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

Ruiqi Zhang; Simon H. Tindemans

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arXiv · 2505.20930

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

Ruiqi Zhang·Simon H. Tindemans

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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.

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Ruiqi Zhang, Simon H. Tindemans. 2025-05-27. MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models. https://arxiv.org/abs/2505.20930

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