Low-Voltage Power Restoration Based on Fog Computing Load Forecasting and Data-Driven Wasserstein Distributionally Robust Optimization

Ruoxi Liu, Yifan Song, Yuan Gui, Hanqi Dai, Zhiyong Wang, Chengdong Yin, Qinglei Qin, Wenqin Yang and Yue Wang ()
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Ruoxi Liu: State Grid Beijing Electric Power Research Institute, Beijing 100192, China
Yifan Song: State Grid Beijing Electric Power Research Institute, Beijing 100192, China
Yuan Gui: State Grid Beijing Electric Power Research Institute, Beijing 100192, China
Hanqi Dai: State Grid Beijing Electric Power Research Institute, Beijing 100192, China
Zhiyong Wang: State Grid Beijing Electric Power Company, Beijing 100192, China
Chengdong Yin: State Grid Beijing Electric Power Research Institute, Beijing 100192, China
Qinglei Qin: State Grid Beijing Yizhuang Electric Power Company, Beijing 100071, China
Wenqin Yang: Department of Electrical Engineering, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Yue Wang: Department of Electrical Engineering, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Energies, 2025, vol. 18, issue 8, 1-20

Abstract: This paper proposes a fault self-healing recovery strategy for passive low-voltage power station areas (LVPSAs). Firstly, being aware of the typical structure and communication conditions of the LVPSAs, a fog computing load forecasting method is proposed based on a dynamic aggregation of incremental learning models. This forecasting method embeds two weighted ultra-short-term load forecasting techniques of complementary characteristics and mines real-time load to learn incrementally, and thanks to this mechanism, the method can efficiently make predictions of low-voltage loads with trivial computational burden and data storage. Secondly, the low-voltage power restoration problem is overall formulated as a three-stage mixed integer program. Specifically, the master problem is essentially a mixed integer linear program, which is mainly intended for determining the reconfiguration of binary switch states, while the slave problem, aiming at minimizing load curtailment constrained by power flow balance along with inevitable load forecast errors, is cast as mixed integer type-1 Wasserstein distributionally robust optimization. The column-and-constraint generation technique is employed to expedite the model-resolving process after the slave problem with integer variables eliminated is equated with the Karush–Kuhn–Tucker conditions. Comparative case studies are conducted to demonstrate the performance of the proposed method.

Keywords: low-voltage station area; fault self-healing; fog computing; ultra-short-term load forecasting; incremental learning; Wasserstein distributionally robust optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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