A Deterministic Hopfield Model To Dynamic Economic Dispatch With Ramp Limit And Prohibited Zones

Yıl 2012, Cilt: 12 Sayı: 1, 1464 - 1471, 02.09.2013

Farid Benhamıda , Abdelber Bendaoud Abdelghani Ayad1

https://izlik.org/JA28ND74HG

Öz

A solution to the dynamic economic dispatch (DED) for 24-hour dispatch intervals (one day) with practical constraints using a Hopfield neural network (HNN) is proposed in this paper. The HNN is a deterministic model with mutual coupling and of non-hierarchical structure. A continuous and monotonically increasing transfer function is adopted in this model. The DED in this paper must satisfy the following constraints: (1) the system load demand, (2) the spinning reserve capacity, (4) the ramping rate limits and (5) finally the prohibited operating zone. The line losses are included in the algorithm using an iterative procedure where the load demand is augmented in each time interval with a maximum estimation of line losses. The feasibility of the proposed approach is demonstrated using two power systems, and it is compared with the other methods in terms of solution quality and computation efficiency.

Anahtar Kelimeler

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Kaynakça

• X. S. Han, H. B. Gooi, D. S. Kirschen, “Dynamic economic dispatch: feasible and optimal solutions”, IEEE Trans. Power Syst., vol.16, no.1, pp.22-28, 2001.
• X. Xia, A.M. Elaiw, “Optimal dynamic economic dispatch of generation: A review”, Elect. Power Syst. Res., vol.80, pp.975-986, 2010.
• D. L. Travers and R. J. Kaye, “Dynamic dispatch by constructive dynamic programming”, IEEE Trans. Power Syst., vol.13, no.1, pp.72-78, 1998.
• F. N. Lee, A. M. Breipohl, “Reserve constrained economic dispatch with prohibited operating zones”, IEEE Trans. Power Syst., vol.8, no.1, pp.246-254, 1993.
• D. C. Walters, G. B. Sheble, “Genetic algorithm solution of economic dispatch with valve point loading”, IEEE Trans. Power Syst., vol.8, no.3, pp.1325-1332, 1993.
• P. Venkatesh, R. Gnanadass, N. P. Padhy, “Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints”, IEEE Trans. Power Syst., vol.18, no.2, pp.688-697, 2003.
• Z. -L. Gaing, et al. “Constrained dynamic economic dispatch solution using particle swarm optimization”, IEEE Power Eng. Soc. Gen. Meet., vol.1,pp.153–158, 2004.
• R. H. Liang, “A Neural-based redispatch approach to dynamic generation allocation”, IEEE Trans. Power Syst., vol.14, no. 4, pp.388-1393. 1999.
• F. Benhamida et al. “Generation allocation problem using a hopfield-bisection approach ıncluding transmission losses”, Elect. Power and energ. Syst., vol.33, no.5, pp.1165-1171. 2011.
• S. Pothiya et al., “Application of multiple tabu search algorithm considering generator constraints”, Ener. Conv. Manag., vol.49, pp.506-5162, 2008. economic dispatch
• P. H. Chen, H. C. Chang, “Large-scale economic dispatch by genetic algorithm”, IEEE Trans. Power Syst., vol. 10, no.4, pp.1919-1926, 1995.
• J. M. Zurada, “Introduction to artificial neural network systems”, Jaiko Publishing house, Mumbai, 1996.
• C.-T. Su, G.-J. Chiou, “An enhanced hopfield model for economic dispatch considering prohibited zones”, Elect. Power Syst. Res., vol.4, pp. 71-76, 1997.