A modeler's guide to handle complexity in energy systems optimization

Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models

“…The time consumption for loading the data, adding the components, clustering the input data, setting up the mathematical problem structure using Pyomo, and mapping the solution from Gurobi back to the model's variables, is negligible. This can be explained by the mathematical connectivity [7] of the variables and constraints caused by a complex component structure and time step linking storage equations whereas, on the other hand, only one region and five time series are considered.…”

“…Therefore, a multitude of techniques to reduce the computational complexity of energy system models have emerged [6,7]. Temporal aggregation takes a prominent role among these, which strive to reduce the amount of input data from time series without significantly affecting the solutions of energy system models.…”