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Generator Cost Coefficients Inference Attack via Exploitation of Locational Marginal Prices in Smart Grid

Junfei Wang, Pirathayini Srikantha

TL;DR

This paper proves the existence of a closed-form solution for recovering coefficients in cost functions when LMPs and disaggregated power generation data are available and establishes the convergence conditions for inference the quadratic coefficients of cost functions when LMPs and aggregated generation data are given.

Abstract

Real-time price signals and power generation levels (disaggregated or aggregated) are commonly made available to the public by Independent System Operators (ISOs) to promote efficiency and transparency. However, they may inadvertently reveal crucial private information about the power grid, such as the cost functions of generators. Adversaries can exploit these vulnerabilities for strategic bidding, potentially leading to financial losses for power market participants and consumers. In this paper, we prove the existence of a closed-form solution for recovering coefficients in cost functions when LMPs and disaggregated power generation data are available. Additionally, we establish the convergence conditions for inference the quadratic coefficients of cost functions when LMPs and aggregated generation data are given. Our theoretical analysis provides the conditions under which the algorithm is guaranteed to converge, and our experiments demonstrate the efficacy of this method on IEEE benchmark systems, including 14-bus and 30-bus and 118-bus systems.

Generator Cost Coefficients Inference Attack via Exploitation of Locational Marginal Prices in Smart Grid

TL;DR

This paper proves the existence of a closed-form solution for recovering coefficients in cost functions when LMPs and disaggregated power generation data are available and establishes the convergence conditions for inference the quadratic coefficients of cost functions when LMPs and aggregated generation data are given.

Abstract

Real-time price signals and power generation levels (disaggregated or aggregated) are commonly made available to the public by Independent System Operators (ISOs) to promote efficiency and transparency. However, they may inadvertently reveal crucial private information about the power grid, such as the cost functions of generators. Adversaries can exploit these vulnerabilities for strategic bidding, potentially leading to financial losses for power market participants and consumers. In this paper, we prove the existence of a closed-form solution for recovering coefficients in cost functions when LMPs and disaggregated power generation data are available. Additionally, we establish the convergence conditions for inference the quadratic coefficients of cost functions when LMPs and aggregated generation data are given. Our theoretical analysis provides the conditions under which the algorithm is guaranteed to converge, and our experiments demonstrate the efficacy of this method on IEEE benchmark systems, including 14-bus and 30-bus and 118-bus systems.

Paper Structure

This paper contains 13 sections, 1 theorem, 18 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Formulation of ACOPF
  4. Locational Marginal Prices
  5. Assumptions in This Paper
  6. Proposed Algorithm
  7. Attack Mechanism Using LMPs and Individual Generations
  8. Attack Algorithm Exploiting LMPs and Aggregated Power Generations
  9. Experiment
  10. Environment and Dataset
  11. Attack Performance on Scenario 1
  12. Attack Performance on Scenario 2
  13. Conclusion and Future Work

Key Result

Theorem 1

The MFPI attack for recovering cost coefficients via LMPs and aggregated power generations is guaranteed to converge to an unique solution when the following conditions hold: where $a_{\min}$ is defined in Eq. eq:bound, and $a_{\max}$ is a predefined upper bound for $a_i$.

Figures (1)

  • Figure 1: Error of MFPI Algorithm on IEEE 14-bus.

Theorems & Definitions (2)

  • Theorem 1
  • proof