# A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications

arXiv: https://arxiv.org/abs/2409.16256 · HTML: https://mlsanity.com/paper/2409.16256/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 92 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## 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, 19 of 19)

- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Discovering Reinforcement Learning Algorithms](https://mlsanity.com/paper/2007.08794/) (2020)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Computationally Efficient Safe Reinforcement Learning for Power Systems](https://mlsanity.com/paper/2110.10333/) (2021)
- [Deep Learning for Biomedical Image Reconstruction: A Survey](https://mlsanity.com/paper/2002.12351/) (2020)
- [Safe Reinforcement Learning for Emergency LoadShedding of Power Systems](https://mlsanity.com/paper/2011.09664/) (2020)
- [Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems](https://mlsanity.com/paper/2103.14186/) (2021)
- [Contingency-constrained economic dispatch with safe reinforcement learning](https://mlsanity.com/paper/2205.06212/) (2022)
- [Safe reinforcement learning for multi-energy management systems with known constraint functions](https://mlsanity.com/paper/2207.03830/) (2022)
- [District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2112.10949/) (2021)
- [Understanding the Safety Requirements for Learning-based Power Systems Operations](https://mlsanity.com/paper/2110.04983/) (2021)
- [Coordinated Frequency Control through Safe Reinforcement Learning](https://mlsanity.com/paper/2202.00530/) (2022)
- [Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees](https://mlsanity.com/paper/2207.03329/) (2022)
- [Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network](https://mlsanity.com/paper/2311.15594/) (2023)
- [A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants](https://mlsanity.com/paper/2401.13020/) (2024)
- [Multi-agent Deep Reinforcement Learning for Distributed Load Restoration](https://mlsanity.com/paper/2306.14018/) (2023)
- [Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units](https://mlsanity.com/paper/2112.10459/) (2021)
- [Safe Reinforcement Learning for Grid Voltage Control](https://mlsanity.com/paper/2112.01484/) (2021)

## Built on by (most cited first, 1 of 1)

- [Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses](https://mlsanity.com/paper/2510.20314/) (2025)

## Abstract (arXiv)

The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased complexity and difficulty in the operation and control of power systems. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) has been widely implemented in recent years to handle the uncertainty in power systems. However, in critical infrastructures such as power systems, safety issues always receive top priority, while DRL may not always meet the safety requirements of power system operators. The concept of safe reinforcement learning (safe RL) is emerging as a potential solution to overcome the shortcomings of conventional DRL in the operation and control of power systems. This study provides a rigorous review of the latest research efforts focused on safe RL to derive power system control policies while accounting for the unique safety requirements of power grids. Furthermore, this study highlights various safe RL algorithms applied in diverse applications within the power system sector, from single grid-connected power converters, residential smart homes, and buildings to large power distribution networks. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in the operation and control of power system applications is also presented. This review aims to support research in the area of safe RL algorithms, embracing smart power system operation with safety constraints amid high uncertainty from DERs.