Reliable Confidence Intervals for Monte Carlo-Based Resource Adequacy Studies

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Quantitative risk analysis is essential for power system planning and operation. Monte Carlo methods are frequently employed for this purpose, but their inherent sampling uncertainty means that accurate estimation of this uncertainty is essential. Basic Monte Carlo procedures are unbiased and, in the limit of large sample counts, have a well-characterised error distribution. However, for small time budgets and ill-behaved distributions (such as those for rare event risks), we may not always operate in this limit. Moreover, multilevel Monte Carlo was recently proposed as a computationally efficient alternative to regular Monte Carlo. In this approach, great asymptotic speedups are achieved by reducing the number of full model evaluations. This further challenges the assumption that normally distributed errors can be used. This paper investigates the sampling error distributions for a practical resource adequacy case study, in combination with the Multilevel Monte Carlo method. It further proposes a practical test for validating error estimates, based on a bootstrap approach.

Original languageEnglish
Title of host publicationProceedings of the 2024 International Conference on Smart Energy Systems and Technologies (SEST)
Place of PublicationDanvers
PublisherIEEE
Number of pages6
ISBN (Electronic)979-8-3503-8649-3
ISBN (Print)979-8-3503-8650-9
DOIs
Publication statusPublished - 2024
Event2024 International Conference on Smart Energy Systems and Technologies, SEST 2024 - Torino, Italy
Duration: 10 Sept 202412 Sept 2024

Conference

Conference2024 International Conference on Smart Energy Systems and Technologies, SEST 2024
Country/TerritoryItaly
CityTorino
Period10/09/2412/09/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • central limit theorem
  • Monte Carlo methods
  • Multilevel Monte Carlo
  • resource adequacy
  • statistical testing

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