A novel approach for load profiling in smart power grids using smart meter data

被引:27
|
作者
Khan, Zafar A. [1 ]
Jayaweera, Dilan [1 ]
Alvarez-Alvarado, Manuel S. [1 ]
机构
[1] Univ Birmingham, Dept Elect Elect & Syst Engn, Birmingham B15 2TT, W Midlands, England
关键词
Load modelling; Load profiling; Particle swarm optimization; Stochastic modelling; CLASSIFICATION; SYSTEM;
D O I
10.1016/j.epsr.2018.09.013
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Increasing penetration of distributed energy resources, varying load demands and big data of smart meters require new load models to support power system studies. The big data of smart meters and non-linearities in the load demand require the smart meter data to be represented in an alternative way to use in stochastic simulations to enhance processing. This paper proposes a novel method for stochastic load modelling of smart meter data. The approach turns smart meter data to a manageable level by linearizing energy consumption patterns producing energy classifications. A case study, using real world smart meter data, simulated scenarios to prove the robustness and accuracy of the method. The accuracy of results validates the stability and robustness of the approach and model validation provided substantiation for application in probabilistic studies.
引用
收藏
页码:191 / 198
页数:8
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