Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore, the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function (PDF). In this paper, three objective functions, i.e., total generation cost, transmission losses, and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the single objective optimization problems are solved using the Glowworm Swarm Optimization (GSO) algorithm, whereas the MO-OPF problems are solved using the multi-objective GSO (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 and 300 bus test systems with wind farms located at different buses in the system. The simulation results obtained show the suitability and effectiveness of proposed MO-OPF approach.