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Novel Gaussian-Decrement-Based Particle Swarm Optimization with Time-Varying Parameters for Economic Dispatch in Renewable-Integrated Microgrids

Author

Listed:
  • Yuan Wang

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Wangjia Lu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Wenjun Du

    (Zhejiang Institute of Communications Co., Ltd., Hangzhou 310030, China
    Key Laboratory of Transport Industry of Comprehensive Transportation Theory, Hangzhou 310030, China)

  • Changyin Dong

    (School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
    National Key Laboratory of Aircraft Configuration Design, Xi’an 710072, China)

Abstract

Background: To address the uncertainties of renewable energy power generation, the disorderly charging characteristics of electric vehicles, and the high electricity cost of the power grid in expressway service areas, a method of economic dispatch optimization based on the improved particle swarm optimization algorithm is proposed in this study. Methods: Mathematical models of photovoltaic power generation, energy storage systems, and electric vehicles were established, thereby constructing the microgrid system model of the power load in the expressway service area. Taking the economic cost of electricity consumption in the service area as the objective function and simultaneously meeting constraints such as power balance, power grid interactions, and energy storage systems, a microgrid economy dispatch model is constructed. An improved particle swarm optimization algorithm with time-varying parameters of the inertia weight and learning factor was designed to solve the optimal dispatching strategy. The inertia weight was improved by adopting the Gaussian decreasing method, and the asymmetric dynamic learning factor was adjusted simultaneously. Findings: Field case studies demonstrate that, compared to other algorithms, the improved Particle Swarm Optimization algorithm effectively reduces the operational costs of microgrid systems while exhibiting accelerated convergence speed and enhanced robustness. Value: This study provides a theoretical mathematical reference for the economic dispatch optimization of microgrids in renewable-integrated transportation systems.

Suggested Citation

  • Yuan Wang & Wangjia Lu & Wenjun Du & Changyin Dong, 2025. "Novel Gaussian-Decrement-Based Particle Swarm Optimization with Time-Varying Parameters for Economic Dispatch in Renewable-Integrated Microgrids," Mathematics, MDPI, vol. 13(15), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:15:p:2440-:d:1712579
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    References listed on IDEAS

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