The field of power systems is witnessing significant developments in the areas of stability analysis, optimization, and smart grid technologies. Researchers are exploring innovative methods to ensure the stability of power systems with increasing penetration of inverter-based resources and renewable energy sources. The use of advanced machine learning algorithms, such as support vector machines and graph neural networks, is becoming prominent in predicting stability regions and optimizing power system operations. Additionally, the integration of blockchain technology and artificial intelligence is being investigated to enhance the security and efficiency of energy transactions and grid management. Noteworthy papers in this area include the proposal of an adaptive sampling method for training a support vector machine classifier to estimate the probability of stability of a power system, and the development of a parallel graph neural network method for efficient extreme operating condition search in online relay setting calculation. Another significant contribution is the introduction of a novel metric, energy modularity, to evaluate community partitions in energy networks and optimize collective self-sufficiency. Furthermore, the application of recursive-ARX system identification for online power-system fault detection and the use of data-driven approaches for topology correction in low-voltage networks with distributed energy resources are also noteworthy.