A new alliance between the power and tech sectors is tackling a major hurdle to grid modernization—data access.
The electric grid is undergoing one of the most ambitious transformations in its history, driven by the urgent need to integrate clean energy, electrify transportation, and enhance reliability. At the center of this shift is artificial intelligence (AI), a tool with the potential to optimize power system operations like never before. But there’s a catch: AI needs data—and most of it is locked away.
Recognizing this challenge, the Electric Power Research Institute (EPRI) has launched the Open Power AI Consortium, a collaborative effort between utilities, technology firms, and regulators to break down the data silos that hinder AI development in the energy sector. The goal? Accelerate grid modernization by creating standardized, secure, and accessible datasets that fuel innovation.
The Data Bottleneck
AI thrives on high-quality, diverse data. Yet, roughly 95% of power system data is shielded by cybersecurity protocols and customer privacy protections. This includes everything from grid performance metrics to household-level energy usage. While these safeguards are necessary, they’ve made it difficult for AI developers to train tools that can predict failures, optimize energy flows, or manage distributed energy resources like rooftop solar and batteries.
“Every major advancement in AI stems from access to data shaped by human knowledge,” said Scale AI CEO Alexandr Wang. “Without it, even the best algorithms are powerless.”
Utilities face a similar dilemma. They’re sitting on mountains of potentially transformative data but lack the legal and technical infrastructure to share it safely and meaningfully.
Bridging Sectors, Building Solutions
The Open Power AI Consortium aims to change that by developing shared data repositories, testing environments, and feedback systems that let utilities and tech firms co-develop AI solutions. These “sandboxes” will allow for experimentation without compromising real-world systems or customer trust.
Meanwhile, other initiatives, such as Schneider Electric’s partnership with Microsoft and Esri, are building data hubs that turn information from smart devices into real-time insights. These systems can help utilities predict demand spikes, automate responses, and reduce costly infrastructure upgrades.
Shinjini Menon, Senior Vice President at Southern California Edison, underscored the stakes: “The grid is far too complex for yesterday’s methods. We need AI not just for speed, but for smarter, more adaptive operations.”
States Leading the Charge
While federal legislation on AI and energy data remains in early stages, many states are moving ahead. California, Minnesota, Massachusetts, and others are pushing utilities to adopt open data standards and advanced metering technologies. These moves aim to make anonymized customer data more available for planning and innovation—without sacrificing privacy.
In New York, a recent analysis suggests that unlocking data-driven grid flexibility could save up to $3 billion annually by 2040. However, realizing those savings will require better tools to collect, analyze, and share grid data in real time.
Enter DERMS and the AI Edge
To manage the growing swarm of distributed energy resources (DERs)—from electric vehicles to smart thermostats—utilities are investing in Distributed Energy Resource Management Systems (DERMS). These platforms help balance supply and demand at the local level and are increasingly integrating AI for faster, more precise decision-making.
There are two main types: Edge DERMS, which focus on behind-the-meter devices like home batteries and appliances, and Grid DERMS, which coordinate larger-scale resources like solar farms and aggregated DER fleets.
“AI can provide a single window into complex, siloed systems,” said Jesse Demmel, CTO of DER aggregator Uplight. “With the right data, it becomes possible to pinpoint the most cost-effective upgrades—and avoid unnecessary ones.”
Moving Forward
Though questions about security, accountability, and investment remain, the energy sector is coalescing around a simple truth: unlocking the potential of AI means unlocking the data. And with climate challenges growing more urgent, the timeline for modernizing the grid is tightening.
By aligning technical innovation with thoughtful policy, initiatives like EPRI’s consortium could help chart a path to a smarter, more resilient grid.
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