Joint Route Selection and Charging Discharging Scheduling of EVs in V2G Energy Network
Liu, P; Wang, C; Hu, J; et al.Fu, T; Cheng, N; Zhang, N; Shen, X
Date: 20 August 2020
Journal
IEEE Transactions on Vehicular Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
Thanks to the advantages of zero carbon dioxide
emissions and low operation cost, the number of on-road electric
vehicles (EVs) is expected to keep increasing. They usually
get charged through charging stations powered by either the
grid or renewable plants. Due to the potential difference in
electricity price between the grid and ...
Thanks to the advantages of zero carbon dioxide
emissions and low operation cost, the number of on-road electric
vehicles (EVs) is expected to keep increasing. They usually
get charged through charging stations powered by either the
grid or renewable plants. Due to the potential difference in
electricity price between the grid and the renewable plants,
an EV may purchase electricity at charging stations powered
by renewable plants, and then discharge the surplus energy
in the battery to the grid, to gain profits and enhance the
overall renewable energy utilization. In this work, we aim to
optimize the route selection and charging/discharging scheduling
to improve the overall economic profits of EVs, taking into
account the constraints, including the time-varying energy supply
caused by the intermittent generation of renewable energy, the
limited number of charging piles in a charging station, and the
traveling delay tolerance of EVs. Firstly, a time-expanded vehicleto-grid graph is designed to model the objective and related
constraints. Then, we apply an AI-based A* algorithm to find
K-shortest paths for each EV. Finally, a joint routing selection and
charging/discharging algorithm, namely, K-Shortest-Paths-JointRouting-Scheduling (KSP-JRS), is proposed to minimize the total
cost of EVs by maximizing their revenue from energy discharging
under time constraints. The proposed approach is evaluated
using the real traffic map around Santa Clara, California. The
simulation, with different numbers of testing EVs, shows the
feasibility and superiority of the proposed algorithm.
Computer Science
Faculty of Environment, Science and Economy
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