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Quantum-behaved Particle Swarm Optimization for Power Economic Dispatch Problem of Units with Multiple Fuel Option

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  • Chao Lung Chiang

Abstract

This paper presents a quantum-behaved particle swarm optimization (QPSO) with a multiple updating (MU) for solving the power economic dispatch problem (PEDP) of generators with multiple fuel options (MFOs). The QPSO assists the proposed method efficaciously find and precisely search. The MU helps the proposed method prevent deforming the augmented Lagrange function (ALF) and caused difficultly in searching optimal solution. The proposed approach combines the QPSO and the MU that has benefits of adopting a widespread area of punishment parameters and a small-size population. The proposed algorithm has been demonstrated on a practical ten generating units system; every one unit is composed of two or three fuel changes. The entire fuel price got by the proposed QPSO-MU has been competed with former studies for validating its efficacy. Compared achievements clearly express that the presented method is an effective alternative for resolving PEDP of units with MFOs in the realistic operations of power system.

Suggested Citation

  • Chao Lung Chiang, 2017. "Quantum-behaved Particle Swarm Optimization for Power Economic Dispatch Problem of Units with Multiple Fuel Option," European Journal of Engineering and Technology Research, European Open Science, vol. 2(12), pages 11-16, December.
    • Handle: RePEc:epw:ejeng0:v:2:y:2017:i:12:id:60492
    DOI: 10.24018/ejeng.2017.2.12.492
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    References listed on IDEAS

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    1. Wang, Yujia & Yang, Yupu, 2010. "Particle swarm with equilibrium strategy of selection for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 200(1), pages 187-197, January.
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    3. Vo, Dieu Ngoc & Ongsakul, Weerakorn, 2012. "Economic dispatch with multiple fuel types by enhanced augmented Lagrange Hopfield network," Applied Energy, Elsevier, vol. 91(1), pages 281-289.
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