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.
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.
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.
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.
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
