Dynamic Modeling of Cascading Failure in Power Systems

TLDR

Cascading failure modeling in power systems is challenging because many mechanisms exist, and understanding their relative importance is essential for selecting appropriate models. The study introduces a dynamic simulation model of power networks and protection systems that can simulate a broader range of cascading outage mechanisms than existing quasi‑steady‑state models, and details its modules and their interactions. The model incorporates dynamic representations of load and protection systems, enabling testing of their impact on cascading outage sizes through simulations of random N‑2 contingencies across various static load configurations. Simulations show that the dynamic model’s blackout size and event‑length distributions align with historical trends, that load models significantly affect cascading risk, and that while it agrees with a quasi‑steady‑state dc model in early stages, it diverges substantially in later stages.

Abstract

The modeling of cascading failure in power systems is difficult because of the many different mechanisms involved; no single model captures all of these mechanisms. Understanding the relative importance of these different mechanisms is an important step in choosing which mechanisms need to be modeled for particular types of cascading failure analysis. This work presents a dynamic simulation model of both power networks and protection systems, which can simulate a wider variety of cascading outage mechanisms, relative to existing quasi-steady state (QSS) models. The model allows one to test the impact of different load models and protections on cascading outage sizes. This paper describes each module of the developed dynamic model and demonstrates how different mechanisms interact. In order to test the model we simulated a batch of randomly selected $N-2$ contingencies for several different static load configurations, and found that the distribution of blackout sizes and event lengths from the proposed dynamic simulator correlates well with historical trends. The results also show that load models have significant impacts on the cascading risks. This dynamic model was also compared against a QSS model based on the dc power flow approximations; we find that the two models largely agree, but produce substantially different results for later stages of cascading.