Research on Data-Driven Distribution Network Planning Method

• Published in: Scientific programming 2022-08, Vol.2022, p.1-7
• Main Authors: Liu, Xingdong, Xu, Daolin, Peng, Hui, Xu, XiaoChuan, Liu, HuiDeng, Zhang, Xin
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Automation; Big Data; Construction; Data analysis; Economic development; Electricity; Electricity distribution; Feature recognition; Interactive systems; Network reliability; Network topologies; Optimization; Optimization models; Pattern recognition; Planning; Power consumption; Power supply; Production management; Reconfiguration; Reconstruction; Society; Wiring; Workloads
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2022/7838269

With the development of the intelligent and interactive power system, the elements of distribution network planning continue to increase. The distribution network connects the transmission system and individuals, directly affects the individual’s power consumption experience, and is a key link in the power system. A reasonable planning scheme can not only improve the power supply capacity and reliability of the distribution network but also fully apply the data of each system in the distribution network to realize the optimal planning of the medium-voltage distribution network driven by data. Firstly, this paper constructs the CIM model and the distribution network topology model and establishes the wiring pattern recognition feature library. The network reconstruction and planning method research was carried out for the target line, and a typical operation scenario of the distribution network was generated. At the same time, based on the time period network loss index, the distribution network reconfiguration optimization model and distribution network expansion planning model are established, and the solution method of the distribution network reconstruction and expansion planning model is expounded. A reconstruction optimization scheme with the best overall network loss performance is in the network operation scenario. The experimental results of the final example show that based on the proposed time period network loss index, the overall operation loss of the distribution network in a period can be calculated more accurately, and the optimized planning scheme is more suitable for the load power consumption characteristics of the region, and the method has certain feasibility.