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Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method

Author

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  • Klemen Deželak

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
    Statistical Office of the Republic of Slovenia, Litostrojska Cesta 54, 1000 Ljubljana, Slovenia)

  • Klemen Sredenšek

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia)

  • Sebastijan Seme

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
    Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia)

Abstract

The widespread adoption of electric vehicles poses certain challenges to the distribution grid, which refers to the network of power lines, transformers, and other infrastructure that delivers electricity from power plants to consumers. This higher demand can strain the distribution grid, particularly in areas with a high concentration of electric vehicles. Grid operators need to ensure that the grid infrastructure can handle this additional load and prevent overloading and consequences in terms of additional losses. As part of the task, a methodology was developed for the assessment of the electricity consumption of battery electric vehicles in Slovenia. The approach used for the calculation includes the number of electric cars, average consumption, distance travelled and efficiency of the system. Additionally, the results of the modelling approach for an integrated distribution grid model in terms of steady-state simulations are presented. The regular situation of the power losses within the distribution grid is managed together with an optimal result. In this sense, an application of the particle swarm optimisation-based strategy is suggested to minimise reliance on grid systems.

Suggested Citation

  • Klemen Deželak & Klemen Sredenšek & Sebastijan Seme, 2023. "Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method," Energies, MDPI, vol. 16(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5393-:d:1194566
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    References listed on IDEAS

    as
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