Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914985106472960 |
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| author | Bossart, Matthew Lara, Jose Daniel Roberts, Ciaran Henriquez-Auba, Rodrigo Callaway, Duncan Hodge, Bri-Mathias |
| author_facet | Bossart, Matthew Lara, Jose Daniel Roberts, Ciaran Henriquez-Auba, Rodrigo Callaway, Duncan Hodge, Bri-Mathias |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_06827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate Bossart, Matthew Lara, Jose Daniel Roberts, Ciaran Henriquez-Auba, Rodrigo Callaway, Duncan Hodge, Bri-Mathias Systems and Control 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. |
| title | Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2405.06827 |