The recent developments in the research area of power systems and energy management are marked by a shift towards more integrated, robust, and efficient solutions. A notable trend is the adoption of end-to-end frameworks that jointly optimize various aspects of system operation, such as forecast models and decision conservativeness, leading to enhanced cost efficiency and reliability. These frameworks often leverage advanced mathematical techniques, including gradient descent and convex optimization, to handle the complexities of modern power grids. Another significant direction is the incorporation of physics-based models and probabilistic approaches to better understand and manage the behavior of renewable energy sources and storage systems. This includes the development of new parameter estimation methods and the use of behavioral aggregation techniques to simplify complex grid dynamics while maintaining accuracy. Additionally, there is a growing focus on safety and robustness in system control, with the introduction of safe trajectory sets and model-free detection algorithms for critical issues like internal short circuits in battery packs. These advancements collectively aim to address the challenges posed by the increasing complexity and variability in modern power systems, paving the way for more sustainable and reliable energy management solutions.

Noteworthy papers include one that presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs, and another that introduces a model-free Koopman Mode-based detection algorithm for internal short circuits in lithium-ion battery packs.