Market Value of Differentially-Private Smart Meter Data
This addresses the trade-off between data utility and privacy in smart grid applications for energy consumers and providers, though it appears incremental as it applies existing methods (differential privacy, ANNs) to a specific domain.
The paper tackles the problem of valuing privacy-protected smart meter data sharing between consumers and energy providers by developing a framework that combines differential privacy, load forecasting, and market optimization. It demonstrates that when consumer load profiles differ significantly from the system average (quantified using Kullback-Leibler divergence), sharing such data has substantial market value while preserving privacy.
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.