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Integrating model predictive control and deep learning for the management of an EV charging station

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  • D’Amore, G.
  • Cabrera-Tobar, A.
  • Petrone, G.
  • Pavan, A. Massi
  • Spagnuolo, G.

Abstract

Explicit model predictive control (EMPC) maps offline the control laws as a set of regions as a function of bounded uncertain parameters using multi-parametric programming. Then, in online mode, it seeks the best solution within these areas. Unfortunately, the offline solution can be computationally demanding because the number of regions can grow exponentially. Thus, this paper presents the application of a deep neural network (DNN) to learn the EMPC’s regions for a photovoltaic-based charging station. The main uncertain parameters in this study are the forecast error of photovoltaic power production and the battery’s state of charge. Additionally, the connection or disconnection of an electric vehicle is considered a disruption. The final controller creates the regions at the start of each prediction time or when a disruption occurs, only using the previously created DNN. The obtained solution is validated using data from an e-vehicle charging station installed at the University of Trieste, Italy.

Suggested Citation

  • D’Amore, G. & Cabrera-Tobar, A. & Petrone, G. & Pavan, A. Massi & Spagnuolo, G., 2024. "Integrating model predictive control and deep learning for the management of an EV charging station," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 33-48.
    • Handle: RePEc:eee:matcom:v:224:y:2024:i:pb:p:33-48
    DOI: 10.1016/j.matcom.2023.04.016
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    References listed on IDEAS

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    1. Huo, Yuchong & Bouffard, François & Joós, Géza, 2022. "Integrating learning and explicit model predictive control for unit commitment in microgrids," Applied Energy, Elsevier, vol. 306(PA).
    2. Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
    3. Bhatti, Abdul Rauf & Salam, Zainal & Aziz, Mohd Junaidi Bin Abdul & Yee, Kong Pui & Ashique, Ratil H., 2016. "Electric vehicles charging using photovoltaic: Status and technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 34-47.
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