Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

Conference · 11 January 2023

DOI:https://doi.org/10.1109/ISGTAsia54193.2022.10003627· OSTI ID:1957999

Tan, Bendong; Zhao, Junbo; Liu, Weijia; Duan, Nan

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Grid Modernization Laboratory Consortium (GMLC)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1957999
• Report Number(s): NREL/CP-5D00-85359; MainId:86132; UUID:ebe6c0d9-616e-48bd-9df2-06a08dbf1761; MainAdminID:68791
• Country of Publication: United States
• Language: English

Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
deep kernel learning
interpretability
surrogate model
uncertainty quantification
voltage stability