1. Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate
- Author
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Bossart, Matthew, Lara, Jose Daniel, Roberts, Ciaran, Henriquez-Auba, Rodrigo, Callaway, Duncan, and Hodge, Bri-Mathias
- Subjects
Electrical Engineering and Systems Science - Systems and Control - Abstract
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., Comment: This work has been submitted to the IEEE Transactions on Energy Conversion for possible publication
- Published
- 2024