期刊
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
卷 5, 期 4, 页码 1887-1897出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCNS.2017.2773025
关键词
Battery swapping; convex relaxation; Dist-Flow equations; electric vehicle (EV); generalized Benders decomposition
资金
- Zhejiang Provincial Natural Science Foundation of China [LR16F030002]
- NSF [CCF 1637598, ECCS 1619352, CNS 1545096]
- ARPA-E [DE-AR0000699]
- GRID DATA program
- DTRA [HDTRA 1-15-1-0003]
- Advance Queensland Research Fellowship [AQRF11016-17RD2]
- State of Queensland through the Department of Science, Information Technology and Innovation
- University of Queensland
- Redback Technologies
- NSFC [61750110529]
- SUTD-MIT International Design Center
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.
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