dispatch) problems with reserve sharing in power system operations. The proposed hybridizing sum-local search optimizer, entitled HSLSO, is a relatively simple but powerful technique. The HSLSO algorithm is used in this study for solving different MAED problems with non-smooth cost function. The effectiveness and efficiency of the HSLSO algorithm is first tested on a number of benchmark test functions. Experimental results showe the HSLSO has a better quality solution with the ability to converge for most of the tested functions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.energy.2016.04.002
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 182
    Subjects:
      – SubjectFull: Differential evolution
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
      – SubjectFull: Electric power systems
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      – SubjectFull: Cost functions
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              Text: Jul2016
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