Transmission Network Reduction Method using Nonlinear Optimization

TL;DR

This paper introduces a nonlinear optimization method to accurately determine susceptances in reduced transmission networks, improving approximation quality and computational efficiency over existing techniques.

Contribution

The paper proposes a novel nonlinear optimization approach for susceptance determination using PTDFs, outperforming traditional methods in accuracy and speed.

Findings

Reduced injection-independent PTDFs provide the best approximation.

The nonlinear method lowers mean power flow deviation errors.

Approach achieves higher accuracy with reasonable computation times.

Abstract

This paper presents a new method to determine the susceptances of a reduced transmission network representation by using nonlinear optimization. We use Power Transfer Distribution Factors (PTDFs) to convert the original grid into a reduced version, from which we determine the susceptances. From our case studies we find that considering a reduced injection-independent evaluated PTDF matrix is the best approximation and is by far better than an injection-dependent evaluated PTDF matrix over a given set of arbitrarily-chosen power injection scenarios. We also compare our nonlinear approach with existing methods from literature in terms of the approximation error and computation time. On average, we find that our approach reduces the mean error of the power flow deviations between the original power system and its reduced version, while achieving higher but reasonable computation times.