Additional information for the stratigic evaluation of the Energy Research Field, POF IV
The national and world-wide energy transition aims at establishing a globally sustainable energy system. The related transformation process will be based on a variety of novel technologies, which have to be integrated in the overall system design and fundamentally increase system complexity. The new Energy System Design (ESD) Program tackles this system complexity challenge and provides respective solutions to support the transformation towards a globally sustainable energy system.
TOPIC 1: ENERGY SYSTEM TRANSFORMATION
“Energy System Transformation” addresses the sociotechnical design and integrated assessment of sustainable future energy systems embedded in their full technical, economic, environmental, societal and political contexts. For this purpose, Topic 1 will develop quantitative and qualitative transformation scenarios as a basis for discussion and decision-making by policymakers in societal real-world labs aimed at demonstrating societal feasibility, while ensuring technical effectiveness. The quantitative assessment of transformation pathways for energy systems will be based on technoeconomic simulation and optimization models with a temporal resolution and timeframe ranging from sub-hourly to decades, where model parameters will be iteratively refined based on insights gained in societal real-world laboratories.
TOPIC 2: DIGITALIZATION AND SYSTEM TECHNOLOGY
“Digitalization and System Technology” addresses R&D on technical system design and resilient (real-time) operation of decentralized integrated energy systems. For this purpose, Topic 2 will develop models, methods and tools for optimization-based design, scheduling and control of energy systems and demonstrate the technical feasibility of different hardware and software solutions in smart energy system labs, e.g. Energy Lab 2.0 at the KIT, Living Lab Energy Campus at the FZJ and the DLR Grid-Lab. The temporal resolution and timeframe of the quantitative technical simulation and optimization models ranges from μs to years, whereby model structures and parameters will be iteratively refined, e.g. based on insights gained in the technical real-world laboratories.
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