Session Chair: Venkat Banunarayanan- NRECA

As artificial intelligence moves from the research into applications in grid planning and operations, the electric grid control room stands at the frontier of what could be one of the most consequential transformations in the history of the power system. This session explores what it means to place AI at the operator’s side—where split-second decisions balance supply and demand, integrate surging volumes of both firm and intermittent energy, and keep the lights on across increasingly complex and volatile networks. A key development is the emergence of grid foundation models: large, pre-trained AI systems that learn the underlying physics and behavior of the power network from vast operational data, promising a shift from narrow, task-specific tools toward versatile models that can forecast, diagnose, and reason across the full breadth of control room challenges.
We will confront the hard questions: How can AI deliver actionable insight fast enough to matter, while remaining transparent and trustworthy enough for operators to rely on when the stakes are highest? Can grid foundation models and machine learning algorithms anticipate cascading failures, optimize dispatch, and manage uncertainty in ways that surpass traditional tools, without introducing new risks around data quality, cybersecurity, and automation bias? And critically, how do we design a partnership between human expertise and machine intelligence that augments rather than replaces the seasoned judgment of the control room? Bringing together perspectives from system operators, researchers, and technology developers, this panel will chart both the immense opportunities and the real-world barriers to making AI a safe, reliable, and indispensable presence in the control room of tomorrow

AI for the Control Room – An utility perspective

Speaker: Brandon Harrington – AECI

Human-Centred AI for Power System Control Rooms

Speaker: Ricardo Bessa – INESC-TEC

This talk presents the AI4REALNET framework for human-centric AI in critical infrastructures, with a particular focus on power system operation. It discusses key challenges in supporting control-room decision-making, including the rapid processing and prioritization of alarms and the need for AI models that are transparent, interpretable, and auditable.

TBD

Speaker: Adrian Kelly – EPRI Europe

TBD

The Control Room of the Future: Human Factors in AI Adoption

Speaker: Seong Lok Choi – NLR

The rapid integration of AI into operational control rooms raises a key human-factors question about when operators will willingly give the driver’s seat to an AI system and when they will retain direct command, a paradox illustrated by autonomous vehicles that are measurably safer than human drivers yet still prompt passengers to reach for the steering wheel. This preference is grounded in a cognitive distinction between safety and controllability, with delegation accepted in low-risk, repetitive, well-defined tasks where the operator can assume a passenger role, but rejected in high-stakes or unusual emergencies where direct control is demanded. In those high-stakes contexts, AI is most effective as a trusted assistant that is demonstrably accurate, procedurally faithful, transparent, and auditable, augmenting rather than replacing human judgment. We propose conditional delegation — “if it ain’t broke, don’t fix it” — in which AI takes the primary role only when human capacity is exceeded, existing processes have failed, or the domain is new, and we recommend that control-room interfaces be designed to preserve operator action while leveraging AI’s accuracy, consistency, and speed.