The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations
Abstract
In this letter, a novel autonomous control framework “Grid Mind” is proposed for the secure operation of power grids based on cutting-edge artificial intelligence (AI) technologies. The proposed platform provides a data-driven, model-free and closed-loop control agent trained using deep reinforcement learning (DRL) algorithms by interacting with massive simulations and/or real environment of a power grid. The proposed agent learns from scratch to master the power grid voltage control problem purely from data. It can make autonomous voltage control (AVC) strategies to support grid operators in making effective and timely control actions, according to the current system conditions detected by real-time measurements from supervisory control and data acquisition (SCADA) or phasor measurement units (PMUs). Two state-of-the-art DRL algorithms, namely deep Q-network (DQN) and deep deterministic policy gradient (DDPG), are proposed to formulate the AVC problem with performance compared. Case studies on a realistic 200-bus test system demonstrate the effectiveness and promising performance of the proposed framework.
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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
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
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
Diao, R., Wang, Z., Shi, D., Chang, Q., Duan, J., & Zhang, X. (2019) · 2019 IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm40551.2019.8973924 · Cited by 115
Self-citation
Cited by
Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100169 · Cited by 552
Reinforcement 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
Self-citationShi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327
Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems
Zhang, Y., Wang, X., Wang, J., & Zhang, Y. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.3010130 · Cited by 299
Glavic, M. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.09.008 · Cited by 163
Metadata
- Authors
- Duan, J., Shi, D., Diao, R., Li, H., Wang, Z., Zhang, B., Bian, D., & Yi, Z.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2020-01
- Volume
- 35
- Issue
- 1
- Pages
- 814-817
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2972852318
- Cited by
- 434
- References
- 3
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Duan, J., Shi, D., Diao, R., Li, H., Wang, Z., Zhang, B., Bian, D., & Yi, Z. (2020). Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2019.2941134
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