OpenStreetMap is the largest freely accessible geographic database of the world. The necessary processing steps to extract information from this database, namely reading, converting and filtering, can be very consuming in terms of computational time and disk space. esy-osmfilter is a Python library designed to read and filter OpenStreetMap data under optimization of disc space and computational time. It uses parallelized prefiltering for the OSM pbf-files data in order to quickly reduce the original data size. It can store the prefiltered data to the hard drive. In the main filtering process, these prefiltered data can be reused repeatedly to identify different items with the help of more specialized main filters. At the end, the output can be exported to the GeoJSON format.

Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model’s capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany.

The large-scale storage of hydrogen in salt caverns, modelled on today’s natural gas storage, is a promising approach to storing renewable energy over a large power range and for the required time period. An essential subsystem of the overall gas storage is the surface facility and, in particular, the compressor system. The future design of compressor systems for hydrogen storage strongly depends on the respective boundary conditions. Therefore, this work analyses the requirements of compressor systems for cavern storage facilities for the storage of green hydrogen, i.e., hydrogen produced from renewable energy sources, using the example of Lower Saxony in Germany. In this course, a hydrogen storage demand profile of one year is developed in hourly resolution from feed-in time series of renewable energy sources. The injection profile relevant for compressor operation is compared with current natural gas injection operation modes.

The current transition in the European energy sector towards climate neutrality requires detailed and reliable energy system modeling. The quality and relevance of the energy system modeling highly depend on the availability and quality of model input datasets. However, detailed and reliable datasets are still missing, especially for the gas infrastructure. In this contribution, we present our approach for developing an opensource model of the gas transport network in Europe. Various freely available data sources were used to collect gas transport data. The datasets from multiple sources were merged, and further, statistical methods were used to generate missing data.As a result, we successfully created a gas transport network model only using open-source data. The SciGRID_gas model contains 206,000 km of pipeline data which is roughly in accordance to former estimations. In addition, datasets of compressor stations, LNG terminals, storages, production sites, gas power plants, border points, and demand time series are provided. Finally, we have discussed data gaps and how they can potentially be closed.

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