Locating and Sizing of Series FACTS Devices Using Line Outage Sensitivity Factors and Harmony Search Algorithm

Abstract This paper proposes the use of series FACTS devices to relieve congestion and enhance the security in restructured power system. Harmony search algorithm as a novel heuristic algorithm is employed for optimal locating and sizing of series FACTS devices. In order to reduce the solution space and to pinpoint the lines which are more suitable for FACTS device placement line outage sensitivity factors is employed. Two different objective functions are considered in the optimization problem, the first one is the total congestion cost and the other is total generation cost. To validate the effectiveness of the proposed method and show its efficiency, the simulations are carried out on IEEE 14-bus test system. The results of the proposed method are compared with those obtained by particle swarm optimization and with those obtained by congestion rent contribution method.

[1]  Kankar Bhattacharya,et al.  Transmission congestion management in bilateral markets: An interruptible load auction solution , 2005 .

[2]  Nadarajah Mithulananthan,et al.  A proposal for investment recovery of FACTS devices in deregulated electricity markets , 2007 .

[3]  Seema Singh,et al.  Optimal location of FACTS devices for congestion management , 2001 .

[4]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[5]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[6]  G.B. Shrestha,et al.  Congestion-driven transmission expansion in competitive power markets , 2004, IEEE Transactions on Power Systems.

[7]  S. C. Srivastava,et al.  Optimal power dispatch in deregulated market considering congestion management , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[8]  Masoud Rashidinejad,et al.  Distributed generation placement for congestion management considering economic and financial issues , 2010 .

[9]  S. Iwamoto,et al.  Line Flow Sensitivities of Line Reactances for Congestion Management , 2007, 2007 IEEE Power Engineering Society General Meeting.

[10]  Muwaffaq I. Alomoush,et al.  Contingency-constrained congestion management with a minimum number of adjustments in preferred schedules , 2000 .

[11]  Seyed Hamid Hosseini,et al.  Locating series FACTS devices using line outage sensitivity factors and particle swarm optimization for congestion management , 2009, 2009 IEEE Power & Energy Society General Meeting.

[12]  Naresh Acharya,et al.  Locating series FACTS devices for congestion management in deregulated electricity markets , 2007 .

[13]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[14]  G. MadhusudhanaRao,et al.  Optimal location of TCSC and SVC for enhancement of ATC in a de-regulated environment using RGA , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[15]  K. L. Lo,et al.  Congestion management in deregulated electricity markets , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[16]  Mario Montagna,et al.  Optimal network reconfiguration for congestion management by deterministic and genetic algorithms , 2006 .