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A Business Service Model of Smart Home Appliances Participating in the Peak Shaving and Valley Filling Based on Cloud Platform
Mingrui ZHU Yangjian JI Wenjun JU Xinjian GU Chao LIU Zhifang XU
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2021/08/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Computational Intelligence and Big Data for Scientific and Technological Resources and Services)
power system, peak shaving and valley filling, home appliance business resources, business model, load aggregator,
Full Text: PDF(3.7MB)>>
With the development of power market demand response capability, load aggregators play a more important role in the coordination between power grid and users. They have a wealth of user side business data resources related to user demand, load management and equipment operation. By building a business model of business data resource utilization and innovating the content and mode of intelligent power service, it can guide the friendly interaction between power supply, power grid and load, effectively improve the flexibility of power grid regulation, speed up demand response and refine load management. In view of the current situation of insufficient utilization of business resources, low user participation and imperfect business model, this paper analyzes the process of home appliance enterprises participating in peak shaving and valley filling (PSVF) as load aggregators, and expounds the relationship between the participants in the power market; a business service model of smart home appliance participating in PSVF based on cloud platform is put forward; the market value created by home appliance business resources for each participant under the joint action of market-oriented means, information technology and power consumption technology is discussed, and typical business scenarios are listed; taking Haier business resource analysis as an example, the feasibility of the proposed business model in innovating the content and value realization of intelligent power consumption services is proved.