2021/03/30 by Maximilian Hoffmann, Hoffmann, Maximilian, Jan Priesmann +9 · 4 citations
Engineering · #Integrated Energy Systems Optimization #Energy Load and Power Forecasting #Electric Power System Optimization
paper · pdf · doi:10.48550/arxiv.2103.16657
Energy system models are challenged by the need for high temporal and spatial\nresolutions in or-der to appropriately depict the increasing share of\nintermittent renewable energy sources, storage technologies, and the growing\ninterconnectivity across energy sectors. This study evaluates methods for\nmaintaining the computational viability of these models by ana-lyzing different\ntemporal aggregation techniques that reduce the number of time steps in their\nin-put time series. Two commonly-employed approaches are the representation of\ntime series by a subset of single (typical) time steps, or by groups of\nconsecutive time steps (typical periods). We test these techniques for two\ndifferent energy system models that are implemented using the Frame-work for\nIntegrated Energy System Assessment (FINE) by benchmarking the optimization\nresults based on aggregation to those of the fully resolved models and\ninvestigating whether the optimal aggregation method can, a priori, be\ndetermined based on the clustering indicators. The results reveal that typical\ntime steps consistenly outperform typical days with respect to cluster-ing\nindicators, but do not lead to more accurate optimization results when applied\nto a model that takes numerous storage technologies into account. Although both\naggregation techniques are ca-pable of coupling the aggregated time steps,\ntypical days offer more options to depict storage oper-ations, whereas typical\ntime steps are more effective for models that neglect time-linking\ncon-straints. In summary, the adequate choice of aggregation methods strongly\ndepends on the mathematical structure of the considered energy system\noptimization model, and a priori decisions of a sufficient temporal aggregation\nare only possible with good knowledge of this mathematical structure.\n