Multi-Objective TLBO and GWO-based Optimization for Placement of Renewable Energy Resources in Distribution System

The use of renewable solar and wind resources as distributed generation sources in distribution networks has been welcomed by network operators. In order to exploit the maximum benefits using these products, location installation their capacity should be determined optimally network. this paper, optimize placement panels turbines with aim reducing losses improving reliability based on Energy Not Supplied subscribers (ENS), a multi-objective evolutionary algorithm fuzzy decision method, called Multi-Objective Hybrid Training Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) proposed that High optimization speed not trapped at all optimal local. At first, candidate buses are set for Loss Sensitivity Factor (LSF). Then method is used determine through bases. Proposed issues have implemented single-objective multiobjective manner 33 bus IEEE radial Also, effect distributing characteristics evaluated. results obtained from compared other algorithms demonstrate superiority losses, reliability, increasing financial profit Simulation show better performance comparison Teaching-Learning Optimization (TLBO) Grey Optimiser (GWO) methods past studies achieve results. leads further reduction improvement criterion.