Abstract
The constantly spreading smart meters open new possibilities for load profiling. With smart meter measurements, standard customer class load profiles can be updated, customers can be clustered into similarly behaving groups, or individual load profiles can be created. This paper studies how these new smart meter data -based load profiling methods affect the distribution system state estimation accuracy. Extensive simulations were made with a real distribution network containing both medium and low voltage networks. Results achieved with different load profiling methods were compared with each other, with publicly available standard customer class load profiles, and with other currently used load modelling methods. In all studied cases, the new smart meter data -based load profiles provided superior state estimation accuracy. The results presented in this paper should motivate distribution network operators to utilize smart meter measurements more efficiently in load profiling and state estimation.
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