Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases

Journal Article (2022)
Author(s)

Hans Christian Gils (Stuttgart Research Initiative on Integrated Systems Analysis for Energy (STRise), Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Hedda Gardian (Stuttgart Research Initiative on Integrated Systems Analysis for Energy (STRise), Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Martin Kittel (DIW Berlin)

Wolf Peter Schill (DIW Berlin)

Alexander Zerrahn (DIW Berlin)

Alexander Murmann (Research Center for Energy Economics (FfE))

Jann Launer (Reiner Lemoine Institut)

Alexander Fehler (RWTH Aachen University)

Felix Gaumnitz (RWTH Aachen University)

Jonas van Ouwerkerk (RWTH Aachen University, JARA-Energy)

Christian Bußar (JARA-Energy, RWTH Aachen University)

Jennifer Mikurda (Universität Duisburg-Essen)

Laura Torralba-Díaz (Stuttgart Research Initiative on Integrated Systems Analysis for Energy (STRise), University of Stuttgart)

Tomke Janßen (Wuppertal Institut für Klima, Umwelt, Energie)

Christine Krüger (Wuppertal Institut für Klima, Umwelt, Energie)

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DOI related publication
https://doi.org/10.1016/j.rser.2021.111995
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Publication Year
2022
Language
English
Affiliation
External organisation
Journal title
Renewable and Sustainable Energy Reviews
Volume number
158
Article number
111995
Downloads counter
192

Abstract

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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