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A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior

Author

Listed:
  • Leonardo Nogueira Fontoura da Silva

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Marcelo Bruno Capeletti

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Alzenira da Rosa Abaide

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Luciano Lopes Pfitscher

    (Sustainability and Energy Departament, Federal University of Santa Catarina, Araranguá 88906-072, Santa Catarina, Brazil)

Abstract

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.

Suggested Citation

  • Leonardo Nogueira Fontoura da Silva & Marcelo Bruno Capeletti & Alzenira da Rosa Abaide & Luciano Lopes Pfitscher, 2024. "A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior," Energies, MDPI, vol. 17(7), pages 1-27, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1764-:d:1371547
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    References listed on IDEAS

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