New classification-based global optimization approach for sustainable active power distribution networks

Abstract Active power distribution networks are evolving with the integration of distributed energy resources (DERs) and advanced optimization techniques to enhance grid flexibility and efficiency. However, radial distribution networks suffer from significant voltage drops and high-power losses due to their inherent topology and unidirectional power flow.This study proposes a novel Classification-based Global Optimization (CGO) approach that integrates electrical engineering principles with a structured optimization framework—a departure from conventional metaheuristic methods. Unlike black-box algorithms, CGO classifies distribution buses based on voltage sensitivity and power flow characteristics before applying a deterministic global optimization function for optimal placement and sizing of distributed generation (DG) and capacitor banks (CBs). The methodology is validated on IEEE 33-bus and IEEE 69-bus test systems. For the IEEE 33-bus system, simultaneous DG and CB integration achieved a 94.75% reduction in active power losses, while for the IEEE 69-bus system, losses were reduced by 98.061%. Voltage stability was significantly improved, with the voltage stability index (VSI) increasing to 0.9740 and 0.9773 for the 33-bus and 69-bus systems, respectively. The proposed CGO approach demonstrates superior computational efficiency, with average simulation times of 18.62 s (33-bus) and 21.45 s (69-bus) for combined DG and CB optimization. By enhancing energy efficiency and renewable integration, the method directly supports Sustainable Development Goals (SDGs) 7, 9, 11, and 13, offering a scalable and interpretable solution for modern active distribution networks.