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The problem of customizing electricity retail prices using data mining techniques is studied in this paper. The density-based spatial clustering of applications with noise is first applied to load profile analysis, in order to explore end-users' inherent electricity consumption patterns from their historical load data. Then, statistical analysis of end-users' historical consumption is conducted to better capture their consumption regularity. After extracting these load features, a mixed integer nonlinear programming model for customizing electricity retail prices is proposed. In the proposed model, both the structure of time-of-use (TOU) retail price and the price level are optimized once given the number of price blocks. It is among the first that the optimization of TOU price structure is studied in electricity retail pricing research. The proposed model is mathematically reformulated and solved by online commercial solvers provided by the network-enabled optimization system server. Electricity usage data collected by the smart grid, smart city project in Australia is used to demonstrate the feasibility and efficiency of the developed models and algorithms.
Yang et al. (Tue,) studied this question.