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Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

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Tingyang Meng, Zongli Lin and Yacov A. Shamash, "Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 606-616, Mar. 2021. doi: 10.1109/JAS.2021.1003874
Citation: Tingyang Meng, Zongli Lin and Yacov A. Shamash, "Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 606-616, Mar. 2021. doi: 10.1109/JAS.2021.1003874

Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids

doi: 10.1109/JAS.2021.1003874
Funds:  This work relates to Department of Navy award (N00014-20-1-2858) issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein
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  • The control of battery energy storage systems (BESSs) plays an important role in the management of microgrids. In this paper, the problem of balancing the state-of-charge (SoC) of the networked battery units in a BESS while meeting the total charging/discharging power requirement is formulated and solved as a distributed control problem. Conditions on the communication topology among the battery units are established under which a control law is designed for each battery unit to solve the control problem based on distributed average reference power estimators and distributed average unit state estimators. Two types of estimators are proposed. One achieves asymptotic estimation and the other achieves finite time estimation. We show that, under the proposed control laws, SoC balancing of all battery units is achieved and the total charging/discharging power of the BESS tracks the desired power. A simulation example is shown to verify the theoretical results.

     

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    Highlights

    • Balancing of the state-of-charge of networked battery units is considered.
    • The problem is formulated and solved as a distributed control problem.
    • Distributed average reference power and average unit state estimators are built.
    • Asymptotic and finite time estimators are proposed and their performance analyzed.
    • Simulation results illustrate the effectiveness of the proposed control algorithms.

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