Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation

Teichgraeber, Holger; Küpper, Lucas Elias; Brandt, Adam R.

doi:10.1016/j.apenergy.2021.117696

Cited by 19 publications

(12 citation statements)

References 31 publications

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“…Our framework customises aggregation to the energy system model using its operational variables (generation, transmission and storage patterns). It unifies methods by Hilbers et al (2020) and Teichgraeber et al (2021) -which use operational variables in models without storage -with that of Kotzur et al (2018b) -which allow chronology-preserving aggregation for storage technologies.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Sun et al (2019) and Zhang et al (2022) cluster vectors of planning model outputs (run on each individual day) instead of the time series itself. Bahl et al (2018) and Teichgraeber et al (2021) alternate between a planning model on aggregated data and an operation model on the full time series to iteratively identify and include days with unmet demand; this ensures design estimates have adequate generation capacity for such events. Hilbers et al (2020) identify and include system-relevant extreme events using their generation cost, also calculated using an operation model.…”

Section: Most Time Series Aggregation Schemes Are Whatmentioning

confidence: 99%

“…We introduce a general aggregation framework that uses a model's operational variables -generation, transmission and storage patterns -to improve aggregation while maintaining chronology. It builds on work by Hilbers et al (2020) and Teichgraeber et al (2021) -which uses operational variables in aggregation for models without storage -and the chronology-preserving aggregation introduced by Kotzur et al (2018b).…”

Section: This Paper's Contributionmentioning

confidence: 99%

“…The second is unserved energy, which occurs only at times of insufficient supply. These are roughly those used by Hilbers et al (2020) and Teichgraeber et al (2021) respectively for models without storage. Expert knowledge can motivate others, e.g.…”

Section: Remarksmentioning

confidence: 99%

“…Methods D-F are a posteriori, using operational variables as described in Section 3.2. D and E include days with high unserved energy and generation cost, which serve as the importance functions and correspond roughly to those used by Teichgraeber et al (2021) and Hilbers et al (2020) respectively in models without storage. Method F is the same as E, but uses storage (dis)charge decisions in the second aggregation (step 2(d)).…”

Section: Setupmentioning

confidence: 99%

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Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

Hilbers¹,

Brayshaw²

2022

Preprint

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The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory.To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology.In this paper, we introduce a posteriori time series aggregation schemes for models with storage. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. Furthermore, they preserve chronology and hence allow modelling of storage technologies.We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.

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Section: Discussionmentioning

confidence: 99%