ADVANCED RESEARCH
DIRECTIONS ON
AI FOR
ENERGY

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 CO2 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 stakeholder datasets, facilitating international preparation, response, and recovery, can enhance preparedness and resilience solutions and inform faster recovery. Science-based models enhanced with AI multi-modeling approaches can improve predictions of subsurface properties and systems to improve resource discovery for domestic critical materials, geothermal reservoirs, uranium, and water opportunities. This capability could create a national subsurface AI and data testbed to enable responsible commercial, regulatory, and science-based discovery and development. AI can improve the forecasting and prediction of subsurface properties and systems, informing and transforming our ability to reduce risks and responsibly interact with the subsurface. Energy material innovation is key to realizing national clean energy goals. Increasing automation in materials laboratories, such as autonomous laboratories, can transform the design and discovery of new materials. AI can also accelerate materials qualification through automation of materials testing, leading to new energy technologies such as advanced nuclear reactors and new battery certifications. In addition to these cross-cutting opportunities, there are unique use cases in nuclear, renewable, and carbon management energy systems. For example, while emissions, prediction, measurement, and mitigation are uniquely important to carbon management, the underlying computational infrastructure could be shared across grand challenges. Unattended operation of nuclear reactors has unique life-safety considerations; however, many plant-level digital twins of piping, valve, heat exchanger, and cooling towers could be shared across applied energy domains. A DOE consortium model from all energy domains, integrated with expertise from subject-matter experts from the laboratories, could help ensure and drive efficiency across research challenges. To accomplish these grand challenges, key developments are needed. The laboratories must establish a leadership computing ecosystem to train and host data and foundation models at ever-increasing scales. Fine-tuned models need to be developed for each domain that are coupled, where possible, with ground-truth, first-principles physics. Although the laboratories have hundreds of petabytes’ worth of data, only small amounts of these data are cataloged, warehoused, and ready for AI model ingestion. Curation of one-of-a-kind, ground-truth data coupled with energy industry data will be essential to building models at these scales. Most important, partnerships across laboratories, government, industry, and academia are essential to realizing the transformational benefits of AI for energy. This AI for Energy report further details grand challenges that provide significant opportunities for energy applications across nuclear energy, the power grid, carbon management, energy storage, and energy materials over the next decade. The main conclusions and opportunities from this study are available in the Key Findings section of this report.