Scheduling of EV Battery Swapping-Part I: Centralized Solution

• Published in: IEEE transactions on control of network systems Vol. 5; no. 4; pp. 1887 - 1897
• Main Authors: You, Pengcheng, Low, Steven H., Tushar, Wayes, Geng, Guangchao, Yuen, Chau, Yang, Zaiyue, Sun, Youxian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> DistFlow</tex-math> </inline-formula> </named-content> equations; Algorithms; Alternating current; Batteries; Battery swapping; Benders decomposition; Computer simulation; convex relaxation; electric vehicle (EV); Electric vehicles; Electricity pricing; Flow equations; generalized Benders decomposition; Mathematical model; Optimal scheduling; Optimization; Power flow; Production scheduling; Public transportation; Rechargeable batteries; Scheduling; State of charge
• ISSN: 2325-5870; 2372-2533
• DOI: 10.1109/TCNS.2017.2773025

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery 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. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information.