An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters

Abstract

The increasing integration of renewable energy sources and power electronic devices has significantly increased the complexity of modern power systems, making modeling and simulation challenging due to multi-time scale dynamics and multi-physics coupling. To address these challenges, this paper proposes a multi-level simulation framework based on unified energy flow theory. The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. A multi-level simulation architecture leveraging Field Programmable Gate Arrays (FPGAs) enables faster-than-real-time system-level simulation and real-time component-level modeling with time resolution as small as 5 nanoseconds. A DC microgrid case study with photovoltaic generation, battery storage, and power electronic converters demonstrates the proposed method, achieving up to a 500× speedup over traditional Simulink models while maintaining high accuracy. The results confirm the framework’s ability to capture multiphysics interactions, optimize energy distribution, and ensure system stability under dynamic conditions, providing an efficient and scalable solution for advanced DC microgrid simulations.