Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to th...
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| Published in: | IEEE transactions on smart grid Vol. 13; no. 4; pp. 2935 - 2958 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1949-3053, 1949-3061 |
| Online Access: | Get full text |
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| Abstract | With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well. |
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| AbstractList | With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well. |
| Author | Li, Na Qu, Guannan Tang, Yujie Chen, Xin Low, Steven |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0000-0002-1357-3970 surname: Chen fullname: Chen, Xin email: chenxin2336@gmail.com organization: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA – sequence: 2 givenname: Guannan orcidid: 0000-0002-5466-3550 surname: Qu fullname: Qu, Guannan email: gqu@andrew.cmu.edu organization: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA – sequence: 3 givenname: Yujie orcidid: 0000-0002-4921-8372 surname: Tang fullname: Tang, Yujie email: yujietang@seas.harvard.edu organization: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA – sequence: 4 givenname: Steven orcidid: 0000-0001-6476-3048 surname: Low fullname: Low, Steven email: slow@caltech.edu organization: Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA – sequence: 5 givenname: Na orcidid: 0000-0001-9545-3050 surname: Li fullname: Li, Na email: nali@seas.harvard.edu organization: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA |
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| SubjectTerms | Communication networks Decision making Distributed generation Energy management Energy sources Frequency regulation Heuristic algorithms Learning Markov processes Mathematical models Power system dynamics Power systems Reinforcement learning smart grid Smart sensors voltage control |
| Title | Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges |
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