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In which way, and in which sectors, will renewable energy be integrated in the German Energy System by 2030, 2040, and 2050? How can the resulting energy system be characterised following a −95% greenhouse gas emission reduction scenario? Which role will hydrogen play? To address these research questions, techno-economic energy system modelling was performed. Evaluation of the resulting operation of energy technologies was carried out from a system and a business point of view. Special consideration of gas technologies, such as hydrogen production, transport, and storage, was taken as a large-scale and long-term energy storage option and key enabler for the decarbonisation of the non-electric sectors. The broad set of results gives insight into the entangled interactions of the future energy technology portfolio and its operation within a coupled energy system. Amongst other energy demands, CO2 emissions, hydrogen production, and future power plant capacities are presented. One main conclusion is that integrating the first elements of a large-scale hydrogen infrastructure into the German energy system, already, by 2030 is necessary for ensuring the supply of upscaling demands across all sectors. Within the regulatory regime of 2020, it seems that this decision may come too late, which jeopardises the achievement of transition targets within the horizon 2050.

Renewable & sustainable energy reviews 158, 111995 (2022). doi:10.1016/j.rser.2021.111995 special issue: "MODEX: energy system model comparisons through harmonized applications / Edited by Hans Christian Gils, Jochen Linßen, Dominik Möst, Christoph Weber", Published by Elsevier Science, Amsterdam [u.a.]

Renewable & sustainable energy reviews 159, 112177 (2022). doi:10.1016/j.rser.2022.112177 special issue: "MODEX: energy system model comparisons through harmonized applications / Edited by Hans-Christian Gils, Christoph Weber, Dominik M��st, Jochen Lin��en", Published by Elsevier Science, Amsterdam [u.a.]

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. In a model comparison, we examine the optimal expansion of such technologies with six capacity expansion power system models. The technologies under investigation include base- and peak-load power plants, electricity storage, and transmission. We define four highly simplified and harmonized use cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all use cases. Fixed energy-to-power-ratios of storage can entirely change model optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and end storage levels can impact the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure significantly deviate from linear optimization models, as limited foresight and flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.

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A variety of models have emerged in the field of energy system analysis to answer a wide range of research questions centred around a sustainable future for the energy sector. Even models designed to address similar issues often have a different focus or modelling approach. Thus, model experiments are a vital tool to provide an overview of the range of models and enable decision-makers to make meaningful model choices. Such comparisons are executed based on a harmonised data set to ensure a high degree of comparability. In the MODEX project cluster, six model experiments, including 40 energy system models, were conducted, and efforts were made to harmonise the input data within the individual comparison and beyond them in the consortium. The experiences and findings of the consortium on how data harmonisation could be performed are presented in this paper. In particular, the focus lies on data transparency to ensure a high degree of reproducibility. A key finding is that while model heterogeneity complicates harmonisation, an early focus on data research and scenario design promotes the creation of a common data set. The metadata collection can provide a significant advantage for the use of model experiment results by external scientists and the data acquisition process itself because of the predefined machine-readable and standardised format.

Im Projekt MuSeKo wird der kombinierte Einsatz unterschiedlicher Flexibilitäten und Sektorenkopplungen in einem künftigen Energiesystem mit einem hohen Anteil erneuerbarer Energien modellbasiert untersucht. Die Analyse erfolgt mit einem zeitlich und räumlich aufgelösten, techno-ökonomischen Ansatz. Anhand dessen Ergebnissen werden unterschiedliche energiewirtschaftliche Rahmenbedingungen im Hinblick auf Investitionen und den Anlageneinsatz bewertet. Der Neuheitswert des Projekts liegt einerseits in der Fokussierung auf die Ausgestaltung der Kopplung von Strom- und Gassystem und andererseits auf der Kombination der gesamtwirtschaftlichen mit der betriebswirtschaftlichen Perspektive. Die Projektergebnisse zeigen einerseits die Chancen einer engeren Verzahnung der Strom-, Wärme- und Gasversorgung, und andererseits die damit einhergehenden regulatorischen Herausforderungen für die Anreizung systemdienlicher Investitionen und Anlageneinsätze. Die Arbeiten bieten in den erweiterten Methoden und erhobenen Daten eine wichtige Grundlage zur weiteren Erforschung der Umsetzung der flexiblen Sektorenkopplung in der Energiewende.