Comparing different temporal dimension representations in distributed energy system design models

Abstract Energy models based on optimization principles are valuable tools for optimizing the design elements and the operating strategies of multiple distributed energy systems (DES). Such models, commonly formulated as Mixed-Integer Linear Programs (MILP), achieve a good trade-off between model accuracy and computational complexity. However, the latter aspect depends heavily on the number of variables. Hence, problems can become intractable when large spatial or temporal resolutions are considered. In this paper, the focus is placed on the temporal dimension and different representations of it are evaluated. The model is solved for a full year in hourly time-steps, for a set of optimally-selected typical days, and, finally, using a rolling horizon formulation in which the DES operation is optimized sequentially. Results show the possibility of decreasing the computational burden by several orders of magnitude without sacrificing the accuracy of the optimization results, by appropriately selecting the parameters of the temporal reduction method.