Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

One type of Evolutionary Optimization Technique namely Particle Swarm Optimization (PSO) have been proposed in this paper to obtain the optimal placement a Shunt Flexible AC Transmission System (FACTS) Controller i.e. Static VAr Compensator (SVC) in the network. An Optimal Power Flow (OPF) problem with mixed integer programming has been formulated for simultaneously optimizing multi-objectives optimization problem viz., enhancing the power system load ability, minimizing the active power loss of transmission line, and by considering installation cost of the controller whereas maintaining the system security and stability margins, e.g., small signal stability, fast voltage stability index, and line stability factor in their acceptable margins. The effectiveness of the proposed methodology has been investigated on a practical Java-Bali 24-bus Indonesia system. Results demonstrate that the static and dynamic performances of the power system can be effectively enhanced by the optimal allocation of the SVC in the network.

[1]  S. J. Cheng,et al.  Application of Evolutionary Optimization Techniques for Optimal Location and Parameters Setting of Multiple UPFC Devices , 2007, Third International Conference on Natural Computation (ICNC 2007).

[2]  N. Hingorani.  Role of FACTS in a deregulated market , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[3]  M.V. Suganyadevia,et al.  Estimating of loadability margin of a power system by comparing Voltage Stability Indices , 2009, 2009 International Conference on Control, Automation, Communication and Energy Conservation.

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

[5]  D. Povh.  Modeling of FACTS in power system studies , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[6]  I. Musirin,et al.  Novel fast voltage stability index (FVSI) for voltage stability analysis in power transmission system , 2002, Student Conference on Research and Development.

[7]  Brian Birge,et al.  PSOt - a particle swarm optimization toolbox for use with Matlab , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[8]  Georgios C. Stamtsis,et al.  Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[9]  S. J. Cheng,et al.  Optimal Location and Parameter Settings of Multiple TCSCs for Increasing Power System Loadability Based on GA and PSO Techniques , 2007, Third International Conference on Natural Computation (ICNC 2007).

[10]  F. Milano,et al.  An open source power system analysis toolbox , 2005, 2006 IEEE Power Engineering Society General Meeting.

[11]  Ni Putu Agustini,et al.  Optimal placement of UPFC for maximizing system loadability and minimizing active power losses in system stability margins by NSGA-II , 2011, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.

[12]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  W. Ongsakul,et al.  Optimal placement of UPFC for maximizing system loadability and minimize active power losses by NSGA-II , 2011, 2011 International Conference & Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE).

[15]  N.G. Hingorani,et al.  Flexible AC transmission , 1993, IEEE Spectrum.

[16]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.