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Market Value of Differentially-Private Smart Meter Data

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  • Saurab Chhachhi
  • Fei Teng

Abstract

This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load profile of a consumer group differs from the system average, which is quantified using the Kullback-Leibler divergence, there is significant value in sharing smart meter data while retaining individual consumer privacy.

Suggested Citation

  • Saurab Chhachhi & Fei Teng, 2021. "Market Value of Differentially-Private Smart Meter Data," Papers 2104.09898, arXiv.org.
    • Handle: RePEc:arx:papers:2104.09898
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    References listed on IDEAS

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    1. Farhad Farokhi, 2019. "Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon," Papers 1908.03995, arXiv.org, revised Jan 2020.
    2. Carissa Véliz & Philipp Grunewald, 2018. "Protecting data privacy is key to a smart energy future," Nature Energy, Nature, vol. 3(9), pages 702-704, September.
    3. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    4. Jong Wook Kim & Beakcheol Jang & Hoon Yoo, 2018. "Privacy-preserving aggregation of personal health data streams," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
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    Cited by:

    1. Xu, Luo & Guo, Qinglai & Sheng, Yujie & Muyeen, S.M. & Sun, Hongbin, 2021. "On the resilience of modern power systems: A comprehensive review from the cyber-physical perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    2. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).

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