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Stability Constrained Reinforcement Learning for Real-Time Voltage Control

Shi, Yuanyuan and Qu, Guannan and Low, Steven and Anandkumar, Anima and Wierman, Adam (2022) Stability Constrained Reinforcement Learning for Real-Time Voltage Control. In: 2022 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 2715-2721. ISBN 978-1-6654-5196-.

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Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of formal stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in distribution grids and we prove that the proposed approach provides a formal voltage stability guarantee. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of the approach in case studies, where the proposed method can reduce the transient control cost by more than 30% and shorten the response time by a third compared to a widely used linear policy, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability.

Item Type:Book Section
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URLURL TypeDescription ItemDiscussion Paper ItemCode and data
Shi, Yuanyuan0000-0002-6182-7664
Qu, Guannan0000-0002-5466-3550
Low, Steven0000-0001-6476-3048
Anandkumar, Anima0000-0002-6974-6797
Wierman, Adam0000-0002-5923-0199
Additional Information:Code and data are available at
Record Number:CaltechAUTHORS:20230315-336401000.3
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120062
Deposited By: George Porter
Deposited On:16 Mar 2023 19:03
Last Modified:16 Mar 2023 19:03

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