A Joint Energy and Differentially-Private Smart Meter Data Market

TL;DR

This paper introduces a joint energy and data market framework that leverages differentially-private smart meter data to improve energy procurement decisions while preserving consumer privacy, integrating forecasting and optimization.

Contribution

It presents a novel integrated market model combining energy and data trading, utilizing Wasserstein distance for valuation and privacy preservation, addressing interdependence overlooked in prior work.

Findings

Joint market model improves procurement accuracy.

Differential privacy maintains consumer data confidentiality.

Numerical case studies validate the approach.

Abstract

Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, existing data market frameworks rely on a trusted entity to clear the market. This paper proposes a joint energy and data market focusing on the day-ahead retailer energy procurement problem with uncertain demand. The retailer can purchase differentially-private smart meter data from consumers to reduce uncertainty. The problem is modelled as an integrated forecasting and optimisation problem providing a means of valuing data directly rather than valuing forecasts or forecast accuracy. Value is determined by the Wasserstein distance, enabling privacy to be preserved during the valuation and procurement process. The value of joint energy and data clearing is highlighted through numerical case studies using both synthetic and real smart meter data.