Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment

TL;DR

This paper introduces a spatio-temporal deep learning model combining GNN and LSTM to improve the efficiency of security-constrained unit commitment in power systems by capturing spatial and temporal correlations in data.

Contribution

It presents a novel ST deep learning approach that enhances SCUC computational efficiency through spatial-temporal pattern recognition, validated on multiple test systems.

Findings

Effective prediction of generator commitment schedules.

Successful classification of critical and non-critical lines.

Achieved computational speed-up without sacrificing solution quality.

Abstract

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