Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment

TL;DR

This paper introduces the use of machine-learned surrogate models within multilevel Monte Carlo methods to significantly accelerate power system reliability assessments without sacrificing accuracy.

Contribution

It demonstrates how machine learning surrogates can replace manual model tuning in MLMC, enabling faster and more efficient resource adequacy evaluations.

Findings

Machine-learned surrogates achieve high accuracy and inference speed.

Significant speedups in resource adequacy assessment using MLMC with surrogates.

Effective strategies for constructing and training surrogate models.

Abstract

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.