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arXiv AI By Sulyman Iyanda, Mansur Arief

Learned Control Variates for Rare Grid-Outage Estimation at Electric Vehicle Charging Stations

Read the original on arXiv AI →

The paper introduces learned control variates (CVs) to estimate rare grid‑outage probabilities at electric vehicle charging stations more efficiently. By training a classifier on inexpensive features such as temperature and load forecasts, the CV reduces variance in Monte Carlo simulations, with performance tied to the classifier’s recall and false‑positive rate. Experiments on a feeder‑overload simulator and a real Surabaya network show that even a weak learner can cut variance by a quarter, while a boosted‑tree learner achieves about 60% reduction, leading to a one‑third decrease in city‑wide blackout days compared to standard Monte Carlo.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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