The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems
Open accessAbstract
This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.
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Cites
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Control of Power Converters in AC Microgrids
Rocabert, J., Luna, A., Blaabjerg, F., & Rodríguez, P. (2012) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2199334 · Cited by 3,723
Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977374 · Cited by 1,306
Distribution System Modeling and Analysis
Kersting, W. (2012) · Electric Power Engineering Series · doi:10.1201/b12056-31 · Cited by 1,241
Georgilakis, P. S., & Hatziargyriou, N. D. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2237043 · Cited by 875
Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations
Duan, J., Shi, D., Diao, R., Li, H., Wang, Z., Zhang, B., Bian, D., & Yi, Z. (2020) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2941134 · Cited by 434
Yan, Z., & Xu, Y. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2881359 · Cited by 387
Wang, W., Yu, N., Gao, Y., & Shi, J. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2962625 · Cited by 331
Distribution System Modeling and Analysis
Kersting, W. H. (2006) · CRC Press eBooks · doi:10.1201/9781420006162 · Cited by 231
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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
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
Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach
Zhao, T., Wang, J., Lu, X., & Du, Y. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3102857 · Cited by 75
Self-citationWu, Z., Li, Y., Zhang, X., Zheng, S., & Zhao, J. (2025) · Applied Energy · doi:10.1016/j.apenergy.2024.124984 · Cited by 29
Jung, Y., Han, C., Lee, D., Song, S., & Jang, G. (2021) · Applied Sciences · doi:10.3390/app11198979 · Cited by 15
Metadata
- Authors
- Zhang, Y., Wang, X., Wang, J., & Zhang, Y.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2021-01
- Volume
- 12
- Issue
- 1
- Pages
- 361-371
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W3043475934
- Cited by
- 299
- References
- 30
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- green
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