Due to the steady increase in renewable energy capacities, future power systems are expected to exhibit flexibility at different timescales. Demand-side response and energy storage are two key elements for providing this flexibility. In this paper, we analyze how a large energy consumer with flexibility options interacts with the energy system. In this context, the interaction is not only limited to the operational aspect, but also extends to investment decisions in flexibility options. To perform this analysis, a simple power system model is considered, coupled with a grid-connected electrolysis plant and storage facility. Then, a linear programming problem is solved to optimize the energy costs as well as the investment costs of hydrogen facilities. For this analysis, a new mapping method for near-optimal regions is formulated and implemented. This implementation enables an extended sensitivity analysis, where the evolution of the near-optimal region is analyzed rather than the evolution of the optimal point. Finally, an abstract definition of flexibility in hydrogen consumption is presented, and its influence is interpreted. The results indicate that varying power system settings lead to distinct patterns of investment in flexibility capacities. In addition, they emphasize the complementary relationship between flexible consumption and the need for storage.

We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models. Thus, our simulation model can operate without measurements of a PV device. It combines wave and ray optics and a dedicated semiconductor simulation to model the optoelectronic PV device properties resulting in the IV-curve. The system surroundings are described via spectrally resolved ray tracing resulting in a cell resolved irradiance distribution, and via the fluid dynamics-based thermal model, in the individual cell temperatures. A lumped-element model is used to calculate the IV-curves of each solar cell for every hour of the year. These are combined factoring in the interconnection to obtain the PV module IV-curves, which connect to the inverter for calculating the AC energy yield. In our case study, we compare two types of 2 terminal perovskite/silicon tandem modules with STC PV module efficiencies of 27.7% and 28.6% with a reference c-Si module with STC PV module efficiency of 20.9%. In four different climates, we show that tandem PV modules operate at 1–1.9 °C lower yearly irradiance weighted average temperatures compared to c-Si. We find that the effect of current mismatch is significantly overestimated in pure optical studies, as they do not account for fill factor gains. The specific yields in kWh/kWp of the tandem PV systems are between −2.7% and +0.4% compared to the reference c-Si system in all four simulated climates. Thus, we find that the lab performance of the simulated tandem PV system translates from the laboratory to outdoors comparable to c-Si systems.