Soft-Coupling Energy and Power System Models to Analyze Pathways Toward a De-fossilized German Transport Sector

Danial Esmaeili Aliabadi (), Niklas Wulff (), Matthias Jordan (), Karl-Friedrich Cyffka () and Markus Millinger ()
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Danial Esmaeili Aliabadi: Helmholtz Centre for Environmental Research
Niklas Wulff: Institute of Engineering Thermodynamics, German Aerospace Center
Matthias Jordan: Helmholtz Centre for Environmental Research
Karl-Friedrich Cyffka: Deutsches Biomasseforschungszentrum gGmbH
Markus Millinger: Chalmers University of Technology

Chapter Chapter 38 in Operations Research Proceedings 2022, 2023, pp 313-320 from Springer

Abstract: Abstract The transport sector is a major consumer of energy worldwide. Unfortunately, there is no silver bullet to de-fossilize the transport sector due to its intricacy; therefore, many concepts and technologies should be combined to have a noteworthy impact on this hard-to-abate sector. As such, the required diverse set of expertise for making correct decisions cannot be achieved by merely utilizing one model. In this study, we connect multiple datasets and models that employ various methodologies with different purposes to exhibit a pathway to a green transport sector. The extended bioenergy optimization (BENOPTex) and renewable energy mix (REMix) models are coupled iteratively to produce coherent results while considering different sets of constraints. The combined effects of bioenergy and synthetic fuel—using renewable electricity—on the German transport sector are investigated via a scenario. Two demand models are also used to capture the specificities of the energy demands of the mainly behavior-driven road transportation as well as technology-driven aviation sector. The outcome of the resulted soft-coupled model respects biomass availability, regulatory circumstances, techno-economic properties, and power sector expansion for the production of synthetic fuels.

Keywords: Soft-coupling; Bioenergy; Renewable energy; Transportation; Optimization models (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-24907-5_38

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