Safe Reinforcement Learning-based Automatic Generation Control

TL;DR

This paper introduces a control barrier function-based framework to ensure safety in reinforcement learning algorithms applied to automatic generation control in power systems, aiming to improve reliability and trustworthiness.

Contribution

It presents a novel safety framework integrating control barrier functions with reinforcement learning for power system control, addressing safety concerns in machine learning applications.

Findings

Developed safety barriers for reinforcement learning in power systems

Established a framework for safe automatic generation control

Provided foundation for future verification and deployment studies

Abstract

Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement learning agents for power system control applications, specifically in the context of automatic generation control. We develop the safety barriers and reinforcement learning framework necessary to establish trust in reinforcement learning as a safe option for automatic generation control - as foundation for future detailed verification and application studies.