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Probabilistic Method to Assess the Impact of Charging of Electric Vehicles on Distribution Grids

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  • Eduardo Valsera-Naranjo

    (Centre of Technological Innovation in Static Converters and Drives, Departament of Electrical Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, College of Industrial Engineering of Barcelona, Carrer Comte d’Urgell, 187-08036 Barcelona, Spain)

  • Andreas Sumper

    (Centre of Technological Innovation in Static Converters and Drives, Departament of Electrical Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, College of Industrial Engineering of Barcelona, Carrer Comte d’Urgell, 187-08036 Barcelona, Spain
    Centre of Technological Innovation in Static Converters and Drives, Departament of Electrical Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, School of Industrial Engineering of Barcelona, Av. Diagonal, 647, Pl. 2. 08028 Barcelona, Spain
    IREC Catalonia Institute for Energy Research, Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Barcelona, Spain)

  • Roberto Villafafila-Robles

    (Centre of Technological Innovation in Static Converters and Drives, Departament of Electrical Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, College of Industrial Engineering of Barcelona, Carrer Comte d’Urgell, 187-08036 Barcelona, Spain
    Centre of Technological Innovation in Static Converters and Drives, Departament of Electrical Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, School of Industrial Engineering of Barcelona, Av. Diagonal, 647, Pl. 2. 08028 Barcelona, Spain)

  • David Martínez-Vicente

    (Centre of Technological Innovation in Static Converters and Drives, Departament of Electrical Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, College of Industrial Engineering of Barcelona, Carrer Comte d’Urgell, 187-08036 Barcelona, Spain)

Abstract

This paper describes a grid impact analysis of charging electric vehicles (EV) using charging curves with detailed battery modelling. A probabilistic method using Monte Carlo was applied to a typical Spanish distribution grid, also using mobility patterns of Barcelona. To carry out this analysis, firstly, an IEEE test system was adapted to a typical distribution grid configuration; secondly, the EV and its battery types were modeled taking into account the current vehicle market and the battery characteristics; and, finally, the recharge control strategies were taken into account. Once these main features were established, a statistical probabilistic model for the household electrical demand and for the EV charging parameters was determined. Finally, with these probabilistic models, the Monte Carlo analysis was performed within the established scenario in order to study the lines’ and the transformers’ loading levels. The results show that an accurate model for the battery gives a more precise estimation about the impact on the grid. Additionally, mobility patterns have been proved to be some of the most important key aspects for these type of studies.

Suggested Citation

  • Eduardo Valsera-Naranjo & Andreas Sumper & Roberto Villafafila-Robles & David Martínez-Vicente, 2012. "Probabilistic Method to Assess the Impact of Charging of Electric Vehicles on Distribution Grids," Energies, MDPI, vol. 5(5), pages 1-29, May.
  • Handle: RePEc:gam:jeners:v:5:y:2012:i:5:p:1503-1531:d:17829
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    Citations

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    Cited by:

    1. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    2. Miguel Carrión & Rafael Zárate-Miñano & Ruth Domínguez, 2020. "Integration of Electric Vehicles in Low-Voltage Distribution Networks Considering Voltage Management," Energies, MDPI, vol. 13(16), pages 1-23, August.
    3. Paweł Pijarski & Piotr Kacejko, 2021. "Voltage Optimization in MV Network with Distributed Generation Using Power Consumption Control in Electrolysis Installations," Energies, MDPI, vol. 14(4), pages 1-21, February.
    4. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    5. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    6. Dillman, Kevin Joseph & Fazeli, Reza & Shafiei, Ehsan & Jónsson, Jón Örvar G. & Haraldsson, Hákon Valur & Davíðsdóttir, Brynhildur, 2021. "Spatiotemporal analysis of the impact of electric vehicle integration on Reykjavik's electrical system at the city and distribution system level," Utilities Policy, Elsevier, vol. 68(C).
    7. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    8. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
    9. Pol Olivella-Rosell & Roberto Villafafila-Robles & Andreas Sumper & Joan Bergas-Jané, 2015. "Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks," Energies, MDPI, vol. 8(5), pages 1-28, May.
    10. Francisco J. Ruiz-Rodríguez & Jesús C. Hernández & Francisco Jurado, 2017. "Probabilistic Load-Flow Analysis of Biomass-Fuelled Gas Engines with Electrical Vehicles in Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-23, October.

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