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Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer

Author

Listed:
  • Wisam Kareem Meteab

    (Department of Electrical Engineering, University of Jaén, EPS Linares, 23700 Jaén, Spain)

  • Salwan Ali Habeeb Alsultani

    (Department of Electrical Engineering, University of Jaén, EPS Linares, 23700 Jaén, Spain)

  • Francisco Jurado

    (Department of Electrical Engineering, University of Jaén, EPS Linares, 23700 Jaén, Spain)

Abstract

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy.

Suggested Citation

  • Wisam Kareem Meteab & Salwan Ali Habeeb Alsultani & Francisco Jurado, 2023. "Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer," Energies, MDPI, vol. 16(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6038-:d:1219510
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    References listed on IDEAS

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    Cited by:

    1. Natascia Andrenacci & Antonino Genovese & Giancarlo Giuli, 2025. "Strategies for Workplace EV Charging Management," Energies, MDPI, vol. 18(2), pages 1-32, January.

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