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Variable time-step: A method for improving computational tractability for energy system models with long-term storage

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  • de Guibert, Paul
  • Shirizadeh, Behrang
  • Quirion, Philippe

Abstract

Optimizing an energy system model featuring a large proportion of variable (non-dispatchable) renewable energy requires a fine temporal resolution and a long period of weather data to provide robust results. Many models are optimized over a limited set of ‘representative’ periods (e.g. weeks) but this precludes a realistic representation of long-term energy storage.

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  • de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220321319
    DOI: 10.1016/j.energy.2020.119024
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    Cited by:

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    2. ZareAfifi, Farzan & Mahmud, Zabir & Kurtz, Sarah, 2023. "Diurnal, physics-based strategy for computationally efficient capacity-expansion optimizations for solar-dominated grids," Energy, Elsevier, vol. 279(C).
    3. Shirizadeh, Behrang & Quirion, Philippe, 2022. "Do multi-sector energy system optimization models need hourly temporal resolution? A case study with an investment and dispatch model applied to France," Applied Energy, Elsevier, vol. 305(C).
    4. Klemm, Christian & Wiese, Frauke & Vennemann, Peter, 2023. "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution," Applied Energy, Elsevier, vol. 334(C).
    5. 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).
    6. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2022. "A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems," Energy, Elsevier, vol. 258(C).

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