A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm

Abstract

The clustering of any type of consumers (residential, commercial, industrial) is of great importance in the operation of Smart Grids. In this paper, we propose a three-stage hierarchical scheme for residential consumers' partitioning using the Hierarchical clustering algorithm. The aim of this study is to cluster the consumers in well-separated and compact clusters using information from the near past (almost real time). The usage of electricity from a resident to another varies and this information can be used from the system operator for improving the efficiency of the distribution network. The first stage corresponds to the consumers' clustering of the distribution grid using data driven every three minutes (simulation time) from the meter of each residency. The procedure of the second stage takes part every a specific number of hours, called h, that is defined by the user. The average value of each of the k*20 clusters formed the last h hours is used as input for the hierarchical algorithm. In the third stage and in the end of each h hours, the average value of the data in each cluster is calculated and each consumer is reassigned to the cluster where the a distance metric is minimized. The results of the second stage provide deeper information about the load patterns existing each hour in the distribution grid. This information can be used from suppliers to design the energy tariffs for suiting better to the consumers' needs. This approach is tested using the IEEE-13 test feeder. The data are driven from 56 residencies.

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

“…In the current study, the delineation of city zones is predicated on electricity-related criteria, as outlined in [35]. Significantly, the process of determining zones in smart cities facilitates the introduction of specialized and customized services tailored to specific zones [6,35]. It should be noted that that the partitioning of a smart city into zones can be approached in diverse ways, influenced by various factors.…”

“…In the current study, the delineation of city zones is predicated on electricity-related criteria, as outlined in [35]. Significantly, the process of determining zones in smart cities facilitates the introduction of specialized and customized services tailored to specific zones [6,35].…”

“…Efforts to promote efficient electricity consumption have facilitated the division of urban areas into distinct electricity zones, as noted in [6]. These zones bear resemblance to microgrids, as they may house their own generation sources, such as renewables, alongside their consumer base [7].…”

A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data

“…On the other hand, Nasiakou et al [31] introduced a three-stage scheme with hierarchical clustering algorithm to partition consumers. The first hierarchical clustering is performed on a three minute basis data in h hours to result in 20 × h sets of k clusters, and the second one is performed on the average values of these clusters.…”