Optimal Power Flow Solution Based on Multi-verse Optimizer Algorithm Incorporating Renewable Energy Sources

Due to the escalating complexity and rapid growth of electrical power networks, transmission lines are experiencing unprecedented stress. This leads to overloads, increased power losses, and heightened operational costs. Addressing these challenges requires the execution of optimal power flow (OPF) for secure and economical power system functionality. This research introduces the application of a recent multi-verse optimizer (MVO) algorithm for solving OPF problems within modern power systems. These systems incorporate both thermal power plants and renewable energy sources (RESs), such as wind and solar energy facilities. The uncertainty associated with the power generation of wind and solar power plants is effectively addressed using Weibull and Log-normal probability density functions, respectively. The proposed algorithm's efficacy and efficiency is evaluated against various optimization techniques considering the modified IEEE-30 bus system. Simulation outcomes indicate that, relative to alternative approaches, the MVO algorithm effectively finds the most precise optimal solution to the power systems OPF problems.