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Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System

Author

Listed:
  • Djamila Rekioua

    (Laboratoire de Technologie Industrielle et de l’Information (LTII), Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Toufik Rekioua

    (Laboratoire de Technologie Industrielle et de l’Information (LTII), Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Ahmed Elsanabary

    (Department of Electrical Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Saad Mekhilef

    (Department of Electrical Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia
    School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

Abstract

The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) manages the energy production from different sources, while the power flow method is used to balance the load and renewable power. The study shows that integrating the HMPPT algorithm and power flow approach results in improved system performance, including increased power generation and reduced stress on the batteries. The study also proposes an accurate sizing method to further improve system efficiency. The study demonstrates the effectiveness of the proposed approach by presenting results for twelve different days with varying weather conditions. The results show that the proposed approach effectively manages the energy production and load, resulting in optimal system performance. This study provides valuable insights into the optimal control of hybrid renewable energy systems, and highlights the importance of considering different energy sources and optimal sizing for maximizing system efficiency.

Suggested Citation

  • Djamila Rekioua & Toufik Rekioua & Ahmed Elsanabary & Saad Mekhilef, 2023. "Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System," Energies, MDPI, vol. 16(5), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2286-:d:1082058
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    References listed on IDEAS

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    1. Lu, D. & Fakham, H. & Zhou, T. & François, B., 2010. "Application of Petri nets for the energy management of a photovoltaic based power station including storage units," Renewable Energy, Elsevier, vol. 35(6), pages 1117-1124.
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    9. Ewa Chomać-Pierzecka & Andrzej Kokiel & Joanna Rogozińska-Mitrut & Anna Sobczak & Dariusz Soboń & Jacek Stasiak, 2022. "Analysis and Evaluation of the Photovoltaic Market in Poland and the Baltic States," Energies, MDPI, vol. 15(2), pages 1-16, January.
    10. Khiareddine, Abla & Ben Salah, Chokri & Rekioua, Djamila & Mimouni, Mohamed Faouzi, 2018. "Sizing methodology for hybrid photovoltaic /wind/ hydrogen/battery integrated to energy management strategy for pumping system," Energy, Elsevier, vol. 153(C), pages 743-762.
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    Cited by:

    1. Hasan Hamdan & Sharul Sham Dol & Abdelrahman Hosny Gomaa & Aghyad Belal Al Tahhan & Ahmad Al Ramahi & Haya Fares Turkmani & Mohammad Alkhedher & Rahaf Ajaj, 2023. "Experimental and Numerical Study of Novel Vortex Bladeless Wind Turbine with an Economic Feasibility Analysis and Investigation of Environmental Benefits," Energies, MDPI, vol. 17(1), pages 1-30, December.
    2. Djamila Rekioua, 2023. "Energy Storage Systems for Photovoltaic and Wind Systems: A Review," Energies, MDPI, vol. 16(9), pages 1-26, May.
    3. Vladislav N. Kovalnogov & Ruslan V. Fedorov & Andrei V. Chukalin & Vladimir N. Klyachkin & Vladimir P. Tabakov & Denis A. Demidov, 2024. "Applied Machine Learning to Study the Movement of Air Masses in the Wind Farm Area," Energies, MDPI, vol. 17(16), pages 1-27, August.

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