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Coordinating electric vehicle flow distribution and charger allocation by joint optimization

Coordinating electric vehicle flow distribution and charger allocation by joint optimization
Coordinating electric vehicle flow distribution and charger allocation by joint optimization
A two-stage stochastic programming model is established to minimize EV’s expected total journey time under stochastic traffic conditions, by jointly optimizing the allocation of chargers and the distribution of EV flows. Based on sample average approximation, a feasible deterministic equivalent of the original stochastic problem is obtained. Then, a hybrid solution method, composing of a Tabu-based search and sequential quadratic programming (SQP), is proposed. The Tabu heuristic manages the charger allocation problem, where each solution candidate undergoes a second-stage EV flow optimization. SQP is applied to optimially distribute the EV flows, which is proved to be a convex problem. Extensive simulations are carried out using the eastern Massachusetts highway network. Results show that the proposed algorithm outperforms existing approaches by finding high-quality charger allocation and optimal flow distribution policies under different traffic conditions. Additionally, the two-stage model designates charging resource sufficiency by statistically estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources.
Electric vehicle (EV), traffic flow distribution, charger allocation, two-stage stochastic programming, convex optimization
1941-0050
Bi, Xiaowen
6f314c97-3283-4439-a3ea-3444d4f70cd7
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340
Tang, Wallace K.S.
a6174fc4-3efe-4e99-8858-57d213f9f9e3
Bi, Xiaowen
6f314c97-3283-4439-a3ea-3444d4f70cd7
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340
Tang, Wallace K.S.
a6174fc4-3efe-4e99-8858-57d213f9f9e3

Bi, Xiaowen, Chipperfield, Andrew and Tang, Wallace K.S. (2021) Coordinating electric vehicle flow distribution and charger allocation by joint optimization. IEEE Transactions on Industrial Informatics. (doi:10.1109/TII.2021.3059288).

Record type: Article

Abstract

A two-stage stochastic programming model is established to minimize EV’s expected total journey time under stochastic traffic conditions, by jointly optimizing the allocation of chargers and the distribution of EV flows. Based on sample average approximation, a feasible deterministic equivalent of the original stochastic problem is obtained. Then, a hybrid solution method, composing of a Tabu-based search and sequential quadratic programming (SQP), is proposed. The Tabu heuristic manages the charger allocation problem, where each solution candidate undergoes a second-stage EV flow optimization. SQP is applied to optimially distribute the EV flows, which is proved to be a convex problem. Extensive simulations are carried out using the eastern Massachusetts highway network. Results show that the proposed algorithm outperforms existing approaches by finding high-quality charger allocation and optimal flow distribution policies under different traffic conditions. Additionally, the two-stage model designates charging resource sufficiency by statistically estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources.

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TII_R1_v3 - Accepted Manuscript
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Accepted/In Press date: 7 February 2021
e-pub ahead of print date: 15 February 2021
Keywords: Electric vehicle (EV), traffic flow distribution, charger allocation, two-stage stochastic programming, convex optimization

Identifiers

Local EPrints ID: 447508
URI: http://eprints.soton.ac.uk/id/eprint/447508
ISSN: 1941-0050
PURE UUID: cdb9f1d8-e157-44aa-bf86-f0d392b36ddc
ORCID for Andrew Chipperfield: ORCID iD orcid.org/0000-0002-3026-9890

Catalogue record

Date deposited: 12 Mar 2021 17:35
Last modified: 13 Mar 2021 02:38

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Contributors

Author: Xiaowen Bi
Author: Wallace K.S. Tang

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