Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

In this paper, quasi-oppositional teaching-learning based optimization (QOTLBO) is introduced and successfully applied for solving an optimal power flow (OPF) problem in power system incorporating flexible AC transmission systems (FACTS). The main drawback of the original teaching-learning based optimization (TLBO) is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning (OBL) concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization (HDE-BBO). This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.