Genetic algorithm-based fuzzy multi-objective approach to congestion management using FACTS devices

This paper investigates a novel optimization-based methodology for placement of Flexible AC Transmission Systems (FACTS) devices in order to avoid congestion in the transmission lines while increasing static security margin and voltage profile of a given power system. The optimizations are carried out on the basis of location, size, and number of FACTS devices. Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are two FACTS devices which are implemented in this investigation to achieve the determined objectives. The problem is formulated according to Sequential Quadratic Programming (SQP) problem in the first stage to accurately evaluate static security margin with congestion alleviation constraint in the presence of FACTS devices and estimated annual load profile. In the next stage a Genetic Algorithm (GA)-based fuzzy multi-objective optimization approach is used to find the best trade-off between conflicting objectives. The IEEE 14-bus test system is selected to validate the proposed approach.

[1]  H. Sasaki,et al.  A New Formulation for FACTS Allocation for Security Enhancement against Voltage Collapse , 2002, IEEE Power Engineering Review.

[2]  C. S. Chang,et al.  Optimal multiobjective SVC planning for voltage stability enhancement , 1998 .

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  B. Barkovich,et al.  Charting a new course in California [electricity market restructuring] , 1996 .

[5]  James D. McCalley,et al.  TCSC controller design for damping interarea oscillations , 1998 .

[6]  N. K. Sharma,et al.  A Novel Placement Strategy for FACTS Controllers , 2002, IEEE Power Engineering Review.

[7]  Kwang Y. Lee,et al.  Dynamic and static voltage stability enhancement of power systems , 1993 .

[8]  Ali Abur,et al.  Static security enhancement via optimal utilization of thyristor-controlled series capacitors , 2002 .

[9]  Weerakorn Ongsakul,et al.  Optimal power flow with FACTS devices by hybrid TS/SA approach , 2002 .

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

[11]  Narayana Prasad Padhy,et al.  Power flow control and solutions with multiple and multi-type FACTS devices , 2005 .

[12]  Y. Huang.  Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration , 2002 .

[13]  G. J. Berg,et al.  Determination of voltage stability limit in multimachine power systems , 1988 .

[14]  Rachid Cherkaoui,et al.  Optimal location of FACTS devices to enhance power system security , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[15]  B. Gao,et al.  Voltage Stability Evaluation Using Modal Analysis , 1992, IEEE Power Engineering Review.

[16]  H. F. Wang,et al.  Selection of robust installing locations and feedback signals of FACTS-based stabilizers in multi-machine power systems , 1999 .

[17]  L. L. Freris,et al.  Investigation of the load-flow problem , 1967 .

[18]  Hiroshi Okamoto,et al.  A method for identification of effective locations of variable impedance apparatus on enhancement of steady-state stability in large scale power systems , 1995 .

[19]  Seung-Il Moon,et al.  Installation and operation of FACTS devices for enhancing steady-state security , 2004 .

[20]  H. Glavitsch,et al.  Estimating the Voltage Stability of a Power System , 1986, IEEE Transactions on Power Delivery.