Real-Time Contingency Analysis with Corrective Transmission Switching - Part I: Methodology
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Abstract (arXiv)
Transmission switching (TS) has gained significant attention recently. However, barriers still remain and must be overcome before the technology can be adopted by the industry. The state of the art challenges include AC feasibility and performance, computational complexity, the ability to handle large-scale real power systems, and dynamic stability. This two-part paper investigates these challenges by developing an AC TS-based real-time contingency analysis (RTCA) tool that can handle large-scale systems within a reasonable time. The tool proposes multiple corrective switching actions, after detection of a contingency with potential violations. To reduce the computational complexity, three heuristic algorithms are proposed to generate a small set of candidates for switching. Parallel computing is implemented to further speed up the solution time. Furthermore, stability analysis is performed to check for dynamic stability of proposed TS solutions. Part I of the paper presents a comprehensive literature review and the methodology. The promising results, tested on the Tennessee Valley Authority (TVA) system and actual energy management system (EMS) snapshots from Pennsylvania New Jersey Maryland (PJM) and the Electric Reliability Council of Texas (ERCOT), are presented in Part II. It is concluded that RTCA with corrective TS significantly reduces potential post-contingency violations and is ripe for industry adoption.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2016.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2025GridMind: LLMs-Powered Agents for Power System Analysis and Operationscited by 15
- 2024N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Networkcited by 3
- 2022Towards Practical Physics-Informed ML Design and Evaluation for Power Gridcited by 1
- 2026Scalable and Reliable State-Aware Inference of High-Impact N-k Contingenciescited by 0
- 2026Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distributioncited by 0
- 2026Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution4 Aug 2026
- 2026Scalable and Reliable State-Aware Inference of High-Impact N-k Contingencies10 Feb 2026
- 2025GridMind: LLMs-Powered Agents for Power System Analysis and Operations2 Sep 2025
- 2024N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network9 Feb 2024
- 2022Towards Practical Physics-Informed ML Design and Evaluation for Power Grid7 May 2022