Descripción del proyecto
Un método holístico para el suministro de energía
Nuestra vida depende de las redes de calefacción, electricidad y gas. La ecologización de estas redes resulta fundamental para alcanzar los objetivos europeos en materia de eficiencia energética y consumo de recursos. En este contexto, el equipo del proyecto financiado con fondos europeos RELIC explorará un método holístico para saber cómo se suministran los recursos y la energía a la industria a través de las redes de distribución. Estudiará cómo la incorporación del aprendizaje basado en datos en el diseño de algoritmos de control conduce a un mejor rendimiento medioambiental. En la actualidad, las escalas temporales que van desde milisegundos hasta días o meses, para garantizar el funcionamiento seguro de las bombas o los generadores, complican su funcionamiento. Hay incertidumbre respecto a las condiciones de funcionamiento y la información incompleta. En el proyecto se desarrollarán nuevas estrategias de funcionamiento para las redes de distribución.
Objetivo
"Increasing energy and resource efficiency in industrial systems is key to decrease harmful emissions by 90% by 2050. Reaching the environmental targets requires a holistic approach to how resources and energy are delivered to the industry by means of distribution networks, such as heat networks, electricity networks, or gas transport networks. I will devise new control strategies that ensure robust operation of distribution networks while ensuring safety and satisfaction of environmental objectives.
The environmental performance of the whole system hinges on the performance of distribution networks. Optimal control of such networks is complex due to timescales, from milliseconds to ensure safe operation of pumps or generators, to days or months to include environmental goals, spatial complexity, uncertainty related to varying operating conditions, incomplete information available, and limited computational power. Existing control frameworks are usually application specific and have limited use in large-scale systems. In the project, I will advance theory in data analytics and optimisation, and build on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets.
There is a potential in integrating machine learning in control design to overcome the complexity while satisfying safety constraints, as shown in robotics and automotive industry. However, IPCC indicated that ""The key challenge for making an assessment of the industry sector is the diversity in practices, which results in uncertainty, lack of comparability, incompleteness, and quality of data available in the public domain on process and technology specific energy use and costs"". The research question I will address in this project is if and how incorporating data-driven learning in design of control algorithms leads to improved environmental performance and safe operation of large-scale industrial networks."
Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringpower engineeringelectric power distribution
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinador
7491 Trondheim
Noruega