Monte Carlo simulation is often used for the reliability assessment of power systems, but it converges slowly when the system is complex. Multilevel Monte Carlo (MLMC) can be applied to speed up computation without compromises on model complexity and accuracy that are limiting real-world effectiveness. In MLMC, models with different complexity and speed are combined, and having access to fast approximate models is essential for achieving high speedups. This paper demonstrates how machine-learned surrogate models are able to fulfil this role without excessive manual tuning of models. Different strategies for constructing and training surrogate models are discussed. A resource adequacy case study based on the Great Britain system with storage units is used to demonstrate the effectiveness of the proposed approach, and the sensitivity to surrogate model accuracy. The high accuracy and inference speed of machine-learned surrogates result in very large speedups, compared to using MLMC with hand-built models.
|Title of host publication||Proceedings of the 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)|
|Number of pages||6|
|Publication status||Published - 2022|
|Event||PMAPS 2022: The 17th International Conference on Probabilistic Methods Applied to Power Systems - Online at Manchester, United Kingdom|
Duration: 12 Jun 2022 → 15 Jun 2022
Conference number: 17th
|City||Online at Manchester|
|Period||12/06/22 → 15/06/22|
Bibliographical noteGreen 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.
- Monte Carlo methods
- multilevel Monte Carlo
- resource adequacy
- storage dispatch
- surrogate model