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

TL;DR

This paper introduces a reinforcement learning approach for distributed transient frequency control in power systems, ensuring stability and safety while reducing conservativeness through dynamic budget assignment.

Contribution

It develops a novel distributed control framework with less conservative stability conditions and trains neural network controllers using reinforcement learning.

Findings

Guaranteed stability and safety in simulations

Significant improvement in control optimality

Less restrictive control policy constraints

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.