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An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems

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  • Xu Chen
  • Bin Xu
  • Wenli Du

Abstract

Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeography-based learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeography-based learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

Suggested Citation

  • Xu Chen & Bin Xu & Wenli Du, 2018. "An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems," Complexity, Hindawi, vol. 2018, pages 1-15, July.
    • Handle: RePEc:hin:complx:7289674
    DOI: 10.1155/2018/7289674
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    References listed on IDEAS

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    1. Xiong, Guojiang & Shi, Dongyuan & Duan, Xianzhong, 2013. "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Applied Energy, Elsevier, vol. 111(C), pages 801-811.
    2. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
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    Cited by:

    1. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    2. Chun-Yao Lee & Maickel Tuegeh, 2020. "An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem," Energies, MDPI, vol. 13(14), pages 1-16, July.
    3. Chen, Xu, 2020. "Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects," Energy, Elsevier, vol. 203(C).

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