Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping

Min-Seung Ko; Seonghan Kim; Jae-Kyeong Kim; Taesik Nam; Hao Zhu; Kyeon Hur

doi:10.35833/MPCE.2025.000644

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Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping

Original Paper | 更新时间：2026-09-28

- Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1820-1832(2026)
- 作者机构：
  
  1.Chandra Family Department of Electrical and Computer Engineering (ECE), The University of Texas at Austin, Austin, USA
  2.2.School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
  3.Korea Electrotechnology Research Institute, Uiwang, 16029, Republic of Korea
  4.Korea Institute of Industrial Technology, Gwangju, Republic of Korea
- 作者简介：
  
  Min-Seung Ko received the B.S. and Ph.D. degrees in electrical engineering from Yonsei University, Seoul, Republic of Korea, in 2018 and 2024, respectively. He is currently a Postdoctoral Fellow of electrical and computer engineering at The University of Texas at Austin, Austin, USA. In 2024, he was a Senior Researcher with the Net-zero Laboratory, Gwangmyung, Republic of Korea. His current research interests include data-driven planning and operation of inverter-based resource (IBR)-dominant power system, stochastic stability analysis, and seamless integration of artificial intelligence (AI) datacenter. Email: kms4634500@utexas.edu
  Seonghan Kim received the B.S. degree in electrical engineering from Konkuk University, Seoul, Republic of Korea, in 2019. He is currently working toward the Ph.D. degree in electrical engineering from Yonsei University, Seoul, Republic of Korea. His research interests include power system dynamics, and power system modeling and stability analysis. Email:seonghan07@yonsei.ac.kr
  Jae-Kyeong Kim received the B.S. and Ph.D. degrees in electrical engineering from Yonsei University, Seoul, Republic of Korea, in 2012 and 2018, respectively. In 2018, he was a Postdoctoral Researcher at Yonsei University. Since December 2018, he has been with Korea Electrotechnology Research Institute (KERI), Uiwang, Republic of Korea. His research interests include power system dynamics, power system modeling and stability analysis, power system model validation, data-driven analysis, and application of deep learning. Email: jkkim@keri.re.kr
  Taesik Nam received the B.S. and Ph.D. degrees in electrical engineering from Yonsei University, Seoul, South Korea, in 2008 and 2015, respectively. He was an R&D engineer with LS Electric, Anyang, Republic of Korea, between 2015 and 2019. Since September 2019, he has been with the Korea Institute of Industrial Technology (KITECH), Gwangju, Republic of Korea. His research interests include grid-forming inverter, integration of variable renewable energy source, distribution grid optimization, power system operation, international standards for power system, and application of machine learning and deep learning in power system. Email:tsnam@kitech.re.kr
  Hao Zhu received the B.S. degree from Tsinghua University, Beijing, China, in 2006, and the M.Sc. and Ph.D. degrees from the University of Minnesota, Minneapolis, USA, in 2009 and 2012, respectively. She is an Associate Professor of electrical and computer engineering at The University of Texas at Austin, Austin, USA. Her research interests include machine learning for power system operation and resilience enhancement, and nexus between power grid and new cyber/computing requirements such as generative artificial intelligence (GenAI) workload. Email:haozhu@utexas.edu
  Kyeon Hur received the B.S. and M.S. degrees in electrical engineering from Yonsei University, Seoul, Republic of Korea, in 1996 and 1998, respectively, and the Ph.D. degree in electrical and computer engineering from The University of Texas at Austin, Austin, USA, in 2007. He has rejoined Yonsei University since 2010 and leads a smart-grid research group. His research interests include dynamic performance modeling and analysis of power grid with high level of stochastic and power electronic-interfaced resources, and high-voltage direct current (HVDC) and flexible AC transmission system (FACTS) control. Email:khur@yonsei.ac.kr
- 基金信息：
- DOI：10.35833/MPCE.2025.000644
  CLC：
- Received：13 July 2025，
  
  Revised：2025-11-07，
  
  Accepted：05 February 2026，
  
  Online First：27 April 2026，
  
  Published：15 September 2026
- 稿件说明：
移动端阅览
Ko Min-Seung,Kim Seonghan,Graduate ,et al.Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1820-1832.
DOI：

Ko Min-Seung,Kim Seonghan,Graduate ,et al.Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1820-1832. DOI： 10.35833/MPCE.2025.000644.

摘要

Abstract

This paper proposes a structure-inspired parameter estimation method for the composite load model with distributed generation (CMPLDWG) developed by Western Electricity Coordinating Council (WECC). The high dimensionality and strong nonlinearity of this model

due to the aggregated distributed energy resource (DER_A) component

greatly complicate the reliable parameter estimation. We put forth a parameter interdependency analysis to partition the entire parameter space into smaller subsets

thereby decomposing the original high-dimensional estimation problem into　multiple tractable subproblems. After applying the interdependency-based parameter grouping

the estimation for each subset is performed using both the Levenberg-Marquardt (LM) algorithm and the enhanced snake optimizer (ESO)

demonstrating the solver-agnostic improvements in the convergence stability and estimation accuracy. An initialization strategy is developed to improve the robustness of subsequent optimization. Case studies in the New England 68-bus system confirm that the interdependency-based parameter grouping significantly improves the convergence speed

numerical stability

and estimation accuracy across different disturbance scenarios. The ability of the proposed estimation method is further validated through real-world measurements

and it can be broadly applicable to other modeling problems.

关键词

Keywords

references

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