Computational analysis of cascading failures in power networks

Abstract

This paper focuses on cascading line failures in the transmission system of the power grid. Such a cascade may have a devastating effect not only on the power grid but also on the interconnected communication networks. Recent large-scale power outages demonstrated the limitations of epidemic- and percolation-based tools in modeling the cascade evolution. Hence, based on a linearized power flow model (that substantially differs from the classical packet flow models), we obtain results regarding the various properties of a cascade. Specifically, we consider performance metrics such as the the distance between failures, the length of the cascade, and the fraction of demand (load) satisfied after the cascade. We show, for example, that due to the unique properties of the model: (i) the distance between subsequent failures can be arbitrarily large and the cascade may be arbitrarily long, (ii) a large set of initial line failures may have a smaller effect than a failure of one of the lines in the set, and (iii) minor changes to the network parameters may have a significant impact. Moreover, we show that finding the set of lines whose removal has the most significant impact (under various metrics) is NP-Hard. Moreover, we develop a fast algorithm to recompute the flows at each step of the cascade. The results can provide insight into the design of smart grid measurement and control algorithms that can mitigate a cascade.

Interaction graph learning of line cascading failure in power networks and its statistical properties

“…From the computational perspective, the component failure does not have monotonicity property in the physical domain, i.e., failure of a set of components may have a smaller effect than the failure of a subset of them (Guo et al 2017;Mazauric et al 2013), and the problem of finding the set of lines whose removal has the most impact is NP-hard with different metrics. The authors of Ghasemi and Kantz (2022) show that the failure cascading in power networks involves higher-order interactions when the simultaneous states of a group of more than two lines affect the process dynamics.…”