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A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique

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  • Elkholy, M.H.
  • Senjyu, Tomonobu
  • Metwally, Hamid
  • Farahat, M.A.
  • Irshad, Ahmad Shah
  • Hemeida, Ashraf M.
  • Lotfy, Mohammed Elsayed

Abstract

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe.

Suggested Citation

  • Elkholy, M.H. & Senjyu, Tomonobu & Metwally, Hamid & Farahat, M.A. & Irshad, Ahmad Shah & Hemeida, Ashraf M. & Lotfy, Mohammed Elsayed, 2024. "A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s096014812301683x
    DOI: 10.1016/j.renene.2023.119768
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