# Chance-Constrained Outage Scheduling using a Machine Learning Proxy

arXiv: https://arxiv.org/abs/1801.00500 · HTML: https://mlsanity.com/paper/1801.00500/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2018.

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

- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality](https://mlsanity.com/paper/2211.04584/) (2022)

## Abstract (arXiv)

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates.