Using Causal Inference to Measure Residential Consumers Demand Response Elasticity

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019-06-23

Autores

João Tomé Saraiva (Autor) FEUP; Kamalanathan Ganesan (Autor) Outra; Ricardo Jorge Bessa (Autor) Outra

Ata de Conferência Internacional

Título: 2019 IEEE Milan PowerTech

Páginas: 1-6

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 1 Citação

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrotécnica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6