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"Methods and Models for Energy Transformation and Integration Systems", funded by BMWi, funding code: 03ET4064B
Ongoing project (10/2018 – 09/2021)

The energy system today is already characterized by a very high degree of complexity, which will continue to increase in the future due to the energy transition and the digitalization of the energy industry. This complexity needs to be handled in an appropriate way within energy system models to be able to answer relevant research questions and to contribute to the political debate. For this purpose, approaches for an adequate complexity management in energy system models are to be developed and evaluated within the research project METIS. These approaches include, for example, complexity management, spatial and temporal aggregation of time series, improved use of high-performance computers and the application of new optimization methods.

In the joint project, an interdisciplinary consortium with representatives from the fields of computer science, mathematics, engineering and economics is working together. In addition to researchers of the assistant professorship for energy resource and innovation economics, this consortium includes the following participants:

  • Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung, Elektrochemische Verfahrenstechnik (IEK-3)
  • Forschungszentrum Jülich GmbH, Institute for Advanced Simulation – Jülich Supercomputing Centre (IAS-JSC)
  • Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Mathematik, Lehrstuhl für Wirtschaftsmathematik (EDOM)

The goal of the contributions of the researchers based at the School of Business and Economics at RWTH Aachen University is in particular the elaboration of a suitable complexity management. For this purpose, appropriate methods for complexity reduction with the associated inaccuracies regarding results as well as the reductions gained in computing time are determined. Subsequently, inaccuracies and runtime advantages are converted into decision models in order to be able to assess different application cases. In particular, the entire process of energy system modeling (acquisition of exogenous data, system modeling, simulation, interpretation of results, etc.) is to be taken into account.




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