A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis

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 th...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2019-05, Vol.10 (3), p.3374-3386
Main Authors: Yang, Jiajia, Zhao, Junhua, Wen, Fushuan, Dong, Zhaoyang
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Clustering algorithms
clustering analysis
Customization
customized retail price
Data mining
Electricity consumption
Electricity retailing
Feature extraction
Indexes
Load modeling
Mixed integer
Nonlinear programming
optimal structure of TOU price
Optimization
Prices
Pricing
Real-time systems
Smart grid
Solvers
Statistical analysis
Time of use electricity pricing
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Summary: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.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2018.2825335