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A Joint Energy and Differentially-Private Smart Meter Data Market

Saurab Chhachhi, Fei Teng

TL;DR

This paper proposes a privacy-preserving joint energy and data market for differentially-private smart meter data, integrating data valuation directly with day-ahead retailer procurement. It introduces a Wasserstein distance-based valuation within an integrated forecasting and optimisation framework (IFOF) and co-optimises data purchases with energy procurement via a MISOCP, all while maintaining consumer privacy through differential privacy and multi-party computation. A Lipschitz-calibrated approach, including Transfer Function and local Lipschitz relaxation illustrated by Gaussian Newsvendor, provides tractable risk bounds. Case studies on forecast procurement and real smart-meter data procurement demonstrate that Wasserstein-based valuations align closely with actual profit improvements and outperform forecast-based metrics, while preserving budget feasibility and revealing trade-offs in privacy, risk, and payments.

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.

A Joint Energy and Differentially-Private Smart Meter Data Market

TL;DR

This paper proposes a privacy-preserving joint energy and data market for differentially-private smart meter data, integrating data valuation directly with day-ahead retailer procurement. It introduces a Wasserstein distance-based valuation within an integrated forecasting and optimisation framework (IFOF) and co-optimises data purchases with energy procurement via a MISOCP, all while maintaining consumer privacy through differential privacy and multi-party computation. A Lipschitz-calibrated approach, including Transfer Function and local Lipschitz relaxation illustrated by Gaussian Newsvendor, provides tractable risk bounds. Case studies on forecast procurement and real smart-meter data procurement demonstrate that Wasserstein-based valuations align closely with actual profit improvements and outperform forecast-based metrics, while preserving budget feasibility and revealing trade-offs in privacy, risk, and payments.

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.

Paper Structure

This paper contains 30 sections, 2 theorems, 8 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Existing Approaches
  4. Contributions
  5. Joint Energy and Data Market Framework
  6. Retailer Energy Procurement Problem
  7. Integrated Forecasting and Optimisation
  8. Forecasting Model
  9. Optimisation
  10. Joint Optimisation Mechanism
  11. Consumers and Data Value
  12. Retailer and Market Platform
  13. Lipschitz Calibration
  14. Transfer Function
  15. Lipschitz Relaxation
  16. ...and 15 more sections

Key Result

Theorem 2.1

The integrated retailer energy forecasting and procurement problem is equivalent to a quantile regression problem for the $\tau$-th quantile, between historical demand data, $d_t$, and a set of features, $\mathbf{x}_t$ (Adapted from Huber2019): where, $q_t(\mathbf{\Psi},\mathbf{x}_t)$ is the output of the ANN-based forecasting model with weight matrix, $\mathbf{\Psi}$.

Figures (4)

  • Figure 1: Overview of Joint Optimisation Mechanism
  • Figure 2: Lipschitz Relaxation of Gaussian Newsvendor
  • Figure 3: Effect of Differential Privacy on Forecast Value. Top: Percentage Change in Shapley Allocation and $\sigma^{dp}-\sigma^{s}$, under $W^{DP}$, for Four consumers. Bottom: Shapley Payments with Increasing Noise, $\gamma$, under Different Privacy Scenarios.
  • Figure 4: Valuation of Real Smart Meter Data $(\rho=0)$. Top: Base Results for Proposed Mechanism $(\delta = 0.95, K = 2\lambda^u, B(X_R) = \Pi(X_T^{va}) - \Pi(X_R^{va}))$. Bottom: Effect of Risk Adjustment/Calibration of Conservatism by varying $\delta$, $K$ and $B(X_R)$.

Theorems & Definitions (3)

  • Theorem 2.1: Newsvendor Regression
  • Definition 2.1: Locally Lipschitz
  • Proposition 2.2: Locally Lipschitz Constant