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Outage Identification from Electricity Market Data: Quickest Change Detection Approach

Milad Hoseinpour, Shubhanshu Shekhar, Vladimir Dvorkin

TL;DR

This paper addresses the problem of rapidly detecting and identifying power system outages using publicly available electricity market signals. It combines parametric quickest change detection with a CuSum-based framework that leverages multi-parametric programming to derive region-specific densities for market-clearing outputs, enabling timely discrimination between nominal operation and outages. The authors develop probabilistic models of real-time market data, design a two-stage CuSum detector with offline critical-region computation and online likelihood testing, and adapt the method for bounded perturbations and multiple outage hypotheses. Numerical experiments on a stylized PJM testbed demonstrate fast outage detection and competitive identification performance, highlighting substantial potential for independent, data-driven outage awareness and hedging by market participants.

Abstract

Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market signals grounded in the parametric quickest change detection (QCD) theory. Parametric QCD operates on stochastic data streams, distinguishing pre- and post-change regimes using the ratio of their respective probability density functions. To derive the density functions for normal and post-outage market signals, we exploit multi-parametric programming to decompose complex market signals into parametric random variables with a known density. These densities are then used to construct a QCD-based statistic that triggers an alarm as soon as the statistic exceeds an appropriate threshold. Numerical experiments on a stylized PJM testbed demonstrate rapid line outage identification from public streams of electricity demand and price data.

Outage Identification from Electricity Market Data: Quickest Change Detection Approach

TL;DR

This paper addresses the problem of rapidly detecting and identifying power system outages using publicly available electricity market signals. It combines parametric quickest change detection with a CuSum-based framework that leverages multi-parametric programming to derive region-specific densities for market-clearing outputs, enabling timely discrimination between nominal operation and outages. The authors develop probabilistic models of real-time market data, design a two-stage CuSum detector with offline critical-region computation and online likelihood testing, and adapt the method for bounded perturbations and multiple outage hypotheses. Numerical experiments on a stylized PJM testbed demonstrate fast outage detection and competitive identification performance, highlighting substantial potential for independent, data-driven outage awareness and hedging by market participants.

Abstract

Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market signals grounded in the parametric quickest change detection (QCD) theory. Parametric QCD operates on stochastic data streams, distinguishing pre- and post-change regimes using the ratio of their respective probability density functions. To derive the density functions for normal and post-outage market signals, we exploit multi-parametric programming to decompose complex market signals into parametric random variables with a known density. These densities are then used to construct a QCD-based statistic that triggers an alarm as soon as the statistic exceeds an appropriate threshold. Numerical experiments on a stylized PJM testbed demonstrate rapid line outage identification from public streams of electricity demand and price data.
Paper Structure (16 sections, 28 equations, 2 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 28 equations, 2 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Probabilistic Models of Real-Time Market
  3. Perturbed Market-Clearing Optimization Problem
  4. Mapping Load Perturbations to Market Outcomes
  5. Probabilistic Models of Electricity Market Data Streams
  6. CuSum-Based Outage Identification from Streams of Market-Clearing Data
  7. CuSum Design using Probabilistic Market Models
  8. Adapting CuSum for Bounded Demand Perturbations
  9. Threshold For Outage Detection and Identification
  10. Outage Identification Algorithm
  11. Numerical Experiments
  12. Settings
  13. Distinguishing Line Outages from Demand Variations
  14. Performance Analysis of the CuSum Statistic
  15. Conclusion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: PJM system and the corresponding critical regions of the real-time market-clearing problem in the nominal and post-outage operation.
  • Figure 2: Demand (left) and LMP (middle) data streams and the dynamics of the six CuSum statistics (right). The outage occurs at $T=500$ (red dashed line).