Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction

Journal Article · 04 September 2023 · IEEE Transactions on Power Systems

DOI: https://doi.org/10.1109/tpwrs.2023.3311638 · OSTI ID:2283428

Su, Tong  ^[1]; Zhao, Junbo  ^[1]; Pei, Yansong  ^[1]; Ding, Fei  ^[2]

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1. University of Connecticut, Storrs, CT (United States)
2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Here this letter proposes a novel data-driven probabilistic physics-informed graph convolutional network (GCN) for active distribution system voltage prediction with PVs and EVs. It leverages both measurements and network topology to accurately and efficiently predict node voltages without the need for an accurate distribution system power flow model. The dropout-enabled Bayesian inference is developed to achieve uncertainty quantification of the voltage prediction. Thanks to the network model embedding, it also has robustness against topology changes, a key difference with existing machine learning-based approaches. Comparison results with other state-of-the-art machine learning methods on a realistic 759-node distribution system demonstrate that the proposed method can achieve better accuracy and robustness under different scenarios.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 2283428
• Report Number(s): NREL/JA--5D00-87747; MainId:88522; UUID:85450061-992f-44a1-91dd-3e022239984d; MainAdminId:71694
• Journal Information: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 6 Vol. 38; ISSN 0885-8950
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English