Multi-objective sizing and location of DG in distribution network by hybrid gorilla troops optimization-genetic algorithm: a real case study.

The efficient incorporation of distributed generators optimal DG in distribution networks represents a fundamental strategy for ensuring economical and efficient power system operation. The optimal DG problem can be described as a Mixed Discrete-Continuous Optimization Problem (MDCOP). In this study, a new hybrid approach combining Gorilla Troops Optimization and Genetic Algorithm, namely the Hybrid GTO-GA, is proposed. The proposed hybrid is utilized to decide the optimal sizing and location of DG units in distribution networks to reduce power losses and ameliorate the voltage profile, either individually (i.e., single-objective) or simultaneously (i.e., multi-objective). The effectiveness of the suggested hybrid GTO-GA approach is examined using 23 test functions and applied to two practical test systems of different sizes: the IEEE 33-bus distribution system and a real 143-bus system. The simulation findings display that the hybrid GTO-GA approach ensures better convergence and improved exploration of the search space compared to standalone GTO and GA algorithms. It has also proven effective in significantly reducing economic losses by minimizing power loss and voltage deviation. In the IEEE 33-bus system, yearly economic losses decreased from 91,988.14 $ to 13,773.960 $, resulting in savings of 78,214.18 $. Similarly, in the real143-bus system, losses were reduced from 177,898.08 $ to 32,401.56 $, yielding savings of 145,496.52 $.