| For decades, researchers have developed various models and algorithms to look for the optimal power flow (OPF) in different applications. Still research is ongoing to find OPF problems for the present day power system challenges such as a liberalized market or a deregulated power system. Traditional OPF provided a tool to achieve such task and has initially dealt with fuel cost only. Later, other objectives were incorporated into the OPF in the form of single objective. Recently, with the progress in evolutionary optimization techniques, it is possible to deal with multi-objective optimization problems.;This thesis presents a true multi-objective formulation of the OPF problem taking into consideration different operational constraints in order to ensure proper system operation. A multi-objective particle swarm optimization (MOPSO) has been proposed, developed and successfully implemented to solve the multi-objective OPF. The objective functions are to minimize fuel cost, wheeling cost and congestion management using TCSC device. A clustering algorithm is applied to manage the size of the Pareto set. Also, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Two case studies have been used to test the proposed approach. The first case is IEEE 30-bus test system and the second case is 87-bus practical system. The results are compared with the available literature, it show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well distrusted Pareto solutions. |
