SPECIAL REPORT | AI OPPORTUNITIES EXPLORED
AI’s nuclear opportunity
Artificial intelligence could clearly benefit the US energy system but, an expert
symposium concluded, it offers specific and transformational opportunities to its nuclear industry to rapidly deploy clean energy assets, secure critical networks and reduce the capital and operational costs of next-generation energy technologies
IN DECEMBER 2023 AROUND 100 experts in energy, artificial intelligence (AI) and machine learning (ML) convened at Argonne National Laboratory. They were there to map out the future potential for utilisation of AI in energy, considering five ‘energy mission’ areas that have previously been identified - fossil fuel, carbon management, nuclear power, renewables and energy efficiency. The outcome is a report, ‘AI for Energy’ that sets out ‘grand challenges’ for AI applications in nuclear energy over the next decade, as well as for the power grid, carbon management, energy storage and energy materials. The experts were optimistic: they found that AI offers the
ability to assess much larger system models, make more use of computational resources and capture knowledge from top scientists. As a result, it offers a “transformational opportunity” to rapidly deploy clean energy assets, secure critical networks and reduce the capital and operational costs of next-generation energy technologies. It can also help the energy industry grapple with new issues like connected systems and demand-side response. AI use cases across the broader energy industry are
varied and perhaps sometimes unexpected. Among those noted in the report that followed the Argonne meeting were:
● Commercial power plant design and licensing, which can account for up to 50% of the time-to-market for new energy deployments. The experts said AI has the potential to reduce schedules by approximately 20% across new clean energy designs, partly by augmenting the large energy development workforce that will be required.
● Responding to a rapid increase in requirements for planning, operation and controls as demand-side response activities increase alongside generation capabilities. That encompasses more flexibility from assets and improving the reliability and agility of the grid by increasing the visibility of assets to operators and consumers. The resulting volumes of communications, controls, data and information are increasing and require an AI-based response.
● Natural disasters and human-caused events, which are occurring more frequently and with more intensity, with adverse weather events increasingly disrupting supply chains, damaging assets and making some areas less habitable. An AI-based, all-hazards global response system can ingest global and stakeholder datasets, to enhance preparedness and allow for faster recovery.
Above: Experts in energy, artificial intelligence (AI) and machine learning (ML) convened at Argonne National Laboratory to map out the future potential for utilisation of AI Photo credit: Henry C Jorgenson/
Shutterstock.com
34 | August 2024 |
www.neimagazine.com
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