The Pareto-optimal temporal aggregation of energy system models. (1st June 2022)

• Record Type: Journal Article
• Authors: Hoffmann, Maximilian; Kotzur, Leander; Stolten, Detlef
• Is Part Of: Applied energy. Volume 315(2022)
• Publication Date: 2022-06-01
• Subjects: Typical days -- Segmentation -- Time slices -- Energy system models -- Time series aggregation -- Temporal aggregation -- Temporal resolution -- Renewable energy systems -- Clustering -- Computation time -- Pareto optimality; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119029
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The ratio of typical days and time steps therein is crucial for a good optimization result. An algorithm for an improved ratio of typical days and segments is introduced. The duration curves of time series are crucial for the correct sizing of the components. An algorithm is introduced that precisely replicates the original value distribution. The methods are open source and available at: https://github.com/FZJ-IEK3-VSA/tsam. Abstract: The growing share of intermittent renewable energy sources, storage technologies, and the increasing degree of so-called sector coupling necessitates optimization-based energy system models with high temporal and spatial resolutions, which significantly increases their runtimes and limits their maximum sizes. In order to maintain the computational viability of these models for large-scale application cases, temporal aggregation has emerged as a technique for reducing the number of considered time steps by reducing the original time horizon down to fewer, more representative ones. This study presents advanced but generally applicable clustering techniques that allow for ad-hoc improvements of current approaches without requiring profound knowledge of the individual energy system model. These improvements comprise a method to find the optimal tradeoff between the number of typical days and inner-daily temporal resolutions, and a representation method that can reproduce the value distribution of the original time series. We prove the superiority of these approaches by applying them to two fundamentally different model types and benchmarking them against state-of-the-art approaches. This is performed for a variety of temporal resolutions, which leads to many hundreds of model runs. The results show that the proposed improvements on current methods strictly dominate the status quo with respect to Pareto-optimality in terms of runtime and accuracy. Although a speeding up factor of one magnitude could be achieved using traditional aggregation methods within a cost deviation range of two percent, the algorithms proposed herein achieve this accuracy with a runtime speedup by a factor of two orders of magnitude.