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

Xiaohong Kong (), Kunyan Li, Yihang Zhang, Guocai Tian and Ning Dong
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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

Energies, 2024, vol. 17, issue 24, 1-29

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.

Keywords: Combined Heat and Power; Artificial Hummingbird Algorithm; chaotic mapping; intelligent algorithm; economic dispatch (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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