The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Open accessAbstract
Driven by the recent advances in electric vehicle (EV) technologies, EVs have 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 the 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.
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In the citing paper
“For load monitoring and electric vehicle charging scheduling, transfer learning [200] and deep reinforcement learning [215] methods are proposed.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [215]
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reinforcement Learning: An Introduction
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
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A Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
Lipton, Z. C., Berkowitz, J. A., & Elkan, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00019 · Cited by 3,159
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y. H., & Zhang, Y. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2753802 · Cited by 2,806
Practical Recommendations for Gradient-Based Training of Deep Architectures
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Cited by
Sorted by citations
Das, H. S., Rahman, M. M., Li, S., & Tan, C. W. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109618 · Cited by 1,140
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
“For load monitoring and electric vehicle charging scheduling, TL [200] and deep RL [215] methods are proposed.”
BackgroundMethodReinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000552 · Cited by 460
“Reference [80] proposes a DQN-based approach for the optimization of EV charging while [81] extends this work by integrating the DQN algorithm with the long-short term memory (LSTM) NN.”
MethodConstrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning
Li, H., Wan, Z., & He, H. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2955437 · Cited by 442
“If we maximize the lower bound to generate the policy π k + 1 when at the k th iteration, we are guaranteed that an improving policy can be obtained according to Wan et al. [16], i.e., Now, we can use the update rule in (38) to improve the policy π k to maximize the return J (π k ) .”
BackgroundMethodInfluentialSelf-citationA Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things
Chang, Z., Liu, S., Xiong, X., Cai, Z., & Tu, G. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3088875 · Cited by 431
Metadata
- Authors
- Wan, Z., Li, H., He, H., & Prokhorov, D.
- Affiliations
- University of Rhode Island (US)
- Shenyang Institute of Automation (CN)
- Toyota Motor North America Research & Development (United States) (US)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2019-09
- Volume
- 10
- Issue
- 5
- Pages
- 5246-5257
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2899639849
- Cited by
- 548
- References
- 46
- Citations by year
- 2026: 61 · 2025: 71 · 2024: 83 · 2023: 95 · 2022: 87 · 2021: 74 · 2020: 57 · 2019: 20
- FWCI
- 14.43
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Journal of Media Literacy Education (submittedVersion, cc-by)
- SIA OpenIR (Chinese Academy of Sciences) (submittedVersion)
- Funders
- Office of Naval Research (N00014-18-1-2396)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- electric vehicle charging scheduling, real-time pricing, demand response, deep reinforcement learning, model-free learning, unknown transition probabilities, Markov decision process, Q-network, electricity price forecasting, smart grid
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