Distributed approach for temporal: spatial charging coordination of plug-in electric taxi fleet

Zaiyue YANG; Tianci GUO; Pengcheng YOU; Yunhe HOU; S. Joe QIN

doi:10.1109/TII.2018.2879515

Distributed approach for temporal: spatial charging coordination of plug-in electric taxi fleet

Zaiyue YANG^*, Tianci GUO, Pengcheng YOU, Yunhe HOU, S. Joe QIN

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

29 Citations (Scopus)

Abstract

This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance Utilization ratio for CSs, and also flatten Unevenness of charging request for power grid.

Original language	English
Article number	8521690
Pages (from-to)	3185-3195
Number of pages	11
Journal	IEEE Transactions on Industrial Informatics
Volume	15
Issue number	6
DOIs	https://doi.org/10.1109/TII.2018.2879515
Publication status	Published - Jun 2019
Externally published	Yes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China under Grant 61873118 and in part by the Shenzhen Committee on Science and Innovations under Grant GJHZ20180411143603361 and Grant 20160207. Paper no. TII-18-1161.

Funding

This work was supported in part by the Natural Science Foundation of China under Grant 61873118 and in part by the Shenzhen Committee on Science and Innovations under Grant GJHZ20180411143603361 and Grant 20160207. Paper no. TII-18-1161.

Keywords

Backward induction
game-theoretical approach
plug-in electric taxi (PET)
spatial selection
temporal scheduling

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10.1109/TII.2018.2879515

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title = "Distributed approach for temporal: spatial charging coordination of plug-in electric taxi fleet",

abstract = "This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance Utilization ratio for CSs, and also flatten Unevenness of charging request for power grid.",

keywords = "Backward induction, game-theoretical approach, plug-in electric taxi (PET), spatial selection, temporal scheduling",

author = "Zaiyue YANG and Tianci GUO and Pengcheng YOU and Yunhe HOU and QIN, {S. Joe}",

note = "This work was supported in part by the Natural Science Foundation of China under Grant 61873118 and in part by the Shenzhen Committee on Science and Innovations under Grant GJHZ20180411143603361 and Grant 20160207. Paper no. TII-18-1161.",

year = "2019",

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AU - GUO, Tianci

AU - YOU, Pengcheng

AU - HOU, Yunhe

AU - QIN, S. Joe

N1 - This work was supported in part by the Natural Science Foundation of China under Grant 61873118 and in part by the Shenzhen Committee on Science and Innovations under Grant GJHZ20180411143603361 and Grant 20160207. Paper no. TII-18-1161.

PY - 2019/6

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N2 - This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance Utilization ratio for CSs, and also flatten Unevenness of charging request for power grid.

AB - This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance Utilization ratio for CSs, and also flatten Unevenness of charging request for power grid.

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Distributed approach for temporal: spatial charging coordination of plug-in electric taxi fleet

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