Network Model Management

Effective network models require reliable data

From working with numerous utilities, we’ve seen that an incomplete data foundation is often seen as a major hurdle to digitizing the grid. The Intelligent Grid Platform overcomes this by streamlining and automating data quality workflows, making the process far more efficient – even when initial data is incomplete. All of our customers started with imperfect data and still achieved fast, tangible results without years of upfront preparation.

No intelligent grid planning without clean data

High quality data is essential for digitizing and automating processes. Utilities that have complete and precise network data can make quicker and more informed decisions:

Accelerate interconnection: Identify appropriate points of interconnection in real-time

Maximize system utilization: Gain clearer visibility into available capacity

Invest efficiently and improve reliability: Prioritize expansion projects with greater precision

Many grid planners recognize a pressing need for a more reliable data foundation. Key challenges include:

Different formats and sources in evolving system environments make it difficult to link data

The dynamics of the distribution system, driven by ever-increasing numbers of generators and loads, make it essential to keep data up-to-date

Missing or inconsistent data makes it difficult to perform precise calculations

Recognizing data quality challenges is not enough on its own. The key is to identify which parts need to be optimized: Which data issues have the most negative impact? Which data issues can be resolved with little effort? Estimating the effort-benefit-ratio should play a crucial part in prioritizing the resolution of data issues. Given the large volume and high complexity of the data, that is no easy task.

No more chaos: Smart solution for systematic data integration

Our response to these challenges: The Intelligent Grid Platform. As an automated data platform, the IGP connects various source systems, merges different data pools, aggregates and analyzes data, and effectively supports data improvement. Starting with a limited data foundation is no problem. The IGP’s algorithms compensate for missing objects and parameters by applying fallback assumptions that are then validated step by step.

The clear and precise documentation of gaps and anomalies in the data foundation enables staff to quickly forward data issues to the appropriate department. This structured process organizes the data and increases transparency, providing a solid foundation for system planning. The process for automatic data validation is divided into the following steps:

The Sync (the IGP’s process to update data) combines and maps data from source systems

Elemental testing ensures that the data of each element, e.g. line segments, network participants, or transformers, is tested for plausibility

Topological testing verifies that galvanically connected elements are energized and at the correct voltage level, and ensures outgoing lines are properly connected to busbars

Electrical testing confirms that calculation results, grid topology and the electrical parameters are plausible

Thanks to this data quality loop, imported data is always validated, ensuring high data quality.

Advantages for utilities:

High data quality & continuous data maintenance, guaranteed by a fully automated, efficient process and automated source data testing

Reporting & documentation of data issues – such as those imported from GIS – ensures the appropriate department is informed and can effectively resolve them.

Accepting only updated data into the network model to ensure an up-to-date and high-quality data foundation that also benefits other interfaced systems

Data aggregation & full transparency in a centralized network model as a single source of truth, including a convenient overview of all data gaps and issues

The changes in the energy mix as well as rapid electrification across various sectors poses new challenges for electric utilities, large and small.

With our Intelligent Grid Platform, we offer a suite of tools that address many of these challenges: from streamlining and accelerating interconnections processes to conducting reliable future scenario analyses for long-term planning.

Case Study: E.DIS Netz GmbH

Data quality that makes the difference for utilities

Effective network models require reliable data

Example from practice

No intelligent grid planning without clean data

No more chaos: Smart solution for systematic data integration

Advantages for utilities:

Success figures from practice

Conclusion: Reaching the goal with reliable grid data

Related Solutions

Hosting Capacity Maps

Interconnection Pre-Application