Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/73889
Title: Robust planning of electric vehicle charging facilities with advanced evaluation method
Authors: Wang, G
Zhang, X 
Wang, H
Peng, J
Jiang, H
Liu, Y
Wu, C
Xu, Z 
Liu, W
Keywords: Data envelopment analysis
Data envelopment analysis
Electric vehicle
Load modeling
Multistage charging facility planning
Planning
Robustness
Smart grids
State of charge
Uncertainty
Uncertainty
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on industrial informatics, 2017, p. 2 How to cite?
Journal: IEEE transactions on industrial informatics 
Abstract: The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multi-stage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multi-stage planning. Then, the well-established data envelopment analysis (DEA) is utilized to evaluate the planning schemes, while the novel EV Expected Energy Not Supplied (EVEENS) cost is defined to measure the service ability of charging facilities. The final planning result obtained by the proposed framework will not only have good performance in the current stage, but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.
URI: http://hdl.handle.net/10397/73889
ISSN: 1551-3203
EISSN: 1941-0050
DOI: 10.1109/TII.2017.2748342
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