AI Resilience for the Grid to Safeguard Energy Systems
Artificial intelligence has steadily made its way into critical sectors, and the energy grid is no exception. For decades, utilities have relied on data-driven tools for maintenance planning, load forecasting and operational oversight. Now, AI is amplifying these capabilities, offering unprecedented insights drawn from vast historical datasets, including operator logs and control records.
By processing and interpreting mountains of data in real time, AI can forecast outages, optimise power generation mixes and propose operator actions in the moment. It can suggest when to ramp up wind energy, switch to nuclear reserves, or balance natural gas with renewables for peak efficiency. However, alongside the promise comes a set of formidable risks.
From misleading “hallucinations” to cyberattacks that subtly alter data or model architecture, AI-driven systems can introduce vulnerabilities. These aren’t hypothetical scenarios—they could translate into flawed decisions with the potential to destabilise entire sections of the grid.
As Andy Bochman, Idaho National Laboratory’s senior grid strategist, cautioned: “AI can rapidly analyse mountains of history from operator logs and trend forecasting to suggest the optimal use of generation sources such as natural gas, nuclear, wind or a combination of all. But AI can also hallucinate, recommending actions that human experts would know not to take.”
A First-of-its-Kind Safeguard
Recognising the high stakes, Idaho National Laboratory (INL) has rolled out the Testing for AI Grid Resilience (TAIGR) initiative. This pioneering programme aims to identify, evaluate and mitigate risks associated with AI-enhanced grid management systems.
TAIGR’s mission is to bring together a wide range of players—utilities, equipment vendors, regulators, suppliers, operators and researchers—into a collaborative space. Here, they can tackle AI’s unique challenges for grid operations head-on, sharing knowledge and building safeguards.
Earlier this year, INL convened its first workshop under TAIGR. Representatives from utilities, technology suppliers, federal regulators and academic bodies spent two days dissecting AI’s role in grid management. The event revealed both a hunger for rapid AI adoption and a glaring lack of structured risk evaluation methods.
Alex Jenkins, senior AI solutions engineer at Aveva, suggested supplier engagement must be part of the equation: “Future researcher-industry collaborations should include supplier input to broaden the exchange of ideas and perspectives on AI safety.”
Strengthening AI’s Foundations
Out of this collaboration emerged AMARANTH (Artificial Intelligence Management and Research for Advanced Networked Testbed Hub), a project funded by the Department of Energy’s Grid Deployment Office. AMARANTH zeroes in on AI safety and resilience, working to ensure these systems can withstand real-world challenges.
Patience Yockey, research data scientist at INL, underscored the importance of deep testing: “We need to understand any possible weaknesses, not just in the data but in the AI’s design and implementation as well. We want to ensure that AI systems can be trusted to help produce consistent power under any conditions.”
Learning from CyTRICS
TAIGR draws inspiration from DOE’s CyTRICS (Cyber Testing for Resilient Industrial Control Systems), which has bolstered the cybersecurity of industrial control systems and supply chains. Much like CyTRICS, TAIGR will operate voluntarily and transparently, with input from industry veterans, national labs and government agencies.
INL is already in conversation with influential trade bodies such as the Edison Electric Institute, the North American Electric Reliability Corporation and the Electric Power Research Institute. According to Bochman: “They all want to engage and contribute.”
Scott Aaronson, senior vice president of Energy Security & Industry Operations at Edison Electric Institute, echoed the sentiment: “Edison Electric Institute and our member electric companies are committed to exploring the full capabilities of advanced AI systems and to engaging with the full range of stakeholders who are developing, enhancing, and ensuring safe and secure integration of those systems. INL’s work with TAIGR is an essential part of the process, helping us all better understand how to leverage the benefits of these systems while mitigating the risks.”
The Testing Edge
What sets TAIGR apart is access to INL’s full-scale power grid testbed—a rare and invaluable asset. This facility can manage up to 138 kilovolts, enabling rigorous and safe stress-testing of energy systems. It’s a controlled environment where AI-enhanced grid technologies can be pushed to their limits without jeopardising real-world networks.
Bochman summed it up: “This is why national labs exist—to solve hard problems that require expertise and infrastructure you won’t find anywhere else.”
With decades of experience in testing critical energy infrastructure, INL is uniquely equipped to lead. Its grid can simulate diverse operational scenarios, from renewable integration to large-scale storage testing, offering a proving ground for AI-driven solutions.
Building Industry-Wide Confidence
TAIGR’s next steps involve creating detailed testing protocols and approaches to attract asset owners and technology vendors. These procedures will ensure that any AI introduced into grid operations is both safe and reliable.
The ultimate aim is not to hold AI at bay but to prepare the industry for its safe, resilient adoption. As AI continues to evolve, TAIGR provides a pathway for energy stakeholders to innovate without gambling with grid stability.
A Smarter, Safer Future
The electric grid underpins the economy, national security and public safety. As such, introducing any new technology—especially one as transformative as AI—requires both enthusiasm and caution.
Bochman put it plainly: “AI is too efficient to overlook, too helpful to ignore. Yet when AI is fed bad data or its results go unchecked, it may become unreliable, and that’s not acceptable, especially when dealing with critical infrastructure.”
TAIGR stands as a proactive shield, ensuring AI’s benefits can be embraced without compromising the lifeblood of modern society.
