Abstract
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.
Highlights
With the development of smart grids and in response to carbon dioxide (CO2) reduction targets, penetrations of intermittent renewable energy sources (RES), wind and solar power have been intensified
This paper 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 advanced metering infrastructure (AMI)
The paper proposes a methodology for smart meter load modelling for Monte Carlo (MC) simulation applications to reduce the computing time compared with traditional alternatives
Summary
With the development of smart grids and in response to carbon dioxide (CO2) reduction targets, penetrations of intermittent RES, wind and solar power have been intensified. As given by [18] owing to the applicability, proven robustness, higher processing speed, and proven efficiency, a direct clustering technique namely k-means clustering is incorporated in this approach for the analysis of energy consumption data of smart meters in the first stage and it is extended to extract clear load patterns. Another added advantage of using k-means clustering in this study is the fast processing speed of integrated operation of k-mean clustering and MC simulation. The energy classifiers are defined which carry the information of the magnitude of load, frequency and classification number according to the individual profile
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