Please use this identifier to cite or link to this item:
DC FieldValueLanguage
dc.contributor.authorHuang, Chih-Chengen_US
dc.identifier.citation[1] Carrion M, Arroyo JM. A Computationally Efficient Mixed-Integer Linear Formulation for the Thermal Unit Commitment Problem. IEEE Trans Power Syst 2006;21(3):1371-78. [2] Senjyu T, Shimabukuro K, Funabashi T. A Fast Technique for Unit Commitment Problem by Extended Priority List. IEEE Trans Power Syst 2003;18(2):882-88. [3] Lopez JA, Gomez RN, Moya IG. Commitment of Combined Cycle Plants Using a Dual Optimization–Dynamic Programming Approach.IEEE Trans Power Syst 2011;26(2):728-37. [4] Ongsakul W, Petcharaks N. Unit Commitment by Enhanced Adaptive Lagrangian Relaxation. IEEE Trans Power Syst 2004;19(1):620-8. [5] Seki T, Yamashita N, Kawamoto K. the Lagrangian-Relaxation-Based Unit Commitment Solution. IEEE Trans Power Syst 2010;25(1):272-82. [6] Chen CL. BRANCH-AND-BOUND SCHEDULING FOR THERMAL GENERATING UNITS. IEEE Trans Energy conver 1993;8(2):184-9. [7] Li T, Shahidehpour M. Price-Based Unit Commitment: A Case of Lagrangian Relaxation Versus Mixed Integer Programming. IEEE Trans Power Syst 2005;20(4):2015-25. [8] Kazarlis SA, Bakirtzis AG, Petridis V. A GENETIC ALGORITHM SOLUTION TO THE UNIT COMMITMENT PROBLEM. Trans Power Syst 1996;11(1):83-92. [9] Damousis IG, Bakirtzis AG, Dokopoulos PS. A Solution to the Unit-Commitment Problem Using Integer-Coded Genetic Algorithm. IEEE Trans Power Syst 2004;19(2):1165-71. [10] Juste KA, Tanaka KE, Hasegawa J. An Evolutionary Programming Solution to the Unit Commitmnet Problem. IEEE Trans Power Syst 1999;14(4):1452-59. [11] Simopoulos DN, Kavatza SD, Vournas CD. Reliability Constrained Unit Commitment Using Simulated Annealing. IEEE Trans Power Syst 2006;21(4):1699-06. [12] Simopoulos DN, Kavatza SD, Vournas CD. Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 2006;21(1):68–76. [13] Ting TO, Rao MV, Loo CK. A Novel Approach for Unit Commitment Problem via an Effective Hybrid Particle Swarm Optimization. IEEE Trans Power Syst 2006;21(1):411-418. [14] Tsoi E, Wong KP. Artificial intelligence algorithms for short term scheduling of thermal generators and pumped-storage. IEE Gener Transm Distrib. 1997;144(2):193-200. [15] Cheng CP, Liu CW, Liu CC. Unit Commitment by Lagrangian Relaxation and Genetic Algorithms. IEEE Trans Power Syst 2000;15(2):707-14. [16] Rajan CC, Mohan MR. An Evolutionary Programming-Based Tabu Search Method For Solving The Unit Commitment Problem. IEEE Trans Power Syst 2004;19(1):577-85. [17] Huang SJ. Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches. IEEE Trans. Energy Conversion 2001;16(3):296–301.. [18] Simon SP, Padhy NP, Anand RS. “An ant colony system approach for unit commitment problem. Int J Electr Power Energy Syst 2006;28(5):315-23. [19] Yu IK, Song YH. A novel short-term generation scheduling technique of thermal units using ant colony search algorithms. Int J Electr Power Energy Syst 2001;23(6):471–9. [20] Shi L, Hao j, Zhou j, Xu G. Ant colony optimization algorithm with random perturbation behaviour to the problem of optimal unit commitment with probabilistic spinning reserve determination. Electr. Power Syst. Res 2004;69(2-3): 295–303. [21] Columbus CC, Chandrasekaran K, Simon SP. Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs. Applied Soft Computing 2012;12(1):145-60. [22] Vaisakh K, Srinivas LR. Evolving ant colony optimization based unit commitment. Applied Soft Computing 2011;11(2):2863-70. [23] Lin D, Cai Y. Taguchi method for solving the economic dispatchproblem with nonsmooth cost functions. IEEE Trans. Power Syst 2005;20(4):2006-12. [24] Taguchi G, Chowdhury S, Taguchi S, “ Robust Engineering,” New York: McGraw-Hill, 2000. [25] Ross PJ. Taguchi Techniques for Quality Engineering. New York: McGraw-Hill, 1989. [26] Dorigo M, Maniezzo V, Colorni A. The ant system: An autocatalytic optimizing process. Technical Report no. 91-016 Revised, Politecnico di Milano, Italy, 1991 [27] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans Power Syst, Man, Cybernetics 1996;26(1):29-41. [28] Dorigo M, Gambardella LM. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans Evolutionary Computation 1997;1(1):53-66. [29] Dorigo M, Stutale T. A short convergence proof for a class of ant colony optimization algorithms. IEEE Trans. Evolutionary Computation 2002;6(4):358-65. [30] John J. Grainger & William D. Stevenson, Jr., “Power System Analysis”, McGraw-Hill, Inc, 1994en_US
dc.description.abstract本論文提出一個整合蟻群演算法及田口方法(HTACS) 處理火力機組調派問題。HTACS 提供一個強大的全域搜尋能力。當蟻群系統每支螞蟻走完所有行程,在全域費洛蒙更新前,將田口方法整合到蟻群系統中。利用田口方法有系統的推論能力將這次疊代產生的機組排程和目前最佳解的機組排程輸入到田口方法,讓田口方法利用正交矩陣及信號雜訊分析,重組這兩個機組排程,並能快速的找出較好因子組合進而產生更佳的機組排程,進而增強了螞蟻的搜尋能力。因此HTACS較傳統ACS更為強健、準確及快速收歛效果。本論文所提出的方法運用在10部火力機組的排程問題上,分別將ACS及HTACS產生的結果進行比較,証明HTACS其性能優於傳統ACS。所得到的結果是可行、強健及更為有效。 關鍵詞: 蟻群系統、田口方法、機組調派。zh_TW
dc.description.abstractIn this paper, a hybrid Taguchi-Ant Colony System algorithm (HTACS) is proposed to deal with the unit commitment (UC) problem. The HTACS integrates the Taguchi method and the traditional Ant Colony System algorithm (ACS), providing a powerful global exploration capability. The Taguchi method is incorporated into the ACS process before its global pheromone update mechanism. The systematic reasoning ability of the Taguchi method is employed in selecting better UC solution quickly for representing new potential UC schedules and consequently, enhances the ACS algorithm. Therefore, the HTACS can be more robust, statistically sound and quickly convergent. The proposed model has been demonstrated on a practical ten-unit system to demonstrate its performance. Our results show that the proposed method is feasible, robust, and more effective on the UC problem than traditional ACS methods. Keywords: Ant Colony System (ACS), Taguchi Method, Unit Commitmenten_US
dc.description.tableofcontents誌謝辭 i 中文摘要 ii Abstract iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 1-1 Motivation 1 1-2 Literature Review and Propose of the Thesis 1 1-3 Organization of the Thesis 3 Chapter 2 Problem formulation 4 2-1 Description of the problem 4 2-2 Problem formulation 4 Chapter 3 A Brief Introduction on Taguchi Method 9 3-1 Orthogonal arrays 9 3-2 Signal-to-noise ratio (SNR) 10 Chapter 4 A Brief Introduction on Ant colony system (ACS) 12 4-1 Initialization 13 4-2 Transition strategy 13 4-3 Pheromone trail update rule 14 Chapter 5 Hybrid Taguchi-ACS Algorithm, HTACS 15 Chapter 6 Result and Discussion 17 Chapter 7 Conclusion 19 Reference 20 Appendix 33zh_TW
dc.subjectAnt Colony System (ACS)en_US
dc.subjectTaguchi Methoden_US
dc.subjectUnit Commitmenten_US
dc.titleIntegration of Ant Colony System and Taguchi Method for Thermal Unit Commitmenten_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextwith fulltext-
Appears in Collections:電機工程學系所
Files in This Item:
File Description SizeFormat Existing users please Login
nchu-101-5099064012-1.pdf377.03 kBAdobe PDFThis file is only available in the university internal network    Request a copy
Show simple item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.