MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models

TL;DR

This paper combines MLMC with active learning to efficiently assess resource adequacy in power systems, reducing variance within limited computational budgets by minimizing data labeling efforts.

Contribution

It introduces a speed metric for MLMC efficiency that incorporates training time and proposes an active learning approach to reduce labeling calls in surrogate models.

Findings

Active learning significantly reduces labeling calls.

MLMC with active learning achieves lower variance.

The method is effective within limited computational budgets.

Abstract

Multilevel Monte Carlo (MLMC) is a flexible and effective variance reduction technique for accelerating reliability assessments of complex power system. Recently, data-driven surrogate models have been proposed as lower-level models in the MLMC framework due to their high correlation and negligible execution time once trained. However, in resource adequacy assessments, pre-labeled datasets are typically unavailable. For large-scale systems, the efficiency gains from surrogate models are often offset by the substantial time required for labeling training data. Therefore, this paper introduces a speed metric that accounts for training time in evaluating MLMC efficiency. Considering the total time budget is limited, a vote-by-committee active learning approach is proposed to reduce the required labeling calls. A case study demonstrates that, within a given computational budget, active learning in combination with MLMC can result in a substantial reduction variance.