The field of power system modeling and optimization is witnessing significant developments, driven by the need for more accurate and efficient analysis of complex power systems. Researchers are exploring new approaches to improve the modeling of power systems, including the use of symbolic equation modeling, Gaussian copula-based methods, and learning-based techniques. These advancements aim to address the challenges posed by the increasing penetration of distributed energy resources and the growing complexity of power systems. Notably, the development of open-source frameworks and tools, such as QUEENS, is facilitating the composition and management of simulation analyses, while also providing a platform for the implementation of cutting-edge algorithms. Furthermore, the application of machine learning techniques, such as adaptive ensemble learning and learning interior point methods, is improving the accuracy and efficiency of power system optimization. Overall, these developments are expected to enhance the reliability, efficiency, and sustainability of power systems. Noteworthy papers include: Solving Three-phase AC Infeasibility Analysis to Near-zero Optimality Gap, which presents a novel method for solving three-phase infeasibility analysis problems with near-zero optimality gap. QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models, which introduces an open-source framework for composing and managing simulation analyses. Learning Interior Point Method for AC and DC Optimal Power Flow, which proposes a feasibility-guaranteed learning interior point method for solving AC and DC optimal power flow problems.