Optimal EV Fast Charging Station Deployment Based on a Reinforcement Learning Framework

马尔可夫决策过程 强化学习 软件部署 计算机科学 维数之咒 数学优化 马尔可夫过程 服务质量 循环神经网络 人工智能 人工神经网络 计算机网络 统计 数学 操作系统
作者
Zhonghao Zhao,C. K. M. Lee,Jingzheng Ren,Yung Po Tsang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:24 (8): 8053-8065 被引量:1
标识
DOI:10.1109/tits.2023.3265517
摘要

This study aims to determine the optimal deployment plan for EV fast charging stations in a transportation network with a limited budget. The objective of the deployment problem is to maximize the quality of service (QoS) with respect to both waiting time and range anxiety from the perspective of EV customers. With the rapid growth of the electric vehicle (EV) market penetration, state-of-the-art algorithms based on mathematical programming are limited in handling high-dimensional optimization problems adequately. Unlike previous studies, we make the first attempt to formulate the fast charging station deployment problem (FCSDP) as a finite discrete Markov decision process (MDP) in a novel reinforcement learning (RL) framework to alleviate the curse of dimensionality problem. Since creating a supervised training dataset is impractical due to the high computational complexity of the FCSDP, we propose a recurrent neural network (RNN) with an attention mechanism to learn the model parameters and determine the optimal policy in a completely unsupervised manner. Finally, numerical experiments are conducted on multiple problem sizes to evaluate the performance of the RNN-based RL framework. Simulation results show that the proposed approach outperforms the comparing algorithms in terms of solution quality and computation time.
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