Fast and Scalable Dynamic Gas Network Modeling and Simulation Framework With Gas Composition Tracking
Yifei Lu, Andrew Ivan Sulimro, Thiemo Pesch, Andrea Benigni · IEEE Access · 2025
The gas sector is playing an increasingly important role in the decarbonization of energy systems. As a result, tools that enable the integrated analysis of power and gas networks are becoming increasingly essential. Hydrogen-blending is regarded as a promising bridging technology that can accelerate the adoption of hydrogen. However, it raises the complexity of the gas network simulation as it involves solving complex partial differential equation systems. This paper presents a new framework for the dynamic modeling and simulation of gas pipeline flows, capable of tracking the composition of gas mixtures. The proposed approach decouples the dynamic gas flow simulation and the composition tracking. The dynamic flow simulation is modeled using the central differencing and the IMEX integration scheme and represented as an equivalent electric circuit model. The derived model can then be solved using a power system EMT solver. On top of that, a batch-tracking algorithm is implemented to accurately track the propagation of the gas mixture composition in the gas pipeline network. Simulations of network models of different sizes demonstrate the advantages of this proposed modeling and simulation framework and show its potential applications in gas pipeline network analyses.