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Implementation of a microgrid energy management system considering fair EV charging, uncertainties and contingencies: A multi-objective approach

Author

Listed:
  • Tairo, Derian C.
  • Silva, Jéssica Alice A.
  • López, Juan Camilo
  • Rider, Marcos J.

Abstract

The integration of distributed energy resources (DERs), such as battery energy storage systems (BESSs), photovoltaic (PV) systems, and electric vehicle (EV) chargers, presents new challenges for energy management system (EMS) in microgrids. A key challenge is developing models that incorporate real-time analysis, three-phase systems, and transitions between grid-connected and isolated modes while accounting for uncertainties in PV generation and demand. Additionally, ensuring fairness in EV charging within microgrids is essential for user satisfaction and system performance. This work addresses a multi-objective optimization problem (MOOP) aimed at minimizing operational costs from the main grid and energy non-supplied (ENS) for EVs, incorporating a fairness index to ensure equitable energy distribution among connected vehicles. This index considers factors such as state of charge (SoC), energy capacities, and charging time availability at electric vehicle charging station (EVCS). The proposed model is simulated using Hardware-in-the-Loop (HIL) within a practical Internet of Things (IoT) framework, accounting for multiple contingencies and uncertainties. To evaluate the approach, data from CAMPUSGRID, a three-phase AC microgrid at Universidade Estadual de Campinas (UNICAMP), is utilized. The results present a Pareto front, enabling the selection of an optimal compromise solution for the MOOP. This solution achieves low ENS for EVs while yielding nearly 18 % savings in operational costs. Furthermore, the fairness index is tested with competing factors (SoC, energy capacities, and available charging time), demonstrating that EVs with higher energy capacity and demand are slightly favored, ensuring equitable energy distribution under diverse conditions.

Suggested Citation

  • Tairo, Derian C. & Silva, Jéssica Alice A. & López, Juan Camilo & Rider, Marcos J., 2025. "Implementation of a microgrid energy management system considering fair EV charging, uncertainties and contingencies: A multi-objective approach," Applied Energy, Elsevier, vol. 396(C).
  • Handle: RePEc:eee:appene:v:396:y:2025:i:c:s0306261925009729
    DOI: 10.1016/j.apenergy.2025.126242
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    References listed on IDEAS

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    1. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    2. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    3. Soares, João & Lezama, Fernando & Faia, Ricardo & Limmer, Steffen & Dietrich, Manuel & Rodemann, Tobias & Ramos, Sergio & Vale, Zita, 2024. "Review on fairness in local energy systems," Applied Energy, Elsevier, vol. 374(C).
    4. Silva, Jéssica Alice A. & López, Juan Camilo & Guzman, Cindy Paola & Arias, Nataly Bañol & Rider, Marcos J. & da Silva, Luiz C.P., 2023. "An IoT-based energy management system for AC microgrids with grid and security constraints," Applied Energy, Elsevier, vol. 337(C).
    5. Zaneti, Letícia A.L. & Arias, Nataly Bañol & de Almeida, Madson C. & Rider, Marcos J., 2022. "Sustainable charging schedule of electric buses in a University Campus: A rolling horizon approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    6. Silva, Jéssica Alice A. & López, Juan Camilo & Arias, Nataly Bañol & Rider, Marcos J. & da Silva, Luiz C.P., 2021. "An optimal stochastic energy management system for resilient microgrids," Applied Energy, Elsevier, vol. 300(C).
    7. Ullah, Zia & Rehman, Anis Ur & Wang, Shaorong & Hasanien, Hany M. & Luo, Peng & Elkadeem, Mohamed R. & Abido, Mohammad A., 2023. "IoT-based monitoring and control of substations and smart grids with renewables and electric vehicles integration," Energy, Elsevier, vol. 282(C).
    8. Kim, Jangkyum & Oh, Hyeontaek & Choi, Jun Kyun, 2022. "Learning based cost optimal energy management model for campus microgrid systems," Applied Energy, Elsevier, vol. 311(C).
    9. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    10. Restrepo, Mauricio & Cañizares, Claudio A. & Simpson-Porco, John W. & Su, Peter & Taruc, John, 2021. "Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility," Applied Energy, Elsevier, vol. 290(C).
    11. Zandrazavi, Seyed Farhad & Guzman, Cindy Paola & Pozos, Alejandra Tabares & Quiros-Tortos, Jairo & Franco, John Fredy, 2022. "Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles," Energy, Elsevier, vol. 241(C).
    12. Çimen, Halil & Bazmohammadi, Najmeh & Lashab, Abderezak & Terriche, Yacine & Vasquez, Juan C. & Guerrero, Josep M., 2022. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring," Applied Energy, Elsevier, vol. 307(C).
    13. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    14. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
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