Author: Harsh Anand

Publisher:

Published: 2021

Total Pages:

ISBN-13:

The energy infrastructure facilitates energy flows across industry sectors throughout the nation. This energy flow is susceptible to any disruptive event that threatens the functionality of the energy supply-demand network and impacts economic productivity throughout a multi-regional interdependent economy. The study on the systematic framework for analyzing energy infrastructure resilience proposes a systematic framework to analyze energy infrastructure resilience by (i) evaluating the economic vulnerability of the interdependent network of energy infrastructure and industry sectors, (ii) seeking strategies to allocate a limited budget to harden the network, and (iii) assessing the value of information in decision making under uncertainty. The proposed resilience analysis framework is implemented in a stylized case study of the U.S. energy and the industry sectors. The holistic framework helps policymakers with decisions related to enhancing energy infrastructure resilience. Such decisions include investments to (i) reduce the vulnerability of the energy infrastructure network and (ii) develop a more effective mix of generation sources (e.g., renewable vs. nonrenewable). The U.S. has defined a number of critical infrastructures, the disruption of which "would have a debilitating impact on security, national economic security, national public health or safety, or any combination of those matters." Among these critical infrastructures is the electric power network that has a crucial role in enabling the operation of societies and industries. In the past decades, the functionality of the power network has been vulnerable to numerous disruptive events, including natural hazards, human-made events, or common failures. The power network component vulnerability analysis work leverages several publicly available big data sets to lay the foundation for a comprehensive characterization and analysis of the U.S. power network in order to propose a network component vulnerability measure adopting machine learning techniques. The non-linear machine learning model is implemented to create smarter component cataloging for vulnerability analysis based on its geographic location and criticality. 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 and moving toward resilient smart grids. 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. The power network component importance considering economic impacts study 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.