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Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network

Author

Listed:
  • Dechang Yang

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Wenlong Liao

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yusen Wang

    (School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

  • Keqing Zeng

    (Tandon School of Engineering, New York University, New York, NY 11201, USA)

  • Qiuyue Chen

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Dingqian Li

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

Abstract

To improve the reliability and reduce power loss of distribution network, the dynamic reconfiguration is widely used. It is employed to find an optimal topology for each time interval while satisfying all the physical constraints. Dynamic reconfiguration is a non-deterministic polynomial problem, which is difficult to find the optimal control strategy in a short time. The conventional methods solved complex model of dynamic reconfiguration in different ways, but only local optimal solutions can be found. In this paper, a data-driven optimization control for dynamic reconfiguration of distribution network is proposed. Through two stages that include rough matching and fine matching, the historical cases which are similar to current case are chosen as candidate cases. The optimal control strategy suitable for the current case is selected according to dynamic time warping (DTW) distances which evaluate the similarity between the candidate cases and the current case. The advantage of the proposed approach is that it does not need to solve complex model of dynamic reconfiguration, and only uses historical data to obtain the optimal control strategy for the current case. The cases study shows that the optimization results and the computation time of the proposed approach are superior to conventional methods.

Suggested Citation

  • Dechang Yang & Wenlong Liao & Yusen Wang & Keqing Zeng & Qiuyue Chen & Dingqian Li, 2018. "Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network," Energies, MDPI, vol. 11(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2628-:d:173298
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    Cited by:

    1. Yuxing Li & Xiao Chen & Jing Yu & Xiaohui Yang & Huijun Yang, 2019. "The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy," Energies, MDPI, vol. 12(3), pages 1-18, January.

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