multi-objective particle swarm optimization based on global margin ranking

Increased demand for electrical energy and the lack of development of the transmission network, has made transmission systems, as an intermediary between production and consumption, be more pressed. If the transmission network is not able to correct the desired interactions in the market congestion occurs in the electricity market. If this density is not controlled leading to a rise in market prices and monopolies in sales. One of the effective ways to deal with the issue is the use of FACTS devices such as TCSC, by changing the transmission line impedance, it has the ability to control the power flow of the transmission lines. Therefore, finding the capacity and location of these devices, it is a necessity for power system operators. In this thesis, an optimization method is used to find the number, the installation location and optimal capacity of the TCSC are intended to control network congestion. The goal of the optimization problem is to improve the voltage profile, improve transient stability and reduce operating costs. For this purpose, the Multi-objective particle swarm optimization based on global margin ranking has been used to solve the problem. In this research, several scenarios have been presented to illustrate the effect of adding TCSC to the network. The proposed method has been implemented on the New England Testing System, which has been used for this purpose by the Matlab software environment. The results obtained by the proposed algorithm indicate the accuracy and efficiency of the proposed method.