  Security-constrained unit commitment (SCUC) is solved for power system
day-ahead generation scheduling, which is a large-scale mixed-integer linear
programming problem and is very computationally intensive. Model reduction of
SCUC may bring significant time savings. In this work, a novel approach is
proposed to effectively utilize machine learning (ML) to reduce the problem
size of SCUC. An ML model using logistic regression (LR) algorithm is proposed
and trained with historical nodal demand profiles and the respective commitment
schedules. The ML outputs are processed and analyzed to reduce variables and
constraints in SCUC. The proposed approach is validated on several standard
test systems including IEEE 24-bus system, IEEE 73-bus system, IEEE 118-bus
system, synthetic South Carolina 500-bus system and Polish 2383-bus system.
Simulation results demonstrate that the use of the prediction from the proposed
LR model in SCUC model reduction can substantially reduce the computing time
while maintaining solution quality.
