Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases
Hans Christian Gils, Hedda Gardian, Martin Kittel, Wolf-Peter Schill, Alexander Zerrahn, Alexander Murmann, Jann Launer, Alexander Fehler, Felix Gaumnitz, Jonas van Ouwerkerk, Christian Bußar, Jennifer Mikurda, Laura Torralba-Díaz, Tomke Janßen, Christine Krüger, Renewable and Sustainable Energy Reviews, Vol. 158, 29 January 2022.
Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model-specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model’s degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches.
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