Fast Infeasibility Analysis of Network-constrained Unit Commitment Model

Yanhong Liu; Xinfei Yan; Haiwang Zhong; Chongqing Kang

doi:10.35833/MPCE.2025.000736

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Fast Infeasibility Analysis of Network-constrained Unit Commitment Model

Original Paper | 更新时间：2026-09-28

- Fast Infeasibility Analysis of Network-constrained Unit Commitment Model
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1696-1707(2026)
- 作者机构：
  
  1.State Key Laboratory of Power System Operation and Control, Department of Electrical Engineering, Tsinghua University, Beijing, China
  2.Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China
- 作者简介：
  
  Yanhong Liu received the B.S. degree in electrical engineering from Tsinghua University, Beijing, China, in 2024, where he is currently working toward the Ph.D. degree in electrical engineering. His research interests focus on power system operation. Email:lyhthu@126.com;
  Xinfei Yan received the B.S. and Ph.D. degrees in electrical engineering from Tsinghua University, Beijing, China, in 2019 and 2025, respectively. He is currently a Post-doctor in Tsinghua University. His research interests include distributed optimization algorithm and power system operation. Email:yanxinfei1994@126.com
  Haiwang Zhong received the B.S. and Ph.D. degrees in electrical engineering from Tsinghua University, Beijing, China, in 2008 and 2013, respectively. He is currently an Associate Professor with the Department of Electrical Engineering, Tsinghua University. He is also the Director of Energy Internet Trading and Operation Research Department of Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China. He was a recipient of the ProSPER.Net Young Scientist Award and the Outstanding Postdoctoral Fellow of Tsinghua University. He currently serves as the Convenor of CIGRE C1.54 Working Group on assessment of system reserves and flexibility needs in the power systems of the future. His research interests include power system operation and optimization and electricity market. Email:zhonghw@tsinghua.edu.cn
  Chongqing Kang received the Ph.D. degree from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 1997. He is currently a Professor with Tsinghua University. His research interests include load forecasting, electricity market, power system planning, and generation scheduling optimization. Email:cqkang@tsinghua.edu.cn
- 基金信息：
- DOI：10.35833/MPCE.2025.000736
  CLC：
- Received：13 August 2025，
  
  Revised：2025-11-18，
  
  Accepted：05 January 2026，
  
  Online First：27 February 2026，
  
  Published：15 September 2026
- 稿件说明：
移动端阅览
Liu Yanhong,Yan Xinfei,Zhong Haiwang,et al.Fast Infeasibility Analysis of Network-constrained Unit Commitment Model[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1696-1707.
DOI：

Liu Yanhong,Yan Xinfei,Zhong Haiwang,et al.Fast Infeasibility Analysis of Network-constrained Unit Commitment Model[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1696-1707. DOI： 10.35833/MPCE.2025.000736.

摘要

Abstract

With the rapid development of the electricity market and the increasingly stringent operational standards

the longstanding infeasible network-constrained unit commitment (NCUC) issue in market operations is calling for effective and systematic methods that can analyze and repair infeasible NCUC models. Studies show that a set of irreducible infeasible subsets (IISs) may lead to infeasibility in a model. The identification and repair of IISs can correct the original infeasible model

while traditional filtering algorithms for IIS location often exhibit computational inefficiency. A fast infeasibility analysis framework of NCUC and a relaxation filtering algorithm are proposed in this paper to analyze infeasible NCUC models more comprehensively and effectively. The infeasibility analysis framework employs subsystem-based IIS analysis method

generating and analyzing simplified models to repair the infeasible NCUC. The relaxation filtering algorithm can achieve greater effectiveness of IIS identification in the holistic NCUC compared with traditional general filtering algorithms. The case studies show that the proposed framework and algorithm can analyze and repair infeasible NCUC models effectively

reducing the analytical time by at least one order of magnitude compared with that of traditional general methods.

关键词

Keywords

references

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