Data-model Hybrid-driven Approach for Unit Commitment Considering Conditional Transmission Section Limits

Hong Yu; Yong Zhao; Manli Yan; Yuanzheng Li; Yaowen Yu

doi:10.35833/MPCE.2025.000658

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Data-model Hybrid-driven Approach for Unit Commitment Considering Conditional Transmission Section Limits

Original Paper | 更新时间：2026-09-28

- Data-model Hybrid-driven Approach for Unit Commitment Considering Conditional Transmission Section Limits
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1708-1719(2026)
- 作者机构：
  
  1.School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
  2.Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology), Ministry of Education, Wuhan, China
- 作者简介：
  
  Hong Yu received the bachelor’s degree from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2023. She is currently pursuing the master’s degree in control science and engineering with the School of Artificial Intelligence and Automation, HUST. Her research interests include scheduling optimization in power systems and artificial intelligence method. Email:m202373572@hust.edu.cn
  Yong Zhao received the B.S., M.S., and Ph.D. degrees from Huazhong University of Science and Technology (HUST), Wuhan, China, in 1989, 1992, and 1996, respectively. He is currently working in the School of Artificial Intelligence and Automation, HUST. His research interests include machine learning, power market, power dispatching, and decision theory. Email:zhiwei98530@hust.edu.cn
  Manli Yan received the bachelor’s and master’s degrees from Tongji University, Shanghai, China, in 2019 and 2021, respectively. She is currently pursuing the Ph.D. degree in control engineering with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. Her research interests include electricity market, artificial intelligence-based bidding, and multi-agent system. Email:d202481469@hust.edu.cn
  Yuanzheng Li received the M.S. degree in electrical engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2011, and the Ph.D. degree in electrical engineering from the South China University of Technology, Guangzhou, China, in 2015. He is currently a Full Professor with HUST. His research interests include deep learning, reinforcement learning, smart grid, optimal power system and microgrid scheduling, and decision making. Email:Yuanzheng_ Li@hust.edu.cn
  Yaowen Yu
- 基金信息：
- DOI：10.35833/MPCE.2025.000658
  CLC：
- Received：18 July 2025，
  
  Revised：2025-11-13，
  
  Accepted：23 January 2026，
  
  Online First：02 April 2026，
  
  Published：15 September 2026
- 稿件说明：
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Yu Hong,Zhao Yong,Yan Manli,et al.Data-model Hybrid-driven Approach for Unit Commitment Considering Conditional Transmission Section Limits[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1708-1719.
DOI：

Yu Hong,Zhao Yong,Yan Manli,et al.Data-model Hybrid-driven Approach for Unit Commitment Considering Conditional Transmission Section Limits[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1708-1719. DOI： 10.35833/MPCE.2025.000658.

摘要

Abstract

Conditional transmission section limits (C-TSLs) depend on the actual operational state of the power grid

such as commitment status and reserve capacity

exacerbating the computational complexity of the unit commitment problem. To overcome this complexity

this paper proposes a data-model hybrid-driven approach for solving unit commitment problems. Furthermore

a unit commitment prediction algorithm is proposed to reduce the problem scale by fixing a subset of unit commitment variables

thereby eliminating inactive C-TSL intervals. Specifically

the proposed algorithm incorporates a convolutional attention module to perform deep feature extraction and enhancement on time series data

complemented by a multi-head cross-attention mechanism designed to synthesize temporal and non-temporal features. The introduction of attention mechanisms enhances the prediction performance of the proposed algorithm. Numerical results demonstrate that the proposed approach significantly reduces the number of variables and constraints in the unit commitment model

thereby expediting the solution process while maintaining high solution accuracy.

关键词

Keywords

references

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