A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
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
More Information
  • 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.

     

  • loading
  • [1]
    H. Chen, T. N. Cong, W. Yang, C. Tan, Y. Li, and Y. Ding, “Progress in electrical energy storage system: A critical review,” Progress in Natural Science, vol. 19, no. 3, pp. 291–312, 2009. doi: 10.1016/j.pnsc.2008.07.014
    [2]
    M. T. Lawder, B. Suthar, P. W. Northrop, S. De, C. M. Hoff, O. Leitermann, M. L. Crow, S. Santhanagopalan, and V. R. Subramanian, “Battery energy storage system (bess) and battery management system (bms) for grid-scale applications,” Proc. the IEEE, vol. 102, no. 6, pp. 1014–1030, 2014. doi: 10.1109/JPROC.2014.2317451
    [3]
    C. A. Hill, M. C. Such, D. Chen, J. Gonzalez, and W. M. Grady, “Battery energy storage for enabling integration of distributed solar power generation,” IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 850–857, 2012. doi: 10.1109/TSG.2012.2190113
    [4]
    B. M. Gundogdu, S. Nejad, D. T. Gladwin, M. P. Foster, and D. A. Stone, “A battery energy management strategy for UK enhanced frequency response and triad avoidance,” IEEE Trans. Industrial Electronics, vol. 65, no. 12, pp. 9509–9517, 2018. doi: 10.1109/TIE.2018.2818642
    [5]
    T. Feehally, A. Forsyth, R. Todd, M. Foster, D. Gladwin, D. Stone, and D. Strickland, “Battery energy storage systems for the electricity grid: UK research facilities,” in Proc. the 8th IET Int. Conf. on Power Electronics, Machines and Drives (PEMD), IET, Glasgow, UK, 2016.
    [6]
    H. Rahimi-Eichi, U. Ojha, F. Baronti, and M. Y. Chow, “Battery management system: An overview of its application in the smart grid and electric vehicles,” IEEE Industrial Electronics Magazine, vol. 7, no. 2, pp. 4–16, 2013. doi: 10.1109/MIE.2013.2250351
    [7]
    H. Cai and G. Hu, “Distributed control scheme for package-level stateof-charge balancing of grid-connected battery energy storage system,” IEEE Trans. Industrial Informatics, vol. 12, no. 5, pp. 1919–1929, 2016. doi: 10.1109/TII.2016.2601904
    [8]
    T. Morstyn, A. V. Savkin, B. Hredzak, and V. G. Agelidis, “Multiagent sliding mode control for state of charge balancing between battery energy storage systems distributed in a DC microgrid,” IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 4735–4743, 2017.
    [9]
    Y. Xu, Z. Li, J. Zhao, and J. Zhang, “Distributed robust control strategy of grid-connected inverters for energy storage systems state-of-charge balancing,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 5907–5917, 2017.
    [10]
    L. Xing, Y. Mishra, Y. C. Tian, G. Ledwich, C. Zhou, W. Du, and F. Qian, “Distributed state-of-charge balance control with event-triggered signal transmissions for multiple energy storage systems in smart grid,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 49, no. 8, pp. 1601–1611, 2019. doi: 10.1109/TSMC.2019.2916152
    [11]
    L. Xing, X. Qianwen, C. Wen, Y. C. Tian, Y. Mishra, G. Ledwich, and Y. D. Song, “Robust event-triggered dynamic average consensus against communication link failures with application to battery control,” IEEE Trans. Control of Network Systems, vol. 7, no. 3, pp. 1559–1570, 2020.
    [12]
    J. Cao, N. Schofield, and A. Emadi, “Battery balancing methods: A comprehensive review,” in Proc. IEEE Vehicle Power and Propulsion Conf.. IEEE, 2008, pp. 1–6.
    [13]
    R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proc. the IEEE, vol. 95, no. 1, pp. 215–233, 2007. doi: 10.1109/JPROC.2006.887293
    [14]
    R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Trans. Automatic Control, vol. 49, no. 9, pp. 1520–1533, 2004. doi: 10.1109/TAC.2004.834113
    [15]
    J. A. Fax and R. M. Murray, “Information flow and cooperative control of vehicle formations,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1465–1476, 2004. doi: 10.1109/TAC.2004.834433
    [16]
    W. Ren and R. W. Beard, “Consensus seeking in multiagent systems under dynamically changing interaction topologies,” IEEE Trans. Automatic Control, vol. 50, no. 5, pp. 655–661, 2005. doi: 10.1109/TAC.2005.846556
    [17]
    H. Liang, H. Zhang, Z. Wang, and J. Wang, “Consensus robust output regulation of discrete-time linear multi-agent systems,” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 2, pp. 204–209, 2014. doi: 10.1109/JAS.2014.7004551
    [18]
    A. Elahi, A. Alfi, and H. Modares, “H consensus control of discrete-time multi-agent systems under network imperfections and external disturbance,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 3, pp. 667–675, 2019. doi: 10.1109/JAS.2019.1911474
    [19]
    C. Deng, W. Gao, and W. Che, “Distributed adaptive fault-tolerant output regulation of heterogeneous multi-agent systems with coupling uncertainties and actuator faults,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 4, pp. 1098–1106, 2020. doi: 10.1109/JAS.2020.1003258
    [20]
    N. M. L. Tan, T. Abe, and H. Akagi, “Design and performance of a bidirectional isolated DC–DC converter for a battery energy storage system,” IEEE Trans. Power Electronics, vol. 27, no. 3, pp. 1237–1248, 2011.
    [21]
    G. Hu, “Robust consensus tracking of a class of second-order multiagent dynamic systems,” Systems &Control Letters, vol. 61, no. 1, pp. 134–142, 2012.
    [22]
    S. S. Kia, J. Cortés, and S. Martinez, “Dynamic average consensus under limited control authority and privacy requirements,” Int. Journal of Robust and Nonlinear Control, vol. 25, no. 13, pp. 1941–1966, 2015. doi: 10.1002/rnc.3178
    [23]
    J. George, R. A. Freeman, and K. M. Lynch, “Robust dynamic average consensus algorithm for signals with bounded derivatives,” in Proc. American Control Conf. (ACC), IEEE, 2017, pp. 352–357.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(25)

    Article Metrics

    Article views (2257) PDF downloads(136) Cited by()

    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.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return