Service restoration strategy for active distribution networks considering source-load uncertainty

• Model comparison and experimental validation are conducted. • A service restoration strategy for ADNs considering source-load uncertainty is proposed. • Proposed the priority restoration evaluation coefficient to quantify load criticality levels and time-varying characteristics for prioritized critical load restoration. • A robust restoration optimization model is developed, integrating uncertainty constraints to simulate global system fluctuations. Active distribution networks (ADNs) incorporate numerous distributed generations (DGs) and energy storage systems. When sudden faults occur in the system, service restoration can be enhanced through microgrid formation and network reconfiguration. However, the uncertainties inherent in DGs output and load demands complicates the service restoration process for distribution networks. Therefore, this paper proposes a service restoration strategy for ADNs that explicitly accounts for source-load uncertainty. First, load priority restoration evaluation coefficients are calculated based on load criticality levels and time-varying demand characteristics. Building upon this, An microgrid formation model incorporating DGs and energy storage systems is built. This model is then solved using Depth-First Search (DFS) and Breadth-First Search (BFS) algorithms to prioritize the restoration of critical loads. Subsequently, a robust restoration optimization model for the ADN is developed. To model the uncertainties in DGs output and load demands, a polyhedral uncertainty set is introduced. The resulting two-stage optimization problem is solved using the Column-and-Constraint Generation (C&CG) algorithm to achieve the restoration of remaining loads. Finally, simulation analysis is conducted on a modified IEEE 69-node active distribution network test system. The results demonstrate that the proposed method exhibits strong robustness under fluctuating DGs output and time-varying load demands.