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

Sähkötekniikka

Tutkimustuotos: Konferenssiartikkeli › Scientific › vertaisarvioitu

3 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings - 2021 IEEE Sustainable Power and Energy Conference
Alaotsikko	Energy Transition for Carbon Neutrality, iSPEC 2021
Kustantaja	IEEE
Sivut	1830-1834
Sivumäärä	5
ISBN (elektroninen)	9781665414395
DOI - pysyväislinkit	https://doi.org/10.1109/iSPEC53008.2021.9736065
Tila	Julkaistu - 2021
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE Sustainable Power and Energy Conference - Nanjing, Kiina Kesto: 22 jouluk. 2021 → 24 jouluk. 2021

Conference

Conference	IEEE Sustainable Power and Energy Conference
Maa/Alue	Kiina
Kaupunki	Nanjing
Ajanjakso	22/12/21 → 24/12/21

Julkaisufoorumi-taso

Jufo-taso 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

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Pääsy asiakirjaan

10.1109/iSPEC53008.2021.9736065

Customized_Retail_Pricing_Design_with_a_Hybrid_Data_driven_Method
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Accepted author manuscript, 600 KBLisenssi: Unspecified

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

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@inproceedings{b6c1b910bf394a7dabad5c7a70f04c81,

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. julkaisussa Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. IEEE, Sivut 1830-1834, IEEE Sustainable Power and Energy Conference, Nanjing, Kiina, 22/12/21. https://doi.org/10.1109/iSPEC53008.2021.9736065

TY - GEN

T1 - Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method

AU - Chen, Tao

AU - Mutanen, Antti

AU - Järventausta, Pertti

PY - 2021

Y1 - 2021

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.

KW - automatic meter reading

KW - dynamic pricing

KW - electricity retail market

U2 - 10.1109/iSPEC53008.2021.9736065

DO - 10.1109/iSPEC53008.2021.9736065

M3 - Conference contribution

AN - SCOPUS:85128097421

SP - 1830

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

Abstrakti

Conference

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

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