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
- Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping
- MPCE 2026年14卷第5期页码：1820-1832
- 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：
  
  the International Cooperation Research Program through National Research Foundation of Korea grant funded by the Korea Government (MSIT)(RS-2023-00218377);Basic Science Research Program through National Research Foundation of Korea grant funded by the Korea Government (MSIT)(2021R1A2C209550313);the US National Science Foundation(2150571);the US Department of Energy grant from the Advanced Manufacturing Office
- DOI：10.35833/MPCE.2025.000644
  中图分类号：
- 收稿：2025-07-13，
  
  修回：2025-11-07，
  
  录用：2026-02-05，
  
  网络首发：2026-04-27，
  
  纸质出版：2026-09-15
- 稿件说明：
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Min-Seung Ko, Seonghan Kim, Jae-Kyeong Kim, 等. Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping[J]. MPCE, 2026,14(5):1820-1832.

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.
Min-Seung Ko, Seonghan Kim, Jae-Kyeong Kim, 等. Structure-inspired Parameter Estimation of Composite Load Model with Distributed Generation via Interdependency-based Parameter Grouping[J]. MPCE, 2026,14(5):1820-1832. DOI： 10.35833/MPCE.2025.000644.

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

Y. Lin and J. Wang, “A generic dynamic load model framework,” Journal of Modern Power Systems and Clean Energy , vol. 10 , no. 2 , pp. 399 - 406 , Mar. 2022 .

WECC, “WECC dynamic composite load model specifications,” Western Electricity Coordinating Council (WECC), Tech. Rep., 2015.

Z. Ma, Z. Wang, Y. Wang et al. , “Mathematical representation of WECC composite load model,” Journal of Modern Power Systems and Clean Energy , vol. 8 , no. 5 , pp. 1015 - 1023 , Sept. 2020.

P. Pourbeik, J. Weber, D. Ramasubramanian et al. , “An aggregate dynamic model of distributed energy resources or power system stability studies,” CIGRE Science & Engineering , vol. 14 , pp. 38 - 48 , Jun. 2019 .

J.-K. Kim, S. Lee, J. S. Kim et al. , “The need for modeling the impact of behind-the-meter generation trip on primary frequency response through operational experiences in Korea power system,” IEEE Transactions on Power Systems , vol. 37 , no. 2 , pp. 1661 - 1664 , Mar. 2022 .

N. Rao and S. Tripathy, “Power system static state estimation by the Levenberg-Marquardt algorithm,” IEEE Transactions on Power Apparatus and Systems , no. 2 , pp. 695 - 702 , Mar. 1980 .

J. -K. Kim, K. An, J. Ma et al. , “Fast and reliable estimation of composite load model parameters using analytical similarity of parameter sensitivity,” IEEE Transactions on Power Systems , vol. 31 , no. 1 , pp. 663 - 671 , Mar. 2015 .

S. Son, S. H. Lee, H. Choi et al. , “Improvement of composite load modeling based on parameter sensitivity and dependency analyses,” IEEE Transactions on Power Systems , vol. 29 , no. 1 , pp. 242 - 250 , Sept. 2013.

B. Tan, J. Zha o, and N. Duan, “Amortized Bayesian parameter estimation approach for WECC composite load model,” IEEE Transactions on Power Systems , vol. 39 , no. 1 , pp. 1517 - 1529 , Feb. 2023 .

J. Ma, D. Han, R. He et al. , “Reducing identified parameters of measurement-based composite load model,” IEEE Transactions on Power Systems , vol. 23 , no. 1 , pp. 76 - 83 , Feb. 2008 .

J.-K. Kim and K. Hur, “On the perturbation size of the finite difference method for trajectory sensitivity-based assessment of power system dynamics with non-smooth behavior,” International Journal of Electrical Power & Energy Systems , vol. 155 , p. 109647 , Jan. 2024 .

S. R. Khazeiynasab, J. Zhao, and N. Duan, “WECC composite load model parameter identification using deep learning approach,” in Proceedings of 2022 IEEE PES General Meeting , Denver, USA, Jul. 2022 , pp. 1 - 5 .

F. Bu, Z. Ma, Y. Yuan et al. , “WECC composite load model parameter identification using evolutionary deep reinforcement learning,” IEEE Transactions on Smart Grid , vol. 11 , no. 6 , pp. 5407 - 5417 , Nov. 2020 .

X. Wang, Y. Wang, D. Shi et al. , “Two-stage WECC composite load modeling: a double deep Q -learning networks approach,” IEEE Transactions on Smart Grid , vol. 11 , no. 5 , pp. 4331 - 4344 , Sept. 2020.

P. Regulski, D. S. Vilchis-Rodriguez, S. Djurović et al. , “Estimation of composite load model parameters using an improved particle swarm optimization method,” IEEE Transactions on Power Delivery , vol. 30 , no. 2 , pp. 553 - 560 , Feb. 2014 .

J. Ma, R. He, Z. Y. Dong et al. , “Measurement-based load modeling using genetic algorithms,” in Proceedings of 2007 IEEE Congress on Evolutionary Computation , Singapore, Sept. 2007, pp. 2909 - 2916 .

F. A. Hashim and A. G. Hussien, “Snake optimizer: a novel meta-heuristic optimization algorithm,” Knowledge-based Systems , vol. 242 , p. 108320 , Apr. 2022 .

L. Yao, P. Yuan, C.-Y. Tsai et al. , “ESO: an enhanced snake optimizer for real-world engineering problems,” Expert Systems with Applications , vol. 2023 , p. 120594 , Nov. 2023 .

N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES),” Evolutionary Computation , vol. 11 , no. 1 , pp. 1 - 18 , Mar. 2003 .

K. Zhang, H. Zhu, and S. Guo, “Dependency analysis and improved parameter estimation for dynamic composite load modeling,” IEEE Transactions on Power Systems , vol. 32 , no. 4 , pp. 3287 - 3297 , Nov. 2016 .

NERC, “Parameterization of the DER_A model for aggregate DER,” North American Electric Reliability Corporation (NERC), Tech. Rep., 2023.

P. Pourbeik, J. J. Sanchez-Gasca, J. Senthil et al. , “Generic dynamic models for modeling wind power plants and other renewable technologies in large-scale power system studies,” IEEE Transactions on Energy Conversion , vol. 32 , no. 3 , pp. 1108 - 1116 , Dec. 2016 .

S. Guo and T. J. Overbye, “Parameter estimation of a complex load model using phasor measurements,” in Proceedings of 2012 IEEE Power and Energy Conference at Illinois , Champaign, USA, Feb. 2012 , pp. 1 - 6 .

S. A. Foroutan and A. Srivastava, “Generator model validation and calibration using synchrophasor data,” in Proceedings of 2019 IEEE Industry Applications Society Annual Meeting , Baltimore, USA, Sept. 2019, pp. 1 - 6 .

A. Chen, Z. Ren, W. Guo et al. , “An efficient adaptive differential grouping algorithm for large-scale black-box optimization,” IEEE Transactions on Evolutionary Computation , vol. 27 , no. 3 , pp. 475 - 489 , Jun. 2022 .

Y. Sun, M. Kirley, and S. K. Halgamuge, “A recursive decomposition method for large scale continuous optimization,” IEEE Transactions on Evolutionary Computation , vol. 22 , no. 5 , pp. 647 - 661 , Oct. 2017 .

A. Srivastava, J. Zhao, H. Zhu et al. , “Distribution system behind-the-meter DERs: estimation, uncertainty quantification, and control,” IEEE Transactions on Power Systems , vol. 40 , no. 1 , pp. 1060 - 1077 , Jan. 2024 .

NERC, “Dynamic load modeling,” North American Electric Reliability Corporation (NERC), Tech. Rep., 2016.

NERC, “Parameterization of the DER_A model,” North American Electric Reliability Corporation (NERC), Tech. Rep., 2019.

S. Li, S. Lewandowsky, and V. E. DeBrunner, “Using parameter sensitivity and interdependence to predict model scope and falsifiability,” Journal of Experimental Psychology: General , vol. 125 , no. 4 , p. 360 , Dec. 1996 .

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