An increasing part of European electricity is provided by renewable sources, whose output varies considerably. In order to offset these variations, gas can be used as an auxiliary source of energy for power production, but extra power can also be used to produce hydrogen or methane for storage. As a consequence, gas and power networks are growing more and more interdependent and thus need to be modelled together in order to make meaningful predictions. A growing body of literature deals with the modelling and simulation of such coupled networks, but the main focus of most sources is accuracy. On the other hand, Gasunie and TenneT’s Infrastructure Outlook report aimed to study hourly snapshots of a coupled network over one year, in a variety of different scenarios. Therefore, a simplified and computationally inexpensive model was needed. Literature dealing with such models is lacking, so a linear model consisting of transport load minimization was constructed for this purpose. It was used for both gas and power simulations, but it is inadequate for the latter.
In this thesis, the transport load model is combined with the standard DC model of power flow in order to obtain more accurate results. The resulting combined linear model is tested on small sample networks, showing that it is functional given a set of modelling assumptions. In addition, the transport load model is compared to the DC model in a sample set of Dutch power networks. A large difference is observed between the two, confirming that there is no reason to use transport load as a model for electricity. Further research is needed to evaluate the accuracy of the transport load model for gas.

Despite the uncertainties on the generation end and the consumption end, the transmission network thrives to be reliable. This includes its effort to predict the power flows in its lines and maintain the flows under a safety margin. Due to the said uncertainties and approximations, predicted power flows in the lines can exceed the acceptable limits. Under such a scenario, the responsible TSO has to take actions to ensure that this predicted violation won’t occur in the reality. It needs to be noted that the flow in the line that mandated these actions could be caused partly by power exchanges outside the borders of the TSO. The responsible TSO bears the costs of these actions initially. The incurred expenses can be split in a fair manner by partitioning the power flow in the line into power flows caused by the power exchanges of all zones. Full Line Decomposition(FLD) is an application developed by the FB4INV team at TenneT that calculates the power flow partitions. To compute the power flow partitions, it requires all power exchanges occurring in the system. The existing method of computing power exchanges(called the Bialek method) models the power network as a directed graph based on power flows in the branches. It traces the flow of power from the generator to the load(or vice-versa) along the paths available. If a power flow path is not available due to the directed modelling, an error of zero power exchange is introduced. There exists multiple ways of computing the power exchanges. This paper attempts to find an unquestionable method. A method of computing power exchanges based on electrical distance reflects the behaviour of the power system in distributing flows. Through the literature review, the superposition method was found that makes use of circuit theory to find generator-to-load power contribution. In the course of the thesis, the superposition method was found to have a flaw in the equations. A new approach was developed in this thesis where power exchange is computed by defining it as an optimisation problem. The objective function of the optimisation problem reflects the excepted behaviour of the power system. The optimisation process was implemented using a genetic algorithm. The existing and developed methods were tested on an IEEE 30 bus system.