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 cities and communities depend, and maintaining its reliable service and resilient operation is essential. Recently, extreme events such as natural disasters and man-made attacks have revealed the fragility of the grid, and the widespread consequences that people can face as a result. In general, the grid’s performance 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, which demonstrate the efficacy of our approach, and demonstrate how the ordering of attacks can result in different levels of disruption.