teaching learning based optimization algorithm algorithm

Optimal reactive power dispatch plays an important role in economic operation of network by reducing the loss. In this paper, the teaching learning based optimization algorithm is used to optimal reactive power dispatch and voltage control. This algorithm is an evolutionary and population based algorithm which has a great capability to solve the nonlinear problems. This problem is formulated as a mixed integer nonlinear problem including both continues and discrete variables. Optimal solution of problem contains set of generators voltage, tap changers and compensative reactive components. In the proposed approach, using real wind speed data and considering the wind uncertainty, the two points estimate power flow is used to model the uncertainties. The proposed method has been implemented on 57-bus IEEE test case. A comparing has been done between the teaching learning based optimization algorithm and particle swarm optimization and differential evolution algorithms in order to verify the efficiency of the proposed algorithm. The results demonstrate the efficiency of the proposed method in reducing losses and handling the constraints.