Monte Carlo Based Method for Managing Risk of Scheduling Decisions with Dynamic Line Ratings

Binayak Banerjee, Dilan Jayaweera, Syed Islam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
228 Downloads (Pure)

Abstract

Dynamic line ratings have been shown as an attractive alternative to conventional congestion management methods that can potentially improve wind integration. However, the modelling of dynamic line ratings is dependent on effectively modelling the risk of thermal overload which usually has a high amount of uncertainty. This paper uses a sample average approximation method to model the uncertainty in risk function and determine how scheduling decisions are affected. It also presents a sensitivity analysis to determine the level of uncertainty in the risk function that can be managed and how the sampling process should be adjusted. Test cases indicate that there is a high level of confidence in scheduling decisions for sample sizes less than 100. A larger sample size can maintain the high level of confidence if there is a greater uncertainty associated with the risk function.
Original languageEnglish
Title of host publicationMonte Carlo Based Method for Managing Risk of Scheduling Decisions with Dynamic Line Ratings
PublisherIEEE Xplore
ISBN (Electronic)978-1-4673-8040-9
DOIs
Publication statusPublished - 5 Oct 2015
EventIEEE Power & Energy Society General Meeting, 2015 - Denver, United States
Duration: 26 Jul 201530 Jul 2015

Publication series

NameIEEE Power Engineering Society
PublisherIEEE
ISSN (Print)1932-5517

Conference

ConferenceIEEE Power & Energy Society General Meeting, 2015
Country/TerritoryUnited States
CityDenver
Period26/07/1530/07/15

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