IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924023468.html
   My bibliography  Save this article

Multi-scale spatio-temporal transformer: A novel model reduction approach for day-ahead security-constrained unit commitment

Author

Listed:
  • Liu, Mao
  • Kong, Xiangyu
  • Xiong, Kaizhi
  • Wang, Jimin
  • Lin, Qingxiang

Abstract

Security-constrained unit commitment (SCUC) in large-scale power systems faces significant computational challenges, particularly with increasing renewable energy integration. This paper introduces a multi-scale spatio-temporal transformer (MSTT) model for efficient SCUC problem reduction through three key innovations: a multi-scale ST attention mechanism integrating both hierarchical temporal attention and electrical distance-based spatial attention to capture complex system dependencies, a physics-informed position encoding method incorporating power system domain knowledge including electrical distance, power flow sensitivity, and generator stability characteristics, and an adaptive reduction strategy with dynamic threshold adjustment mechanism that automatically balances computational efficiency and solution reliability based on system states and prediction confidence. Experimental results on IEEE test systems demonstrate that the MSTT model achieves up to 69.5 % computational time reduction while maintaining solution optimality (base-normalized cost (BNC) ≤ 0.05 %), significantly outperforming existing approaches.

Suggested Citation

  • Liu, Mao & Kong, Xiangyu & Xiong, Kaizhi & Wang, Jimin & Lin, Qingxiang, 2025. "Multi-scale spatio-temporal transformer: A novel model reduction approach for day-ahead security-constrained unit commitment," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924023468
    DOI: 10.1016/j.apenergy.2024.124963
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924023468
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124963?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Boyou & Guo, Chuangxin & Chen, Zhe, 2024. "Frequency constrained unit commitment considering reserve provision of wind power," Applied Energy, Elsevier, vol. 361(C).
    2. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
    3. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky & Al-Awami, Ali Taleb, 2023. "Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid," Applied Energy, Elsevier, vol. 335(C).
    4. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).
    5. Ferahtia, Seydali & Houari, Azeddine & Cioara, Tudor & Bouznit, Mohammed & Rezk, Hegazy & Djerioui, Ali, 2024. "Recent advances on energy management and control of direct current microgrid for smart cities and industry: A Survey," Applied Energy, Elsevier, vol. 368(C).
    6. Cheng, Xianliang & Feng, Suzhen & Zheng, Hao & Wang, Jinwen & Liu, Shuangquan, 2022. "A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency," Energy, Elsevier, vol. 251(C).
    7. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Quasi-oppositional turbulent water flow-based optimization for cascaded short term hydrothermal scheduling with valve-point effects and multiple fuels," Energy, Elsevier, vol. 251(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).
    2. Hossein Lotfi & Mohammad Hasan Nikkhah, 2024. "Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method," Sustainability, MDPI, vol. 16(4), pages 1-28, February.
    3. Sifat, Md. Mhamud Hussen & Choudhury, Safwat Mukarrama & Das, Sajal K. & Pota, Hemanshu & Yang, Fuwen, 2025. "Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals," Applied Energy, Elsevier, vol. 377(PC).
    4. Wang, Wenhao & Tang, Aihong & Wei, Feng & Yang, Huiyuan & Xinran, Li & Peng, Jiao, 2025. "Electric vehicle charging load forecasting considering weather impact," Applied Energy, Elsevier, vol. 383(C).
    5. Mingxiang Li & Tianyi Zhang & Haizhu Yang & Kun Liu, 2024. "Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer," Energies, MDPI, vol. 17(20), pages 1-16, October.
    6. Saqib Akram & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Nasim Ullah & Alsharef Mohammad & Mohamed Emad Farrag, 2022. "Introducing Adaptive Machine Learning Technique for Solving Short-Term Hydrothermal Scheduling with Prohibited Discharge Zones," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    7. Zhao, Xiaoyu & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Hu, Jinxue & Zhang, Chenyang & Yuan, Xiaoyang, 2025. "A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening," Energy, Elsevier, vol. 327(C).
    8. Ba-Alawi, Abdulrahman H. & Nguyen, Hai-Tra & Yoo, ChangKyoo, 2024. "Coordinated operation for a resilient and green energy-water supply system: A co-optimization approach with flexible strategies," Energy, Elsevier, vol. 304(C).
    9. Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
    10. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    11. Tian, Zhirui & Sun, Wenpu & Wu, Chenye, 2025. "MLP-Carbon: A new paradigm integrating multi-frequency and multi-scale techniques for accurate carbon price forecasting," Applied Energy, Elsevier, vol. 383(C).
    12. Bunnak, Phumthep & Coniglio, Stefano & Galelli, Stefano, 2025. "Bridging theory and practice: Efficiently solving the unit commitment problem in production cost models," Energy, Elsevier, vol. 322(C).
    13. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2025. "Generative probabilistic forecasting of wind power: A Denoising-Diffusion-based nonstationary signal modeling approach," Energy, Elsevier, vol. 317(C).
    14. Wang, Danhao & Peng, Daogang & Huang, Dongmei & Zhao, Huirong & Qu, Bogang, 2025. "MMEMformer: A multi-scale memory-enhanced transformer framework for short-term load forecasting in integrated energy systems," Energy, Elsevier, vol. 322(C).
    15. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    16. Latify, Mohammad Amin & Mokhtari, Ali & Alavi-Eshkaftaki, Amin & Rajaei Najafabadi, Fatemeh & Hashemian, Seyed Nasrollah & Khaleghizadeh, Ali & Nezamabadi, Hossein & Yousefi Ramandi, Mostafa & Mozdawa, 2025. "Security-constrained unit commitment: Modeling, solutions and evaluations," Applied Energy, Elsevier, vol. 390(C).
    17. Fan, Jingmin & Zhong, Mingwei & Guan, Yuanpeng & Yi, Siqi & Xu, Cancheng & Zhai, Yanpeng & Zhou, Yongwang, 2024. "An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration," Energy, Elsevier, vol. 299(C).
    18. Dong, Jizhe & Li, Yuanhan & Zuo, Shi & Wu, Xiaomei & Zhang, Zuyao & Du, Jiang, 2023. "An intraperiod arbitrary ramping-rate changing model in unit commitment," Energy, Elsevier, vol. 284(C).
    19. Nguyen, Hai-Tra & Ba-Alawi, Abdulrahman H. & Yoo, ChangKyoo, 2024. "Flexible supply-demand side management towards a sustainable decentralized distribution network: A net-negative Water-energy-emissions Nexus assessment," Applied Energy, Elsevier, vol. 375(C).
    20. Devine, Mel & Lynch, Muireann Á, 2023. "Cournot competition in an integerconstrained electricity market model," Papers WP766, Economic and Social Research Institute (ESRI).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924023468. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.