Optimal power flow incorporating stochastic wind power using Artificial gorilla troops optimizer

This paper presents the application of a new nature-inspired metaheuristic technique to solve optimal power flow (OPF) problems considering stochastic wind power. This algorithm is called the Artificial gorilla troops optimizer (GTO) which is inspired by the behaviors of gorillas. The main objective of this work is to reduce the total cost of generation (TGC) according to the optimal scheduling of thermal and wind units, satisfying the equality and inequality constraints. The Weibull probability density (PDF) function will be included in the optimization problem to model the wind power output uncertainty. The function of wind power cost includes the direct cost, the penalty cost for underestimation and the reserve cost for overestimation of available wind power. To assess the performance of the GTO algorithm, the IEEE-30 bus system will be used incorporating wind farms. A comparative study suggests that GTO produces very competitive performance compared to the Artificial hummingbird algorithm (AHA).