A tractable ellipsoidal approximation for voltage regulation problems

TL;DR

This paper introduces a machine learning method that approximates the feasible region in voltage regulation problems with an ellipsoid, enabling efficient chance constrained optimization in power systems.

Contribution

It proposes a novel SVM-like learning model to approximate uncertainty regions with ellipsoids for voltage regulation, improving computational efficiency.

Findings

Effective ellipsoidal approximation of feasible regions

Successful application on IEEE test feeders

Enhanced efficiency in chance constrained optimization

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.