Optimal Power Flow Solution With Stochastic Renewable Energies Using Nature Inspired Algorithm

The use of the Moth Flame Optimization (MFO) algorithm to solve optimal power flow as an objective optimization problem in power system operation and control is described in this thesis. Given the environmental consequences of pollution from fossil-fueled power plants, the optimal power flow that minimises only the overall cost of fuel appears to be no longer relevant as a single objective constraint. The optimization method, which is based on statistical models to solve optimal power flow and problems, shall be defined as a method for solving problems with a single identical objective function. Using the relevant equation, which is not violating the moth flame's system that has been developed as their base, the testing will run for a number of iterations, and after achieving the iterations, the testing will print out the output which is at their best optimal outcome, and this testing must run for a number of times to find the steady output for data collection. This method was tested on three different generation systems under varying load conditions. The results obtained using the proposed approach are comparable to those obtained using the other approaches discussed in the literature review. By the end of this study, this algorithm should have been demonstrated to be a process that is simple to use and capable of searching for a nearglobal optimal solution with significant convergence and effectiveness when compared to other algorithms