Worldwide Data centers (DC) are estimated to account for 1 to 2% of electricity usage. Regarding the European context, it is expected that data centres will account for 98.5 TWh/year in 2030. So it is evident that there is an important potential to recover waste heat from the cooling processes of DCs. The THUNDER project aims to overcome existing barriers hampering a wide adoption of DCs waste heat recovery strategies, providing an innovative, efficient and cost attractive Seasonal Thermal storage based on Thermochemical Materials. THUNDER solutions stretch across the value chain (data centre innovative storage providers, heat pump manufacturers and district energy company operators). The THUNDER solutions will be validated in field conditions at the Demosite in Bulgaria where the practice of WHR from DC is not widely diffused thus boosting the market also in those areas. Deepened replicability assessment will be done and pre-feasibility analysis developed in 10 further Demosites across all over Europe. Co-design and training workshops will be organized at the replicability identified sites to promote stakeholders engagement and social awareness thus unlocking barriers and make it real THUNDER replication.

Nowadays, the interest in direct current (DC) power transmission and distribution (T&D) systems among academia and industry has been rekindled mainly due to the proliferation of power electronic based (or interfaced through power electronics) loads and the increasing deployment of distributed energy resources (DERs), which operate intrinsically in DC or have a DC stage. Reacting to the recent events in Ukraine, the European Commission (EC) unveiled the REPowerEU Plan. This plan aims to reduce Europe’s dependence on Russian energy imports by increasing the total renewable energy generation capacities to 1236 GW by 2030 (compared to 1067 GW originally envisaged under the “Fit for 55” package). To achieve this goal, the energy sector, including DC technologies, is expected to play a prominent role. While considerable progress has been made in increasing electricity generation from variable RES in various member states, additional efforts are needed to reach a carbon-neutral power system. Within this context, the widespread adoption of offshore wind generation is anticipated to play a significant role in the years to come, as it is favored for its higher availability rates and greater public acceptance. The HYNET projects aims to: (i) develop innovative technologies for transnational design and planning of AC/DC hybrid power systems, (ii) establish standardized methodologies and interoperability for multi-terminal, multi-vendor MVDC and LVDC systems, (iii) define and validate functional requirements for AC and DC grid forming capabilities, (iv) design, implement and demonstrate a complete workbench of innovative solutions that promotes the adoption and deployment of DC power systems across all voltage levels while evaluating the technoeconomic benefits of DC vs AC systems, (v) demonstrate HYNET innovations in 4 countries across Europe in existing and planned AC/DC hybrid grids.

The European energy system is undergoing a significant transformation: decarbonization, security of supply, deployment of renewables and their integration into the market, generating significant opportunities and challenges for energy stakeholders. Despite all energy efficiency efforts, overall demand for decarbonized electricity is set to be significantly higher in 2050 than today due to the decarbonization of the heating, cooling, transport and many industrial sectors, which can only be achieved via efficient and smart electrification. Hydropower is a key technology in supporting the European pathway to a decarbonized energy system and to achieve global leadership in renewable energy generation. It consists a renewable and highly sustainable electricity resource and can supply the European power system with stability and valuable flexibility. In addition, hydropower reduces EU’s dependency on fossil imports and renders multiple extra benefits for society in the river basins such as support to irrigation, water supply and flood control. The D-HYDROFLEX project will advance excellence in research on digital technology for hydropower paving the way towards more efficient, more sustainable, and more competitive hydropower plants in modern power markets. D-HYDROFLEX will develop a toolkit for digitally ‘renovating’ the existing hydroelectric power plants based on sensors, digital twins, AI algorithms, hybridization modelling (power-to-hydrogen), cloud-edge computing and image processing. The core pillars of the project will be: (i) digitalization, (i.e., digital twins for hydro dams and machinery, weather and flow forecasts, cyber-resilience), (ii) flexibility, (i.e., coordination with hydrogen, storage and VPP operation) and (iii) sustainability, (i.e., biodiversity environmental issues). Validation will take place in real hydro plants of EDF (France), TEE (Poland), PPC (Greece), TASGA (Spain) and INTEX (Romania), covering different geographical areas of Europe.

DC grids attractiveness has been increased in the last years due to the high proliferation of renewable energy sources together with the increase in DC loads (electronics, LED lighting, electric vehicles, energy storage…). The main drivers behind this paradigm shift are related to the improved efficiency, flexibility, security and reliability DC grids may provide, thus increasing the sustainability of the energy distribution system. However, there is a need for demonstration of DC technologies and grid topologies so that these solutions are able to evolve from a promising solution for the future smart grids to a commercially available technological option. Under this context, TIGON aims to achieve a smooth deployment and integration of intelligent DC-based grid architectures within the current energy system while providing ancillary services to the main network. To do so, TIGON proposes a four-level approach aiming at improving 1) Reliability, 2) Resilience 3) Performance, and 4) Cost Efficiency of hybrid grids through the development of an innovative portfolio of power electronic solutions and software systems and tools focused on the efficient monitoring, control and management of DC grids. These solutions will be demonstrated in two main Demo-Sites located in France and Spain, while additional use cases in the residential and urban railway sectors will act as niche markets for analysing and further solidifying the replication of TIGON developments after the project’s end. TIGON has involved for this purpose a multidisciplinary team of 15 partners from 8 different European Member States with a well-balanced consortium integrated by 7 non-profit entities and 7 companies. This partnership provides the required expertise from fields such as power electronics, cybersecurity, standardisation, etc. to design the solutions proposed within TIGON as well as the industrial capabilities required for the manufacturing, integration and validation of the whole TIGON concept.

HumAIne will research, develop, validate and promote a novel operating system for Human-AI collaboration, which will enable the development of advanced decision making applications in dynamic, unstructured environments in different industrial sectors. The HumAIne OS will empower AI solution integrators to implement Human-AI collaboration systems that outperform AI systems and humans when working in isolation. HumAIne’s developments will be integrated into a single OS platform, which will coordinate four interwind components offering Active Learning (AL), Neuro-Symbolic Learning, Swarm Learning (SL) as well eXplainable AI (XAI) capabilities. These advanced AI paradigms are ideal for exploiting true Human-AI collaboration since, in each of them, the worker is the key actor with complete control and understanding of the performed operations. AL enables the development of effective Human-in-the-Loop systems that involve humans when AI faces increased uncertainty. Neuro-Symbolic Learning combines DL with semantics and rules to complete highly complex tasks with high accuracy while requiring considerably less training data than current AI models. Advanced XAI models will be made available, providing explanations of models’ predictions while considering the global context instead of just analysing the feature importance of a single AI model. HumAIne’s XAI will provide guidance to humans to enable the timely optimisation of AL and SL models where human participants provide feedback dynamically as well as fine-tuning of Neuro-Symbolic models. The platform will handle various types of structured and unstructured data, including inputs from humans that will be semantically correlated through ontologies, knowledge graphs, and semantic interoperability. HumAIne will complement its platform with complementary resources (e.g., training) and will be build a vibrant community of interested parties around it, to drive exploitation and wider use of the project's results.