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Optimal Location of Electric Vehicle Charging Stations in Distribution Grids Using Genetic Algorithms

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Optimization, Learning Algorithms and Applications (OL2A 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1981))

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Abstract

In recent years, as a result of population growth and the strong demand for energy resources, there has been an increase in greenhouse gas emissions. Thus, it is necessary to find solutions to reduce these emissions. This will make the use of electric vehicles (EV) more attractive and reduce the high dependency on internal combustion vehicles. However, the integration of electric vehicles will pose some challenges. For example, it will be necessary to increase the number of fast electric vehicle charging stations (FEVCS) to make electric mobility more attractive. Due to the high power levels involved in these systems, there are voltage drops that affect the voltage profile of some nodes of the distribution networks. This paper presents a methodology based on a genetic algorithm (GA) that is used to find the optimal location of charging stations that cause the minimum impact on the grid voltage profile. Two case studies are considered to evaluate the behavior of the distribution grid with different numbers of EV charging stations connected. From the results obtained, it can be concluded that the GA provides an efficient way to find the best charging station locations, ensuring that the grid voltage profile is within the regulatory limits and that the value of losses is minimized.

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Acknowledgments

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.

Author information

Authors and Affiliations

  1. Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801, Vila Real, Portugal

    Eduardo Gomes

  2. Department of Mathematics, University of Trás-os-Montes and Alto Douro, INESC-TEC, UTAD’s Pole, 5000-801, Vila Real, Portugal

    Adelaide Cerveira

  3. Department of Engineering, University of Trás-os-Montes and Alto Douro, INESC-TEC, UTAD’s Pole, 5000-801, Vila Real, Portugal

    José Baptista

Authors
  1. Eduardo Gomes
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  2. Adelaide Cerveira
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  3. José Baptista
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Corresponding author

Correspondence to José Baptista .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Azores, Ponta Delgada, Portugal

    Armando Mendes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

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Gomes, E., Cerveira, A., Baptista, J. (2024). Optimal Location of Electric Vehicle Charging Stations in Distribution Grids Using Genetic Algorithms. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_38

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  • DOI: https://doi.org/10.1007/978-3-031-53025-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53024-1

  • Online ISBN: 978-3-031-53025-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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