The recent developments in the research area of power systems and renewable energy integration have shown a significant shift towards advanced control strategies, optimization techniques, and robust management of grid infrastructure under varying conditions. There is a growing emphasis on integrating renewable energy sources, such as solar and wind, into the grid while addressing the challenges of voltage regulation, transformer temperature management, and grid stability. Innovative control frameworks for inverters are being developed to ensure seamless transitions between different operation modes, enhancing grid integration and stability. Additionally, there is a surge in the use of deep reinforcement learning and other AI-driven methods for optimizing energy conversion and storage systems, particularly in wave energy and microgrid scheduling. These advancements aim to improve energy harvesting efficiency, reduce the levelized cost of energy, and manage voltage risks more effectively. Notably, the integration of physical-informed deep reinforcement learning in bi-level programming for microgrid scheduling represents a significant leap in coordinating operator and user interests under complex operating conditions. Furthermore, the robustness of control systems for electric vehicles and the stability analysis of offshore wind power plants are areas where substantial progress has been made, ensuring a more sustainable and resilient power grid. Overall, the field is progressing towards more intelligent, adaptive, and resilient power systems capable of handling the complexities introduced by high penetration levels of renewable energy.