Residential Demand Side Aggregation of Privacy-Conscious Consumers

Jun-Xing Chin, Gabriela Hug, Andrey Bernstein

Research output: Contribution to conferencePaperpeer-review


The increasing adoption of smart meters has led to growing concerns regarding privacy risks stemming from the high resolution measurements. This has given rise to privacy protection techniques that physically alter the consumer's energy load profile, masking private information by using localised devices, e.g. batteries or flexible loads. Meanwhile, there has also been increasing interest in aggregating the distributed energy resources (DERs) of residential consumers to provide services to the grid. In this paper, we propose an online distributed algorithm to aggregate the DERs of privacy-conscious consumers to provide services to the grid, whilst preserving their privacy. Results show that the optimisation solution from the distributed method converges to one close to the optimum computed using an ideal centralised solution method, balancing between grid service provision, consumer preferences and privacy protection. More importantly, the distributed method preserves consumer privacy, and does not require high-bandwidth two-way communications infrastructure.

Original languageAmerican English
Number of pages6
StatePublished - 28 Jun 2021
Event2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Spain
Duration: 28 Jun 20212 Jul 2021


Conference2021 IEEE Madrid PowerTech, PowerTech 2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

NREL Publication Number

  • NREL/CP-5D00-80831


  • Ancillary Services
  • Consumer Privacy
  • Mutual Information
  • Online Gradient Descent
  • Smart Meter


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