Optimal Placement and Parameters Based on Multi-Objective Predictability

When it comes to making decisions under uncertainty, electrical system operators have a challenging problem. Systems that exhibit stochastic behavior, like those that use a lot of renewable energy, demand a high level of system state prediction. Of course, Power Systems that can be accurately predicted are more appealing to operators. This research offers a multi-objective framework for the ideal positioning and parameter setting of a unified power flow controller (UPFC) taking into account system predictability. In the presence of operational constraints and uncertainties, the well-known multiobjective nondominated sorting genetic algorithm is used to manage a variety of objective functions, including active power losses and system predictability. The point estimate method is used to model wind power’s stochastic nature. The proposed method has many benefits, including the ability to gather statistical data on the voltage magnitude and perceived power of UPFC converters. The extensive simulations are carried out on the IEEE 57-bus test system. Additionally, a multi-objective particle swarm optimization technique is used, and the outcomes of two different algorithms are compared to one another to verify the outcomes attained.