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Developing Use Cases in Energy Data Spaces

Data spaces enable the creation of collaborative solutions where different actors in the energy, urban and mobility ecosystem can combine data, algorithms and services within a shared framework of trust. This section explains how use cases are designed and deployed within the Empower-X ecosystem, following a practical, results-oriented methodology aligned with European principles of interoperability, data sovereignty and federated architectures.

Empower-X operates as the lighthouse for the Data Space for Positive Energy Districts (DS4PED), part of the European DS4SSCC-DEP project.
In the Rubí pilot, municipal systems, public solar plants, charging points, energy communities, electric-mobility services and the ZertiPower platform share data through a federated data space that ensures traceability, sovereignty and secure computation.

Use cases are the primary mechanism to solve energy and urban challenges through federated data sharing. They rely on:

  • a shared technical infrastructure (Web3 identities, semantic catalogues, EDC/IDS connectors, Data Rooms, Compute-to-Data, marketplace),
  • a shared governance model,
  • verifiable usage policies ensuring data sovereignty, privacy and interoperability.

Methodology for Developing a Use Case in Empower-X​

Developing a use case inside an energy data space follows an eight-phase structured cycle. This ensures that the solution is feasible, secure, interoperable and scalable within a federated and multi-actor environment.

1. Identifying the challenge and opportunity​

Participants identify a shared need related to:

  • distributed renewable integration,
  • real-time energy traceability,
  • flexibility management,
  • electric-mobility and infrastructure planning,
  • urban energy efficiency,
  • optimisation of charging networks,
  • coordination between actors (municipalities, DSOs, energy communities, mobility operators).

Examples from the Rubí pilot:
– ensuring 100% renewable EV charging via ZEAC and real-time traceability;
– tokenising municipal solar surpluses to balance night-time consumption.

2. Understanding and structuring the available data​

This phase analyses:

  • which datasets exist and who owns them,
  • their quality, granularity and frequency,
  • applicable semantic models (SAREF, Smart Data Models, NGSI-LD, CEEDS Blueprint),
  • which data must remain protected using Compute-to-Data (e.g. individual consumption or personal mobility).

The data model is defined and decisions are made regarding the need for:

  • energy or mobility forecasting,
  • urban simulation,
  • flexibility aggregation,
  • optimisation algorithms.

3. Alignment between participants​

Stakeholders establish a common framework:

  • participation conditions,
  • access and permission policies,
  • expected value for each actor,
  • roles and responsibilities,
  • governance and control mechanisms.

This step builds trust before technical integration begins.

4. Functional and technical design​

The design document defines:

  • the functional logic of the use case,
  • required datasets and data-exchange flows,
  • which data-space components will be used (Compute-to-Data, Data Rooms, marketplace, federated catalogues, EDC/IDS connectors),
  • legal requirements (GDPR, DGA, Data Act),
  • semantic and integration requirements.

The design follows patterns from the CEEDS Blueprint and the DS4PED pilot architecture.

5. Solution development​

The solution is built by:

  • developing or adapting algorithms (flexibility, forecasting, certification),
  • creating real-time data pipelines,
  • integrating municipal systems, CPOs or energy communities,
  • defining performance metrics (renewable share, efficiency, mobility KPIs).

Examples:

  • algorithm for generating ZEAC from municipal solar production;
  • combined forecasting of charging demand and solar availability.

6. Integration of technologies and services​

This phase integrates all required elements:

  • EDC/IDS connectors,
  • verifiable identities and Web3 wallets,
  • federated catalogues with FAIR metadata,
  • governance and auditing services,
  • Data Rooms and Compute-to-Data,
  • DLT infrastructure for ZEAC, EKW and renewable-energy certification.

The goal is to guarantee full data-lifecycle execution inside the data space.

7. Deployment and validation​

The use case is validated within Empower-X through:

  • functional and integration tests,
  • access tests with real policies,
  • Compute-to-Data validation,
  • compliance and traceability checks,
  • final acceptance by all participants.

Examples validated in Rubí:

  • 100% renewable traceability for EV charging using ZEAC
  • PED operational simulation combining energy, mobility and climate data

8. Operation, scaling and continuous improvement​

Once operational:

  • the generated energy, economic and environmental value is measured,
  • enhancements are incorporated,
  • new datasets and stakeholders are added,
  • interoperability is enabled with other data spaces (CEEDS, Mobility, Built Environment),
  • the use case is replicated in new territories.

This cycle ensures the data space grows federatively while preserving sovereignty and transparency.

Tools for Evaluating and Designing Use Cases​

1. Viability assessment​

This evaluates:

  • the challenge and value hypothesis,
  • energy and urban impact potential,
  • economic and social benefits,
  • collaboration needs,
  • risks and complexity,
  • go/no-go decision.

2. Detailed design​

If viable, the use case is further defined:

  • scope and objectives,
  • energy-sector roles (DSO, CPO, municipality, prosumers),
  • technical, legal and organisational requirements,
  • integration architecture,
  • access and usage policies,
  • roadmap and resources.

This process transforms an initial idea into an implementable and scalable use case inside Empower-X, replicable across European territories.