Generation Unit Capacity Expansion Planning Analysis: Approach Using Real Coded Improved Genetic Algorithm

M. Jeraldin Ahila; S. Joseph Jawhar; N. Albert Singh

doi:10.4028/www.scientific.net/AMM.626.190

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 626Generation Unit Capacity Expansion Planning...

Generation Unit Capacity Expansion Planning Analysis: Approach Using Real Coded Improved Genetic Algorithm

Abstract:

This paper provides information about the development of an algorithm called Real Coded Improved Genetic Algorithm (RCIGA). And it leads to a plan for generating units of power with minimum cost and that plan is called as Generation Unit Expansion Planning (GUEP) problem. GUEP is a fully forced non linear system. And this can be solved by technique called genetic algorithm. RCIGA helps in providing faster speed and the space which helps in searching also is increased. RCIGA helps in calculating the combination of units through which the minimum cost can be obtained and units of power should meet out the conditions of the forecasted demands.

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Periodical:

Applied Mechanics and Materials (Volume 626)

Pages:

190-196

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.626.190

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Online since:

August 2014

Authors:

M. Jeraldin Ahila*, S. Joseph Jawhar, N. Albert Singh

Keywords:

Generation Unit Expansion Planning, Real Coded Improved Genetic Algorithm, Reserve Margin

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* - Corresponding Author

References

[1] X. Wang, Mc Donald, Modern Power System Planning, McGraw-Hill International Limited, Singapore, p.311–373 ( 1994).

Google Scholar

[2] Jose L. Ceciliano Meza, Mehmet Bayram Yildirim, and Abu S. M. Masud, A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning, IEEE Trans, Man And Cybernetics Systems and Humans, 39 (5) (2009).

DOI: 10.1109/tsmca.2009.2025868

Google Scholar

[3] P. Murugan, S. Kannan, and S. Baskar, Application of NSGA-II Algorithm to Single-Objective Transmission Constrained Generation Expansion Planning, IEEE Trans. Power systems 24 (4) (2009)1790-1797.

DOI: 10.1109/tpwrs.2009.2030428

Google Scholar

[4] P. Murugan, S. Kannan, S. Baskar, NSGA-II algorithm for multi- objective generation expansion planning problem, Electric Power Systems Research 79(2009) 622–628.

DOI: 10.1016/j.epsr.2008.09.011

Google Scholar

[5] Jong-Bae Park, Young-Moon Park, Jong-Ryul Won, and Kwang Y. Lee, An Improved Genetic Algorithm for Generation Expansion Planning, IEEE Trans. Power systems 15 (3) (2000) 916-922.

DOI: 10.1109/59.871713

Google Scholar

[6] A.K. David, Zhao Rongda, An Expert System With Fuzzy Sets For Optimal Planning, IEEE Trans. Power systems 6 (1) (1991) 59-65.

DOI: 10.1109/59.131092

Google Scholar

[7] David E. Goldberg, JH Holland, Genetic algorithms and machine learning, Springer Netherlands, 3 (2) (1988) 95- 99.

Google Scholar

[8] S. Kannan, S. Mary Raja Slochanal, P. Subbaraj, Narayana Prasad Padhy, Application of particle swarm optimization technique and its variants to generation expansion planning problem, Electric Power Systems Research, 70 (2004) 203-210.

DOI: 10.1016/j.epsr.2003.12.009

Google Scholar

[9] Hamid Bouzeboudia, Abdelkader Chaker, Ahmed Allali, Bakhta Naama, Economic Diapatch solution using a real-Coded Genetic Algorithm , Acta Electrotechnica et informatica, 5 (4) (2005).

Google Scholar

[10] Ioannis G. Damousis, Anastasios, G. Bakirtzis, and Petros S. Dokopoulos, "Network-Constrained Economic Dispatch Using Real-Coded Genetic Algorithm, IEEE Trans. Power systems, 18 (1) (2003) 197-205.

DOI: 10.1109/tpwrs.2002.807115

Google Scholar

[11] Sonja Wogrin, Efraim Centeno, and Julian Barquin, Generation Capacity Expansion Analysis: Open Loop Approximation of Closed Loop equilibria, IEEE Trans. Power systems, 28 (3) (2013) 3362-3371.

DOI: 10.1109/tpwrs.2013.2252632

Google Scholar