A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours

Cavadas, Joana; Correia, Gonçalo Homem de Almeida; Gouveia, João

doi:10.1016/j.tre.2014.11.005

Cited by 118 publications

(56 citation statements)

References 23 publications

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“…More similar to the methodology used in this paper, Baouche et al (2014) and Cavadas et al (2015) used integer optimization techniques to identify the location of charging stations for passenger EVs in the cities of Lyon, France and Coimbra, Portugal. However, these studies differ from the present study since they have dealt with electric Light-Duty Vehicles (LDVs), and not Heavy-Duty Vehicles (HDVs), such as buses.…”

Section: Figure 1: Map Of the Stockholm Bus Network -Bus Routes (Leftmentioning

confidence: 99%

Locating charging infrastructure for electric buses in Stockholm

Xylia

Leduc

Patrizio

et al. 2017

Transportation Research Part C: Emerging Technologies

227

101

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Highlights Presenting an optimization model for the location of electric bus charging stations.  Model application to a large-scale case study for the bus network of Stockholm, Sweden.  Results show that low fuel costs for electricity can balance annual infrastructure costs.  Emissions decrease up to 51% and energy consumption up to 34% with electrification.  The model may assist decision-making for investments in public transport.Keywords electric bus; charging infrastructure; optimization; Mixed Integer Linear Programing; public transport; Sweden AbstractCharging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

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Section: Figure 1: Map Of the Stockholm Bus Network -Bus Routes (Leftmentioning

confidence: 99%

Locating charging infrastructure for electric buses in Stockholm

Xylia

Leduc

Patrizio

et al. 2017

Transportation Research Part C: Emerging Technologies

227

101

View full text Add to dashboard Cite

show abstract

“…Then, the location of this same type of station was applied to the case of passenger vehicles (Wang and Wang, 2010). Another extension is the one proposed by Cavadas et al (2015), in which for the location of recharging stations is considered so that the demands of the customers can be transferred between one station and another.…”

Section: Literature Reviewmentioning

confidence: 99%

The multi-depot electric vehicle location routing problem with time windows

Paz¹,

Echeverri²,

Escobar³

2018

10.5267/j.ijiec

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In this paper, the Multi-Depot Electric Vehicle Location Routing Problem with Time Windows (MDVLRP) is addressed. This problem is an extension of the MDVLRP, where electric vehicles are used instead of internal combustion engine vehicles. The recent development of this model is explained by the advantages of this technology, such as the diminution of carbon dioxide emissions, and the support that they can provide to the design of the logistic and energy-support structure of electric vehicle fleets. There are many models that extend the classical VRP model to take electric vehicles into consideration, but the multi-depot case for location-routing models has not been worked out yet. Moreover, we consider the availability of two energy supply technologies: the "Plug-in" Conventional Charge technology, and Battery Swapping Stations; options in which the recharging time is a function of the amount of energy to charge and a fixed time, respectively. Three models are proposed: one for each of the technologies mentioned above, and another in which both options are taken in consideration. The models were solved for small scale instances using C++ and Cplex 12.5. The results show that the models can be used to design logistic and energy-support structures, and compare the performance of the different options of energy supply, as well as measure the impact of these decisions on the overall distance traveled or other optimization objectives that could be worked on in the future.

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“…An impressively detailed model of both spatial and temporal charging demand is provided by Cavadas et al (2015), where a mixed integer optimization program is suggested to find optimal places for slow-charging stations within an urban area. The authors suggest an objective function that maximizes vehicle-owners' demand satisfaction in terms of reaching their ultimate destination of travel.…”

Section: Literature Reviewmentioning

confidence: 99%

A Model of Charging Service Demand for the Czech Republic

Pekárek

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The paper introduces a standalone model of electric vehicle charging demand based on large-scale travel survey data of the Czech Republic. This demand model has been intended as a comprehensive input model for following charging infrastructure problem, where a spatial view of charging demand is usually needed. The model uses publicly available data, whose mutual incompatibility and information richness had to be overcome. The necessary data transformations are described and final data representation in the form of a mathematical graph allows the introduction of a point-defined (vertex-defined) charging demand model. Several drawbacks of the model are identified and their effect, as well as an application of whole model, is demonstrated on the large-scale numerical example. Sound demand model is a cornerstone for demand-related problems, such as general large-scale charging infrastructure problem, which is a common issue for countries that stand at the very beginning of the electric vehicle adoption process.

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A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours

Cited by 118 publications

References 23 publications

Locating charging infrastructure for electric buses in Stockholm

Locating charging infrastructure for electric buses in Stockholm

The multi-depot electric vehicle location routing problem with time windows

A Model of Charging Service Demand for the Czech Republic

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