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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Vydáno v:IEEE transactions on smart grid Ročník 13; číslo 4; s. 2935 - 2958
Hlavní autoři: Chen, Xin, Qu, Guannan, Tang, Yujie, Low, Steven, Li, Na
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
ISSN:1949-3053, 1949-3061
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2022.3154718