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研究生:陳永峰
研究生(外文):Yung-Fang Chen
論文名稱:電源規劃最佳化之決策支援系統
論文名稱(外文):The Decision Support System for Optimal Power Generation Planning
指導教授:邱昭彰邱昭彰引用關係
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:66
中文關鍵詞:電源規劃基因演算法染色體機率運轉模擬法
外文關鍵詞:Power PlanningGenetic Algorithm(GA)chromosomeProbabilistic Production Cost
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電源規劃問題,是一個非線性且包含很多限制的複雜問題,若以人力規劃耗力費時。此規劃依據未來的電力需求及穏定度限制、結合規劃興建及規劃退休機組之資訊,從候選機組中決定在什麼時候、建什麼樣的機組。電源規劃問題可以切為二個子問題來看,即機組間的組合問題及個別機組運轉上的問題。而機組間的組合問題直接影響到後續的運轉問題。本研究運用基因演算法(Genetic Algorithm, GA)此最佳化搜尋方法來解決此電源規劃的問題。機組的組合問題可由GA獲得答案,至於評估運轉方面的問題是用機率運轉模擬法(Probabilistic Production Cost),最後藉綜合此二者之整體評量,來判斷規劃結果的良窳,從而得到最佳的規劃。
Power Generation Planning is a non-linear and complex and has many constraints on it. If one want to do the planning without computer’s assistant, it is time consuming. According to the power requirement in the future, the power reliability limited and the information of planning and retiring power generators, the planning is to decide “when” and “what” to build the power generators.
The Power Generation Planning can be viewed as two sub-problems. They are generator invest sub-problem and generator operation sub-problem. In this paper, we apply Genetic Algorithm(GA) to solve this problem. We can obtain the result of invest sub-problem directly from the GA’s chromosome. As to the operation sub-problem, we use Probabilistic Production Cost method to simulate. Assemble these two sub-problems’ judgment scores, we can obtain the optimal planning solution.
1 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與範圍 2
1.3 研究架構與方法 3
1.4 研究限制 7
1.5 論文架構 7
2 文獻探討 8
2.1 電力機組概述 8
2.2 電源規劃 9
2.3 機率運轉模擬法 11
2.3.1 分段線性近似法 11
2.3.2 區塊法 16
2.3.3 等能量法 19
2.4 基因演算法 22
3 研究方法 27
3.1 總成本現值最小化的規劃 27
3.1.1 數學模型 28
3.1.2 GA染色體編碼 29
3.1.3 基因操作 30
3.1.4 系統流程 30
3.1.5 適應值的計算 31
3.1.6 停止條件 33
3.2 年燃料成本最小之規劃 34
3.2.1 數學模式 34
3.2.2 GA染色體編碼 34
3.2.3 基因操作 35
3.2.4 系統流程 36
3.2.5 適應值評估 36
3.2.6 停止條件 37
4 系統實作與分析 38
4.1 機組資料 38
4.2 負載預測 38
4.3 實驗結果與分析 39
4.3.1 總成本現值最小之規劃 40
4.3.2 年二氧化碳排量固定限制下,燃料成本最小之規劃 45
4.3.3 實驗結果分析 49
5 結論與未來發展 51
5.1 結論 51
5.2 未來發展 51
參考文獻 53
附錄 57
中文部分
[1]賴耿陽,電能原理概論,復漢出版社,1988。
[2]林士煥等譯,電力系統分析(輸配電),高立圖書有限公司,1996,pp. 491-505。
[3]台灣電力公司網站,http://www.taipower.com.tw。
[4]陳南鳴等,電力系統可靠度之研究第三章,經濟部能源委員會,1990。
[5]台灣電力公司,93年統計年報,台灣電力公司企劃處,2005。
[6]台灣電力公司,火力發電廠空氣污染改善之績效指標,環境保護處,1997。
[7]經濟部能源局,我國未來最適供電可靠度規畫之探討,經濟情勢暨評論第四卷第四期,1999,pp. 157-165。

英文部分
[1]Zhu, J. X., & Chow, M. Y., “A Review of Emerging Techniques on Generation Expansion Planning,” IEEE Transactions on Power Systems, Vol. 12, No. 4, 1997, pp. 1722-1728.
[2]Chen, H. Y., Wang, X. F., & Zhao, X. Y., “Generation Planning Using Lagrangian Relaxation and Probabilistic Production Simulation,” Electrical Power and Energy Systems, Vol. 26, 2004, pp. 597-605.
[3]Chen, P. H., & Chang, H. C., “Large-Scale Economic Dispatch by Genetic Algorithm,” IEEE Transactions on Power Systems, Vol. 10, No. 4, 1995, pp. 1919-1925.
[4]Park, J. B., Park, Y. M., Won, J. R., & Lee, K. Y., “An Improved Genetic Algorithm for Generation Expansion Planning,” IEEE Transactions on Power Systems, Vol. 15, No. 3, 2000, pp. 1043-1047.
[5]Park, Y. M., Park, J. B., & Won, J. R., “A Hybrid Genetic Algorithm/Dynamic Programming Approach to Optimal Long-Term Generation Expansion Planning,” Electrical Power and Energy System, Vol. 20, No. 4, 1998, pp. 295-303 .
[6]Fukuyama, Y., “A Parallel Genetic Algorithm for Generation Expansion Planning,” IEEE Transactions on Power Systems, Vol.11, No. 2, 1996, pp. 955-961.
[7]Nara, K., “Genetic Algorithm for Power Systems Planning,” Proceedings of the 4th International Conference on Advances in Power System Control, Operation and Management, APSCOM-97, 1997, pp. 60-64.
[8]Firmo, H. T., & Legey, L. F. L., “Generation Expansion Planning: An Iterative Genetic Algorithm Approach,” IEEE Transactions on Power Systems, Vol. 17, No. 3, 2002, pp. 901-906.
[9]Antunes, C. H., Martins, A. G., & Oliveira, I., “A Multiple Objective Mixed Integer Linear Programming Model for Power Generation Expansion Planning,” Energy, Vol. 29, 2004, pp. 613-627.
[10]Slochanal, S. M. R., Kannan, S., & Rengaraj, R., “Generation Expansion Planning the Competitive Environment,” 2004 International Conference on Power System Technology – POWERCON 2004 Singapore, 21-24 November 2004, pp. 1546-1549.
[11]Sevilgen, S. H., Erdem, H. H., Cetin, B., Akkaya, A.V., & Dağdaş, A., “Effect of economic parameters on power generation expansion planning,” Energy Conversion and Management , Vol. 46, 2005, pp. 1780-1789.
[12]Lin, W. M., Zhan, T. S., Tsay, M. T., & Hung, W. C., “The Generation Expansion Planning of the Utility in a Deregulated Environment,” 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies(DRPT2004), 2004 , pp. 702-707.
[13]Kannan, S., Slochanal, S. M. R., & Padhy, N. P., “Application and Comparison of Metaheurisic Technique to Generation Expansion Planning Problem,” IEEE Transactions on Power Systems, Vol. 20, No. 1, 2005, pp. 466-475.
[14]Booth, R. R., “Power system Simulation Model Based on Probability Analysis,” IEEE Transactions on Power Apparatus and Systems, Vol. PAS-91, 1972, pp. 62-69.
[15]Schenk, K. F., “A New Method for the Evaluation of Expected Energy Generation and Loss of Load Probability,” IEEE Transactions on Power Apparatus and Systems, Vol. PAS-103, No. 2, 1984, pp. 294-303.
[16]Wang, X. F., “Equivalent Energy Function Approach to Power System Probabilistic Modeling,” IEEE Transactions on Power Systems, Vol. 3, No. 3, 1988, pp. 823-829.
[17]Lin, M., Breipohl, A. B., & Lee, F. N., “Comparison of Probabilistic Production Cost Simulation Methods,” IEEE Transactions on Power Systems, Vol. 4, No. 4, 1989, pp. 1326-1334.
[18]Manhire, B., & Jenkins, R. T., “A New Technique for Simulating the Operation of Multiple Assigned-Energy Generating Units Suitable for Use in Generation System Expansion Planning Models,” IEEE Transactions on Power Apparatus and Systems, Vol. PAS-101, No. 10, 1982, pp. 3861-3868.
[19]Malik, A. S., & Cory, B. J., “Assigned- and Demand- Energy Units in Probabilistic Production Costing,” Imperial College of Science, Technology and Medicine, pp. 299-304.
[20]Malik, A. S., & Cory, B. J., “Efficient algorithm to optimize the energy generation by pumped storage units in probabilistic production costing,” IEE Proceedings Generation, Transmission and Distribution, Vol. 143, No. 6, 1996, pp. 546-552.
[21]Malik, A. S., Cory, B. J., & Wijayatunga, P.D.C., “Applications of Probabilistic Peak-Shaving Technique in Generation Planning,” IEEE Transactions on Power Systems, Vol. 14, No. 4, 1999, pp. 1543-1548.
[22]Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989.
[23]Gen, M., & R., Cheng, Genetic Algorithms & Engineering Design, WILEY, New York, 1997.
[24]Kalyanmoy, D., Multi-Objective Optimization using Evolutionary Algorithms, WILEY, New York, 2001.
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