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 language | English |
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Title of host publication | Proceedings of the 2024 International Conference on Smart Energy Systems and Technologies (SEST) |
Place of Publication | Danvers |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 979-8-3503-8649-3 |
ISBN (Print) | 979-8-3503-8650-9 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 International Conference on Smart Energy Systems and Technologies, SEST 2024 - Torino, Italy Duration: 10 Sept 2024 → 12 Sept 2024 |
Conference
Conference | 2024 International Conference on Smart Energy Systems and Technologies, SEST 2024 |
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Country/Territory | Italy |
City | Torino |
Period | 10/09/24 → 12/09/24 |
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-careOtherwise 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