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

Alexis Tantet, Marc Stéfanon, Philippe Drobinski, Jordi Badosa, Silvia Concettini, Anna Cretì, Claudia D’Ambrosio, Dimitri Thomopulos and Peter Tankov
Additional contact information
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
Anna Cretì: Département d’Economie, École polytechnique, IP Paris, 91128 Palaiseau, 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

Energies, 2019, vol. 12, issue 22, 1-37

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.

Keywords: renewable energy; climate variability; energy mix; mean-variance; sensitivity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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