Refueling-station location problem under uncertainty

Hosseini, Meysam; MirHassani, S. A.

doi:10.1016/j.tre.2015.10.009

Cited by 70 publications

(37 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FRLM and flow covering problems in general have recently been used in the literature to locate charging stations ( Chung and Kwon, 2015;Hosseini and MirHassani, 2015;Jochem et al, 2015;Wang, 2011;Wang and Lin, 2009;. Along with the ideas behind all these applications of flow based models to the charging station location problem, Nie and Ghamami (2013) identified Level 3 fast charging as necessary to achieve a reasonable level of service in intercity charging station location.…”

Section: Introductionmentioning

confidence: 99%

A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles

Arslan

Karaşan

2016

Transportation Research Part B: Methodological

117

View full text Add to dashboard Cite

The flow refueling location problem (FRLP) locates p stations in order to maximize the flow volume that can be accommodated in a road network respecting the range limitations of the vehicles. This paper introduces the charging station location problem with plug-in hybrid electric vehicles (CSLP-PHEV) as a generalization of the FRLP. We consider not only the electric vehicles but also the plug-in hybrid electric vehicles when locating the stations. Furthermore, we accommodate multiple types of these vehicles with different ranges. Our objective is to maximize the vehicle-miles-traveled using electricity and thereby minimize the total cost of transportation under the existing cost structure between electricity and gasoline. This is also indirectly equivalent to maximizing the environmental benefits. We present an arc-cover formulation and a Benders decomposition algorithm as exact solution methodologies to solve the CSLP-PHEV. The decomposition algorithm is accelerated using Pareto-optimal cut generation schemes. The structure of the formulation allows us to construct the subproblem solutions, dual solutions and nondominated Pareto-optimal cuts as closed form expressions without having to solve any linear programs. This increases the efficiency of the decomposition algorithm by orders of magnitude and the results of the computational studies show that the proposed algorithm both accelerates the solution process and effectively handles instances of realistic size for both CSLP-PHEV and FRLP.

show abstract

Section: Introductionmentioning

confidence: 99%

A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles

Arslan

Karaşan

2016

Transportation Research Part B: Methodological

117

View full text Add to dashboard Cite

show abstract

“…They considered uncertainty in the demand for batteries, loads, and generation of renewable power sources. The research in [22] incorporated uncertainty regarding the traffic flow into both capacitated and uncapacitated versions of a two-stage stochastic model to locate EVCSs. With the objective of maximizing both the miles traveled by EVs and environmental benefits, the research in [1] presented the EVCS problem as an extension of the flow refueling location problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities

Fazeli

Venkatachalam

Chinnam

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. Some of the key random data parameters considered in this study are: EV's dwell time at parking location, battery's state of charge, distance from home, willingness to walk, drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.

show abstract

“…The presented model was more efficient than previous set covering models. Hosseini and MirHassani in [29] considered the traffic flow uncertainty in determining the locations for both permanent and portable refueling stations. Hosseini and MirHassani in [30] considered the waiting time to build recharging stations at capacitated recharging stations for given locations.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal recharging framework and simulation for electric vehicle fleet

Usman

Knapen

Yasar

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

Electrical vehicles are considered sustainable transport alternatives as compared to conventional combustion engine vehicles due to their lower energy consumption and less pollutants production. The increased penetration of electric vehicles in the market depends upon technology to overcome the driving range barrier. That can be achieved by significantly planning the use of available charging stations. In this paper, a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered at home, at work or at quick charging stations in Flanders, Belgium. The proposed procedure plans a charging strategy for each electric vehicle so that entire scheduled tours of the individual can be executed successfully. The simulation starts by activating an agent for each electric vehicle that takes the daily schedule of the driver, registered charging requests at each charging station, and devises a charging strategy that may or may not require a detour to a charging station. Detouring to a charging station causes the time loss that result in the utility drop due to decreased participation in the planned activities. The charging station that leads to the minimum detour travel time, waiting time and recharging time is selected to minimize the time loss. The simulation uses a realistic travel demand predicted by an activity-based model. The results show the percentage of the population that can drive electric vehicle with charging only at home and/or work location; it also shows those who need a stop for recharging at a charging station. The results indicate the use of all charging stations over the day and also the waiting time as function of charging points at each charging station.

show abstract

Refueling-station location problem under uncertainty

Cited by 70 publications

References 34 publications

A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles

A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles

Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities

Optimal recharging framework and simulation for electric vehicle fleet

Contact Info

Product

Resources

About