Authors : M.M Muhammad; Abdulsalam B. Muhammad; Bukar U. Musa; Lawan Dauda

Volume/Issue : Volume 8 - 2023, Issue 12 - December

Google Scholar : http://tinyurl.com/49ykjzvk

Scribd : http://tinyurl.com/47uhz5eb

DOI : https://doi.org/10.5281/zenodo.10464623

Abstract : The optimal location and sizing of Distributed Generation (DG) are crucial factors in integrating DGs into a network to minimize power losses and improve the voltage profile. This paper presents a hybridized solution that combines the Crow Search Algorithm (CSA) and Smell Agent Optimization (SAO) algorithm for determining the optimal location and sizing of DGs. The CSA, SAO, and the proposed CSA-SAO method were modeled and applied to validate their effectiveness using the standard IEEE-33 test bus. The results were compared with the base case scenario, which did not include DGs. For the 33-bus system, the CSA method achieved a 37.54% reduction in system losses and a 40.99% improvement in the overall voltage profile. The SAO method resulted in a 45.43% reduction in losses and a 54.88% improvement in the average voltage profile. The proposed hybrid CSA-SAO method demonstrated even better performance, with a 47.56% reduction in losses and a 63.47% improvement in the average voltage profile. This comparison indicates that the proposed hybrid model is valid for solving optimal DG allocation problems. The results suggest that combining the CSA and SAO algorithms in a hybrid approach produces superior results compared to using the methods independently.

Keywords : Crow Search Algorithm; Smell Agent Optimization; Voltage Profile; Power Losses; Distributed Generation.