# Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints

arXiv: https://arxiv.org/abs/2407.18989 · HTML: https://mlsanity.com/paper/2407.18989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems](https://mlsanity.com/paper/2007.01002/) (2020)
- [Online Mixed-Integer Optimization in Milliseconds](https://mlsanity.com/paper/1907.02206/) (2019)
- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs](https://mlsanity.com/paper/2004.07156/) (2020)
- [Relaxations of AC Maximal Load Delivery for Severe Contingency Analysis](https://mlsanity.com/paper/1710.07861/) (2017)
- [Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning](https://mlsanity.com/paper/1809.04153/) (2018)
- [Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations](https://mlsanity.com/paper/2111.11980/) (2021)
- [Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization](https://mlsanity.com/paper/2204.06543/) (2022)
- [Managing Wildfire Risk and Promoting Equity through Optimal Configuration of Networked Microgrids](https://mlsanity.com/paper/2306.03325/) (2023)
- [Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency](https://mlsanity.com/paper/2405.05521/) (2024)
- [Fairly Extreme: Minimizing Outages Equitably](https://mlsanity.com/paper/2310.01348/) (2023)
- [Equitable Networked Microgrid Topology Reconfiguration for Wildfire Risk Mitigation](https://mlsanity.com/paper/2402.04444/) (2024)

## Abstract (arXiv)

Timely and effective load shedding in power systems is critical for maintaining supply-demand balance and preventing cascading blackouts. To eliminate load shedding bias against specific regions in the system, optimization-based methods are uniquely positioned to help balance between economic and fairness considerations. However, the resulting optimization problem involves complex constraints, which can be time-consuming to solve and thus cannot meet the real-time requirements of load shedding. To tackle this challenge, in this paper we present an efficient machine learning algorithm to enable millisecond-level computation for the optimization-based load shedding problem. Numerical studies on both a 3-bus toy example and a realistic RTS-GMLC system have demonstrated the validity and efficiency of the proposed algorithm for delivering fairness-aware and real-time load shedding decisions.