Decentralized Charging Coordination of Large-Population PEVs Under a Hierarchical Structure

  • Zhongjing MaEmail author


Centralized or decentralized charging schedules of large-scale PEVs coordinated by a system operator usually require significant management, computation and communication capabilities on the system. Alternatively in this chapter, it constructs a hierarchical model for the PEV charging coordination problems where a collection of agents are introduced between the system operator and individual vehicles, and proposes an off-line decentralized method for the constructed hierarchical optimization problems. Under the decentralized method, each PEV implements its best behavior with respect to a given local charging price curve set by its agent. Each agent submits the collected aggregated charging behaviors under this agent to the system operator who then updates the electricity generation price and broadcasts it to PEVs via agents. To reimburse the transaction operation costs on agents, the charging price on the PEVs under an agent comprises both the generation price broadcasted from the system operator and the operation price set by this agent. It is shown that, under certain conditions, the proposed dynamical procedure converges to the efficient (or socially optimal) solution. The proposed method under the hierarchical structure presents the advantage of the autonomy of the individual PEVs and the low computation and communication capability requirements on the system.


  1. 1.
    O. Sundstrom, C. Binding, Planning electric-drive vehicle charging under constrained grid conditions. Technical Report (IBM - Zurich, Switzerland, 2010)Google Scholar
  2. 2.
    K. Clement-Nyns, E. Haesen, J. Driesen, The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans. Power Syst. 25(1), 371–380 (2010)Google Scholar
  3. 3.
    R. Hermans, M. Almassalkhi, I.A. Hiskens. Incentive-based coordinated charging control of plug-in electric vehicles at the distribution-transformer level, in American Control Conference (ACC), Montreal, Canada (2012), pp. 264–269Google Scholar
  4. 4.
    M.D. Galus, G. Andersson, Demand management of grid connected plug-in hybrid electric vehicles (PHEV), in IEEE Energy 2030, Atlanta, Georgia, 17–18 November 2008Google Scholar
  5. 5.
    Z. Li, Q. Guo, H. Sun, S. Xin, and J. Wang. A new real-time smart-charging method considering expected electric vehicle fleet connections. IEEE Trans. Power Syst., pp(99), 1–2 (2014)Google Scholar
  6. 6.
    S. Wang, L. Han, D. Wang, M. Shahidehpour, Z. Li, Hierarchical charging management strategy of plug-in hybrid electric vehicles to provide regulation service, in 2012 3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe) (IEEE, Berlin, 2012), pp. 1–6Google Scholar
  7. 7.
    W. Qi, Z. Xu, Z.J.M. Shen, Z. Hu, Y. Song, Hierarchical coordinated control of plug-in electric vehicles charging in multifamily dwellings. IEEE Trans. Smart Grid 5(3), 1465–1474 (2014)Google Scholar
  8. 8.
    W. Yao, J. Zhao, F. Wen, Y. Xue, G. Ledwich, A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles. IEEE Trans. Power Syst. 28(3), 2768–2778 (2013)Google Scholar
  9. 9.
    W. Tang, R. Jain, Hierarchical auction mechanisms for network resource allocation. IEEE J. Sel. Areas Commun. 30(11), 2117–2125 (2012)Google Scholar
  10. 10.
    F. Kelly, A. Maulloo, D. Tan, Rate control for communication networks: shadow prices, proportional fairness and stability. J. Oper. Res. Soc. 49(3), 237–252 (1998)Google Scholar
  11. 11.
    Zhong Fan, A distributed demand response algorithm and its application to PHEV charging in smart grids. IEEE Trans. Smart Grid 3(3), 1280–1290 (2012)Google Scholar
  12. 12.
    S. Bashash, S.J. Moura, J.C. Forman, H.K. Fathy, Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity. J. Power Sources 196(1), 541–549 (2011)Google Scholar
  13. 13.
    J. Wang, P. Liu, J. Hicks-Garner, E. Sherman, S. Soukiazian, M. Verbrugge, H. Tataria, J. Musser, P. Finamore, Cycle-life model for graphite-LiFePO\(_4\) cells. J. Power Sources 194(8), 3942–3948 (2011)Google Scholar
  14. 14.
    K.W.E. Cheng, B.P. Divakar, H. Wu, K. Ding, H.F. Ho, Battery-management system (BMS) and SOC development for electrical vehicles. IEEE Trans. Veh. Technol. 60(1), 76–88 (2011)Google Scholar
  15. 15.
    S. Boyd, L. Vandenberghe, Convex optimization (Cambridge University Press, Cambridge, 2004)Google Scholar
  16. 16.
    Z. Ma, D.S. Callaway, I.A. Hiskens, Decentralized charging control of large populations of plug-in electric vehicles. IEEE Trans. Control Syst. Technol. 21(1), 67–78 (2013)Google Scholar
  17. 17.
    L. Gan, U. Topcu, S.H. Low, Optimal decentralized protocol for electric vehicle charging. IEEE Trans. Power Syst. 28(2), 940–951 (2013)Google Scholar
  18. 18.
    D. Bertsekas, Dynamic Programming and Optimal Control, vol. I (Athena Scientific, Singapore, 1995)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of AutomationBeijing Institute of TechnologyBeijingChina

Personalised recommendations