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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:

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    2. Göke, Leonard & Schmidt, Felix & Kendziorski, Mario, 2024. "Stabilized Benders decomposition for energy planning under climate uncertainty," European Journal of Operational Research, Elsevier, vol. 316(1), pages 183-199.
    3. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
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    6. 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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