A tractable ellipsoidal approximation for voltage regulation problems

Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto L. Sangiovanni-Vincentelli, Baosen Zhang

Abstract: We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of our approach resides in approximating the feasible region of uncertainty with an ellipsoid. We formulate this problem using a learning model similar to Support Vector Machines (SVM) and propose a sampling algorithm that efficiently trains the model. We demonstrate our approach on a voltage regulation problem using standard IEEE distribution test feeders.