Computationally Efficient Spline-Based Modeling of DER Dynamics for Voltage Stability in Active Distribution Networks
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912693117517824 |
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| author | Asiedu, Shadrack T. Aryal, Tara Wang, Zongjie Rekabdarkolaee, Hossein Moradi Hansen, Timothy M. |
| author_facet | Asiedu, Shadrack T. Aryal, Tara Wang, Zongjie Rekabdarkolaee, Hossein Moradi Hansen, Timothy M. |
| contents | The increasing integration of Distributed Energy Resources (DERs) into power systems necessitates the accurate representation of their dynamic behavior at the transmission level. Traditional electromagnetic transient models (EMT), while effective, face scalability challenges due to their reliance on detailed system information. Data-driven approaches, such as System Identification (SysID), offer a promising alternative by modeling system dynamics without detailed system knowledge. However, SysID and similar methods are computationally intensive, requiring the computation of complex ordinary differential equations (ODEs) or transfer functions estimation. This makes them less effective for real-time operation. We therefore propose a novel data-driven approach that simplifies the modeling of DERs dynamics by leveraging B-splines to transform discrete system data into continuous differentiable functions. This enables the estimation of lower order linear ordinary differential equations with simple linear regression to represent the underlying dynamics at a very low computational cost. Furthermore, the extracted dynamic equations are discretized by the backward Euler method for potential integration into discrete-time power dispatch models. Validation results indicate a goodness-of-fit (GoF) of 98.74%, comparable to the 99.03% GoF of the SysID method, yet, 4.8 times faster. Our proposed model's execution time of less than one minute makes it more suitable for real-time applications in power system operations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_04917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient Spline-Based Modeling of DER Dynamics for Voltage Stability in Active Distribution Networks Asiedu, Shadrack T. Aryal, Tara Wang, Zongjie Rekabdarkolaee, Hossein Moradi Hansen, Timothy M. Systems and Control The increasing integration of Distributed Energy Resources (DERs) into power systems necessitates the accurate representation of their dynamic behavior at the transmission level. Traditional electromagnetic transient models (EMT), while effective, face scalability challenges due to their reliance on detailed system information. Data-driven approaches, such as System Identification (SysID), offer a promising alternative by modeling system dynamics without detailed system knowledge. However, SysID and similar methods are computationally intensive, requiring the computation of complex ordinary differential equations (ODEs) or transfer functions estimation. This makes them less effective for real-time operation. We therefore propose a novel data-driven approach that simplifies the modeling of DERs dynamics by leveraging B-splines to transform discrete system data into continuous differentiable functions. This enables the estimation of lower order linear ordinary differential equations with simple linear regression to represent the underlying dynamics at a very low computational cost. Furthermore, the extracted dynamic equations are discretized by the backward Euler method for potential integration into discrete-time power dispatch models. Validation results indicate a goodness-of-fit (GoF) of 98.74%, comparable to the 99.03% GoF of the SysID method, yet, 4.8 times faster. Our proposed model's execution time of less than one minute makes it more suitable for real-time applications in power system operations. |
| title | Computationally Efficient Spline-Based Modeling of DER Dynamics for Voltage Stability in Active Distribution Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.04917 |