Enhancing resilience of DC microgrids with model predictive control based hybrid energy storage system

Fuyao Ni, Zixuan Zheng*, Qi Xie, Xianyong Xiao, Yi Zong, Chunjun Huang

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    Due to the renewable energy resources fluctuations, load changes, failures and unplanned disconnection from the utility grid, DC microgrids (DCMGs) may at various risks of different time scale power mismatch and dc bus voltage instability. The hybrid energy storage system (HESS) composed of power-type energy storage and energy-type energy storage devices is considered as a cost-effective measure to enhance the resilience of DCMGs against those disturbances. This paper proposes a fast model predictive control (MPC) based voltage control and power allocation optimization method for HESS. In this MPC controller, only local information in the HESS is utilized, and the DC bus voltage can be regulated quickly by one-step prediction horizon and simplified switching states, which enhances the resilience of DCMGs against various disturbances. Besides, the power allocation command is optimally achieved by the residual capacity triggered activating-sequence of different types of ESSs based on a dynamic voltage control. Several cases conducted in MATLAB/Simulink demonstrate the feasibility and superiority of fast response ability without causing DC bus voltage oscillation. Furthermore, reducing the charge/discharge cycles and rates of the battery effectively extends the service lifetime of HESS.

    Original languageEnglish
    Article number106738
    JournalInternational Journal of Electrical Power and Energy Systems
    Volume128
    Number of pages11
    ISSN0142-0615
    DOIs
    Publication statusPublished - Jun 2021

    Bibliographical note

    Funding Information:
    This work was supported by the National Key Research and Development Project under Grant 2020YFF0305800.

    Funding Information:
    This work was supported by the National Key Research and Development Project under Grant 2020YFF0305800.

    Publisher Copyright:
    © 2020

    Keywords

    • DC microgrid
    • Hybrid energy storage system
    • Model predictive control
    • SMES control

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