计算机科学
网格
汽车工程
充电站
车辆到电网
电气工程
电动汽车
工程类
功率(物理)
物理
几何学
数学
量子力学
作者
Zuzhao Ye,Yuanqi Gao,Nanpeng Yu
出处
期刊:IEEE Transactions on Smart Grid
[Institute of Electrical and Electronics Engineers]
日期:2022-07-01
卷期号:13 (4): 3038-3048
被引量:42
标识
DOI:10.1109/tsg.2022.3165479
摘要
The rapid adoption of electric vehicles (EVs) calls for the widespread installation of EV charging stations. To maximize the profitability of charging stations, intelligent controllers that provide both charging and electric grid services are in great need. However, it is challenging to determine the optimal charging schedule due to the uncertain arrival time and charging demands of EVs. In this paper, we propose a novel centralized allocation and decentralized execution (CADE) reinforcement learning (RL) framework to maximize the charging station’s profit. In the centralized allocation process, EVs are allocated to either the waiting or charging spots. In the decentralized execution process, each charger makes its own charging/discharging decision while learning the action-value functions from a shared replay memory. This CADE framework significantly improves the scalability and sample efficiency of the RL algorithm. Numerical results show that the proposed CADE framework is both computationally efficient and scalable, and significantly outperforms the baseline model predictive control (MPC). We also provide an in-depth analysis of the learned action-value function to explain the inner working of the reinforcement learning agent.
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