Optimization of position and rating of shunt and series connected FACTS devices for transmission congestion management in deregulated power networks

Author:

Alagapuri Vengadesan12,Radhakrishnan Ashok Bakkiyaraj1,Padaiyatchi S. Sakthivel3

Affiliation:

1. Department of Electrical Engineering, Annamalai University, Chidambaram, India

2. Sri Venkateshwaraa College of Engineering and Technology, Puducherry, India

3. Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India

Abstract

<abstract><p>Transmission congestions are caused by electricity trading between generators and distribution companies in a deregulated environment. Power system operation and security in liberalized scenarios are maintained by removing branch overloads. A flexible alternating current transmission system (FACTS) controller is installed in a suitable location to redistribute the power flow among the transmission lines so that the power flows are brought within the capacity of the lines. In this work, series-connected thyristor-controlled switched compensators (TCSCs) and shunt-connected Volt-Ampere reactive (VAR) static compensators (SVCs) are installed in appropriate locations to alter the power flow patterns and to remove overloads. It is proposed to reduce the overload of transmission lines by locating series and shunt connected FACTS devices at proper locations. The size and location of TCSC and SVC devices greatly affect their ability to meet a congestion management goal. An optimization process optimizes the location and size of these devices to maximize the congestion mitigation benefits of the TCSC and SVC controllers. In this work, the whale optimization algorithm (WOA) is used to optimize the value of the objective function by appropriately choosing the location and size of the FACTS controllers. This algorithm has a few parameters that are tuned to give the best overall results. A WOA-based method is proposed to optimize the size and location of the FACTS devices and is implemented on the IEEE-30 bus test case. The results were compared and found to be improved with those of other algorithms such as the particle swarm optimization algorithm (PSO) and the firefly algorithm (FFA).</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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