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

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

Abstract

Generation Capacity Expansion Planning (GCEP) requires high temporal resolution to account for the volatility of renewable energy supply. Because the GCEP optimization problem is often computationally intractable, time-series input data are often aggregated to representative periods using clustering. However, clustering removes extreme events, which are important to achieve reliable system designs. We present a method to include extreme periods into time-series aggregation for GCEP that guarantees reliable system designs on the full input data even though only the reduced data set is used for system design. Our method iteratively adds extreme periods to the set of representative periods based on information from the optimization problem itself until the energy system provides power reliably.

Suggested Citation

  • Teichgraeber, Holger & Küpper, Lucas Elias & Brandt, Adam R., 2021. "Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921010424
    DOI: 10.1016/j.apenergy.2021.117696
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    References listed on IDEAS

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    Cited by:

    1. Hilbers, Adriaan P. & Brayshaw, David J. & Gandy, Axel, 2023. "Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage," Applied Energy, Elsevier, vol. 334(C).
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    3. Müller, Inga M., 2022. "Energy system modeling with aggregated time series: A profiling approach," Applied Energy, Elsevier, vol. 322(C).
    4. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    5. Kuepper, Lucas Elias & Teichgraeber, Holger & Baumgärtner, Nils & Bardow, André & Brandt, Adam R., 2022. "Wind data introduce error in time-series reduction for capacity expansion modelling," Energy, Elsevier, vol. 256(C).

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