3 Ways OT-IT Integration Helps Energy and Utilities Providers Modernize Grid Operations

Synchronize Digital Twins for the Grid and Infrastructure

Increasingly, energy providers are turning to digital twins to model and simulate critical infrastructure across generation, transmission and distribution environments.

By feeding live telemetry from supervisory control and data acquisition systems, intelligent electronic devices and other OT assets into IT-based simulation platforms, utilities can create real-time digital replicas of substations, turbines, transformers and even entire grid segments. This enables teams to test load-balancing strategies, maintenance schedules or DER integrations without disrupting service.

Tools such as NVIDIA Omniverse can support the development of end-to-end digital replicas of complex operational environments.

Tim Mirth, senior specialist solution architect at Red Hat, notes that real-time simulation is transformative for industrial environments — including energy infrastructure.

“Within industrial, timing is everything, and it can’t be off, even by milliseconds,” he explains. “Being able to simulate that in a digital landscape means you no longer need specialized hardware just to validate performance. IT has matured to meet OT’s real-time requirements.”

For utilities, that precision is essential. Even minor timing discrepancies in grid operations can affect frequency stability or protective relays. By validating changes in a digital environment first, IT and OT teams can reduce risk while accelerating modernization initiatives.

READ MORE: Digital twins are delivering operational efficiency for energy, oil and gas companies.

However, technology alone isn’t enough.

“There’s a difference in lingo between IT and OT,” Mirth says. “You can use the same words, but they mean completely different things.”

Utilities must foster collaboration between grid engineers and IT architects, ensuring both teams understand operational constraints and cybersecurity requirements. Building trust through accurate digital twins can pave the way for more agile, software-defined infrastructure.

“Once you’ve built the digital twin and shown that it works with the same quality and performance OT teams expect, you can move toward a more modern architecture,” Mirth says. “That’s when the value of IT and OT working together really becomes clear.”

Combine Private 5G and TSN for Grid Communications

Reliable, deterministic connectivity is foundational for modern utilities, particularly as distributed assets — such as solar farms, wind turbines and battery storage systems — expand across wide geographic areas.

“The challenge in the OT space is that it’s extremely expensive and time-consuming to run wires,” Mirth says. “OT would love nothing else but to remove some of those wires and go wireless. But historically, wireless has not been trustworthy in this space.”

Private 5G networks offer a compelling alternative. Designed for high reliability and low latency, private 5G can operate effectively in interference-heavy environments such as substations or generation facilities. When paired with TSN, utilities can achieve deterministic, sub-millisecond communication between protection systems, controllers and analytics platforms.

“One of the promises of 5G is that it can work better in those environments by being a little bit more reliable,” Mirth explains. “Another aspect is that you need determinism. You need to know that the data is coming in a timely manner.”

For utilities, this precision supports faster fault detection, automated switching and improved outage response. It also enables more advanced grid orchestration platforms that rely on real-time IT directives to optimize load flow and balance supply and demand.

“The trick is to get the two protocols to sync together,” Mirth says. “Then, you would be able to troubleshoot errors and improve processes in ways that weren’t possible before.”

Use Federated Machine Learning To Optimize Energy Assets

Energy providers often operate remote and distributed sites, from offshore wind farms to rural substations. Connectivity at these locations can be intermittent, and sensitive operational data may be subject to regulatory controls.

Federated machine learning allows utilities to train AI models locally at the edge — analyzing equipment performance, detecting anomalies and refining predictive maintenance strategies — without centralizing raw operational data.

For industries such as energy and oil, remote sites can run local anomaly detection models tailored to site-specific conditions, while still sharing insights that strengthen enterprisewide safety and operational protocols.

CDW’s cloud platform can help aggregate those insights securely to enhance resilience across the organization.

DISCOVER: Explore the latest tech trends in the energy, oil and gas industry.

Mirth emphasizes the importance of local adaptability.

“Having it local to that thing, only really caring about what’s happening at that particular device, makes a lot of sense,” he explains. “It helps improve processes, spot anomalies and support preventive maintenance — critical in industries like oil and gas where it’s never stopping.”

Intermittent connectivity further reinforces the value of this approach.

“Often, the communication is intermittent at best,” Mirth says. “By not relying on the cloud, machine learning can still happen locally, keeping operations running.”

Ultimately, energy and utilities organizations that embrace digital twins, private 5G networks and federated learning can strengthen grid reliability; accelerate decarbonization initiatives; and deliver more resilient, data-driven operations.