Sensitivity analysis of time aggregation techniques applied to capacity expansion energy system models

Buchholz, Stefanie; Gamst, Mette; Pisinger, David

doi:10.1016/j.apenergy.2020.114938

Cited by 12 publications

(5 citation statements)

References 70 publications

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“…Selecting the correct temporal representation to effectively answer a research question is difficult, with numerous papers discussing the importance and challenges of this task 13 , 23 , 24 . Especially in systems with large shares of variable renewable energy sources, capturing the intermittent nature of the sources is often needed to obtain credible results.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, the “Timeslice Data” script will generate the time slice structure defined by the user in the configuration file, and update parameters that rely on time slice definition respectively, such as the capacity factor and electricity demand profiles. OSeMOSYS Global uses representative days to timeslice the model 13 , 24 . This approach involves representing a time period using average values for variable parameters, such as loads or renewable generation profiles, over a specified time period.…”

Section: Methodsmentioning

confidence: 99%

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OSeMOSYS Global, an open-source, open data global electricity system model generator

et al. 2022

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This paper describes OSeMOSYS Global, an open-source, open-data model generator for creating global electricity system models for an active global modelling community. This version of the model generator is freely available and can be used to create interconnected electricity system models for both the entire globe and for any geographically diverse subset of the globe. Compared to other existing global models, OSeMOSYS Global allows for full user flexibility in determining the time slice structure and geographic scope of the model and datasets, and is built using the widely used fully open-source OSeMOSYS energy system model. This paper describes the data sources, structure and use of OSeMOSYS Global, and provides illustrative workflow results.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

OSeMOSYS Global, an open-source, open data global electricity system model generator

et al. 2022

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show abstract

“…Selecting the correct temporal representation to effectively answer a research question is difficult, with numerous papers discussing the importance and challenges of this task [13], [28], [29]. Especially in systems with large shares of variable renewable energy sources, capturing the intermittent nature of the sources is often needed to obtain credible results.…”

Section: Flexible Temporal Resolutionmentioning

confidence: 99%

“…Finally, the "Timeslice Data" script will generate the time slice structure defined by the user in the configuration file, and update parameters that rely on time slice definition respectively, such as the capacity factor and electricity demand profiles. OSeMOSYS Global uses representative days to timeslice the model [13], [29]. This approach involves representing a time period using average values for variable parameters, such as loads or renewable generation profiles, over a specified time period.…”

Section: Scenario Creationmentioning

confidence: 99%

OSeMOSYS Global: An open-source, open data global electricity system model generator

Barnes

Shivakumar

Brinkerink

et al. 2022

Preprint

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“…However, despite their past usefulness, new trends like the increasing share of variable renewable energy sources (VRES) in power systems, pose a challenge to temporal aggregation techniques as they rely on adding multiple time steps, e.g., daily or weekly averages from hourly data [9] [10] [11], or breaking the inter-temporal linking of time [12] [13], but VRES' technical constraints require a highly detailed temporal modeling [14]. The consequences of these aggregation procedures have been researched [15] [16] [17], and results show that they lead to inaccurate results as they go against these fundamental technical constraints and even those of short-term storage technologies.…”

mentioning

confidence: 99%

On the aggregation of input data for energy system models

Cardona-Vasquez

DiTondo

Wogrin

2022

Elektrotech. Inftech.

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The increasing share of variable renewable energy sources in power systems poses new challenges to policy makers and network planners alike because of the sources’ variability and insufficient energy storage capability. This requires the development of new optimization models that consider the inter-temporal connection between different periods aggregating them on a more general level (e.g., days or weeks). However, aggregation models are often empirical and based on common clustering algorithms. In this paper, we carry out a numerical exploration of the relationship between the structure of the system and the hyperparameters required for these aggregation procedures. Our findings indicate that there is valuable information from the power system that can be used to improve the aggregation. This is important because, in most cases, the accuracy of the aggregation cannot be measured exactly.

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Sensitivity analysis of time aggregation techniques applied to capacity expansion energy system models

Cited by 12 publications

References 70 publications

OSeMOSYS Global, an open-source, open data global electricity system model generator

OSeMOSYS Global, an open-source, open data global electricity system model generator

OSeMOSYS Global: An open-source, open data global electricity system model generator

On the aggregation of input data for energy system models

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