Logarithmic resilience risk metrics that address the huge variations in blackout cost

TL;DR

This paper introduces new resilience risk metrics for power systems that effectively handle the heavy-tailed distribution of blackout costs by using logarithmic transformations and tail slope analysis.

Contribution

It proposes novel metrics based on the mean log blackout cost, tail slope index, and blackout frequency to better estimate large blackout risks.

Findings

Metrics effectively capture large blackout risk despite heavy-tailed data

Improved estimation of blackout risk with logarithmic and tail slope measures

Addresses variability in blackout cost data for more reliable risk assessment

Abstract

Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the logarithm of the cost of large-cost blackouts, the slope index of the heavy tail, and the frequency of large-cost blackouts.