AI Empowers the Power Industry: Tech Giants like Nvidia and IBM Drive Smart Grid Advancement, but Regulatory and Data Privacy Challenges Remain

For a long time, digital technology suppliers have been frustrated by electric utilities' reliability-first approach that suppresses innovation. However, rapid load growth and the potential of AI solutions are now driving cooperation between the two sides, especially in the use and sharing of power system data.

Electric utilities have seen thesignificant expected load growthbrought by electrification of transportation, manufacturing, buildings, and data centers. Executives at technology companies such as Nvidia, Microsoft, IBM, and Schneider Electric say they are gradually understanding the regulatory barriers that hinder electric utilities from transitioning to advanced AI computing strategies.

The surge in digital technology computing power and hyperscale cloud resources are changing previous assumptions aboutAI learning, execution, and optimization of system operations, pushing the power industry into what is called a "technology transition period."

"The power industry is relatively conservative, but it faces hard requirements for clean energy and emission reduction," said Marc Spieler, senior managing director of energy business at Nvidia, the microprocessor market leader. "Real-time prediction from advanced computing can optimize" decisions such as batch scheduling and operations and maintenance, and Nvidia's "specialized modules will apply learnings from other industries to the energy transition," he added.

Analysts point out that electric utilities have already applied advanced computing inwildfire mitigation, vegetation management, and predictive maintenanceand are beginning to consider its potential in optimizing system dispatch.

"The tech industry's business model is 'move fast and break things,' and they don't always understand why regulated electric utilities cannot move just as quickly," said Emily Sanford Fisher, general counsel, corporate secretary, and executive vice president of clean energy at the Edison Electric Institute (EEI). "But there seems to be a new spirit of cooperation emerging in solving power industry challenges."

One obstacle could slow this technology transition. Technology companies have seen how advanced computing innovation in other industries maximizes AI capabilities, but electric utilities still need to validate this potential and convince regulators that investing in implementing advanced computing capabilities is justified.

Challenges and Potential

The rapid load growth is evident.

"Over the past year, grid planners have nearly doubled their 5-year load growth forecast, from 2.6% to 4.7%,"a December 2023 Grid Strategies research reportshowed. The report added that 2024 forecasts "may show even higher national growth rates," driven mainly by new manufacturing, industrial, and data center investments.

Jeremy Renshaw, senior technical executive (AI, quantum, and innovation) at the Electric Power Research Institute (EPRI), said the widespread application of advanced computing is providing "some decision support for bulk system operators" managing new load. But fully optimizing distribution system operations and dispatch "could be a breakthrough, but it may also take years," Renshaw added.

Many technology companies are investing in advanced computing in hopes of finding this breakthrough.

Technology Company Work

Advanced computing is beginning to serve electric utilities.

BrightNight's PowerAlpha platform can help design, operate, and optimize clean energy projects, significantly improving load factors and reducing costs, said Kiran Kumaraswamy, chief technology officer at BrightNight. Its machine learning and AI algorithms focus on utility-scale assets but "do not have the granularity to optimize distribution systems," he said.

Machine learning-based weather forecasting enables Amperon customers to purchase cheaper electricity before extreme events,avoiding high scarcity prices, said Sean Kelly, CEO and co-founder of Amperon. "A human cannot calculate hourly updates of a 15-day forecast, along with weekly long-term forecasts covering28 weather variables across 30,000 locations,"he added.

"Whether you call it machine learning or AI,"the "computerized replica" of Southern California Edison's system built byNeara will "apply more variables than a human can integrate,"for wildfire mitigation, said Rob Brook, senior vice president and managing director of the Americas at advanced computing provider Neara. It "identifies improvements" and "eliminates human error and cost," he added.

Although current AI applications in the power industry focus mostly on bulk systems and maintenance, one company based on advanced computing appears poised for a breakthrough at the distribution system level.

"Utilidata is deploying the first distribution system AI platform," using "custom Nvidia modules," said Jess Melanson, president and chief operating officer at Utilidata. The platform is currently being installed alongsideAclara metersbut "will eventually be used for other system hardware such as transformers," he added.

Both Nvidia and Utilidata see "enormous opportunities" in power system applications, Melanson said. "Previously, Utilidata's analysis used incomplete, outdated, or erroneous data," but world-standard Nvidia chips enable Utilidata'sKarman platform analysisto detail "what is happening in the system, what is likely to happen next, and what the best response is," he added.

This level of intelligence at the distribution system level could enable customer-owned resources to play a greater role in reliability and reduce overall customer costs.

A key potential obstacle to realizing the benefits of advanced computing is limited access to utility and private customer data. This is a challenge that advanced computing players, including Nvidia and IBM, take seriously.

Foundation Models and Federated Learning

Nvidia's software enables advanced computing to "predict data patterns and identify the next best action," said Nvidia's Spieler. For example, it can give electric utilities a better understanding of where outages are likely to occur and enable proactive maintenance, multiple analysts said.

The challenge of electric utilities not sharing proprietary data needed to develop a more detailed understanding of the power system is real, "but federated learning (used in healthcare to protect patient data) can be the solution," Spieler said. "With federated learning, collaborators can build their data models and share them in a central location," he added.

NVIDIA FLAREis a federated learning software tool that "builds additional synthetic data to address new problems," which addresses privacy concerns, Spieler said.

"Electric utilities take data security seriously," and data must be shared "in the right way," said EEI's Fisher. "There must be protocols to protect critical energy infrastructure information," although "there are constructive conversations about how to cooperate to achieve this, which is good news," she added.

Some believe that federated learning may be too limited for the diverse regional characteristics of power systems and the complexity of different resource mixes.

"Foundation models are emerging to scale advanced computing capabilities," said Bryan Sacks, global chief technology officer and leader of energy, environment, and utilities solutions at IBM.

"Instead of applying AI components to a single problem, orders of magnitude more data are pre-trained into foundation models to solve multiple problems," Sacks said. Foundation models can capture the diversity of power systems without revealing proprietary details of any operator. But "data from metering and monitoring systems that electric utilities use for operational decisions is not in current large language models and is protected from external sharing," he added.

"For foundation models to understand power system operations, they need time- and location-specific data for each connected asset, but that data must be anonymized," Sacks said. "IBM has launched working groups to build a foundation model trained on anonymized data for real-time and day-ahead operations and long-term planning of power systems," he added.

Different power system stakeholders will be able to "fine-tune that foundation model to address their respective different problems," Sacks said. But "regulatory barriers or restrictions on market participants' access to data needed to train models are real concerns," he added.

According to Sacks, IBM is working with global stakeholders to contribute foundational research. It will also engage with regulators "to help establish governance systems that promote data sharing" and build effective "guardrails" to protect systems and data, he said.

IBM's awareness of regulatory issues aligns it with what electric utilities call their top concern.

Electric Utilities and Regulators

Electric utilities seem to view the regulatory obstacles to advanced computing more realistically than technology companies.

Using advanced computing to optimize distribution systems in real time requires "sufficient confidence" from electric utilities and regulators, said Steve Smith, head of strategy, innovation, and market analysis at National Grid Group and president of National Grid Partners corporate venture capital fund. "We may get there in 10 or 15 years," he added.

"Technology companies and electric utilities have fundamentally different business models," and tech companies "don't understand why it takes 10 years to bring a new transmission line into operation," EEI's Fisher added. In reality, regulated electric utilities "can build a transmission line in just 18 to 24 months, but siting, permitting, and litigation take eight years," she said.

Introducing advanced computing requires "concrete evidence" to convince electric utilities and regulators concerned about rising electricity rates that "necessary grid modernization spending will lower customer costs," Fisher said.

"Foundation models have great potential for the energy industry," and data federation "is absolutely necessary to extract and coordinate data from the different siloed systems of the power system," said Scott Harden, chief technology officer for global innovation at Schneider Electric.

The ideal power system architecture should be built on a power industry foundation model that captures key features of its diversity and challenges, Harden said. It should also deploy more broadlyphasor measurement units(hardware devices that can record and transmit transmission and distribution system data), as well as "fully deploy smart meters at the edge of the system, with all data federated," he added.

Building that architecture can start with regulators supporting "new computing capabilities and the technologies that make them work," Harden said. It is unclear how large the cost and time burden of deployment will be, "but the more important question is what the cost will be if we don't deploy," he added.

"For everyone, it's early days in the broad AI space, and the question now is how to navigate it," Allison Clements, commissioner at the Federal Energy Regulatory Commission (FERC), told Utility Dive. Current advanced computing applications "can create a positive feedback loop if federal and state regulators push for it," she added.

"Regulators must maintain a growth mindset during this transformative moment, because this is the early stage of the messy middle of grid modernization," Clements said. Electric utilities "are handling rate cases case by case to understand how to achieve this transition while protecting reliability and affordability," she added.

"Federal and state regulators need to be actively engaged because AI capabilities are coming," Clements added. "Whether it benefits society or causes problems depends on electric utilities, policymakers, legislators, and other leaders."