Intelligent algorithm-based efficient planning of electric vehicle charging station: A case study of metropolitan city of India

Document Type : Article

Authors

1 Department of Electrical Engineering, Delhi Technological University, Delhi-110042, India

2 - Department of Electrical Engineering, Delhi Technological University, Delhi-110042, India. - Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Qassim, Saudi Arabia

Abstract

Electric vehicles are gaining popularity and going to become the mainstream mode of transportation in urban and rural areas not only in India but globally in the next few years. In the adoption of electric vehicles, there are certain issues like proper charging infrastructure, charging time etc. and out of these the sizing and siting of the charging station particularly in urban areas where the cost of land and location plays an important role. Thus, it is important that the charging station location should be easily accessible for the electric vehicle users and cost effective as well. This paper presents an intelligent algorithm based efficient planning of electric vehicle charging station considering geographical information and road network. The cost function has considered such as investment cost, charging station electrification cost, electric vehicle energy loss cost and travel time cost. An intelligent algorithm-based approach is employed to solve the planning problem of electric vehicle charging stations. Further, the impact on reliability of the grid is also evaluated by determining the charging cost loss on each considered location. The result shows that the applied method provides better optimized solutions which are beneficial for electric vehicles users, charging station operator and utility grid.

Keywords

References

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Scientia Iranica
Volume 30, Issue 2
Transactions on Computer Science & Engineering and Electrical Engineering (D)
March and April 2023
Pages 559-576

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History

  • Receive Date: 06 January 2021
  • Revise Date: 20 May 2021
  • Accept Date: 02 August 2021

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