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

被引:2
|
作者
Azzouz, Imen [1 ]
Fekih Hassen, Wiem [2 ]
机构
[1] Univ Carthage, Higher Sch Commun Tunis SupCom, Ariana 2083, Tunisia
[2] Univ Passau, Chair Distributed Informat Syst, Innstr 41, D-94032 Passau, Germany
关键词
smart EV charging; day-ahead planning; deep Q-Network; data-driven approach; waiting time; cost minimization; real dataset;
D O I
10.3390/en16248102
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:18
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