A Novel Method for Dynamic Load Restoration of Transmission Networks After Faults with Large-Scale Renewable Energy Integration

The large-scale integration of renewable energy sources introduces significant temporal variability into power systems, rendering traditional static network reconfiguration methods inadequate for post-fault load restoration in transmission networks. To address this challenge, a novel dynamic restoration method is proposed that explicitly accounts for uncertainty in renewable energy. Wind and photovoltaic output uncertainties are modeled using Weibull and Beta distributions, respectively. Typical operating scenarios are generated via Latin hypercube sampling combined with an improved K-means clustering algorithm, while the node contraction method is employed to quantify the topological importance of load buses. A multi-period optimization model is formulated to maximize weighted load restoration and minimize line switching costs, subject to comprehensive constraints including network topology, AC power flow, and short-circuit current limits. This mixed-integer nonlinear problem is solved using a GA-PSO parallel hybrid algorithm. Simulation results on the modified IEEE 39-bus system under four typical renewable energy scenarios show that the proposed method can generate scenario-specific restoration schemes and that cross-scenario feasibility verification is necessary when the source-load imbalance is severe.