期刊
IEEE TRANSACTIONS ON SMART GRID
卷 11, 期 4, 页码 3291-3301出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2020.2967082
关键词
Transportation; Routing; Computational modeling; Charging stations; Numerical models; Power grids; Plug-in electric vehicle; routing; charging navigation; transportation; power system
资金
- National Natural Science Foundation of China [U1766205]
- Guangdong Natural Science Foundation, China [2019A1515012023]
- Science and Technology Development Fund, Macau [SKL-IOTSC-2018-2020]
- University of Macau [SRG2019-00162-IOTSC]
Increasing plug-in electric vehicle (PEV) charging stations are coupling the power and transportation systems tightly together. The distribution of PEV traffic flows will be constrained by and inversely affect both systems. In this paper, we study the PEV routing problem on a coupled transportation and power network from the perspective of a social coordinator. We formulate an interdisciplinary second order cone programming model that optimizes PEVs' driving paths and charging locations to minimize the system's social costs, which include driving and charging time costs of PEV drivers and power supply costs. The model employs: 1) an expanded transportation network model to explicitly describe PEVs' driving range constraints on the transportation network; 2) the AC power flow model to describe the electrical constraints of the power system. We then design an iterative column generation algorithm to efficiently solve it. We validate the proposed method on a coupled transportation and power network with distributed renewable generation. Numerical simulation results show that routing PEV traffic flows adopting the proposed strategy can effectively improve social welfare and promote renewable generation integration.
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