Optimal power flow based on gray wolf optimization algorithm using probability density functions extraction considering wind power uncertainty

In recent years, utilization of the renewable based power plants has become widespread in the power systems. One of the most widely used renewable based power plants is wind power plants. Due to the utilization of wind energy to generate electricity, wind turbines have not emitted any environmental pollution. Thus, in addition to economic benefits, utilization of these power plants is of great interest from the environmental view point. As the uncertainty in the generated power of these plants is due to the fluctuating nature of wind speed, some problems related to the power system operation including the optimal Power Flow (OPF) issue are revealed. In this paper, instead of using the predetermined probability distribution functions (such as Weibull distribution), the probability density functions extracted based on previous information of wind power plants are used to consider the generated power uncertainty of the wind power plants. The OPF issue has been modeled as an optimization problem aimed at minimizing the network operating cost. The direct power flow constraints are used to model the power grid. To evaluate the above method, the uncertainty scenarios are divided into two categories of sample scenarios and tests that the network performance in sample scenarios will be evaluated by test scenarios. To solve the above optimization problem, a new Random Walk Gray Wolf Optimization (RWGWO) has been used. The simulation of the above method has been performed using MATLAB software for two test networks of IEEE 30 buses and IEEE 118 buses. The simulation results indicate that the utilization of the extracted probability density function has been more consistent with the actual power network conditions and therefore has reduced the network costs as compared to the predetermined probability density function.