The restoration of modern power systems after large-scale outages poses significant challenges due to the increasing integration of renewable energy sources (RES) and electric vehicles (EVs), both of which introduce new dimensions of uncertainty and flexibility. This paper presents a Hierarchical Modern Power System Restoration (HMPSR) model that employs a two-level architecture to enhance restoration efficiency and system resilience. At the upper level, Graph Neural Networks (GNNs) are used to predict fault locations and optimize network topology by analyzing the spatial and topological features of the grid. At the lower level, Distributionally Robust Optimization (DRO) is applied to manage uncertainty in generation and demand through scenario-based dispatch planning. The model specifically considers solar and wind power as the primary RES, and incorporates both grid-connected and mobile EVs as flexible energy resources to support the restoration process. Simulation results on an enhanced IEEE 33-bus test system demonstrate that the HMPSR model reduces restoration time by 18.6% and total cost by 15.4%, while maintaining a Grid Stability Index above 85% under high variability conditions. These results confirm the effectiveness of a tightly integrated, hierarchical strategy for power system restoration, providing a robust and adaptive framework for real-world deployment.
Alhazmi et al. (Wed,) studied this question.