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

Ghasemi, M; Aghaei, J; Akbari, E; Ghavidel, S; Li, L

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

Ghasemi, M Aghaei, J Akbari, E Ghavidel, S Li, L

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Publication Type:: Journal Article
Citation:: Energy, 2016, 107 pp. 182 - 195
Issue Date:: 2016-07-15

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Field	Value	Language
dc.contributor.author	Ghasemi, M	en_US
dc.contributor.author	Aghaei, J	en_US
dc.contributor.author	Akbari, E	en_US
dc.contributor.author	Ghavidel, S	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.date.available	2020-05-25T19:02:38Z
dc.date.issued	2016-07-15	en_US
dc.identifier.citation	Energy, 2016, 107 pp. 182 - 195	en_US
dc.identifier.issn	0360-5442	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122805
dc.description.abstract	© 2016 Elsevier Ltd. 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.	en_US
dc.relation.ispartof	Energy	en_US
dc.relation.isbasedon	10.1016/j.energy.2016.04.002	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Energy	en_US
dc.title	A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems	en_US
dc.type	Journal Article
utslib.citation.volume	107	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	0914 Resources Engineering and Extractive Metallurgy	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	107	en_US

Abstract:

© 2016 Elsevier Ltd. 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.

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http://hdl.handle.net/10453/122805