METIS - Methods and Models for Energy Transformation and Integration Systems

  Logo METIS

Project duration: 10/2018 – 09/2021

Funded by: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

Funding code: 03ET4064B

Project partners:

  • Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research, Techno-economic Systems Analysis (IEK-3)
  • Forschungszentrum Jülich GmbH, Institute for Advanced Simulation, Jülich Supercomputing Centre (IAS-JSC)
  • Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Mathematics, Economics, Discrete Optimization, and Mathematics (EDOM)

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 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. An overview of the research questions in the METIS project is shown in Figure 1.

  Figure 1: Overview of the research questions in the METIS project Copyright: © FCN-ESE Figure 1: Overview of the research questions in the METIS project

In the joint project, an interdisciplinary consortium with representatives from the fields of computer science, mathematics, engineering, and economics is working together.

The goal of the contributions of the researchers based at FCN is in particular the elaboration of 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 to be able to assess different application cases (for a schematic representation, see Figure 2). 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.

  Figure 2: Schematic representation of systematic assessment of accuracy and complexity in power system models. Copyright: © FCN-ESE Figure 2: Schematic representation of systematic assessment of accuracy and complexity in power system models.  

The complexity manager tool developed within the project can be accessed here. Taking into account the (1) research focus as well as (2) the preference in terms of complexity and accuracy, it provides a decision guidance on the optimal model formulation for modelers of energy systems.


Selected publications of project results

Journal articles (full scholarly peer-review)
Hoffmann, M., Priesmann, J., Nolting, L., Praktiknjo, A., Kotzur, L. Stolten, D. (2021). Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models. Applied Energy, 304, 117825. [ScienceDirect]

Kotzur, L., Nolting, L., Hoffmann, M., Groß, T., Smolenko, A., Priesmann, J., Büsing, H., Beer, R., Kullmann, F., Singh, B., Praktiknjo, A., Stolten, D., Robinius, R. A modeler's guide to handle complexity in energy systems optimization. Advances in Applied Energy, 4, 100063. [ScienceDirect]

Ridha, E., Nolting, L., Praktiknjo, A. (2020). Complexity Profiles: A Large-Scale Review of Energy System Models in Terms of Complexity, Energy Strategy Reviews, 30, 100515. [ScienceDirect]
Priesmann, J., Nolting, L., Praktiknjo, A. (2019). Are Complex Energy System Models More Accurate? An Intra Model Comparison of Energy System Optimization Models, Applied Energy 255(12), 2019. [ScienceDirect]
  Supported by BMWK