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Exponential hybrid mutation differential evolution for economic dispatch of large-scale power systems considering valve-point effects

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Abstract

Economic dispatch (ED) is a key foundational issue for optimal power system operation and scheduling control. It is a complex multi-constraint optimization problem, especially taking into account the valve-point effects of thermal power generators. As the power system continues to grow in size, the ED problem becomes more sophisticated and the solution space will have more local extrema, which makes the solution methods more prone to premature convergence. Thus, the existing methods encounter difficulties in achieving satisfactory solutions. To address this issue, this study presents an exponential hybrid mutation differential evolution (EHMDE), which utilizes two improved strategies including exponential population size reduction and hybrid mutation operation to adaptively equilibrate exploitation and exploration during the iteration process. The former strategy can maintain population diversity to avoid getting stuck in a local optimum in the preceding period and enhance the convergence speed in the later period by reducing the population size progressively. The latter strategy can explore wide search ranges and aggregate the individuals by two mutation operators EHMDE/current-to-rand/1 and EHMDE/pbest/1 based on a variation probability. Simulation results of 23 benchmark functions and five ED cases verify the superiority of EHMDE over other peer methods. Furthermore, they also demonstrate that these two improved strategies work well together to strengthen EHMDE.

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Data Availability

The data are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the editor and the reviewers for their constructive comments. This research was funded by the National Natural Science Foundation of China (52167007, 52367006), the Natural Science Foundation of Guizhou Province (QiankeheBasic-ZK[2022]General121), the Open Project Program of Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education (2021FF06), and the Open Project Program of Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System (2022A0008).

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Authors and Affiliations

  1. College of Electrical Engineering, Guizhou University, Guiyang, 550025, China

    Derong Lv, Guojiang Xiong, Xiaofan Fu & Jing Zhang

  2. Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing, 400065, China

    Guojiang Xiong

  3. Department of Electrical Engineering, University of Hafr Al Batin, 31991, Hafr Al Batin, Saudi Arabia

    Mohammed Azmi Al-Betar

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE

    Houssem R. E. H. Bouchekara

  5. Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System, Quanzhou, 362216, China

    Hao Chen

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  1. Derong Lv
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Contributions

Derong Lv: Software, Methodology, Writing—original draft; Guojiang Xiong: Conceptualization, Supervision, Formal analysis, Writing—review & editing; Xiaofan Fu: Writing—review & editing, Formal analysis; Mohammed Azmi Al-Betar: Writing—review & editing; Jing Zhang: Formal analysis, Validation; Houssem R.E.H. Bouchekara: Writing—review & editing; Hao Chen: Funding acquisition.

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Correspondence to Guojiang Xiong or Hao Chen.

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Lv, D., Xiong, G., Fu, X. et al. Exponential hybrid mutation differential evolution for economic dispatch of large-scale power systems considering valve-point effects. Appl Intell 53, 31046–31064 (2023). https://doi.org/10.1007/s10489-023-05180-5

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