Risk-based Probabilistic Quantification of Power Distribution System Operational Resilience

TL;DR

This paper introduces probabilistic, risk-based metrics to quantify the operational resilience of power distribution systems against rare, high-impact weather events, aiding in planning and mitigation strategies.

Contribution

It proposes novel $VaR_\alpha$ and $CVaR_\alpha$ metrics for resilience assessment and develops a simulation framework for their evaluation under various weather scenarios.

Findings

Metrics effectively quantify resilience loss under extreme weather.

Simulation results demonstrate the impact of planning measures on resilience.

Framework applicable to real-world power distribution networks.

Abstract

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 ($VaR_\alpha$) and Conditional Value-at-Risk ($CVaR_\alpha $) 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, $\alpha$. 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.