Publikationen

For two-phase geothermal sources, Organic Rankine Cycle (ORC) based binary plant is often applied for power production. In this work, a network topology is designed with the open-source Thermal Engineering Systems in Python (TESPy) software to simulate the stationary operation of the ORC plant. With this topology, the performance of six different working fluids are compared. From the thermodynamic perspective, the gross and net power output are optimized respectively. Results show that R600 has the highest gross power output of 17.55 MW, while R245fa has the highest net power output of 12.93 MW. However, the turbine inlet temperatures for these two working fluids need to be designed at the upper theoretical limit. R245ca and R601a require the heat exchange rates of internal heat exchanger to be larger than 1.51 MW and 0.99 MW to satisfy the re-injection temperature limit, which are smaller than the R600 (6.7 MW) and R245fa (6.0 MW) cases. Besides, the working fluid with lower critical state is preferred for a geothermal source with smaller steam fraction to establish a stable ORC plant. The workflow for the ORC design and optimization in this work is generic, and can be further applied to thermo-economic investigation.

Exergy-based methods support the identification of thermodynamic inefficiencies and the discovery of optimization potentials in thermal engineering applications. Although a large variety of simulation software is available in this field, most do not offer an integrated solution for exergy analysis. While there are commercial products on the market with such capabilities, their access for research and educational purposes is limited. The presented open-source software offers an integrated and fully automated exergy analysis tool for thermal conversion processes. In a first step, physical exergy is implemented, and the tool is then applied to three different example plants to highlight its capabilities and validate the implementation: A solar thermal power plant, a supercritical CO2 power cycle, and an air refrigeration cycle. The respective models and the results of the analyses are presented briefly. By providing the results in modern data structures, they are easily accessible and postprocessible. Future work will include chemical exergy to enable analyses of applications with conversion of matter. Additionally, the implementation of the exergoeconomic analysis and optimization is envisaged.

The transition to renewable energy sources to mitigate climate change will require large-scale energy storage to dampen the fluctuating availability of renewable sources and to ensure a stable energy supply. Energy storage in the geological subsurface can provide capacity and support the cycle times required. This study investigates hydrogen storage, methane storage and compressed air energy storage in subsurface porous formations and quantifies potential storage capacities as well as storage rates on a site-specific basis. For part of the North German Basin, used as the study area, potential storage sites are identified, employing a newly developed structural geological model. Energy storage capacities estimated from a volume-based approach are 6510 TWh and 24,544 TWh for hydrogen and methane, respectively. For a consistent comparison of storage capacities including compressed air energy storage, the stored exergy is calculated as 6735 TWh, 25,795 TWh and 358 TWh for hydrogen, methane and compressed air energy storage, respectively. Evaluation of storage deliverability indicates that high deliverability rates are found mainly in two of the three storage formations considered. Even accounting for the uncertainty in geological parameters, the storage potential for the three considered storage technologies is significantly larger than the predicted demand, and suitable storage rates are achievable in all storage formations.

Porous media compressed air energy storage (PM-CAES) systems that use porous geological formations such as sandstone may provide large storage capacities in future energy systems based primarily on fluctuating renewable energy sources. In CAES systems, the instantaneous power and stored energy are closely linked to the storage pressure and the mass flow rate achievable in the geological reservoir. Therefore, a coupled simulator that accurately represents the power plant, the geostorage site, and their interactions during all potential PM-CAES system operation modes is presented in this paper. Using adiabatic and diabatic power plant topology test designs, strong feedback between the achievable storage rates and capacities of the chosen power plant design and geostorage site are found, thus confirming the benefit of this integrated modelling approach. Using a generic, highly cyclic load profile for daily peak shaving with charging and discharging rates of 100 MW and an adiabatic power plant topology, it is found that all discharging targets can be met but the achievable charging rates decrease to approximately 95 MW due to increased pressure in the geostorage after approximately 10 cycles. When a diabatic power plant design is considered, a long-term decrease in the geostorage pressure is found. Correspondingly, the charging power always meets the specifications, while the discharging power decreases slowly from the 20th storage cycle onwards to 79 MW in the 31st cycle. The newly developed simulation tool thus allows one to predict achievable power rates and geostorage pressures for PM-CAES systems, enabling the identification of efficient PM-CAES designs.