Coordinating Electric Vehicle Flow Distribution and Charger Allocation by Joint Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Original languageEnglish
Journal / PublicationIEEE Transactions on Industrial Informatics
Online published15 Feb 2021
Publication statusOnline published - 15 Feb 2021

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. Additionally, the two-stage model designates charging resource sufficiency by estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources.

Research Area(s)

  • charger allocation, Charging stations, convex optimization, Electric vehicle (EV), Informatics, Manganese, Optimization, Resource management, Roads, Stochastic processes, traffic flow distribution, two-stage stochastic programming