EXECUTIVE SUMMARY
EXECUTIVE SUMMARY
Artificial intelligence (AI) provides a transformational opportunity to
rapidly deploy new clean energy, secure critical grid energy assets
from threat actors, and reduce capital and operational costs of
next-generation energy technologies and the connected systems
that embody the demand side of the transformation. The United
States will need to invest trillions of dollars in energy infrastructure
to reach the nation’s clean, resilient goals by 2050. At the
Department of Energy (DOE) national laboratories, AI has
incredible potential across nuclear, renewable, and carbon
management domains due to the ability to represent
unprecedented system model sizes, provide intense computational
resources, and capture knowledge from a workforce of the nation’s
top scientists. In aggregate, AI could reduce the cost to design,
license, deploy, operate, and maintain energy infrastructure by
hundreds of billions of dollars if the following applied energy
challenges are realized.
AI provides a breakthrough opportunity to accelerate the design,
deployment, and licensing of new energy capacity. Commercial powerplant design and licensing are a multi-year effort that
can account for up to 50% of time to market for new energy deployments. DOE estimates the onboarding of 1.6 TW of new
solar capacity and 200 GW of new nuclear capacity, while enabling hydrogen, geothermal, critical minerals, and other clean
energy resources by 2050, with a cost that could approach trillions of dollars in national investment to meet growing global
clean energy demand. Additionally, DOE estimates the need to reduce costs to less than $100/net metric ton of CO
2
equivalent for both carbon capture and storage to address carbon pollution. AI has the potential to reduce schedules by
approximately 20% across new clean energy designs, with potential savings in the hundreds of billions of dollars by 2050.
Additionally, AI can augment and extend the energy development workforce that will be in high demand.
The energy grid’s generation capabilities and demand-side needs are experiencing rapid changes in requirements for secure,
reliable, and resilient planning and operations controls. The increasing volumes of communications, controls, data, and
information are growing the digital landscape, increasing flexibility and improving the reliability and agility of the grid by
increasing visibility to operators and consumers. Integrating energy systems together across grid operations could save
billions of dollars annually by automatically optimizing generation and demand-side needs.
Autonomous operation technologies can provide monitoring, control, and maintenance automation across various clean
energy technologies. Distributed, consumer-sited technologies are changing the power load with electric vehicles (EVs),
distributed storage, smart buildings, and appliances adding new intelligence to loads while also requiring the integration of
consumer-sited controllability. Furthermore, new advanced nuclear technologies, such as microreactors, will likely need to
operate autonomously to realize economies of scale. Delivering AI capabilities across the operations and maintenance
lifecycle can transform safety, efficiency, and innovation within national energy production and distribution infrastructure.
The siting of new energy capacity is a complex challenge balancing energy generation options, community needs,
environmental factors, and resiliency considerations. AI could aid community energy planning based on a comprehensive
dataset and a trained community energy foundation model that captures characteristics of and interactions between physical
infrastructure, human behavior, and climate/weather impacts. AI tools can achieve national clean energy goals by
democratizing community-level clean energy resources and facilitating the identification of energy transition pathways that
reflect local objectives, demographics, and legacy infrastructure.
Natural disasters and human-caused events are occurring more frequently and with more intensity, delivering significant
impacts to the nation. Adverse weather events are increasingly disrupting supply chains, damaging property and assets,
and making certain areas less habitable. The U.S. experienced a record 28 unique weather/climate disasters that cost at least
$1 billion in 2023. Climate change, urbanization, population growth, aging infrastructure, and deferred maintenance increase
risks to communities and human survival. An AI-based, all-hazards global response system that has ingested global and
EXEMPLAR GRAND CHALLENGES FROM THE
CHAPTERS OF THE AI FOR ENERGY REPORT
01 Nuclear Energy: Accelerating the Licensing and
Regulatory Process
02 Power Grid: Building Cyber- and All-Hazards
Resilient and Secure Energy Systems
03 Carbon Management: Realizing A Virtual
Subsurface Earth Model
04 Energy Storage: Equitable and Accessible
Deployment
05 Energy Materials: Advancing Beyond Material
Properties and Performance to Achieve Lifecycle-
Aware Materials Design
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