Transmission Network Reduction Method using Nonlinear Optimization
For power system engineers, this method improves accuracy in network reduction, though it is incremental over existing approaches.
The paper introduces a nonlinear optimization method to determine susceptances for reduced transmission network representations using PTDFs, achieving lower mean power flow deviation errors compared to existing methods, albeit with higher computation time.
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.