Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization

Abstract In this paper, a new method for optimal locating multi-type FACTS devices in order to optimize multi-objective voltage stability problem is presented. The proposed methodology is based on a new variant of particle swarm optimization (PSO) specialized in multi-objective optimization problem known as non-dominated sorting particle swarm optimization (NSPSO). The crowding distance technique is used to maintain the Pareto front size at the chosen limit, without destroying its characteristics. To aid the decision maker choosing the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed for this task. NSPSO is used to find the optimal location and setting of two types of FACTS namely: Thyristor controlled series compensator (TCSC) and static var compensator (SVC) that maximize static voltage stability margin (SVSM), reduce real power losses (RPL), and load voltage deviation (LVD). The optimization is carried out on two and three objective functions for various FACTS combinations considering. For ensure the robustness of the proposed method and gives a practical sense of our study, N − 1 contingency analysis and the stress of power system is considered in the optimization process. The thermal limits of lines and voltage limits of load buses are considered as the security constraints. The proposed method is validated on IEEE 30-bus and realistic Algerian 114-bus power system. The simulation results are compared with those obtained by particle swarm optimization (PSO) and non-dominated sorting genetic algorithms (NSGA-II). The comparisons show the effectiveness of the proposed NSPSO to solve the multi-objective optimization problem and capture Pareto optimal solutions with satisfactory diversity characteristics.

[1]  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).

[2]  Cao Yijia,et al.  Multiple objective particle swarm optimization technique for economic load dispatch , 2005 .

[3]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[6]  T. Niimura,et al.  Multiobjective tradeoff analysis of deregulated electricity transactions , 2003 .

[7]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[8]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

[9]  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.

[10]  K.Y. Lee,et al.  Placement of SVCs and Selection of Stabilizing Signals in Power Systems , 2007, IEEE Transactions on Power Systems.

[11]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[12]  M. A. Abido,et al.  Optimal VAR dispatch using a multiobjective evolutionary algorithm , 2005 .

[13]  K.Y. Lee,et al.  Multi-objective VAr Planning with SVC for a Large Power System Using PSO and GA , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[14]  S. C. Srivastava,et al.  Optimal Placement of SVC for Static and Dynamic Voltage Security Enhancement , 2005 .

[15]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[16]  Jonathan E. Fieldsend,et al.  A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and , 2002 .

[17]  W. F. Long,et al.  Determination of Needed FACTS Controllers That Increase Asset Utilization of Power Systems , 1997 .

[18]  Russell C. Eberhart,et al.  Particle swarm with extended memory for multiobjective optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[19]  Tapabrata Ray,et al.  A Swarm Metaphor for Multiobjective Design Optimization , 2002 .

[20]  B. Badrzadeh,et al.  Modeling and simulation of SVC and TCSC to study their limits on maximum loadability point , 2004 .

[21]  Fernando L. Alvarado,et al.  SVC placement using critical modes of voltage instability , 1993 .

[22]  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..

[23]  Jong-Bae Park,et al.  A New Optimal Routing Algorithm for Loss Minimization and Voltage Stability Improvement in Radial Power Systems , 2007, IEEE Transactions on Power Systems.

[24]  A. Sharma.  Optimal Number and Location of TCSC and Loadability Enhancement in Deregulated Electricity Markets Using MINLP , 2006 .

[25]  A. David,et al.  Placement of FACTS devices in open power market , 2000 .

[26]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).