• Energy Research

Countries around the world suffer the dramatic impact of earthquakes and other natural hazards reflected in casualties, infrastructure damage, service interruptions, and recovery costs. Although disaster exposure consciousness of electric power systems has increased in recent years, mitigation and adaptation actions, such as reserve scheduling and infrastructure investments, are usually performed without quantitative tools to account for the underlying stochasticity of these events. This article first discusses why an integrated assessment, which incorporates sources of uncertainty (risk) and manages the time-dependency of the recovery process (resilience), should be used to assess the impact of seismic events on electric power systems. Thereafter, a probabilistic methodology that considers the hazard, vulnerability, operation, and recovery of the system is presented. As a case study, the probabilistic seismic resilience of the electric power system of Northern Chile is assessed using different risk measures, including expected annual loss, value at risk, and conditional value-at-risk. Finally, a novel criticality assessment based on these metrics is developed to demonstrate that, for certain networks such as the study case, retrofit of selective components can notably improve the resilience of the complete system to seismic events. For example, if one specific component from the 152 components of the study system is assumed invulnerable, expected annual interruption costs decrease by 8%.

Even though the concept of resilience is becoming widely used in electric power systems, there is no consensus on how to systematically model or quantify it. This article begins by proposing a classification for different types of system risk analyses, associating them to the concept of power systems resilience. Thereafter, it describes and applies a resilience assessment and adaptation framework to the Northern Chilean electric power system in the context of its exposure to seismic events. This time-dependent analysis is evaluated throughout the disaster's impact and the network's operation and restoration timeframes with two indices: Energy not Supplied and Energy Index of Unreliability. Finally, the article compares a base case with three resilience adaptation strategies, namely, robust, redundant and responsive cases, in terms of their expected energy annual loss and the return period of different levels of network performance.