Data-Driven Charging Strategy of PEVs under Transformer Aging Risk

Chaojie Li; Chen Liu; Ke Deng; Xinghuo Yu; Tingwen Huang

doi:10.1109/TCST.2017.2713321

Data-Driven Charging Strategy of PEVs under Transformer Aging Risk

Chaojie Li
, Chen Liu
, Ke Deng
, Xinghuo Yu
, Tingwen Huang

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

52 Citations (Scopus)

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 risk-averse 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 mechanism-based 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. © 1993-2012 IEEE.

Original language	English
Pages (from-to)	1386-1399
Journal	IEEE Transactions on Control Systems Technology
Volume	26
Issue number	4
DOIs	https://doi.org/10.1109/TCST.2017.2713321
Publication status	Published - 1 Jul 2018
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the National Priorities Research Program from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 9-166-1-031, in part by the ARC Discovery Projects under Project DP140100544 and Project DP0102303, in part by the Research Foundation of Key Laboratory of Machine Perception, and in part by the Children’s Intelligence Development Fund CQUE under Grant 16xjpt07.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Conditional value at risk (CVaR)
data-driven
plug-in electric vehicle (PEV)
risk-averse decision
stochastic game

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title = "Data-Driven Charging Strategy of PEVs under Transformer Aging Risk",

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 risk-averse 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 mechanism-based 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. {\textcopyright} 1993-2012 IEEE.",

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author = "Chaojie Li and Chen Liu and Ke Deng and Xinghuo Yu and Tingwen Huang",

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AU - Yu, Xinghuo

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AB - 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 risk-averse 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 mechanism-based 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. © 1993-2012 IEEE.

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KW - data-driven

KW - plug-in electric vehicle (PEV)

KW - risk-averse decision

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JO - IEEE Transactions on Control Systems Technology

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ER -

Data-Driven Charging Strategy of PEVs under Transformer Aging Risk

Abstract

Bibliographical note

Funding

UN SDGs

Research Keywords

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