Gray-Box Modeling for Distribution Systems With Inverter-Based Resources: Integrating Physics-Based and Data-Driven Approaches

Journal Article · 23 April 2024 · IEEE Transactions on Industry Applications

DOI: https://doi.org/10.1109/tia.2024.3392710 · OSTI ID:2441424

Zhang, Junhui  ^[1]; Men, Yuxi  ^[1]; Ding, Lizhi  ^[1]; Lu, Xiaonan  ^[1]; Du, Wei  ^[2]

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1. Purdue Univ., West Lafayette, IN (United States)
2. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Here, in this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, we utilize partial physical knowledge of the system, including the inverters’ structures and control diagrams, as well as the equivalent network model simplified through Kron reduction. The white-box model containing unknown parameters is then constructed with mathematical equations and an optimization-based method is subsequently employed to identify these unknown parameters within the white-box model. Next, the graybox modeling framework is then constructed by embedding the output variables of the white-box model into the input vector of a black-box model (represented using a neural network). Finally, the black-box section is trained using the collected input-output datasets and the gray-box model is then obtained. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 2441424
• Report Number(s): PNNL-SA--193812
• Journal Information: IEEE Transactions on Industry Applications, Journal Name: IEEE Transactions on Industry Applications Journal Issue: 4 Vol. 60; ISSN 0093-9994
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English