Publicly-Funded Projects

From a scientific standpoint, this project opens a world of possibilities
by studying the interactions of the vehicle digital twins in every aspect of the vehicle operation to their integration as digital twins in vehicle fleets and for their second life applications once their vehicle life has ended.

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In collaboration with CAOS and LSI research teams.

In this project, we will design the Energy management policy for a single Autonomous Electric Vehicle as well as for a fleet of Electric vehicles. The energy management policies will be based on Reinforcement Learning techniques which will depend on both the nature of the mobility behavior of EVs (scheduled distance, battery energy consumption during driving, and arrival and departure connection times to the charging station) and the distribution grid operator and market information (grid technical constraints and retail energy prices). Moreover, in this project, we will design, develop and validate multiagent Reinforcement learning processes for energy management in Electric Vehicle fleets providing “energy-as-a-service” (G2V-V2G) to the grid. The energy flexibility as a service will consider the variability and uncertainty of each individual EV and also from the renewable generation installed at the fleet depot.

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This project applies the latest developments in power system optimization to the maximization of the production of renewable energy. The methods applied in the project ensure the secure operation of the power system by combining static and dynamic constraints.

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In collaboration with Mecatran, Public Policy and Politics, CAOS, and LSI research teams.

The general objective of this multidisciplinary project, AMBULATE-CM, is the design of an advanced, sustainable and dynamic solution for the management of the ambulance service in a pandemic situation. For this, it has been necessary to design new strategies and tools to guarantee an agile, effective and dynamic response to the management of patient transport and, especially, of ambulances to ensure rapid and adequate patient care. In this project, the REDES research group has been in charge of the energy management of electric ambulance batteries and has designed an electric ambulance route planner based on the METRIC-FF heuristic. The objective of the planner is to transport patients with symptoms of COVID-19 to the nearest hospital in the shortest possible time, optimizing the use of electric ambulance batteries.

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In collaboration with LSI research team.

This project addresses technological challenges as well as business aspects to promote the integration of Autonomous Electric Vehicles (AEV) into smart energy grids. This will be accomplished by adopting power load prediction and network modelling approaches for accurate AEV smart charging/discharging decisions. The electric battery charging process (Grid-to-vehicle G2V) will be based on an optimization process where the battery state, smart grid constraints, and trip information will be considered. In this project, the role of the AEV aggregator will be defined considering the electricity market aspects and the smart grid network constraints.

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In collaboration with Gas Natural Fenosa, ZIV, ORBIS and TECNALIA.

The objective of this collaborative project is the development of knowledge, tools and equipment that allow optimization of the supervision of smart networks, mainly for the remote management in real time of residential meters in low voltage networks. In this project, the REDES research group has developed an algorithm for real-time estimation of technical losses and the energy balance of smart low-voltage networks based on the information provided by Smart meters.

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The IDE4L project will define, develop and demonstrate the entire distribution network automation system, IT systems and applications for active network management. Active distribution networks will utilize distributed energy resources (DERs) for network management, including both real time operation and long-term network planning viewpoints. DERs consist of aggregated distributed generation, demand response and other controllable loads. Demonstrations of integrated automation system and applications will be realized in real life networks in different parts of Europe where large and small scale PV are connected, as well as wind power, heat pumps and EVs located in urban and rural areas. The outcome of the project will be applicable in the very near future all over Europe.

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