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Methodology to Evaluate the Impact of Electric Vehicles on Electrical Networks Using Monte Carlo

Author

Listed:
  • Daniel Betancur

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Luis F. Duarte

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Jesús Revollo

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Carlos Restrepo

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Andrés E. Díez

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Idi A. Isaac

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Gabriel J. López

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Jorge W. González

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

Abstract

In preparation for the electric mobility technological transition in Colombia, an impact assessment of the electric power system is required, considering the increasing loading that must be able to be managed in the future. In this paper, a plug-in electric vehicle (PEV) charging simulation methodology is developed in order to dimension the impact of this type of load on power grids. PEV electric properties, user charging behaviors, geographic location, trip distances, and other variables of interest are modeled from empirical or known probability distributions and later evaluated in different scenarios using Monte Carlo simulation and load flow analysis. This methodology is later applied to the transmission network of Antioquia (a department of Colombia) resulting in load increases of up to 40% on transmission lines and 20% on transformers in a high PEV penetration scenario in 2030, increases that are well within the expected grid capacity for that year, avoiding the need for additional upgrades.

Suggested Citation

  • Daniel Betancur & Luis F. Duarte & Jesús Revollo & Carlos Restrepo & Andrés E. Díez & Idi A. Isaac & Gabriel J. López & Jorge W. González, 2021. "Methodology to Evaluate the Impact of Electric Vehicles on Electrical Networks Using Monte Carlo," Energies, MDPI, vol. 14(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1300-:d:506928
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    References listed on IDEAS

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    1. Li, Ying & Davis, Chris & Lukszo, Zofia & Weijnen, Margot, 2016. "Electric vehicle charging in China’s power system: Energy, economic and environmental trade-offs and policy implications," Applied Energy, Elsevier, vol. 173(C), pages 535-554.
    2. García-Villalobos, J. & Zamora, I. & San Martín, J.I. & Asensio, F.J. & Aperribay, V., 2014. "Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 717-731.
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    Cited by:

    1. Marco Toledo-Orozco & Eddy Bravo-Padilla & Carlos Álvarez-Bel & Diego Morales-Jadan & Luis Gonzalez-Morales, 2023. "Methodological Evaluation to Integrate Charging Stations for Electric Vehicles in a Tram System Using OpenDSS—A Case Study in Ecuador," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    2. Daud Mustafa Minhas & Josef Meiers & Georg Frey, 2022. "Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets," Energies, MDPI, vol. 15(5), pages 1-29, February.
    3. Lijuan Sun & Menggang Chen & Yawei Shi & Lifeng Zheng & Songyang Li & Jun Li & Huijuan Xu, 2022. "Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System," Energies, MDPI, vol. 15(24), pages 1-13, December.
    4. Pokpong Prakobkaew & Somporn Sirisumrannukul, 2022. "Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data," Energies, MDPI, vol. 15(11), pages 1-19, May.
    5. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    6. Andrés E. Díez & Mauricio Restrepo, 2021. "A Planning Method for Partially Grid-Connected Bus Rapid Transit Systems Operating with In-Motion Charging Batteries," Energies, MDPI, vol. 14(9), pages 1-22, April.

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