Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control

Understand

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.