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Consumer privacy protection using flexible thermal loads: Theoretical limits and practical considerations

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  • Chin, Jun-Xing
  • Baker, Kyri
  • Hug, Gabriela

Abstract

The increasing adoption of smart meters introduces growing concerns about consumer privacy risks stemming from high resolution metering data. To counter these risks, there have been various works in actively shaping the grid-visible energy consumption profile using controllable loads such as energy storage systems (ESSs) and flexible consumer loads. In this paper, we compare the use of flexible thermal-based consumer loads (FTLs) against ESSs for consumer privacy protection. By first assuming ideal conditions, and subsequently bringing them closer to reality, the limitations of using FTLs for privacy protection are identified. Through theoretical analyses and realistic simulations, it is shown that, due to the limitations in the operation of FTLs, without significant over-sizing of systems and sacrifices in consumer comfort, FTLs of much higher equivalent energy storage capacity are required to afford the same level of protection as ESSs. Nonetheless, given their increasing ubiquity, controllable FTLs should be considered for use in consumer privacy protection.

Suggested Citation

  • Chin, Jun-Xing & Baker, Kyri & Hug, Gabriela, 2021. "Consumer privacy protection using flexible thermal loads: Theoretical limits and practical considerations," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920315026
    DOI: 10.1016/j.apenergy.2020.116075
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    References listed on IDEAS

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    1. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    2. Jin, Xin & Baker, Kyri & Christensen, Dane & Isley, Steven, 2017. "Foresee: A user-centric home energy management system for energy efficiency and demand response," Applied Energy, Elsevier, vol. 205(C), pages 1583-1595.
    3. McKenna, Eoghan & Richardson, Ian & Thomson, Murray, 2012. "Smart meter data: Balancing consumer privacy concerns with legitimate applications," Energy Policy, Elsevier, vol. 41(C), pages 807-814.
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    1. Cheng, Haoyuan & Lu, Tianguang & Hao, Ran & Li, Jiamei & Ai, Qian, 2024. "Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection," Applied Energy, Elsevier, vol. 358(C).

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