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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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    References listed on IDEAS

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    1. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    2. Qian, Lixian & Soopramanien, Didier, 2014. "Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market," Journal of Business Research, Elsevier, vol. 67(6), pages 1226-1232.
    3. Orakanya Kanjanatarakul & Komsan Suriya, 2012. "Comparison of sales forecasting models for an innovative agro-industrial product: Bass model versus logistic function," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 89-106, December.
    4. Chu, Wen-Lin & Wu, Feng-Shang & Kao, Kai-Sheng & Yen, David C., 2009. "Diffusion of mobile telephony: An empirical study in Taiwan," Telecommunications Policy, Elsevier, vol. 33(9), pages 506-520, October.
    5. Anders F. Jensen & Elisabetta Cherchi & Stefan L. Mabit & Juan de Dios Ortúzar, 2017. "Predicting the Potential Market for Electric Vehicles," Transportation Science, INFORMS, vol. 51(2), pages 427-440, May.
    6. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    7. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    8. Sudtasan, Tatcha & Mitomo, Hitoshi, 2017. "Comparison of Diffusion Models for Forecasting the Growth of Broadband Markets in Thailand," 14th ITS Asia-Pacific Regional Conference, Kyoto 2017: Mapping ICT into Transformation for the Next Information Society 168541, International Telecommunications Society (ITS).
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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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