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.
The integration of renewables into electrical grids calls for the development of tailored control schemes which in turn require reliable grid models. In many cases, the grid topology is known but the actual parameters are not exactly known. This pape
r proposes a new approach for online parameter estimation in power systems based on optimal experimental design using multiple measurement snapshots. In contrast to conventional methods, our method computes optimal excitations extracting the maximum information in each estimation step to accelerate convergence. The performance of the proposed method is illustrated on a case study.
