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Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain

Author

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  • Aina Maimó-Far

    (Physics Department, Meteorology Group, Universitat de les Illes Balears, 07122 Palma de Mallorca, Spain)

  • Alexis Tantet

    (LMD/IPSL, Ecole Polytechnique, IP Paris, ENS, PSL Université, Sorbonne Université, CNRS, 91120 Palaiseau, France)

  • Víctor Homar

    (Physics Department, Meteorology Group, Universitat de les Illes Balears, 07122 Palma de Mallorca, Spain)

  • Philippe Drobinski

    (LMD/IPSL, Ecole Polytechnique, IP Paris, ENS, PSL Université, Sorbonne Université, CNRS, 91120 Palaiseau, France)

Abstract

We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.

Suggested Citation

  • Aina Maimó-Far & Alexis Tantet & Víctor Homar & Philippe Drobinski, 2020. "Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain," Energies, MDPI, vol. 13(19), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5132-:d:422969
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    Cited by:

    1. Alexis Tantet & Philippe Drobinski, 2021. "A Minimal System Cost Minimization Model for Variable Renewable Energy Integration: Application to France and Comparison to Mean-Variance Analysis," Post-Print hal-03350191, HAL.
    2. Alexis Tantet & Philippe Drobinski, 2021. "A Minimal System Cost Minimization Model for Variable Renewable Energy Integration: Application to France and Comparison to Mean-Variance Analysis," Energies, MDPI, vol. 14(16), pages 1-38, August.

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