Grid-Forming Inverter Modeling & Operation in High-Inverter Based Resources Electrical Network

Inverter-based resources are becoming part of our modern electrical grid as they pave the way for renewable energy resources integration into the electrical grid. Most of these resources are connected to the grid by grid-following inverters (GFLIs), and they are replacing rotating synchronous generators. However, these inverters are designed to operate with strong system operation preserved by the rotating synchronous machines (RSMs). As a result, the system dynamics behavior becomes faster, and the GFLIs may fail to remain synchronized with the system. These challenges direct attention to grid-forming inverter (GFMI) control techniques due to their capability to control/regulate voltage and frequency in a very short time scale. These capabilities improve stability and increase the resiliency of the electrical grid. This dissertation has two main objectives. The first objective is to develop several models to enhance the integration of GFMIs in the electrical grid and to maximize their capability. First, we propose a model for balanced power-sharing between droop-controlled GFMIs in islanded microgrids using machine learning algorithms. Second, we aim to develop an RMS virtual oscillator control GFMI for distribution network dynamic studies. Third, we propose an adaptive controller to change the virtual oscillator parameters depending on the operation mode (grid-connected or islanded) In addition, we introduce an auxiliary controller to provide a seamless transition between the two operation modes. Finally, we introduce an aggregation model to a distribution network with different participation ratios of GFLIs and GFMIs to reduce the computational burden of the transmission network planning studies. The second objective is to investigate the operation of transmission and distribution networks with 100% inverter-based resources. We investigate the behavior of the frequency and voltage levels, and the power exchange between the two networks based on different participation ratios of GFMIs and GFLIs on different grid events