PNNL

Abstract

The increasing intensity and frequency of extreme events have raised significant concerns about the elec- trical grid’s resilience. Numerous extreme events such as hurricanes, tornadoes, earthquakes, geomagnetic storms, high-altitude electromagnetic pulses, ice storms, cyber-attacks, and physical attacks, among others, threaten the entire electrical grid and its various components. Fragility curves play a crucial role in assessing asset vulnerability having a significant impact in all studies that evaluate the power system resilience for extreme events. Real-world data for such extreme events are often significantly limited, and thus in this paper a method for calibrating fragility curves with limited real-world data is presented. This paper also introduces a methodology to generate likely temporal sequences of failures during a hurricane event leveraging the newly calibrated fragility curves and the geospatial knowledge utilizing the electric grid resilience & assessment system (EGRASS) tool. Furthermore, the methodology evaluates the sequential contingencies using the dynamic contingency analysis tool (DCAT) for power system dynamic simulations. The main contribution of this work is the development and analysis of the methodology that enables dynamic simulations of power systems under climate-related extreme events using limited real-world data. The methodologies and case studies presented in this paper focus on the hurricanes faced by Puerto Rico’s grid as an extreme event but are agnostic of the type of extreme event and can be applied to other such events.