一般火力機組排程(Unit Commitment)的目的為，決定各發電機組在所規劃時段之狀態以及其發電量，並滿足各項限制條件，使得總運轉成本為最小。而本論文提出實數型基因演算法(Real Genetic Algorithms, RGA)與混合田口方法之基因演算法(Hybrid Taguchi-Genetic Algorithms, HTGA)，來求解火力機組排程問題，並將所求得的結果和傳統基因演算法(GA)做一比較。文中應用田口方法(Taguchi Method)所具有的系統規劃參數設計，來強化實數型基因演算法的效能，而將田口方法於突變之後做操作。其應用田口方法的目的為改善子代的品質，因為經由隨機性的交配與突變所產生的子代，並不能確保其品質優於父代，因此藉由田口方法讓所求得的子代自身做優化，進而產生更佳的子代，此不但可以增加局部搜尋的能力，還可以快速搜索到所要求的最佳解，並且提早收斂。最後將以機組排程的模擬測試結果，來說明HTGA比RGA擁有較佳的尋優能力。

The objective of thermal unit commitment is to schedule the on or off status and the real power outputs of units and minimize the system production cost during the period while simultaneously satisfying operational constraints. In this thesis, the Real Genetic Algorithms (RGA) and the Hybrid Taguchi-Genetic Algorithm (HTGA) approaches are presented to solve the thermal unit commitment problem, and comparison with the results obtained using GA. Then this thesis applied the systematic reasoning ability of the Taguchi method operated after mutation can promote the RGA efficiency. The objective of Taguchi method is to improve the quality of offsprings by optimizing themselves to generate a better result, because the offsprings produced randomly by crossover and mutation process is not necessary better than the parents. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. Finally, it will be shown that the HTGA outperforms RGA by comparing simulation results of unit commitment.

目錄

摘要……………………………………………………………I
Abstract………………………………………………………II
目錄……………………………………………………………III
圖目錄………………………………………………………VII
表目錄………………………………………………………VIII

第一章 緒論…………………………………………………1

1.1 研究背景與動機………………………………………………1
1.2 文獻回顧與研究目的…………………………………………2
1.3 論文內容架構…………………………………………………6

第二章 機組排程之問題描述與數學模式…………………8

2.1 簡介……………………………………………………………8
2.2 問題描述與數學模式…………………………………………9
2.2.1 目標函數………………………………………………9
2.2.2 限制條件………………………………………………10
2.3 本章結論……………………………………………………14

第三章 田口方法……………………………………………15

3.1 簡介…………………………………………………………15
3.2 田口方法之程序……………………………………………15
3.3 直交表………………………………………………………18
3.4 參數設計之原理……………………………………………20
3.5 本章結論……………………………………………………23

第四章 基因演算法…………………………………………24

4.1 簡介…………………………………………………………24
4.2 原理介紹……………………………………………………25
4.2.1 初始族群………………………………………………26
4.2.2 適應值…………………………………………………27
4.2.3 複製……………………………………………………27
4.2.4 交配……………………………………………………29
4.2.5 突變……………………………………………………30
4.2.6 取代……………………………………………………30
4.3 傳統基因演算法……………………………………………31
4.3.1 編碼……………………………………………………33
4.3.2 解碼……………………………………………………34
4.3.3 BGA交配策略…………………………………………34
4.3.4 BGA突變策略…………………………………………36
4.4 實數型基因演算法…………………………………………37
4.4.1 RGA交配策略…………………………………………39
4.4.2 RGA突變策略…………………………………………41
4.5 混合田口方法之基因演算法………………………………41
4.5.1 田口方法之應用………………………………………44
4.6 本章結論…………………………………………………46

第五章 方法分析與模擬測試………………………………48

5.1 簡介…………………………………………………………48
5.2 應用分析……………………………………………………48
5.2.1 RGA應用分析…………………………………………48
5.2.1 HTGA應用分析………………………………………56
5.3 模擬測試……………………………………………………60
5.3.1 10部發電機模擬測試…………………………………60
5.3.2 20部發電機模擬測試…………………………………64
5.4 本章結論……………………………………………………74

第六章 結論與未來研究方向………………………………75

6.1 結論…………………………………………………………75
6.2 未來研究方向………………………………………………76
參考文獻………………………………………………………78

[1] T. Senjyu, K. Shimabukuro, K. Uezato and T. Funabashi, “A fast technique for unit commitment problem by extended priority list,” IEEE Transactions on Power Systems, Vol. 18, No. 2, pp. 882 – 888, May 2003.
[2] W. J. Hobbs, G. Hermon, S. Warner and G. B. Shelbe, “An enhanced dynamic programming approach for unit commitment,” IEEE Transactions on Power Systems, Vol. 3, No. 3, pp. 1201 – 1205, Aug. 1988.
[3] Z. Ouyang and S. M. Shahidehpour, “An Intelligent Dynamic Programming for Unit Commitment Application,” IEEE Transactions on Power Systems, Vol. 6, No. 3, pp. 1203-1209, 1991.
[4] C. L. Chen, “Dynamic Programming for Unit Commitment,” Master Dissertation, National Taiwan University, 1992.
[5] 莊景勝, “線性整數規劃與拉氏鬆弛法求解火力機組排程之分析比較”, 碩士論文, 國立中正大學, 民國93年.
[6] A. J. Wood and B. F. Wollenberg, “Power Generation Operation and Control,” Second Edition, John Wiley & Sons, Inc, 1996.
[7] Chuan-Ping Cheng, Chih-Wen Liu and Chun-Chang Liu, “Unit commitment by Lagrangian relaxation and genetic algorithms,” IEEE Transactions on Power Systems, Vol. 15, No. 2, pp. 707 – 714, May 2000.
[8] C. L. Chen and S. C. Wang, “Branch-and-bound scheduling for thermal generating units,” IEEE Transactions on Energy Conversion, Vol. 8, No. 2, pp. 184 – 189, June 1993.
[9] A. P. Rewagad and V. L. Soanawane, “Artificial neural network based short term load forecasting,” TENCON '98, 1998 IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control, Vol. 2, pp. 588 – 595, 17-19 Dec. 1998.
[10] S. J. Kiartzis, C. E. Zoumas, J. B. Theocharis, A. G. Bakirtzis and V. Petridis, “Short-term load forecasting in an autonomous power system using artificial neural networks,” IEEE Transactions on Power Systems, Vol. 12, No. 4, pp. 1591 – 1596, Nov. 1997.
[11] H. Sasaki, M. Watanabe, J. Kubokawa, N. Yorino and R. Yokoyama, “A solution method of unit commitment by artificial neural networks,” IEEE Transactions on Power Systems, Vol. 7, No. 3, pp. 974 – 981, Aug. 1992.
[12] F. Zhuang and F. D. Galiana, “Unit commitment by simulated annealing,” IEEE Transactions on Power System, Vol. 5, No. 1, pp. 311 – 318, Feb. 1990.
[13] A. Viana, J. P. Sousa and M. Matos, “Simulated annealing for the unit commitment problem,” 2001 IEEE Porto Power Tech Conference Portugal, pp. 4-7, 10-13 Sept. 2001.
[14] C. C. A. Rajan and M. R. Mohan, “An evolutionary programming method for solving the unit commitment problem,” IEEE on Power Engineering Society General Meeting, Vol. 1, pp. 1149, 6-10 June 2004.
[15] N. Sinha, R. Chakrabarti and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load dispatch,” IEEE Transactions on Evolutionary Computation, Vol. 7, No. 1, pp. 83 – 94, Feb. 2003.
[16] M. Gen and R. Cheng, “Genetic Algorithms and Engineering Optimization,” John Wiley and Sons, 2000.
[17] J. T. Tsai, T. K. Liu and J. H. Chou, “Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, No. 4, pp. 365-377, Aug. 2004.
[18] I. G. Damousis, A. G. Bakirtzis, P. S. Dokopoulos, “Network-constrained economic dispatch using real-coded genetic algorithm,” IEEE Transactions on Power Systems, Vol. 18, No. 1, pp. 198 – 205, Feb. 2003.
[19] Hong-Tzer Yang, Pai-Chuan Yang and Ching-Lien Huang, “Applications of the genetic algorithm to the unit commitment problem in power generation industry,” IEEE International Conference on Fuzzy Systems, Vol. 1, pp. 267 – 274, March 1995.
[20] D. Dasgupta and D. R. McGregor, “Thermal unit commitment using genetic algorithms,” IEE Proceedings-Generation, Transmission and Distribution, Vol. 141, No. 5, pp. 459 – 465, Sept. 1994.
[21] A. Rudolf and R. Bayrleithner, “A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system,” IEEE Transactions on Power Systems, Vol. 14, No. 4, pp. 1460 – 1468, Nov. 1999.
[22] S. A. Kazarlis, A. G. Bakirtzis and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Transactions on Power Systems, Vol. 11, No. 1, pp. 83 – 92, Feb. 1996.
[23] 鄭泉泙, “以基因演算法為基礎之火力機組排程研究”, 博士論文, 國立台灣大學, 民國88年.
[24] 許博傑, “實數編碼與二位元編碼遺傳演算法之性能比較研究”, 碩士論文, 國立台灣科技大學, 民國90年.
[25] 李坤洲, “應用田口基因演算法設計數位IIR濾波器”, 碩士論文, 國立高雄第一科技大學, 民國93年.
[26] 周鴻程, “遺傳演算法原理與應用”, 全華書局, 2001.
[27] A. H. Mantawy, Y. L. Abdel-Magid and S.Z. Selim, “Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem,” IEEE Transactions on Power Systems, Vol. 14, No. 3, pp. 829 – 836, Aug. 1999.
[28] Hyunchul Kim, Y. Hayashi and K. Nara, “The performance of hybridized algorithm of GA SA and TS for thermal unit maintenance scheduling,” IEEE International Conference on Evolutionary Computation, Vol. 1, pp. 114-119, 29 Nov.-1 Dec., 1995.
[29] C. C. A. Rajan and M. R. Mohan, “An evolutionary programming-based tabu search method for solving the unit commitment problem,” IEEE Transactions on Power Systems, Vol. 19, No. 1, pp. 577 – 585, Feb. 2004.
[30] 李輝煌, “田口方法-品質設計的原理與實務”, 高立圖書有限公司, 2000.
[31] Hadi Saadat, “電力系統分析”, 東華書局, 譯者:陳在相, 吳瑞南 and 張宏展, 民國89年.