SEQUIN: A Network Science and Physics-based Approach to Identify Sequential N-k Attacks in Electric Power Grids

The electric grid is a vital infrastructure that supplies power on which modern society depends, and maintaining its reliable service and resilient operation is essential. The grid's performance typically relies on a few key components. However, efficiently finding these components is challenging, due to the geo-distributed scale of the grid, complex physics governing power flows, and automated network response. Realistically identifying these key components must also consider the temporal aspect of how failures affect the network. In this paper, we address the problem of identifying worst-case disruptions to the grid, under the sequential failure of components. We present SEQUIN, a framework leveraging network science principles and physics-based constraint optimization to explore such failures in the grid. We formulate the problem using a sequential N-k interdiction model, which provides a methodology to explore and capture interactions between the failures and network response. Our approach defines several network properties to assess the contribution of each component towards its operation, and provides an efficient guided exploration of attacks. We also provide a toolkit to help reason about the impact on the grid. Extensive experiments on multiple benchmark grid networks are conducted to show the efficacy of our approach and demonstrate how the varying the sequence of attacks can result in different levels of disruption.