Title :
A stochastic flow capturing location and allocation model for siting electric vehicle charging stations
Author :
Jingzi Tan ; Wei-Hua Lin
Author_Institution :
Analytics Anal. &Optimization, IBM, Chicago, IL, USA
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.
Keywords :
electric vehicles; stochastic processes; EV charging stations; allocation model; backup flow capturing model; demand uncertainty; deterministic flow capturing location-allocation problem; electric vehicle charging stations; service coverage; stochastic flow capturing location; stochastic model; transportation network; Buildings; Charging stations; Electric vehicles; Linear programming; Resource management; Stochastic processes;
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on
Conference_Location :
Qingdao
DOI :
10.1109/ITSC.2014.6958140