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Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

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Listed:
  • Francesco Lo Franco

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

  • Mattia Ricco

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

  • Vincenzo Cirimele

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

  • Valerio Apicella

    (R&D and Innovation Group, Movyon s.p.a., 50013 Florence, Italy)

  • Benedetto Carambia

    (R&D and Innovation Group, Movyon s.p.a., 50013 Florence, Italy)

  • Gabriele Grandi

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

Abstract

Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

Suggested Citation

  • Francesco Lo Franco & Mattia Ricco & Vincenzo Cirimele & Valerio Apicella & Benedetto Carambia & Gabriele Grandi, 2023. "Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach," Energies, MDPI, vol. 16(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2076-:d:1074866
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    References listed on IDEAS

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    1. Zhiyuan Zhuang & Xidong Zheng & Zixing Chen & Tao Jin & Zengqin Li, 2022. "Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification," Energies, MDPI, vol. 15(19), pages 1-13, September.
    2. Benjamin Schaden & Thomas Jatschka & Steffen Limmer & Günther Robert Raidl, 2021. "Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers," Energies, MDPI, vol. 14(22), pages 1-33, November.
    3. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    4. Francesco Lo Franco & Mattia Ricco & Riccardo Mandrioli & Gabriele Grandi, 2020. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization," Energies, MDPI, vol. 13(19), pages 1-25, September.
    5. Dan Zhou & Zhonghao Guo & Yuzhe Xie & Yuheng Hu & Da Jiang & Yibin Feng & Dong Liu, 2022. "Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting," Energies, MDPI, vol. 15(17), pages 1-15, August.
    6. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
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    Cited by:

    1. Maria Carmela Di Piazza, 2024. "Volume II: Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids," Energies, MDPI, vol. 17(8), pages 1-3, April.
    2. Saeed Alyami, 2024. "Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach," Sustainability, MDPI, vol. 16(14), pages 1-15, July.

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