The field of power systems is undergoing significant transformations with the integration of renewable energy sources and advancements in power electronics. Recent developments are focused on improving the accuracy of power system modeling and control, enabling the efficient and reliable operation of modern power grids. Researchers are exploring innovative approaches, such as topology-aware graph neural networks and reinforcement learning, to predict power system states and optimize control strategies. These advances have the potential to enhance the stability and resilience of power systems, particularly in the presence of high renewable penetration. Noteworthy papers in this area include: PowerGNN, which proposes a topology-aware graph neural network framework for predicting power system states, achieving substantial improvements in predictive accuracy. Nuclear Microreactor Control with Deep Reinforcement Learning, which demonstrates the effectiveness of deep reinforcement learning for real-time drum control in microreactors, showcasing its ability to generalize and extrapolate to longer, more complex transients.