A modified artificial bee colony optimization for distributed generation insertion in electrical distribution networks

Optimizing the location of distributed generation units in electrical distribution networks is a well-established approach to improving grid performance, particularly for enhancing reliability and reducing operational losses. Recent large-scale blackouts in Spain and Portugal have underscored the vulnerability of power systems and the urgent need for more resilient, cost-effective, and efficient grid configurations. However, integrating distributed generation involves significant investment, operational, and maintenance costs, making optimal placement a critical challenge. Integrating optimally located distributed generation units into distribution networks using a modified artificial bee colony algorithm will substantially reduce power losses, voltage deviations, and total electricity costs compared to the conventional artificial bee colony and other state-of-the-art optimization methods. The main contributions of this work are: (1) the development of an enhanced artificial bee colony algorithm incorporating inverse population initialization and a bounded-round search strategy to improve diversity and avoid premature convergence; (2) the introduction of a chaotic cosine-based neighborhood search around the global best to accelerate convergence and enhance solution quality; and (3) the application and validation of the proposed method on IEEE 33-bus and 69-bus distribution systems, demonstrating its effectiveness in reducing power losses and improving voltage profiles. The proposed algorithm achieved reductions in total power loss of up to 82.33% for the IEEE 33-bus system and 86.6% for the IEEE 69-bus system, outperforming the basic artificial bee colony and other recent optimization techniques. The findings confirm that the modified artificial bee colony algorithm is a robust and efficient tool for distributed generation allocation, providing significant improvements in grid resilience, reliability, and operational efficiency in the face of increasing energy demands and instability risks.