A Joint Energy and Differentially-Private Smart Meter Data Market

ArXiv ID: 2412.07688 “View on arXiv”

Authors: Unknown

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.

Keywords: Data Markets, Differential Privacy, Wasserstein Distance, Energy Procurement, Stochastic Optimization, Commodities (Energy)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including Wasserstein distances, differential privacy, Lipschitz constants, and integrated forecasting-optimization models. Empirical rigor is strong, demonstrated through numerical case studies using both synthetic and real smart meter data to validate the proposed joint energy and data market mechanism.

  flowchart TD
    Start(["Research Goal<br/>How can data markets optimize<br/>energy procurement under uncertainty?"]) --> Inputs
    subgraph Inputs ["Input Data & Constraints"]
        direction LR
        I1["Energy Market Data"]
        I2["Consumer Smart Meter Data"]
        I3["Differential Privacy<br/>Constraint"]
    end
    Inputs --> Methodology
    subgraph Methodology ["Integrated Methodology"]
        direction LR
        M1["Value Estimation via<br/>Wasserstein Distance"]
        M2["Joint Market Clearing<br/>Retailer + Consumers"]
    end
    Methodology --> Computation
    subgraph Computation ["Computational Process"]
        direction LR
        C1["Stochastic<br/>Optimization"]
        C2["Privacy-Preserving<br/>Valuation"]
    end
    Computation --> Outcomes
    subgraph Outcomes ["Key Findings"]
        direction LR
        O1["Value of Joint<br/>Energy & Data Clearing"]
        O2["Direct Data Valuation<br/>Framework"]
        O3["Privacy-Utility<br/>Trade-off Quantified"]
    end
    Outcomes --> End(["End: New Market Mechanism<br/>for Energy & Data Procurement"])