Outage Identification from Electricity Market Data: Quickest Change Detection Approach

TL;DR

This paper introduces a rapid outage detection method for power systems using a quickest change detection approach on market data, enabling prompt identification of outages to mitigate financial risks.

Contribution

It develops a novel outage detection technique based on parametric QCD and multi-parametric programming applied to electricity market signals.

Findings

Rapid outage detection demonstrated on PJM testbed

Effective identification from demand and price data streams

Method reduces detection delay compared to traditional approaches

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.