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Reinforcement Learning for Decision-Making and Control in Power Systems: Tutorial, Review, and Vision

Chen, Xin and Qu, Guannan and Tang, Yujie and Low, Steven and Li, Na (2021) Reinforcement Learning for Decision-Making and Control in Power Systems: Tutorial, Review, and Vision. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210831-203850710

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Abstract

With large-scale integration of renewable generation and distributed energy resources (DERs), 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. In this paper, we provide a tutorial on various RL techniques and how they can be applied to decision-making in power systems. We illustrate RL-based models and solutions in three key applications, frequency regulation, voltage control, and energy management. We conclude with three critical issues in the application of RL, i.e., safety, scalability, and data. Several potential future directions are discussed as well.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2102.01168arXivDiscussion Paper
ORCID:
AuthorORCID
Chen, Xin0000-0002-0952-0008
Qu, Guannan0000-0002-5466-3550
Tang, Yujie0000-0002-4921-8372
Low, Steven0000-0001-6476-3048
Additional Information:Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Subject Keywords:Reinforcement learning, smart grid, frequency regulation, voltage control, energy management
Record Number:CaltechAUTHORS:20210831-203850710
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210831-203850710
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110643
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Sep 2021 14:55
Last Modified:01 Sep 2021 14:55

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