Modern facilities generate more electrical data than ever before. Power meters, protective relays, smart breakers, and monitoring systems continuously produce information about how electrical infrastructure is performing. In many facilities, that data is already being collected in real time. The International Energy Agency estimates that connected devices with automated controls could exceed 25 billion by 2030, yet only 2-4% of available meter data is used to improve facility operations.
Collecting data and using it effectively are very different things. The issue isn’t visibility—it’s turning data into actionable insight. Many teams can identify when something happened, but still struggle to understand why, what it means, or what actions to take next.
This is also where many digital twin initiatives lose momentum: teams try to do too much too quickly. The phrase “digital twin” can make it feel like organizations need a fully connected model of the entire facility before they can create value. In reality, the most effective approach is often much more practical: start with the electrical data you already collect and focus on solving the operational problems your teams face every day.
Scale from the problem, not the platform
One of the most common mistakes organizations make with digital twin initiatives is starting with the technology instead of the operational challenge. Electrical systems don’t operate as isolated assets. A switching sequence, load transfer, maintenance activity, or configuration change can affect fault levels, voltage stability, protection coordination, overload conditions, and overall system reliability. Without a system-level model, teams often rely on experience, judgment, and disconnected documentation to understand these relationships.
An electrical digital twin—a living model of the electrical network that evolves alongside the facility—provides a centralized view of system performance and asset interactions across the broader electrical environment. But that doesn’t mean organizations need to model everything from the start.
Instead, teams can begin where better visibility would create the clearest operational value, including:
- Critical electrical substations or electrical rooms
- Recurring reliability issues
- High-value process lines
- Specific geographic areas or campus zones
- Priority disciplines, such as electrical distribution
- Lifecycle phases like commissioning, maintenance, or operations
Start with the unresolved problem. Build the model around that need. Then expand as the value becomes clear.
Test the decision before taking action
One of the most valuable aspects of an electrical digital twin is the ability to evaluate changes before they are implemented in the real world. Instead of reacting to issues after they occur, teams can simulate scenarios in advance to better understand potential operational impacts.
This could include:
- Validating switching sequences before execution,
- Evaluating load transfer strategies,
- Assessing maintenance impacts,
- Identifying potential overload conditions, or
- Understanding how system changes may affect protection coordination and reliability.
By testing decisions in a virtual environment first, organizations can reduce operational risk, improve system resilience, and support more confident decision-making across engineering and operations teams.
Preserve the knowledge behind the system
Electrical digital twins can also help address a quieter but equally important operational risk: knowledge loss. Historically, much of a facility’s operational context has lived in the heads of a few experienced personnel. They understand how the system behaves, why certain decisions were made, and where risks are most likely to emerge. Documentation helps, but it doesn’t always capture how systems behave together in practice.
A digital twin helps preserve system knowledge by capturing it within a shared, continuously evolving model. Instead of leaving critical operational insight tied to a single individual, that knowledge remains accessible across teams and over time.
As experienced personnel retire or transition out of the workforce, organizations risk losing critical operational knowledge that may not exist in formal documentation. In 2025, nearly 40% of organizations experienced a major outage stemming from procedures that were either not followed or flawed. New operators, technicians, and engineers need more than manuals and static drawings. They need a way to understand how the system behaves, how assets relate to one another, and how today’s decisions may affect tomorrow’s operations.
Connect monitoring and modeling
Power monitoring systems and electrical models have traditionally operated separately. Monitoring platforms provide real-time operational data, while engineering models are often used independently for analysis, planning, or system studies. When these systems remain disconnected, teams can struggle to maintain a complete and current view of electrical system behavior.
Connecting platforms such as EcoStruxure Power Monitoring Expert and ETAP helps bridge that gap by linking real-time operational data with system-level electrical models. This integration allows organizations to move beyond passive monitoring and toward a more dynamic understanding of system performance, operational risk, and decision-making.
By combining monitoring and modeling, teams can better visualize system conditions, evaluate operational scenarios, and maintain a more accurate representation of the electrical network over time.
Begin with what you can’t explain
Many organizations already collect large amounts of electrical system data but still struggle to explain certain operational events. Why did that disturbance occur? What caused the issue? Why does the same problem keep happening?
These unanswered questions are often the best place to begin. Rather than trying to model the entire electrical system at once, organizations can start by focusing on the areas where visibility is limited and operational uncertainty remains highest.
With solutions from Schneider Electric, organizations can begin connecting electrical monitoring, system modeling, and operational insight to better understand system behavior and support more informed decision-making over time.
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