IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1060-d1596892.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1060/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lupangu, C. & Bansal, R.C., 2017. "A review of technical issues on the development of solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 950-965.
    2. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    3. Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
    4. Aliashim Albani & Mohd Zamri Ibrahim, 2017. "Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia," Energies, MDPI, vol. 10(3), pages 1-21, March.
    5. Saugat Upadhyay & Ibrahim Ahmed & Lucian Mihet-Popa, 2024. "Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique," Energies, MDPI, vol. 17(16), pages 1-18, August.
    6. Sharma, Pavitra & Dutt Mathur, Hitesh & Mishra, Puneet & Bansal, Ramesh C., 2022. "A critical and comparative review of energy management strategies for microgrids," Applied Energy, Elsevier, vol. 327(C).
    7. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    8. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pavel Ilyushin & Vladislav Volnyi & Konstantin Suslov & Sergey Filippov, 2023. "State-of-the-Art Literature Review of Power Flow Control Methods for Low-Voltage AC and AC-DC Microgrids," Energies, MDPI, vol. 16(7), pages 1-35, March.
    2. McCluskey, Jac & Druitt, Tom & Larkin, Charles, 2025. "Sustainability in transit: Assessing the economic case for electric bus adoption in the UK," Transport Policy, Elsevier, vol. 162(C), pages 493-508.
    3. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    4. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    5. Wesly Jean & Marcel Bursztyn & Nelson Bernal & Antonio C. P. Brasil Junior & Gabriela Litre & Daniela Nogueira, 2024. "Linking Energy Transition to Income Generation for Vulnerable Populations in Brazil: A Win-Win Strategy," Sustainability, MDPI, vol. 16(17), pages 1-18, August.
    6. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    7. Tan, Kang Miao & Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Mansor, Muhamad & Teh, Jiashen & Guerrero, Josep M., 2023. "Factors influencing global transportation electrification: Comparative analysis of electric and internal combustion engine vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    8. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    9. Wu, Jiajun & Liu, Hui & Ren, Xiaolei & Nie, Shida & Qin, Yechen & Han, Lijin, 2025. "A multi-objective optimization approach for regenerative braking control in electric vehicles using MPE-SAC algorithm," Energy, Elsevier, vol. 318(C).
    10. Kim, Hyunjung & Kim, Dae-Wook & Kim, Man-Keun, 2022. "Economics of charging infrastructure for electric vehicles in Korea," Energy Policy, Elsevier, vol. 164(C).
    11. Quintero Fuentes, Abel & Hickman, Mark & Whitehead, Jake, 2025. "Zone substations' readiness to embrace electric vehicle adoption: Brisbane case study," Energy, Elsevier, vol. 322(C).
    12. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    13. Gualtieri, Giovanni, 2019. "A comprehensive review on wind resource extrapolation models applied in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 215-233.
    14. Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    15. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    16. AL-Rasheedi, Majed & Gueymard, Christian A. & Al-Khayat, Mohammad & Ismail, Alaa & Lee, Jared A. & Al-Duaj, Hamad, 2020. "Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    17. Andrea Marchioni & Carlo Alberto Magni & Davide Baschieri, 2020. "Investment and Financing Perspectives for a Solar Photovoltaic Project," MIC 2020: The 20th Management International Conference,, University of Primorska Press.
    18. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    19. Yu, Kaize & Yan, Pengyu & Liu, Yang & Chen, Zhibin & Kong, Xiang T.R., 2025. "Battery degradation mitigation-oriented strategy for optimizing e-hailing electric vehicle operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).
    20. Jakov Topić & Jure Soldo & Filip Maletić & Branimir Škugor & Joško Deur, 2020. "Virtual Simulation of Electric Bus Fleets for City Bus Transport Electrification Planning," Energies, MDPI, vol. 13(13), pages 1-24, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1060-:d:1596892. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.