Modeling of Natural Disasters and Extreme Events in Power System Resilience Enhancement and Evaluation Methods

TL;DR

This paper reviews current methods for modeling natural disasters and extreme events affecting power systems, emphasizing their importance for resilience assessment and highlighting research gaps and future challenges.

Contribution

It provides a comprehensive review of existing modeling practices for extreme events and discusses research gaps and potential solutions for improving power system resilience.

Findings

Existing models vary in accuracy and computational efficiency.

Identified gaps in modeling impacts on system components.

Highlighted need for integrated, realistic modeling approaches.

Abstract

The frequency of disruptive and newly emerging threats (e.g. man-made attacks--cyber and physical attacks; extreme natural events--hurricanes, earthquakes, and floods) has escalated dramatically in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vitally important as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in the modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a very important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.