Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Ming Zeng; Supeng Leng; Jianhua He; Yan Zhang; Guanhua Qiao

doi:10.1109/ICCChina.2016.7636887

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Ming Zeng, Supeng Leng^*, Jianhua He, Yan Zhang, Guanhua Qiao

^*Corresponding author for this work

Adaptive Communications Networks Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

The frequency, time and places of charging have large impact on the Quality of Experience (QoE) of EV drivers. It is critical to design effective EV charging scheduling system to improve the QoE of EV drivers. In order to improve EV charging QoE and utilization of CSs, we develop an innovative travel plan aware charging scheduling scheme for moving EVs to be charged at Charging Stations (CS). In the design of the proposed charging scheduling scheme for moving EVs, the travel routes of EVs and the utility of CSs are taken into consideration. The assignment of EVs to CSs is modeled as a two-sided many-to-one matching game with the objective of maximizing the system utility which reflects the satisfactory degrees of EVs and the profits of CSs. A Stable Matching Algorithm (SMA) is proposed to seek stable matching between charging EVs and CSs. Furthermore, an improved Learning based On-LiNe scheduling Algorithm (LONA) is proposed to be executed by each CS in a distributed manner. The performance gain of the average system utility by the SMA is up to 38.2% comparing to the Random Charging Scheduling (RCS) algorithm, and 4.67% comparing to Only utility of Electric Vehicle Concerned (OEVC) scheme. The effectiveness of the proposed SMA and LONA is also demonstrated by simulations in terms of the satisfactory ratio of charging EVs and the the convergence speed of iteration.

Original language	English
Title of host publication	2016 IEEE/CIC International Conference on Communications in China, ICCC 2016
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-5090-2143-7
DOIs	https://doi.org/10.1109/ICCChina.2016.7636887
Publication status	Published - 21 Oct 2016
Event	2016 IEEE/CIC International Conference on Communications in China - Chengdu, China Duration: 27 Jul 2016 → 29 Jul 2016

Conference

Conference	2016 IEEE/CIC International Conference on Communications in China
Abbreviated title	ICCC 2016
Country/Territory	China
City	Chengdu
Period	27/07/16 → 29/07/16

Bibliographical note

-© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

on-line scheduling
stable matching
traveling plan aware
vehicle-to-grid

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10.1109/ICCChina.2016.7636887

Travel plan aware charging algorithms in V2G smart grid networks
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 157 KB

http://ieeexplore.ieee.org/document/7636887/

Cite this

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abstract = "The frequency, time and places of charging have large impact on the Quality of Experience (QoE) of EV drivers. It is critical to design effective EV charging scheduling system to improve the QoE of EV drivers. In order to improve EV charging QoE and utilization of CSs, we develop an innovative travel plan aware charging scheduling scheme for moving EVs to be charged at Charging Stations (CS). In the design of the proposed charging scheduling scheme for moving EVs, the travel routes of EVs and the utility of CSs are taken into consideration. The assignment of EVs to CSs is modeled as a two-sided many-to-one matching game with the objective of maximizing the system utility which reflects the satisfactory degrees of EVs and the profits of CSs. A Stable Matching Algorithm (SMA) is proposed to seek stable matching between charging EVs and CSs. Furthermore, an improved Learning based On-LiNe scheduling Algorithm (LONA) is proposed to be executed by each CS in a distributed manner. The performance gain of the average system utility by the SMA is up to 38.2% comparing to the Random Charging Scheduling (RCS) algorithm, and 4.67% comparing to Only utility of Electric Vehicle Concerned (OEVC) scheme. The effectiveness of the proposed SMA and LONA is also demonstrated by simulations in terms of the satisfactory ratio of charging EVs and the the convergence speed of iteration.",

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Matching theory based travel plan aware charging algorithms in V2G smart grid networks

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Keywords

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