学术文献库

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.