Online Learning of Stable Integer Variables in Unit Commitment Using Internal Information

Abstract

In a set of similar unit commitment (UC) problems, a variable is called a stable integer variable (SIV) if its optimal value is 0 or 1 on a regular basis, which reflects an inherent pattern in UC solutions. The computational complexity can be significantly reduced by fixing SIVs with a minor chance of accuracy loss. To identify SIVs, this letter proposes a method to collect internal solution information from the initial branch-and-bound process of artificial mixed-integer programming problems generated from the given problem. A distinct advantage of this method is that because no prior offline training is needed, learning is instance-specific and has no dependency on the training set. With identified SIVs, machine learning-based acceleration is achieved. Based on 50 test cases from publicly available and practical data, the proposed method improves the percentage of solved problems within the clearing time window from 68% to 96% compared with commercial solvers, counting the total time of data collection, prediction, and optimization.