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Abstract

As one of the most pressing challenges of the 21st century, global climate change demands 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. In the context of the United States, this paper refers to this system-of-systems as ``The American Multi-Modal Energy System (AMES)". These combined changes necessitate an understanding of the AMES interdependencies, both structurally and behaviorally, to develop and enact effective policies. This work focuses on behavioral analysis methods to provide examples of how to analyze system behavior and the critical matter and energy flows through the system. Building upon past works, two regions of the AMES are modeled, and their behavior is analyzed using Hetero-functional Graph Theory (HFGT). More specifically, the work presents a weighted least square error state estimation model of the AMES. State estimation has played a major role in the operation and development of the American Electric Power System. This work extends the state estimation analysis beyond the single-operand electric grid environment into the heterogeneous environment of the AMES. Employing a data-driven and model-based systems engineering approach in combination with HFGT, a Weighted Least Squares Error Hetero-functional Graph State Estimation (WLSEHFGSE) optimization program is developed to estimate the optimal flows of mass and energy through the AMES. This work is the first to integrate state estimation methods with HFGT. Furthermore, it demonstrates how such a WLSEHFGSE recovers the mass and energy flows in a system-of-systems like the AMES with asset-level granularity.