Research Article | OPEN ACCESS
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
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,
References
-
Alawode, K.A., A.M. Jubril and O.A. Komolafe, 2010. Multiobjective optimal reactive power flow using elitist nondominated sorting genetic algorithm: Comparison and improvement. J. Electr. Eng. Technol., 5(1): 70-78.
CrossRef
-
Alsac, O. and B. Scott, 1974. Optimal load flow with steady state security. IEEE T. Power Syst., PAS-93(3): 745-751.
CrossRef
-
Banu, R.N. and D. Devaraj, 2012. Multi-objective GA with fuzzy decision making for security enhancement in power system. Appl. Soft Comput., 12(9): 2756-2764.
CrossRef
-
Bompard, E., P. Correia, G. Gross and M. Amelin, 2003. Congestion management schemes: A comparative analysis under a unified framework. IEEE T. Power Syst., 18(1): 346-352.
CrossRef
-
Christie, R.D., B.F. Wollenberg and I. Wangensteen, 2000. Transmission management in the deregulated environment. P. IEEE, 88(2): 170-195.
CrossRef
-
Deb, K., A. Pratap, S. Agarwal and T. Meyarivan, 2002. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE T. Evolut. Comput., 6(2).
CrossRef
-
Deependra, S. and K.S. Verma, 2011. Utility exhibition on power and energy systems: Issues & prospects for Asia (ICUE). Proceeding of International Conference, 2011.
-
Dutta, S. and S.P. Singh, 2008. Optimal rescheduling of generators for congestion management based on PSO. IEEE T. Power Syst., 23(4): 1560- 1569.
CrossRef
-
Fang, R.S. and A.K. David, 1999. Optimal dispatch under transmission contracts. IEEE T. Power Syst., 14(2): 732-737.
CrossRef
-
Hazra, J. and A.K. Sinha, 2007. Congestion management using multi objective particle swarm optimization. IEEE T. Power Syst., 22(4): 1726-1734.
CrossRef
-
Joorabian, M., M. Saniei and H. Sepahvand, 2011. Locating and parameters setting of TCSC for congestion management in deregulated electricity market. Proceeding of 6th IEEE Conference on Industrial Electronics and Applications, pp: 2185-2190.
CrossRef
-
Kumar, A., S.C. Srivatsava and S.N. Singh, 2004. A zonal congestion management approach using ac transmission congestion distribution factors. Electr. Pow. Syst. Res., 72: 85-93.
CrossRef
-
Raghuwanshi, M.M. and O.G. Wakde, 2008. Survey on multi-objective evolutionary and real coded genetic algorithms. Complex Int., 11: 150.
-
Reddy, S.S., M.S. Kumari and M. Sydulu, 2009. Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi objective genetic algorithm. J. Electr. Eng. Technol., 4: 467-475.
CrossRef
-
Srinivas, N. and K. Deb, 1993. Multi-objective optimization using non-dominated sorting in genetic algorithms. Technical Report, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 1993.
-
Talukdar, B.K., A.K. Sinha, S. Mukhopadhyay and A. Bose, 2005. A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management. Int. J. Electr. Power Energ. Syst., 27(5-6): 379-388.
CrossRef
-
Yesuratnam, G. and D. Thukaram, 2007. Congestion management in open access based on relative electrical distances using voltage stability criteria. Electr. Pow. Syst. Res., 77: 1608-1618.
CrossRef
-
Zimmerman, R.D., C.E. Murillo-Sanchez and R.J. Thomas, 2011. Matpower: Steady-state operations, planning and analysis tools for power systems research and education. IEEE T. Power Syst., 26(1): 12-19.
CrossRef
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.
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The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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