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Real-Time Energy Management for DC Microgrids Using Artificial Intelligence

Author

Listed:
  • Aiman J. Albarakati

    (Department of Computer Engineering, Faculty of Computer and Information Sciences, Majmaah University, Majmaah 11952, Saudi Arabia)

  • Younes Boujoudar

    (Department of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, Morocco)

  • Mohamed Azeroual

    (Department of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, Morocco)

  • Reda Jabeur

    (Department of Electrical and Mechanical Engineering, Faculty of Science and Technology, Hassan 1 University, Settat BP 577, Morocco)

  • Ayman Aljarbouh

    (Department of Computer Science, School of Arts and Sciences, University of Central Asia, Naryn 722918, Kyrgyzstan)

  • Hassan El Moussaoui

    (Department of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, Morocco)

  • Tijani Lamhamdi

    (Department of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, Morocco)

  • Najat Ouaaline

    (Department of Electrical and Mechanical Engineering, Faculty of Science and Technology, Hassan 1 University, Settat BP 577, Morocco)

Abstract

Microgrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flows along the entire microgrid, an energy management strategy (EMS) is necessary. This paper describes a microgrid energy management system, which is composed of solar panels and wind turbines as renewable sources, Li-ion batteries, electrical grids as backup sources, and AC/DC loads. The proposed EMS is based on the maximum extraction of energy from the renewable sources, by making them operate under Maximum Power Point Tracking (MPPT) mode; both of those MPPT algorithms are implemented with a multi-agent system (MAS). In addition, management of the stored energy is performed through the optimal control of battery charging and discharging using artificial neural network controllers (ANNCs). The main objective of this system is to maintain the power balance in the microgrid and to provide a configurable and a flexible control for the different scenarios of all kinds of variations. All the system’s components were modeled in MATLAB/Simulink, the MAS system was developed using Java Agent Development Framework (JADE), and Multi-Agent Control using Simulink with Jade extension (MACSIMJX) was used to insure the communication between Simulink and JADE.

Suggested Citation

  • Aiman J. Albarakati & Younes Boujoudar & Mohamed Azeroual & Reda Jabeur & Ayman Aljarbouh & Hassan El Moussaoui & Tijani Lamhamdi & Najat Ouaaline, 2021. "Real-Time Energy Management for DC Microgrids Using Artificial Intelligence," Energies, MDPI, vol. 14(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5307-:d:622872
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    References listed on IDEAS

    as
    1. Gao, Hongjun & Xu, Song & Liu, Youbo & Wang, Lingfeng & Xiang, Yingmeng & Liu, Junyong, 2020. "Decentralized optimal operation model for cooperative microgrids considering renewable energy uncertainties," Applied Energy, Elsevier, vol. 262(C).
    2. Meng, Lexuan & Sanseverino, Eleonora Riva & Luna, Adriana & Dragicevic, Tomislav & Vasquez, Juan C. & Guerrero, Josep M., 2016. "Microgrid supervisory controllers and energy management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1263-1273.
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    Cited by:

    1. Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.
    2. Toni Alex Reis Borges & Filipe Cardoso Brito & Rafael Guimarães Oliveira dos Santos & Paulo de Tarso Nascimento & Celso Barreto da Silva & Roberta Mota Panizio & Hugo Saba & Aloísio Santos Nascimento , 2025. "Smart Technologies Applied in Microgrids of Renewable Energy Sources: A Systematic Review," Energies, MDPI, vol. 18(11), pages 1-20, May.
    3. Nachat Nasser & Meghdad Fazeli & Ahmed A. Aboushady, 2022. "Buffered Microgrids with Modular Back-to-Back Converter Grid Interface," Energies, MDPI, vol. 15(21), pages 1-16, October.
    4. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    5. Wei-Tzer Huang & Wu-Chun Chung & Chao-Chin Wu & Tse-Yun Huang, 2024. "A Parallel Framework for Fast Charge/Discharge Scheduling of Battery Storage Systems in Microgrids," Energies, MDPI, vol. 17(24), pages 1-21, December.

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