The American Multi-Modal Energy System (AMES)

The problem

In envisioning a future where our energy systems work sustainably and seamlessly together, a fundamental shift is needed from analysing them in isolation to embracing a holistic, multi-energy, systems engineering approach. This encompasses various energy sources like electric power, coal, natural gas, oil, and renewable fuels, viewed through the lens of both asset-level resilience and national-level sustainability.

The United States has faced a significant hurdle to this outcome. There was a lack of comprehensive data on how these energy systems depended on each other. Existing data was fragmented and confined within individual infrastructure sectors, making it challenging to see the bigger picture of how they worked together. Compounding this challenge was the absence of cohesive national-level strategies for the sustainable energy transition that could be implemented at the facility-level scale; risking overbuilding or uncoordinated changes.

The response

To address this, the US National Science Foundation and the Department of Homeland Security launched an initiative to design, develop, and share simulated and synthetic data on interdependent critical infrastructures. This effort aimed to improve our understanding and performance. In response, Professor Amro M. Farid, Smart Energy Mission Principal Systems Scientist, developed an open-access, physically-informed, data-driven, machine-learning model of the entire American Multi-Modal energy system (AMES). In so doing, the AMES model allows systematic study of the AMES’ sustainability and resilience as it goes through the sustainable energy transition.

Using a meticulously crafted workflow grounded in GIS analytics, Model-Based Systems Engineering (MBSE), and hetero-functional graph (HFG) theory, Professor Farid’s team gathered data on the AMES into its constituent parts, identified their interdependencies, and integrated them back together again.

Model-Based Systems Engineering (MBSE) is a way of designing and managing complex systems using graphical models instead of traditional text-based documents. It’s like building a virtual blueprint of a system, where you can see how all the parts fit together and how they work.

Hetero-functional graph theory is a way of understanding complex systems by using graphs. Instead of just looking at how the parts of a system are connected, like in a regular graph, hetero-functional graph theory allows us to understand the many functions of these parts and how they depend on each other.

Professor Farid’s response to the US National Science Foundation and the Department of Homeland Security initiative involved integrating various GIS data layers to build a model that visually depicts how different systems interact without losing our understanding of the behaviour of each energy asset.

The first step involved leveraging GIS data-driven development to construct an architecture of the AMES. This entailed integrating various GIS data layers to visually depict how systems such as coal, oil, gas, and electricity interact with each other and with their broader natural and human environments.

Using MBSE, they broke down the AMES into its basic parts and individual components, studying how each asset works to understand what goes in and what comes out.

With this foundational understanding in place, they seamlessly stitched together the individual infrastructure systems to grasp the holistic functioning of the AMES. This graphical representation illuminated the connections between infrastructures and their intricate interplay with the broader environment.

To deepen their insights, they used mathematical and computational models, supported by HFG theory. HFG helped change the graphical models into computer simulations, making it easier to analyse on a larger scale.

 

The result

Armed with these tools, Professor Farid’s team provided a comprehensive understanding of the AMES to the US National Science Foundation and Department of Homeland Security, paving the way for a sustainable and resilient energy future. They can now grasp the dynamics of resilience under various challenges like bushfires, heatwaves, cyclones, and cyber-attacks with precision.

Furthermore, they can conduct detailed analyses at national and state levels, gaining insights into energy needs on a month-by-month basis and generating heat maps to visualise energy consumption and production across regions.

Additionally, they have a deeper understanding of network flows for different energy commodities.
This has led to the creation of an AMES state estimator , providing near-real-time insights into the functioning of the holistic system. Leveraging HFG estimation, this tool serves as a granular asset-level guide for achieving net-zero pathways, supporting informed decision-making as they transition towards a more sustainable energy future.