Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology

TL;DR

This paper introduces an influence graph model that captures non-local cascade propagation in power systems, providing insights for reducing blackout risks by identifying critical components.

Contribution

The paper presents a novel influence graph approach that models cascade propagation non-locally and Markovian, validated against simulation data, and offers a method to identify system modifications to mitigate risks.

Findings

Influence graph closely matches cascade size distribution from simulations.

Model captures non-local cascade propagation patterns.

Equation for identifying system modifications to reduce cascade risk.

Abstract

In a cascading power transmission outage, component outages propagate non-locally, after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting "influence graph" model is Markovian, in that component outage probabilities depend only on the outages that occurred in the prior generation. To validate the model we compare the distribution of cascade sizes resulting from $n-2$ contingencies in a $2896$ branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.