Global initiatives to decarbonize the energy system combined with technological innovation lead to a major expansion of the renewable energy generation capacity around the globe. This development brings advantages, but also new challenges for the overall energy system. One major challenge is the intermittency of renewable electricity sources, such as solar- and wind energy. An extensive expansion of these energy sources leads to fluctuating residual loads. This means that times with a lack of electricity from renewable energy sources can rapidly alternate with times of electricity surplus.
This Ph.D. thesis analyzes the effects of fluctuating residual loads on various hierarchical levels. The first considered level is a single electricity consumer with an assigned load profile, referred to as Single-Nodal system. The major goal of the work at this level is to develop a methodology, which determines the ideal PV generation- and energy storage capacity for any corresponding load profile. This task is performed by a novel mathematical optimization methodology, with the objective of minimizing the system size for a given degree of self-sufficiency within the considered single node. The optimization is applied to a wide range of household consumer types. All corresponding results are presented in this work.
The fact that energy systems consist of numerous different consumers and suppliers, interconnected by electricity-, gas- and district heating grids, leads to the second introduced modelling framework, called HyFlow. It is designed to consider multiple nodes of the energy system across all considered energy carriers. Fluctuating residual loads in these nodes lead to quickly changing load flows between them, with possible grid congestions. The presented modelling framework calculates these load flows for all considered grids, based on physical principles. This allows for the analysis of the locations and times of grid congestions in energy systems of all sizes. Furthermore, HyFlow is capable of evaluating the effectiveness of possible solution strategies to deal with these congestions, by implementing energy storage systems and cross-energy carrier sector coupling technologies. These technologies can be freely placed and configured in the presented software. This technical, scenario-based approach helps to identify weak spots in energy grids and allows for the analysis of different energy system scenarios with a broad range of spatial depth. The analyzed scenarios can contain increased renewable energy sources, but also changed consumer load profiles of individual energy carriers, due to a possible shift caused by electrification. Examples for this shift in consumption are electric vehicles, heat pumps or industrial processes, as well as technologies for balancing generation and demand. Results achieved with HyFlow, therefore, may provide valuable decision-support for grid operators and political decision-makers.