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Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Author

Listed:
  • Wenqiang Yang

    (School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Yihang Zhang

    (School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Xinxin Zhu

    (School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Kunyan Li

    (School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Zhile Yang

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.

Suggested Citation

  • Wenqiang Yang & Yihang Zhang & Xinxin Zhu & Kunyan Li & Zhile Yang, 2024. "Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm," Energies, MDPI, vol. 17(6), pages 1-29, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1491-:d:1360977
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    References listed on IDEAS

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    1. Shen, Xin & Zou, Dexuan & Duan, Na & Zhang, Qiang, 2019. "An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch," Energy, Elsevier, vol. 186(C).
    2. Yang, Xiaohui & Leng, Zhengyang & Xu, Shaoping & Yang, Chunsheng & Yang, Li & Liu, Kang & Song, Yaoren & Zhang, Liufang, 2021. "Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method," Renewable Energy, Elsevier, vol. 172(C), pages 408-423.
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    Cited by:

    1. Xiaohong Kong & Kunyan Li & Yihang Zhang & Guocai Tian & Ning Dong, 2024. "Research on the Economic Scheduling Problem of Cogeneration Based on the Improved Artificial Hummingbird Algorithm," Energies, MDPI, vol. 17(24), pages 1-29, December.
    2. Yang, Wenqiang & Dong, Ning & Yang, Zhile & Nie, Fuquan & Li, Kunyan & Cai, Jingkao & Wang, Yang, 2025. "Research on dynamic economic scheduling of plug in electric vehicles based on improved ant-lion algorithm," Energy, Elsevier, vol. 335(C).

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