Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models. (15th December 2021)

• Record Type: Journal Article
• Authors: Hoffmann, Maximilian; Priesmann, Jan; Nolting, Lars; Praktiknjo, Aaron; Kotzur, Leander; Stolten, Detlef
• Is Part Of: Applied energy. Volume 304(2021)
• Publication Date: 2021-12-15
• Subjects: Typical Days -- System States -- Snapshots -- Energy System Models -- Time Series Aggregation -- Temporal Aggregation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117825
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The impact of temporal aggregation on a dispatch and a capacity expansion model. Time-linking constraints are crucial for choosing the best aggregation technique. If storage is considered, typical days are the pareto-optimal aggregation. If no storage is considered, typical time steps are the pareto-optimal aggregation. All applied aggregation techniques are capable to model seasonal storage operation. Abstract: Energy system models are challenged by the need for high temporal and spatial resolutions in order to appropriately depict the increasing share of intermittent renewable energy sources, storage technologies, and the growing interconnectivity across energy sectors. This study compares different temporal aggregation strategies, which reduce the number of considered time steps, to maintain computational viability of these models. The work focuses on the representation of time series by a subset of single time steps (i.e., typical time steps ), or by groups of consecutive time steps (i.e., typical periods ), which are commonly applied in the literature using clustering. We test these techniques for two different energy system models and benchmark the optimization results based on aggregation to those of the fully resolved models. Further, centroids and medoids are used to represent the clustered datasets and it is investigated whether the optimal aggregation method can be determined based on clustering indicators only. The results reveal that typical time steps consistently outperform typical periods with respect to clustering indicators, but do not lead to more accurate optimization results when applied to a model that takes numerous storage technologies into account. Although both aggregation techniques are capable of coupling the aggregated time steps, typical periods offer more options to depict storage operations, whereas typical time steps are more effective for models that neglect time-linking constraints. Further, this observation is independent from the choice of centroids or medoids to represent the clustered time series. In summary, the adequate choice of aggregation methods strongly depends on the mathematical structure of the considered energy system optimization model, and a priori decisions of a sufficient temporal aggregation are only possible with good knowledge of the mathematical structure of the underlying optimization problem.