Modeling Static Grid Equivalent with Artificial Neural Networks Including Optimally Designed Local Reactive Power Control

During the energy system transition, it has become increasingly important to intelligently control the provision of reactive power from distributed energy resources (DERs) to support grid stability. However, the applied reactive power controller and the variable grid states pose significant challenges to implementing a static equivalent grid, which is used as a standard solution for data confidentiality and computational efficiency. This thesis proposes an innovative time series optimizationbased approach for calculating optimal characteristic curves for each DER. The individually optimized characteristic curves prove to be more effective than conventional settings, offering substantial benefits such as enhanced voltage stability. To address variable grid states, a static grid equivalent method based on artificial neural networks is developed. This method not only enhances the accuracy of the grid equivalent compared to existing methods but also ensures data confidentiality.