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Intersecting near-optimal spaces: European power systems with more resilience to weather variability

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  • Grochowicz, Aleksander
  • van Greevenbroek, Koen
  • Benth, Fred Espen
  • Zeyringer, Marianne

Abstract

We suggest a new methodology for designing robust energy systems. For this, we investigate so-called near-optimal solutions to energy system optimisation models; solutions whose objective values deviate only marginally from the optimum. Using a refined method for obtaining explicit geometric descriptions of these near-optimal feasible spaces, we find designs that are as robust as possible to perturbations. This contributes to the ongoing debate on how to define and work with robustness in energy systems modelling.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:eneeco:v:118:y:2023:i:c:s0140988322006259
    DOI: 10.1016/j.eneco.2022.106496
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    Cited by:

    1. Frysztacki, Martha Maria & Hagenmeyer, Veit & Brown, Tom, 2023. "Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?," Energy, Elsevier, vol. 281(C).
    2. Dubois, Antoine & Dumas, Jonathan & Thiran, Paolo & Limpens, Gauthier & Ernst, Damien, 2023. "Multi-objective near-optimal necessary conditions for multi-sectoral planning," Applied Energy, Elsevier, vol. 350(C).
    3. 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.

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    More about this item

    Keywords

    Climate-resilient energy systems; Energy system optimisation model; Weather uncertainty; Near-optimal solutions; Robust energy systems; Modelling to generate alternatives;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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