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

  • 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 effectivenessThe 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.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Wiem Fekih HassenORCiD, Imen Azzouz Imen Azzouz
URN:urn:nbn:de:bvb:739-opus4-13985
DOI:https://doi.org/10.3390/en16248102
Titel des übergeordneten Werkes (Englisch):Energies
Verlag:MDPI
Verlagsort:Basel
Dokumentart:Artikel in einer Fachzeitschrift
Sprache:Englisch
Datum der Erstveröffentlichung:16.12.2023
Veröffentlichende Institution:Universität Passau
Datum der Freischaltung:13.03.2024
Freies Schlagwort / Tag:cost minimization; data-driven approach; day-ahead planning; deep Q-Network; real dataset; smart EV charging; waiting time
Jahrgang:16
Aufsatznummer:8102
Seitenzahl:18 Seiten
Fakultät/Einrichtung:Fakultät für Informatik und Mathematik
DDC-Klassifikation:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
open_access (DINI-Set):open_access
Fördervermerk:Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International