PhD Thesis Defense: Dakota Thompson

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"The American Multi-modal Energy System: Model Development with Structural and Behavioral Analysis using Hetero-functional Graph Theory"

In the 21st century, infrastructure is playing an ever greater role in our daily life. Presidential Policy Directive 21 emphasizes that infrastructure is critical to public confidence, the nation's safety, and its well-being. With global climate change demanding a host of changes across at least four critical energy infrastructures: the electric grid, the natural gas system, the oil system, and the coal system, it is imperative to study models of these infrastructures to guide future policies and infrastructure developments. Traditionally these critical energy systems have been studied independently, usually in their own fields of study. Therefore, infrastructure data sets often lack the structural and dynamic elements to describe the interdependencies with other infrastructures.

This thesis refers to the integration of the aforementioned energy infrastructures into a singular system-of-systems within the context of the United States of America as the American Multi-modal Energy System (AMES). This work develops a structural and behavioral model of the AMES using Hetero-functional Graph Theory (HFGT) to provide an open-source dataset. Following a data driven approach and utilizing model-based systems engineering practices, the proposed model of the AMES is produced in the Following steps. First, the HFGT toolbox code is made available on GitHub and advanced to produce HFGs of systems on the scale of the AMES using the languages Python and Julia. Second, the analytical insights that HFGs can provide relative to formal graphs are investigated through structural analysis of the American Electric Power System (AEPS) which demonstrates how HFGs are better equipped to describe changes in system behavior. Third, a reference architecture of the AMES is developed which provides a standardized foundation to develop future models of the AMES. Forth, the AMES reference architecture is instantiated into a structural model from which structural properties are investigated. Finally, a physically informed Weighted Least Squares Error Hetero-functional Graph State Estimation (WLSEHFGSE) analysis of the AMES' socio-economic behavior is implemented to investigate the behavior of the AMES with asset level granularity. These steps provide a reproducible and reusable structural and behavioral model of the AMES for guiding future policies and infrastructural developments to the critical energy infrastructures.

Thesis Committee

• Prof. Mark Laser (Chair)
• Prof. Geoffrey Parker
• Prof. Steven Taylor
• Prof. Amro M. Farid (External: Stevens Institute of Technology)

Contact

For more information, contact Julia Abraham at julia.s.abraham@dartmouth.edu.