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Data-augmentation acceleration framework by graph neural network for near-optimal unit commitment

Author

Listed:
  • Wei, Lishen
  • Ai, Xiaomeng
  • Fang, Jiakun
  • Cui, Shichang
  • Gao, Liqian
  • Li, Kun
  • Wen, Jinyu

Abstract

The acceleration of large-scale unit commitment (UC) problems has been a long-standing challenge in the power industry. Independent system operators are required to find a near-optimal solution within a short timeframe, which is a challenging task in practice. In this paper, to obtain near-optimal commitments rapidly, we develop a data-augmentation acceleration framework by neural network. First, a data augmentation technique is developed to enrich the training dataset with numerous near-optimal solutions instead of just incorporating the optimal one. Secondly, we employ the graph neural network (GNN) to learn the commitments, where the UC problems are treated as graphs to represent the relationship between variables and constraints. Last, a repair method is introduced to address potential inaccuracies caused by GNN, converting infeasible or suboptimal solutions into near-optimal ones. The proposed framework was validated on the RTS-GMLC and Jiangsu province systems. Numerical experiments with the Gurobi solver demonstrated a remarkable acceleration in finding near-optimal solutions, achieving speed-ups of 10.7× and 15.9×, respectively. The quickly obtained near-optimal solutions also greatly aid in finding optimal solutions. Furthermore, integrating the proposed framework into the open-source solver significantly outperformed the Gurobi solver in finding near-optimal commitments.

Suggested Citation

  • Wei, Lishen & Ai, Xiaomeng & Fang, Jiakun & Cui, Shichang & Gao, Liqian & Li, Kun & Wen, Jinyu, 2025. "Data-augmentation acceleration framework by graph neural network for near-optimal unit commitment," Applied Energy, Elsevier, vol. 377(PD).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s030626192401715x
    DOI: 10.1016/j.apenergy.2024.124332
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    References listed on IDEAS

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    1. de Mars, Patrick & O’Sullivan, Aidan, 2021. "Applying reinforcement learning and tree search to the unit commitment problem," Applied Energy, Elsevier, vol. 302(C).
    2. Álinson S. Xavier & Feng Qiu & Shabbir Ahmed, 2021. "Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 739-756, May.
    3. Bernard Knueven & James Ostrowski & Jean-Paul Watson, 2020. "On Mixed-Integer Programming Formulations for the Unit Commitment Problem," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 857-876, October.
    4. Zhang, Menghan & Yang, Zhifang & Lin, Wei & Yu, Juan & Dai, Wei & Du, Ershun, 2021. "Enhancing economics of power systems through fast unit commitment with high time resolution," Applied Energy, Elsevier, vol. 281(C).
    5. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
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