Electricity demand is becoming more dynamic. Electric vehicles, data centers, building electrification, and distributed energy resources are changing not only how much power is needed, but when and where that power is needed. For utilities, this creates a difficult planning problem: how to preserve reliability without overbuilding grid infrastructure.
Recent research from the National Laboratory of the Rockies (NLR), developed in partnership with Xcel Energy, highlights the potential of smart energy management to shift flexible demand and reduce pressure on distribution equipment. The work focuses on electric vehicle charging, but the same logic can apply to other flexible loads connected at homes, businesses, and local energy communities.
Why Flexibility Matters
Traditional grid planning often responds to load growth with physical upgrades such as new transformers, feeders, or distribution equipment. Those investments are sometimes necessary, but they can be slow, expensive, and ultimately paid for by customers. Smart energy management offers another layer of options by coordinating flexible energy use around grid conditions.
Instead of treating every charging session as an immediate peak demand event, grid-aware controls can spread energy use across periods when the local network has more available capacity. In the NLR analysis, one studied feeder was able to fully satisfy more than 94% of residential charging sessions without increasing transformer overloads, compared with a case without smart energy management.
From Time-of-Use to Grid-Aware Control
Smart energy management can take several forms. A simple version is time-of-use pricing, which encourages customers to consume energy when demand is lower. More advanced approaches use real-time grid signals to modulate charging or other controllable loads so that power delivery stays within safe operating limits.
This is where digitalisation becomes central. Utilities need high-resolution network models, demand forecasts, and control strategies that understand both feeder-level and transformer-level constraints. The value is not only lower peak demand, but better visibility into where infrastructure upgrades are truly needed and where software-based flexibility can defer or avoid them.
Modeling the Grid at Useful Detail
For Xcel Energy, NLR researchers combined transportation energy demand scenarios with detailed distribution network data in service areas around Boulder and Aurora, Colorado. That model connected vehicle charging demand with grid assets such as substations, feeders, lines, and transformers.
This level of detail matters because broad feeder-level analysis can miss small local constraints. A feeder might appear manageable overall, while a specific transformer or low-voltage segment still experiences stress. Combining higher-level and property-level analysis helps utilities balance algorithmic energy management with targeted infrastructure investment.
EVI-DiST and Open-Source Utility Planning
The project also led to the Electric Vehicle Infrastructure - Distribution System Integration Tool, known as EVI-DiST. The tool is intended to help utilities evaluate how EV charging and other flexible resources may affect distribution networks, and how different energy management strategies can reduce cost and operational risk.
EVI-DiST includes two modes. A lighter mode provides faster feeder or transformer insights over a week without detailed power-flow simulation. A deeper mode simulates power flows and voltage impacts on a shorter time horizon, allowing utilities to investigate specific local grid constraints. By releasing the tool in an open-source format, NLR is making it easier for utilities and researchers to test, adapt, and improve the methodology.
Why This Matters for Digital Energy
The lesson is clear: the next phase of grid modernization is not only about adding hardware. It is also about using data, modeling, and intelligent control to make existing infrastructure more flexible. For organizations working on AI, energy analytics, and digital platforms, smart energy management is a practical example of how software can unlock real operational value.
As electrification accelerates, utilities will need tools that can connect customer behavior, mobility patterns, local network constraints, and cost-sensitive planning. Smart energy management can become one of the key bridges between decarbonisation goals and affordable, reliable grid operation.