A new power user clustering method based on metric learning algorithm (MLA) considering business value and demand response value. (11th September 2019)

• Record Type: Journal Article
• Authors: Li, Sitao; Zhang, Sufang; He, Yongxiu; Chen, Wenjun
• Is Part Of: International journal of energy research. Volume 43:Number 15(2019)
• Page Start: 9001
• Page End: 9012
• Publication Date: 2019-09-11
• Subjects: business value -- clustering method -- demand response value -- metric learning -- power retailer -- power user; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.4865
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: The conventional method of power user clustering only considers physical and demand side information such as the amount and the peak and valley difference of power consumption. It is not appropriate in a new era where wholesale and retail power markets have been liberalized and power system has becoming increasingly intelligent. The new method developed in this study introduces price signals at both wholesale and retail power markets into the power user clustering and considers both business value and demand response value of power users on the basis of metric learning algorithm (MLA). The case study shows that this new method significantly improves the degree of separation between the business value and demand response value indicators of clusters and reveals the relationship between the wholesale power price and the weight of power consumption for a specific target. This new method can help power retailers in their business decision making and has strong applicability and expandability. Abstract : Power market deregulation and smart grid system change power system environment. Conventional power user clustering method is not ideal in the new environment. The new power user clustering method considers power market price signal through metric learning algorithm (MLA), and the new method can better serve power retailers in their marketing strategy.