Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit
  Commitment
Arun Venkatesh Ramesh Xingpeng Li
Security-constrained unit commitment (SCUC) is a computationally complex
process utilized in power system day-ahead scheduling and market clearing. SCUC
is run daily and requires state-of-the-art algorithms to speed up the process.
The constraints and data associated with SCUC are both geographically and
temporally correlated to ensure the reliability of the solution, which further
increases the complexity. In this paper, an advanced machine learning (ML)
model is used to study the patterns in power system historical data, which
inherently considers both spatial and temporal (ST) correlations in
constraints. The ST-correlated ML model is trained to understand spatial
correlation by considering graph neural networks (GNN) whereas temporal
sequences are studied using long short-term memory (LSTM) networks. The
proposed approach is validated on several test systems namely, IEEE 24-Bus
system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina
(SC) 500-Bus system. Moreover, B-{\theta} and power transfer distribution
factor (PTDF) based SCUC formulations were considered in this research.
Simulation results demonstrate that the ST approach can effectively predict
generator commitment schedule and classify critical and non-critical lines in
the system which are utilized for model reduction of SCUC to obtain
computational enhancement without loss in solution quality