The increasing integration of distributed energy resources (DERs)—such as renewable generation, energy storage systems, and responsive loads—has introduced significant variability and uncertainty into modern power systems. Traditional static energy management frameworks are often inadequate to cope with these rapid fluctuations in generation and demand. Learning-based Dynamic Energy Management Systems have emerged as a key paradigm for achieving flexible, adaptive, and real-time coordination of energy resources.
In this context, we mainly focus on applying multi-agent and learning-based methods such as reinforcement learning, deep learning under DEMS’s framework. In order to produce a more intelligent power system, we will focus on intent-driven DEMS in the future, which endow the power system with the ability of self-configuring, self-managing, and self-optimizing.
