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Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

Author

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)

  • Riccardo Mandrioli

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

  • Gabriele Grandi

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

Abstract

In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5003-:d:418002
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    References listed on IDEAS

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    Cited by:

    1. Mansur Arief & Yan Akhra & Iwan Vanany, 2023. "A Robust and Efficient Optimization Model for Electric Vehicle Charging Stations in Developing Countries under Electricity Uncertainty," Papers 2307.05470, arXiv.org.
    2. Francesco Lo Franco & Vincenzo Cirimele & Mattia Ricco & Vitor Monteiro & Joao L. Afonso & Gabriele Grandi, 2022. "Smart Charging for Electric Car-Sharing Fleets Based on Charging Duration Forecasting and Planning," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    3. 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.
    4. Raoul Bernards & Werner van Westering & Johan Morren & Han Slootweg, 2020. "Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models," Energies, MDPI, vol. 13(22), pages 1-21, November.
    5. Lazher Mejdi & Faten Kardous & Khaled Grayaa, 2022. "Impact Analysis and Optimization of EV Charging Loads on the LV Grid: A Case Study of Workplace Parking in Tunisia," Energies, MDPI, vol. 15(19), pages 1-18, September.
    6. Giuseppe Aiello & Mario Cacciato & Francesco Gennaro & Santi Agatino Rizzo & Giuseppe Scarcella & Giacomo Scelba, 2020. "A Tool for Evaluating the Performance of SiC-Based Bidirectional Battery Chargers for Automotive Applications," Energies, MDPI, vol. 13(24), pages 1-17, December.
    7. Najmat Celene Branco & Carolina M. Affonso, 2020. "Probabilistic Approach to Integrate Photovoltaic Generation into PEVs Charging Stations Considering Technical, Economic and Environmental Aspects," Energies, MDPI, vol. 13(19), pages 1-18, September.

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