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Behavioral Modeling Paradigm for DC Nanogrid Based Distributed Energy Systems

Author

Listed:
  • Muhammad Saad

    (Department of Automation, School of Electronic and Control Engineering, Chang’an University, Xi’an 710072, China)

  • Yongfeng Ju

    (Department of Automation, School of Electronic and Control Engineering, Chang’an University, Xi’an 710072, China)

  • Husan Ali

    (Department of Electrical Engineering, Air University, Aerospace & Aviation Campus, Kamra 43570, Pakistan)

  • Sami Ullah Jan

    (Department of Electrical Engineering, University of Engineering & Technology, Peshawar 25120, Pakistan)

  • Dawar Awan

    (Department of Electrical Technology, University of Technology, Nowshera 24100, Pakistan)

  • Shahbaz Khan

    (Department of Electrical Engineering, Air University, Aerospace & Aviation Campus, Kamra 43570, Pakistan)

  • Abdul Wadood

    (Department of Electrical Engineering, Air University, Aerospace & Aviation Campus, Kamra 43570, Pakistan)

  • Bakht Muhammad Khan

    (Department of Electrical Engineering, Air University, Aerospace & Aviation Campus, Kamra 43570, Pakistan)

  • Akhtar Ali

    (Department of Electrical Engineering, University of Engineering & Technology, Peshawar 25120, Pakistan)

  • Tahir Khurshaid

    (Department of Electrical Engineering, Yeungnam University, 280, Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Korea)

Abstract

The remarkable progress of power electronic converters (PEC) technology has led to their increased penetration in distributed energy systems (DES). Particularly, the direct current (dc) nanogrid-based DES embody a variety of sources and loads, connected through a central dc bus. Therefore, PECs are required to be employed as an interface. It would facilitate incorporation of the renewable energy sources and battery storage system into dc nanogrids. However, it is more challenging as the integration of multiple modules may cause instabilities in the overall system dynamics. Future dc nanogrids are envisioned to deploy ready-to-use commercial PEC, for which designers have no insight into their dynamic behavior. Furthermore, the high variability of the operating conditions constitute a new paradigm in dc nanogrids. It exacerbates the dynamic analysis using traditional techniques. Therefore, the current work proposes behavioral modeling to perform system level analysis of a dc nanogrid-based DES. It relies only on the data acquired via measurements performed at the input–output terminals only. To verify the accuracy of the model, large signal disturbances are applied. The matching of results for the switch model and its behavioral model verifies the effectiveness of the proposed model.

Suggested Citation

  • Muhammad Saad & Yongfeng Ju & Husan Ali & Sami Ullah Jan & Dawar Awan & Shahbaz Khan & Abdul Wadood & Bakht Muhammad Khan & Akhtar Ali & Tahir Khurshaid, 2021. "Behavioral Modeling Paradigm for DC Nanogrid Based Distributed Energy Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7904-:d:687542
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    References listed on IDEAS

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    1. Maria Fotopoulou & Dimitrios Rakopoulos & Dimitrios Trigkas & Fotis Stergiopoulos & Orestis Blanas & Spyros Voutetakis, 2021. "State of the Art of Low and Medium Voltage Direct Current (DC) Microgrids," Energies, MDPI, vol. 14(18), pages 1-27, September.
    2. Burmester, Daniel & Rayudu, Ramesh & Seah, Winston & Akinyele, Daniel, 2017. "A review of nanogrid topologies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 760-775.
    3. Liyuan Gao & Yao Liu & Huisong Ren & Josep M. Guerrero, 2017. "A DC Microgrid Coordinated Control Strategy Based on Integrator Current-Sharing," Energies, MDPI, vol. 10(8), pages 1-17, August.
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