PJM Interconnect AI-Planning Tools
Returning from our July 4th Holiday hiatus, the Minerva Institute continues its weekly case study series on AI and autonomous systems in industrial control systems and critical infrastructure, with our fourth release, “PJM Interconnection AI-Planning Tools.”
PJM, the largest grid operator in the United States, faced a major interconnection bottleneck as renewable and storage applications outpaced traditional engineering review processes. This case study explores how PJM partnered with Google’s Tapestry project to apply AI-enabled planning tools, including computational knowledge graphs, automated document ingestion, and parallelized simulations, to accelerate long-term grid planning.
Importantly, the case is not about replacing engineers or automating real-time grid operations. It is about using transparent, human-in-the-loop AI (an issue we will address in more detail in a separate series devoted to HITL) to improve planning throughput while preserving reliability, regulatory accountability, and engineering judgment.
For energy leaders, policymakers, technologists, and security professionals, PJM’s experience offers timely lessons on process reform, data discipline, and responsible AI adoption.
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