Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network

Dechang Yang, Wenlong Liao, Yusen Wang, Keqing Zeng, Qiuyue Chen and Dingqian Li
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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

Energies, 2018, vol. 11, issue 10, 1-18

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.

Keywords: dynamic reconfiguration; data-driven; coarse matching; fine matching; dynamic time warping (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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