How utilities can make grid digital twins work — a practical framework

Grid digital twins do not have to fail quietly. 

Across the industry, utilities are moving beyond pilot projects and beginning to realize real operational value from digital twins. The difference is not more advanced technology. It is a clearer focus on decisions, context, workflows, and ownership.

Industry research reflects both progress and frustration. While many utilities report experimenting with or adopting digital twins, far fewer believe those initiatives are delivering consistent operational impact — underscoring a gap between deployment and day-to-day use (EY, 2024).

This article outlines a practical framework for closing that gap.

 

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Reframe the Objective: From Models to Decisions

Successful initiatives start with a shift in mindset:

A grid digital twin is not a model of the grid. It is a system for making better operational decisions.

As grids become more constrained — with growing interconnection queues, changing load patterns, and increasing weather-driven risk — decision speed and confidence matter more than model fidelity. In the U.S. alone, proposed generation and storage capacity in interconnection queues now exceeds 2,000 GW, adding operational complexity well before assets are ever built (FERC, 2024).

When the objective is decision quality, scope becomes clearer. Utilities focus on which decisions matter most, not on integrating every possible data source.

 

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A Practical Framework for Grid Digital Twin Success

Utilities that succeed consistently apply the principles below.

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1. Anchor the Twin to High-Value Operational Decisions

Effective digital twins start with operational pain points, not technology roadmaps.

Typical entry points include congestion management, flexibility activation, outage response, maintenance deferrals, and interconnection impact assessment. These decisions are time-critical, risk-sensitive, and highly visible — making them ideal anchors for adoption.

In practice:
The Utilities Commission of New Smyrna Beach (Florida) focused its digital twin effort on improving asset accuracy and field operations, establishing a reliable operational foundation before expanding scope (Esri case study).

Kognitwin® Grid is designed around this principle — structuring digital twins to support specific operational decisions rather than serving as static network replicas.

 

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2. Establish a Shared, Contextual View of the Grid

Integration alone does not create understanding.

Operational value comes from contextualised intelligence — aligning data around how the grid actually operates:

• Network topology and live state
• Asset condition and risk
• Constraints and dependencies
• Available flexibility and response options

Utilities consistently report that digital twins are most valuable when they reduce the need for operators to reconcile information across multiple systems, providing a single, trusted operational view (Energy Central).

This is where an operational digital twin acts as a contextual backbone — connecting disparate grid data into a decision-ready representation aligned to how operators work.

 

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3. Embed the Twin Where Work Happens

Adoption follows execution.

Digital twins deliver value when they are embedded directly into operational workflows — where decisions are made, actions are initiated, and outcomes are reviewed. When operators can explore scenarios, understand trade-offs, and move seamlessly from insight to action, the twin becomes indispensable.

In practice:
Utilities such as Southern California Edison are using AI-enabled digital twin platforms to model vegetation and environmental risk, directly informing operational mitigation rather than producing standalone analysis (TIME).

Solutions like Kognitwin® Grid, delivered through an industrial work surface, are designed to place digital twin intelligence directly inside operational workflows — not alongside them.

 

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4. Assign Clear Operational Ownership

Governance is often the deciding factor.

Digital twins owned solely by IT or innovation teams rarely achieve sustained operational use. Utilities that succeed place ownership with operations, supported by digital and IT partners. This ensures the twin evolves with real operational needs and that value is measured in outcomes, not activity.

Kognitwin® Grid is intentionally designed to be governed this way — with digital teams enabling the platform, and operations accountable for decisions and results.

This aligns with broader reliability findings emphasizing tools embedded in daily operations rather than isolated analytical systems (NERC).

 

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Preparing for AI — Without Losing Trust

This framework also establishes the foundation for AI-enabled grid operations.

AI systems depend on high-quality context and explainability. When grounded in an operational digital twin, AI can support scenario analysis and advisory workflows while remaining transparent and aligned with operator judgment (ICF).

An operational digital twin provides the contextual grounding AI requires, ensuring recommendations are explainable, defensible, and usable in real operations.

Rather than replacing expertise, AI augments it.

 

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A Final Thought

The question for utilities is no longer whether digital twins matter. It’s whether the digital twin is designed to support the operational decisions that keep the grid reliable, resilient, and defensible every day.

When digital twins are anchored to decisions, embedded in workflows, and owned by operations, they stop failing quietly — and start delivering confidence where it matters most.

Most digital twin initiatives fail because they’re built through integrations alone - stitching together data platforms, visualisation tools, and analytics. That approach rarely scales or survives operational pressure. Kongsberg Digital took a different path: we productised the digital twin as a SaaS platform, designed to operate, evolve, and scale.