# Safe Reinforcement Learning-based Automatic Generation Control

arXiv: https://arxiv.org/abs/2507.17868 · HTML: https://mlsanity.com/paper/2507.17868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Computationally Efficient Safe Reinforcement Learning for Power Systems](https://mlsanity.com/paper/2110.10333/) (2021)
- [Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems](https://mlsanity.com/paper/2103.14186/) (2021)
- [On the Use of Reinforcement Learning for Attacking and Defending Load Frequency Control](https://mlsanity.com/paper/2303.15736/) (2023)
- [On the Use of Safety Critical Control for Cyber-Physical Security in the Smart Grid](https://mlsanity.com/paper/2303.02197/) (2023)

## Abstract (arXiv)

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.