mlsanityData to 7 Oct 2026

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

arXiv 2306.01570 ↗First posted 2 Jun 2023cs.LG cs.SY eess.SYcited by 6

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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

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

22 of 40 references.

References · 22

  1. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  3. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  4. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  5. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
  6. 2019Learning to Solve Large-Scale Security-Constrained Unit Commitment Problemscited by 38
  7. 2022Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitmentscited by 12
  8. 2019Data-Driven Screening of Network Constraints for Unit Commitmentcited by 11
  9. 2022Reduced Optimal Power Flow Using Graph Neural Networkcited by 11
  10. 2021Neural Network-based Power Flow Modelcited by 10
  11. 2021Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurementscited by 8
  12. 2021Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Modelcited by 8
10 more
  1. 2022Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operationscited by 6
  2. 2019An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment with Corrective Network Reconfigurationcited by 5
  3. 2020Security Constrained Unit Commitment with Corrective Transmission Switchingcited by 5
  4. 2021Machine Learning Assisted Approach for Security-Constrained Unit Commitmentcited by 5
  5. 2021Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Gridscited by 4
  6. 2020Reducing Congestion-Induced Renewable Curtailment with Corrective Network Reconfiguration in Day-Ahead Schedulingcited by 3
  7. 2021Network Reconfiguration Impact on Renewable Energy System and Energy Storage System in Day-Ahead Schedulingcited by 3
  8. 2022Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradationcited by 3
  9. 2018Two-Stage Robust Unit Commitment for Co-Optimized Electricity Markets: An Adaptive Data-Driven Approach for Scenario-Based Uncertainty Setscited by 2
  10. 2022The Benefits of Hydrogen Energy Transmission and Conversion Systems to the Renewable Power Grids: Day-ahead Unit Commitmentcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 1.

6 papers.

  1. 2023Graph Neural Network-based Power Flow Modelcited by 5
  2. 2024N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Networkcited by 3
  3. 2023Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecastingcited by 2
  4. 2024Graph neural networks for power grid operational risk assessment under evolving grid topologycited by 1
  5. 2024Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splittingcited by 1
  6. 2026A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitmentcited by 0