Efficient Allocation of Electric Vehicles Charging Stations: Optimization Model and Application to a Dense Urban Network

Baouche, Fouad; Billot, Romain; Trigui, Rochdi; Faouzi, Nour‐Eddin El

doi:10.1109/mits.2014.2324023

Cited by 118 publications

(74 citation statements)

References 21 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

237

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

237

101

View full text Add to dashboard Cite

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“…Baouche et al [14] used OD data to develop an integer linear programming algorithm mixed with a dynamic consumption demand model, for the City of Lyon. The proposed model aims at minimizing the fixed charge charging station and the vehicle travel cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric vehicles charging infrastructure location: a genetic algorithm approach

Efthymiou

Chrysostomou

Morfoulaki

et al. 2017

Eur. Transp. Res. Rev.

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Introduction As part of the overall goal of carbon emissions reduction, European cities are expected to encourage the electrification of urban transport. In order to prepare themselves to welcome the increased number of electric vehicles circulating in the city networks in the near future, they are expected to deploy networks of public electric vehicle chargers. The Electric Vehicle Charging Infrastructure Location Problem is an optimization problem that can be approached by linear programming, multi-objective optimization and genetic algorithms. Methods In the present paper, a genetic algorithm approach is presented. Since data from electric vehicles usage are still scarce, origin -destination data of conventional vehicles are used and the necessary assumptions to predict electric vehicles' penetration in the years to come are made. The algorithm and a user-friendly tool have been developed in R and tested for the city of Thessaloniki. ResultsThe results indicate that 15 stations would be required to cover 80% of the estimated electric vehicles charging demand in 2020 in the city of Thessaloniki and their optimal locations to install them are identified. Conclusions The tool that has been developed based on the genetic algorithm, is open source and freely available to interested users. The approach can be used to allocate charging stations at high-level, i.e. to zones, and the authors encourage its use by local authorities of other cities too, in Greece and worldwide, in order to deploy a plan for installing adequate charging infrastructure to cover future electric vehicles charging demand and reduce the electric vehicle Bdriver anxiety( i.e. the driver's concern of running out of battery) encouraging the widespread adoption of electromobility.

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“…The potential PEV charging demand is satisfied if at least one charging facility is located along the pre-determined travel route. Traffic flows between each OD pair can be provided by navigation systems [44], generated based on household traffic survey [53], or artificially generated by traffic simulator models [12]. After the PEV traffic flows are determined, charging facilities can be located on the traveling routes in order to maximize the captured PEV flows.…”

Section: Traffic Flow-based Modelmentioning

confidence: 99%

“…The multipath-refueling location model (MPRLM) enables PEV users to utilize multiple deviation paths between every OD pairs on the transportation network [47]. Other planning objectives based on the nodal demand density model can be used in sizing and sitting of charging facilities [48,53]. One objective is called p-median, to minimize the total travel cost between demand points and facility locations.…”

Section: Objective Functions Associated With Transportation Systemmentioning

confidence: 99%

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Abdalrahman

Zhuang

2017

Energies

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Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

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Efficient Allocation of Electric Vehicles Charging Stations: Optimization Model and Application to a Dense Urban Network

Cited by 118 publications

References 21 publications

Locating charging infrastructure for electric buses in Stockholm

Locating charging infrastructure for electric buses in Stockholm

Electric vehicles charging infrastructure location: a genetic algorithm approach

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

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