# Risk-based Probabilistic Quantification of Power Distribution System Operational Resilience

arXiv: https://arxiv.org/abs/1912.03344 · HTML: https://mlsanity.com/paper/1912.03344/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory](https://mlsanity.com/paper/2208.11543/) (2022)
- [Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets](https://mlsanity.com/paper/2211.07645/) (2022)

## Abstract (arXiv)

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks but also evaluate the potential improvements offered by different planning measures. In this paper, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: Value-at-Risk (VaRα) and Conditional Value-at-Risk (CVaRα ) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively for the events beyond a prespecified risk threshold, α. Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.