A Model for Location of Capacitated Alternative‐Fuel Stations

Upchurch, Christopher; Kuby, Michael; Lim, Seow

doi:10.1111/j.1538-4632.2009.00744.x

Cited by 251 publications

(109 citation statements)

References 14 publications

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“…In its current form, our methodology does not accommodate for the capacities of the refueling stations which is in the scope of recent works such as Upchurch, Kuby, and Lim (2009a) and Jung et al (2014). Driver's different preferences such as stopping tolerance can also be accommodated into this framework and the number of refueling stops on a given path might also be limited by a constant.…”

Section: Discussionmentioning

confidence: 99%

“…The presented example oversees capacity issues related to stations. Capacitated refueling stations are within the scope of recent studies such as Upchurch, Kuby, and Lim (2009b) and Jung, Chow, Jayakrishnan, and Park (2014). Though not within our scope, the flexibility provided by non-simple paths might prove useful in capacitated networks as well.…”

Section: Introductionmentioning

confidence: 99%

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A branch and price approach for routing and refueling station location model

Yıldız

Arslan

Karaşan

2016

European Journal of Operational Research

104

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a b s t r a c tThe deviation flow refueling location problem is to locate p refueling stations in order to maximize the flow volume that can be refueled respecting the range limitations of the alternative fuel vehicles and the shortest path deviation tolerances of the drivers. We first provide an enhanced compact model based on a combination of existing models in the literature for this relatively new operations research problem. We then extend this problem and introduce the refueling station location problem which adds the routing aspect of the individual drivers. Our proposed branch and price algorithm relaxes the simple path assumption generally adopted in the existing studies and implicitly takes into account deviation tolerances without the pregeneration of the routes. Therefore, the decrease in solution times with respect to existing models is significant and our algorithm scales very efficiently to more realistic network dimensions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A branch and price approach for routing and refueling station location model

Yıldız

Arslan

Karaşan

2016

European Journal of Operational Research

104

View full text Add to dashboard Cite

show abstract

“…The problem of optimally locating such refuelling stations has been investigated by several researchers using flow refuelling location model. [18][19][20] To enable mobility of EVs, models of the placement of least charging stations on the shortest path are proposed to avoid detours for charging. 21,22 A conceptual optimization model is proposed to analyse travel by EVs along a long corridor.…”

Section: Charging Station Design and Location Studiesmentioning

confidence: 99%

Electric vehicles: A review of network modelling and future research needs

Jing

Yan

Kim

et al. 2016

Advances in Mechanical Engineering

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Electric vehicles are believed to be an effective solution for reducing greenhouse gas emissions. Despite extensive study on the attributes and characteristics of electric vehicles and their charging infrastructure design, the development and network modelling of electric vehicles are still evolving and limited. This article provides a comprehensive review of electric vehicle studies and identifies existing research gaps in the aspects of theories, modelling approaches, solution algorithms and applications. This article first describes the electric vehicles' concepts, market share, characteristics and charging infrastructures. Then, the studies on traffic assignment problem with electric vehicles in the network and limited charging facilities are particularly discussed. We conclude that it is of great importance to take into account electric vehicles' special characteristics (e.g. range limit) in predicting their routing behaviour and charging infrastructure design networks.

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“…L. Feng [2] models the charging demand by a weighted voronoi diagram to minimize the detours But it only considers the size and location in traffic networks. C. Upchurch [3] and M. Kuby [4] model the charging demand as a flow refueling location model to maximize the captured flows. A. Lam [5] provides a structure for charging station placement including the formulation, complexity and solutions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Charging Station Deployment

Sun

Zhao

et al. 2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Lack of charging stations has been a main obstacle to the promotion of electric vehicles. This paper studies deploying charging stations in traffic networks considering grid constraints to balance the charging demand and grid stability. First, we propose a statistical model for charging demand. Then we combine the charging demand model with power grid constraints and give the formulation of the charging station deployment problem. Finally, we propose a theoretical solution for the problem by transforming it to a Markov Decision Process. IntroductionOne critical issue holding back the widespread of electric vehicles (EVs) is the scarcity of charging facilities. For example, EVs in ShenZhen had increased by 6958 during 2009-2014, but only 3091 new chargers were constructed during this period. Even so, the utilization of chargers is less than 33%. On the other hand, numerous EVs charging synchronously will shake the power networks seriously. K. Clement-Nyns [1] shows that, when the EV penetration reaches 30%, the uncoordinated EV charging will cause 5% total power loss and 10% voltage deviation. Thus, when deploying charging stations, both the charging demand in traffic networks and the stability in power grid should be considered.There are some papers focusing on the charging station deployment. L. Feng [2] models the charging demand by a weighted voronoi diagram to minimize the detours But it only considers the size and location in traffic networks. C. Upchurch [3] and M. Kuby [4] model the charging demand as a flow refueling location model to maximize the captured flows. A. Lam [5] provides a structure for charging station placement including the formulation, complexity and solutions. In spite of the various models and solutions proposed, the impacts on power grid are untouched.There are also some papers focusing on deploying charging stations in power networks. M. Aghaebrahimi [6], P. Sadeghi-Barzani [7], Z. Liu [8] locate charging stations in distribution systems to minimize costs. But mapping the power network constraints to costs is not proper, because violating the constraints may cause disasters. N. Ariyapim [9] proposes an ant colony optimization method to optimize the charging station locations to minimize the feeder loss in distribution systems. Although these papers study the deployment of charging stations in power networks, none of them consider the spatio-temporal features of charging demand.

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A Model for Location of Capacitated Alternative‐Fuel Stations

Cited by 251 publications

References 14 publications

A branch and price approach for routing and refueling station location model

A branch and price approach for routing and refueling station location model

Electric vehicles: A review of network modelling and future research needs

A Novel Methodology for Charging Station Deployment

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