A dynamic programming based fast computation Hopfield neural network for unit commitment and economic dispatch

Electric Power Systems Research

Volumn 77, Issue 8, 2007, Pages 917-925

  Kumar, S Senthil ^a   Palanisamy, V ^b  

^a GOVERNMENT COLLEGE OF ENGINEERING   (India)

^b GOVERNMENT COLLEGE OF TECHNOLOGY   (India)

Author keywords

Dynamic programming; Economic dispatch; Hopfield network; Optimization; Unit commitment

Indexed keywords

COMPUTATIONAL METHODS; DYNAMIC PROGRAMMING; ELECTRIC GENERATORS; ITERATIVE METHODS; PROBLEM SOLVING; THERMAL EFFECTS;

ECONOMIC DISPATCH; HOPFIELD NETWORK; UNIT COMMITMENT;

NEURAL NETWORKS;

EID: 33947648446     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2006.08.005     Document Type: Article

References (20)

1. Wood A.J., and Wollenberg B.F. Power Generation Operation and Control. second ed. (1996), Wiley, New York
2. Padhy N.P. Unit commitment-a bibliographical survey. IEEE Trans. Power Syst. 19 May (2004) 1196-1205
3. Snyder W.L., Powel H.D., and Rayburn J.C. Dynamic programming approach to unit commitment. IEEE Trans. Power Syst. 2 May (1987) 339-350
4. Hobbs W.J., Hermon G., Warner S., and Sheble G.B. An enhanced dynamic programming approach for unit commitment. IEEE Trans. Power Syst. 3 August (1988) 1201-1205
5. Cohen A.I., and Yoshimura M. A branch and bound algorithm for unit commitment. IEEE Trans. Power Apparatus Syst. 102 February (1983) 444-451
6. Dekrajangpetch S., Sheble G.B., and Conejo A.J. Auction implementation problems using Lagrangian relaxation. IEEE Trans. Power Syst. 14 February (1999) 82-88
7. Ongsakul W., and Petcharaks N. Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Trans. Power Syst. 19 February (2004) 620-628
8. Senjyu T., Shimalkumar K., Vezato K., and Funabashi T. A fast technique for unit commitment problem by extended priority list. IEEE Trans. Power Syst. 18 May (2003) 882-888
9. Sasaki H., Watanabe M., and Yokoyama R. A solution method of unit commitment by artificial neural networks. IEEE Trans. Power Syst. 7 August (1992) 974-981
10. Cheng C.P., Liu C.W., and Liu C.C. Unit commitment by annealing-genetic algorithm. Electr. Power Energy Syst. 24 (2002) 149-158
11. Swarup K.S., and Yamashiro S. A genetic algorithm approach to generator unit commitment. Electr. Power Energy Syst. 25 (2003) 679-687
12. Tong S.K., Shahidehpour, and Ouyang Z. A heuristic short term unit commitment. IEEE Trans Power Apparatus Syst. 6 August (1991) 1210-1216
13. Mantawy A.H., Abdel-Magid Youssef L., and Selim Shokri Z. A simulated annealing algorithm for unit commitment. IEEE Trans. Power Syst. 13 February (1998) 197-204
14. Rudolf A., and Bayrleithner R. A genetic algorithm for solving unit commitment problem of a hydro thermal power system. IEEE Trans. Power Syst. 14 November (1999) 1460-1468
15. Juste K.A., Kita H., Tanaka E., and Hasegawa J. An evolutionary solution to the unit commitment problem. IEEE Trans. Power Syst. 14 November (1999) 1452-1459
16. Mantawy A.H., Abdel-Magid Youssef L., and Selin Shokri Z. Integrating genetic algorithms, Tabu Search and simulated annealing for the unit commitment problem. IEEE Trans. Power Syst. 14 August (1999) 829-836
17. Walsh M.P., and Malley M.J.O. Augmented Hopfield network for constrained generator scheduling. IEEE Trans. Power Syst. 14 May (1999) 765-771
18. Lee F.N., and Breipohi A.M. Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans. Power Syst. 8 February (1993) 246-254
19. Walters C., and Sheble G.B. Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans. Power Syst. 8 (1993) 1325-1332
20. Yalcinoz T., Cory B.J., and Short M.J. Hopfield neural network approaches to economic dispatch problems. Electr. Power Energy Syst. 23 (2001) 435-442