Scheduling of EV Battery Swapping-Part II: Distributed Solutions

Pengcheng You, Steven Low, Liang Zhang, Ruilong Deng, Georgios B. Giannakis, Youxian Sun, Zaiyue Yang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In Part I of this paper, we formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs' travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. We propose there a centralized solution based on second-order cone programming relaxation of optimal power flow and generalized Benders decomposition that is applicable when global information is available. In this paper, we propose two distributed solutions based on the alternating direction method of multipliers and dual decomposition, respectively, that are suitable for systems where the distribution grid, stations, and EVs are managed by separate entities. Our algorithms allow these entities to make individual decisions, but coordinate through privacy-preserving information exchanges to solve a convex relaxation of the global problem. We present simulation results to show that both algorithms converge quickly to a solution that is close to optimum after discretization.

Original languageEnglish (US)
Article number8110720
Pages (from-to)1920-1930
Number of pages11
JournalIEEE Transactions on Control of Network Systems
Volume5
Issue number4
DOIs
StatePublished - Dec 2018

Bibliographical note

Funding Information:
Manuscript received June 25, 2017; revised October 3, 2017; accepted October 26, 2017. Date of publication November 15, 2017; date of current version December 14, 2018. This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR16F030002; in part by the NSF through Grant CCF 1637598, Grant ECCS 1619352 and Grant CNS 1545096; in part by the ARPA-E through Grant DE-AR0000699 and the GRID DATA program; in part by the DTRA through Grant HDTRA 1-15-1-0003; and in part by the Alberta Innovates—Technology Futures (AITF) postdoctoral fellowship. Recommended by Associate Editor L. Schenato. (Corresponding author: Zaiyue Yang.) P. You and Y. Sun are with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China (e-mail: pcyou@zju.edu.cn; yxsun@iipc.zju.edu.cn).

Funding Information:
This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR16F030002; in part by the NSF through Grant CCF 1637598, Grant ECCS 1619352 and Grant CNS 1545096; in part by the ARPA-E through Grant DE-AR0000699 and the GRID DATA program; in part by the DTRA through Grant HDTRA 1-15-1-0003; and in part by the Alberta Innovates - Technology Futures (AITF) postdoctoral fellowship.

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Distributed algorithms
  • electric vehicle (EV)
  • joint battery swapping
  • optimal power flow (OPF)

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