# Safe Reinforcement Learning for Grid Voltage Control

arXiv: https://arxiv.org/abs/2112.01484 · HTML: https://mlsanity.com/paper/2112.01484/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions](https://mlsanity.com/paper/2004.07584/) (2020)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [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)
- [Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control](https://mlsanity.com/paper/2006.12667/) (2020)

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

- [A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications](https://mlsanity.com/paper/2409.16256/) (2024)

## Abstract (arXiv)

Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently. Reinforcement learning (RL) has been adopted as a promising approach to circumvent the issues; however, RL approach usually cannot guarantee the safety of the systems under control. In this paper, we discuss a couple of novel safe RL approaches, namely constrained optimization approach and Barrier function-based approach, that can safely recover voltage under emergency events. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark are performed to demonstrate the effectiveness of the proposed safe RL emergency control.