Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control

TL;DR

This paper introduces a stability-constrained reinforcement learning approach for real-time voltage control in power systems, ensuring system stability through Lyapunov functions and monotone neural networks, and demonstrating significant improvements over traditional methods.

Contribution

It proposes a novel RL method with explicit stability guarantees using Lyapunov functions and monotone neural networks for decentralized voltage control.

Findings

Reduces transient control cost by over 25%.

Shortens voltage recovery time by 21.5%.

Always maintains voltage stability, unlike standard RL methods.

Abstract

Deep reinforcement learning 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 explicit stability and safety guarantees. In this paper, we propose a stability-constrained reinforcement learning (RL) method for real-time voltage control, that guarantees system stability both during policy learning and deployment of the learned policy. The key idea underlying our approach is an explicitly constructed Lyapunov function that leads to a sufficient structural condition for stabilizing policies, i.e., monotonically decreasing policies guarantee stability. We incorporate this structural constraint with RL, by parameterizing each local voltage controller using a monotone neural network, thus ensuring the stability constraint is satisfied by design. We demonstrate the effectiveness of our approach in both single-phase and three-phase IEEE test feeders, where the proposed method can reduce the transient control cost by more than 25% and shorten the voltage recovery time by 21.5% on average compared to the widely used linear policy, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability.