HEURISTIC METHODS TO SOLVE OPTIMAL POWER FLOW PROBLEM

Abstract

Optimal Power Flow (OPF) is one of the most effective tools for both analysis of current and planning of new power systems. The Manuscript is about an Artificial Intellicence (AI) application based on Heuristic methods can solve OPF problems with an more extreme accuracy compared to conventional methods. In this paper, the total hourly generation cost of generator units are minimized as an objective function to meet the load demand and system losses. Real Coded Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods developed using MATLAB are applied to IEEE 14 and IEEE 30 standart test systems to solve OPF problem. In consequence of the OPF carried with the use of PSO and GA, the optimum solutions were compared to similar studies in the literature. It was determined that the PSO algorithm developed within the scope of this paper provides lower-cost results than GA developed for this study and the GA studies that are  present in the literature.

Keywords

• Optimal power flow, heuristic methods, particle swarm optimization, real coded genetic algorithm, total generation cost minimization.

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23. Power Systems, 2010. Rengin Idil Cabadag received the B.Sc. degree in Electrical Engineering from Yildiz Technical University in 2009. Then she got her M.S. degree from Energy Institute of Istanbul Technical University in 2012. She is currently a Research Assistant at TU Dresden in Germany. Her research interests include Power
24. Systems and Impact of Renewable Energy Sources on Power Systems. Belgin Emre Turkay received the B.Sc, M.S. and Ph.D. degrees in Electrical Engineering from Istanbul Technical University, Turkey. She is currently working as a Professor at Istanbul Technical University and she is also Istanbul Technical University Electrical Engineering Program Coordinator since 2011. Her research areas consist of
25. Distribution Systems, Electric Power Generation, Power Quality, Automation and Control, Power System Operation, Control and Optimization, Renewable Energy Sources.