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Online Distribution Network Scheduling via Provably Robust Learning Approach

Author

Listed:
  • Naixiao Wang

    (Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510220, China)

  • Xinlei Cai

    (Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510220, China)

  • Linwei Sang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Tingxiang Zhang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Zhongkai Yi

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Ying Xu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS.

Suggested Citation

  • Naixiao Wang & Xinlei Cai & Linwei Sang & Tingxiang Zhang & Zhongkai Yi & Ying Xu, 2024. "Online Distribution Network Scheduling via Provably Robust Learning Approach," Energies, MDPI, vol. 17(6), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1361-:d:1355620
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    References listed on IDEAS

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    1. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
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