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Open AccessArticle

e4clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy

LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France
IRJI, Université de Tours, 37200 Tours, France
Energy and Prosperity Chair, 75002 Paris, France
Département d’Economie, École polytechnique, IP Paris, 91128 Palaiseau, France
Université Paris Dauphine, PSL, Leda-CGEMP, 75775 Paris, France
LIX, École polytechnique, IP Paris, CNRS, 91128 Palaiseau, France
CREST, ENSAE, École Polytechnique, IP Paris, 91128 Palaiseau, France
Author to whom correspondence should be addressed.
Energies 2019, 12(22), 4299;
Received: 22 August 2019 / Revised: 31 October 2019 / Accepted: 6 November 2019 / Published: 11 November 2019
(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)
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 e4clim’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. View Full-Text
Keywords: renewable energy; climate variability; energy mix; mean-variance; sensitivity renewable energy; climate variability; energy mix; mean-variance; sensitivity
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Tantet, A.; Stéfanon, M.; Drobinski, P.; Badosa, J.; Concettini, S.; Cretì, A.; D’Ambrosio, C.; Thomopulos, D.; Tankov, P. e4clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy. Energies 2019, 12, 4299.

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