The popularity of smart meters promotes the development of big data in smart power distribution and consumption systems. Data mining for smart meter data and awareness of electricity consumption characteristics are of great significance for consumption patterns recognition

demand response potential evaluation

and electricity price design. However

on one hand

the volume of smart meter data will grow dramatically with higher data collection frequency; on the other hand

the collected smart meter data has strong dispersion. To tackle the challenges brought by the massive and distributed smart meter big data

a novel distributed clustering algorithm is proposed. Firstly

the adaptive k-means algorithm is applied to each local data center so the typical load profiles can be extracted and the local model can be built. Then slightly revised traditional clustering algorithms are applied to the local models for secondary clustering analysis

thus the global model is built. Finally

the effectiveness of the proposed algorithm is verified by an actual example from Ireland. This work is supported by National Science Fund for Distinguished Young Scholars(No. 51325702)and China Southern Power Grid Company Limited(No. GD-KJXM-20150902).