Determination and allocation of typical load profiles to the eligible consumers

Abstract

Abshaci-Information concerning the consumer's Load Profile (LP) in a deregulated environment is of initial importance lor load balancing purposes and for billing the consumers, which deviated from contracted schedules. Establishment of a system that enables consumers, without the quarter-hourly load measuremeuts, to participate at the retail market, requires the Typical Load Profiles (TLP) of the various types of consumers. Furthermore, simple and straightforward method for the TLP assignment to the particular eligible consumers should be established. In the paper, a methodology for consumer's load profdes classification is presented. The Measured Load Profiles (MLP) are classified by hierarchic clustering method and fuzzy logic. Probability neural network is used to assign the TLP to the particular group of consumers. The results demonstrate that the proposed method is efficient for and assigning TLP to the consumers.

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

“…In [32], hierarchical clustering and FCM with PNNs were used in order to study the distribution of deviations between the estimated purchased electricity at the power market and the actual consumption. In [33][34], a probabilistic model using beta probability distribution function (beta pdf) and a formulating method for energy LP for domestic buildings were proposed respectively to analyze residential consumer loads.…”

Load Time-Series Classification Based on Pattern Recognition Methods

“…It is obvious that the a priori index of customer's activity is not representative for load curves, which is also confirmed by (Chicco et al, 2003a;-, 2003b;-, 2006;Figueiredo et al, 2003). This can not be generalized since it may vary within countries and distribution companies depending on the respective data of customers (Gerbec et al, 2003;. But the proposed methodology can be applied directly to the respective set of customers, in order to study their respective load behaviour.…”

“…• the mean index adequacy (Chicco et al, 2003a;-, 2003b;, • the clustering dispersion indicator (Chicco et al, 2003a;-, 2003b;-, 2006), • the similarity matrix indicator (Chicco et al, 2004), • the Davies-Bouldin indicator (Beccali et al, 2004;-, 2006;Gerbec et al, 2004;, • the modified Dunn index , • the scatter index ) and • the mean square error (Gerbec et al, 2003;.…”

“…In the second stage, classification of customers is performed by the same methods and measures, together with the representative load patterns of customers being obtained from the first stage. The basic contribution of this methodology is that its first stage enables the modification of the representative day, such as the most populated day, and avoids the a priori definition of a single day or the "mean" day of a specific time period (as it is suggested by previously published methodologies (Chicco et al, 2002 ;-,2003a;-,2003b;-,2006;Figueiredo et al, 2003;Gerbec et al, 2003 ;). The results of the second stage provide valuable information for electricity suppliers in competitive energy markets.…”

“…This can not be generalized since it may vary within countries and distribution companies, depending on the respective data of customers (Gerbec et al, 2003 ;. Finally, the pattern recognition methodology for the classification of the daily chronological load curves of the Greek power system is presented, in order to estimate their respective representative daily load profiles, which can be used for load forecasting and the feasibility studies of demand side management programs.…”