# Security Constrained Unit Commitment with Corrective Transmission Switching

arXiv: https://arxiv.org/abs/2001.00597 · HTML: https://mlsanity.com/paper/2001.00597/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

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

- [Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations](https://mlsanity.com/paper/2208.06742/) (2022)
- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [Machine Learning Assisted Approach for Security-Constrained Unit Commitment](https://mlsanity.com/paper/2111.09824/) (2021)
- [Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow](https://mlsanity.com/paper/2410.17460/) (2024)
- [Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering](https://mlsanity.com/paper/2606.20950/) (2026)

## Abstract (arXiv)

Traditionally, power system operations use a static network to deliver power and meet demand optimally. Network topology reconfiguration through transmission switching (TS) has gained significant interest recently to reduce the operational cost of power system operations. However, implementation of TS also causes large disturbance in the network and as a result the use of corrective transmission switching (CTS) in response to power system contingencies is currently being researched extensively. This paper emphasizes the importance of CTS to accomplish flexible transmission in N-1 security-constrained unit commitment (SCUC) model. An N-1 SCUC mathematical model implementing a dynamic network in the post-contingency scenario is proposed as opposed to current industry practices of static network in short-term operations. The proposed model is tested and validated on the IEEE 24-bus system. The proposed model results in cost-effective implementation and leads to overall reduced cost, and congestion reduction in the post-contingency scenario.