A solution to the unit commitment problem—a review

Abstract

Unit commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads under different constraints and environments. Many algorithms have been invented in the past five decades for optimization of the UC problem, but still researchers are working in this field to find new hybrid algorithms to make the problem more realistic. The importance of UC is increasing with the constantly varying demands. Therefore, there is an urgent need in the power sector to keep track of the latest methodologies to further optimize the working criterions of the generating units. This paper focuses on providing a clear review of the latest techniques employed in optimizing UC problems for both stochastic and deterministic loads, which has been acquired from many peer reviewed published papers. It has been divided into many sections which include various constraints based on profit, security, emission and time. It emphasizes not only on deregulated and regulated environments but also on renewable energy and distributed generating systems. In terms of contributions, the detailed analysis of all the UC algorithms has been discussed for the benefit of new researchers interested in working in this field.

Key words： unit commitment (UC); optimization; deterministic load; stochastic load; evolutionary programming (EP); hybrid

References

1 Catalao J P S, Mariano S J P S, Mendes V M F, Ferreira L A F M. Profit based unit commitment with emission limitation: A multiobjective approach. In: Proceedings of IEEE Power Tech. Lausanne, Switzerland, 2007, 1417-1422

2 Lu B, Shahidehpour M. Unit commitment with flexible generating units. IEEE Transactions on Power Systems, 2005, 20(2): 1022-1034

3 Wang Y, Xia Q. A novel security stochastic unit commitment for wind thermal system operation. In: Proceedings of 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Weihai, China, 2011, 386-393

4 Raglend I J, Kumar R, Karthikeyan S P, Palanisamy K, Kothari D P. Profit based unit commitment problem under deregulated environment. In: Proceedings of 2009 Power Engineering Conference Australasian Universities (AUPEC 2009), Adelaide, Australia, 2009, 1-6

5 Zendehdel N, Karimpour A, Oloomi M. Optimal Unit Commitment using equivalent linear minimum up and down time constraints. In: Proceedings of 2008 IEEE 2nd International Power and Energy Conference (PECon 2008), Johor Bahru, Malaysia, 2008, 1021-1026

6 Tan T S, Huang G B. Time constrain optimal method to find the minimum architectures for feedforward neural networks. In: Wang L P, Rajapakse J C, Fukeshima K, Lee S Y, Yao X, eds. Proceedings of 9th International Conference on Neural Information Processing (ICONIP’02), Singapore, 2002

7 Catalão J P S, Mariano S J P S, Mendes V M F, Ferreira L A F M. Short term scheduling of thermal units: emission constraints and trace off curves. European Transactions on Electrical Power, 2008, 18(1): 1-14

8 Moussouni F, Tran T V, Brisset S, Brochet P. Optimization methods. 2007-<month>05</month>-<day>30</day>, http://l2ep.univ-lille1.fr/come/benchmark-transformer_fichiers/Method_EE.htm

9 Land A H, Doig A G. An automatic method of solving discrete programming problems. Econometrica, 1960, 28(3): 497-520

10 Singhal P K, Sharma R N. Dynamic programming approach for large scale unit commitment problem. In: Proceedings of International Conference on Communication Systems and Network Technologies, Katra, Jammu, 2011, 714-717

11 Chang G W, Tsai Y D, Lai C Y, Chung J S. A practical mixed integer linear programming based approach for unit commitment. In: Proceedings of IEEE Power Engineering Society General Meeting, Piscataway, USA, 2004, 221-225

12 Wong S Y W. An enhanced simulated annealing approach to unit commitment. International Journal of Electrical Power & Energy Systems, 1998, 20(5): 359-368

13 Purushothama G K, Jenkins L. Simulated annealing with local search—A hybrid algorithm for unit commitment. IEEE Transactions on Power Systems, 2003, 18(1): 273-278

14 Ebrahimi J, Hosseinian S H, Gharehpetian G B. Unit commitment problem solution using shuffled frog leaping algorithm. IEEE Transactions on Power Systems, 2011, 26(2): 573-581

15 Salam S. Unit commitment solution methods. Proceedings of World Academy of Science, Engineering and Technology, 2007, 26: 320-325

16 Mori H, Hayashi T. An efficient method for capacitor placement with parallel tabu search. In: Proceedings of the 1997 International Conference on Intelligent System Applications to Power Systems, Seoul, Korea, 1997, 387-391

17 Mori H, Hayashi T. New parallel tabu search for voltage and reactive power control in power systems. In: Proceedings of IEEE ISCAS’98. Monterey, USA, 1998, 431-434

18 Mori H, Matsuzaki O. A parallel tabu search approach to unit commitment in power systems. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, Japan, 1999, 509-514

19 Ouyang Z, Shahidehpour S M. An intelligent dynamic programming for unit commitment application. IEEE Transactions on Power Systems, 1991, 6(3): 1203-1209

20 Ouyang Z, Shahidehpour S M. Short-term unit commitment expert system. Electric Power Systems Research, 1990, 20(1): 1-13

21 Saneifard S, Prasad N R, Smolleck H A. A fuzzy logic approach to unit commitment. IEEE Transactions on Power Systems, 1997, 12(2): 988-995

22 Zhai D, Breipohl A M, Lee F N, Adapa R. The effect of load uncertainty on unit commitment risk. IEEE Transactions on Power Systems, 1994, 9(1): 510-517

23 Sasaki H, Watanabe M, Kubokawa J, Yorino N, Yokoyama R. A solution method of unit commitment by artificial neural networks. IEEE Transactions on Power Systems, 1992, 7(3): 974-981

24 Liang R H, Kang F C. Thermal generating unit commitment using an extended mean field annealing neural network. IEEE Proceedings on Generation Transmission Distribution, 2000, 147(3): 164-170

25 Walsh M P, O’Malley M J. Augmented hopfield network for unit commitment and economic dispatch. IEEE Transactions on Power Systems, 1997, 12(4): 1765-1774

26 Kurban M, Filik U B. Unit commitment scheduling by using the autoregressive and artificial neural network models based short-term load forecasting. In: Proceedings of 10th International Conference on Probabilistic Methods Applied to Power Systems. Rincon, USA, 2008, 1-5

27 Ma R, Huang Y M, Li M H. Unit commitment optimal research based on the improved genetic algorithm. In: Proceedings of 2011 International Conference on Intelligent Computation Technology and Automation. Shenzhen, China, 2011, 291-294

28 Abookazemi K, Mustafa M W. Unit commitment optimization using improved genetic algorithm. In: Proceedings of IEEE Bucharest Power Technology Conference, Bucharest, Romania, 2009, 1-6

29 Atashpaz-Gargari E, Hashemzadeh F, Lucas C. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In: Proceedings of IEEE Congress on Evolutionary Computation, Singapore, 2007, 4661-4667

30 Withironprasert K, Chusanapiputt S, Nualhong D, Jantarang S, Phoomvuthisarn S. Hybrid ant system/priority list method for unit commitment problem with operating constraints. In: Proceedings of IEEE International Conference on Industrial Technology, Gippsland, Australia, 2009, 1-6

31 Sum-im T, Ongsakul W. Ant colony search algorithm for unit commitment. In: Proceedings of 2003 IEEE International Conference on Industrial Technology, 2003, 72-77

32 Yu D R, Wang Y Q, Guo R. A Hybrid Ant Colony Optimisation Algorithm based lambda iteration method for unit commitment. In: Proceedings of IEEE Second WRI Global Congress on Intelligence Systems. Wuhan, 2010, 19-22

33 Kazarlis S, Bakirtzis A, Petridis V. A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems, 1996, 11(1): 83-92

34 Zhang X H, Zhao J Q, Chen X Y. A hybrid method of lagrangian relaxation and genetic algorithm for solving UC problem. In: Proceedings of International Conference on Sustainable Power Generation and Supply, Nanjing, 2009, 1-6

35 Kumar S S, Palanisamy V. A new dynamic programming based hopfield neural network to unit commitment and economic dispatch. In: Proceedings of IEEE International Conference on Industrial Technology, 2006, 887-892

36 Lal raja Singh R, Christober Asir Rajan C. A hybrid approach based on EP and PSO for proficient solving of unit commitment problem. Indian Journal of Computer Science and Engineering, 2011, 2(3): 281-294

37 Chang W P, Luo X J. A solution to the unit commitment using hybrid genetic algorithm. In: Proceedings of 2008 IEEE Region 10 Conference, Hyderabad, India, 2008, 1-6

38 Alshareef A. An application of artificial intelligent optimization techniques to dynamic unit commitment for the western area of Saudi Arabia. In: Proceedings of 3rd International Conference on Computational Intelligence, Communication Systems and Networks, Bali, Indonesia, 2011, 17-21

39 Mantawy A H, Abdel-Magid Y L. A new fuzzy unit commitment model and solution. In: Proceedings of 14th Power System Computation Conference (14th PSCC), Sevilla, Spain, 2002, 1-6

40 Nascimento F R, Silva I C, Oliveira E J, Dias B H, Marcato A L M. Thermal unit commitment using improved ant colony optimization algorithm via lagrange multipliers. In: 2011 IEEE Conference on Power Technology, Trondheim 2011, 1-5

41 Eusuff M M, Lansey K E, Pasha F. Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization, 2006, 38(2): 129-154

42 Kumar C, Alwarsamy T. A novel algorithm unit commitment problem by a fuzzy tuned particle swarm optimization. European Journal of Scientific Research, 2011, 64(1): 157-167

43 Dimitroulas D K, Georgilakis P S. A new memetic algorithm approach for the price based unit commitment problem. Applied Energy, 2011, 88(12): 4687-4699

44 Chandrasekaran K, Simon S P. Binary/real coded particle swarm optimization for unit commitment problem. In: Proceedings of International Conference on Power, Signals, Controls and Computation (EPSCICON), Kerala, India, 2012, 1-6

45 Li T, Shahidehpour M. Price based unit commitment: a case of Langrangian relaxation versus mixed integer programming. IEEE Transactions on Power Systems, 2005, 20(4): 2015-2025

46 Pokharel B K, Shreshtha G B, Lie T T, Fleten S E. Price-based unit commitment for GENCOs in deregulated markets. In: Proceedings of IEEE Power Engineering Society General Meeting, San Francisco, USA, 2005, 428-433

47 Richter C W, Sheble G B. A profit-based unit commitment GA for the competitive environment. IEEE Transactions on Power Systems, 2000, 15(2): 715-721

48 Collett R, Quaicoe J. Security constrained unit commitment using particle swarms. In: Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering, Ottawa, Canada, 2006, 1125-1129

49 Padhy N P. Unit commitment problem under deregulated environment-A literature review. In: Proceedings of IEEE Power Engineering Society General Meeting, 2003, 1088-1094

50 Madrigal M, Quintana V H. Existence and determination of competitive equilibrium in unit commitment power pool auctions: Price setting and scheduling alternatives. IEEE Transactions on Power Systems, 2001, 16(3): 380-388

51 Valenzuela J, Mazumdar M. Making unit commitment decisions when electricity is traded at spat market prices. In: Proceedings of IEEE Power Engineering Society Winter Meeting, 2001, 1: 1509-512

52 Lasen T J, Wangensteen I, Gjengedal T. Sequential timestep unit commiunent. In: Proceedings of IEEE Power Engineering Society Winter Meeting. Columbus, USA, 2001, 1524-1529

53 Sen S, Kothari D P. Optimal thermal generating unit commitment: A review. International Journal of Electrical Power & Energy Systems, 1998, 20(7): 443-451

54 Padhy N P. Unit commitment-A bibliographical survey. IEEE Transactions on Power Systems, 2004, 19(2): 1196-1205

55 Wang Q F, Guan Y P, Wang J H. A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output. IEEE Transactions on Power Systems, 2012, 27(1): 206-215

56 Álvarez López J, Gómez R N, Moya I G. Commitment of combined cycle plants using a dual optimization-dynamic programming approach. IEEE Transactions on Power Systems, 2011, 26(2): 728-737

57 Lotfjou A, Shahidehpour M, Fu Y. Hourly scheduling of DC transmission lines in SCUC with voltage source converters. IEEE Transactions on Power Delivery, 2011, 26(2): 650-660

58 Chen P H. Two-level hierarchical approach to unit commitment using expert system and elite PSO. IEEE Transactions on Power Systems, 2012, 27(2): 780-789

59 Moghimi Hadji M, Vahidi B. A solution to the unit commitment problem using imperialistic competition algorithm. IEEE Transactions on Power Systems, 2012, 27(1): 117-124

60 Wang Y, Xia Q, Kang C Q. Unit commitment with volatile node injections by using interval optimization. IEEE Transactions on Power Systems, 2011, 26(3): 1705-1713

61 Street A, Oliveira F, Arroyo J M. Contingency-constrained unit commitment with n-K security criterion: A robust optimization approach. IEEE Transactions on Power Systems, 2011, 26(3): 1581-1590

62 Inostroza J C, Hinojosa V H. Short-term scheduling solved with a particle swarm optimiser. IET Generation. Transmission & Distribution, 2011, 5(11): 1091-1104

63 Chung C Y, Yu H, Wong K P. An advanced quantum-inspired evolutionary algorithm for unit commitment. IEEE Transactions on Power Systems, 2011, 26(2): 847-854

64 Khodaei A, Shahidehpour M, Bahramirad S. SCUC with hourly demand response considering intertemporal load characteristics. IEEE Transaction on Smart Grid, 2011, 2(3): 564-571

65 Wu H Y, Guan X H, Zhai Q Z, Ye H X. A systematic method for constructing feasible solution to SCUC problem with analytical feasibility conditions. IEEE Transactions on Power Systems, 2012, 27(1): 526-534

66 Daneshi H, Srivastava A K. Security-constrained unit commitment with wind generation and compressed air energy storage. IET Generation. Transmission & Distribution, 2012, 6(2): 167-175

67 Saber A Y, Venayagamoorthy G K. Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles. IEEE Systems Journal, 2012, 6(1): 103-109

68 Wu L, Shahidehpour M, Li Z Y. Comparison of Scenario-based and interval optimization approaches to stochastic SCUC. IEEE Transactions on Power Systems, 2012, 27(2): 913-921

69 Ostrowski J, Anjos M F, Vannelli A. Tight mixed integer linear programming formulations for the unit commitment problem. IEEE Transactions on Power Systems, 2012, 27(1): 39-46

70 Wu L. A tighter piecewise linear approximation of quadratic cost curves for unit commitment problems. IEEE Transactions on Power Systems, 2011, 26(4): 2581-2583

71 Restrepo J F, Galiana F D. Assessing the yearly impact of wind power through a new hybrid deterministic/stochastic unit commitment. IEEE Transactions on Power Systems, 2011, 26(1): 401-410