A Hybrid Approach of GA- TS based Multi-Tasking Optimisation for Optimal Location and Sizing of Distributed Generation in Distribution Networks

Abstract

Using hybrid approach of genetic algorithm and tabu search algorithm based multi-tasking optimisation in distribution networks with static load models, this article has attempted to improve system performance indices for the best placement and sizing of various types of distributed generation from the perspective of minimising the overall real power loss of the distribution networks. Indicators of system performance such real power loss, reactive power loss, voltage deviation, line capacity, and voltage regulation are taken into account when developing distributed generating systems with static load models. For the 16-bus, 37-bus, and 69-bus test systems, the suggested practise has been illustrated. The suggested approach should to produce improved outcomes with high accuracy for the ideal positioning and sizing of distributed generations with static load models in the distribution networks. The distribution networks' loadability, frequency stability, and voltage stability can be improved by placing distributed generation equipment in the best possible locations and sizing it for static load models.