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Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach

Author

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  • Imen Azzouz

    (Higher School of Communication of Tunis (Sup’Com), University of Carthage, 2083 Ariana, Tunisia)

  • Wiem Fekih Hassen

    (Chair of Distributed Information Systems, University of Passau, Innstraße 41, 94032 Passau, Germany)

Abstract

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.

Suggested Citation

  • Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8102-:d:1301578
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.
    3. Jaehyun Lee & Eunjung Lee & Jinho Kim, 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme," Energies, MDPI, vol. 13(8), pages 1-18, April.
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