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

arXiv: https://arxiv.org/abs/2306.01570 · HTML: https://mlsanity.com/paper/2306.01570/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments](https://mlsanity.com/paper/2211.15755/) (2022)
- [Data-Driven Screening of Network Constraints for Unit Commitment](https://mlsanity.com/paper/1907.04694/) (2019)
- [Reduced Optimal Power Flow Using Graph Neural Network](https://mlsanity.com/paper/2206.13591/) (2022)
- [Neural Network-based Power Flow Model](https://mlsanity.com/paper/2112.08418/) (2021)
- [Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements](https://mlsanity.com/paper/2112.00926/) (2021)
- [Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model](https://mlsanity.com/paper/2112.13681/) (2021)
- [Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations](https://mlsanity.com/paper/2208.06742/) (2022)
- [An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment with Corrective Network Reconfiguration](https://mlsanity.com/paper/1912.01764/) (2019)
- [Security Constrained Unit Commitment with Corrective Transmission Switching](https://mlsanity.com/paper/2001.00597/) (2020)
- [Machine Learning Assisted Approach for Security-Constrained Unit Commitment](https://mlsanity.com/paper/2111.09824/) (2021)
- [Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids](https://mlsanity.com/paper/2110.11498/) (2021)
- [Reducing Congestion-Induced Renewable Curtailment with Corrective Network Reconfiguration in Day-Ahead Scheduling](https://mlsanity.com/paper/2002.01090/) (2020)
- [Network Reconfiguration Impact on Renewable Energy System and Energy Storage System in Day-Ahead Scheduling](https://mlsanity.com/paper/2103.13321/) (2021)
- [Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation](https://mlsanity.com/paper/2202.12416/) (2022)
- [Two-Stage Robust Unit Commitment for Co-Optimized Electricity Markets: An Adaptive Data-Driven Approach for Scenario-Based Uncertainty Sets](https://mlsanity.com/paper/1803.06676/) (2018)
- [The Benefits of Hydrogen Energy Transmission and Conversion Systems to the Renewable Power Grids: Day-ahead Unit Commitment](https://mlsanity.com/paper/2206.14279/) (2022)

## Built on by (most cited first, 6 of 6)

- [Graph Neural Network-based Power Flow Model](https://mlsanity.com/paper/2307.02049/) (2023)
- [N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network](https://mlsanity.com/paper/2402.06226/) (2024)
- [Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting](https://mlsanity.com/paper/2310.08793/) (2023)
- [Graph neural networks for power grid operational risk assessment under evolving grid topology](https://mlsanity.com/paper/2405.07343/) (2024)
- [Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting](https://mlsanity.com/paper/2411.06268/) (2024)
- [A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment](https://mlsanity.com/paper/2604.21891/) (2026)

## Abstract (arXiv)

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-θ 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