Chance-Constrained Outage Scheduling using a Machine Learning Proxy

TL;DR

This paper introduces a chance-constrained outage scheduling method that leverages machine learning to efficiently predict system outcomes, resulting in more cost-effective and reliable maintenance plans for large power networks.

Contribution

It presents a novel distributed scenario-based optimization framework incorporating machine learning proxies to improve outage scheduling in complex power systems.

Findings

Achieves cheaper outage plans with higher reliability on IEEE networks.

Demonstrates the effectiveness of machine learning proxies in large-scale optimization.

Outperforms traditional methods in cost and reliability metrics.

Abstract

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.