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SUMMARY:Distributionally Robust Optimal Power Flow with Contextual Informa
 tion
DTSTART:20211129T140000
DTEND:20211129T150000
DTSTAMP:20260406T214432Z
UID:552d7764eb6a9f7ba717a0a2cb14e7066348cb2ecc69912c8abf341c
CATEGORIES:Conferences - Seminars
DESCRIPTION:Adrian Esteban Pérez\, University of Malaga\n\n \nAbstract :
 \nIn this talk\, we introduce a distributionally robust chance-constrained
  formulation of the Optimal Power Flow problem (OPF) whereby the system op
 erator can leverage contextual information. For this purpose\, we exploit 
 an ambiguity set based on probability trimmings and optimal transport thro
 ugh which the dispatch solution is protected against the incomplete knowle
 dge of the relationship between the OPF uncertainties and the context that
  is conveyed by a sample of their joint probability distribution. We provi
 de an exact reformulation of the proposed distributionally robust chance-c
 onstrained OPF problem under the popular conditional-value-at-risk approxi
 mation. By way of numerical experiments run on a modified IEEE-118 bus net
 work with wind uncertainty\, we show how the power system can substantiall
 y benefit from taking into account the well-known statistical dependence b
 etween the point forecast of wind power outputs and its associated predict
 ion error. Furthermore\, the experiments conducted also reveal that the di
 stributional robustness conferred on the OPF solution by our probability-t
 rimmings-based approach is superior to that bestowed by alternative approa
 ches in terms of expected cost and system reliability.\n 
LOCATION:ODY 4 03 https://plan.epfl.ch/?room==ODY%204%2003
STATUS:CONFIRMED
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