臺灣博碩士論文加值系統

摘要
本文旨在針對一背壓式與抽汽冷凝式蒸汽渦輪發電機組的實際汽電共生系統，在滿足廠內製程蒸汽為優先，同時配合時間電價及相關限制條件下，應用“基因遺傳演算法”(Genetic Algorithms, GA)求得此系統於非夏月時段蒸汽渦輪機組之最佳機組排程調度，即於調度時段內決定各部蒸汽渦輪發電機組何時停機、何時起機，並同時求解每一小時各部鍋爐及發電機組之最佳運轉模式，以達到降低整體運轉成本及節約能源的目的。
“基因遺傳演算法”為採機率性的轉變原則，且其對於目標方程式無連續性、可微分性等限制，亦不受限於時間相關的限制式，因此有較大的機會在非凸面的搜尋空間避免搜尋到局部最佳解(Local Optimal)，而本文為了增加求得全域最佳解的機率及節省整個演化過程的運算時間，提出一些改進的方法，將使基因遺傳演算法在求解過程更有效率；本文將以一實際汽電共生系統為實例，驗證所提方法為實際可行。

ABSTRACT
The main purpose of this thesis is to find the optimal unit commitment schedule in non-summer season for a real cogeneration system with back-pressure and extraction turbines by using “Genetic Algorithms”. This thesis is first to determine at which hour the unit should be shut down and at which hour the unit should be started up during specified period, and then to solve the economic dispatch of units at each hour while considering the time-of-use (TOU) rates and the relative constraints subject to satisfying the process steam demand to reduce production costs and save energy.
Genetic algorithms are based on probabilistic transition rules and can adapt to nonlinearities and discontinuities commonly found in power systems, so they are less likely to converge to local optima. In addition, the GAs can manage time-dependent constraints. This thesis also proposes some improved methods to increase the probability of finding the global optima and save the computation time. A real cogeneration system will be used to verify the feasibility of the proposed scheme.

CONTENTS
Page
ABSTRACT (in Chinese) I
ABSTRACT (in English) II
ACKNOWLEDGMENTS
III CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VII
CHAPTER 1 INTRODUCTION 1
1.1 Research background and motives 1
1.2 Research approach 2
1.3 Thesis framework 4
CHAPTER 2 COGENERATION SYSTEM INTRODUCTION AND
THE I/O CURVE ANALYSIS OF UNITS 6
2.1 Introduction 6
2.2 System introduction 6
2.3 The I/O curve analysis of steam turbine generators and boilers 9
2.3.1 The I/O curve analysis of boilers 9
2.3.2 The I/O curve analysis of steam turbine generators 10
CHAPTER 3 GENETIC ALGORITHMS 14
3.1 Introduction 14
3.2 The principle of genetic algorithms 14
3.2.1 Operators of genetic algorithms 15
3.2.2 Parameter setting of genetic algorithms 19
3.2.3 Improvement of genetic algorithms 20
3.3 The principle of SADP 24
3.4 Application of GAs to unit commitment 27
CHAPTER 4 SIMULATED SYSTEM ANALYSIS AND
MATHEMATIC MODEL 34
4.1 Introduction 33
4.2 Mathematical model of the unit commitment problem 34
4.2.1 Objective function and constraints 35
4.3 Mathematical model of the economic dispatch problem 44
4.3.1 Objective function and constraints 44
4.4 The optimization of the entire system 48
CHAPTER 5 CASE SIMULATION AND ANALYSIS 52
5.1 Introduction 52
5.2 Case analysis 55
5.2.1 Case1 56
5.2.2 Case2 58
5.2.3 Case3 61
5.2.4 Comprehensive discussion 61
5.3 Influence of the variance of fuel price 63
CHAPTER 6 CONCLUSION AND SUGGESTION 79
APPENDIX 83
REFERENCE 95
LIST OF FIGURES
Page
Fig.2.1 The scheme of the cogeneration system 8
Fig.2.2 The schema of a extraction turbine 12
Fig.3.1 The basic framework of GAs 16
Fig.3.2 The binary representation of a unit commitment problem solution 16
Fig.3.3 Multi-point crossover 18
Fig.3.4 Mutation 18
Fig.3.5 Repair operator 21
Fig.3.6 Swap-window hill-climb operator 23
Fig.3.7 Decision variables modification 23
Fig.3.8 Flowchart of SADP 26
Fig.4.1 The cold start-up diagram of the extraction condenser turbine generator 41
Fig.4.2 The shut-down diagram of the extraction condenser turbine generator 42
Fig.4.3 Flowchart of ED by SADP 49
Fig.4.4 (a) Flowchart of unit commitment by GA 50
Fig.4.4 (b) Flowchart of unit commitment by GA (continued) 51
Fig.5.1 (a) In-plant electric demand, (b) High-pressure process steam demand
and (c) Medium-pressure process steam demand 53
Fig.5.2 Cost reduction at different oil price in unit commitment problem 63
LIST OF TABLES
Page
Table 2.1 Coefficients of I/O curves of boilers 10
Table 2.2 Coefficients of I/O curves of the steam turbines 12
Table 3.1 Unit characteristics 27
Table 3.2 Load pattern 27
Table 4.1 Steam capacity limits for boilers 38
Table 4.2 Limits of generated power from the steam turbines 38
Table 4.3 Limits of steam flow rates for steam turbines 39
Table 4.4 Minimum up/down time of turbine generators 43
Table 4.5 Ramp rate limits of turbine generators 43
Table 5.1 Priority list of boilers 56
Table 5.2 In-plant operation record (case1) 66
Table 5.3 Simulated results of dispatching the same units (boilers & turbines) as in-plant operation records by performing ED (case1) 68
Table 5.4 Simulated results of the case2-state1
(All turbines on line & using priority list method for boilers) 70
Table 5.5 Simulated results of the case2-state2
(Dispatch the same turbines as in-plant operation record &
using priority list method for boilers) 72
Table 5.6 Simulated results of the case2-state3
(Unit commitment for turbines & using priority list method for boilers) 74
Table 5.7 Simulated results of the case3
(Units’ state the same as case2-state3 without considering contract constraint) 76
Table 5.8 The total cost of each state in case1, case2 and case3 78
Table A.1 In-plant load data 83
Table A.2 In-plant operation records (case1) 85
Table A.3 Simulated results of dispatching the same units (boilers & turbines) as in-plant operation records by performing ED in detail (case1) 87
Table A.4 Simulated results of the case2-state1 in detail
(All turbines on line & using priority list method for boilers) 89
Table A.5 Simulated results of the case2-state2 in detail
(Dispatch the same turbines as in-plant operation record &
using priority list method for boilers) 91
Table A.6 Simulated results of the case2-state3 in detail
(Unit commitment for turbines & using priority list method for boilers) 93

REFERENCES
[1] T. T. Liu, “Optimal operation for cogeneration systems by successive approximations dynamic programming based method,” Master Thesis, Electrical Engineering Department, Tatung Institute of Technology, June 1998.
[2] 蔡明堂，“汽電共生系統多時段排程的運轉調度”，技術學刊，第十六卷第二期，民國九十年。
[3] A. J. Wood and B. F. Wollenberg, Power Generation, Operation and Control. New York: John Wiley and Sons, Inc. 1996.
[4] S. K. Tong, S. M. Shahidehpour, and Z. Ouyang, “A heuristic short-term unit commitment,” IEEE Trans. on Power Systems, Vol. 6, no. 3, pp. 1210-1216, Aug. 1991.
[5] C. K. Pang and H. C. Chen, “Optimal short-term thermal unit commitment,” IEEE Trans. on Power Systems, vol. 95, no. 4, pp. 1336-1345, July/Aug. 1976.
[6] W. L. Snyder, H. D. Powell Jr., and J. C. Rayburn, “Dynamic programming approach to unit commitment,” IEEE Trans. on Power Systems, Vol. 2, pp. 339-350, May 1987.
[7] A. Merlin and P. Sandrin, “A new method for unit commitment at Electricite de France,” IEEE Trans. on Power Apparatus and Systems, Vol. PAS-102, pp. 1218-1225, May 1983.
[8] X. Guan, P. B. Luh, and H. Yan, “An optimization-based method for unit commitment,” Elect. Power Energy Syst., Vol. 14, no. 1, pp. 9-17, Feb. 1992.
[9] S. A. Kazarlis, A. G. Bakirtzis and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. on Power Systems, Vol. 11, No. 1, Feb. 1996.
[10] T. T. Maifeld and G. B. Sheble, “Genetic-based unit commitment algorithm,” IEEE Trans. on Power Systems, Vol. 11, No. 3, Aug. 1996.
[11] K. S. Swarup and S. Yamashiro, “Unit commitment solution mtthodology using genetic algorithm,” IEEE Trans. on Power Systems, Vol. 17, No. 1, Feb. 2002.
[12] J. A. Momoh, Electric Power System Applications of Optimization. Marcel Dekker, Inc. 2001.
[13] Z. X. Liang and J. D. Glover, “Improved cost functions for economic dispatch computations,” IEEE Trans. on Power Systems, Vol. 6, No. 2, May 1991.
[14] N. R. Draper and H. Smith, Applied regression analysis. New York：John Wiley and Sons, Inc. 1981.
[15] EPRI Report, COGEN3：A computer model for design, costing, and economic optimization of cogeneration projects.
[16] J. H. Holland, “Adaptation in natural and artificial systems,” Ann Arbor, Michigan：The University of Michigan Press, 1975.
[17] D. E. Goldberg, Genetic Algorithm in Search, Optimization and Control. Addison Wesley, 1989.
[18] D. Walters, C. Sheble, B. Gerald, “Genetic algorithm solution of economic dispatch with valve point loading,” IEEE Trans. on Power Systems, Vol. 8, No. 3, Aug. 1993.
[19] J. Valenzuela and A. E. Smith, “A seeded memetic algorithm for large unit commitment problems,” Journal of Heuristics, Sep. 1999.
[20] S. H. Huang, “Optimal unit commitment scheduling for cogeneration by genetic algorithms,” Master Thesis, Electrical Engineering Department, Tatung Institute of Technology, June 1999.
[21] A. J. Korsak and R. E. Larson, “A dynamic programming successive approximations technique with convergence proofs-parts I & II,” Automatica, vol. 6, Dec. 1969.