臺灣博碩士論文加值系統

為減少電壓驟降及斷電事故造成之傷害，大型電力用戶多會自備發電機組，以提升供電品質。在考慮台電電費結構及自備發電機組發電成本下，台電電費支出及自備發電機組運轉方式與所訂定之契約容量息息相關。有鑑於此，本文研究主旨乃在如何制訂最佳契約容量，配合自備發電機組運轉，使用戶總用電成本為最低。
差值演算法透過突變、交配及選擇操作，在搜尋最佳解時具有快速及強健的收斂性。文化演算法則可擷取並儲存進化過程中從個體中所獲得之專門知識或問題特性，在差值演算法之突變操作中加入文化演算法的專門知識，可使得解之搜尋更有效率。因此，本文乃提出文化差值演算法作為求解最佳契約容量的方法，達到節省總用電成本之目的。
為驗證所提方法之可行性，本文使用某光電廠實際用電數據資料，包括台電用電度數、自備發電機組容量與燃料及運轉成本函數，以及欲制訂契約容量月份的負載需求預測。由實際系統結果證實，比較文化差值演算法及該廠現行方法，總用電成本可節省比例為14.56%，本文所提方法和其他現有最佳化演算法比較，文化差值演算法運用於自備發電機組契約容量訂定亦具有最佳化之效果。

To reduce the damages caused by voltage sag and interruption events, many customers have their self-owned generating units in attempt to improve the power supply quality. With the power tariff structure of the utilities and the cost functions of self-owned generating units considered at the same time, expenses due to the utility power consumed and the operation of self-owned generating units are highly related to the contracted capacity. Taking into account the corresponding operations of self-owned generating units, the thesis is thus aimed at determining the optimal contracted capacity with the utilities to obtain the lowest total power expenditure.
The differential computation algorithm provides fast and robust converging characteristics in searching the optimal solution through operations of mutation, crossover, and selection. The cultural algorithm can extract and save the domain knowledge or problem properties during the evolution process. The domain knowledge in cultural algorithm can be added to the mutation operation in differential computation algorithm to make the searching more efficient. Accordingly, the thesis proposes the cultured differential computation algorithm to determine the optimal contracted capacity in order to reach the goal of saving the total power expenses.
To verify feasibility of the proposed method, the thesis employs the real data obtained from an optoelectronics factory in Taiwan, data which include the amounts of power consumption from the utilities, capacities and cost functions of self-owned generating units, and load demand forecasting in the months of planning period. It is shown from the simulation results that 14.56% of electrical power expenses can be saved from the proposed cultured differential computation algorithm as compared with the method currently adopted by the factory. Also, in comparison with the other optimization methods, the proposed approach has superior results to the other existing optimization methods as revealed in the numerical results.

目 錄
中文摘要.............................................................I
Abstract............................................................II
誌 謝.............................................................III
目 錄..............................................................IV
圖目錄.............................................................VII
表目錄............................................................VIII
符號表..............................................................IX
第一章 緒論..........................................................1
1-1 研究背景.........................................................1
1-2 研究動機.........................................................2
1-3 文獻回顧.........................................................3
1-4 研究方法.........................................................6
1-5 論文貢獻.........................................................7
1-6 論文架構.........................................................7
第二章 自備發電機組系統架構與運轉成本................................9
2-1 簡介.............................................................9
2-2 自備發電機組系統介紹............................................10
2-2-1 發電機組主機設備..............................................10
2-2-2 輔機設備......................................................12
2-3 自備發電機組運轉成本分析........................................14
2-3-1 固定成本......................................................14
2-3-2 變動成本......................................................15
2-4 本章結論........................................................16
第三章 台電電費結構及計算方式.......................................17
3-1 簡介............................................................17
3-2 契約容量........................................................17
3-3 季節電價與時間電價..............................................19
3-4 台電電費成本....................................................21
3-4-1 基本電費......................................................22
3-4-2 流動電費......................................................24
3-4-3 功率因數調整費................................................24
3-4-4 超約附加費....................................................25
3-4-5 契約容量調整費................................................28
3-5 加入自備發電機組後之電費計算....................................34
3-5-1 基本電費......................................................34
3-5-2 流動電費......................................................35
3-5-3 功率因數調整費................................................36
3-6 本章結論........................................................37
第四章 文化差值演算法求解最佳契約容量...............................38
4-1 簡介............................................................38
4-2 未裝設自備發電機組之用電成本計算................................38
4-3 裝設自備發電機組之用電成本計算..................................40
4-3-1 台電電費成本..................................................40
4-3-2 自備發電機組發電成本..........................................42
4-3-3 用戶總用電成本................................................42
4-4 文化演算法......................................................43
4-5 差值演算法......................................................45
4-6 文化差值演算法..................................................50
4-6-1 狀況知識......................................................51
4-6-2 規範知識......................................................51
4-6-3 拓墣知識......................................................52
4-6-4 歷史知識......................................................53
4-6-5 認可函數......................................................54
4-6-6 影響函數......................................................54
4-6-7 演算法參數設定................................................55
4-7 以文化差值演算法訂定自備發電機組最佳契約容量....................55
4-8 本章結論........................................................57
第五章 數值計算與結果分析...........................................58
5-1 簡介............................................................58
5-2 未裝設發電機組之最佳契約容量訂定................................59
5-3 使用轉移方式訂定最佳契約容量....................................59
5-4 使用基因演算法訂定最佳契約容量..................................66
5-5 使用改良型田口方法訂定最佳契約容量..............................66
5-6 使用差值及文化差值演算法訂定最佳契約容量........................73
5-7 各方法模擬結果比較..............................................78
5-8 本章結論........................................................81
第六章 結論與未來研究方向...........................................82
6-1 結論............................................................82
6-2 未來研究方向....................................................83
參考文獻............................................................84

圖目錄
圖2.1 自備發電機組系統設備架構圖[8].................................11
圖3.1 以不同供電時間來表示需量契約容量..............................18
圖3.2 契約容量與電費關係圖..........................................19
圖4.1 未裝設自備發電機組用戶電費計算流程圖..........................39
圖4.2 裝設自備發電機組用戶總用電成本計算流程圖......................43
圖4.3 文化演算法之空間利用..........................................45
圖4.4 差值演算法突變方式............................................47
圖4.5差值演算法交配方式.............................................48
圖4.6 差值演算法之演化流程圖........................................49
圖4.7 應用文化差值演算法求解最佳契約容量流程圖......................56
圖5.1 轉移方式契約容量調整流程圖[9].................................63
圖5.2 應用基因演算法求解最佳契約容量流程圖[8].......................67
圖5.3 應用改良型田口方法求解最佳契約容量流程圖[9]...................70
圖5.4 差值演算法及文化差值演算法之搜尋路徑..........................73
圖5.5 各種方法之各月份用電成本比較..................................80
圖5.6 各種方法之各月份累計用電成本比較..............................80

表目錄
表2.1 自備發電機組固定成本[8].......................................15
表2.2 輔機費用[8]...................................................16
表3.1 台電電價表規定全日為離峰日之時間[30]..........................20
表3.2 台電三段式時間電價表[30]......................................21
表3.3 擴建補助費單價表[30]..........................................32
表3.4 供電設備維持費單價表[30]......................................32
表5.1 預估之各月份中各時段最高需量及用電度數[8].....................60
表5.2 應用差值演算法之台電用電度數及台電電費........................61
表5.3 應用文化差值演算法之台電用電度數及台電電費....................62
表5.4 轉移方式之台電用電度數及發電機組發電度數[9]...................64
表5.5 轉移方式之台電電費及發電機組成本[9]...........................65
表5.6 應用基因演算法之台電用電度數及發電機組發電度數[8].............68
表5.7 應用基因演算法之台電電費及發電機組成本[8].....................69
表5.8 應用改良型田口方法之台電用電度數及發電機組發電度數[9].........71
表5.9 應用改良型田口方法之台電電費及發電機組成本[9].................72
表5.10 應用差值演算法之台電用電度數及發電機組發電度數...............74
表5.11 應用差值演算法之台電電費及發電機組成本.......................75
表5.12 應用文化差值演算法之台電用電度數及發電機組發電度數...........76
表5.13 應用文化差值演算法之台電電費及發電機組成本...................77
表5.14 各種契約容量訂定方法之各月份用電成本列表.....................79
表5.15 各方法發電機組發電量所占比例.................................81

[1]蔡明堂、陳松齡與鄭富升，“時間電價工業用戶最佳契約容量之訂定”，正修學報，第11期，117-124頁，1998年。
[2]王瑋民、陳建富與黃慶連，“最佳契約容量訂定與用戶並聯發電關係之探討”，台電工程月刊，第519期，137-156頁，1991年。
[3]陳政宏，大型用戶負載最佳省電策略規劃，碩士論文，中原大學，民國92年。
[4]李清吟與楊富貿，“南港軟體工業園區之最佳化負載管理”，中華民國第二十七屆電力工程研討會論文集，2006年。
[5]吳建欣，校園電能監控系統研製，碩士論文，國立台灣大學，民國92年。
[6]顧克平，台電契約容量之動態調整研究，碩士論文，元智大學，民國91年。
[7]張隆益，多媒介校區電能管理系統之研製，碩士論文，逢甲大學，民國90年。
[8]蔡德濃，自備發電機組用戶之契約容量最佳化，碩士論文，中原大學，民國94年。
[9]黃崇熙，應用改良型田口方法於自備發電機組用戶之契約容量最佳化，碩士論文，中原大學，民國95年。
[10]A. J. Wood and B. F. Wollenberg, Power Generation, Operation, and Control, John Wiley & Sons, Inc, 1996.
[11]H. H. Happ, “Optimal Power Dispatch a Comprehensive Survey,” IEEE Transactions on Power Apparatus and Systems, Vol. 96, No. 3, pp. 841-854, May 1977.
[12]G. B. Sheble, “Solution of the Unit Commitment Problem by the Method of Unit Periods,” IEEE Transactions on Power System, Vol. 5, No. 1, pp. 257–260, February 1990.
[13]G. Opoku, “Optimal Power System VAR Planning,” IEEE Transactions on Power Systems, Vol. 5, No. 1, pp. 53-60, 1990.
[14]A. I. Cohen and M. Yoshimura, “A Branch-and-Bound Algorithm for Unit Commitment,” IEEE Transactions on Power Apparatus and Systems, Vol. 102, No. 2, pp. 444–451, 1983.
[15]A. A. E1-Keib, H. Ma, and J. L. Hart, “Environmentally Constrained Economic Dispatch Using the Lagrangian Relaxation Method,” IEEE Transactions on Power Systems, Vol. 9, No. 4, pp. 1723-1729, November 1994.
[16]K. A. Papadogiannis and N. D. Hatziargyriou, “Optimal Allocation of Primary Reserve Services in Energy Markets,” IEEE Transactions on Power Systems, Vol. 19, No. 1, February 2004.
[17]K. P. Wong and C. C. Fung, “Simulated Annealing Based Economic Dispatch Algorithm,” IEE Proceedings of Generation, Transmission and Distribution, Vol. 140, No. 6, pp. 509-515, November 1993.
[18]R. H. Liang and Y. Y. Hsu, “Scheduling of Hydroelectric Generations Using Artificial Neural Networks,” IEE Proceedings of Generation, Transmission and Distribution, Vol. 141, No. 5, pp. 425-458, 1994.
[19]D. C. Walters and G. B. Sheble, “Genetic Algorithm Solution of Economic Dispatch with Valve Point Loading,” IEEE Transactions on Power Systems, Vol. 8, No. 3, pp. 1325-1332, August, 1993.
[20]K. S. Swarup, and S. Yamashiro, “Unit Commitment Solution Methodology Using Genetic Algorithm,” IEEE Transactions on Power Systems, Vol. 17, No. 1, February 2002.
[21]T. O. Ting, M. V. C. Rao, and C. K. Loo, “A Novel Approach for Unit Commitment Problem Via an Effective Hybrid Particle Swarm Optimization,” IEEE Transactions on Power System, Vol. 21, No. 1, February 2006.
[22]蘇木春、張孝德，機器學習 類神經網路、模糊系統以及基因演算法則，全華科技圖書股份有限公司，民國92年。
[23]R. G. Reynolds and W. Sverdlik, “Problem Solving Using Cultural Algorithms,” Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. 2, pp. 645-650, July 1994.
[24]R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of global optimization, Vol. 11, no. 4, pp. 341-359, December, 1997.
[25]L. B. Ricardo and C. A. Coello, “Cultured Differential Evolution for Constrained Optimization,” Computer methods in applied mechanics & engineering, Vol. 195, pp. 4303-4322, 2006.
[26]林文輝，人造空氣對密閉式循環柴油引擎之性能影響分析，碩士論文，國立成功大學，民國94年。
[27]空氣污染防治法，行政院環保署，民國94年。
[28]固定污染源空氣污染物排放標準，行政院環保署，民國91年。
[29]陳宗賢，汽電共生鍋爐系統之類神經網路最佳化模組建立及模擬，碩士論文，國立清華大學，民國91年。
[30]台灣電力公司電價表，經濟部，民國94年。
[31]台灣電力公司營業規則，經濟部，民國95年。
[32]C. A. Coello and R. L. Becerra, “Evolutionary Multiobjective Optimization Using a Cultural Algorithm,” Proceedings of the IEEE Conference on Swarm Intelligence Symposium, April 2003.
[33]王永富，應用混合型差分進化法於多區域電力系統之經濟調度與運轉，碩士論文，國立中正大學，民國92年。
[34]S. M. Mcgovern and S. M. Gupta, “Greedy Algorithm for Disassembly Line Scheduling,” Proceedings of the IEEE Conference on System, Man and Cybernetics, Vol. 2, pp. 1737-1744, October 2003.
[35]呂少君，應用混合差分進化演算法及動態規劃法於發輸電系統虛功補償與調度，碩士論文，國立中正大學，民國93年。
[36]Y. N. Guo, D. W. Gong, and Z. G. Xue, “Hybrid Optimization Method Based on Genetic Algorithm and Cultural Algorithm,” Proceedings of the Sixth World Congress on Intelligent Control and Automation, June 21-23, 2006, Dalian, China.
[37]C. L. Dos Santos and V. C. Mariani, “An Efficient Particle Swarm Optimization Approach Based on Cultural Algorithm Applied to Mechanical Design,” Proceedings of the IEEE Congress on Evolutionary Computation, July 16-21, 2006, Vancouver, Canada.
[38]S. M. Saleem, Knowledge-Based Solution to Dynamic Optimization Problems Using Cultural Algorithms, Ph.D. Dissertation, Wayne State University, Detroit, Michigan, 2001.