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

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.