Data-driven charging strategy of PEVs under transformer aging risk

Li, C, Liu, C, Deng, K, Yu, X and Huang, T 2018, 'Data-driven charging strategy of PEVs under transformer aging risk', IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1386-1399.

Document type: Journal Article
Collection: Journal Articles

Title Data-driven charging strategy of PEVs under transformer aging risk
Author(s) Li, C
Liu, C
Deng, K
Yu, X
Huang, T
Year 2018
Journal name IEEE Transactions on Control Systems Technology
Volume number 26
Issue number 4
Start page 1386
End page 1399
Total pages 14
Publisher IEEE
Abstract Big data analytics and plug-in electric vehicle (PEV) are the important elements of smart grids in the future. This paper introduces a data-driven charging strategy for PEV-based taxis, where driving behaviors of taxis and load profiles of buildings are characterized by data analysis to make the riskaverse decision on PEV charging. First, the framework of data driven risk-averse PEV charging is introduced, where a stochastic game model is proposed. Specifically, the pricing mechanismbased charging cost and the conditional value-at-risk (CVaR) measurement are used to determine the objective function for each PEV. Then, the existence of a generalized Nash equilibrium and its seeking algorithm is studied. The convergence analysis of the algorithm is also given. Second, the big data analysis of the statistical information about PEV-based taxis and the load profile of buildings are presented by applying various data process techniques. Finally, the performance of the method is numerically illustrated by the case study via real global positioning system information of 490 PEV-based taxis and the smart meter data from local commercial buildings.
Subject Control Systems, Robotics and Automation
Keyword(s) Conditional value at risk (CVaR)
plug-in electric vehicle (PEV)
risk-averse decision
stochastic game
DOI - identifier 10.1109/TCST.2017.2713321
Copyright notice © 2017 IEEE
ISSN 1558-0865
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