Grid reduction for energy system analysis

Energy system models play an important role in evaluating potential pathways to a low-carbon energy system. Modeling the electrical grids within an energy system model raises the computational burden significantly. This is the reason why the grid is often reduced to a lower amount of nodes. While grid reduction is common in energy system models, the partitioning of the grid is mostly done manually. A grid reduction method is proposed that takes electrical parameters of the grid as well as other parameters, such as political borders or region-specific renewable potentials, into account. The proposed reduction method uses clustering based on electrical distance to identify regions with high electrical connectivity to minimize load flow deviations after the reduction. The method is compared against another state-of-the-art partitioning-based reduction method. The comparison shows that the partitioning plays an important role on the accuracy of line power flows when reducing grids for energy system analysis. An additional investigation is carried out on the ratio of recognized overloaded lines in the reduced grids. With respect to studied parameters, the proposed reduction method prevails. The findings can thus be used to more accurately reduce and represent electrical grids in energy system models.