The Cluster Method of Heterogeneous Distributed Units in a Low Voltage Distribution Network

Tao Wang, Hongshan Li, Huihui Song, Meng Liu and Hongchen Liu
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Tao Wang: Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Hongshan Li: Department of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China
Huihui Song: Department of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China
Meng Liu: Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250001, China
Hongchen Liu: Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China

Energies, 2022, vol. 15, issue 13, 1-11

Abstract: With the large amounts of small capacity and heterogeneous distributed electricity units connected to the distribution power network, there exist increasingly complex management challenges. In this paper, a new management scheme that can classify and divide the distributed units according to their adjustable characteristics is proposed, which consequently forms an effective collection of fragmented adjustable ability and promotes the utilization of micropower resources. Inspired by the social division of labor in the biological community, the approach is based on a logical aggregation with the division of labor. A feature extraction method was acquired on the basis of the daily output curve, which reduces the data dimension and, subsequently, clusters the output feature points by the K-means algorithm. The simulation is performed by taking the measured output curve of low voltage distributed units on the low voltage side. The experimental results analyze the characteristics of seven classes of distributed units, allocate two main features, and reorganize them into a cluster; so, the “5-dimensional feature array” is reduced to “2-dimensional feature points”. The results demonstrate that the proposed cluster method can enable the power grid to identify and classify the distributed units automatically.

Keywords: logical aggregation; day-ahead output curve; cluster; K-means algorithm; feature extraction (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: 2022
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