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Review of Computer Engineering Research

March 2017, Volume 4, 1, pp 11-29

Solving Nonlinear Single-Unit Commitment Problem by Genetic Algorithm Based Clustering Technique

M.A. El-Shorbagy

,

A.A. Mousa

,

M. Farag

M.A. El-Shorbagy 1 ,

A.A. Mousa 2
M. Farag 1 
  1. Department of Basic Engineering Science, Faculty of Engineering, Shebin El-Kom, Menoufia University, Egypt 1

  2. Department of Basic Engineering Science, Faculty of Engineering, Shebin El-Kom, Menoufia University, Egypt & Department of Mathematics and Statistics, Faculty of sciences, Taif University, Saudi Arabia 2

Pages: 11-29

DOI: 10.18488/journal.76.2017.41.11.29

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Article History:

Received: 26 October, 2016
Revised: 18 January, 2017
Accepted: 16 February, 2017
Published: 15 March, 2017


Abstract:

Nonlinear single-unit commitment problem (NSUCP) is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems. This paper presents a new algorithm for solving NSUCP using genetic algorithm (GA) based clustering technique. The proposed algorithm integrates the main features of binary-real coded GA and K-means clustering technique. Clustering technique divides population into a specific number of subpopulations. In this way, different operators of GA can be used instead of using one operator to the whole population to avoid the local minima and introduce diversity. The effectiveness of the proposed algorithm is validated by comparison with other well-known techniques.  By comparison with the previously reported results, it is found that the performance of the proposed algorithm quite satisfactory.

Contribution/ Originality
This study presents a new algorithm for solving nonlinear single-unit commitment problem using genetic algorithm based clustering technique; where it integrates the main features of binary-real coded genetic algorithm and K-means clustering technique. The tests demonstrated that the proposed approach has a satisfactory performance compared to previous studies.

Keywords:

Nonlinear single-unit commitment problem, Genetic algorithm, Clustering technique, Optimization.

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Funding:

This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.

Acknowledgement:

All authors contributed equally to the conception and design of the study.

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