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Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models

Author

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  • Paolo Thiran

    (Institute of Mechanics, Materials and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

  • Hervé Jeanmart

    (Institute of Mechanics, Materials and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

  • Francesco Contino

    (Institute of Mechanics, Materials and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

Abstract

Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., a priori evaluation). However, its impact on the results of the energy system model (i.e., a posteriori evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up a posteriori metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this a priori metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.

Suggested Citation

  • Paolo Thiran & Hervé Jeanmart & Francesco Contino, 2023. "Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models," Energies, MDPI, vol. 16(6), pages 1-20, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2772-:d:1099315
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    References listed on IDEAS

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    1. DeCarolis, Joseph F., 2011. "Using modeling to generate alternatives (MGA) to expand our thinking on energy futures," Energy Economics, Elsevier, vol. 33(2), pages 145-152, March.
    2. Grochowicz, Aleksander & van Greevenbroek, Koen & Benth, Fred Espen & Zeyringer, Marianne, 2023. "Intersecting near-optimal spaces: European power systems with more resilience to weather variability," Energy Economics, Elsevier, vol. 118(C).
    3. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
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    1. Limpens, Gauthier & Rixhon, Xavier & Contino, Francesco & Jeanmart, Hervé, 2024. "EnergyScope Pathway: An open-source model to optimise the energy transition pathways of a regional whole-energy system," Applied Energy, Elsevier, vol. 358(C).
    2. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alberg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2023. "Review of Hot Topics in the Sustainable Development of Energy, Water, and Environment Systems Conference in 2022," Energies, MDPI, vol. 16(23), pages 1-20, December.
    3. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alberg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2024. "Hot Topics at the 18th SDEWES Conference in 2023: A Conference Report," Energies, MDPI, vol. 17(18), pages 1-19, September.

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