Online power system parameter estimation and optimal operation

TL;DR

This paper introduces a joint optimization method for power system parameter estimation and operation, reducing costs and improving accuracy by integrating experimental design with control.

Contribution

It presents a novel approach that simultaneously minimizes operational costs and estimates grid parameters more accurately than traditional methods.

Findings

Significant cost reduction in parameter estimation.

Higher accuracy in line parameter estimation.

Effective application demonstrated on a benchmark system.

Abstract

The integration of renewables into electrical grids calls for optimization-based control schemes requiring reliable grid models. Classically, parameter estimation and optimization-based control is often decoupled, which leads to high system operation cost in the estimation procedure. The present work proposes a method for simultaneously minimizing grid operation cost and optimally estimating line parameters based on methods for the optimal design of experiments. This method leads to a substantial reduction in cost for optimal estimation and in higher accuracy in the parameters compared with standard Optimal Power Flow and maximum-likelihood estimation. We illustrate the performance of the proposed method on a benchmark system.