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We present a machine learning approach to the solution of chance constrainedoptimizations in the context of voltage regulation problems in power systemoperation. The novelty of our approach resides in approximating the feasibleregion of uncertainty with an ellipsoid. We formulate this problem using alearning model similar to Support Vector Machines (SVM) and propose a samplingalgorithm that efficiently trains the model. We demonstrate our approach on avoltage regulation problem using standard IEEE distribution test feeders.

A tractable ellipsoidal approximation for voltage regulation problems