Optimal Location of Compressed Natural Gas (CNG) Refueling Station Using the Arc Demand Coverage Model

Boostani, Abtin; Ghodsi, Reza; Miab, Ali Kamali

doi:10.1109/ams.2010.49

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Relatively few literature has been published on optimization problems related to alternative fuels. Most articles deal with the question how to place refueling stations in an infrastructureoriented context, either for refueling vehicles using compressed natural gas (CNG) (Boostani et al 2010) or electricity (Qiu et al 2011). The development of an infrastructure consisting of refueling stations, differing in terms of refueling speed and capacity, has been realized to be crucial for the promotion of AFVs.…”

Section: Literature Reviewmentioning

confidence: 99%

The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations

Schneider

Stenger

Goeke

2014

Transportation Science

881

550

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Driven by new laws and regulations concerning the emission of greenhouse gases, carriers are starting to use battery electric vehicles (BEVs) for last-mile deliveries. The limited battery capacities of BEVs necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning in order to avoid inefficient vehicle routes with long detours. We introduce the Electric Vehicle Routing Problem with Time Windows and Recharging Stations (E-VRPTW), which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme. Furthermore, we consider limited vehicle freight capacities as well as customer time windows, which are the most important constraints in real-world logistics applications. As solution method, we present a hybrid heuristic, that combines a Variable Neighborhood Search algorithm with a Tabu Search heuristic. Tests performed on newly designed instances for the E-VRPTW as well as on benchmark instances of related problems demonstrate the high performance of the heuristic proposed as well as the positive effect of the hybridization.

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Section: Literature Reviewmentioning

confidence: 99%

The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations

Schneider

Stenger

Goeke

2014

Transportation Science

881

550

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“…Therefore, arc-based approaches emerged that provide good coverage of flows in the absence of O-D data (e.g. Boostani et al, 2010;Zhong et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Operational Methods for Charging of Electric Vehicles

Csonka

Havas

Csiszár

et al. 2020

Period. Polytech. Transp. Eng.

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The increasing number of electric vehicles induces a new relationship between the electric vehicles, transportation network and electric network. The deployment of the charging infrastructure is a prerequisite of the widespread of electric vehicles. Furthermore, the charging process and energy management have a significant influence on the operation of both the transportation and electric networks. Therefore, we have elaborated novel operational methods that support the deployment of charging infrastructure for electric cars and buses operating in public bus service, and the energy management. Weighted sum-models were developed to assess candidate sites for public charging stations. The mathematical model of public bus services was elaborated that supports the optimization of static charging infrastructure at bus stops and terminals without schedule adjustments. The flexibility and predictability of charging sessions were identified as the main differences between charging infrastructure deployment for cars and buses. Furthermore, the flows of energy, information and value have been revealed among the components of charging with a focus on commercial locations, which is the basis of energy flow optimization on the smart grid.

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“…Another group of models locates stations to serve passing traffic, not proximity to homes or companies. Higher amounts of traffic volume, measured in terms of average annual daily traffic (AADT) or vehicle-miles traveled (VMT), signals more demand for fueling (Boostani et al, 2010;Brey et al, 2016;Goodchild and Noronha, 1987;Lin et al, 2008). While it makes sense to locate stations on busy roads or at the intersection of busy roads, several kinds of demandcounting errors can arise because drivers link together a sequence of arcs into a route.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Scale Framework for Fuel Station Location: From Highways to Street Intersections

Zhao¹,

Kelley²,

Fan³

et al. 2019

Preprint

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Electric drive vehicles (plug-in electric vehicle or hydrogen fuel cell vehicles) have been promoted by governments to foster a more sustainable transportation future. Wider adoption of these vehicles, however, depends on the availability of a convenient and reliable refueling/recharging infrastructure. This paper introduces a path-based, multi-scale, scenario-planning modeling framework for locating a system of alternative-fuel stations. The approach builds on 1) the Flow Refueling Location Model (FRLM), which assumes that drivers stop along their origin-destination routes to refuel, and checks explicitly whether round trips can be completed without running out of fuel, and 2) the Freeway Traffic Capture Method (FTCM), which assesses the degree to which drivers can conveniently reach sites on the local street network near freeway intersections. This paper extends the FTCM to handle cases involving clusters of nearby freeway intersections, which is a limitation of its previous specification. Then, the cluster-based FTCM (CFTCM) is integrated with the FRLM and the DFRLM (FRLM with Deviations) to better conduct detailed geographic optimization of this multi-scale location planning problem. The main contribution of this research is the introduction of a framework that combines multi-scale planning methods to more effectively inform the early development stage of hydrogen refueling infrastructure planning. The proposed multi-scale modeling framework is applied to the Hartford, Connecticut region, which is one of the next areas targeted for fuel-cell vehicle (FCV) market and infrastructure expansion in the United States. This method is generalizable to other regions or other types of fast-fueling alternative fuel vehicles.

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Optimal Location of Compressed Natural Gas (CNG) Refueling Station Using the Arc Demand Coverage Model

Cited by 7 publications

References 26 publications

The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations

The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations

Operational Methods for Charging of Electric Vehicles

A Multi-Scale Framework for Fuel Station Location: From Highways to Street Intersections

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