# Fairly Extreme: Minimizing Outages Equitably

arXiv: https://arxiv.org/abs/2310.01348 · HTML: https://mlsanity.com/paper/2310.01348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2023.

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

- [Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints](https://mlsanity.com/paper/2407.18989/) (2024)

## Abstract (arXiv)

This paper focuses on the problem of minimizing the outages due to extreme events on the power grid equitably among all customers of the grid. The paper presents two ways of incorporating fairness into the existing formulations that seek to minimize the total outage in the power grid. The first method is motivated by existing literature on incorporating fairness in optimization problems and this is done by modifying the problem's objective function. The second method introduces a novel notion of fairness, termed varepsilon-fairness, that can be incorporated into existing problem formulations through a single second-order cone constraint. Both these methods are very general and can be used to incorporate fairness in existing planning and operational optimization problems in the power grid and beyond. Extensive computational case studies that examine the effectiveness of both these methods to characterize fairness is presented followed by conclusions and ways forward.