Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method

Tao Chen; Antti Mutanen; Pertti Järventausta

doi:10.1109/iSPEC53008.2021.9736065

Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method

Tao Chen, Antti Mutanen, Pertti Järventausta

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

Rapid growth of smart metering data in smart grids provides great opportunities for the retailer to design customized price schemes and demand side management (DSM) programs for different customer groups. This paper proposes a hybrid data-driven method of clustering customers' daily load profiles and optimizing different electricity retail plan recommendations for electricity retailers. By combing the user-side information with the risk-aware decision-making framework, specifically using conditional value-at-risk (CVaR) modeling method, the retailer could guarantee its accumulated revenue without doing any harm to the customers' benefit, while guiding their energy consumption behavior instead. Through large-scale experiments, it is observed that a slight increase in the customers' possible payment would be compensated by their big gain in more demand response opportunities. The retailers' profit could also be increased by roughly 49%-51% and 33%-38% with or without enabling demand response programs.

Original language	English
Title of host publication	Proceedings - 2021 IEEE Sustainable Power and Energy Conference
Subtitle of host publication	Energy Transition for Carbon Neutrality, iSPEC 2021
Publisher	IEEE
Pages	1830-1834
Number of pages	5
ISBN (Electronic)	9781665414395
DOIs	https://doi.org/10.1109/iSPEC53008.2021.9736065
Publication status	Published - 2021
Publication type	A4 Article in conference proceedings
Event	IEEE Sustainable Power and Energy Conference - Nanjing, China Duration: 22 Dec 2021 → 24 Dec 2021

Conference

Conference	IEEE Sustainable Power and Energy Conference
Country/Territory	China
City	Nanjing
Period	22/12/21 → 24/12/21

Keywords

automatic meter reading
dynamic pricing
electricity retail market

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Energy Engineering and Power Technology

UN SDGs

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

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10.1109/iSPEC53008.2021.9736065

Customized_Retail_Pricing_Design_with_a_Hybrid_Data_driven_Method
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Accepted author manuscript, 600 KBLicence: Unspecified

https://urn.fi/URN:NBN:fi:tuni-202204284072

Cite this

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title = "Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method",

abstract = "Rapid growth of smart metering data in smart grids provides great opportunities for the retailer to design customized price schemes and demand side management (DSM) programs for different customer groups. This paper proposes a hybrid data-driven method of clustering customers' daily load profiles and optimizing different electricity retail plan recommendations for electricity retailers. By combing the user-side information with the risk-aware decision-making framework, specifically using conditional value-at-risk (CVaR) modeling method, the retailer could guarantee its accumulated revenue without doing any harm to the customers' benefit, while guiding their energy consumption behavior instead. Through large-scale experiments, it is observed that a slight increase in the customers' possible payment would be compensated by their big gain in more demand response opportunities. The retailers' profit could also be increased by roughly 49%-51% and 33%-38% with or without enabling demand response programs.",

keywords = "automatic meter reading, dynamic pricing, electricity retail market",

author = "Tao Chen and Antti Mutanen and Pertti J{\"a}rventausta",

note = "Funding Information: This work is supported by National Natural Science Foundation of China under Grant No.52107079. Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE Sustainable Power and Energy Conference ; Conference date: 22-12-2021 Through 24-12-2021",

year = "2021",

doi = "10.1109/iSPEC53008.2021.9736065",

language = "English",

pages = "1830--1834",

booktitle = "Proceedings - 2021 IEEE Sustainable Power and Energy Conference",

publisher = "IEEE",

}

Chen, T, Mutanen, A & Järventausta, P 2021, Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method. in Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. IEEE, pp. 1830-1834, IEEE Sustainable Power and Energy Conference, Nanjing, China, 22/12/21. https://doi.org/10.1109/iSPEC53008.2021.9736065

Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method. / Chen, Tao; Mutanen, Antti; Järventausta, Pertti.

Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. IEEE, 2021. p. 1830-1834.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Järventausta, Pertti

PY - 2021

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N2 - Rapid growth of smart metering data in smart grids provides great opportunities for the retailer to design customized price schemes and demand side management (DSM) programs for different customer groups. This paper proposes a hybrid data-driven method of clustering customers' daily load profiles and optimizing different electricity retail plan recommendations for electricity retailers. By combing the user-side information with the risk-aware decision-making framework, specifically using conditional value-at-risk (CVaR) modeling method, the retailer could guarantee its accumulated revenue without doing any harm to the customers' benefit, while guiding their energy consumption behavior instead. Through large-scale experiments, it is observed that a slight increase in the customers' possible payment would be compensated by their big gain in more demand response opportunities. The retailers' profit could also be increased by roughly 49%-51% and 33%-38% with or without enabling demand response programs.

AB - Rapid growth of smart metering data in smart grids provides great opportunities for the retailer to design customized price schemes and demand side management (DSM) programs for different customer groups. This paper proposes a hybrid data-driven method of clustering customers' daily load profiles and optimizing different electricity retail plan recommendations for electricity retailers. By combing the user-side information with the risk-aware decision-making framework, specifically using conditional value-at-risk (CVaR) modeling method, the retailer could guarantee its accumulated revenue without doing any harm to the customers' benefit, while guiding their energy consumption behavior instead. Through large-scale experiments, it is observed that a slight increase in the customers' possible payment would be compensated by their big gain in more demand response opportunities. The retailers' profit could also be increased by roughly 49%-51% and 33%-38% with or without enabling demand response programs.

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KW - electricity retail market

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DO - 10.1109/iSPEC53008.2021.9736065

M3 - Conference contribution

AN - SCOPUS:85128097421

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EP - 1834

BT - Proceedings - 2021 IEEE Sustainable Power and Energy Conference

PB - IEEE

T2 - IEEE Sustainable Power and Energy Conference

Y2 - 22 December 2021 through 24 December 2021

ER -

Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method

Abstract

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

UN SDGs

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Cite this