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How do you build a strong data foundation to estimate the grid expansion and investment needs accurately?

Helen Electricity Network, a Finnish grid operator, shares their approach. Learn it firsthand 👉

Grid operators often say: „We have the data – but it’s spread across multiple systems.“

GIS, ERP, SCADA, CRM – each shows only part of the grid. When it comes to understanding the grid as a whole, the limits of fragmented data become clear. Some describe it like a puzzle assembled from pieces from different boxes: nothing quite fits together.

A digital twin solves this by bringing together all relevant data – grid topology, assets, load profiles, measurement data – combined into a consistent model to use for simulations and analysis. No more patchwork, but rather a comprehensive digital representation of your distribution grid, updated on a daily basis.

From limited visibility to better grid management – Paper for download

Fragmented data and static models are becoming a bottleneck for grid operators. The paper "A Single Source of Truth for Better Grid Decisions" shows how a continuously updated, shared view of the grid changes how planning and operations make decisions, especially under growing complexity.

You will get a clear understanding of what a “single source of truth” actually means in practice.

What’s inside:

  • Understand why current data setups limit decision quality as grid complexity increases
  • See how a dynamic grid model improves both planning and operations
  • Learn what a “single source of truth” needs to deliver beyond theory
  • Identify concrete value levers like faster connections, better investments, and reduced grid bottlenecks
  • Find out why "100% grid data quality" is a myth and what the realistic number is
Get the paper "A Single Source of Truth for Better Grid Decisions".

How is the digital twin created at envelio?

digital-twin-creation-01

At the heart of the digital twin is the GridHub – our platform that integrates, cleans, and transforms all grid data from various systems into a simulation-ready grid model.

This is not a static data repository, but a dynamic, synchronized environment that continuously updates in response to changes in source systems.

All applications within the Intelligent Grid Platform access this very model – consistently, version-controlled, and centrally managed.

digital-twin-creation-02

To build a high-fidelity digital twin of the grid, GridHub requires detailed input data of what is actually happening in the grid, including:

  • Grid topology and geo data – including normal switching states and feeder routes
  • Asset-specific details such as location, type, capacity, and current-carrying capability of cables, substations, and transformers
  • Information on grid participants such as PV systems, EV chargers, or heat pumps – including technical parameters and connection details

These data points are collected from different source systems, including GIS, ERP, asset management systems, CRM platforms, and smart meter backends, and then united in the GridHub.

digital-twin-creation-03

Keeping a digital twin up to date requires reliable data flows from multiple systems. Standards like CIM or CGMES help in theory, but in practice they rarely enable seamless integration.

That’s why we use a modular approach: systems are connected through reusable, tailored modules that ensure stable and transparent data integration without manual preparation.

We handle the full setup – from interfaces to data delivery. Grid operators only need to provide access to their data, regardless of format.

Thanks to this framework, we’ve successfully integrated more than 60 different systems to date, including GIS, ERP, and SCADA systems.

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Data issues often only become visible once everything is combined into a full grid model. What looks correct in isolation can break at system level.

That’s why envelio uses a two-stage quality approach:

1. Pre-checks before model creation

Each dataset is automatically checked for completeness and plausibility. Inconsistencies – like unrealistic transformer capacities or missing mandatory fields – are flagged, and many of them can be corrected directly by the system.

2. Validation of the complete grid model

Once the data is merged, the grid structure is thoroughly validated. Are all grid areas connected properly? Any open ends or missing links? Automated checks – such as grid tracing and load flow simulations – are applied here. The latter help systematically uncover critical issues like non-converging networks or unrealistic loadings.

GIS data, grid calculations, and SCADA – without a shared view?
Our digital twin brings it all onto one platform. For consistent planning, reliable operations, and faster decision-making.

Schedule a consultation with no commitment

This is how data turns into a practical tool

A digital twin isn’t an end in itself. Its true value lies in being the foundation for real processes and use cases – from strategic planning to day-to-day operations.

Grid studies and strategic grid planning

A complete, simulation-ready grid model is essential to simulate realistic scenarios, identify critical areas early, and prioritize investments efficiently.

Automating connection requests evaluation

Whether PV, EV charger, or heat pump – new connection requests can be evaluated directly within the digital twin, quickly and in full compliance with local regulations, without unnecessary manual effort.

Grid-side flexibility management

Targeted curtailment measures require full visibility into grid load and switching states. The digital twin provides the basis for automated, non-discriminatory control processes.

Grid monitoring and state estimation

Enriched with real-time measurement data, the digital twin becomes the operational backbone – supporting switching decisions, maintenance planning, and congestion detection.

PlanOps-Approach: Planning + Operations

When grid planning, operations, and asset management use the same model, processes become aligned, reaction times shorter, and grid management more integrated.

Reservation management for planned projects

Grid operators must often allocate capacity long before projects go live. The digital twin from envelio helps manage that proactively.

Approved but not yet connected projects can be added as their own layer within the model – and so can planned grid reinforcement measures. Instead of managing this data in siloed tools, reservations and planned measures are embedded directly in the model, where they can be taken into account across all projects.

This brings clarity, avoids double-reservations, and turns the grid model into a tool for anticipatory planning.

Want to learn more?

We’re happy to explore how a digital grid model can work in your existing system landscape – based on your current structure and challenges.

Schedule a consultation from one of our experts – with no commitment.

Ihr Kontakt

Dr. Tobias Falke
VP Global Sales & Marketing

Tobias Falke envelio
+49 221 222 85 80
info@envelio.de