Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees

Abstract

This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees. Building on Lyapunov stability theory and safety-critical control, we derive sufficient conditions on the distributed controller design that ensure the stability and transient frequency safety of the closed-loop system. Our idea of distributed dynamic budget assignment makes these conditions less conservative than those in recent literature, so that they can impose less stringent restrictions on the search space of control policies. We construct neural network controllers that parameterize such control policies and use reinforcement learning to train an optimal one. Simulations on the IEEE 39-bus network illustrate the guaranteed stability and safety properties of the controller along with its significantly improved optimality.

References in corpus (6)

• Constrained Consensus
• Synchronization in Complex Oscillator Networks and Smart Grids
• Design and Stability of Load-Side Primary Frequency Control in Power Systems
• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
• Training robust neural networks using Lipschitz bounds
• Inverse Stability Problem and Applications to Renewables Integration