A probabilistic approach to modeling power network component importance considering economic impacts

The electric power networks have become increasingly interconnected and complex. The resilience of the power network is crucial for the economic productivity of the states and the broader country. This work integrates a network flow formulation with an economic interdependency model to quantify the multi-industry impacts of a disruption in the power network. We aim to measure and rank the importance of components according to their impact on the network's overall resilience. During modeling, we define the measure of importance by combining the probabilistic assumptions under uncertainty. We use data-driven methods to enhance the predictability and interpretability of resilience importance measures in network planning using a Bayesian kernel technique. The findings could be useful by the grid stakeholder and policymakers to (i) evaluate network stability, (ii) understand the risk of cascading failure, and (iii) improve the resilience of the overall network.