This master thesis aimed to develop an optimized operating strategy for an electricity grid incorporating hydrogen storage, while considering three key performance indicators: financial costs, reliability, and sustainability. The central research question guiding this study was: "How can the electricity demand be reliably met by a system combining solar power, conventional power supply, and hydrogen systems, optimized for sustainability, cost-efficiency, and reliability through adjustment of the operating strategy?"

The study quantified all key performance indicators in terms of costs, with sustainability measured by the price of carbon credits, providing a quantifiable measure of CO2 emissions. A discrete event model was utilized to simulate an electricity system on an hourly basis, integrating solar energy, a hydrogen system, and non-sustainable energy sources. The particle swarm optimization algorithm, enhanced with linear decay, was employed to identify the most optimal operating strategies within this model. Four distinct scenarios were formulated to analyze the optimal operating strategy under varying circumstances.
The findings revealed that when the price of hydrogen was significantly higher than that of conventional resources, the optimal strategy favored the avoidance of hydrogen electricity due to its high costs. Electrolysis costs and fuel cell expenses were identified as the primary cost drivers. Conversely, in scenarios where hydrogen was competitive with conventional resources, the optimal operating strategy incorporated a small portion of non-sustainable electricity while maximizing the use of hydrogen-based systems.

In conclusion, this master thesis addressed the research question by optimizing the operating strategy of an electricity grid integrating hydrogen technology. Presently, the financial advantages of non-sustainable electricity outweigh the sustainability benefits of hydrogen electricity. The study highlighted the need for substantial cost reductions in hydrogen system components and an increased price of carbon credits to realize a future scenario where hydrogen electricity competes with natural gas.

This paper introduces an experimental setup for retrieving horizontal wind speed and direction profiles with a high temporal and vertical resolution for process studies and validation of convection-permitting model simulations. The CMTRACE (tracing convective momentum transport in complex cloudy atmospheres) campaign used collocated wind lidar and cloud radar measurements to retrieve seamless wind profiles from near the surface up to cloud tops. It took place in Cabauw, the Netherlands, between 13 September and 3 October 2021. The intermediate processing steps for generating the level 1 and level 2 data, such as second trip echoes filtering, offset correction, wind retrieval, re-gridding, and flagging, are described. In level 1 (https://doi.org/10.5281/zenodo.6926483, Dias Neto, 2022a), the data from lidar and radars are kept in the original spatial and temporal resolution, while in level 2 (https://doi.org/10.5281/zenodo.6926605, Dias Neto, 2022b), they are regridded to a common spatial and temporal resolution. Statistical analyses of the lidar's and radar's wind speed and direction profiles indicate a correlation higher than 0.95 for both variables. The bias of wind direction and speed calculated between radar's and lidar's observations are 0.24∘ and −0.16 m s−1, respectively. The foreseen initial application of the datasets includes the study of convective momentum transport and its validation in regional weather forecasts and large-eddy simulation hindcasts.@en