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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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    13. Behrang Shirizadeh, Quentin Perrier, and Philippe Quirion, 2022. "How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
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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).
    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. 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).
    5. 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).
    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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