Time series aggregation for energy system design: Modeling seasonal storage
By Leander Kotzur, Peter Markewitz, Martin Robinius, Detlef Stolten. 2017
267 citations recorded by the source corpus.
The optimization-based design of renewable energy systems is a computationally demanding task because of the high temporal fluctuation of supply and demand time series. In order to reduce these time series, the aggregation of typical operation periods has become common. The problem with this method is that these aggregated typical periods are modeled independently and cannot exchange energy. Therefore, seasonal storage cannot be adequately taken into account, although this will be necessary for energy systems with a high share of renewable generation.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/a8968d10adc88b876cbbce188d29ea2746f790cc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
Citations in the corpus
- The Pareto-optimal temporal aggregation of energy system models (2021, unknown): “… 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…”
- Optimal Planning for Electricity-Hydrogen Integrated Energy System Considering Power to Hydrogen and Heat and Seasonal Storage (2020, methodology): “Combined with the clustering method, the authors of [13] proposed a novel mathematical model for storage inventories based on the superposition of inter-period and intra-period states, which can achieve seasonal energy complementarity compared with the optimization models based on a full annual time”
- Potential of Power-to-Methane in the EU energy transition to a low carbon system using cost optimization (2018, methodology): “This approach can lead to deviations due to the smoothening of the shape of the profile [93].”