Approach for smart meter load profiling in Monte Carlo simulation applications

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.

[1]  D.S. Kirschen,et al.  A probabilistic indicator of system stress , 2004, IEEE Transactions on Power Systems.

[2]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[3]  Chongqing Kang,et al.  Load profiling and its application to demand response: A review , 2015 .

[4]  Dilan Jayaweera,et al.  Security of Energy Supply With Change in Weather Conditions and Dynamic Thermal Limits , 2014, IEEE Transactions on Smart Grid.

[5]  D. Jayaweera,et al.  Smart meter data taxonomy for demand side management in smart grids , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[6]  Ioannis P. Panapakidis,et al.  Enhancing the clustering process in the category model load profiling , 2015 .

[7]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[8]  D. Jayaweera,et al.  Comparison of risk-based and deterministic security assessments , 2007 .

[9]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[10]  Joydeep Ghosh,et al.  Data Clustering Algorithms And Applications , 2013 .

[11]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[12]  Dilan Jayaweera.  Security Enhancement With Nodal Criticality-Based Integration of Strategic Micro Grids , 2015, IEEE Transactions on Power Systems.

[13]  Mahmud Fotuhi-Firuzabad,et al.  A comprehensive review on uncertainty modeling techniques in power system studies , 2016 .

[14]  Franklin L. Quilumba-Gudino.  Using Advanced Metering Infrastructure Data For Smart Grid Development , 2014 .

[15]  Young-Il Kim,et al.  Methods for generating TLPs (typical load profiles) for smart grid-based energy programs , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[16]  Peter J. Wolfs,et al.  A Hybrid Model for Residential Loads in a Distribution System With High PV Penetration , 2013, IEEE Transactions on Power Systems.

[17]  Lieve Helsen,et al.  The Impact of Load Profile on the Grid-Interaction of Building Integrated Photovoltaic (BIPV) Systems in Low-Energy Dwellings , 2010 .

[18]  J. McDonald,et al.  Customer Security Assessment in Distribution Networks With High Penetration of Wind Power , 2007, IEEE Transactions on Power Systems.

[19]  Dilan JAYAWEERA,et al.  Steady-state security in distribution networks with large wind farms , 2014, ENERGYO.

[20]  Guido Carpinelli,et al.  Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems , 2015 .

[21]  B. Bak-Jensen,et al.  Probabilistic load flow: A review , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.