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Optimized Strategy for Energy Management in an EV Fast Charging Microgrid Considering Storage Degradation

Author

Listed:
  • Joelson Lopes da Paixão

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Alzenira da Rosa Abaide

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Gabriel Henrique Danielsson

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Jordan Passinato Sausen

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil
    Graduate Program in Applied Computing, University of Vale do Itajaí—Univali, Itajaí 88302-901, Santa Catarina, Brazil)

  • Leonardo Nogueira Fontoura da Silva

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Nelson Knak Neto

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

Abstract

Current environmental challenges demand immediate action, especially in the transport sector, which is one of the largest CO 2 emitters. Vehicle electrification is considered an essential strategy for emission mitigation and combating global warming. This study presents methodologies for the modeling and energy management of microgrids (MGs) designed as charging stations for electric vehicles (EVs). Algorithms were developed to estimate daily energy generation and charging events in the MG. These data feed an energy management algorithm aimed at minimizing the costs associated with energy trading operations, as well as the charging and discharging cycles of the battery energy storage system (BESS). The problem constraints ensure the safe operation of the system, availability of backup energy for off-grid conditions, preference for reduced tariffs, and optimized management of the BESS charge and discharge rates, considering battery wear. The grid-connected MG used in our case study consists of a wind turbine (WT), photovoltaic system (PVS), BESS, and an electric vehicle fast charging station (EVFCS). Located on a highway, the MG was designed to provide fast charging, extending the range of EVs and reducing drivers’ range anxiety. The results of this study demonstrated the effectiveness of the proposed energy management approach, with the optimization algorithm efficiently managing energy flows within the MG while prioritizing lower operational costs. The inclusion of the battery wear model makes the optimizer more selective in terms of battery usage, operating it in cycles that minimize BESS wear and effectively prolong its lifespan.

Suggested Citation

  • Joelson Lopes da Paixão & Alzenira da Rosa Abaide & Gabriel Henrique Danielsson & Jordan Passinato Sausen & Leonardo Nogueira Fontoura da Silva & Nelson Knak Neto, 2025. "Optimized Strategy for Energy Management in an EV Fast Charging Microgrid Considering Storage Degradation," Energies, MDPI, vol. 18(5), pages 1-34, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1060-:d:1596892
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    References listed on IDEAS

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    1. Tancredi Testasecca & Francesco Bellesini & Diego Arnone & Marco Beccali, 2025. "A Digital Twin for Real-Time and Predictive Optimization of Electric Vehicle Charging in Microgrids Integrating Renewable Energy Sources," Energies, MDPI, vol. 18(21), pages 1-26, October.

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