Blueprint of the Common European Energy Data Space

Blueprint of the Common European Energy Data Space Version 3.0 September 2025 Blueprint of the CEEDS 2 PUBLISHER COPYRIGHT DOI 10.5281/zenodo.17116750 AUTHORS Alberto Dognini (Fraunhofer FIT) Ludwig Karg (B.A.U.M.) Antonello Monti (Fraunhofer FIT, RWTH Aachen) Maarten Kollenstart (TNO) Antonio Kung (Trialog) Maider Santos Mugica (Tecnalia) Arturo Medela (Eviden) Marc Kurz (FH Hagenberg) Charukeshi Joglekar (Fraunhofer FIT) Marion Arles (Gireve) Carlos Ayon Mac Gregor (B.A.U.M) Markus Stroot (Fraunhofer FIT) Pranav Jayant Kulkarni (Fraunhofer FIT) Apostolos Papafragkakis (Que Technologies) Christoph Schaffer (FH Hagenberg) Martina Galluccio (RINA) Daniele Stampatori (EUI) Massimo Bertoncini (Engineering) Diana Jimenez (Trialog) Maurizio Fantino (Links Foundation) Erik Maqueda (Tecnalia) Oliver Hödl (FH Hagenberg, University of Vienna) Fabio Coelho (INESC TEC) Olivier Genest (Trialog) Florian Mancel (EDF) Ricardo Bessa (INESC TEC) Foroogh Sedighi (RWTH Aachen) Rita Dornmair (B.A.U.M.) Georg Hartner (Entarc.eu GmbH) Sonia Jimenez (IDSA) Gianluca Lipari (EPRI) Tasos Tsitsanis (Suite5) Javier Valiño (Eviden) Thomas Strasser (AIT) Joseba Jimeno Huarte (Tecnalia) Volker Berkhout (Fraunhofer IEE) Laia Guitart (E.DSO) Laurent Schmitt (Digital4Grids) Leonardo Carreras (RWTH Aachen) Interoperability Network for the Energy Transition (int:net) c/o Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. Hansastrasse 27c, 80686 Munich Germany Blueprint of the Common European Energy Data Space © 2024 by Interoperability Network for the Energy Transition (int:net) is licensed under CC BY 4.0 Blueprint of the CEEDS 3 The research leading to these results has received funding from the European Union’s Horizon Europe Research and Innovation Programme, under Grant Agreements no 101070086, 101069831, 101069694, 101069839, 101069287 and 101069510. Blueprint of the CEEDS 4 CONTENTS 1. Introduction ..................................................................................................................................... 6 1.1. Scope ....................................................................................................................................... 6 2. Data Spaces Concept ..................................................................................................................... 9 2.1. Overall Strategies .................................................................................................................. 10 2.2. Defining Data Spaces Across Diverse Uses ......................................................................... 10 3. Business Use Cases for Energy ................................................................................................... 13 3.1. Use case #1 - “Collective self-consumption and optimized sharing for energy communities” 17 3.1.1. Scopes ........................................................................................................................... 17 3.1.2. Description ..................................................................................................................... 17 3.1.3. Scenarios ....................................................................................................................... 18 3.2. Use case #2 – “Residential home energy management integrating DER flexibility aggregation” ...................................................................................................................................... 21 3.2.1. Scopes ........................................................................................................................... 21 3.2.2. Description ..................................................................................................................... 21 3.2.3. Scenarios ....................................................................................................................... 22 3.3. Use case #3 - “TSO-DSO coordination for flexibility” ............................................................ 24 3.3.1. Scope ............................................................................................................................. 24 3.3.2. Description ..................................................................................................................... 24 3.3.3. Scenarios ....................................................................................................................... 26 3.4. Use-case #4 - “Electromobility: services roaming, load forecasting and schedule planning” 29 3.4.1. Scopes ........................................................................................................................... 29 3.4.2. Description ..................................................................................................................... 30 3.4.3. Scenarios ....................................................................................................................... 31 3.5. Use case #5 – “Renewables O&M optimization and grid integration” ................................... 33 3.5.1. Scopes ........................................................................................................................... 33 3.5.2. Description ..................................................................................................................... 33 3.5.3. Scenarios ....................................................................................................................... 34 3.6. Network codes requirements ................................................................................................. 37 Blueprint of the CEEDS 5 4. Proposed Architecture for CEEDS ................................................................................................ 39 4.1. Components of the Data Space Federated Side................................................................... 43 5. EDSCP Implementation Details .................................................................................................... 48 5.1. Energy Dataspace Cluster Projects ...................................................................................... 48 5.2. Technical Building Blocks Implementations across Projects ................................................ 50 5.3. Business Use Case Realization in CEEDS Architecture ....................................................... 56 5.4. Data value creation aspects .................................................................................................. 63 6. CEEDS Governance ..................................................................................................................... 66 6.1. Main Governance building blocks.......................................................................................... 66 6.2. EDSCP Implementations ....................................................................................................... 67 7. Interoperability Aspects ................................................................................................................ 71 7.1.1. Technical Interoperability ............................................................................................... 71 7.1.1.1. Building Blocks .............................................................................................................. 71 7.1.1.2. Actors ............................................................................................................................. 72 7.1.1.3. Data Formats ................................................................................................................. 73 7.1.1.4. Data transmission protocols .......................................................................................... 73 7.1.2. Semantic Interoperability ............................................................................................... 74 7.1.3. Governance interoperability .......................................................................................... 75 7.1.4. Fostering Interoperability in Organizations .................................................................... 81 7.1.5. Joining Forces on Interoperability.................................................................................. 82 8. Conclusions .................................................................................................................................. 83 9. References .................................................................................................................................... 86 10. List of Figures ............................................................................................................................... 88 11. List of Tables ................................................................................................................................. 89 12. List of Abbreviations...................................................................................................................... 90 13. Glossary ........................................................................................................................................ 92 Blueprint of the CEEDS 6 1. Introduction The shift in the energy sector, outlined as a key aspect of the Green Deal and detailed in the REPowerEU plan, necessitates a widespread substitution of fossil-fuel-based power generation with low-CO2 technologies. Although substantial progress has been made towards meeting the targets, achieving a complete transformation remains a lengthy and intricate process. Central to this transformation is the electrical grid, which already plays a crucial role in facilitating our contemporary lifestyle. However, its significance has now heightened due to the increased electrification of sectors like mobility as well as temperature control of buildings. Now more than ever, Europe requires an electrical network that is resilient, cyber-secure, flexible, and reliable. Meeting this demand is contingent on the implementation of advanced automation and power flow optimisation solutions as well as the comprehensive digitalization of entire energy systems. Data spaces play a pivotal role in advancing the digitalization of electrical energy systems, ushering in a new era of efficiency, reliability, and sustainability. In fact, data spaces address both the new business opportunities as well as the existing technical challenges. As the energy landscape undergoes a profound transformation, characterized by the integration of renewable sources, electrification of various sectors, and a growing emphasis on decarbonization, the need for intelligent and interconnected systems becomes increasingly evident. Moreover, data spaces facilitate predictive analytics, enabling proactive maintenance and reducing downtime in critical components of the electrical grid. Additionally, data spaces support the deployment of advanced automation and grid capacity optimisation solutions, enabling adaptive and responsive grids that can dynamically adjust to changing energy demands and supply conditions. Furthermore, the interconnected nature of data spaces promotes collaboration among various stakeholders, including utilities, regulators, technology providers, and consumers. This collaborative environment fosters innovation, accelerates the development of smart technologies, and ensures a more inclusive and participatory approach to the energy transition. Hence, data spaces emerge as catalysts for the digital transformation of electrical energy systems, offering a comprehensive and interconnected approach to managing the complexities of modern energy landscapes. Through the integration of data spaces, the energy sector can harness the power of information to build resilient, efficient, and sustainable electrical systems for the future. 1.1. Scope This document addresses the concept of a Common European Energy Data Space (CEEDS), providing detailed approaches and recommendations for its real-world realization. In particular, the main objective of this blueprint is to guide on enhancing the existing data infrastructures, the energy domain, towards the full embracement of data space solutions. Bridging this gap will empower the introduction of novel energy services, which will increase the efficiency and reliability of the energy systems while providing substantial benefits for every stakeholder. The key scope of this document is to present (i) a framework for new economically feasible business use cases and (ii) the general data space architecture that can enable them. This architecture aims to Blueprint of the CEEDS 7 interconnect the existing data infrastructures, composed of a diversity of heterogeneous systems operated by different actors, with federated data spaces; at this scope, technical specifications have been included. int:net has cooperated with the sister projects, forming the Energy Data Space Cluster Projects (EDSCP), and the energy community to identify the specific vertical capabilities that are needed in an energy data space. The result is the CEEDS architecture blueprint that meets the need of the domain. The objective in the future is that the CEEDS architecture is a specialization of the mandatory part of the Dataspace Support Centre (DSSC) and of future data space standards. This will require further coordination with future initiatives for convergence, e.g., a description of DSSC structured into a reference part and a pattern part as recommended in current standards on reference architectures (ISO/IEC/IEEE 42042 - reference architecture, ISO/IEC 40131 - guidance for reference architecture). It is recommended that the European Commission start a transversal task force between the data space architects in various initiatives to enable this alignment. Figure 1 illustrates the alignment between the CEEDS framework and the DSSC, outlining the progression from high-level reference architectures to the implementation of a CEEDS-based data space. At the foundational level, the DSSC Reference Architecture informs the CEEDS Reference Architecture, which is further refined through CEEDS Reference Architecture Patterns and CEEDS Blueprint Patterns. These patterns contribute to the development of the CEEDS Blueprint, which ultimately serves as the basis for a CEEDS-Based Data Space, ensuring a structured and standardized approach to data space implementation. Figure 1 - Ensuring alignment between the DSSC and the CEEDS Blueprints. Blueprint of the CEEDS 8 The blueprint is organized as follows: Section 2 provides general insights into the data space concepts and, particularly, specifically related to the energy domain; Section 3 describes the reference use cases for CEEDS while Section 4 presents the proposed architecture that enables their realization; Section 5 details the implementations of the building blocks; Section 6 discusses Governance of the CEEDS. Notable insights and references for the technical, semantic and governance interoperability of energy data spaces are discussed in Section 7. Section 8 concludes the document. Blueprint of the CEEDS 9 2. Data Spaces Concept The conceptualization of data spaces was initiated several years ago, providing the basis for characterization in specific domains like energy. Considering a domain-agnostic perspective, a data space is defined in the DSSC Blueprint v1.0 [1] as “Interoperable framework, based on common governance principles, standards, practices and enabling services, that enables trusted data transactions between participants.”. Considering this definition, three transversal features must be considered in the data space deployment: - Security and Privacy: Concentrating on ensuring the security and privacy of the exchanged data within the designated data space. - Quality and Integrity: Relating to the quality and integrity of the data residing within the data space. This encompasses elements associated with metadata, such as data validation, data cleansing, data accuracy, and data consistency. - Governance and Policy: Encompassing the structure of governance and policies dictating the data spaces, addressing decision-making, data governance frameworks (comprising rules and practices for management and operations), policies for data sharing and access, as well as energy-related policies and regulations. Furthermore, the deployment of a data space is performed according to five main dimensions, which reflect the transversal features described above. These dimensions correspond to: - Business: examining the business model related to data exchange, such as utilizing consumption data for managing flexibility transactions in the wholesale market and delineating the business roles of involved parties. - Legal: Delving into the legal framework, encompassing (a) overarching legal frameworks, (b) organizational aspects, and (c) contractual instruments. - Operation: Providing insights into the operational framework, including use cases, processes, and activities. - Functional: Describing the technical and governance building blocks, deployed based on necessary technical services (and their dependencies), as well as adherence to data standards - Technology: Offering specifications on adopted standards or required software components, as identified in the energy domain through the Smart Grid Architecture Model (SGAM). A primary objective is to ensure interoperability among internal parties and with other data spaces. The realization of a data space in the energy domain must, then, address every indicated dimension and implement the required measures to achieve interoperable solutions; even if existing solutions are already in place and well advanced for individual dimensions (e.g., an operational framework for grid management or a standardized data model, with associated data exchange profiles, that addresses a specific interoperability point), consistent work must be deployed to synchronize and align all the different dimensions simultaneously and in a defined system. Blueprint of the CEEDS 16 optimization analytics and infrastructure needs grid performance Governance and Regulation Governance frameworks for community energy sharing Regulations for massproduced DER integration Coordination frameworks between TSOs and DSOs Standards and protocols for electromobility services Regulatory compliance for renewables integration Further sections in this chapter explain in detail the scope, description and the various scenarios in each use case. The Section 3.6 sheds light on how an interoperable dataspace facilitate the implementation of new EU grid codes regulation. Blueprint of the CEEDS 17 3.1. Use case #1 - “Collective self-consumption and optimized sharing for energy communities” 3.1.1. Scopes The general scope of this use case is the instantiation and operation of Jointly Acting Self-Consumers (JASC), Residential Energy Communities (RECs) and Commercial Energy Communities (CECs), aiming at the collective self-consumption, inside the communities, and the optimization of energy sharing, with the electrical system. The specific objectives include: - Size the technical components and conduct an economical evaluation for the deployment of energy communities, based on consumption and generation profiles as well as market data, weather data and the possibility of assets sharing business models. - Provide the mechanisms for the collection and sharing of data, with appropriate granularity at the device level, of the energy consumption and generation, with the final goal of enabling flexibility and energy savings mechanisms. - Extract approximated flexibility models for smart appliances (e.g., using non-intrusive load monitoring data), enabling an overall quantification of flexibility and estimation of energy savings from intelligent load control. 3.1.2. Description The effective and large-scale deployment of energy communities, for collective self-consumption and regulated energy sharing, involves the optimization in both the network design phase (i.e., the size and location of distributed energy resources) and in the deployment of energy sharing mechanisms within the community and with the active role of electrical grid operators. This use case includes two optimization problems, the first one aims at determining the optimal installed capacities in the REC / CEC, considering typical consumption profiles, availability of renewable energy sources, costs of technologies (both capital and operational cost) and opportunity costs of the community members (retailing tariff for the electricity consumed from the grid, and selling price for the electricity sold back to the grid). The second optimization problem considers the operation of the community constrained by the installed capacity from the first optimization problem, in particular its electrical energy sharing / trading, where the optimized dispatch of controllable energy resources (e.g., storage, thermal loads, electric vehicles) is obtained considering the opportunity costs of the community members, together with an internal electricity pricing mechanisms to settle the internal energy transactions among members, which can be computed with different approaches or algorithms, to be used to study different financial schemes for communities. The data space environment enables the exchanges of data that are necessary for the execution of the optimization scenarios among actors, whose roles are described in [3]. In particular, the Service Provider offers, via its broker, the technical algorithms as services to which the Service Consumer has Blueprint of the CEEDS 18 subscribed. Technical parameters (including the type of available devices, assets, and capacity constraints), pricing and financing specifications as well as consumption and generation data profiles are used as the inputs coming from the Data Provider. The consent for data sharing is obtained from the Data Owner; additionally, the data space Clearing House (which is a service for logging data exchange transactions relevant for clearing and billing as well as usage control) works as an intermediary to keep the log of the transactions. The output data are received by the Service Consumers and correspond to the optimal installed capacity, the estimated flexibility schedule and the pricing for internal and external transactions, differentiated according to the energy sharing mechanism. As an additional service, the provision of information regarding the required device maintenance is also included. Moreover, the data exchange outputs allow improving the forecasts on available flexibility (I.e., aggregated demand side flexibility potential of the energy community). The enablement of data space capabilities becomes key given the multiple stakeholders and service providers in this use case, often enrolled through a value-chain enabler (legal or digital platform with an established governance scheme). Thus, the need to procure a data exchange environment built around data sovereignty guarantees allows the translation from common legal contracts to smart contracts, which guides data exchange limits (i.e., usage policies) and the long-term and post-exchange traceability of all data and associated data transactions. Moreover, as exploring aggregated and anonymized models representing the profiles of the community members may be included in as a data monetization scheme, there is a real need for ensuring pre and post data exchange guarantees with identity verification and validation of the involved organizations, or the traceability of data flows as part of a digital passport for data as an asset. 3.1.3. Scenarios The system encompasses three sub-use cases, here named as scenarios, each designed to address specific aspects of energy management within RECs and CECs: DER Sizing and Economic Evaluation of REC/CEC Business Model: Users subscribe to data space for DER sizing and economic evaluation, combining real consumption profiles from historical data. They provide parameters, request data (e.g., real consumption profiles (historical data), and solve optimization problems to determine optimal capacities and schedules, aiming to maximize of collective self-consumption of energy. Estimation of Flexibility Potential and Energy Cost Savings from Thermal Domestic Loads: Consumers subscribe via a Broker for flexibility estimation services. Data is requested, consent is obtained, and an optimization problem enhances the Electric Water Heater (EWH) operation. Output metadata, including flexibility potential, is transferred. Computation of Internal Transaction Price based on REC/CEC Operation: Consumers subscribe to internal pricing and REC/CEC operation services via a broker. Data is requested, consent is obtained, and the selected pricing mechanism is executed. Output metadata, including energy transacted and Blueprint of the CEEDS 19 prices, is transferred. The objective of this service is to simulate the operation of an internal market and extracts price curves that can be used to evaluate different business models (e.g., in terms of asset sharing) and the economic potential of communities for different stakeholders, such as inclusive communities for vulnerable consumers. In Table 4, the scenarios are detailed with respect to the involved actors and the triggering events, which cause the data exchange and transition from the pre-condition to the post-condition of available data and accomplished actions. The use of a data space infrastructure allows trading data between organizations (i.e., the REC/CEC members and service providers, as developers of the running algorithms) while enforcing the data sovereignty stack. Figure 4 further details the interactions and events between different actors in this use case as a sequence diagram. Table 4 - Scenarios for the use case #1. Scenarios Scenario name, description Actors Triggering events Pre-condition Post-condition DER sizing and economic evaluation of the REC / CEC business model Consumer, Energy service company, Energy trader, Market information aggregator, Resource aggregator, FSP, Sub-meter data hub operator Service consumer requests service Consumption and generation profiles / time series available in the data space & tariff data Information available about REC / CEC optimal sizing Estimation of flexibility potential and energy savings from thermal domestic loads Consumer, Energy service company, Energy trader, Market information aggregator, Resource aggregator, FSP, Sub-meter data hub operator Service consumer requests service Technical information from the EWH available; typical profiles or historical info about shower duration and start; sensor for outlet water Data available about estimated energy cost savings and flexibility Computation of energy price within the REC / CEC Consumer, Energy service company, Energy trader, Market information aggregator, Resource aggregator, FSP, Sub-meter data hub operator Service consumer requests service Consumption and generation profiles / time series available in the data space & tariff data Collective and individual operation costs or energy bills Blueprint of the CEEDS 20 Figure 4 - Sequence diagram for the use case #1. Blueprint of the CEEDS 21 3.2. Use case #2 – “Residential home energy management integrating DER flexibility aggregation” 3.2.1. Scopes Prosumers – whether residential, community, city, or industrial scale – are playing a new central focal role to enable cross-sectorial integration using their energy and flexibility data to actively contribute to a variety of flexibility markets. Moreover, the use of flexible DER located in residential environments allows to mitigate critical peak prices through wholesale markets as well as reduces TSO and DSO grid congestions. In this context new digital platforms are leveraging IoT, edge computing as well as federated cognitive cloud architectures with strategic digital features to optimally orchestrate DER through energy data spaces; this is pursued at the lowest voltage levels of the energy value chain, which includes home appliances and behind-the-meter DER and managed by resources operators and FSP that optimise the associated flexibility through their balancing portfolio. This approach requires rethinking the way data is generated from dedicated measurement devices, attached to DER, and exchanged throughout different federated actors of the electricity value chain: requirements involve real-time data exchange and streaming, taking advantage of a variety of domain-specific data exchange standards through consistent data space dictionaries. 3.2.2. Description Future carbon-neutral houses will soon require providing new net-zero analytics as defined through the directive “Energy Performance of Buildings 6 ” and, hence, provide near real-time indications to homeowners about their home energy efficiency as well as their available flexible capacity to respond to grid congestions and emergency events. The home energy use will be continuously optimized while maximizing local PV self-consumption and minimizing electricity costs (associated with new real-time energy and flexibility prices). New flexible DERs are in the meantime introduced through the home environment, such as heat pumps, EV bidirectional chargers as well as home batteries; these devices require new local home edge optimization across these resources. New integration approaches are considered to automate and facilitate the associated integration, such as all-in-one residential home energy stations that integrate bidirectional EV and home stationary battery and solar PV (directly with DC technology, resulting in the default consumer data interfaces). Local home energy management solutions are becoming essential building blocks to share residential DER data through multi-sided data exchange platforms, which are operated through distributed cloud infrastructures of OEM and integrate advanced real-time energy optimization as a service. Multi-sided platforms are accessed, on one side, by prosumers through their DER-specific app or high-level energy management apps while the other side is accessed by FSP accessing consumer data to enrol them (with their consent) in DER-specific flexibility programs. 6 https://energy.ec.europa.eu/topics/energy-efficiency/energy-efficient-buildings/energy-performance-buildings-directive_en Blueprint of the CEEDS 22 This BUC is typically associated with large residential assets offering flexibility to homeowners, namely heat pumps, smart heating equipment, EV chargers (V1G and V2G) as well as residential hybrid inverters for solar and storage applications. Reference DER data dictionaries are managed to enable plug-and-play registration of DER infrastructures in TSO-DSO flexibility markets; moreover, new real-time data stream across key actors of the energy flexibility value chain: from DER operator to energy community managers as well as with FSP and grid operators (TSOs and DSOs), hence automating associated residential DER transactions. The associated data space should allow managing all types of DER integrating the latest power electronics, edge computing and data streaming technologies to exchange relevant residential energy data (obtained from the main smart meter as well as from any other accessible DER submeters/dedicated measurement devices). The data space should be distributed through different federated cloud infrastructures and enable consent based on data exchanges across actors. 3.2.3. Scenarios Table 5 - Scenarios for the use case #2. Scenarios Scenario name, description Actors Additional information Residential energy and carbon footprint monitoring Prosumer, Resource aggregator Residential DER registration by DER operators Prosumer, Resource aggregator, Consent administrator, Flexibility register, Flexible product qualifier Registration consists in messages to registers customers in the DSO flexibility register. Residential home energy optimization DER, Local energy management, Weather forecast provider, FSP, Balancing responsible party Residential baseline calculation Data provider, Resource provider, Resource aggregator, Balancing service provider, FSP Provision of baseline data calculated by the service provider or the final customer, also based on weather/carbon/other data. Residential flexibility intraday calculation Data provider, Resource provider, Resource aggregator, Balancing service provider, FSP Residential flexibility bidding Balancing service provider, FSP, Market operator, TSO, Flexibility buyer Onboarding to market platform (and activation tests/product prequalification). Data exchange and communication requirements need to be tested for balancing services. Residential flexibility activation Market operator, TSO, Flexibility buyer, Balancing service provider, FSP, Resource provider, DER, Prosumer When flexibility is activated (either through a bare execution of a bid, or via set points), a controllable unit can receive these signals either via the Service Provider or directly from Blueprint of the CEEDS 23 the System or Market Operator. Service Providers may use the Kafka-based streaming infrastructure for both communication with the market, but also with their units under control. Residential flexibility observability Market operator, TSO, DSO, Resource aggregator, Resource provider, DER After the delivery phase, measurements at different points need to be transferred to the Flexibility Registry Operator, to make them in turn available to the Settlement Responsible Party for service validation and perimeter correction. Residential flexibility transaction management Flexibility settlement party, Metered data responsible, Metered data collector, Balancing service provider, FSP, Resource provider, DER, Prosumer Figure 5 - Sequence diagram for the use case #2. Blueprint of the CEEDS 24 3.3. Use case #3 - “TSO-DSO coordination for flexibility” 3.3.1. Scope With the increasing decentralization and decarbonization of the energy system, TSOs and DSOs are faced with the challenge of ensuring the resilience of the energy system, while enabling the integration of large RES to contribute to the achievement of ambitious RES deployment targets. The uncertainty of loads and generation flows poses increased challenges over-optimized network operations. Congestions and voltage issues that have been typically addressed with costly network upgrades need to be tackled with smarter, cheaper, non-cable alternative solutions that flexible DER offers through their power electronics interfaces and technologically existing aggregation potential. Active network management regimes for network control need to be developed, which require advanced forecasting of loads and generation for resource scheduling and real-time control. Moreover, a variety of analytics are necessary to ensure that appropriate measures exist to satisfy compliance with evolving reliability standards and security of supply. In their role as system operators, TSOs and DSOs are required to explore, evaluate, and deploy non-network alternatives that include the operation of market-based approaches such as frequency containment and reserves. The development of new market-based approaches shall be non-discriminatory and services might be offered from all eligible participants (either aggregated or direct end-users) at different voltage levels, while the operation of the transmission and distribution networks shall be performed collaboratively between TSOs and DSOs to ensure synergetic service provision and avoidance of conflicting actions while co-optimizing the operation of both systems (distribution-transmission-national) and reducing the overall OPEX. As electricity network management evolves towards more collaborative management structures, it is of utmost importance that TSOs and DSOs are involved in bilateral data-sharing agreements (facilitated by energy data spaces) towards exchanging flexibility requirements, enabling the identification of critical operational events at both levels of electricity grid operation and allowing for their common criticality prioritization while identifying available flexibility resources. This is pursued through federated flexibility registers, as defined through the new demand side flexibility code, towards ensuring the optimal operation of power grids under evolving real-time conditions via optimal collaborative operational scheduling, maximisation of capacity usage, activation of offered flexibility as well as deployment of flexible connection agreements. System operators also need to engage in data sharing with FSP (as identified in the use case #2) towards gaining increased visibility over available flexibility sources and proper clusters of them based on information shared by the relevant actors. 3.3.2. Description The exploitation of flexibility, stemming from generation, demand, storage and EV assets, for solving network issues, such as balancing and congestion, is not a novel idea. However, on the one hand the sparsity of adequate real-time information about the available flexibility and on the other hand the fact that most flexible assets and several sources of flexible generation are connected to the distribution Blueprint of the CEEDS 25 system, poses significant barriers to the efficient exploitation of the flexibility by the transmission system operator. To this end it is of utmost importance that novel approaches (data-driven and intelligenceenabled) are defined, at first for the real-time or near-to-real-time aggregation of the available flexibility provided by distributed energy resources located in the distribution network. Since the majority of the resources located in the distribution system are small-scale, they need to be aggregated to be efficiently included in the operational planning of either DSO or TSO. Moreover, tools enhancing the fast and efficient coordination between TSO and DSO should be developed, so that flexibility from the distribution system to be transferred to the TSO for balancing the system or solving network issues. In fact, electricity networks are progressively being dominated by small, dispersed prosumers (as also highlighted in use case #2), not only in terms of number but also in terms of criticality for the system resilience since they are associated to the ever growing number of small-scale DERs connected to the network, that continuously expand the energy system “edge”, in terms of controllability and operational complexity. The progressive decentralization, which is also accompanied by the introduction of new digitalized assets (EVs, IoT, batteries), poses significant challenges for the resilience of the system, while introducing increased uncertainty in traditional control routines, given the stochastic and intermittent character of renewable generation and the new control variables (not currently addressed in existing tools for the system management) introduced by new assets. Under these circumstances, energy systems need to evolve towards integrated ecosystems and, more specifically, integrated data value chains, to enable the data-driven optimization at system and DER level in a coordinated manner, by stepping on trustful data (intelligence) sharing models facilitated by energy data spaces. Such models and approaches will increase stakeholders’ data outreach, enhance their intelligence and facilitate the realization of innovative energy services and collaboration models for improving networks operations in a resilient manner by utilizing the untapped flexibility potential of small-scale dispersed DERs. As technology advances and becomes more affordable, prosumers and DER owners are no longer perceived as passive elements of the energy system, but are transforming themselves into active nodes that can effectively contribute to its optimized operation since: • they comprise in a huge source of flexibility able to support distribution and transmission system operators with the needed services to balance demand & supply and manage power quality and system resilience, and, at the same time, • they are associated with the generation of vast amounts of asynchronous streamed-data, spanning smart metering and sub-metering information, IoT device information (sensing/control), distributed generation (RES), storage, building systems (heating/ cooling) and electric vehicle data, becoming more and more essential for improving observability and orchestrating the resilient operation of a decentralized and complex energy system that effectively achieves the decarbonization advantages that come with the increasing penetration of RES and the progressive electrification of the mobility and building sectors. Hence, it becomes obvious that the real value of data produced by prosumers and DERs at the edge of the energy system (and beyond it) is hidden in the (real-time) sharing of such (previously non-reachable) information with the rest of the energy data value chain stakeholders that their operations are directly or Blueprint of the CEEDS 32 Figure 7 - Sequence diagram for the use case #4 - EV Booking Roaming Service. Blueprint of the CEEDS 33 Figure 8: Sequence diagram for the use case #4 - EV Flexibility Service 3.5. Use case #5 – “Renewables O&M optimization and grid integration” 3.5.1. Scopes The main challenges of renewable energies for getting larger deployment are cost competitiveness and smart grid integration. Therefore, the scopes of this use case are: 1. Develop more robust algorithms for optimizing the O&M of renewable energy assets by leveraging data from multiple renewable energy plant owners. This will allow a more reliable and earlier fault detection, automated diagnosis and maintenance prescription resulting in reduced operation and maintenance costs (OPEX). 2. Develop data analytics to enable efficient integration of distributed energy sources into the smart grid by monitoring data from different actors such as consumers and producers and data from the grid itself anticipating potential issues, like congestion or voltage volatility, impacting on quality and security of service. This can facilitate decision making on the optimal location and size of renewable resources in the overall system. 3.5.2. Description An optimized O&M of renewable assets along their lifetime is key to reduce the Levelized Cost Of Energy (LCOE) by increasing the Performance Ratio (PR) and reducing O&M costs and Weighted Average Cost of Capital (WACC). However, nowadays data are normally kept in silos within companies. This is one of the main blockers for AI since the ability of the algorithms to learn and generalize is limited by the company´s data, which generally covers a limited range of possible operating conditions. Data Blueprint of the CEEDS 34 Spaces enable access to a wider range of information than the one related to one single portfolio, enhancing the generalization capacity of AI algorithms for different operating conditions. Furthermore, in some domains, such as wind energy, some relevant actors, such as component manufacturers (Tier 2-3 categories), ICT companies, SMEs and academia do not have access to operational data, causing the block of their capacity to improve existing products and develop innovative digital services. Moreover, high penetration rates of Renewable Energy Sources require special measures by the DSO to ensure the quality and security of energy supply. In this context, it is crucial to develop innovative digital services that leverage existing data from different stakeholders (prosumers, DSO, aggregator) to optimise the power flows in the gird. Consequently, it is necessary to foster data exchange amongst different actors of the energy system, while ensuring data security, privacy and sovereignty. In this BUC, the category of data providers includes RES plant owners, RES plant operators, OEMs, DSOs and consumers/producers, while the data users are component manufacturers (Tier 2-3 categories) and data analytics service providers. 3.5.3. Scenarios In terms of the crucial datasets for exchange, this encompasses SCADA data for RES operation, meteorological data, smart grid data, and prosumer energy consumption (smart meter) data. Regarding the extent of data exchange, it varies with the specific application. For O&M optimization purposes, the scope is global, aiming to gather real operational data from similar assets in diverse operating conditions worldwide. On the other hand, for smart grid integration, the scope is more localized or regional. The majority of the required datasets are proprietary and, in some instances, contain business-critical information. Additionally, certain datasets, such as prosumer data, may include personal information that needs to comply with GDPR. Notably, meteorological data is typically open source. Concerning the willingness of data providers to engage in a European data space and share data across borders, this largely depends on the renewable technology involved. For example, solar PV data are typically owned by PV plant owners/operators who are open to sharing data. Conversely, in the wind energy sector, this data is predominantly owned by OEMs who are less inclined to share. This difference is because the wind energy sector is shifting its business model from selling wind turbines to provide O&M services, and data is a key competitive advantage to provide this type of services. The data is exchanged through the Common European Energy Data Space through the so-called connectors ensuring data privacy, security and sovereignty. This data is used to provide energy services by processing raw data through data-driven AI algorithms. These services include for example, RES O&M optimization service, Digital Twins for RES assets and Smart Grid, Prosumer Energy Demand/Generation forecast, smart grid reinforcement planning service, etc. The actors, events, preand post-conditions for each scenario are detailed in Table 8 - Scenarios for the use case #5. Figure 9 Blueprint of the CEEDS 35 further details the interactions and events between different actors in this use case as a sequence diagram. Table 8 - Scenarios for the use case #5. Scenarios Scenario name, description Actors Triggering events Pre-condition Post-condition RES O&M optimization OEM, RES plant owners/operators, TIER2-3 component manufacturer, Data analytics service providers RES plant owners/ operators requests service RES operational data available in the data space Early detection of failures, optimized maintenance schedule, optimal operation prescription. RES smart grid integration RES plant operators, prosumers, DSO DSO requests service Smart meter data and RES operational data available in the data space. Anticipate potential issues (congestion or voltage volatility, etc.) and prescribe corrective actions. Optimal RES sizing (prosumer/ community) Consumer/Producer, Data analytics service providers, DSO Customer/ Community request Generation, consumption and storage data available, geographic parameters, EV and prices Provide optimal size for RES integration DSO resources optimal location DSO, Consumer/ Produces, Data analytics service providers DSO Request Generation, consumption and storage data available, grid model (info for digital twin) grid information (existing problems), assets that can be installed Provide optimal location for DSO resources Blueprint of the CEEDS 36 Figure 9 - Sequence diagram for the use case #5. Blueprint of the CEEDS 37 3.6. Network codes requirements A crucial area where energy data spaces can potentially act as a game changer is in the implementation of new rules mandated by the network code on demand response; particularly relevant for the presented use cases #1 “Collective self-consumption and optimized sharing for energy communities” and #2 “Residential home energy management integrating DER flexibility aggregation”. Experts from the EU DSO Entity and ENTSO-E are collaboratively drafting the legal text proposal in close cooperation with European stakeholders. Market actors are increasingly calling for efficient valuestacking options between market platforms and various participants on the demand side. To gain a better understanding of the matter, it is worthwhile to review how future legislation is likely to define specific concepts and allocate responsibilities. To allocate responsibility in the future energy scenario, it is necessary to categorize key assets that play active roles in the market mechanisms under transformation. Referring to Figure 10, assets will be categorized as follows: - “Technical resource”: an individual power generation, energy storage, or demand module. - “Controllable unit”: a single technical resource or a group of technical resources behind the same connection point, provided that these technical resources can be collectively controlled. In this context, the controllable unit remains under the full sovereignty of the final customer, who has the authority to decide which aggregator or service provider will market the flexibility of the asset. - “Service providing unit” (SPU): a single controllable unit or a group of controllable units, a “service providing group” (SPG), connected to the same connection point. SPUs and SPGs are defined by the service provider to deliver local or balancing services. - “Service provider” or “aggregator” is a market participant with a legal or contractual obligation to supply local or balancing services from at least one SPU or SPG. Figure 10 - Definitions as basis for rules on demand response. Blueprint of the CEEDS 38 With this conceptual framework as a foundation, regulations govern complex services, and the markets associated with them. High-level real-time monitoring requirements will need to be managed by service providers. Simultaneously, the provision of local services must be coordinated and potentially constrained by system operators to avoid violating grid limitations, through local congestion-based markets and, potentially, flexible connection agreements. Submetering, together with embedded measurement devices in control unit equipment, will be integrated into the European regulatory framework, and multiple FSP, as well as multiple suppliers, will be permitted to operate behind a final customer’s single connection point. Controllable units are required to be “switchable” between aggregators (through dedicated control units), restoring grid users' sovereignty over the hardware they have purchased and effectively separating hardware from aggregation markets. These rules represent a significant leap forward, posing substantial data management challenges for all stakeholders in the field. Relevant data exchange standards are currently discussed by ENTSO-E, the DSO Entity as well as industries to ensure the end-to-end interoperability of demand side flexibility data through harmonised ontologies, as defined in the Common Information Model (CIM). Anyway, the markets they facilitate will not function without full digitalization and efficient data exchange environments, defined at European level to ensure level playing access to distributed controllable units (such as those associated with heat pumps and EVs). Blueprint of the CEEDS 39 4. Proposed Architecture for CEEDS The reference BUCs for CEEDS, described in Section 3 of this document, are based on an ecosystem of data spaces (following the approaches presented in section 2.1) that is strictly necessary to deploy regulated and efficient exchange of energy-related data. In fact, the scenarios of BUCs exploit the availability of data and services, indexed, and discovered in the data spaces catalogues, to operate the energy services. The implementation of this data space approach allows, moreover, to enlarge the set of involved actors as active participants in the energy systems operations, with socio-economic benefits (in terms of monetary savings as well as the quality of the services and reliability of electricity distribution) for every actor of the energy value chain. As already mentioned, the data spaces ecosystem, which sustains the execution of the presented BUCs, will not be constructed entirely from scratch. Instead, it will constitute an extension and enhancement of the prevailing data exchange ecosystem, which presently operates in isolation in countries with very limited pan-European interconnections. The objective is to establish a data infrastructure that facilitates the seamless and equitable exchange of data at pan-European level, transcending local barriers and limitations. In the current section, the existing solutions for energy data exchange are taken as a starting point; the goal is to describe the necessary adaptations to realize the CEEDS through implementing the proposed energy data space infrastructure. Focusing on the realization, the proposed model corresponds to the creation of an energy data space as the combination of (1) multiple “distributed data exchange platforms” with (2) overarching layers defined as the “federated data space” orchestration framework (centralized or distributed). This approach reflects the concept of DERA 3.0 (Data Exchange Reference Architecture 3.0 7 ), which has been defined in the Bridge Data Management WG based on SGAM. Specifications of local and federated parts of the architecture are described hereafter. The (1) “distributed data exchange platforms” layer (with reference to Figure 11) of the architecture refers to data platforms (including the already existing ones), either associated with (i) regulated infrastructures or (ii) unregulated actors and entities, in line with the key applications and functions defined in the SGAM. On one hand, examples of regulated data exchange platforms typically include grid control room platforms – such as EMS and ADMS - market platforms, meter data hubs and flexibility registers; on the other hand, the category of unregulated actors and entities entails the DERMS, VPP, Charging Point Management, Community Energy Management, DER Technical Aggregators, Building Energy Management. In general, these currently existing data exchange platforms are already capturing and persisting their own data, which is usually inputted into tailored applications; they are typically operated by energy stakeholders that assume the roles of actors as presented in the BUCs scenarios, each data exchange 7 https://op.europa.eu/en/publication-detail/-/publication/dc073847-4d35-11ee-9220-01aa75ed71a1/language-en/formatPDF/source-294051153 Blueprint of the CEEDS 40 platform behaving as data providers and/or data consumers. The set of energy stakeholders typically include all actors defined through the HEMRM; namely, among many others: DSOs, TSOs, market operators, OEMs, energy communities, charge point operators, customers, BRPs and BSPs. Most of these actors already have such data platforms in place, to manage, process and visualize different sets of operational data. Therefore, since different data space participants are associated with different data exchange platforms, the CEEDS guarantees data exchange among them. The endpoints for energyrelated data correspond to entities that act as sources and/or receivers of data, for example: field devices that provide real-time measurements (sensors, voltage and current transformers, PMUs, RTUs, smart metering devices and embedded dedicated measurement devices) and receive actuating commands, scheduled operational setpoints or price-based transactive controls (IEDs, tap-changers, switching devices, behind-the-meter DERs), SCADA, EMS and ADMS infrastructures that contains real-time databases and forecasts data, inputs from prosumers regarding the loads schedule, EVs and DERs actual and forecasted power consumption and generation. These data are bidirectionally exchanged with the distributed data ecosystems via the existing communication infrastructures, which accommodate different technologies such as 5G, LTE, fiber optics, PLC, secured internet, etc. Looking inside the data platforms on the “distributed data ecosystems” side, various strategies for data collection and storage originate from various implementation approaches for data management. These existing strategies for data management are described by two significant sources: the TSO-DSO Data Management Report 8 and the GEODE Data Management Fact Sheet 9 . Notably, the latter extensively explores the implications of adhering to Article 23 of Directive (EU) 2019/944, which delegates the responsibility for shaping the approach to data management for energy services to Member States. This empowers them to address European legal requirements based on their specific subsidiary needs. Consequently, the strategies result in three primary architectural approaches observed in numerous Member States, often applied in parallel for different types of data (i.e., from different sectors or applications), and described hereafter. a) In the decentralized model, data remains at its point of origin (e.g., metering information at DSO, contract information at the supplier and generation for DER). Collaborative efforts among market actors are underway to establish standardized market communication and exchange data, either with explicit consent from the data subject or within clearly defined business processes. Examples of frameworks adopting this approach can be found in Austria (EDA), the German market communication, and France. b) The centralized model involves a data hub that receives and stores data. All business processes operate within this hub, and outcomes are transmitted back to its clients. This model is managed and developed by a specific entity or service provider, with market participants utilizing its functionalities. This approach is implemented, for instance, in Finland and Estonia. 8 https://www.entsoe.eu/2016/07/27/tso-dso-data-management-report/. 9 https://www.geode-eu.org/wp-content/uploads/2020/05/202005-Fact-sheet-GEODE-Data-Management-FINAL.pdf. Blueprint of the CEEDS 41 c) The hybrid model combines elements from both previous models. While all market participants can communicate in a decentralized manner, specific central structures are employed in certain use cases (e.g., compliance monitoring or facilitating access to data brokerage). In the context of smart metering, Spain serves as an example where data remains with the DSO as the "metered data administrator," and access for end customers and third parties is facilitated through the AELEC-operated DataDis 10 . The (2) “Federated Data Space” side of the architecture (with reference to Figure 11) refers to where data is indexed, making it discoverable and providing a sort of marketplace for sharing (and, possibly, trading) both data and data services. In doing so, the data space will rely on multiple actors and data platforms (the previously described ones, in the distributed data ecosystems side) federating through the data space connectors and offering their data under pre-recorded policies, verified credentials, data models and contractual agreements. At this scope, the federated data space side includes a set of components to implement foundational building blocks that perform the required functionalities of the data space; these components are described in detail in Section 4.1. Figure 11 - Exchange of energy-related data among different data platforms (as data space participants). 10 DATADIS - https://aelec.es/datadis/ Blueprint of the CEEDS 48 5. EDSCP Implementation Details This section provides a concise overview of the ongoing projects associated with BUCs, followed by a description of the various building blocks utilized in the implementation of these projects. This section presents how the building blocks are being implemented by the EDSCP in their pilots, deriving approaches for further replication in the CEEDS. 5.1. Energy Dataspace Cluster Projects Project EDDIE Project EDDIE has developed the Open-Source EDDIE Framework, to facilitate energy data exchanges across EU member states. Key in the EDDIE Framework is a consent-based European-wide interoperable mechanism which allows the consumer to independently manage data exchange with energy service providers, energy community operators, flexibility service providers and grid operators. EDDIE Framework currently supports connectivity with validated consumption and accounting point master data for 7 Member States and United States (US)/Canada (CAN) Green Button CMD with a common and easily integrable API, standardized data and a common process, with most remaining member states to be attached soon. Furthermore, EDDIE Framework features the Administrative Interface for In-house Data Access (AIIDA), a secure and reliable tool for accessing data from smart meters and behind-the-meter assets close to real-time, based on customer consent and tailored to the respective use case. The EDDIE Marketplace, an off-the-shelve data marketplace for entities involved in energy data management (e.g., DSOs, TSOs, Energy Communities (ECs), etc.), provides facilitation services for new data-based solutions. EDDIE Online is a Platform as a Service (PaaS) to allow for energy data-driven services achieve data integration within minutes - is in development and close-to-production mode since March 2023.In parallel to the development of the EDDIE technical components, a multidisciplinary analysis is ongoing to examine the economic properties and commercial opportunities arising from the establishment of an energy data space; assess the behavioural aspects associated with the availability and use of energy data; clarify rights, duties and principles of consumers’ energy data sharing; investigate alternative regulatory arrangements; and explore security and safety aspects of alternative data-sharing options. EDDIE’s innovative approach significantly reduces data integration costs, allowing energy service companies to operate and compete seamlessly in a unified European market. This not only enhances the operational efficiency of these companies but also promotes a more cohesive and integrated energy sector across Europe. Additionally, AIIDA ensures secure and reliable access to valuable real-time data from smart meters strictly based on customer consent. EDDIE components are LIVE and usable by a broad audience already and they have led to smart and close-to-industrialization solutions. Blueprint of the CEEDS 49 Project SYNERGIES SYNERGIES has introduced a reference Energy Data Space Implementation to unleash the data-driven innovation and sharing potential across the energy data value chain by leveraging on data and intelligence coming from diverse energy actors (prioritizing on consumers and introducing them as data owners/ providers) and coupled sectors (buildings, mobility) and effectively making them reachable and widely accessible. In turn, it has facilitated the transition from siloed data management approaches to collaborative ones which promotes the creation of a data and intelligence ecosystem around energy (and other types of) data and enables the realization of data (intelligence)-driven innovative energy services that (i) value the flexibility capacity of consumers in optimizing energy networks’ operation, maximizing RES integration and self-consumption at different levels of the system (community, building), (ii) evidently support network operators in optimally monitoring, operating, maintaining and planning their assets and coordinating between each other (TSO-DSO collaboration) for enhancing system resilience, (iii) create an inclusive pathway towards the energy transition, through consumer empowerment, awareness and informed involvement in flexibility market transactions, (iv) step on real data streams and intelligence to deliver personalized and automated features to increase prosumer acceptance and remove intrusiveness, (v) facilitate the establishment of sustainable Local Energy Communities (LECs) by enhancing their role with Aggregator and BSP functions, and (vi) establish solid grounds for the creation of a new economy around energy data produced and shared across a complex value chain, in a secure, trustful, fair and acceptable manner. Project DATA CELLAR DATA CELLAR has created a federated energy dataspace that will support the creation, development and management of local energy communities in the EU. The data space population was facilitated via an innovative rewarded private metering approach, with a focus on an easy onboarding and interaction, guaranteeing a smooth integration with other EU energy data spaces, providing to LEC stakeholders services and tools for developing their activities. The DATA CELLAR platform is built around a collection of data sources—such as Local Energy Communities and energy installations—referred to as Validation Cases within the project. These sources deploy software services designed to process and expose raw datasets for integration into the DATA CELLAR ecosystem. The services include adapting data to the project’s model, integrating with a Dataspace Protocol-compliant connector, and publishing datasets in a Gaia-X federated catalogue. In addition, DATA CELLAR implements data-driven, energy-related services, such as a Decision-Support System, which utilise the Validation Case data sources to generate valuable insights for users of the data space. Users can access these through a user-friendly web Dashboard that serves as a gateway, abstracting the complexities of data space concepts and protocols. Advanced users, however, have the option to deploy their own wallet, connector, and related services to interact directly with the data space if they wish. Blueprint of the CEEDS 50 Project OMEGA-X OMEGA-X has developed an Energy Data Space that enables multiple actors to share data and services while ensuring privacy, security and sovereignty. This specifically addressed the current problem of low availability of data for innovative uses in the energy sector and beyond. OMEGA-X collaborated with stakeholders to identify where energy-based service improvements and innovation are required to guarantee that companies and organizations can share their data safely. At the same time, the OMEGAX solution helped existing market actors (including SMEs and start-ups) to have access to a variety of datasets to improve their AI models and thus be able to upgrade existing services and/or bring innovative services that otherwise could not be developed. The availability of data empowers new participants and market roles such as aggregators and local energy community managers. This has the potential to facilitate the large-scale penetration of renewables in the local grid without significant investments in grid infrastructure and will also create an opportunity for new business models to emerge. OMEGA-X put a prominent focus on developing and promoting inclusive and collaborative behaviours, which will lead to a multitude of societal and economic benefits, such as an increase in energy autonomy and a reduction in CO2 emissions. Project Enershare Enershare has leveraged the potential of energy data by developing a reference architecture for a European Energy Data Space. This architecture integrates cloud security solutions with digital security, artificial intelligence, and data exchange frameworks globally. By enhancing interoperability, establishing trust, and increasing the value of data building blocks, Enershare has tailored its solutions to the specific needs of the energy sector. The project has tested its innovative solutions and approaches in seven pilot sites across eleven use cases. Thes use cases demonstrated the value of energy data in providing services within and beyond the energy sector. Enershare demonstrated the potential to positively impact the development of new economic activities and cross-sector services by creating new business opportunities and jobs in the energy sector. Additionally, it enhanced awareness and engagement among energy consumers and communities. The project also envisioned creating new value-added services based on energy data, such as energy management, efficiency, auditing, forecasting, and trading. 5.2. Technical Building Blocks Implementations across Projects Table 9 summarizes the implementation of each building block (BB) within the individual cluster projects. If an implementation for a specific building block does not exist within a project, the corresponding field is left blank in the table. Blueprint of the CEEDS 51 Table 9 - Project specific implementations of CEEDS Building Blocks CEEDS Building Block EDDIE Synergies DataCellar Omega-X Enershare Connector EDDIE Framework Synergies Dataspace Connector Eclipse Dataspace Components Eclipse Dataspace Components (via Sovity connector) TNO Security Gateway & Energy Dataspace Connector Identity Management Keycloak, eID Security, Authentication & Authorisation Engine based on Keycloak Hybrid approach with OIDC (Keycloak) and Gaia-Xcompliant Verifiable Credentials via OID4VC IDSA – DAPS plus Gaia-X Verifiable Credentials Keycloak and TSG Identity Provider Logging Standard Logging Frameworks Contract Settlement Engine Eclipse Dataspace Components system monitoring interface Kubernetes Logging operator Clearing House Vocabulary Hub European Master Data Model CIM Network Manager Custom Implementatio n Common Semantic Data Model (CSDM) Semantic Treehouse Contract Framework Data Marketplace DLT Smart Contract Management Engine IDS Contract Negotiation Gaia-X specifications Blockchain Publication & Discovery European Master Data Model Data Marketplace XFSC Gaia-X Federated Catalogue The Metadata Broker Provenance & Traceability X.509 Contract Settlement Engine Gaia-X Digital Clearing House Gaia-X Digital Clearing House Clearing House Blueprint of the CEEDS 52 Access & Usage Policy Access Policy Engine Eclipse Dataspace Components Gaia-X Digital Clearing House Eclipse Dataspace Components Further details on as well as the rationale behind choosing specific implementation is explained below. EDDIE The Data Space Connector in the EDDIE project is built on the EDDIE Framework and AIIDA, avoiding commercialized connectors due to their limited maturity and applicability to the project's use cases. Instead, interoperability is ensured through Kafka connectors, particularly for cross-sectoral data integration with other projects such as SYNERGIES. It follows the IEC CIM standard for the information model and supports multiple communication protocols, including OASIS AS4, REST, Kafka, AMQP, and MQTT5 for edge deployments. With a maturity level of TRL7+, this implementation is well-suited for data-driven companies and ensures seamless integration across different domains. For Identity Management, EDDIE uses Keycloak and OpenID Connect for centralized identity and access management (IAM), with connections to eID and eIDAS wherever applicable. The implementation aligns with the DSSC reference architectures followed by many connected data spaces, ensuring compatibility across various identity frameworks. With a TRL9 maturity level, it meets all necessary requirements for authentication and authorization, providing a secure and scalable solution for federated data access. The Logging system employs standard logging frameworks designed for integration into modern containerized infrastructures. It offers flexibility by adapting to the logging requirements of each connected data space, ensuring compliance with AS4 standards for features such as repeatability, nonreputability, and auditability. With a maturity level of TRL9, this approach ensures robust log management for monitoring and security across federated environments. For the Vocabulary Hub, the EDDIE Framework translates national information models into IEC CIM where needed and provides a European Master Data Model. This ensures a unified data representation across different energy systems, facilitating seamless data exchange. With a TRL7+ maturity level, the implementation standardizes data interpretation, making integration with diverse datasets more efficient. The Contracting Framework is managed through the EDDIE Data Marketplace, which enables the definition and execution of contracts for data transactions. While specific standards are not mentioned, the implementation follows DSSC practices to ensure structured and legally sound agreements. This approach supports data monetization and crowdsourcing valuable datasets, ensuring that contractual processes align with the needs of the energy sector. For Publication & Discovery, the EDDIE Framework employs a European Master Data Model to connect multiple data spaces, ensuring broad data accessibility. By making datasets available through standardized publication and consumption methods, this component supports efficient data retrieval and sharing. With a TRL7+ maturity level, it enhances discoverability and accessibility of relevant datasets across federated environments. Blueprint of the CEEDS 53 Provenance & Traceability are ensured by establishing trust between federated components using X.509-based signing and encryption. This implementation follows eIDAS and X.509 standards, ensuring secure data authentication and integrity verification. With a TRL9 maturity level, it provides a standardized and widely accepted method for tracking data origins and ensuring trustworthiness across federated data transactions. For Access & Usage Policies, the project translates national regulations into enforceable access rules using the EDDIE Data Needs definition language. This ensures compliance with GDPR and the Data Governance Act, embedding usage policies and consent mechanisms within the system. With a TRL9 maturity level, the approach ensures that data access aligns with legal requirements, facilitating secure and policy-compliant data sharing across stakeholders. Synergies The Data Space Connector in SYNERGIES builds upon the implementation validated in the SYNERGY project and has been extended to support the data-sharing needs of the energy value chain. It aligns with the Gaia-X architecture (22.10 Release) and IDS RAM, ensuring compliance, interoperability, and data sovereignty. The implementation is at TRL6 and expected to reach TRL7 after validation in largescale demonstrators. This approach ensures robust, framework-aligned integration with various data governance and interoperability services. For Identity Management, SYNERGIES employs the Security, Authentication & Authorization Engine, which centralizes user and organization authentication using Keycloak, which is currently integrated with OpenID Connect. This implementation complies with OAuth 2.0 and aligns with the Gaia-X Trust Framework and IDS RAM. Currently at TRL6, it is expected to reach TRL7, providing a standardized and interoperable IAM system for secure access across the data space. The Logging component is managed by the Contract Settlement Engine, which oversees data transaction contracts, ensuring compliance and settlement monitoring. Fully compliant with the IDS Clearing House, it verifies contract terms, supports crypto-payments, and tracks remuneration. At TRL6 and progressing toward TRL7, it ensures transparency and trust in data-sharing agreements. For the Vocabulary Hub, SYNERGIES uses the CIM Network Manager to maintain and harmonize cross-sectoral Common Information Models (CIM), ensuring semantic interoperability. It supports standards such as IEC 62325, OpenADR3.0, SAREF4ENER, and OCPP, aligning with IDS RAM and Gaia-X. With a TRL7 maturity level, this implementation facilitates seamless data model evolution and integration across sectors. The Contracting Framework is based on the DLT Smart Contract Management Engine, leveraging Ethereum for automated and legally binding data-sharing agreements. It aligns with the IDS RAM and Gaia-X Clearing House, enabling contract negotiation, execution, and compliance tracking. Currently at TRL6 and advancing to TRL7, it enhances trust in federated data transactions. For Publication & Discovery, SYNERGIES implements a Data Marketplace that provides a centralized catalogue for data asset exploration. It fully aligns with the specifications of the IDS RAM Metadata Broker and App Store, ensuring metadata-level transparency while allowing stakeholders to navigate Blueprint of the CEEDS 54 and assess datasets efficiently. At TRL6, progressing to TRL7, it streamlines data access and discovery across federated environments. Provenance & Traceability mechanisms build on the Logging and Contracting Framework, ensuring transaction accountability through distributed ledger technologies and smart contracts. These mechanisms comply with IDS RAM and Gaia-X, maintaining trust and integrity in the data-sharing ecosystem. Finally, Access & Usage Policies are enforced by the Access Policy Engine, which enables finegrained control over dataset access and usage permissions. It supports organization-based access, confidentiality settings, and policy enforcement in alignment with DSSC and IDS RAM. Currently at TRL7, it ensures compliance with data security and privacy regulations, preventing unauthorized disclosures. Data Cellar The Data Space Connector in Data Cellar is implemented using the Eclipse Dataspace Components (EDC) framework, which provides modular building blocks for data exchange. It follows the International Data Spaces (IDS) Dataspace Protocol and is currently at TRL5, chosen for its maturity, available documentation, and acceptance within related projects. The Identity Management component employs a hybrid approach using OpenID Connect (Keycloak) for centralized authentication and the walt.id framework for issuing Verifiable Credentials (VCs) and Decentralized Identifiers (DIDs). It complies with the Gaia-X Trust Framework and OpenID Connector for Verifiable Credentials (OID4VC), ensuring compatibility with emerging self-sovereign identity standards, with a current maturity level of TRL5. Logging in Data Cellar lacks a unified implementation, as different services utilize independent logging mechanisms, making it non-applicable (N/A) in terms of maturity and standards compliance. For the Vocabulary Hub, Data Cellar has developed a bespoke ontology using OWL, RDF, JSON-LD, and well-known vocabularies like SAREF and ThinkHome. However, it lacks collaborative editing and curation features, making it N/A in maturity level and standard compliance applicability. The Contracting Framework is based on the IDS Dataspace Protocol's Contract Negotiation Protocol, implemented via the EDC Connector framework. It enables automated contract negotiation between connectors and integrates a custom Marketplace Ecosystem for dataset access. At TRL5, this approach ensures structured and enforceable data-sharing agreements. The Publication & Discovery component is based on a modified fork of the XFSC Federated Catalogue, ensuring compliance with the Gaia-X Trust Framework. It provides a standardized way for participants to publish and query data offerings, currently at TRL5. Provenance & Traceability mechanisms are indirectly supported through the Gaia-X Digital Clearing House (GXDCH) for verifying dataset provenance and the Marketplace for transaction tracking. Though not explicitly developed for this function, they provide a baseline for trust and accountability, with a TRL5 maturity level. Lastly, Access & Usage Policies are enforced using the EDC policy engine, complemented by a custom extension for Verifiable Presentation exchange. It follows standards such as ODRL, JWT, and Blueprint of the CEEDS 55 W3C Verifiable Credentials and is currently at TRL5, with an external Policy Decision Point (PDP) planned to enhance decision-making capabilities. Omega-X The Data Space Connector in OMEGA-X is implemented using Eclipse Dataspace Components (EDC), with an extended enterprise-ready "Connector-as-a-Service" (CaaS) provided by sovity. It follows the Data Space Protocol (DSP) for contract negotiation and secure data transfers, ensuring interoperability. With a TRL7 maturity level, this approach simplifies adoption for non-technical partners, broadening participation in data space solutions. The Identity Management solution consists of an IDSA-compatible DAPS, a PKI based on EJBCA, and a Verifiable Credential (VC) issuer using OID4VCI. It complies with both IDSA and Gaia-X Trust Frameworks and is currently at TRL5, chosen for its seamless integration with the connector and interoperability with other projects. For Logging, a Kubernetes logging operator using Fluentd and Fluentbit provides memoryand filebased buffering to prevent data loss. While it follows standard logging practices, it is at TRL4, as further development is needed for full-scale implementation. The Vocabulary Hub consists of a Common Semantic Data Model (CSDM), available on GitHub, and will later be published in the Semantic Treehouse of the ENERSHARE project. It follows ontology-based REST API standards such as OPENAPI and JSON-LD and is at TRL5, chosen for its ability to enhance market adoption and reusability of data models. The Contracting Framework is based on Gaia-X specifications and is implemented as a marketplace federator for managing offerings and transactions. It ensures interoperability, compliance with regulations, and transparency, making it ideal for multi-provider ecosystems. Currently at TRL4, it facilitates trust and flexibility in contract management. For Publication & Discovery, OMEGA-X uses a Gaia-X Federated Catalogue instance, compliant with Docker and Python v3.12. It is at TRL5, providing efficient data sharing while ensuring compliance, scalability, and transparency. The Provenance & Traceability component is managed through the Gaia-X Digital Clearing House (GXDCH), which ensures data integrity, traceability, and auditability across providers. With TRL4 maturity, it supports secure and immutable transaction logging. Access & Usage Policies are also governed by the GXDCH, providing a standardized framework for managing data flow, ensuring compliance, and enforcing security measures. At TRL4, it enhances accountability and control over data usage within the ecosystem. Enershare Enershare primarily uses the TNO Security Gateway (TSG) version 1 as dataspace connector. The TSG is an IDSA-certified connector aligned with the IDSA Reference Architecture Model v4, with a high maturity level (TRL 8-9). The Energy Data Space Connector v1.1 is also used, based on the OneNet Connector, but it does not yet support the Data Space Protocol (DSP); its TRL is 7-8, with full DSP integration planned for 2025. Blueprint of the CEEDS 56 Identity Management is handled by Keycloak for individual users and the TSG identity provider (IDP) for data space participants, with compliance to IDSA RAM v4. Keycloak has a TRL of 9, while the TSG IDP is at TRL 8-9, ensuring secure authentication and authorization across services. For Logging, the Clearing House logs metadata and transaction details, ensuring compliance with data usage policies and contract agreements. It does not follow specific standards beyond REST APIs and has a TRL of 5-6, chosen for its ability to integrate with connectors and marketplaces to provide verifiable transaction records. The Vocabulary Hub is implemented through Semantic Treehouse, compliant with IDSA RAM v4 and supporting various open standards like RDFS/OWL, SHACL, and JSON schema. Core modules are at TRL 9, while newer modules like the message model wizard are at TRL 5-7, ensuring structured data management and interoperability. The Contracting Framework in ENERSHARE uses blockchain and smart contracts based on the ERC20 standard for tokenized transactions. With a TRL of 6-7, this implementation ensures secure and automated contract execution, enhancing transparency and trust in data exchanges. For Publication & Discovery, the TSG-based Metadata Broker is used, compliant with IDS-RAM v4, with a TRL of 7-8. It enables structured metadata management and integrates with the marketplace for efficient data discovery. Access & Usage Policies are enforced using TSG and Energy Data Space Connector modules, with XACML-based policies, and Eclipse Dataspace Components (EDC), using ODRL. The TSGand TRUEbased implementations have a TRL of 6-7, while the EDC-based implementation is at TRL 5-6, ensuring flexible and standards-compliant policy enforcement for secure data access. 5.3. Business Use Case Realization in CEEDS Architecture This section explains the realization of selected BUC scenarios using the CEEDS architecture and deployed building blocks. The corresponding sequence diagrams (in section 3) illustrate the data exchange within these scenarios, providing insight into how CEEDS can facilitate seamless data flow. BUC#1: Collective self-consumption and optimized sharing for energy communities Scenario 1: In BUC#1, the DER sizing and economic evaluation of the REC/CEC business model is initiated when a consumer requests the service, involving actors such as energy service companies, traders, market information aggregators, resource aggregators, FSPs, and sub-meter data hub operators. To support this use case, consumption and generation profiles, as well as tariff data, must already be available in the data space. These inputs are accessed using the Publication & Discovery building block to ensure the right datasets are discoverable and accessible to authorized service providers. The Data Space Connector enables secure data exchange between the data providers (such as submetering hubs or aggregators) and the analytics services operated by ESCOs or aggregators. Identity Blueprint of the CEEDS 57 Management is used to verify and authorize the requesting service provider and ensure trusted access to consumer data. Once the necessary data is retrieved and processed, the optimal REC/CEC sizing information is produced. To ensure the output data and any contractual terms (if applicable) are verifiable and traceable, Provenance & Traceability and Logging components may be used. If the service is offered through a marketplace model, the Contracting Framework could be used to formalize service agreements or pricing models associated with the sizing recommendations. These CEEDS components, working together, facilitate secure, discoverable, and interoperable data access while preserving trust and transparency across the involved actors in the data space. Scenario 2: Estimation of Flexibility Potential and Energy Cost Savings from Thermal Domestic Loads The scenario is triggered when a consumer requests the service, engaging actors such as energy service companies, traders, market information aggregators, resource aggregators, flexibility service providers (FSPs), and sub-meter data hub operators. The successful execution of this use case requires that technical metadata about the EWH (e.g. tank volume, heating power), historical shower patterns (duration and timing), and sensor data (such as outlet water temperature) are already available in the data space. These inputs are discovered through the Publication & Discovery building block, ensuring authorized actors can identify and access the necessary datasets. Data exchange between the submeter data hub and the analytical services run by ESCOs or aggregators is secured using a Connector. Identity Management components authenticate and authorize access to ensure only verified entities interact with sensitive household-level data. Once retrieved, the data is analysed—typically through digital twins or load disaggregation models—to estimate thermal flexibility and compute potential cost savings by shifting EWH operation to off-peak hours or aligning with dynamic pricing. To ensure auditability and trust, Provenance & Traceability components record data flows and transformations. Logging mechanisms further reinforce transparency in data processing. If the service is offered commercially, a Contract Framework may be employed to automate pricing and formalize agreements for ongoing flexibility service provision. Scenario 3: Evaluation of Potential Revenues from Flexibility Market Participation This scenario is initiated when a consumer requests and involves actors such as energy service companies, traders, market information aggregators, resource aggregators, FSPs, and sub-meter data hub operators. The service relies on the availability of consumption and generation series, along with relevant tariff data, which are accessed through the Publication & Discovery component. Data exchange is secured using a Connector, and Identity Management) ensures authorized access to sensitive consumption data. Once collected, these datasets are processed by the ESCO or aggregator to compute individual and collective energy bills within the community. The Contract Framework may define billing logic or compensation models among members. Provenance & Traceability supports transparency in data origin and processing, while Logging mechanisms ensure auditability of the billing Blueprint of the CEEDS 64 The value-added mechanisms involve a variety of services; SYNERGIES categorizes them in (i) data services (including the monitoring and certification of data asset origins as well as data observability service to monitor the status of each active data check-in pipeline), (ii) generic services (e.g., privacy preservation services, encryption service, access policy service – which define the resolution and which part of the data asset is accessed - security, authentication & authorisation services) and (iii) AI services as well as (iv) application services dedicated to the data analysis, insight extraction (even pre-trained for energy applications) also related to the project use cases. ENERSHARE identifies two added-value services to support the roll-out of services in the CEEDS: (i) barter monetization and incentives module, which evaluates the intrinsic data value and enabling data monetization schemes, and (ii) data transformation service, based on a syntactic model to translate primary data into a semantic data representation. For example, ENERSHARE’s federated learning platform enables training decentralized data across multiple devices, allowing seamless aggregation of models trained on local data while promoting knowledge sharing. Additionally, the added value of the enhanced service for multi-energy flexibility potential assessment is the support of data-driven models for user profiling, rather than just statistically-based models of the household. In general, the projects highlight the need to establish clear incentives for data sharing, while still ensuring data privacy as a complement for plain data exchange. Additionally, DATA CELLAR project delivers a comprehensive suite of value-added services, strategically designed to maximize the benefits of rich data transactions within the data space. Reflecting on the crucial aspects of participation management and user interaction, the project focuses on enhancing user training and engagement to maximize the adoption and usability of the deployed technologies. The business mechanisms of the compensations rely on transaction schemes that will be regulated by formalized data contract templates and enable secure and trusted data asset sharing, trading and bartering, while allowing energy data value chain stakeholders to efficiently search for data assets of interest and providing them with intelligent recommendations for relevant data assets or data assets’ providers. The compensation is implemented with three different approaches: - Data by tokens, in which the access to assets (data, apps, services) is granted based on payment using a cryptographic token (specific for each data space) - Data by data, in which the access to assets (data, apps, services) is granted according to intrinsic value of data (through the barter exchange and incentives module) allowing a data set to be exchange for another data set with equivalent value. - Data by currency (limited to ENERSHARE), in which the access to assets (data, apps, services) is granted based on payment on FIAT currency (which can be handled through the marketplace). Blueprint of the CEEDS 65 Moreover, the marketplace can generate revenues charging a small percentage as a transaction fee for each transaction; as the platform also accommodates auctions, which do not involve token transactions, a fee is applied for the participation in it. To incentivize platform usage, a strategy could be to offer free access to auctions for a user's initial participation and then, a fixed subscription cost. SYNERGIES implements a “Contract Settlement Engine” which is responsible for handling the payment of the monetary cost or the fulfilment of the counter price (e.g. other dataset) in order to activate a smart contract that has been already duly signed by the legal representatives of the involved parties. The Contract Settlement Engine enables (i) settlement of data bartering agreements (e.g., granularity levels and time frames), (ii) Settlement of monetary transactions of data sharing agreements (verifying the money exchange between the related data asset provider(s) and data consumer), (iii) monitoring of any active contract to ensure compliance with the agreed terms (e.g., consistent data quality, freshness, and update rate as agreed) issuing alerts in case the terms of a data sharing contract are not respected, and for terminating a contract in case of breached terms. The Smart Contract Settlement Engine consists of: (a) a back-end component that is developed on NodeJS and in particular on the NestJS framework, (b) a blockchain layer, leveraging the Ethereum distributed platform and (c) a front-end component that builds on VueJS and TailwindCSS. DATA CELLAR solutions work with licenses associated with the digitized objects that represent energy assets (both datasets and AI models). It works with blockchain, using specific smart contracts written in Solidity that administer the exchanges in terms of economics and assets. In particular, two main standards have been used to define the digitization of assets, licenses and the currency used to buy and sell on the platform: ERC721 and ERC20, associated with the creation of non-fungible and fungible tokens, respectively. Licenses can be of two types: “period” or “usage”; the former allows the associated energy data to be used an unlimited number of times, while usage licenses are consumed each time they are used. Every license will be associated with a specific amount of DATA CELLAR Token, which represents the license price. The setup includes also a “balancer”, which handles the monetary exchange between tokens and licenses. This component is responsible for making the practical exchange between these two assets, verifying all the constraints associated with the payment (buyer's funds and availability of the license). EDDIE features a Data Marketplace, where data consumers can submit their data needs in a featurebased way, enabling e.g. machine-learning based solutions to crowd-source data based on characteristics of electricity prosumers. As the access to realising, tailored, high-quality data is an important pre-requisite for trustworthy models, the EDDIE Data Marketplace closes a very important gap. Apart from the access to real-world crowd-sourced data it offers compensation mechanisms for distributed data providers and data-service vendors, whilst preserving customer sovereignty and full GDPR compliance. Blueprint of the CEEDS 66 6. CEEDS Governance This section presents provides an overview of the governance frameworks and approaches employed by the EDSCP within their pilot projects for intra-dataspace governance. At the same time, identifying best practices and actionable insights that can be applied to the CEEDS initiative. 6.1. Main Governance building blocks Data space governance aims to address fundamental questions about regulatory dynamics, decisionmaking authority, stakeholder participation, and accountability within a given data space. It involves a collective effort by relevant actors who share a common goal, focusing on determining how decisions are reached, who has the authority to make them, and how they are communicated and enforced. Governance of an energy data space involves establishing a comprehensive framework that dictates how data is managed, accessed, and utilized within the energy sector. The new paradigms in the management of energy flows in the energy systems (e.g., associated with the active roles of DER, e-mobility, flexibility solutions) are favouring unprecedented interactions among stakeholders, detailed based on the HEMRM, and, consequently, new streams for data exchange according to SGAM. Foremost importance is then assigned to the identification of these necessary interactions (i.e., the stakeholders to be involved) while equipping the data spaces with systems that respect policies and regulations as well as fostering the development and adoption of new services for reliable energy systems. The governance framework of data spaces is divided into four distinct layers (cf. [4]): - Common European framework for data ecosystem: private-public data governance (e.g., Data Act or Data Innovation Board); - Domain-specific building blocks governance: inter-data spaces governance; - Data space governance: intra-data space governance; - Governance of a soft infrastructure: operational level of data space to provide essential services. This framework encompasses a range of policies, procedures, and technologies designed to ensure the data space operates securely, efficiently, and in compliance with regulatory standards. It may entail the identification of stakeholders and the definition of their roles, data management policies (classification, lifecycle etc.), access control and security aspects (authentication / authorization), data sharing agreements and governance bodies. Within the context of intra-data space governance, the DSSC blueprint v2.0 deepens the organizational and business building blocks, reaching the definition of the following governance building blocks [5]: - Organisational Form and Governance Authority: Governance in a data space is multi-faceted and encompasses various key decisions. Examples of these key decision points include the scope of the data space, the position the data space initiative wishes to take in the ecosystem, openness concerning entering participants, the support it wishes to arrange for its participants, Blueprint of the CEEDS 67 or the principles it wishes to implement (e.g. democratic). The specific choices made will differ between data spaces, but they should aim to promote collaborative, multi-stakeholder governance for effective data space operation. Data Spaces can be categorized as either unincorporated (lacking legal personality) or incorporated (possessing legal personality). For the Common European Data Space, such as CEEDS, the DSSC Blueprint v2.0 [5] offers several examples of the legal forms that a data space may adopt: o European Digital Infrastructure Consortium (a special legal form created for Common European Data Spaces and special multi-country projects); o European Company (a for-profit legal entity, similar to the limited liability company); o European Cooperative Society (a non-profit with characteristics of a cooperative and public limited company); and o European Economic Interest Grouping (a non-profit legal entity similar to a partnership). - Participation Management: The participation management building block outlines the operational processes that are implemented through the technical building blocks. It concerns how data transactions are facilitated within the data space. As a part of the data space governance framework, a governance authority can mandate rules and standards for the security, performance, interoperability and observability of data transactions. Clear data-sharing rules are essential for building trust between data space participants and directly reflect the functionality of the data space. In the following sections, the implementation of peculiar elements (i.e., access control and security, governance rules and data sharing agreements) in the EDSCP will be presented. Aspects related to inter-data space governance, including interoperability governance, are presented in Section 7.1.3. 6.2. EDSCP Implementations In the following sections, the implementation of peculiar elements (i.e., access control and security, governance rules and data sharing agreements) in the EDSCP will be presented. 6.2.1.Access control and security Identity management is a critical component in the governance of an energy data space, which ensures that data access and usage are controlled, secure, and compliant with regulatory requirements. As described in Section 5.2, five cluster projects — Data Cellar, SYNERGIES, OMEGA-X, ENERSHARE and EDDIE — have implemented identity management systems using various approaches and technologies. Despite differences in their methods, all aim to ensure security, authentication, and interoperability of identities, whether for individual users or organizations. The primary focus is on managing digital identities to secure data and facilitate integration with other components and services. Blueprint of the CEEDS 68 In the area of security and authentication, all projects utilize certificates or similar mechanisms to ensure secure communication between entities. Further, Decentralized Identifiers (DID) and Verifiable Credentials (VC) are commonly used and the implementing solutions are based on established standards (e.g., W3C, OpenID, SAML, OAuth). Specifically for managing dynamic, secure identities and ensuring the authenticity and integrity of interactions between connectors, Omega-X and ENERSHARE focus on creating an environment compatible with IDSA and GAIA-X trust frameworks. Both projects base the organizational identity management in the implementation of identity provider solution, as defined by IDSA, including a Certificate Authority (CA) and Dynamic Attribute Provisioning Service (DAPS). This combination contributes to the CEEDS System Use Case for onboarding and demonstrating decentralized identity solutions. Moreover, in the Data Cellar project, a dedicated server is used to manage organizational identities and request trust anchors for credential signing as per defined in GAIA-X framework. For Data Cellar and OMEGA-X projects, the identity management solution is based on Self-Sovereign Identity (SSI) principles, primarily utilizing W3C Verifiable Credentials and Decentralized Identifiers (DID). Instead, in ENERSHARE pilots “Keycloak” is used for managing individual users' identities, integrating with marketplace services via OpenID, SAML, and OAuth and, additionally, it has adopted the Dataspace Protocol for connector interoperability and aims to implement a participant wallet using DID, OID4VP, and OID4VCI. Nevertheless, in OMEGA-X, the Marketplace Federator is in charge of managing user registrations and approvals, inspired by Gaia-X specifications. On the other side, SYNERGIES utilizes a “security, authentication & authorisation” service responsible for identity and access management across the energy data space and related marketplaces. This service handles user and organizational lifecycle management, including registration, verification, and authentication. The solution includes single sign-on functionality which facilitates secure communication and authorization permissions across various SYNERGIES components. EDDIE is utilising as far as possible European electronic Identification and Authentication Services (eIDAS), not only for the authentication of data space participants, but also to close chains-of-trust with cloud-edge assets and distributed communication participants. Apart from the use of eIDAS, eID and compliant certificate infrastructures, it highlights and promotes how the European way of identification and access management should be adopted throughout the whole value chain. Further information on the EDDIE approach can be found in [6]. Lastly, Identity management doesn’t need to be created anew for data space environments, especially in a European context. Infrastructures regulated and deployed via electronic Identification and Authentication Services eIDAS [7] provide proven-in-use electronic IDs (eIDs), certification services, and with its 2024 amendments [8] adding the European Digital Identity Framework – even distributed identity wallets. The European federated authentication infrastructure is up and running for years in most Member States and is set to be a very important pillar in a Digital Single Market. With the possibility to share identification handles it serves as a linking pin between different within-sector data spaces as well as cross-sectorally. Blueprint of the CEEDS 69 6.2.2. Design and implementation of data space governance rules Governance rules are another important aspect to guarantee interoperability across energy data space. Delving deeper into the policies and rules designed to ensure the data space operates in compliance with regulatory standards on aspects such as access control, risk mitigation and data sovereignty, it is possible to distinguish different approaches within the different EDSCP. All the cluster projects are designing a model that is focused on fully preserving the rights of the data owner and on the facilitation of assurances for both the consumer and the producer of the data. To this end, most projects include legal and ethical considerations to the design of their governance models. From a legal perspective, the legislative frameworks include data protection, cyber security and energy specific regulations. The ethical aspects of governance are generally considered when utilizing an ethics-by-design methodology and, among others, two following principles are guiding the projects actions: following principles are guiding the projects actions: • Creation of a governance model that enables data use and data access ensuring compliance with ethical, legal and financial requirements applicable to all stakeholders. This enables the effective exercise of available data rights, protect data autonomy, sovereignty, and human dignity as well as fundamental rights of individuals such as the right to privacy and freedom from discrimination. • Implementation of legal agreements, which safeguard and ensure the respect of the governance model; additionally, compensation mechanisms and other adequate remedies are considered and activated in case of fundamental data rights violations. On a different aspect, when considering the rights of the service providers, these are preserved by the contracts and compensations in the incentive schemes (financial and non-financial) that can be agreed beforehand with the tools provided by the data space. Regarding the implementation of the governance model, the EDSCP foresees the creation of a body that exerts the powers both within the data space and with the affairs related to cross-data space issues. The role and functions of the governance authority are still under development, anyway a proposed approach corresponds to a general assembly of members supported by a management board. A federated model could also be adopted for the case of a cross-data space governance, where the positions and opinions of the different data spaces can be represented and considered. 6.2.3. Data sharing agreements Another relevant aspect to consider within the governance model of the CEEDS is the governance rules for participant onboarding and offboarding. These are critical to the governance of data spaces to ensure the integrity, security, and compliance of the data ecosystem. Onboarding rules ensure that new participants meet data governance standards such as security, privacy, and regulatory compliance. Offboarding rules prevent ex-participants from accessing data and services post-exit. In this context, the practice from the different projects can be summarized in the ways described hereafter. Blueprint of the CEEDS 70 • Onboarding agreements. In most projects, the application and evaluation of the prospective participant is conducted by the governance authority. The applicant will express the data space's intended use, and the authority will check compliance with legal and ethical standards and its technical capabilities (capability to deploy software to provide/consume the data). In the evaluation, the authority will clearly outline the potential penalties and consequences for noncompliance and processes for addressing and rectifying compliance violations. Subsequently, a secret and unique API key will be generated for the participant. This key allows communication with the data space services. Some projects are working on a first draft version of the terms and conditions for getting involved in the data space. These will define the types of stakeholders admissible for registration and the roles they can effectively undertake (e.g. data providers, data recipients), the processes and technical means employed for licensing applied over shared data or the means employed for establishing data sharing agreements, stepping on formalized and legally binding data contracts. • Offboarding agreements. When looking into the offboarding process it is important to mention that it represents the termination of the agreement. This includes the notice of termination, data retrieval and deletion as well as the revocation of access. The notice of termination can be either issued by the participant or the data space governance authority. Data retrieval must ensure that all participant data is securely deleted from the data space’s systems to protect privacy and comply with data protection regulations and the revocation of access includes a system audit to ensure the revocation of the participant’s access. Section 7.1.3 will further discuss governance aspects in the energy domain and analysis of the interoperability requirements. Blueprint of the CEEDS 71 7. Interoperability Aspects To fully achieve the deployment of CEEDS, starting from the federation of projects’ data space instances, detailed interoperability measures are necessary. The interoperability requirements described in this blueprint are grouped into technical interoperability, semantic interoperability and governance interoperability; they refer to the European Interoperability Framework (EIF) Toolbox [9], addressing the applicable layers. 7.1.1. Technical Interoperability Technical interoperability refers to the minimum technical framework that is required for all participants of a data space in the energy domain to be able to process and understand the information (metadata) of the services/data offered in the data space and be able to perform data transfers between them (participants). Specifically, this technical interoperability framework covers the following aspects: 1. Building blocks 2. Actors 3. Data formats 4. Data transmission protocols To implement the various capabilities in a data space, technology is needed. In most of the data spaces the component “data space connector”, described above as part of the CEEDS architecture, is used to provide an endpoint, enabling actors to participate in a data space. In addition, (shared) registries and services are needed to provide common/shared functionalities in a data space. For example, to register the participants of a data space. 7.1.1.1. Building Blocks From the technical viewpoint, nine building blocks are defined, which are grouped into: - Data interoperability: capabilities needed for the exchange of data: (semantic) models, data formats and interfaces (APIs). This also includes functionalities for provenance & traceability. - Data sovereignty and trust: capabilities needed for the identification of participants and assets in a data space, the establishment of trust and the possibility to define and enforce policies for access and usage control. - Data value creation: capabilities used to enable value-creation in a data space, e.g. by registering and discovering data offerings or services, providing marketplace functionality and enabling monetization of data sharing. The technical building blocks, initially defined by OPEN DEI and included in the DSSC analysis, are shown in the Figure 13. Blueprint of the CEEDS 72 From an implementation standpoint, there is not a direct one-to-one correspondence between building blocks and technical components. Often, a single technical component may be associated with multiple building blocks. As already introduced in Section 4, it is crucial to differentiate between the control plane and the data plane. The control plane is responsible for determining how data is managed, routed, and processed, including user identification and the enforcement of access and usage policies. On the other hand, the data plane is tasked with the actual movement of data. To illustrate, the control plane addresses user identification, access management, and policy enforcement, while the data plane facilitates the physical exchange of data. Consequently, the control plane can be standardized at a high level, incorporating common standards for identification and authentication. Meanwhile, the data plane may vary across different data spaces, adapting to diverse data exchange requirements. Some data spaces prioritize large dataset sharing, others focus on message exchange, and some follow an event-based approach. There is no universal solution, although certain mechanisms can facilitate the collaboration of different data planes. 7.1.1.2. Actors Apart from the building blocks, it is important to have a common definition of actors, in line with the latest implementation plans of DERA, and their possible interactions. In this sense, DSBA has recently published the technical convergence paper 12 which has defined the main actors: - Data Space Governance Authority - Data Space - Participant - Participant Agent 12 DSBA convergence paper is available online at: https://digital-strategy.ec.europa.eu/en/policies/data-governance-actexplained Figure 13 - Technical building blocks, proposed by OPEN DEI and DSSC [1]. Blueprint of the CEEDS 73 - Data Space Registry - Credential Issuer - Identity/Authentication & Authorization, Identity provider Figure 14 - Relations among data spaces actors (from [10]). The Figure 14 shows the relationships among the actors. 7.1.1.3. Data Formats As the main reference, JSON constitutes a lightweight, language-independent data interchange format, easy to parse and generate. It provides a way to create a network of standards-based machineinterpretable data across different documents. Particularly relevant, as specific proposed solution is the use of JSON-LD, which serializes linked data in JSON. 7.1.1.4. Data transmission protocols The dataspace protocol 13 comprises specifications intended to facilitate interoperable data sharing among entities governed by usage control and utilizing web technologies. These specifications detail the necessary schemas and protocols for entities to publish data, negotiate agreements, and access data within a data space. To share data between autonomous entities, metadata is required to facilitate the transfer of datasets, utilizing a data transfer (or application layer) protocol. The dataspace protocol outlines how this metadata is provisioned, including the deployment of datasets, the syntactic expression, and electronic negotiation of agreements governing data usage, as well as how datasets are accessed using “transfer process protocols”. To summarize, the dataspace protocol supports 13 Further information on the Data Space Protocol: https://docs.internationaldataspaces.org/idsknowledgebase/v/dataspace-protocol/overview/readme Blueprint of the CEEDS 80 categorises types of data space participants into (‘highly likely to be’) private entities (e.g. gatekeepers) and public entities (e.g. public sector bodies); while noting that the distinction between the two types is somewhat blurred (e.g. as in the case of data intermediation service providers, data altruism organisations and researchers and research organisations) 18 ; supporting the approach of fostering interaction between competent authorities for data intermediation (DGA, article 13) and for data altruism (DGA, article 23), and potentially other competent authorities (DGA, article 7). Finally, the European Interoperability Reference Architecture identifies target users (under its scope of application, from within public administrations) as portfolio managers, business analysts and architects; users which may be involved in multiple entities involved in the development of interoperability frameworks [13]. Among other tasks (see DGA, Article 30), the EDIB is to advise the Commission on cross-sector standards for the creation of common European data spaces, as well as to propose guidelines for sectorspecific and cross-sector interoperable frameworks of common standards and practices, while ensuring adequate and non-discriminatory representation of stakeholders in the governance of common European data spaces. In the energy sector, assisted by SGAM, the formulation of said interoperable frameworks may be accompanied by the relevant interactions of involved stakeholders, thus supporting the stakeholder’s adequate representation beyond the business layer, and ensuring addressing framework creation through a holistic and context-aware approach. While data intermediation services under the Data Governance Act have been inspired by common European data spaces 19 , and the corresponding set of rules for data intermediation services aim at ensuring their adequate role within common European data spaces 20 , an intermediary of a data space (i.e. a party performing one, more or all functions of a data space operator) may, but must not necessarily be considered an intermediation service provider, and thus fall within the scope of the Data Governance Act. Hence, the Act’s Chapter III: Requirements applicable to data intermediation services, is highly relevant to data spaces 21 . Going further, and approaching the validation framework, various aspects of technical as well as semantic interoperability for a federation of data spaces were tested by the cluster projects. The key challenges and learnings of this interoperability testing are presented in the Position Paper on Interoperability Framework in Energy Data Spaces [14]. The paper also highlights the importance of tools and standards towards achieving cross-data space interoperability. Additionally, the EU’s interoperability test bed has published a guide on governance interoperability design and conformance 18 Types of participants: https://dssc.eu/space/BVE2/1071253931/Regulatory+Compliance#3.1.2-Types-of-participants 19 Refer to the guidance document on the data governance act: https://digital-strategy.ec.europa.eu/en/library/new-practicalguide-data-governance-act 20 Further explanation on the Data Governance Act: https://digital-strategy.ec.europa.eu/en/policies/data-governance-actexplained 21 Further explanation on regulatory compliance: https://dssc.eu/space/BVE2/1071253931/Regulatory+Compliance Blueprint of the CEEDS 81 testing 22 , which may serve as inspiration for the consideration of governance aspects as part of a conformance testing setup. 7.1.4. Fostering Interoperability in Organizations Interoperability governance as describe in previous chapters must find its expression in concrete action. To that end it is worth mentioning that the int:net project has developed and tested a set of practical tools and guidelines that allow all types of institutions testing and continuously improving their “interoperability maturity”: • EMINENT EMINENT is an interoperability maturity model developed in the int:net project, which addresses the need for interoperability to be fostered as a business capability through several key areas. These include community facilitation – focusing on community growth and maintaining diversity of perspectives – alongside establishing technical agreements and facilitating implementation. EMINENT emphasizes the importance of knowledge retention and operational alignment as organizations evolve their interoperability proficiency. Developing interoperability also requires attention to user base growth, tool/product development, and market creation. Essentially, the EMINENT framework highlights that interoperability isn’t solely a technical issue, but requires a holistic approach encompassing community building, technical standardization, and strategic implementation to mature as a core organizational capability. • IntMAS The “Interoperability Management and Audit System” allows enterprises, associations and any other type of institutions to implement a continuous improvement process in their management practices and daily work. It has been modelled alongside proven management systems such as ISO 9001, ISO 14001 or EMAS. A comprehensive guideline (published as an annex to the int:net Whitebook on “Engagement Towards Interoperability in Governance”[12]) describes a step-by-step approach to implement IntMAS. 22 Guide on governance interoperability and design available online at: https://www.itb.ec.europa.eu/docs/guides/latest/governanceInteroperability/index.html Blueprint of the CEEDS 82 The guideline refers to a set of checklists and templates that allow for assessing the interoperability quality level and creating the required management artefacts with limited efforts. Upon completion of the IntMAS documentation an AI supported assessment process decides if the candidate will be allowed to use the respective quality label. The IntMAS approved organization are supposed to form a community in the framework of IntPPC, the Interoperability People and Project Connector platform 27. 7.1.5. Joining Forces on Interoperability As outlined above, collaboration across multiple governance classes and entities is crucial. Towards the end of the int:net project, a group of representatives from literally all governance classes have formed “The Think Tank on Interoperability Governance” (TTT). As a result of the TTT meeting in Vienna on June 25, 2025, a joint position paper has been prepared outlining insights and recommendations on: • Governance is the Cornerstone of Energy Interoperability. • Standardization Enables Global Energy Integration. • Interoperability Accelerates Innovation and Decarbonization. • Adaptable Governance is needed for a Dynamic Energy Landscape. • Energy transition needs cross-sector collaboration. • A common understanding of infrastructure makes us more resilient. The Think Tank will be maintained and expanded as needed on the IntPPC platform after the end of the int:net project. Blueprint of the CEEDS 83 8. Conclusions The presented blueprint underscores the critical need to adopt data space solutions within the energy domain, marking a pivotal moment for the transformation of the industry. The fundamental pillars of data spaces, as highlighted in this paper, not only foster the active engagement of key stakeholders across the energy value chain but also promise mutual benefits, ranging from monetary compensations or financial benefits (shared across actors of the value chain) to an elevated quality of services. At this scope, the establishment of clear rules, policies and regulatory adaptations is a linchpin in facilitating fair data exchange, paving the way for an open market that fosters the participation of new actors, including data and service providers, as well as data consumers. The document delves into an in-depth analysis of existing challenges within the energy sector and crafts business use cases that form the backbone of the CEEDS implementation. The contribution of this blueprint is twofold. First, complementary reference use cases for energy are defined and chosen with respect to the existing challenges and opportunities in the domain as well as the directions defined in the EU action plan “Digitalising the energy system”. The diversity of these use cases (spanning through areas such as mobility, energy communities, TSO-DSO interactions, residential energy optimization, and renewables O&M) underscores the blueprint's comprehensive approach. The success of these use cases is intricately tied to the widespread adoption of energy data spaces, necessitating a detailed examination of data exchange mechanisms, requirements, and the involved actors. Consequently, to implement the presented use cases, an architecture for the CEEDS is proposed. This architecture - consistent with reference architectures used in the energy domain such as SGAM and Bridge DERA - envisions the integration of existing data platforms, including specific business-related platforms, through the implementation of a federated data space. Moreover, as the blueprint unfolds, it turns its focus toward identifying and addressing existing challenges in interoperability at technical, semantic, and governance levels. Practical actions and recommendations are outlined, guiding stakeholders on the standards and communication protocols crucial for achieving seamless interoperability. Looking ahead, the cluster of energy data spaces projects is committed to further investigations aimed at enhancing interoperability, offering invaluable insights for large-scale replications. The emphasis on the exploitability and interoperability of solutions, coupled with the demonstration of the CEEDS use cases, highlights the commitment to practical applicability and scalability. Therefore, this blueprint is an invitation to a broader audience, extending to stakeholders, decision-makers, and professionals in the energy sector. Their active engagement is crucial for translating the blueprint's vision into reality, as energy data spaces transition from conceptualization to tangible implementation in real-world scenarios. The collaborative efforts of the wider community are essential for shaping the future landscape of the energy sector, ushering in an era defined by innovation, efficiency, and sustainability. Blueprint of the CEEDS 84 The Way Forward: Creating a Synergy and Seeking Alignment with other Domains Energy sits at the cross section of several critical European industries, from automotive to semiconductors where fragmentation often limits innovation and efficiency. Further improvements to the CEEDS blueprint should build on the CEI Sphere Hourglass© model (Figure 18) [15] to provide a simple framework to help diverse stakeholders align using a market-driven, standards-enabled approach. The Hourglass: • Clearly maps business stakeholders to technological roles • Provide a simple layering of functions, from Cloud-Edge-IoT platform infrastructure to data analytics and user interfaces applications • Will create a pathway to standardisation through submission to the 2026 standardisation rolling plan and to ISO/IEC JTC 1/SC 41. Figure 18 – Hourglass Model (From CEI-Sphere [15]). Additionally, further improvements should also draw on the work of the DSSC and O-CEI 23 to create reusable building blocks that can help European technology stakeholders build better, faster. From a problem-solving, business-driven viewpoint, technological bridges such as the offered by O-CEI platform will help materialise a common European view in energy communities supported by open-source Internet of Things and Artificial Intelligence. Together, they will also contribute to shaping standards on the continuum (e.g., through the preliminary work item on architecture considerations on IoT, Cloud, Edge 24 ). The work of CEI-Sphere can support future energy dataspace projects, including INSIEME as well as the project TwinEU 25 , which applies data spaces principles to enable digital twins’ data and model exchange for the energy system. 23 Website of the project O-CEI: (https://o-cei.eu/) 24 Refer to the work of ISO/IEC JTC1/SC41 25 For further information on the TwinEU project, please refer to the project website: https://twineu.net/ Blueprint of the CEEDS 85 Ongoing Project INSIEME Answering Digital Europe Programme Call DIGITAL-2024-CLOUD-AI-06-ENERSPACE, Project INSIEME 26 aims to deploy a reliable, secure, and sustainable CEEDS, applying the abovementioned use cases on further steps towards industrialisation. This initiative aligns with the European Strategy for Data and the EU Action Plan on digitalizing the energy system, adopted in October 2022, as highlighted in the COMMISSION STAFF WORKING DOCUMENT[16] on Common European Data Spaces. The plan outlines key actions to establish the common European energy data space, consolidating a comprehensive and coherent EU framework for data exchange and interoperability in the sector. Many key participants on the five Energy Data Space sister projects and the int:net CSA have teamed up with key European organisations and individuals in the twin transition to provide – in co-operation with the DSSC - a unified, streamlined and securely deployed data space for use cases described in this document. 26 For further information on the INSIEME Project, please refer to the project website: https://insieme.energy/ Blueprint of the CEEDS 86 9. References [1] “Blueprint v1.0 - Data Spaces Support Centre.” Accessed: Sept. 14, 2025. [Online]. Available: https://dssc.eu/space/BVE/357073006/Data+Spaces+Blueprint+v1.0 [2] B. Otto, M. Ten Hompel, and S. Wrobel, Eds., Designing Data Spaces: The Ecosystem Approach to Competitive Advantage. Cham: Springer International Publishing, 2022. doi: 10.1007/978-3-030-93975-5. [3] “IDS-RAM 4 - Roles in the International data spaces”.” [Online]. Available: https://docs.internationaldataspaces.org/ids-knowledgebase/v/ids-ram-4/layers-of-the-referencearchitecture-model/3-layers-of-the-reference-architecture-model/3-1-businesslayer/3_1_1_roles_in_the_ids [4] “OPEN DEI - State of the Art.” Accessed: Sept. 14, 2025. [Online]. Available: https://internationaldataspaces.org/wp-content/uploads/dlm_uploads/Report-OPENDEI-State-ofthe-Art.pdf [5] “Data Spaces Blueprint v2.0 - Home - Blueprint v2.0 - Data Spaces Support Centre.” Accessed: Mar. 28, 2025. [Online]. Available: https://dssc.eu/space/BVE2/1071251457/Data+Spaces+Blueprint+v2.0+-+Home [6] EDDIE Project, Identification and Authentication in a Common Energy Data Space”. Project EDDIE, 2024. [Online]. Available: https://eddie.energy/files/eddie/media/medialibrary/Identification%20and%20Authentication%20in%20a%20Common%20European%20Data %20Space_v1.0.pdf [7] Regulation 910/2014 on ‘electronic Identification and Authentication Services.’” 2014. [Online]. Available: https://eur-lex.europa.eu/eli/reg/2014/910/oj/eng [8] Regulation amending Regulation (EU) 910/2014 adding the ‘European Digital Identity Framework.’” 1183 2024. [Online]. Available: https://eur-lex.europa.eu/eli/reg/2024/1183/oj/eng [9] “European Interoperability Framework (EIF) Toolbox | Interoperable Europe Portal.” Accessed: Sept. 14, 2025. [Online]. Available: https://interoperable-europe.ec.europa.eu/collection/iopeumonitoring/solution/european-interoperability-framework-eif-toolbox [10] “Technical Convergence – Discussion Document”,” Data Spaces Bus. Alliance, Apr. 2023. [11] “European (energy) data exchange reference architecture 3.1 - Publications Office of the EU.” Accessed: Sept. 14, 2025. [Online]. Available: https://op.europa.eu/en/publication-detail/- /publication/6c3b1add-a0a7-11ef-85f0-01aa75ed71a1/language-en [12] L. Karg and M. S. Mugica, “int:net – D4.3 Engagement Towards Interoperability in Governance (V 1.0,” J Jimeno Huarte, Nov. 2023, [Online]. Available: https://intnet.eu/resources/technicalresources [13] “EIRA | Interoperable Europe Portal.” Accessed: Sept. 14, 2025. [Online]. Available: https://interoperable-europe.ec.europa.eu/collection/european-interoperability-referencearchitecture-eira/solution/eira/release/v300 Blueprint of the CEEDS 87 [14] S. Jimenez, Interoperability Framework in Energy Data Spaces. International Data Spaces Association, 2023. doi: 10.5281/zenodo.10117882. [15] “Hourglass Model 2025.” Accessed: Sept. 14, 2025. [Online]. Available: https://zenodo.org/records/16574644 [16] “Second staff working document on data spaces | Shaping Europe’s digital future.” Accessed: Sept. 14, 2025. [Online]. Available: https://digital-strategy.ec.europa.eu/en/library/second-staffworking-document-data-spaces Blueprint of the CEEDS 88 10. List of Figures Figure 1 - Ensuring alignment between the DSSC and the CEEDS Blueprints. ..................................... 7 Figure 2 - Possible ecosystems strategies for data spaces (adapted from [2]). ................................... 10 Figure 3 - Identified reference use cases for CEEDS. .......................................................................... 14 Figure 4 - Sequence diagram for the use case #1. ............................................................................... 20 Figure 5 - Sequence diagram for the use case #2. ............................................................................... 23 Figure 6 - Sequence diagram for the use case #3. ............................................................................... 28 Figure 7 - Sequence diagram for the use case #4 - EV Booking Roaming Service. ............................ 32 Figure 8: Sequence diagram for the use case #4 - EV Flexibility Service ............................................ 33 Figure 9 - Sequence diagram for the use case #5. ............................................................................... 36 Figure 10 - Definitions as basis for rules on demand response. ........................................................... 37 Figure 11 - Exchange of energy-related data among different data platforms (as data space participants). .......................................................................................................................................... 41 Figure 12 – Complete CEEDS architecture. .......................................................................................... 43 Figure 13 - Technical building blocks, proposed by OPEN DEI and DSSC [1].................................... 72 Figure 14 - Relations among data spaces actors (from [10]). ............................................................... 73 Figure 15 - Governance interoperability in the DERA 3.1 model (from [11]). ....................................... 75 Figure 16: SGAM plus: the 6th layer "Framework" (from [12]). ............................................................. 77 Figure 17: Interaction of governance classes and identification of related supporting frameworks (from [12]). ....................................................................................................................................................... 78 Figure 18 – Hourglass Model (From CEI-Sphere [15]). ........................................................................ 84 Blueprint of the CEEDS 89 11. List of Tables Table 1 - Categories of data spaces. .................................................................................................... 11 Table 2 - Summary of the BUCs. .......................................................................................................... 14 Table 3 - Data spaces objectives with respect to the BUCs. ................................................................ 15 Table 4 - Scenarios for the use case #1. ............................................................................................... 19 Table 5 - Scenarios for the use case #2. ............................................................................................... 22 Table 6 - Scenarios for the use case #3. ............................................................................................... 26 Table 7 - Scenarios for the use case #4. ............................................................................................... 31 Table 8 - Scenarios for the use case #5. ............................................................................................... 35 Table 9 - Project specific implementations of CEEDS Building Blocks ................................................. 51 Blueprint of the CEEDS 96 Virtual Power Plants (VPPs): A cloud-based distributed power plant that aggregates the capacities of heterogeneous Distributed Energy Resources (DER) for the purposes of enhancing power generation, as well as trading or selling power on the electricity market. Vocabulary Hub: Provides endpoints for seamless communication with data space connectors and infrastructure components, storing and documenting vocabularies, ensuring compliance within CEEDS. CONTACT Interoperability Network for the Energy Transition (int:net) c/o Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. Hansastrasse 27c, 80686 Munich Germany mail: [email protected]