Optimal Sizing and Localization of Multiple Distributed Generations in Distribution Systems Using an Improved Grey Wolf Optimization Algorithm

Abstract

This study investigates the impact of the localization and sizing of distributed generations in distribution systems using a combined approach of improved grey wolf optimizer (IGWO) and Newton-Raphson load flow algorithms. The suggested method optimizes the size and position of distributed generation generating both real and reactive power while ensuring power system constraints are not violated. The suggested algorithm optimizes the location and sizing of dis-tributed generations. Nevertheless, investigations show that the proposed method outperforms the PSO optimizer and takes less calculation time. Moreover, in contrast with other meta-heuristic algorithms such as JAYA, PSO, SFO, BO, SMA, GA, and GJO, the proposed approach produces a better voltage profile of the distribution system with smaller distributed generator sizes. To demonstrate the advantages of the suggested approach, the IEEE-13, IEEE-37, and IEEE-123 bus distribution systems are used as test cases, and the outcomes are contrasted with those of other meta-heuristic methods. According to simulation data, IGWO outperforms other meta-heuristic algorithms when it comes to the quality of the solution while satisfying all system constraints.