Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

• Published in: IEEE Power & Energy Society General Meeting pp. 1 - 5
• Main Authors: Chung, Sungjoo, Zhang, Ying, Wang, Zhaoyu, Ding, Fei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.07.2024
• Subjects: Accuracy; Computational efficiency; Computational modeling; Data models; data-driven; distributed energy resources; Distributed power generation; Hybrid power systems; Load flow; Load modeling; Measurement uncertainty; outliers; power flow; regression; Robustness; Unbalanced distribution systems
• ISSN: 1944-9933
• DOI: 10.1109/PESGM51994.2024.10689162

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. Moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (DERs). To address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines Taylor series expansion knowledge with a data-driven regression technique. The proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified Taylor expansion. To mitigate the approximation loss that surges due to the DER integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. Comparative analysis in the 13-bus and 123-bus IEEE unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.