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E4CLIM 1.0: The energy for a climate integrated model: Description and application to Italy

Tantet, Alexis; Stéfanon, Marc; Drobinski, Philippe; Badosa, Jordi; Concettini, Sylvia; Creti, Anna; D’Ambrosio, Claudia; Thomopulos, Dimitri; Tankov, Peter (2019), E4CLIM 1.0: The energy for a climate integrated model: Description and application to Italy, Energies, 12, 22, p. 4299. 10.3390/en12224299

Type
Article accepté pour publication ou publié
Date
2019
Journal name
Energies
Volume
12
Number
22
Publisher
MDPI
Pages
4299
Publication identifier
10.3390/en12224299
Metadata
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Author(s)
Tantet, Alexis cc
Laboratoire de Météorologie Dynamique (UMR 8539) [LMD]
Stéfanon, Marc
Laboratoire de Météorologie Dynamique (UMR 8539) [LMD]
Drobinski, Philippe
Laboratoire de Météorologie Dynamique (UMR 8539) [LMD]
Badosa, Jordi
Laboratoire de Météorologie Dynamique (UMR 8539) [LMD]
Concettini, Sylvia

Creti, Anna
Laboratoire d'Economie de Dauphine [LEDa]
D’Ambrosio, Claudia cc
Laboratoire d'informatique de l'École polytechnique [Palaiseau] [LIX]
Thomopulos, Dimitri
Laboratoire d'informatique de l'École polytechnique [Palaiseau] [LIX]
Tankov, Peter
Centre de Recherche en Économie et Statistique [CREST]
Abstract (EN)
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.
Subjects / Keywords
Renewable energy; climate variability; energy mix; mean-variance; sensitivity
JEL
Q56 - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
Q54 - Climate; Natural Disasters and Their Management; Global Warming

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