Equal embedded algorithm for economic dispatch with generator constraints

Key Findings & Data:The proposed algorithm has been tested on a power system having 6,15 and 40 generating units and the results of the proposed method are compared in terms of solution quality, convergence characteristics and computation time with the conventional lambda iterative method.

💡

Conclusion & Impact:From the case studies, it is observed that the proposed algorithm provides qualitative solution with less computational time.

🔬Design:Research Article

📍Scope:Comprehensive Sample

⚙️Variables:Domain Parameters

Abstract

This paper describes Equal Embedded Algorithm (EEA) for solving Economic Dispatch (ED) problem with generator constraints. The proposed algorithm is formulated based on the conventional numerical techniques such as Interpolation and Muller method. The proposed algorithm involves selection of incremental fuel cost (lambda) values, then the output powers of generating units are obtained in terms of lambda by Newton forward interpolation and finally the evaluation of optimal lambda is done by the Muller method at required power demand from the power balance equation. The proposed algorithm has been tested on a power system having 6,15 and 40 generating units and the results of the proposed method are compared in terms of solution quality, convergence characteristics and computation time with the conventional lambda iterative method. From the case studies, it is observed that the proposed algorithm provides qualitative solution with less computational time.

🔖

Cite This Article

SYDULU, M., CHANDRAM, K., SUBRAHMAYAM, N. (2022). Equal embedded algorithm for economic dispatch with generator constraints. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 9 - Sayı: 1, 833–840. https://ajindex.com/articles/543728

📄 Full Text PDF Viewer

Open in New Tab ↗

↗New Tab

📄

PDF görüntüleyici hazırlanıyor...

References (9)

Z.X. Liang, J.D. Glover, “A Zoom feature for a Dynamic Programming solution to economic dispatch including transmission Losses” IEEE Transaction on Power Systems,Vol:7 No.2, May, 1992, pp. 544
J.H. Park, Y.S. Kim, I.K. Eom, and K.Y. Lee, “Economic Load Dispatch for Piecewise Quadratic Cost Function Using Hopfield Neural Networks,” IEEE Transs on Power Systems, Vol: 8, No. 3, August, 1993, pp. 1030-1038
P.H. Chen and H.C. Chang, “Large-scale Economic Dispatch by Genetic Algorithm,” IEEE Trans. on Power Syst., Vol: 10, Nov. 1995, pp. 1919-1926
N. Sinha, R. Chakrabarti, P.K. Chattopadhyay, “Evolutionary Programming techniques for Economic Load Dispatch”, IEEE Trans. on Evol. Comput. 7 (1) (2003) pp. 83-94
Z.L. Giang, “Particle swarm optimization to solving the ED considering the generator constraints”, IEEE Trans. Power System., 2003, 18(03), pp. 1187-1195
L. Dos, S. Coelho, V. Cocco Mariani, “Combining of Chaotic Differential Evolution and Quadratic Programming for Economic Dispatch Optimization with Valve-Point Effect”, IEEE Trans. on Power Syst., 21 (2), (2006), pp. 989-996
W. Ongsakul, S. Dechanupaprittha and I. Ngamroo, “Parallel Tabu Search Algorithm For Constrained Economic Dispatch” IEE Proc.- Gener. Transm. Distrib., Vol: 151, No. 2, March, 2004
A.J. Wood and B.F. Woolenberg, “Power Generation, Operation and Control”, New York, NY: John Wiley Sons, 1984
H. Saadat, “Power System Analysis”, Newyork: McGraw-Hill, 1999 [10] H.M. Antia, “Numerical Methods for Scientists and Engineers”, Birkhäuser publishers, 2nd ed., 2002.