# Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

arXiv: https://arxiv.org/abs/2405.05521 · HTML: https://mlsanity.com/paper/2405.05521/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 15 of 15)

- [Calibrating Deep Neural Networks using Focal Loss](https://mlsanity.com/paper/2002.09437/) (2020)
- [Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions](https://mlsanity.com/paper/2205.10129/) (2022)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks](https://mlsanity.com/paper/2103.14779/) (2021)
- [Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence](https://mlsanity.com/paper/1405.0766/) (2014)
- [Strong SOCP Relaxations for the Optimal Power Flow Problem](https://mlsanity.com/paper/1504.06770/) (2015)
- [Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs](https://mlsanity.com/paper/2004.07156/) (2020)
- [Safe Reinforcement Learning for Emergency LoadShedding of Power Systems](https://mlsanity.com/paper/2011.09664/) (2020)
- [Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning](https://mlsanity.com/paper/2101.05317/) (2021)
- [Risk-Aware Learning for Scalable Voltage Optimization in Distribution Grids](https://mlsanity.com/paper/2110.01490/) (2021)
- [Relaxations of AC Maximal Load Delivery for Severe Contingency Analysis](https://mlsanity.com/paper/1710.07861/) (2017)
- [Dynamic Modeling of Cascading Failure in Power Systems](https://mlsanity.com/paper/1411.3990/) (2014)
- [Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations](https://mlsanity.com/paper/2111.11980/) (2021)
- [Contingency-Risk Informed Power System Design](https://mlsanity.com/paper/1305.0780/) (2013)
- [Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization](https://mlsanity.com/paper/2204.06543/) (2022)

## Built on by (most cited first, 2 of 2)

- [Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints](https://mlsanity.com/paper/2407.18989/) (2024)
- [Transmission Line Outage Probability Prediction Under Extreme Events Using Peter-Clark Bayesian Structural Learning](https://mlsanity.com/paper/2411.11980/) (2024)

## Abstract (arXiv)

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.