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

• URL: http://arxiv.org/abs/2505.20930v2
• Date: Wed, 30 Jul 2025 11:07:49 GMT
• Title: MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
• Authors: Ruiqi Zhang, Simon H. Tindemans,
• Abstract summary: Multilevel Monte Carlo (MLMC) is a flexible and effective variance technique for accelerating reliability assessments.<n>Data-driven surrogate models have been proposed as lower-level models in complex power system framework.
• Score: 6.430258446597413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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