Impact factors analysis on the probability characterized effects of time of use demand response tariffs using association rule mining method

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Kangping Li (Author) Other; Liming Liu (Author) Other; Fei Wang (Author) Other; Tieqiang Wang (Author) Other; Neven Duic (Author) Other; Miadreza Shafie-khah (Author) Other; João P. S. Catalão (Author) FEUP

Journal

Title: Energy Conversion and Management

Vol. 197

ISSN: 0196-8904

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 49 Citations

Scopus - 58 Citations

INSPEC

Other information

Authenticus ID: P-00Q-XX0

DOI: 10.1016/j.enconman.2019.111891

Abstract (EN): Time of use (TOU) rate has been regarded as an effective strategy to associate utility companies to avoid peak time financial risks and make the most profit out of the market, while most programs are not effective as expected to reduce peak time demand of residents. Exploring the impact factors of peak demand reduction (PDR) can help policy makers find reasons that weaken effects of programs and corresponding measures can be carried out to maximize the benefits. However, averaging quantitative indicators for program assessment and incomplete impactor analysis method in existing research show limitations of revealing the complex reasons behind it. In this paper, an association rule mining based quantitative analysis framework is built to explore the impact of household characteristics on PDR under TOU price making up for the deficiencies in current research. Firstly, a probability distribution based customer PDR characterizing model is proposed, in which difference-indifference model is adopted to quantify the effect of PDR and probability distribution fitting method is used to characterize the feature of PDR for households. Then a comprehensive association rule mining analysis using Apriori algorithm is presented to explore the impacts factors of PDR covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Finally, analysis results of a case study based on 2993 household records containing smart metering data and survey data illustrate that PDR level cannot be obtained simply based on the appliance's ownership and its usage habits. Socio-demographic information of households should be taken into consideration together; Internet connection and good house insulation contribute to the increase of PDR level. Moreover, the percentage of renewable generation for households also show a certain relationship with PDR. The proposed analysis framework and findings will associate retailer to improve the benefits of TOU programs and guide policy makers to design more efficient energy saving policies for residents.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15