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A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects

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  • Cai, Jiejin
  • Li, Qiong
  • Li, Lixiang
  • Peng, Haipeng
  • Yang, Yixian

Abstract

In this paper, a hybrid method integrating the fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm and the sequential quadratic programming (SQP) techniques, named FCASO-SQP method, is presented for solving the economic dispatch (ED) problems in power systems. The FCASO algorithm is the main optimizer in the hybrid method and the SQP technique is used to fine tune its results to improve the solution. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters ψd and ri of chaotic ant swarm optimization (CASO). The proposed method was applied to three different cases of power systems with three units, thirteen units and forty units, and the simulation results demonstrate its applicability and effectiveness to solve the ED problems with valve-point effect.

Suggested Citation

  • Cai, Jiejin & Li, Qiong & Li, Lixiang & Peng, Haipeng & Yang, Yixian, 2012. "A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects," Energy, Elsevier, vol. 38(1), pages 346-353.
    • Handle: RePEc:eee:energy:v:38:y:2012:i:1:p:346-353
    DOI: 10.1016/j.energy.2011.11.052
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    References listed on IDEAS

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    1. Liao, Gwo-Ching, 2011. "A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power," Energy, Elsevier, vol. 36(2), pages 1018-1029.
    2. Chang, Hsueh-Hsien, 2011. "Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units," Energy, Elsevier, vol. 36(1), pages 181-190.
    3. Sivasubramani, S. & Swarup, K.S., 2010. "Hybrid SOA–SQP algorithm for dynamic economic dispatch with valve-point effects," Energy, Elsevier, vol. 35(12), pages 5031-5036.
    4. Niknam, Taher & Mojarrad, Hassan Doagou & Nayeripour, Majid, 2010. "A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch," Energy, Elsevier, vol. 35(4), pages 1764-1778.
    5. Yuan, Xiaohui & Su, Anjun & Yuan, Yanbin & Nie, Hao & Wang, Liang, 2009. "An improved PSO for dynamic load dispatch of generators with valve-point effects," Energy, Elsevier, vol. 34(1), pages 67-74.
    6. Niknam, Taher & Mojarrad, Hasan Doagou & Meymand, Hamed Zeinoddini & Firouzi, Bahman Bahmani, 2011. "A new honey bee mating optimization algorithm for non-smooth economic dispatch," Energy, Elsevier, vol. 36(2), pages 896-908.
    7. Niknam, Taher, 2010. "A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem," Applied Energy, Elsevier, vol. 87(1), pages 327-339, January.
    8. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
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