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Optimal control and implementation of energy management strategy for a DC microgrid

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  • Ferahtia, Seydali
  • Djeroui, Ali
  • Rezk, Hegazy
  • Houari, Azeddine
  • Zeghlache, Samir
  • Machmoum, Mohamed

Abstract

This paper proposes an optimal energy management strategy (EMS) for DC microgrid. The studied system presents a commercial building power system that combines a photovoltaic array (PV), fuel cell (FC), a battery storage system and a bidirectional DC/AC grid converter. The integration of multiple power sources like renewables leads to techno-economical challenges including power quality, stability, fuel consumption, and efficiency. The proposed EMS is based on the salp swarm algorithm (SSA). This algorithm has been implemented because of considerable advantages such as its convergence properties and its reduced computing complexity. The step-by-step design of the proposed method is detailed. Then HIL tests are performed to validate the proposed EMS performances. The performance of the proposed EMS is compared with the state machine control strategy (SMC) in terms of system efficiency and fuel consumption where the obtained results prove the superiority of the proposed EMS (5.2 % fuel saving). Regarding the power quality, the proposed EMS is compared with EMS based PSO to investigate the optimizer influence, the obtained results confirm the ability of the proposed EMS to provide a superior power quality. Hence, the proposed EMS responds to the power systems challenges including power quality, fuel-saving and efficiency.

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  • Ferahtia, Seydali & Djeroui, Ali & Rezk, Hegazy & Houari, Azeddine & Zeghlache, Samir & Machmoum, Mohamed, 2022. "Optimal control and implementation of energy management strategy for a DC microgrid," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020259
    DOI: 10.1016/j.energy.2021.121777
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    References listed on IDEAS

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    Cited by:

    1. Wenshuai Bai & Dian Wang & Zhongquan Miao & Xiaorong Sun & Jiabin Yu & Jiping Xu & Yuqing Pan, 2023. "The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    2. Hegazy Rezk & Rania M. Ghoniem & Seydali Ferahtia & Ahmed Fathy & Mohamed M. Ghoniem & Reem Alkanhel, 2022. "A Comparison of Different Renewable-Based DC Microgrid Energy Management Strategies for Commercial Buildings Applications," Sustainability, MDPI, vol. 14(24), pages 1-22, December.
    3. Ferahtia, Seydali & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal techno-economic energy management strategy for building’s microgrids based bald eagle search optimization algorithm," Applied Energy, Elsevier, vol. 306(PB).
    4. Nassef, Ahmed M. & Houssein, Essam H. & Helmy, Bahaa El-din & Rezk, Hegazy, 2022. "Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization," Energy, Elsevier, vol. 254(PA).
    5. Hegazy Rezk & A. G. Olabi & Enas Taha Sayed & Tabbi Wilberforce, 2023. "Role of Metaheuristics in Optimizing Microgrids Operating and Management Issues: A Comprehensive Review," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    6. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).
    7. Aissa Benhammou & Mohammed Amine Hartani & Hamza Tedjini & Hegazy Rezk & Mujahed Al-Dhaifallah, 2023. "Improvement of Autonomy, Efficiency, and Stress of Fuel Cell Hybrid Electric Vehicle System Using Robust Controller," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    8. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).

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