REEFLEX aims to generate niches of opportunities for new cross-sector energy services provided by SMEs and start-ups in demand side flexibility markets and to increase participation of energy consumers in demand side flexibility markets. The project develops a central interoperability platform and a catalogue of services with the capability of maximising distributed energy resources' flexibility while respecting the different end user profiles and needs along with the physical limitations of existing infrastructures. Additionally, the generation of a common operation market model together with AI-driven intelligence services and automation systems, enabled through the utilization of distributed ledger technologies for enhancing transparency and trust, will reduce market entry barriers and costs and achieve a higher participation from energy consumers. They will benefit from new revenues obtained through data and flexibility transactions, while enjoying innovative, personalized, data (intelligence)-driven services for smart, human-centric control of their assets in the frame of demand response or self-consumption. REEFLEX solutions, they will be demonstrated and cross-tested in 4 main demonstrators (Spain, Greece, Switzerland, Bulgaria) with different characteristics, allowing evaluation of their impact under alternative scenarios and integrating a variety of sectors, such as residential buildings, mobility, commercial establishments, industrial sites or data centres, among others. In addition, the services catalogue will be further replicated in three additional replicators across Europe to achieve wider coverage (Turkey, Portugal, Denmark) and adaptation, ending-up in a total of 7 countries for the whole project execution. These cross-tested demonstrators and additional replication campaigns are structured around 8 different use cases that cover a comprehensive set of consumer groups to participate in up to 12 demand side flexibility services.

The transition to the smart grid era is associated with the creation of a meshed network of data contributors that necessitates for the transformation of the traditional top-down business model, where power system optimization relied on centralized decisions based on data silos preserved by stakeholders, to a more horizontal one in which optimization decisions are based on interconnected data assets and collective intelligence. Consequently, the need for “end-to-end” coordination between the electricity stakeholders, not only in business terms but also in exchanging information is becoming a necessity to enable the enhancement of electricity networks’ stability and resilience, while satisfying individual business process optimization targets of all stakeholders involved in the value chain. SYNERGY introduces a novel reference big data architecture and platform that leverages data, primary or secondarily related to the electricity domain, coming from diverse sources (APIs, historical data, statistics, sensors/ IoT, weather, energy markets and various other open data sources) to help electricity stakeholders to simultaneously enhance their data reach, improve their internal intelligence on electricity-related optimization functions, while getting involved in novel data (intelligence) sharing/trading models, in order to shift individual decision-making at a collective intelligence level. To this end SYNERGY will develop a highly effective a Big Energy Data Platform and AI Analytics Marketplace, accompanied by big data-enabled applications for the totality of electricity value chain stakeholders (altogether integrated in the SYNERGY Big Data-driven EaaS Framework). SYNERGY will be validated in 5 large-scale demonstrators, in Greece, Spain, Austria, Finland and Croatia involving diverse actors and data sources, heterogeneous energy assets, varied voltage levels and network conditions and spanning different climatic, demographic and cultural characteristics.

CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

PUZZLE will implement a highly usable cybersecurity, privacy and data protection management marketplace targeted at SMEs&MEs that enables them to monitor, forecast, assess and manage their cyber risks through targeted cybersecurity services, increase their cybersecurity awareness through the efficient heterogeneous information processing, the establishment of knowledge sharing with other SMEs&MEs and extract insights based on advanced analytics. The PUZZLE will track the relationships among the cyber assets of each SME&ME, considering the available network, compute and storage infrastructure and use them to efficiently calculate individual, cumulative and propagated risks, as well as recommend and apply mitigation actions. The PUZZLE will support vulnerabilities and threats assessment in a collaborative manner based on the homogenization of data provided by the SMEs&MEs. Data will be collected by resource handling and monitoring agents applied over Cloud/Edge Computing, IoT and network infrastructure. Such data will be enriched with data provided in relevant open repositories. SMEs&MEs data sharing will take place through blockchain-based technologies for secure data management. Based on the calculated risks and the provided graph topology, the application of resilient defensive strategies will be recommended, considering the type of infrastructure used by the SME&ME. Automated defensive strategies deployment mechanisms are also going to be made available, reducing a lot the complexity and the burden for the deployment of the provided services by cybersecurity managers in SMEs&MEs. Special emphasis will be given on the usability and the rise of competitiveness of the PUZZLE Marketplace, by designing solutions that can be easily on-boarded by external cybersecurity providers and can be seamlessly adopted by the end-users taking into account their preferences. The provided services will be made accessible through the SMEs&MEs Dashboard that will be developed.

Ever since the cloud-centric service provision started becoming incapable for efficiently supporting the emerging end-user needs, compute functionality has been shifted from the cloud, closer to the edge, or delegated to the user equipment at the far-edge. The resources and computing capabilities residing at those locations have been lately considered to collectively make-up a ‘compute continuum’, albeit its unproven assurance to securely accommodate end-to-end information sharing. The continuum-deployed workloads generate traffic that steers through untrusted HW and SW infrastructure (domains) of continuously changing trust-states. CASTOR develops and evaluates technologies to enable trustworthy continuum-wide communications. It departs from the processing of user-expressed high-level requirements for a continuum service, which are turned-to combinations of security needs and network resource requirements, referred to as CASTOR policies. The policies are subsequently enforced on the continuum HW and SW infrastructure to realise an optimised, trusted communication path delivering innovation-breakthroughs to the so-far unsatisfied need: a) for distributed (composable) attestation of the continuum nodes and subsequent elevation of individual outcomes to an adaptive (to changes) continuum trust quantification; b) for the derivation of the optimal path as a joint computation of the continuum trust properties and resources; c) for continuum infrastructure vendor-agnostic trusted path establishment, seamlessly crossing different administrative domains. The CASTOR will be evaluated in operational environments of 4 use-cases whereby varying types of security/safety-critical information is shared. Project innovations will be exhaustively assessed in 3 diverse application domains utilising the carefully-designed CASTOR testbed core for each case. Our results will provide experimental evidence for the CASTOR's efficiency and feed the incomplete trust-relevant (IETF) standards.