Recognising that current storage solutions are unable to stabilize enough the intermittent renewable energy production, new long term energy storage solutions are becoming mandatory. Current long-term energy storage is mainly provided by Pumped-Storage Hydroelectricity (PSH). Compressed Air Energy Storage (CAES) has appeared for decades as a credible alternative but its poor energy efficiency, the need of fossil fuels and the use of existing underground cavities as storage reservoirs have limited its development. Variations to CAES have shown low efficiency, losing a big percentage of energy as heat and mechanical losses. Since the 2010s, there is a strong revival of scientific and industrial interest on CAES, led by China and the European Union (EU). For the EU, leading the new generation of high-efficient, low climate-impact and long-term energy storage research, is key to increase its energy independency. In this context, the main objective of Air4NRG is the development of an innovative, efficient (over 70% RTE), long- term, and sustainable CAES prototype, which can enhance renewable energy availability and offers robustness and safety while increasing cost effectiveness and improving the environmental footprint. At the same time, it will promote innovation and competitiveness in the European energy storage industry, while prioritizing the principles of circular economy and environmental sustainability. Another key factor of the solution is the integrability to the electrical grid system and their intelligent EMS, which will be proven by the end of the project through end user integration activities (TRL5). The project will result in two prototypes: one of them will be a plug and play system fitting into a standard 40ft container with an over ten- hours storage duration, while the other will be a larger-scale system of 200kW with the same duration of storage. The developed system is a rare material-free solution with simple industrial infrastructure needs, allowing its full development within the EU, strengthening Europe’s position in the energy storage system sector.

The CSA Strategic Energy Technology for Industry in Europe (SET-IndEU) is responding to the call HORIZON-CL5-2022-D4-02-06, to propose a structured Secretariat for supporting and strengthening the activities and outcomes of the IWG6 on Energy Efficiency in Industry. The consortium will support the IWG6 to advance the monitoring and revision of the Action 6 Implementation Plan (IP) with the aim of reaching technology targets collectively across SET Plan countries making EU industry less energy, resource and emissions intensive and more competitive. For that purpose, the future needs of the process industry, the R&I targets, and the emerging policy priorities will all be taken into consideration by engaging the IWG6 stakeholders in key activities with industrial and research associations and fora, specifically strengthening the relations, dialogue and exchange with the other SET Plan IWGs and the ETIPs.

STOR-HY aims to minimize CAPEX, OPEX for innovative pumped storage projects. This is achieved by enhancing the lifetime and recyclability of components and equipment, and devising operation strategies for unconventional schemes through sensor-based condition monitoring systems. These systems detect early failure mechanisms, enabling the postponement of unnecessary maintenance actions and avoiding unplanned outages. Furthermore, strategic use of digital tools for operational management is employed to improve efficiency, reliability, and availability of Pumped Storage Plants. Considering energy and market demand dynamics, variable renewable generation, and integration, STOR-HY addresses climate change effects and enhances flexibility and resilience of the EU energy grid. The project focuses on optimizing plant availability, offering increased storage potential, peak shaving, fast response regulation, and ancillary services for grid resilience. The integration of digital tools, real-time controllers, monitoring strategies, and predictive maintenance algorithms is consolidated in a Cyber-physical platform for Advanced Decision Support (CADS). This platform, along with high-tech computational models, enables the realistic estimation of critical component degradation in short- and long-term operations. This information supports informed decision-making in PSP operation and aids in the design of innovative control strategies for challenging conditions. These developments result in a broader operating range and increased flexibility in EU hydropower generation and storage potential. STOR-HY prioritizes building regional connections with local stakeholders, industry, academia, and policy institutions. Sustainability considerations encompass environmental, circularity, economic, and social aspects, drawing insights from previous projects. These insights include on-site impacts, societal acceptance, ecological concerns, and LCC.

Currently, hydraulic turbines are employed across a broad spectrum of operational regimes. A particular case is represented by the environmental flow (E-flows), which are essential for the conservation of fluvial ecosystems but often force to operate out of design parameters or rather switch off the plants. On the other hand, the impact of HPPs on water quality and biodiversity up- and downstream is enormous and should also be a target for refurbishing actions. In this context, the SHERPA project will develop and validate innovative technologies for refurbishing current HPPs, namely, 1) AM metallic patches and coatings to minimize damage and enhance resistance to cavitation, 2) new strategies to adapt rotational speed depending on the flow range, 3) advanced air injection systems to improve water quality and efficiency; 4) new runner designs adapted to E-flows increasing performance. Modelling, simulation, and monitoring tools will assess the new solutions of the in terms of energy output, flexible operation, cost-effectiveness, and impact on biodiversity. The goal is to expand and/or adapt the operational range of the HPP to include lower flows, without this harming their lifetime, economic viability, and environmental and social impact. In order to meet this objective, the project proposes a methodology comprising 8 work packages groups in four blocks to be carried out during 42 months. SHERPA has a well-balanced consortium with 7 partners from 4 countries, covering all the competences and know-how in terms of expertise, resources and positioning in the field, which will ensure the achievement of the project objectives and make an impact at European level.

AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems. Moreover, data quality controlling measures, such as unbiased data and respect to ethical norms, context-aware search, and human-centered design for validation purposes will also be implemented to guarantee the representativeness of the synthetic data generated. Indeed, an augmentation module will be responsible for exploring and developing further the techniques of creating synthetic data, also dynamically on demand for specific use cases. Furthermore, this platform will exploit federated technologies for reproducing un-indentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting the privacy, security, and GDPR-compliant guidelines. The proposed framework will support the development of innovative unbiased AI-based and distributed tools, technologies, and digital solutions for the benefit of researchers, patients, and providers of health services, while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness, while considering the most effective computation configuration. Furthermore, a machine-learning meta-engine will provide information on the quality of the generalized model by analyzing its limits and breaking points, contributing to the creation of a more robust system by supplying on-demand real and/or synthetic data. This platform will be validated against local, national, and cross-border use-cases for both data engineers, ML developers, and aid for clinicians’ operations.