Multiobjective Predictability-Based Optimal Placement and Parameters Setting of UPFC in Wind Power Included Power Systems

Uncertainty management is a challenging task in decision making of the operators of the power systems. Prediction of the system state is vital for the operation of a system with stochastic behavior especially in a power system with a significant amount of renewable energies such as wind power. Predictable power systems are in more interest of operators, of course. This paper proposes a multi-objective framework for optimal placement and parameters setting of a unified power flow controller (UPFC) considering system predictability. The well-known multiobjective nondominated sorting genetic algorithm is implemented to handle various objective functions such as active power losses and predictability of system in the presence of operational constraints and uncertainties. The point estimate method is used for modeling probabilistic nature of the wind power. Using the proposed method, statistical information of voltage magnitude and apparent power of converters of UPFCs can be obtained, which are very useful in making decision on the sizing of UPFCs. Comprehensive discussions are provided using the simulations on the IEEE 57-bus test system. Also, in order to validate the obtained results, a multiobjective particle swarm optimization algorithm is implemented and the results of two algorithms are compared with each other.