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

Jiajia Yang; Junhua Zhao; Fushuan Wen; Zhaoyang Dong

doi:10.1109/TSG.2018.2825335

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

Jiajia Yang
, Junhua Zhao^*
, Fushuan Wen
, Zhaoyang Dong

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

146 Citations (Scopus)

Abstract

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. © 2010-2012 IEEE.

Original language	English
Article number	5165411
Pages (from-to)	3374-3386
Journal	IEEE Transactions on Smart Grid
Volume	10
Issue number	3
DOIs	https://doi.org/10.1109/TSG.2018.2825335
Publication status	Published - 1 May 2019
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Research Keywords

clustering analysis
customized retail price
Electricity retailing
optimal structure of TOU price

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10.1109/TSG.2018.2825335

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title = "A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis",

abstract = "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. {\textcopyright} 2010-2012 IEEE.",

keywords = "clustering analysis, customized retail price, Electricity retailing, optimal structure of TOU price",

author = "Jiajia Yang and Junhua Zhao and Fushuan Wen and Zhaoyang Dong",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].",

year = "2019",

month = may,

day = "1",

doi = "10.1109/TSG.2018.2825335",

language = "English",

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T1 - A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis

AU - Yang, Jiajia

AU - Zhao, Junhua

AU - Wen, Fushuan

AU - Dong, Zhaoyang

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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. © 2010-2012 IEEE.

AB - 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. © 2010-2012 IEEE.

KW - clustering analysis

KW - customized retail price

KW - Electricity retailing

KW - optimal structure of TOU price

UR - https://www.scopus.com/pages/publications/85045331800

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U2 - 10.1109/TSG.2018.2825335

DO - 10.1109/TSG.2018.2825335

M3 - RGC 21 - Publication in refereed journal

SN - 1949-3053

VL - 10

SP - 3374

EP - 3386

JO - IEEE Transactions on Smart Grid

JF - IEEE Transactions on Smart Grid

IS - 3

M1 - 5165411

ER -

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

Abstract

Bibliographical note

UN SDGs

Research Keywords

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