Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment

Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment

Sharifnia, E. (TU Delft Intelligent Electrical Power Grids)
Tindemans, Simon H. (TU Delft Intelligent Electrical Power Grids)

2022

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.

Monte Carlo methods
multilevel Monte Carlo
resource adequacy
storage dispatch
surrogate model

http://resolver.tudelft.nl/uuid:1b844634-ff24-4578-959d-1eb3fb240064

https://doi.org/10.1109/PMAPS53380.2022.9810622

IEEE

2023-01-04

978-1-6654-1212-4

Proceedings of the 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

PMAPS 2022, 2022-06-12 → 2022-06-15, Online at Manchester, United Kingdom

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.

Institutional Repository

conference paper

© 2022 E. Sharifnia, Simon H. Tindemans