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The main objective of the HyCARE project is the development of a prototype hydrogen storage tank with use of a solid-state hydrogen carrier on large scale. The tank will be based on an innovative concept, joining hydrogen and heat storage, in order to improve energy efficiency of the whole system. The developed tank will be installed in the site of ENGIE LAB CRIGEN, which is a research and operational expertise center dedicated to gas, new energy sources and emerging technologies. The center and its 350 staff are located at Plaine Saint-Denis and Alfortville in the Paris Region (F). In particular, the solid-state hydrogen tank will be installed in a Living Lab aimed to develop and explore innovative energy storage solutions. The developed tank will be joined with a PEM electrolyzer as hydrogen provider and a PEM fuel cell as hydrogen user. The following goals are planned in HyCARE: - High quantity of stored hydrogen >= 50 kg - Low pressure < 50 bar and low temperature < 100°C - Low foot print, comparable to liquid hydrogen storage - Innovative design - Hydrogen storage coupled with thermal energy storage - Improved energy efficiency - Integration with an electrolyser (EL) and a fuel cell (FC) - Demonstration in real application - Improved safety - Techno-economical evaluation of the innovative solution - Analysis of the environmental impact via Life Cycle Analysis (LCA) - Exploitation of possible industrial applications - Dissemination of results at various levels - Engagement of local people and institution in the demonstration site

At least 3% of wind production downtime due to breakdowns and maintenance problems that can reach up to 40%. This leads to production losses of over €2.9 billion worldwide annually. Our current SmartCast remotely connects SCADA and sensor data with a virtual database to monitor wind turbines. It involves algorithms based AI, cloud computing and data mining. The SmartGear product is a low cost Condition Monitoring System based on IoT technology which acquires raw data and connects with SmartCast Platform for further processing. The overall objective of the future Phase II Cloud Diagnosis project is to scale-up our SmartGear technology by introducing communication protocols that allow us to extract data from multiple devices allocated in the wind turbines and additional transducers. Additionally, SmartCast cloud diagnosis algorithms need to be improved. Our innovative solution will allow faster detection of wind turbine system failures through complex algorithms implementing intelligent sensor fusion, therefore, optimizing the performance of wind turbines. It does not require onsite visits but provides information online. In this way, our technology will be able to: Reduce wind turbine maintenance cost by 20%: €44 million annually in the Spanish market, €190 million per year in Europe and €440 million annually in the world market. Reduce wind turbine operation cost and component replacement of cost by 20%: A standard park of 50 MW (16 turbines) installation power working 2,100 hours per year faces production losses of at least €378,000 annually. Our system enables 20% savings of €75,600 (€4,725 per turbine). Currently, our SmartCast platform processes real-time data from SCADA and sensors by means of SVM (support vector machine) in 300 turbines. Our SmartGear Solution is present in two wind farms and is being rolled out in five more.

Operation & Maintenance (O&M) costs may account for 30 % of the total cost of energy for offshore wind power. Alarmingly, only after a few years of installation, offshore wind turbines (WT) may need emergency repairs. They also feature an extremely short lifespan hindering investments to green energy, effectively designed to reduce CO2 emissions. We have designed real-time monitoring and diagnostics platform in the context of operation and maintenance scheduling of WT components. Using this architecture, we can quantify the risk of future failure of a given component and trace back the root-cause of the failure. This is business-critical information for Energy Companies and Wind Farm Operators. The platform consists of an autonomous software-hardware solution, implementing an Object Oriented Real-Time Decision Tree learning algorithm for smart monitoring and diagnostics of structural and mechanical WT components. The innovative concept lies in running WT telemetry data through a machine learning based decision tree classification algorithm in real-time for detecting faults, errors, damage patterns, anomalies and abnormal operation. We believe our innovation creates evident value and will raise great interest as decision-support tool for WT manufacturers, Wind Farm Operators, Service Companies and Insurers. In this project, we will carry out pre-commercialisation actions to position ourselves in the market, provide unique selling proposition for future customers as well as raise interest among potential R&D collaborators and pilot customers. We will also establish technology proof of concept for the platform. For the first time, we are applying our design in difficult-to-access energy infrastructure installations and deploying it on a real-world prototype wind turbine. The project will be carried out with technical and commercialisation support from key players within the wind energy industry.