Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Document Type
Article
Date of Original Version
1-1-2018
Abstract
Driven by the recent advances in electric vehicle (EV) technologies, EVs become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user’s commuting behavior, and pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.
Publication Title, e.g., Journal
IEEE Transactions on Smart Grid
Citation/Publisher Attribution
Wan, Zhiqiang, Hepeng Li, Haibo He, and Danil Prokhorov. "Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning." IEEE Transactions on Smart Grid (2018). doi: 10.1109/TSG.2018.2879572.