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Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System

Author

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  • José F. C. Castro

    (Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Davidson C. Marques

    (Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Luciano Tavares

    (Advanced Institute of Technology and Innovation (IATI), Recife 50751-310, PE, Brazil)

  • Nicolau K. L. Dantas

    (Institute of Technology Edson Mororó Moura (ITEMM), Recife 51020-280, PE, Brazil)

  • Amanda L. Fernandes

    (CPFL Energy, Campinas 13087-397, SP, Brazil)

  • Ji Tuo

    (CPFL Energy, Campinas 13087-397, SP, Brazil)

  • Luiz H. A. de Medeiros

    (Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Pedro Rosas

    (Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

Abstract

Electric vehicle (EV) charging may impose a substantial power demand on existing low voltage (LV) and medium voltage (MV) networks, which are usually not prepared for high power demands in short time intervals. The influx of E-mobility may require an increase in grid reinforcements, but these can be reduced and optimized by a combination of new technologies, tools, and strategies, such as the deployment of solar PV generation integrated with aggregated energy storage systems. One of the challenges in the implementation of charging infrastructures in public stations is coupling the projected sizes of energy demand and power requirements in each location for each charger. This paper describes a method to estimate projected values for energy consumption and power demand in EV fast charging stations (CS). The proposed ideas were applied in a concept facility located in Campinas, Brazil, in a structure equipped with two 50 kW DC Fast Chargers, local 12.5 kW/13.2 kWp PV generation (to reduce energy impacts to the grid), and a 100 kW/200 kWh storage system, using electrochemical batteries (to minimize peak power requirements).

Suggested Citation

  • José F. C. Castro & Davidson C. Marques & Luciano Tavares & Nicolau K. L. Dantas & Amanda L. Fernandes & Ji Tuo & Luiz H. A. de Medeiros & Pedro Rosas, 2022. "Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System," Energies, MDPI, vol. 15(17), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6106-:d:895461
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    Cited by:

    1. José F. C. Castro & Ronaldo A. Roncolatto & Antonio R. Donadon & Vittoria E. M. S. Andrade & Pedro Rosas & Rafael G. Bento & José G. Matos & Fernando A. Assis & Francisco C. R. Coelho & Rodolfo Quadro, 2023. "Microgrid Applications and Technical Challenges—The Brazilian Status of Connection Standards and Operational Procedures," Energies, MDPI, vol. 16(6), pages 1-25, March.
    2. Julong Chen & Haoyong Chen, 2023. "Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences," Energies, MDPI, vol. 16(4), pages 1-21, February.
    3. Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.

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