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Neural Network Energy Management-Based Nonlinear Control of a DC Micro-Grid with Integrating Renewable Energies

Author

Listed:
  • Khalil Jouili

    (Laboratory of Advanced Systems, Polytechnic School of Tunisia (EPT), B.P. 743, Marsa 2078, Tunisia)

  • Mabrouk Jouili

    (ETIS, CNRS UMR 8051, CY Cergy Paris University, ENSEA, 6 Avenue du Ponceau, 95014 Cergy, France)

  • Alsharef Mohammad

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Abdulrahman J. Babqi

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Walid Belhadj

    (Physics Department, Faculty of Science, Umm AL-Qura University, P.O. Box 715, Makkah 24382, Saudi Arabia)

Abstract

The broad acceptance of sustainable and renewable energy sources as a means of integrating them into electrical power networks is essential to promote sustainable development. Microgrids using direct currents (DCs) are becoming more and more popular because of their great energy efficiency and straightforward design. In this work, we discuss the control of a PV-based renewable energy system and a battery- and supercapacitor-based energy storage system in a DC microgrid. We describe a hierarchical control approach based on sliding-mode controllers and the Lyapunov stability theory. To balance the load and generation, a fuzzy logic-based energy management system has been created. Using a neural network, maximum power defects for the PV system were determined. The global asymptotic stability of the framework has been verified using Lyapunov stability analysis. In order to simulate the proposed DC microgrid and controllers, MATLAB/SimulinkR (2019a) was utilized. The outcomes show that the system operates effectively with changing production and consumption.

Suggested Citation

  • Khalil Jouili & Mabrouk Jouili & Alsharef Mohammad & Abdulrahman J. Babqi & Walid Belhadj, 2024. "Neural Network Energy Management-Based Nonlinear Control of a DC Micro-Grid with Integrating Renewable Energies," Energies, MDPI, vol. 17(13), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3345-:d:1430923
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    References listed on IDEAS

    as
    1. Divya R. Nair & Manjula G. Nair & Tripta Thakur, 2022. "A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement," Energies, MDPI, vol. 15(15), pages 1-20, July.
    2. Khalil Jouili & Adel Madani, 2023. "Nonlinear Lyapunov Control of a Photovoltaic Water Pumping System," Energies, MDPI, vol. 16(5), pages 1-13, February.
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