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Data · dataset · 2026

D13.1 Regulatory landscape

Listed in D2ET Open Science Portal

The transition toward a sustainable, decarbonized, and highly digitized energy ecosystem represents one of the most profound structural shifts of the modern era.

Description

As centralized fossil-fuel generation gives way to distributed, intermittent renewable energy sources, the operational complexity of energy grids increases exponentially. To navigatethis complexity, the Data-Driven Energy Transition (D2ET) project was established with the ambitious mission of developing a Nationwide Decision Support Platform for the Energy Transition in Luxembourg.

This digital platform is designed to support multiple stakeholders, from network operators, market players, to public authorities in planning and executing energy transition actions by considering both technical limitations and non-technical constraints, such as social acceptability and market dynamics. However, the deployment of such advanced predictive technologies occurs within an intricate, rapidly evolving legal and regulatory environment.

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The convergence of energy law, data protection mandates, and artificial intelligence regulations creates a dense matrix of compliance requirements. This report establishes the foundational regulatory watchframework for the D2ET project, analysing European Union directives, Luxembourgish national implementations, and international comparative models to ensure that theD2ET platform remains compliant, secure, and technologically innovative through the 2030 horizon and beyond.

D13.1tracks 25 binding (hard) regulations and 5 standards (soft law) across data protection, artificial intelligence, data sharing and governance, cybersecurity, open data and energy-market data. The watch is operationalized through a living dashboard that scores each instrument's relevance to each scenario and links it to the PNEC measures it touches . Our central finding is that the regulatory constraint is not uniform: it follows the granularityof the data each scenario needs.

Scenario 1 (heating) relies on building-level and heating demand data, and public geospatial registers; scenario 2 (multi-energy guarantee-of-supply) works mostly on aggregate, system-level data, so its binding constraints shift from privacy toward cybersecurity and critical-infrastructure rules; and scenario 3 (energy-communities) is the most data-intensive and the most heavily constrained, because energy-sharing would require per-member consumption and production profiling.

D13.1 concludes with concrete recommendations: embed privacy-by-design and data protection impact assessments from the outset; align the data models and access procedures with the national energy data platform and the EU interoperability rules for metering data; default to aggregated or pseudonymised data and escalate to granular data only on a clear lawful basis; prepare for the AI Act; adopt recognised security and AI-management standards; and actively engage Luxembourg's regulators and regulatory sandboxes.

The watch will be refreshed yearly for the duration of the project.

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Library and information studies 71%
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