ÖVGW – Efficiency increase in Austrian gas distribution
In the Austrian gas grid, a few measures are in operation, e.g. concerning gas preheating, to make the ongoing gas grid operation more efficient. To summarize this information and make it available to all network operators, this study provides an overview of possible measures; they are then evaluated according to their efficiency effects. In addition to the optimization measures of the actual gas grid operation, a significant focus is put on the future development of the gas grid. The aspired goal of the gas industry is to transport exclusively renewable gases over the infrastructure in 2040. For this case, it becomes relevant to connect many biogas plants to the gas grid to feed in green gas produced from biogenic residues. The second part of this study, therefore, deals with optimizing the interconnection of biogas plants and their connection to the gas grid.
Energy savings by minimizing preheating power
Expansion to a lower pressure cools the gas due to the Joule-Thomson effect. The gas temperature after expansion must be above the dew point to avoid condensation on and in the piping. This is ensured by preheating the gas before expansion. Considering the ambient temperature and humidity, the dew point has been calculated based on a test reference year. The cooling due to expansion depends, among other things, on the gas composition. Pure hydrogen has a reverse Joule-Thomson effect under the conditions present here. This means that hydrogen heats up upon expansion. Increasing the H2 concentration in the gas mixture leads to a reduction in the required preheat energy. From this perspective, an increase in H2 concentration can lead to a reduction in energy use for gas transport and distribution and decarbonization of the gas supply.
Optimization of biogas feed-in
Where exactly existing or future biogas plants should be connected to the distribution or transmission grid is a complex optimization problem. Within this project’s scope, a framework for optimization has already been created in MATLAB using YALMIP and Gurobi. The primary goal is to minimize the distance between the biogas plant and the feed-in point. When additional data is presented, relevant constraints are added to the existing model. For example, specific pipeline costs, pressure levels and compressor costs can be considered. The following figure shows the results of a test with fictitious data. In this model, the shortest connection between biogas plant (x) and injection point (o) was found without further constraints. If the data on potential feed-in points and their feed-in capacity is available, the framework already developed can provide meaningful results.