Market Value of Differentially-Private Smart Meter Data

TL;DR

This paper introduces a framework combining differential privacy, load forecasting, and market analysis to evaluate the value of privacy-protected smart meter data, revealing significant market benefits when consumer load profiles differ from the average.

Contribution

It develops an integrated framework that quantifies the market value of privacy-preserving smart meter data using differential privacy and neural network forecasting.

Findings

Sharing data with distinct load profiles yields high market value.

Differential privacy effectively protects individual identities.

Forecast accuracy impacts market procurement strategies.

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.