Outage Identification from Electricity Market Data: Quickest Change Detection Approach

2026-01-20

AI Summary

This study addresses the critical challenge of timely fault detection in power systems, where undetected failures can lead to substantial financial risks. The authors propose a novel online detection method grounded in parametric quickest change detection (QCD) theory, leveraging only publicly available electricity market data streams—specifically demand and price signals. By employing multiparametric programming, the approach extracts parametric random variables with known probability densities from complex market observations, enabling the construction of a likelihood ratio statistic for rapid fault identification. This work represents the first integration of parametric QCD with multiparametric programming, eliminating the need for internal grid information. Empirical validation on the PJM real-world platform demonstrates the method’s high accuracy and low detection latency in identifying transmission line faults.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

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.

Problem

• outage identification
• electricity market data
• quickest change detection
• power system outages
• financial risk

Innovation

• quickest change detection
• multi-parametric programming
• outage identification
• electricity market data
• parametric density estimation