Optimization and life-cycle assessment of low-carbon energy systems from industrial to international scale
- Optimierung und Ökobilanzierung emissionsarmer Energiesysteme vom industriellen bis zum internationalen Maßstab
Reinert, Christiane; Bardow, André (Thesis advisor); von der Aßen, Niklas Vincenz (Thesis advisor); Brown, Tom (Thesis advisor)
Aachen : RWTH Aachen University (2023)
Dissertation / PhD Thesis
Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023
A fast and efficient transition towards low-carbon energy systems is necessary to limit climate change. Optimization models can identify long-term transition strategies to obtain low-carbon energy systems. Crucial modeling aspects of low-carbon systems comprise spatiotemporal variability in the electricity supply, sector coupling, and suitable environmental indicators. Adding complexity thus is often prohibitive. In this work, we reduce the effort of modeling complex systems while still integrating the crucial modeling aspects. In the first part of this thesis, we present the open-source software framework SecMOD for energy system optimization incorporating life-cycle assessment (LCA). We demonstrate the modularity of SecMOD in three case studies: optimizing a sector-coupled industrial utility system, the sector-coupled German energy system, and the European electricity market. Common simplifications in energy system models involve neglecting external long-term developments, spatial restrictions, and stakeholder representation. In the second part of this thesis, we develop three methods to overcome these simplifications and analyze the influence of complexity representation on the modeling results at the example of the German energy system. First, it is unclear to what extent long-term transitions in the supply chains of an energy system affect optimal technological choices in the energy system. LCA is typically based on historical data and neglects such external long-term developments. We integrate long-term developments in the LCA database and analyze the resulting transition path, observing that dynamic LCA amplifies environmental trends. The second simplification in modeling large-scale energy transitions is spatial aggregation. We present SpArta, a decomposition method to increase spatial resolution in energy models to allow for sufficient spatial detail while maintaining computational tractability. In our case study, SpArta reduces computational time by a factor of 7.5. Last, another common simplification is that market behavior is often neglected, thereby assuming a centralized objective and planner. Using bilevel optimization, we analyze market-based decarbonization strategies in the European energy system. We observe changes in the installed infrastructure by more than 40 % in 11 out of 21 modeled countries when decentral decision-making is introduced. Overall, this work enables the modeling and analysis of complex energy systems by providing a framework to incorporate LCA in optimization and increasing modeling consistency regarding external long-term developments, spatial resolution, and stakeholder representation.
- Chair and Institute of Technical Thermodynamics