This dissertation introduces my research work on modeling and simulation of high IBR-penetration power systems. Grid-forming (GFM) inverter, as the critical device in IBR-dominated system, is modelled in quasi-dynamic domain and protected using the dynamic state estimation-based protection (EBP). The EBP method is evaluated in a real-world PV-integrated distribution system, proving its effectiveness in detecting faults within non-radial distribution systems with bidirectional current flow and low fault current level. GFM inverter is also modelled in time domain to study its performance and limitations in a system with up to 100% IBR-penetration. A Transfer Learning (TL) model for battery pack state prediction with battery degradation and different operating conditions considered is also presented. A generalized dataset from the publicly available datasets is generated to train the model. The generated dataset contains features such as cycling current, voltage, charge/discharge capacity, temperature and battery chemistry, etc. Two prediction models are implemented and compared at cell-level. Then, the model with better performance at cell-level is used as the pre-trained model for the transfer learning model for battery pack-level predictions. The test results indicate that the proposed model can accurately predict the SoC vs. voltage relationship as well as the SoH under different operating conditions. The work proposed in this dissertation paves the road to accurate simulation of high IBR-penetration power systems.