Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79282
Title: Energy management for EV charging in software-defined green vehicle-to-grid network
Authors: Hu, XX
Wang, K 
Liu, XL 
Sun, YF
Li, P
Guo, S 
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE communications magazine, May 2018, v. 56, no. 5, p. 156-163 How to cite?
Journal: IEEE communications magazine 
Abstract: Vehicle-to-grid (V2G) networks are expected to balance the supply and demand in smart grid by reducing the peak-to-average ratio of power grid load curve. We are entering the era of wireless communication, where we can enjoy various advantages such as lower cost, lower battery consumption, and lower access latency. We believe advanced wireless communication techniques have great potential to further promote the economical deployment of V2G network and lower energy consumption. In this article, we address the green V2G network for efficient energy management. However, we still face many challenging issues even if we exploit the promising wireless communication technique in green V2G networks. For example, it becomes more and more challenging to achieve the efficiency and economy of renewable energy resource allocation due to the increasing number of electric vehicles and limited capacity of local aggregators (LAGs). To address the issues, we consider a software-defined green V2G network for energy management, which consists of three planes: management plane, control plane, and data plane. Specifically, the control plane is aimed at guiding both data flow and energy flow to implement an efficient and economic strategy for energy scheduling, while die data plane collects the information through LAGs for the customized services in the management plane. Additionally, we present an energy management scheme of charging stations as a case study. Simulation results reveal that our proposals could achieve delightful performance on global optimization in the software-defined Green V2G network.
URI: http://hdl.handle.net/10397/79282
ISSN: 0163-6804
DOI: 10.1109/MCOM.2018.1700858
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