A new proposal of typing load profiles to support the decision-making in the sector of electricity energy distribution

Abstract

This works presents a method of selection, classification and clustering load curves (SCCL) able to identify a greater diversity of consumption patterns existing in the distribution sector. The method was developed to estimate the features of a sample of load curves aiming to infer the behavior of consumption required by the population of consumers. The algorithm comprises four steps that extract essential features of a load curve of residential users with emphasis on seasonal and temporal profile, among others. The method was successfully implemented and tested in the context of an energy efficiency program developed by a company associated to the sector of electricity distribution (Electric Company of Maranhão, Brazil). This program comprised, among others, the analysis of the impact of replacing refrigerators in a universe of low-income consumers distributed by some cities in the state of Maranhão (Brazil), where it was possible to recognize patterns of load profiles using the typing method developed. The results were compared with a well known method of time series clustering already established in the literature, the Fuzzy C-Means (FCM). Based on the main features of a load profile, the analysis confirmed that the SCCL method was capable to identify a greater diversity of patterns, demonstrating the potential of this method in better characterization of types of demand which represents an important aspect to the process of decision making in the energy distribution sector. Furthermore, a well known index (Silhouette index) was also adopted to quantify the level of uniformity within and between clusters.