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Author Guevara, E.; Babonneau, F.; Homem-de-Mello, T.; Moret, S.
Title A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty Type
Year 2020 Publication Applied Energy Abbreviated Journal Appl. Energy
Volume 271 Issue Pages 18 pp
Keywords (up) Strategic energy planning; Electricity generation; Uncertainty; Distributionally robust optimization; Machine learning
Abstract This paper investigates how the choice of stochastic approaches and distribution assumptions impacts strategic investment decisions in energy planning problems. We formulate a two-stage stochastic programming model assuming different distributions for the input parameters and show that there is significant discrepancy among the associated stochastic solutions and other robust solutions published in the literature. To remedy this sensitivity issue, we propose a combined machine learning and distributionally robust optimization (DRO) approach which produces more robust and stable strategic investment decisions with respect to uncertainty assumptions. DRO is applied to deal with ambiguous probability distributions and Machine Learning is used to restrict the DRO model to a subset of important uncertain parameters ensuring computational tractability. Finally, we perform an out-of-sample simulation process to evaluate solutions performances. The Swiss energy system is used as a case study all along the paper to validate the approach.
Address [Guevara, Esnil] Univ Adolfo Ibanez, PhD Program Ind Engn & Operat Res, Santiago, Chile, Email: frederic.babonneau@uai.cl
Corporate Author Thesis
Publisher Elsevier Sci Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0306-2619 ISBN Medium
Area Expedition Conference
Notes WOS:000540436500003 Approved
Call Number UAI @ eduardo.moreno @ Serial 1188
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