Stevens Researcher Creates First Asset-Level, Nationwide Model of U.S. Energy Systems as Part of National Science Foundation Project

When you flip a light switch, cook dinner on your gas stove or turn up the thermostat on your home’s oil heater, you’re probably not thinking about the energy delivery system behind any of those actions, much less how they’re all connected. Even energy researchers often study these systems independently.

But Amro Farid, professor in the Department of Systems and Enterprises at Stevens Institute of Technology, has taken a broader approach to understanding the U.S. energy system. His groundbreaking research, "American Multi-Modal Energy System Synthetic & Simulated Data (AMES-3D)," is developing an advanced, data-driven, open-source model that maps the interconnections among electricity, natural gas, oil and coal. The work is funded through a National Science Foundation grant. 

Using hetero-functional graph theory—the mathematical framework he developed to model complex systems such as smart grids and transportation networks—Farid has built detailed structural models of the U.S. energy system. His goal is to help policymakers, researchers and industry leaders grasp connections and inefficiencies that might otherwise be overlooked. 

Published in ScienceDirect, his study is the first to apply this approach to the entire national energy network, offering new insights into its structure and the broader implications for energy policy. 

Reenergizing the study of energy systems

"Traditional network graphs work well for analyzing relationships in systems that transport a single type of resource, like an electric grid moving electrons or a natural gas network distributing fuel," Farid explained. "But the U.S. energy system moves and transforms multiple resources, primarily coal, oil, natural gas and electricity. For example, a natural gas power plant will convert and transform natural gas into electricity. The hetero-functional graph theory I developed is, quite frankly, the first analytical method with the potential to fully capture this complexity."

Amro Farid, Department of Systems Engineering

Farid and his co-author, engineering science researcher Dakota Thompson, examined energy networks in New York, California, Texas and the U.S. as a whole. Using mathematical models, their work reveals that geography and state policies greatly impact the nation’s energy infrastructure and its future.

For example, New York, with its colder Northeastern climate and dense population, has a different energy demand than California, which actively leads clean-energy initiatives. Texas, home to nearly half the country’s energy assets, is quite advanced in its energy infrastructure and is a dominant force in oil and gas production.

Farid’s structural analysis shows that certain parts of the network are more interconnected than others. In particular, older energy infrastructure tends to be preserved and expanded, making it difficult to transition to new, more sustainable technologies. 

"If we don’t find ways to overcome this situation, which we call 'infrastructure lock-in,' our energy system will remain dependent on the same incumbent technologies we’ve always used," said Farid. "And that’s not promising news for the sustainable energy transition, which depends on adopting new process technologies rather than those from the last century."

Empowering future research—and the future of energy

By combining physical data with machine learning, the project offers a clearer understanding of how energy flows, what structural challenges must be overcome for a successful energy transition, and how policies can support sustainability, resilience, economic prosperity and equity.

"The energy system must continue to serve the public while undergoing transformation," explained Farid. "It’s like trying to convert a gas-powered car into an electric vehicle while driving down the highway at 70 miles per hour. You need to understand the structure of the system before making changes. That's exactly what we need to do with our energy system. It has to remain reliable while also becoming more sustainable."