Physics-embedded graph learning unlocks integrated energy system modeling

Abstract

Integrated energy system plays a crucial role in global carbon neutrality. Accurate dynamic modeling is essential for optimizing integrated energy system, requiring concurrent modeling of network topology and multi-energy flow dynamics. Existing dynamic modeling approaches often struggle to solve dynamic characteristics with differential-algebraic coupling forms. With the rapid advancements in AI technologies, the integration of AI with energy systems has become not only a promising avenue but also a critical necessity for modeling the modern energy networks. This study innovatively integrates graph neural networks with physical principles, proposing an interpretable neural network methodology. The proposed energy-adapted graph to sequence model (EnG2S) represents a significant advancement for energy systems, pioneering the embedding of fluid dynamics theory to systematically reveal intrinsic connections between multi-energy flow dynamics and neural network characteristics. Overall, this study sets up a new paradigm for energy system modeling, broadening the boundaries of the integration between AI and energy systems.