Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function

Sunil Subedi; Bidur Poudel; Pooja Aslami; Robert Fourney; Hossein Moradi Rekabdarkolaee; Reinaldo Tonkoski; Timothy M. Hansen

doi:10.1016/j.prime.2023.100365

Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function

Sunil Subedi, Bidur Poudel, Pooja Aslami, Robert Fourney, Hossein Moradi Rekabdarkolaee, Reinaldo Tonkoski, Timothy M. Hansen

Grid Systems Modeling and Controls

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

Original language	English
Article number	100365
Journal	e-Prime - Advances in Electrical Engineering, Electronics and Energy
Volume	6
DOIs	https://doi.org/10.1016/j.prime.2023.100365
State	Published - Dec 2023

Funding

The authors thank Nischal Guruwacharya from South Dakota State University for reviewing the manuscript content and Niranjan Bhujel from the University of Maine for his assistance in interface file generation. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). ☆ This work is supported by the U.S. Department of Energy Office of Science, Office of Electricity Microgrid R&D Program, and Office of Energy Efficiency and Renewable Energy Solar Energy Technology Office under the EPSCoR grant number DE-SC0020281. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This work is supported by the U.S. Department of Energy Office of Science, Office of Electricity Microgrid R&D Program, and Office of Energy Efficiency and Renewable Energy Solar Energy Technology Office under the EPSCoR grant number DE-SC0020281.

Keywords

Computational scalability
Converter-dominated power systems
Data-driven partitioned modeling
Power electronic converters

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10.1016/j.prime.2023.100365

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title = "Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function",

abstract = "This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.",

keywords = "Computational scalability, Converter-dominated power systems, Data-driven partitioned modeling, Power electronic converters",

author = "Sunil Subedi and Bidur Poudel and Pooja Aslami and Robert Fourney and Rekabdarkolaee, {Hossein Moradi} and Reinaldo Tonkoski and Hansen, {Timothy M.}",

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AU - Subedi, Sunil

AU - Poudel, Bidur

AU - Aslami, Pooja

AU - Fourney, Robert

AU - Rekabdarkolaee, Hossein Moradi

AU - Tonkoski, Reinaldo

AU - Hansen, Timothy M.

PY - 2023/12

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N2 - This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

AB - This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

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Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function

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