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A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch

Author

Listed:
  • Aokang Pang

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
    These authors contributed equally to this work.)

  • Huijun Liang

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
    These authors contributed equally to this work.)

  • Chenhao Lin

    (College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
    These authors contributed equally to this work.)

  • Lei Yao

    (Enshi Power Supply Company, State Grid Hubei Electric Power Company, Enshi 445000, China
    These authors contributed equally to this work.)

Abstract

Large-scale grids have gradually become the dominant trend in power systems, which has increased the importance of solving the challenges associated with large-scale economic dispatch (LED). An increase in the number of decision variables enlarges the search-space scale in LED. In addition to increasing the difficulty of solving algorithms, huge amounts of computing resources are consumed. To overcome this problem, we proposed a surrogate-assisted adaptive bat algorithm (GARCBA). On the one hand, to reduce the execution time of LED problems, we proposed a generalized regression neural network surrogate model based on a self-adaptive “minimizing the predictor” sampling strategy, which replaces the original fuel cost functions with a shorter computing time. On the other hand, we also proposed an improved hybrid bat algorithm (RCBA) named GARCBA to execute LED optimization problems. Specifically, we developed an evolutionary state evaluation (ESE) method to increase the performance of the original RCBA. Moreover, we introduced the ESE to analyze the population distribution, fitness, and effective radius of the random black hole in the original RCBA. We achieved a substantial improvement in computational time, accuracy, and convergence when using the GARCBA to solve LED problems, and we demonstrated this method’s effectiveness with three sets of simulations.

Suggested Citation

  • Aokang Pang & Huijun Liang & Chenhao Lin & Lei Yao, 2023. "A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch," Energies, MDPI, vol. 16(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:1011-:d:1038029
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    References listed on IDEAS

    as
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