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Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques

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  • Sadegheih, A.

Abstract

This paper proposes a new hybrid algorithm Meta-heuristic for the problem of network planning systems. The main goal of this paper is, to develop an efficient optimization tool which will minimise the cost functions of the stated optimization problems in network planning systems. The following are the objectives of the research: to investigate the capabilities of genetic algorithm, simulated annealing and tabu search for the defined optimization tasks; to develop a hybrid optimization algorithm which will produce improved iterations compared to those found by GA, SA, and TS algorithms. The performance of the hybrid algorithm is illustrated and six hybrid algorithms are developed, to improve the iterations obtained. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. It is advantageous to use exact DC load flow constraint equations based on the modified form of Kirchhoff's Second Law because the iterative process for line addition is not required. Hence, the computation time is decreased. Finally, the hybrid VI shows to be a very good option for network planning systems given that it obtains much accentuated reductions of iteration, which is very important for network planning.

Suggested Citation

  • Sadegheih, A., 2009. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques," Energy, Elsevier, vol. 34(10), pages 1539-1551.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:10:p:1539-1551
    DOI: 10.1016/j.energy.2009.06.047
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    Cited by:

    1. Sadegheih, A., 2010. "A novel formulation of carbon emissions costs for optimal design configuration of system transmission planning," Renewable Energy, Elsevier, vol. 35(5), pages 1091-1097.
    2. Kim, M.K. & Park, J.K. & Nam, Y.W., 2011. "Market-clearing for pricing system security based on voltage stability criteria," Energy, Elsevier, vol. 36(2), pages 1255-1264.
    3. Yuan, Guanghui & Yang, Weixin, 2019. "Study on optimization of economic dispatching of electric power system based on Hybrid Intelligent Algorithms (PSO and AFSA)," Energy, Elsevier, vol. 183(C), pages 926-935.
    4. Fitiwi, Desta Z. & Olmos, L. & Rivier, M. & de Cuadra, F. & Pérez-Arriaga, I.J., 2016. "Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources," Energy, Elsevier, vol. 101(C), pages 343-358.
    5. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    6. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    7. Ara, A. Lashkar & Kazemi, A. & Niaki, S.A. Nabavi, 2011. "Optimal location of Hybrid Flow Controller considering modified steady-state model," Applied Energy, Elsevier, vol. 88(5), pages 1578-1585, May.
    8. Sadegheih, A., 2011. "Optimal design methodologies under the carbon emission trading program using MIP, GA, SA, and TS," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 504-513, January.

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