IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i13p3354-d1431189.html
   My bibliography  Save this article

Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub

Author

Listed:
  • José F. C. Castro

    (Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
    Electrical Engineering Department, IATI—Advanced Institute of Technology and Innovation, Recife 50751-310, PE, Brazil)

  • Augusto C. Venerando

    (School of Electrical and Computer Engineering, State University of Campinas—UNICAMP, Campinas 13083-852, SP, Brazil
    EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil)

  • Pedro A. C. Rosas

    (Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
    Electrical Engineering Department, IATI—Advanced Institute of Technology and Innovation, Recife 50751-310, PE, Brazil)

  • Rafael C. Neto

    (Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
    Electrical Engineering Department, IATI—Advanced Institute of Technology and Innovation, Recife 50751-310, PE, Brazil)

  • Leonardo R. Limongi

    (Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
    Electrical Engineering Department, IATI—Advanced Institute of Technology and Innovation, Recife 50751-310, PE, Brazil)

  • Fernando L. Xavier

    (Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
    Electrical Engineering Department, IATI—Advanced Institute of Technology and Innovation, Recife 50751-310, PE, Brazil)

  • Wesley M. Rhoden

    (EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil)

  • Newmar Spader

    (EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil)

  • Adriano P. Simões

    (EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil)

  • Nicolau K. L. Dantas

    (Institute of Technology Edson Mororó Moura—ITEMM, Belo Jardim 55150-550, PE, Brazil)

  • Antônio V. M. L. Filho

    (Institute of Technology Edson Mororó Moura—ITEMM, Belo Jardim 55150-550, PE, Brazil)

  • Luiz C. P. Silva

    (School of Electrical and Computer Engineering, State University of Campinas—UNICAMP, Campinas 13083-852, SP, Brazil)

  • Pérolla Rodrigues

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

Abstract

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge’s operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil.

Suggested Citation

  • José F. C. Castro & Augusto C. Venerando & Pedro A. C. Rosas & Rafael C. Neto & Leonardo R. Limongi & Fernando L. Xavier & Wesley M. Rhoden & Newmar Spader & Adriano P. Simões & Nicolau K. L. Dantas &, 2024. "Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub," Energies, MDPI, vol. 17(13), pages 1-23, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3354-:d:1431189
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/13/3354/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/13/3354/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3354-:d:1431189. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.