Lambda optimization of constraint violating units in short-term thermal unit commitment using modi ed dynamic programming

Lambda optimization of constraint violating units in short-term thermal unit commitment using modi ed dynamic programming

This paper presents a new approach with a three-stage optimization algorithm for the least-cost optimal solution of the unit commitment problem. In the proposed work, the optimal schedule is obtained by optimizing the lambda operator for the states that violate the inequality constraints. The objective of the work is to minimize the fuel cost when subjected to various constraints such as load balance, minimum up/down time, ramp limit, and spinning reserve. This method of committing the units yields the least-cost solution when applied to the IEEE 10-unit systems and 7-unit Indian utility practical systems scheduled for 24 h and the results obtained are compared with the existing methods.

___

  • [1] Wood AJ, Wollenberg B. Power Generation Operation and Control. New York, NY, USA: John Wiley, 1983.
  • [2] Baldick R. The generalized unit commitment problem. IEEE T Power Syst 1995; 10: 465-475.
  • [3] Tong SK, Shahidehpour SM, Ouyang Z. A heuristic short-term unit commitment. IEEE T Power Ap Syst1991; 6: 1210-1216.
  • [4] Baldwin CJ, Dale KM, Dittrich RF. A study of the economic shutdown of generating units in daily dispatch. AIEE Transactions on Power Apparatus and Systems 1960; 78: 1272-1284.
  • [5] Happ HH, Johnson RC, Wright WJ. Large scale hydro-thermal unit commitment method and results. IEEE T Power Ap Syst 1971; 90: 1373-1383.
  • [6] Merlin A, Sandrin P. A new method for unit commitment at Electricite de France. IEEE T Power Ap Syst 1983; 102: 1218-1225.
  • [7] Muckstadt JA, Wilson RC. An application of mixed-integer programming duality to scheduling thermal generating systems. IEEE T Power Syst 1968; 87: 1968-1978.
  • [8] Cohen AI, Yoshimura MA. Branch and bound algorithm for unit commitment. IEEE T Power Ap Syst 1983; 102: 444-451.
  • [9] Liu C, Shahidehpour M, Wu L. Extended Benders decomposition for two-stage SCUC. IEEE T Power Syst 2010; 25: 1192-1194.
  • [10] Bellman RE. Dynamic Programming Book. Princeton, NJ, USA: Princeton University Press, 1957.
  • [11] Lowery PG. Generating unit commitment by dynamic programming. IEEE T Power Ap Syst 1966; 5: 422-426.
  • [12] Snyder WL, Powel HD, Rayburn JC. Dynamic programming approach to unit commitment. IEEE T Power Syst 1987; 2: 339-350.
  • [13] Juste KA, Kita H, Tanaka E, Hasegawa J. An evolutionary programming solution to the unit commitment problem. IEEE T Power Syst 1999; 14: 1452-1459.
  • [14] Senthil Kumar S, Palanisamy V. A hybrid fuzzy dynamic programming approach to unit commitment. Journal of the Institution of Engineers (India) 2008; 88: 3-9.
  • [15] Senthil Kumar S, Palanisamy V. A dynamic programming based fast computation Hop?eld neural network for unit commitment and economic dispatch. Electr Pow Syst Res 2006; 77: 917-925.
  • [16] Sasaki H, Watanabe M, Yokoyama R. A solution method of unit commitment by arti cial neural networks. IEEE T Power Syst 1992; 7: 974-981.
  • [17] Ouyang Z, Shahidehpour SM. A hybrid arti cial neural network { dynamic programming approach to unit com- mitment. IEEE T Power Syst 1992; 7: 236-242.
  • [18] Mantawy AH, Abdel-Magid YL, Selim SZ. A simulated annealing algorithm for unit commitment. IEEE T Power Syst 1998; 13: 197-204.
  • [19] Xiaomin B, Shahidehpour SM, Erkeng Y. Constrained unit commitment by using tabu search algorithm. In: Proceedings of the International Conference on Electrical Engineering; 1996. pp. 1088-1092.
  • [20] Mantawy AH, Abdel-Magid YL. Unit commitment by tabu search. IEE P-Gener Transm D 1998; 145: 56-64.
  • [21] Kazarlis SA, Bakirtzis AG, Petridis V. A genetic algorithm solution to the unit commitment problem. IEEE T Power Syst 1996; 11: 83-92.
  • [22] Salam S. Unit commitment solution methods. Proc Wrld Acad Sci E 2007; 27:2070-3740.
  • [23] Sheble GB, Fahd GN. Unit commitment literature synopsis. IEEE T Power Syst 1994; 9:128-135.
  • [24] Padhy NP. Unit commitment-a bibliographical survey. IEEE T Power Syst 2004; 19: 1196-1205.
  • [25] Ayoub AK, Patton AD. Optimal thermal generating unit commitment. IEEE T Power Ap Syst 1971; 90: 1752-1756.
  • [26] Van den Bosch PPJ, Honderd G. A solution of the unit commitment problem via decomposition and dynamic programming. IEEE T Power Ap Syst 1985; 104: 1684-1690.
  • [27] Pang CK, Sheble GB, Albuyeh F. Evaluation of dynamic programming based methods and multiple area represen- tation for thermal unit commitments. IEEE T Power Ap Syst 1981; 100: 1212-1218.
  • [28] Hobbs WJ, Hermon G, Warner S, Sheble GB. An enhanced dynamic programming approach for unit commitment. IEEE T Power Syst 1988; 3: 1201-1205.
  • [29] Swarup KS, Yamashiro S. Unit commitment solution methodology using genetic algorithm. IEEE T Power Syst 2002; 17: 87-91.
  • [30] Ganguly D, Sarkar V, Pai J. A new genetic approach for solving the unit commitment problem. In: International Conference on Power System Technology-POWERCON 2004; 21{24 November 2004; Singapore.
  • [31] Mari eld TT, Sheble GB. Genetic based unit commitment algorithm. IEEE T Power Syst 1996; 11: 1359-1370.