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.
. A solution to the unit commitment problem—a review[J]. Frontiers in Energy, 0, (): 223-236.
B. SARAVANAN, Siddharth DAS, Surbhi SIKRI, D. P. KOTHARI. A solution to the unit commitment problem—a review. Front Energ, 0, (): 223-236.
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 doi: 10.1109/TPWRS.2004.840411
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
Catal?o 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 doi: 10.1002/etep.148
8
Moussouni F, Tran T V, Brisset S, Brochet P. Optimization methods. 2007-05-30, 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 doi: 10.2307/1910129
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 doi: 10.1016/S0142-0615(97)00062-8
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 doi: 10.1109/TPWRS.2002.807069
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 doi: 10.1109/TPWRS.2010.2052639
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 doi: 10.1109/59.119267
20
Ouyang Z, Shahidehpour S M. Short-term unit commitment expert system. Electric Power Systems Research , 1990, 20(1): 1-13 doi: 10.1016/0378-7796(90)90020-4
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 doi: 10.1109/59.589804
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 doi: 10.1109/59.317572
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 doi: 10.1109/59.207310
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 doi: 10.1109/59.627889
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 doi: 10.1109/59.485989
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 doi: 10.1080/03052150500384759
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 doi: 10.1016/j.apenergy.2011.06.009
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 doi: 10.1109/TPWRS.2005.857391
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 doi: 10.1109/59.867164
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 doi: 10.1109/59.932272
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 doi: 10.1016/S0142-0615(98)00013-1
54
Padhy N P. Unit commitment-A bibliographical survey. IEEE Transactions on Power Systems , 2004, 19(2): 1196-1205 doi: 10.1109/TPWRS.2003.821611
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 doi: 10.1109/TPWRS.2011.2159522
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 doi: 10.1109/TPWRS.2010.2066584
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 doi: 10.1109/TPWRD.2010.2090908
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 doi: 10.1109/TPWRS.2011.2171197
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 doi: 10.1109/TPWRS.2011.2158010
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 doi: 10.1109/TPWRS.2010.2100050
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 doi: 10.1109/TPWRS.2010.2087367
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 doi: 10.1049/iet-gtd.2011.0117
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 doi: 10.1109/TPWRS.2010.2059716
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 doi: 10.1109/TSG.2011.2157181
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 doi: 10.1109/TPWRS.2011.2165087
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 doi: 10.1049/iet-gtd.2010.0763
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 doi: 10.1109/JSYST.2011.2163012
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 doi: 10.1109/TPWRS.2011.2164947
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 doi: 10.1109/TPWRS.2011.2162008
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 doi: 10.1109/TPWRS.2011.2148370
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 doi: 10.1109/TPWRS.2010.2048345
