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

Key points

• Public charging of electric vehicles (EVs) adds a large and synchronized load to the distribution feeders.1
• Furthermore, we show that control variate based on a Gaussian proxy of the load loses its effect as outages become rarer, while a learner with non-vanishing precision keeps removing a fixed fraction of the variance even in the extreme rare-event regime.1
• We verify these results in numerical experiments using a feeder-overload simulator across three orders of magnitude of outage probability and obtain that even weak learner with a single feature can remove a quarter of the variance, while a boosted-tree learner is able to remove about 60%.1
• These results show that learned control variates can help planners design distribution grids that stay reliable against rare outages.1

Sources (1)

• [1]Learned Control Variates for Rare Grid-Outage Estimation at Electric Vehicle Charging Stations
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:53 PM

  To reduce the number of runs, we propose a control variate (CV) built from a learned classifier of the outage, trained on cheap observable features such as temperature and load forecasts.

  Public charging of electric vehicles (EVs) adds a large and synchronized load to the distribution feeders.

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