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Research on the Economic Scheduling Problem of Cogeneration Based on the Improved Artificial Hummingbird Algorithm

Author

Listed:
  • Xiaohong Kong

    (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)

  • Yihang Zhang

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

  • Guocai Tian

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

  • Ning Dong

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

Abstract

With the increasing application of Combined Heat and Power (CHP) units, Combined Heat and Power Economic Dispatch (CHPED) has emerged as a significant issue in power system operations. To address the complex CHPED problem, this paper proposes an effective economic dispatch method based on the Improved Artificial Hummingbird Algorithm (IAHA). Given the complex constraints of the CHPED problem and the presence of valve point effects and prohibited operating zones, it requires the algorithm to have high traversal capability in the solution space and be resistant to becoming trapped in local optima. IAHA has introduced two key improvements based on the characteristics of the CHPED problem and the shortcomings of the standard Artificial Hummingbird Algorithm (AHA). Firstly, IAHA uses chaotic mapping to initialize the initial population, enhancing the algorithm’s traversal capability. Second, the guided foraging of the standard AHA has been modified to enhance the algorithm’s ability to escape from local optima. Simulation experiments were conducted on CHP systems at three different scales: 7 units, 24 units, and 48 units. Compared to other algorithms reported in the literature, the IAHA algorithm reduces the cost in the three testing systems by up to USD 18.04, 232.7894, and 870.7461. Compared to other swarm intelligence algorithms reported in the literature, the IAHA algorithm demonstrates significant advantages in terms of convergence accuracy and convergence speed. These results confirm that the IAHA algorithm is effective in solving the CHPED problem while overcoming the limitations of the standard AHA.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6411-:d:1548023
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    References listed on IDEAS

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    1. 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.
    2. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    3. Kansal, Veenus & Dhillon, J.S., 2022. "Ameliorated artificial hummingbird algorithm for coordinated wind-solar-thermal generation scheduling problem in multiobjective framework," Applied Energy, Elsevier, vol. 326(C).
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    5. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
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