The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems
Abstract
Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.
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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
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Soft actor-critic Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1801.01290 · Cited by 5,594
Trust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578
Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., & Meger, D. (2018) · arXiv · doi:10.48550/arxiv.1802.09477 · Cited by 3,650
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Radial distribution test feeders
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Cited by
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
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
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids
Zhang, Q., Dehghanpour, K., Wang, Z., Qiu, F., & Zhao, D. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.3034827 · Cited by 137
Cao, D., Zhao, J., Hu, J., Pei, Y., Huang, Q., Chen, Z., & Hu, W. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3267069 · Cited by 103
Zhu, D., Yang, B., Liu, Y., Wang, Z., Ma, K., & Guan, X. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118636 · Cited by 94
Metadata
- Authors
- Wang, W., Yu, N., Gao, Y., & Shi, J.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2020-07
- Volume
- 11
- Issue
- 4
- Pages
- 3008-3018
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2997572754
- Cited by
- 331
- References
- 44
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, W., Yu, N., Gao, Y., & Shi, J. (2020). Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2019.2962625
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