Unit commitment using a new integer coded genetic algorithm

This paper proposes a new Integer‐Coded Genetic Algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units scheduling consists of the sequence of operation/reservation times of the generating units, which is coded into a sequence of alternating sign intege...

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Vydáno v:	European transactions on electrical power Ročník 19; číslo 8; s. 1161 - 1176
Hlavní autoři:	Amjady, Nima, Shirzadi, Ali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Chichester, UK John Wiley & Sons, Ltd 01.11.2009 Wiley
Témata:	Applied sciences Cost engineering crossover Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology Genetic algorithms integer-coded genetic algorithm Integers mutation Mutations Operation. Load control. Reliability Operators Power networks and lines Robustness Scheduling Unit commitment Performance evaluation Dispatching problem mutation crossover integer-coded genetic algorithm unit commitment Stopping time Genetic algorithm Power distribution planning Effectiveness factor Comparative study
ISSN:	1430-144X, 1546-3109
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Abstract	This paper proposes a new Integer‐Coded Genetic Algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units scheduling consists of the sequence of operation/reservation times of the generating units, which is coded into a sequence of alternating sign integer numbers in the proposed ICGA. The minimum up and down time constraints of the generating units are directly coded in the chromosome structure of the ICGA. The proposed ICGA has a new hybrid crossover composed of modified average bound and swapping operators. In addition, a combination of uniform and non‐uniform mutations is used as the mutation operator. As a result, the algorithm robustness is improved. Test results with systems of up to 300 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) methods justifies the effectiveness of the proposed method in light of minimizing the total operation cost. Copyright © 2008 John Wiley & Sons, Ltd.
AbstractList	This paper proposes a new Integer-Coded Genetic Algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units scheduling consists of the sequence of operation/reservation times of the generating units, which is coded into a sequence of alternating sign integer numbers in the proposed ICGA. The minimum up and down time constraints of the generating units are directly coded in the chromosome structure of the ICGA. The proposed ICGA has a new hybrid crossover composed of modified average bound and swapping operators. In addition, a combination of uniform and non-uniform mutations is used as the mutation operator. As a result, the algorithm robustness is improved. Test results with systems of up to 300 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) methods justifies the effectiveness of the proposed method in light of minimizing the total operation cost. This paper proposes a new Integer‐Coded Genetic Algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units scheduling consists of the sequence of operation/reservation times of the generating units, which is coded into a sequence of alternating sign integer numbers in the proposed ICGA. The minimum up and down time constraints of the generating units are directly coded in the chromosome structure of the ICGA. The proposed ICGA has a new hybrid crossover composed of modified average bound and swapping operators. In addition, a combination of uniform and non‐uniform mutations is used as the mutation operator. As a result, the algorithm robustness is improved. Test results with systems of up to 300 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) methods justifies the effectiveness of the proposed method in light of minimizing the total operation cost. Copyright © 2008 John Wiley & Sons, Ltd.
Author	Amjady, Nima Shirzadi, Ali
Author_xml	– sequence: 1 givenname: Nima surname: Amjady fullname: Amjady, Nima email: amjady@tavanir.org.ir organization: Department of Electrical Engineering, Semnan University, Semnan, Iran – sequence: 2 givenname: Ali surname: Shirzadi fullname: Shirzadi, Ali organization: Department of Electrical Engineering, Semnan University, Semnan, Iran
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Cites_doi	10.1023/A:1018947401538 10.1016/j.epsr.2005.10.005 10.1109/TPWRS.2003.820707 10.1109/59.867163 10.1109/TPAS.1983.317714 10.1049/ip-d.1980.0044 10.1016/S0142-0615(03)00003-6 10.1016/S0378-7796(03)00147-0 10.1016/j.epsr.2005.07.002 10.1080/15325000500419185 10.1109/TPWRS.2007.907443 10.1109/TPWRS.2005.860907 10.1109/ICIT.2003.1290244 10.1109/59.485989 10.1109/59.589804 10.1109/59.43211 10.1002/047122412X.ch8 10.1109/TPWRS.2003.811000 10.1016/S0142-0615(01)00048-5 10.1109/TPWRS.2003.821625 10.1049/ip-gtd:20045190 10.1109/TPWRS.1987.4335130 10.1109/TPWRS.2006.876672 10.1016/j.ijepes.2006.02.011 10.1049/ip-gtd:20020460 10.1109/IJCNN.2005.1556046 10.1109/59.801925 10.1109/ICPST.2004.1460285 10.1109/TPAS.1978.354719 10.1016/0378-7796(94)90006-X 10.1109/PESW.2002.984954 10.1023/A:1017960507177
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Keywords	Performance evaluation Dispatching problem mutation crossover integer-coded genetic algorithm unit commitment Stopping time Genetic algorithm Power distribution planning Effectiveness factor Comparative study
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Absolutely stochastic simulated annealing approach to large scale unit commitment problem. Electric Power Components and Systems 2006; 34(6): 619-637. Gollmer R, Nowak MP, Romisch W, Schultz R. Unit commitment in power generation-a basic model and some extensions. Annals of Operation Research 2000; 96: 167-189. Sheble GB, Maifeld TT. Unit commitment by genetic algorithm and expert-system. Electric Power Systems Research 1994; 30(2): 115-121. Carrión M, Arroyo JM. A Computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems 2006; 21(3): 1371-1378. Saneifard S, Prasad NR, Smollech HA. A fuzzy logic approach to unit commitment. IEEE Transactions on Power Systems 1997; 12(2): 988-995. Balci HH, Valenzuela JF. Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method. International Journal of Applied Mathematics and Computer Science 2004; 14(3): 411-421. Senjyu T, Miyagi T, Saber AY, Urasaki N, Funabashi T. Emerging solution of large-scale unit commitment problem by stochastic priority list. Electric Power Systems Research 2006; 76: 283-292. Dang C, Li M. A floating-point genetic algorithm for solving the unit commitment problem. European Journal of Operational Research 2006; 1370-1395. Available online at: http://www.sciencedirect.com/ (in press). Cohen AI, Yoshimura M. A branch-and-bound algorithm for unit commitment. IEEE Transactions on Power Systems 1983; PAS-102(2): 444-451. Sun L, Zhang Y, Jiang C. A matrix real-coded genetic algorithm to the unit commitment problem. Electric Power Systems Research 2006; 76: 716-728. Juste KA, Kitu H, Tunaka E, Hasegawa J. An evolutionary programming solution to the unit commitment problem. IEEE Transactions on Power Systems 1999; 14(4): 1452-1459. Dillon TS, Edwin KW. Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination. IEEE Transactions on Power Systems 1978; PAS-97(6): 2154-2166. Victoire TAA, Jeyakumar AE. Unit commitment by a tabusearch-based hybrid-optimisation technique. IET Proceedings Generation, Transmission and Distribution 2005; 152(4): 563-574. Damousis IG, Bakirtzis AG, Dokopoulos PS. A solution to the unit commitment problem using integer-coded genetic algorithm. IEEE Transactions on Power Systems 2004; 19(2): 1165-1172. Senjyu T, Shimabukuro K, Uezato K, Funabashi T. A fast technique for unit commitment problem by extended priority list. IEEE Transactions on Power Systems 2003; 18(2): 882-888. Baptistella LFB, Geromel JC. A decomposition approach to problem of unit commitment schedule for hydrothermal systems. Proceedings of IEEE, Vol. 149, no. 5, 1980; 250. Ting TO, Rao MVC, Loo CK. A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Transactions on Power Systems 2006; 21(1): 411-418. Wood AJ, Wollenberg BF. 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References_xml	– reference: Hosseini SH, Khodaei A, Aminifar F. A novel straightforward unit commitment method for large-scale power systems. IEEE Transactions on Power Systems 2007; 22(4): 2134-2143. – reference: Senjyu T, Miyagi T, Saber AY, Urasaki N, Funabashi T. Emerging solution of large-scale unit commitment problem by stochastic priority list. Electric Power Systems Research 2006; 76: 283-292. – reference: Senjyu T, Shimabukuro K, Uezato K, Funabashi T. A fast technique for unit commitment problem by extended priority list. IEEE Transactions on Power Systems 2003; 18(2): 882-888. – reference: Saneifard S, Prasad NR, Smollech HA. A fuzzy logic approach to unit commitment. IEEE Transactions on Power Systems 1997; 12(2): 988-995. – reference: Zhao B, Guo CX, Bai BR, Cao YJ. An improved particle swarm optimization algorithm for unit commitment. International Journal of Electrical Power and Energy Systems 2006; 28: 482-490. – reference: Snyder WL, Powell HD, Rayburn JC. Dynamic programming approach to unit commitment. IEEE Transactions on Power Systems 1987; 2(2): 339-350. – reference: Wood AJ, Wollenberg BF. Power Generation, Operation, and Control, 2nd edn. Wiley: New York, 1996. – reference: Ting TO, Rao MVC, Loo CK. A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Transactions on Power Systems 2006; 21(1): 411-418. – reference: Yamin HY, Shabidehpour SM. Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets. Electric Power Systems Research 2003; 68: 83-92. – reference: Sun L, Zhang Y, Jiang C. A matrix real-coded genetic algorithm to the unit commitment problem. Electric Power Systems Research 2006; 76: 716-728. – reference: Balci HH, Valenzuela JF. Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method. International Journal of Applied Mathematics and Computer Science 2004; 14(3): 411-421. – reference: Sheble GB, Maifeld TT. Unit commitment by genetic algorithm and expert-system. Electric Power Systems Research 1994; 30(2): 115-121. – reference: Victoire TAA, Jeyakumar AE. Unit commitment by a tabusearch-based hybrid-optimisation technique. IET Proceedings Generation, Transmission and Distribution 2005; 152(4): 563-574. – reference: Swarup KS, Yamashiro S. A genetic algorithm approach to generator unit commitment. Electrical Power and Energy Systems 2003; 25: 679-687. – reference: Cohen AI, Yoshimura M. A branch-and-bound algorithm for unit commitment. IEEE Transactions on Power Systems 1983; PAS-102(2): 444-451. – reference: Juste KA, Kitu H, Tunaka E, Hasegawa J. An evolutionary programming solution to the unit commitment problem. IEEE Transactions on Power Systems 1999; 14(4): 1452-1459. – reference: Dillon TS, Edwin KW. Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination. IEEE Transactions on Power Systems 1978; PAS-97(6): 2154-2166. – reference: Baptistella LFB, Geromel JC. A decomposition approach to problem of unit commitment schedule for hydrothermal systems. Proceedings of IEEE, Vol. 149, no. 5, 1980; 250. – reference: Mokhtari S, Sing J, Wollenberg B. A unit commitment expert system (power system control). IEEE Transactions on Power Systems 1988; 3(1): 272-277. – reference: Cheng CP, Liu CW, Liu GC. Unit commitment by Lagrangian relaxation and genetic algorithms. IEEE Transactions on Power Systems 2000; 15: 707-714. – reference: Ongsakul W, Petcharaks N. Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Transactions on Power Systems 2004; 19(1): 620-628. – reference: Xing W, Wu FF. Genetic algorithm based unit commitment with energy contracts. Electrical Power and Energy Systems 2002; 24: 329-336. – reference: Senjyu T, Saber AY, Miyagi T, Urasaki N, Funabashi T. Absolutely stochastic simulated annealing approach to large scale unit commitment problem. Electric Power Components and Systems 2006; 34(6): 619-637. – reference: Kazarlis SA, Bakirtzis AG, Petridis V. A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems 1996; 11: 29-36. – reference: Damousis IG, Bakirtzis AG, Dokopoulos PS. A solution to the unit commitment problem using integer-coded genetic algorithm. IEEE Transactions on Power Systems 2004; 19(2): 1165-1172. – reference: Shahidehpour M, Yamin H, Li Z. Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management. Wiley: New York, 2002; 275-310. – reference: Dang C, Li M. A floating-point genetic algorithm for solving the unit commitment problem. European Journal of Operational Research 2006; 1370-1395. Available online at: http://www.sciencedirect.com/ (in press). – reference: Goldberg DE. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley: Reading, MA, 1989. – reference: Gollmer R, Nowak MP, Romisch W, Schultz R. Unit commitment in power generation-a basic model and some extensions. Annals of Operation Research 2000; 96: 167-189. – reference: Carrión M, Arroyo JM. A Computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems 2006; 21(3): 1371-1378. – reference: Valenzuela J, Smith AE. A seeded memetic algorithm for large unit commitment problems. Journal of Heuristic 2002; 8: 173-195. – volume: 19 start-page: 620 issue: 1 year: 2004 end-page: 628 article-title: Unit commitment by enhanced adaptive Lagrangian relaxation publication-title: IEEE Transactions on Power Systems – volume: 76 start-page: 716 year: 2006 end-page: 728 article-title: A matrix real‐coded genetic algorithm to the unit commitment problem publication-title: Electric Power Systems Research – volume: 152 start-page: 563 issue: 4 year: 2005 end-page: 574 article-title: Unit commitment by a tabusearch‐based hybrid‐optimisation technique publication-title: IET Proceedings Generation, Transmission and Distribution – start-page: 593 year: 2002 end-page: 599 – volume: 19 start-page: 1165 issue: 2 year: 2004 end-page: 1172 article-title: A solution to the unit commitment problem using integer‐coded genetic algorithm publication-title: IEEE Transactions on Power Systems – year: 1989 – year: 1996 – volume: 96 start-page: 167 year: 2000 end-page: 189 article-title: Unit commitment in power generation—a basic model and some extensions publication-title: Annals of Operation Research – start-page: 72 year: 2003 end-page: 77 – start-page: 275 year: 2002 end-page: 310 – volume: 18 start-page: 882 issue: 2 year: 2003 end-page: 888 article-title: A fast technique for unit commitment problem by extended priority list publication-title: IEEE Transactions on Power Systems – volume: 24 start-page: 329 year: 2002 end-page: 336 article-title: Genetic algorithm based unit commitment with energy contracts publication-title: Electrical Power and Energy Systems – volume: 14 start-page: 1452 issue: 4 year: 1999 end-page: 1459 article-title: An evolutionary programming solution to the unit commitment problem publication-title: IEEE Transactions on Power Systems – volume: 68 start-page: 83 year: 2003 end-page: 92 article-title: Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets publication-title: Electric Power Systems Research – start-page: 58 year: 2002 end-page: 63 – volume: 21 start-page: 411 issue: 1 year: 2006 end-page: 418 article-title: A novel approach for unit commitment problem via an effective hybrid particle swarm optimization publication-title: IEEE Transactions on Power Systems – volume: 21 start-page: 1371 issue: 3 year: 2006 end-page: 1378 article-title: A Computationally efficient mixed‐integer linear formulation for the thermal unit commitment problem publication-title: IEEE Transactions on Power Systems – volume: 25 start-page: 679 year: 2003 end-page: 687 article-title: A genetic algorithm approach to generator unit commitment publication-title: Electrical Power and Energy Systems – volume: 14 start-page: 411 issue: 3 year: 2004 end-page: 421 article-title: Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method publication-title: International Journal of Applied Mathematics and Computer Science – volume: 30 start-page: 115 issue: 2 year: 1994 end-page: 121 article-title: Unit commitment by genetic algorithm and expert‐system publication-title: Electric Power Systems Research – volume: 22 start-page: 2134 issue: 4 year: 2007 end-page: 2143 article-title: A novel straightforward unit commitment method for large‐scale power systems publication-title: IEEE Transactions on Power Systems – start-page: 1325 year: 2005 end-page: 1330 – volume: PAS‐102 start-page: 444 issue: 2 year: 1983 end-page: 451 article-title: A branch‐and‐bound algorithm for unit commitment publication-title: IEEE Transactions on Power Systems – start-page: 418 year: 2003 end-page: 424 – volume: 2 start-page: 339 issue: 2 year: 1987 end-page: 350 article-title: Dynamic programming approach to unit commitment publication-title: IEEE Transactions on Power Systems – volume: 149 start-page: 250 year: 1980 article-title: A decomposition approach to problem of unit commitment schedule for hydrothermal systems 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Snippet	This paper proposes a new Integer‐Coded Genetic Algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units... This paper proposes a new Integer-Coded Genetic Algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units...
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SubjectTerms	Applied sciences Cost engineering crossover Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology Genetic algorithms integer-coded genetic algorithm Integers mutation Mutations Operation. Load control. Reliability Operators Power networks and lines Robustness Scheduling Unit commitment
Title	Unit commitment using a new integer coded genetic algorithm
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