Multi-Objective Optimization for DG Placement and Sizing in Unbalanced Distribution Networks

The integration of Distributed Generation (DG) in distribution networks plays a critical role in modernizing these systems, transitioning them from passive to active configurations. This study focuses on optimizing the placement and sizing of DG units within unbalanced distribution systems, aiming to minimize power losses and improve voltage profiles. To address these objectives, three advanced optimization techniques are employed: the Flower Pollination Algorithm (FPA), Grasshopper Optimization Algorithm (GOA), and Adaptive GOA (AGOA). The Direct-ZBR method is used for unbalanced load flow analysis. Results indicate that AGOA achieves the best voltage regulation and minimizes power losses by 92.7%, while FPA offers the most balanced solution between power loss reduction and voltage profile improvement. GOA converges rapidly, though its overall performance is slightly inferior to AGOA and FPA. These findings demonstrate the effectiveness of using advanced optimization methods for enhancing the efficiency and stability of unbalanced distribution networks with DG integration.