Developing a hierarchical distributed Model Predictive Control for real-time monitoring and energy management of renewable energy systems with improved features such as sustainability, stability, reliability, and replicability. This objective will be addressed using innovative tools that will bring intelligence to the system, allowing a more preventive than reactive performance, which is fundamental to overcoming the challenges already presented. Such innovations are essential to increase the system's efficiency by improving the control of the energy flows.

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This project will improve collaboration mechanisms between controllers in distributed control systems, equipping them with the ability to cooperate with heterogeneous entities, such as robots or operators. It directly combines methods ranging from artificial intelligence to robotics, including physical system modelling and the intelligent use of communication networks to establish coalitions of controllers based on their dynamic coupling, thereby reducing the cooperation and computing load on the system elements.

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The proposal of this project is contextualized in the area of climate, energy and mobility. Its objectives are the development of resilient predictive control strategies including fault-tolerant systems, risk management, and machine learning algorithms, which will be applied to hydrogen-based energy systems. The development of methodologies that include digital twins will be applied to the control of electrolyzers, their integration with renewable generation, and vehicles. The results obtained will be validated on mobile platforms such as aerial systems (UAVs) and ground-based systems (EV, FCEV), as well as distributed systems within the framework of energy communities.

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Aims to deepen the coalitional MPC framework already created to improve the interaction between heterogeneous entities. To do this, in the first place, it is intended to improve existing methods to achieve greater speed and flexibility in the dynamic partitioning of the system. Secondly, we plan to further develop aspects that until now have been treated superficially, specially the integration of human operators by using digital doubles or twins, and the formulation of resilient and fault-tolerant controllers for cyberattacks. Additionally, data-based methods as well as new case studies for new application fields will be explored.

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The main objective of this project is to develop a new Green Energy Hub (GEH) to achieve greater flexibility and self-sufficiency in local energy communities based on a hybrid storage system comprising green H2, batteries and supercapacitors. During the project, a hybrid storage demonstrator will be built, achieving a TRL 6 by the end of its implementation period, so that the technology will be validated in a relevant environment, in this case connected to a real energy community at the University of Seville, including the photovoltaic plant.

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Project dedicated to promoting the use of renewable energies in rural areas, to strengthen a sustainable and local energy system in the cross-border regions of the POCTEP area. Adopting innovative models of shared energy self-consumption in Local Energy Communities (LECs).

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The AGERAR PLUS project aims to harness the potential of cooperation to consolidate the innovative and scientific framework of Spain and Portugal in the field of energy. The project focuses on improving energy efficiency by promoting the role of small and medium-sized prosumers and energy communities through the use of information and communication technologies.

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The INDALO project is part of the European LifeWatch Infrastructure, an initiative that forms part of a European strategy aimed at providing Europe with research infrastructure accessible to all researchers, in order to improve Europe's competitiveness in all scientific fields and promote innovation and technology. This project aims to study biodiversity in different representative ecosystems in Andalusia (one of the most diverse regions on the European continent, home to 60% of the terrestrial species of flora and fauna present in Spain) and analyse its evolution in order to detect and understand the consequences of global change, including climate change, demographic and productive changes in land use and natural resources.

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AIHRE will promote collaboration between entities that are part of the green hydrogen value chain, such as research groups, technology centers and companies within the Spain-Portugal cross-border space. The project will generate innovative tools to implement an economy based on the potential of renewable hydrogen.

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This project aims to provide solutions for the operation of microgrids with renewable energy within the framework of the Model Predictive Control (MPC). Control of microgrids is evolving considerably in recent years; microgrids can be considered as the most innovative area in the electric power field today, so new control issues are emerging. The range of problems to be addressed by the microgrid control system is very wide. This project focuses on the following aspects: i) design and development of safe energy management systems that integrate fault detection, risk management, cybersecurity and mitigation, ii) development of stochastic methodologies for MPC to integrate uncertainties given by the variables of generation and demand, iii) development of techniques based on machine learning for the recognition of demand profiles, generation and drivers, and iv) analysis of peer-to-peer techniques (P2P) for the energy trading. For this, there are three experimental plants as well as a fleet of electric vehicles that will be used in the experiments for the validation of new techniques.

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