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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 (2021) Stability Constrained Reinforcement Learning for Real-Time Voltage Control. . (Unpublished)

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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:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
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:Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Subject Keywords:reinforcement learning, Lyapunov stability, voltage control
Record Number:CaltechAUTHORS:20220304-172338061
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113732
Deposited By: George Porter
Deposited On:07 Mar 2022 20:33
Last Modified:07 Mar 2022 20:33

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