# Machine Learning Assisted Approach for Security-Constrained Unit Commitment

arXiv: https://arxiv.org/abs/2111.09824 · HTML: https://mlsanity.com/paper/2111.09824/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Solving Mixed Integer Programs Using Neural Networks](https://mlsanity.com/paper/2012.13349/) (2020)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Data-Driven Screening of Network Constraints for Unit Commitment](https://mlsanity.com/paper/1907.04694/) (2019)
- [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)
- [Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids](https://mlsanity.com/paper/2110.11498/) (2021)
- [Unit Commitment using Nearest Neighbor as a Short-Term Proxy](https://mlsanity.com/paper/1611.10215/) (2016)
- [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)
- [Enhancing System Flexibility through Corrective Demand Response in Security-Constrained Unit Commitment](https://mlsanity.com/paper/2006.11519/) (2020)

## Built on by (most cited first, 5 of 5)

- [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)
- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network](https://mlsanity.com/paper/2402.06226/) (2024)
- [Graph neural networks for power grid operational risk assessment under evolving grid topology](https://mlsanity.com/paper/2405.07343/) (2024)

## Abstract (arXiv)

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.