Impact of different time series aggregation methods on optimal energy system design
By Leander Kotzur, Peter Markewitz, Martin Robinius, Detlef Stolten. arXiv.org, 2017
330 citations recorded by the source corpus.
Abstract Modeling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable. To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design. It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself e.g., k-means clustering plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable.
Subfields: Electrical and Electronic Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/0700756c8c43401f7f4523b0391932302f85bdbc
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In Electrical and Electronic Engineering
- Tom Brown
- David P. Schlachtberger
- S. Schramm
- Martin Greiner
- Mirko Schafer
- Leander Kotzur
- M. Hoffmann
- Detlef Stolten
Citations in the corpus
- Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system (2018, background): “A lower intra-annual resolution allows optimisation of investment paths over multiple decades, but does not allow enough resolution to assess the variability and flexibility requirements for high shares of wind and solar power [43, 44].”
- The Pareto-optimal temporal aggregation of energy system models (2021, methodology): “…Bahl et al. [32] 2017 Härtel et al. [33] 2017 Heuberger et al. [34] 2017 Zhu et al. [35] 2017 Stadler et al. [36] 2018 Kotzur et al. [37, 38] 2018 Lara et al. [39] 2018 Liu et al. [40] 2018 Tejada-Arango et al. [41, 42] 2018 Tupper et al. [43] 2018 Welder et al. [44] 2018 …”
- Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints (2018, background): “Previous investigations in [29] and [30] show that the outcomes of energy system models depend to some degree on the sampling and time resolution of the input data as well as the data reduction method.”