  As one of the most pressing challenges of the 21st century, global climate
change demands a host of changes across four critical energy infrastructures:
the electric grid, the natural gas system, the oil system, and the coal system.
Unfortunately, these four systems are often studied individually, and rarely
together as integrated systems. Instead, holistic multi-energy system models
can serve to improve the understanding of these interdependent systems and
guide policies that shape the systems as they evolve into the future. The NSF
project entitled "American Multi-Modal Energy System Synthetic \& Simulated
Data (AMES-3D)" seeks to fill this void with an open-source,
physically-informed, structural and behavioral machine-learning model of the
AMES. To that end, this paper uses a GIS-data-driven, model-based system
engineering approach to develop structural models of the American Multi-Modal
Energy System (AMES). This paper produces and reports the hetero-functional
incidence tensor, hetero-functional adjacency matrix, and the formal graph
adjacency matrix in terms of their statistics. This work compares these four
hetero-functional graph models across the states of New York (NY), California
(CA), Texas (TX), and the United States of America (USA) as a whole. From the
reported statistics, the paper finds that the geography and the sustainable
energy policies of these states are deeply reflected in the structure of their
multi-energy infrastructure systems and impact the full USA's structure.
