How applying AI to some of the grid’s most complex computational demands is helping improve the ‘flow’ of energy.
It’s said that the U.S. electrical grid—a nationwide labyrinth of interconnected power plants, transmission lines, substations, and more—is the largest machine in the world. If that’s the case, this machine is starting to sputter.
After decades of flat electricity demand, this infrastructure-heavy ecosystem has begun an era of dramatic complexity: AI-driven data centers are proliferating rapidly, each demanding hundreds of megawatts; the electric vehicle (EV) industry is booming, along with its fast-growing charging infrastructure sector; and all the while, the energy transition continues to confound utilities, which must ‘park’ new energy from renewables in vast storage systems strewn across the country, until it can be safely released onto the grid.
The upshot: over the next five to 10 years, North America will be at “elevated or high risk” of energy shortfalls, according to the North American Electric Reliability Corp. (NERC).1
There is a way through the complexities, however. For Hitachi, a company with a heritage in energy and industrial technologies, as well as decades in AI, such challenges require a methodical approach. They require boiling down the industrial problems into multiple, workable mathematical problems; tackling one challenge at a time, methodically with AI; and utilizing the unique capabilities and expertise from Hitachi Group companies. This is how Hitachi is easing the mounting pressures on the grid.
Taming computational demand
It’s the kind of approach the company brought to a major regional grid operator in the U.S., that was looking for help reducing the extraordinary length of time needed for the requisite impact studies – formal reports needed before introducing new energy sources onto the grid. It’s also the approach Hitachi takes to build some of the most comprehensive and reputable datasets on energy generation and consumption, from which it can test models to achieve the most reliable outcomes.
And while some view industrial challenges with a disruptive mindset, Hitachi knows when to embrace industrial AI as a “co-pilot.” Its technology works alongside systems in which utilities have made significant investments, rather than replacing them. “We will be part of the utility ecosystem as an accelerator,” says Bo Yang, vice president and head of the Energy Solutions Lab at Hitachi America R&D.
According to Yang, disruptive innovation has its place. But there’s also a practical reason for this approach: the computational demands are enormous. Federal Energy Regulatory Commission (FERC) guidelines require exhaustive analysis of how new power generation affects grid stability, not just under current conditions, but across future scenarios and potential system failures.
Take the grid project. When power companies apply to connect new generators, operator’s software must run tens of thousands of simulations to ensure grid stability. This process traditionally took years. Hitachi’s industrial AI technology speeds up the analysis by running multiple calculations simultaneously, a technique known as parallel processing, while the operator’s existing software stays in place to validate complex cases and final results.
This hybrid approach reduced analysis times by 80%—reducing total review time from 27 months to within a year—while maintaining the rigorous safety standards the industry demands.
The village approach
Connecting new generators to the grid requires complex collaboration across the energy industry. Multiple stakeholders—project developers, independent system operators, transmission operators, regulators, and others—each rely on data-driven analysis to make critical decisions. The entire “village” must reach consensus before new generation can connect to the grid.
Success in this environment requires both deep industry knowledge and advanced AI capabilities. The Hitachi team working on the grid operator’s project includes power engineers with decades of experience who understand the mathematical and operational foundations of power system analysis.
This expertise enables them to pinpoint which parts of the process can be accelerated by AI and which require traditional approaches.
“AI specialists often have strong algorithmic skills, but they may not be focused on the right industry problems,” Yang says. “Meanwhile, legacy analytical tools—some dating back decades—have limitations that new AI methods can overcome when applied thoughtfully.”
Breaking down complex problems
Industrial challenges often involve highly complex, interconnected processes that can be broken down into smaller mathematical problems. In power systems, for example, this means separating the analysis into distinct tasks. Every task focuses on different aspects of grid stability and performance.
Each component presents opportunities for AI acceleration while maintaining the physical constraints and safety requirements that govern power system operations. Industrial AI doesn’t need to reinvent the physics of power systems. It needs to accelerate the computational analysis that utilities already understand.
This systematic approach extends beyond individual projects. The same industrial AI framework developed for power grid applications applies to other physics-based analytical processes across industries—from rail transportation systems where safety is paramount, to manufacturing operations requiring precise control, to mobility solutions demanding real-time optimization.
“The key is understanding the specific problem you’re solving and then determining which AI technique to apply,” Yang says. This problem-first methodology suggests how the same framework might work across different industries.
Industrial AI is different
Utilities carry enormous responsibility for infrastructure that powers entire regions. This creates a fundamental challenge for AI adoption: algorithms alone aren’t enough. Effective implementation requires deep understanding of operational constraints, regulatory requirements, and the physics of power systems that takes years to accumulate and validate.
This reality makes industrial AI fundamentally different. It’s not about disrupting existing systems. It’s about enhancing them with the precision that critical infrastructure demands. The most revolutionary AI is often the most methodical—not because it lacks ambition, but because it simply can’t afford to fail.
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For more information about Hitachi’s industrial AI work, visit: AI Resource Center – Hitachi Digital
