Presenting a stochastic framework for resilient self-healing active distribution networks with integrated distributed generation

This study introduces a novel stochastic framework for self-healing active distribution networks, addressing critical challenges posed by the integration of distribution networks (DNs), such as increased short-circuit capacity, voltage instability, and load variability. By employing graph theory for optimal network restructuring and incorporating stochastic programming to account for load uncertainties, the proposed method ensures robust and efficient fault recovery. The framework integrates innovative security enhancements, including adaptive short-circuit capacity minimization and dynamic loadability improvement. Using the pelican optimization algorithm (POA), the method achieves superior performance in real-world scenarios. The proposed framework’s unique integration of stochastic modeling, adaptive security mechanisms, and optimization techniques sets a new benchmark for self-healing smart grids. Simulations on IEEE 33-bus and 83-bus networks demonstrate the framework’s efficacy. The results highlight a 20% reduction in fault recovery time and a 12.5% decrease in power losses.