Optimal Power Flow with TCSC and TCPS Modeling using Craziness and Turbulent Crazy Particle Swarm Optimization

This paper presents two new Particle swarm optimization methods to solve optimal power flow OPF in power system incorporating flexible AC transmission systems FACTS. Two types of FACTS devices, thyristor-controlled series capacitor TCSC and thyristor controlled phase shifting TCPS, are considered. In this paper, the problems of OPF with FACTS are solved by using particle swarm optimization with the inertia weight approach PSOIWA, real coded genetic algorithm RGA, craziness based particle swarm optimization CRPSO, and turbulent crazy particle swarm optimization TRPSO. The proposed methods are implemented on modified IEEE 30-bus system for four different cases. The simulation results show better solution quality and computation efficiency of TRPSO and CRPSO algorithms over PSOIWA and RGA. The study also shows that FACTS devices are capable of providing an economically attractive solution to OPF problems.

[1]  K.Y. Lee,et al.  Multi-objective Optimization of Power System Performance with TCSC Using the MOPSO Algorithm , 2007, 2007 IEEE Power Engineering Society General Meeting.

[2]  M. Saravanan,et al.  Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability , 2007 .

[3]  G. Fornarelli,et al.  Swarm Intelligence for Electric and Electronic Engineering , 2012 .

[4]  Marijn Janssen,et al.  Architectures for Enabling Flexible Business Processes: A Research Agenda , 2010, Int. J. Organ. Collect. Intell..

[5]  D. Devaraj,et al.  Genetic Algorithm approach for Optimal Power Flow with FACTS devices , 2008, 2008 4th International IEEE Conference Intelligent Systems.

[6]  A. Chatterjee,et al.  Bio-inspired fuzzy logic based tuning of power system stabilizer , 2009, Expert Syst. Appl..

[7]  Richard Chbeir,et al.  Intelligent and Knowledge-Based Computing for Business and Organizational Advancements , 2012 .

[8]  Prithviraj Dasgupta,et al.  Effects of Multi-Robot Team Formations on Distributed Area Coverage , 2011, Int. J. Swarm Intell. Res..

[9]  T. T. Ma Enhancement of power transmission systems by using multiple UPFCs on evolutionary programming , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[10]  Enrique Acha,et al.  A comprehensive Newton-Raphson UPFC model for the quadratic power flow solution of practical power networks , 2000 .

[11]  Rüdiger Oehlmann,et al.  Harmony Strategies for Human-Centered Chance Discovery , 2011, Int. J. Organ. Collect. Intell..

[12]  Zwe-Lee Gaing,et al.  Real-coded mixed-integer genetic algorithm for constrained optimal power flow , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[13]  Weerakorn Ongsakul,et al.  Optimal power flow with multi-type of FACTS devices by hybrid TS/SA approach , 2002, 2002 IEEE International Conference on Industrial Technology, 2002. IEEE ICIT '02..

[14]  T. S. Chung,et al.  Optimal power flow with a versatile FACTS controller by genetic algorithm approach , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[15]  M. A. Abido,et al.  Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization , 2009 .

[16]  S. S. Thakur,et al.  Turbulent Crazy Particle swarm Optimization technique for optimal reactive power dispatch , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[17]  Glauco N. Taranto,et al.  Representation of FACTS devices in power system economic dispatch , 1992 .

[18]  Xin-She Yang,et al.  Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning , 2011, Int. J. Swarm Intell. Res..

[19]  Malabika Basu,et al.  Optimal power flow with FACTS devices using differential evolution , 2008 .

[20]  E. Acha,et al.  Integrated SVC and step-down transformer model for Newton-Raphson load flow studies , 2000, IEEE Power Engineering Review.

[21]  T. S. Chung,et al.  Optimal active power flow incorporating power flow control needs in flexible AC transmission systems , 1999 .

[22]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Lucio Ippolito,et al.  Optimal Allocation of FACTS Devices by Using Multi-Objective Optimal Power Flow and Genetic Algorithms , 2006 .

[24]  C. Fuerte-Esquivel,et al.  Incorporation of a UPFC model in an optimal power flow using Newton's method , 1998 .

[25]  France,et al.  Distributed Task Allocation in Swarms of Robots , 2012 .

[26]  T. J. Stonham,et al.  Combined heat and power economic dispatch by improved ant colony search algorithm , 1999 .

[27]  T. S. Chung,et al.  A hybrid GA approach for OPF with consideration of FACTS devices , 2000 .

[28]  Ge Shaoyun,et al.  Optimal active power flow incorporating FACTS devices with power flow control constraints , 1998 .

[29]  Zhang Qingbin,et al.  An Improved Population-Based Incremental Learning Algorithm , 2006, 2007 Chinese Control Conference.

[30]  C. Fuerte-Esquivel,et al.  Advanced SVC models for Newton-Raphson load flow and Newton optimal power flow studies , 2000 .

[31]  Enrique Acha,et al.  Efficient object oriented power systems software for the analysis of large-scale networks containing FACTS-controlled branches , 1998 .

[32]  S. P. Ghoshal,et al.  Constrained optimal power flow using Craziness Based Particle Swarm Optimization considering valve point loading and prohibited operating zone , 2009, 2009 International Conference on Power Systems.