Congestion Management by Optimal Choice and Allocation of FACTS Controllers using Genetic Algorithm
D. Venugopal1, A. Jayalaxmi2
1D. Venu Gopal, Assoc. Prof. Department of Electrical & Electronics Engineering, KITS, Singapur, Huzurabad, Karimnagar District, Andhra Pradesh, India.
2Dr. A. Jaya Laxmi, Working as Prof., Department of Electrical & Electronics Engineering, and Coordinator, Centre for Energy Studies, JNTUH College of Engineering, Jawaharlal Nehru Technological University, Hyderabad, Kukatpally,
Manuscript received on June 25, 2014. | Revised Manuscript received on July 03, 2014. | Manuscript published on July 05, 2014. | PP: 72-76 | Volume-4, Issue-3, July 2014. | Retrieval Number: C2302074314 /2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective optimization approach for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence, Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. Genetic Algorithms (GA) are best suitable for solution of combinatorial optimization and multi-objective optimization problems. This paper presents optimal location of FACTS controllers considering Branch loading (BL), Voltage Stability (VS) and Loss Minimization (LM) as objectives at a time using GA. The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithm show effective locations for the cases considered for single objective optimization studies.
Keywords: FACTS, Single objective optimization,SVC, TCSC, real parameter Genetic algorithms.