A Joint Energy and Differentially-Private Smart Meter Data Market

Paper: arXiv 2412.07688

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.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"])

Cited References (363)

1. Privacy-preserving aggregation of personal health data streams (2018) — link
2. CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010. (2012) — link
3. Long-term patterns of Eur. PV output using 30 years of validated hourly reanalysis and satellite data (2016) — link
4. Price-Maker Bidding in Day-Ahead Electricity Market for a Retailer With Flexible Demands (2018) — link
5. Building integer programming models I (2013) — link
6. Electric Vehicle Charging Behaviour Study, Final Report for National Grid ESO (2019) — link
7. Artificial neural network-based methodology for short-term electric load scenario generation (2015) — link
8. Variational Bayes for generalized autoregressive models (2002) — link

0 of 363 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.