Using Causal Inference to Measure Residential Consumers Demand Response Elasticity

• Type: Article in International Conference Proceedings Book
• Date: 2019-06-23

Authors

João Tomé Saraiva (Author) FEUP; Kamalanathan Ganesan (Author) Other; Ricardo Jorge Bessa (Author) Other

Conference proceedings International

Title: 2019 IEEE Milan PowerTech

Pages: 1-6

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 1 Citation

Scientific classification

CORDIS: Technological sciences > Engineering > Electrical engineering

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-00R-389

DOI: 10.1109/ptc.2019.8810859

Abstract (EN): Engaging the residential consumers and providing the best tariffs for their randomized behavior is one of the major barriers to demand response (DR) implementation. Additionally, DR offers submitted by aggregators or retailers are not consumer-specific, which turns it even more difficult for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causal inference between dynamic DR tariffs and observed residential electricity consumption (resolution of 30 minutes) to estimate consumers' consumption elasticity. Ultimately, the aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6