Open AccessApproach for smart meter load profiling in Monte Carlo simulation applications
Author(s) -
Khan Zafar A.,
Jayaweera Dilan
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.2084
Subject(s) - cluster analysis , smart grid , smart meter , computer science , monte carlo method , energy consumption , data mining , robustness (evolution) , renewable energy , metering mode , profiling (computer programming) , real time computing , distributed computing , engineering , artificial intelligence , electrical engineering , mathematics , statistics , operating system , mechanical engineering , biochemistry , chemistry , gene
Smart grids introduce new technological elements into power systems which take prevalent challenges to a new level by shaping parameters of power systems towards a complex regime of uncertainties. Rapid proliferation of advanced metering infrastructure (AMI) and integration of renewable energy sources in smart grids increase system‐wide complexities. This study proposes an innovative approach to classify the energy consumptions of smart meter customers with typical profiles by processing with multi‐layered clustering of energy consumption data of smart consumers extracted from the AMI. There are two stages for the approach of which the first stage analyses the data for intra‐cluster similarity of energy consumption patterns and in case the patterns do not have a high intra‐cluster similarity, they are fed back for re‐clustering with multi‐layered clustering process until the clearly identifiable energy patterns with high intra‐cluster similarity is obtained. The second stage linearises the complex energy patterns using interpolant and curve fitting techniques until stabilised profiles are obtained. This study also proposes a methodology for smart meter load modelling for Monte Carlo (MC) simulation applications to reduce the computing time compared with traditional alternatives. This study validates the robustness of the approach and provides the corroboration of the method for MC simulation applications in a smart grid environment.