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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2018

20.07.2016 | Original Article

Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems

verfasst von: M. Kenan Dosoglu, Ugur Guvenc, Serhat Duman, Yusuf Sonmez, H. Tolga Kahraman

Erschienen in: Neural Computing and Applications | Ausgabe 3/2018

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Abstract

This paper presents symbiotic organisms search (SOS) algorithm to solve economic emission load dispatch (EELD) problem for thermal generators in power systems. The basic objective of the EELD is to minimize both minimum operating costs and emission levels, while satisfying the load demand and all equality–inequality constraints. In other research direction, this multi-objective problem is converted into single-objective function by using price penalty factor approach in order to solve it with SOS. The proposed algorithm has been implemented on various test cases, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. The results of the algorithms indicate that SOS gives good results in both systems and very competitive with the state of the art for the solution of the EELD problems.
Vorheriger Artikel Particle classification optimization-based BP network for telecommunication customer churn prediction
Nächster Artikel Similarity measures of bipolar neutrosophic sets and their application to multiple criteria decision making

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Metadaten
Titel
Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems
verfasst von
M. Kenan Dosoglu
Ugur Guvenc
Serhat Duman
Yusuf Sonmez
H. Tolga Kahraman
Publikationsdatum
20.07.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2481-7

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