Trajectory Based Distributionally Robust Optimization Applied to the Case of Electricity Facilities Investment with Significant Penetration of Renewables
Pierre Cayet and
No 2020-34, EconomiX Working Papers from University of Paris Nanterre, EconomiX
As large scale penetration of renewables into electric systems requires increasing flexibility from dispatchable production units, the electricity mix must be adapted to brutal variations of residual demand. Using tools from distributionally robust optimization (DRO), we propose a trajectory ambiguity set including residual demand trajectories answering both support and variability criterion using quantile information, and approximate the level-maximizing and variability-maximizing residual demand trajectories using two simple algorithms. These two limiting trajectories allow us to make investment decisions robust to extremely high levels and brutal variations of residual demand. We provide a numerical experiment using a MILP investment and unit commitment model in the case of France and discuss the results.
Keywords: OR in energy; Uncertainty modelling; Decision analysis; Renewables (search for similar items in EconPapers)
JEL-codes: C61 (search for similar items in EconPapers)
Pages: 30 pages
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-ore
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:drm:wpaper:2020-34
Access Statistics for this paper
More papers in EconomiX Working Papers from University of Paris Nanterre, EconomiX Contact information at EDIRC.
Bibliographic data for series maintained by Valerie Mignon ().