Optimal Power Flow in Presence of Wind Generation Using Artificial Hummingbird Algorithm

paper introduces an artificial hummingbird algorithm (AHA) to solve the optimal power flow (OPF) in the presence of wind generation. The wind velocity is modelled using Weibull probability distribution function (PDF) and Monte-Carlo simulation method is used to calculate the uncertainty cost of wind power generation. The objectives of this stochastic OPF problem are minimizing the total generation cost including the cost for thermal generation and the uncertainty cost of wind power generation, minimizing active power loss, and voltage magnitude deviation. A penalty-based constraint handling method is used to tackle different constraints of the OPF problem. The proposed algorithm has been tested on the IEEE 30-bus test system, and the system has been modified by the addition of a wind farm to one of the load buses. The outcomes that are achieved using AHA is compared with grey wolf optimization (GWO) algorithm. The comparison shows the supremacy of the artificial hummingbird algorithm (AHA) over GWO algorithm.