# Adaptive Power System Emergency Control using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1903.03712 · HTML: https://mlsanity.com/paper/1903.03712/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2019.

## Built on by (most cited first, 27 of 27)

- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis](https://mlsanity.com/paper/2303.05473/) (2023)
- [Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning](https://mlsanity.com/paper/2101.05317/) (2021)
- [Value Activation for Bias Alleviation: Generalized-activated Deep Double Deterministic Policy Gradients](https://mlsanity.com/paper/2112.11216/) (2021)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map](https://mlsanity.com/paper/2405.15831/) (2024)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks](https://mlsanity.com/paper/2408.03685/) (2024)
- [Blackout Mitigation via Physics-guided RL](https://mlsanity.com/paper/2401.09640/) (2024)
- [Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2102.00077/) (2021)
- [Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery](https://mlsanity.com/paper/2111.14352/) (2021)
- [Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control](https://mlsanity.com/paper/2206.01272/) (2022)
- [Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources](https://mlsanity.com/paper/2210.07013/) (2022)
- [Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency](https://mlsanity.com/paper/2405.05521/) (2024)
- [Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer](https://mlsanity.com/paper/2109.01659/) (2021)
- [Robust Stability of Neural Network-controlled Nonlinear Systems with Parametric Variability](https://mlsanity.com/paper/2109.05710/) (2021)
- [Safe Reinforcement Learning for Grid Voltage Control](https://mlsanity.com/paper/2112.01484/) (2021)
- [Why Reinforcement Learning in Energy Systems Needs Explanations](https://mlsanity.com/paper/2405.18823/) (2024)
- [Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents](https://mlsanity.com/paper/2102.08430/) (2021)
- [Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures](https://mlsanity.com/paper/2205.02108/) (2022)
- [Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning](https://mlsanity.com/paper/2212.02715/) (2022)
- [Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning](https://mlsanity.com/paper/2506.08893/) (2025)
- [Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.08310/) (2025)

## Abstract (arXiv)

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.