MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
This work addresses efficiency challenges in resource adequacy assessments for power systems, representing an incremental improvement by integrating active learning into an existing MLMC framework.
The paper tackled the problem of efficiently assessing resource adequacy in power systems by introducing a speed metric that accounts for training time and using a vote-by-committee active learning approach to reduce labeling calls, resulting in a substantial reduction in variance within a limited computational budget.
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.