The field of power systems is rapidly evolving, with a growing focus on optimization, simulation, and data-driven approaches. Recent research has led to the development of innovative methods for solving complex power system problems, such as stochastic optimal power flow and renewable energy integration. These advances have the potential to improve the efficiency, reliability, and resilience of power systems. Notably, the use of machine learning and generative models has emerged as a key trend, enabling the creation of synthetic data and the simulation of complex power system behaviors. Furthermore, the development of scalable and efficient simulation tools has facilitated the analysis of large-scale power systems and the identification of critical security boundaries. Overall, the field is moving towards a more integrated and optimization-based approach, leveraging advances in data analytics, machine learning, and simulation to address the challenges of modern power systems. Noteworthy papers include: Scalable Two-Stage Stochastic Optimal Power Flow via Separable Approximation, which proposes a novel framework for solving two-stage stochastic optimization problems in power systems. A Point-Hyperplane Geometry Method for Operational Security Region of Renewable Energy Generation in Power Systems, which presents a novel method for quantifying the critical security boundaries of renewable energy generation. PRIME: Fast Primal-Dual Feedback Optimization for Markets with Application to Optimal Power Flow, which introduces a novel optimization approach for optimal power flow problems.