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     Research Journal of Applied Sciences, Engineering and Technology


Multi Objective Genetic Algorithm for Congestion Management in Deregulated Power System Using Generator Rescheduling and Facts Devices

1S. Sivakumar and 2D. Devaraj
1EEE Department, Kings College of Engineering, Punalkulam, Thanjavur
2EEE Department, Kalasalingam University, Krishnankoil, Virudhunagar (Dt.), India
Research Journal of Applied Sciences, Engineering and Technology  2014  13:1618-1624
http://dx.doi.org/10.19026/rjaset.8.1142  |  © The Author(s) 2014
Received: September ‎07, ‎2014  |  Accepted: September ‎20, ‎2014  |  Published: October 05, 2014

Abstract

The problem of congestion management is more pronounced in deregulated environment as the participants of the energy market are market oriented rather than socially responsible-as exhibited by the government operated bundled system. Customers would like to purchase the electricity from the cheapest available sources. The seller in energy market would like to derive more benefit out of their investments, engages with contracts that may lead to overloading of the transmission elements of the power system. An Independent System Operator (ISO) who has no vested interest in the energy market, coordinates the trades and make sure that the interconnected power system always operates in a secure state at a minimum cost by meeting the all the load requirements and losses. In this proposed study, Congestion is mitigated by Generator Rescheduling and implementation of FACTS devices. Minimization of rescheduling costs of the generator and minimization of the cost of deploying FACTS devices are taken as the objectives of the given multi-objective optimization problem. Non-dominated sorting genetic algorithm II is used to solve this problem by implementing the series FACTS device namely TCSC and shunt FACTS device namely SVC. The proposed algorithm is tested on IEEE 30 bus system.

Keywords:

Congestion management , generator rescheduling , multi objective optimization , NSGA II, Pareto optimality,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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