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.