Electric vehicles charging infrastructure location: a genetic algorithm approach

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. 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. The 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. 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 “driver anxiety” (i.e. the driver’s concern of running out of battery) encouraging the widespread adoption of electromobility.

• Publication year

  2017

• Venue

  European Transport Research Review

• Publication date

  2017-05-05

• Fields of study

  Engineering, Environmental Science

• Identifiers
  DOI 10.1007/s12544-017-0239-7
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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