Learning-Enabled Residential Demand Response: Automation and Security of Cyberphysical Demand Response Systems

Robert Mieth, Samrat Acharya, Ali Hassan, Yury Dvorkin

Research output: Contribution to specialist publicationArticle

Abstract

Residential Demand Response (DR) Programs have been validated as a viable technology to improve energy efficiency and the reliability of electric power distribution. However, various technical and organizational challenges hinder their full techno-economic potential. In practice, these challenges are related to the small-scale, distributed, heterogeneous, and stochastic nature of residential DR resources. This article investigates state-of-the-art online and reinforcement learning methods that are capable of overcoming these challenges in the context of DR pricing, scheduling, and cybersecurity.

Original languageEnglish (US)
Pages36-44
Number of pages9
Volume9
No1
Specialist publicationIEEE Electrification Magazine
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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