Selection of Severe Transport Situations in the High-Pressure Gas Network of GTS: Introduction of a Similarity Measure based on Optimal Flow Patterns

Gasunie Transport Services (GTS) is the operator of the natural gas transmission network in the Netherlands. This network is an entry-exit system where entry and exit capacities can be booked by parties called shippers. Variations in usage lead to an enormous number of possible entry-exit combinations. GTS must be able to accommodate all realistically possible gas transport scenarios that result from these entry-exit combinations. In principle, hydraulic testing of all these scenarios is required to see if the current network is optimally sized for this task. However, calculating all scenarios is extremely time consuming, so a smaller set of severe scenarios that also covers the less severe ones, is needed to represent the complete set. Reduction of the complete set of transport scenarios is a mathematically challenging task. The current method to reduce the number of these scenarios is workable and probably meets the requirements of day-to-day planning calculations at GTS. This method makes use of the so-called end point representation, which describes transport scenarios by their capacities on entry and exit points only, while e.g. the flow pattern is unknown. The distance function for the current reduction method measures the difference between scenarios by comparing the capacities on end points, where the end points are correlated by their respective transport distances. A new representation is introduced in this report: the flow representation. This representation makes use of the flow patterns that emerge from balanced combinations of entry and exit capacities. A flow pattern follows from an entry-exit combination by determining its minimum associated transport load. Compared to the end point representation, the flow representation is more intricate to obtain (more calculations are required to get flow patterns), but the result is a more accurate representation in terms of the transport physics of the network. The pay-off is that the distance function for the flow representation can be a lot simpler, e.g. a weighted norm which approximates the transport effort. It also turns out to be more adaptable. For example the diameter, pressure drop and other network information can easily be included in this weighted norm. In this report both representations are compared. However, from the experiments no final conclusion can be given for which representation has a better performance. Future studies, e.g. involving hydraulic calculations, are recommended to conclude this matter