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e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy

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  • Alexis Tantet

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • Marc Stéfanon

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • Philippe Drobinski

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • Jordi Badosa

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • Silvia Concettini

    (IRJI, Université de Tours, 37200 Tours, France
    Energy and Prosperity Chair, 75002 Paris, France)

  • Anna Cretì

    (Département d’Economie, École polytechnique, IP Paris, 91128 Palaiseau, France
    Université Paris Dauphine, PSL, Leda-CGEMP, 75775 Paris, France)

  • Claudia D’Ambrosio

    (LIX, École polytechnique, IP Paris, CNRS, 91128 Palaiseau, France)

  • Dimitri Thomopulos

    (LIX, École polytechnique, IP Paris, CNRS, 91128 Palaiseau, France)

  • Peter Tankov

    (CREST, ENSAE, École Polytechnique, IP Paris, 91128 Palaiseau, France)

Abstract

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e 4 clim ’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.

Suggested Citation

  • Alexis Tantet & Marc Stéfanon & Philippe Drobinski & Jordi Badosa & Silvia Concettini & Anna Cretì & Claudia D’Ambrosio & Dimitri Thomopulos & Peter Tankov, 2019. "e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy," Energies, MDPI, vol. 12(22), pages 1-37, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4299-:d:285895
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