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Pré-publication, Document de travail

Optimal wind-solar energy mix in Italy: Impact of climate variability

Abstract : In the context of the 2009 EU directive promoting the use of energy from renewable sources, Italy has reached its 2020 target of a 17% share of renewables in the final energy consumption 6 years in advance. In this study, we evaluate the existing renewable energy mix in Italy at regional scale by comparing it to an optimized mix taking into account climate variability and allowing full decommissioning of the currently installed plants. The variability of the production and of the demand over the 1989-2012 period is resolved by plugging regional climate simulations of this period into a model simulating the renewable energy production as well as the Italian electrical consumption at regional scale. The optimal mix is then inferred from a mean-risk analysis with as objectives both to maximize the mean of the total renewable production and to minimize the variance, or risk, of the latter. We consider two cases: in the first one the analysis takes cross-region correlations in the production and the demand into account and in the second one the analysis is local to each region. The optimal mix maximizing the ratio of the total mean penetration over the total risk for the same renewable capacity as installed in 2015 consists of about two thirds wind and one third solar, i.e. twice as much wind as the actual 2015 Italian renewable mix. The spatial distribution also differs significantly from the actual mix and from what would be obtained ignoring the risk and low-frequency climate variability.
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Contributeur : Alexis Tantet <>
Soumis le : jeudi 20 décembre 2018 - 12:45:04
Dernière modification le : mardi 21 juillet 2020 - 10:48:02
Document(s) archivé(s) le : vendredi 22 mars 2019 - 11:37:29


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  • HAL Id : hal-01962044, version 1
  • ARXIV : 1812.09181


Alexis Tantet, Stéfanon Marc, Drobinski Philippe, Badosa Jordi, Concettini Silvia, et al.. Optimal wind-solar energy mix in Italy: Impact of climate variability. 2018. ⟨hal-01962044v1⟩



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