Economics at your fingertips  

A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty

Esnil Guevara, Fréderic Babonneau, Tito Homem-de-Mello and Stefano Moret

Applied Energy, 2020, vol. 271, issue C, No S0306261920305171

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.

Keywords: Strategic energy planning; Electricity generation; Uncertainty; Distributionally robust optimization; Machine learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.115005

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-10-10
Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920305171