Dynamic Reconfiguration for Distribution Networks with High Renewable Energy Penetration Based on Optimization-Enhanced Stability Assessment
With the increasing penetration of renewable energy sources, active distribution networks are facing severe operational challenges caused by stochastic power fluctuations, which may lead to voltage stability degradation and frequent topology adjustment requirements. Existing dynamic reconfiguration methods mainly rely on repeated power flow calculations or predefined stability evaluations, resulting in high computational burdens and limited adaptability under uncertain renewable scenarios. To address these issues, this paper proposes a stability perception-driven dynamic reconfiguration framework for active distribution networks with high renewable penetration. First, a data-driven voltage stability margin perception model is developed to rapidly characterize the security state under diverse operating conditions. By constructing the nonlinear mapping between network topology, renewable generation, load variations, and stability indicators, repetitive stability calculations during online optimization are effectively reduced. Subsequently, a multi-objective rolling reconfiguration strategy is established to coordinate voltage security, active power loss, and load distribution under stochastic renewable fluctuations. An adaptive evolutionary optimization method is employed to search feasible topology schemes, and the TOPSIS approach is adopted to determine the final compromise solution. Case studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed framework improves voltage stability performance, reduces active power losses, and decreases computational costs compared with conventional approaches. The results verify the effectiveness of the proposed method in enhancing the adaptability and operational resilience of active distribution networks under high renewable penetration conditions.