Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate
<|reference_start|>Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate: The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. In this paper, we propose a data-driven surrogate model based on implicit machine learning -- specifically deep equilibrium layers and neural ordinary differential equations -- to learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.<|reference_end|>
@article{bossart2024acceleration,
    title={Acceleration of Power System Dynamic Simulations using a Deep
  Equilibrium Layer and Neural ODE Surrogate},
    author={Matthew Bossart, Jose Daniel Lara, Ciaran Roberts, Rodrigo
  Henriquez-Auba, Duncan Callaway, Bri-Mathias Hodge},
    journal={arXiv preprint arXiv:2405.06827},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06827},
    primaryClass={eess.SY cs.SY}
}