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Expert articles on digital grid development

How grid data becomes usable, how target grid planning succeeds under uncertainty and what AI really contributes to day-to-day planning – written by the people who build the software.

8 articles
CIM & Data Models14 July 2026

CIM for grid operators: how a shared data model makes grid data usable

A power grid consists not only of lines and transformers, but of thousands of interconnected objects. CIM creates a shared language – the value only emerges once profiles, mapping, data quality and processes work together.

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CIM for grid operators: how a shared data model makes grid data usable

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Grid Planning26 June 2026

Target grid planning under uncertainty: why one scenario is not enough

The future supply task cannot be forecast – it can still be planned. How multi-scenario analyses reveal robust expansion paths.

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CIM & Data Models11 June 2026

Validating CGMES models: the five levels of a robust check

Syntax, profile conformity, references, domain rules, fitness for the target process – why a formally valid file is far from a usable grid model.

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Asset Management28 May 2026

Avoiding stranded investments: thinking asset and grid data together

Condition assessment without grid context leads to expensive wrong decisions. How an integrated view directs investment to where it takes effect.

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AI15 May 2026

What “AI-native” really means for planning software

Not a chatbot on top of an old interface: where machine learning creates real value in grid planning – and where traceability matters more.

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Product Updates30 April 2026

e.planning: timeline playback and rough cost estimation in the dashboard

Viewing measures along the time axis, estimating budgets early: what the latest release changes for investment planning.

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Asset Management16 April 2026

ISO 55000 in practice: from condition index to investment decision

A normative framework alone prioritises no measure. How condition, criticality and grid data combine into a robust basis for decisions.

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CIM & Data Models2 April 2026

SHACL for grid data: rules you can execute

Turning domain knowledge into testable constraints: how shapes are formulated so that findings remain traceable for domain users.

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