# Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

## TL;DR

This paper introduces machine learning techniques to improve the efficiency of solving large-scale security-constrained unit commitment problems in power systems, achieving significant speedups while maintaining solution quality.

## Contribution

The paper presents novel ML methods to predict redundant constraints and feasible regions, significantly accelerating MIP-based SCUC solutions with guarantees.

## Key findings

- Average 4.3x faster solutions with optimality guarantees
- Average 10.2x faster solutions without guarantees
- Method shows robustness against dataset shift

## Abstract

Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via Mixed-Integer Linear Programming, sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning (ML) techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions and affine subspaces where the optimal solution is likely to lie, leading to significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that, using the proposed techniques, SCUC can be solved on average 4.3x faster with optimality guarantees, and 10.2x faster without optimality guarantees, but with no observed reduction in solution quality. Out-of-distribution experiments provides evidence that the method is somewhat robust against dataset shift.