A stochastic flow capturing location and allocation model for siting electric vehicle charging stations

Jingzi Tan; Wei Hua Lin

doi:10.1109/ITSC.2014.6958140

A stochastic flow capturing location and allocation model for siting electric vehicle charging stations

Jingzi Tan, Wei Hua Lin

Systems and Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

With the move of electric vehicle (EV) initiatives in many countries, there is a growing demand for fast-charging stations for recharging EVs. In this paper, we consider the problem of siting these EV charging stations in a transportation network with demand uncertainty. The demand for service considered is the passing flows in the network, i.e., the drive-by customers. We started with formulating the problem as a deterministic flow capturing location-allocation problem and then extended it into a stochastic model. Our results show that the stochastic model more realistically capture the actual coverage of the demand. We also developed a backup flow capturing model for providing secondary or multiple facilities coverage to ensure stability in service coverage and reduce the 'range anxiety.' Test cases with different flow composition and cost parameters are examined.

Original language	English (US)
Title of host publication	2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2811-2816
Number of pages	6
ISBN (Print)	9781479960781
DOIs	https://doi.org/10.1109/ITSC.2014.6958140
State	Published - Nov 14 2014
Event	2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 - Qingdao, China Duration: Oct 8 2014 → Oct 11 2014

Other

Other	2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Country	China
City	Qingdao
Period	10/8/14 → 10/11/14

ASJC Scopus subject areas

Computer Science Applications
Automotive Engineering
Mechanical Engineering

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A stochastic flow capturing location and allocation model for siting electric vehicle charging stations. / Tan, Jingzi; Lin, Wei Hua.

2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2811-2816 6958140.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tan, J & Lin, WH 2014, A stochastic flow capturing location and allocation model for siting electric vehicle charging stations. in 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014., 6958140, Institute of Electrical and Electronics Engineers Inc., pp. 2811-2816, 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, Qingdao, China, 10/8/14. https://doi.org/10.1109/ITSC.2014.6958140

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abstract = "With the move of electric vehicle (EV) initiatives in many countries, there is a growing demand for fast-charging stations for recharging EVs. In this paper, we consider the problem of siting these EV charging stations in a transportation network with demand uncertainty. The demand for service considered is the passing flows in the network, i.e., the drive-by customers. We started with formulating the problem as a deterministic flow capturing location-allocation problem and then extended it into a stochastic model. Our results show that the stochastic model more realistically capture the actual coverage of the demand. We also developed a backup flow capturing model for providing secondary or multiple facilities coverage to ensure stability in service coverage and reduce the 'range anxiety.' Test cases with different flow composition and cost parameters are examined.",

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A stochastic flow capturing location and allocation model for siting electric vehicle charging stations

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