臺灣博碩士論文加值系統

目前台灣的電力系統以火力機組為主，以2009年為例，台灣電力公司全系統「發購電量」，火力發電約佔75.3%，在台灣電力公司自有發電的部分，也還佔有50%，對此數量龐大的運轉機組而言，適當的機組發電排程，所節省之發電成本也是相當可觀。所謂機組排程是根據事先之負載預測，在滿足各項限制條件下，系統應上機、下機的機組，使得排程的總成本降至最低。本論文乃融合量子演算法及基因演算法，來實現一種新的演算法，名為「量子基因演算法」(Quantum Genetic Algorithm)，採用量子機率向量的編碼方式，同時使用量子位元、量子疊加狀態的思想，量子狀態疊加的特性能使排列更多元化，機率表達的特性，是將解的狀態以一定的機率表達出來，有效提高最佳解搜索能力。本論文最後以三個案例作分析，分別以六機組、十機組、二十機組作二十四小時負載機組排程，並且和傳統動態規劃法(Dynamic Programming, DP)、標準基因演算法(Standard Genetic Algorithm, SGA)作比較，模擬結果印證量子基因演算可以達到較低的發電成本，因而適合將此方法使用在求解最佳火力機組排程問題。

Nowadays, Taiwan electric power energy is mainly generated by thermal units. For example, the energy production and purchased for Taiwan Power Company in 2009, the total electric power production by thermal unit is about 75.3% in which 50% is generated by Taiwan Power Company itself and 25.3% by others. For this large number of thermal units currently operating in the power system, optimal unit and commitment schedules to save the total cost is significant importance.The unit commitment is involving different constraints, for example the unit start-up and shut-down schedules to meet the power demand at minimum cost. The other necessary constraints to satisfy the commitment schedules are also required. This thesis combines Quantum Algorithm and Genetic Algorithm to present a new algorithm, called Quantum Genetic Algorithm (QGA). The Quantum Genetic Algorithm uses the coding method of quantum probability vector, and also use the quantum bit and quantum superposition at the same time. The superposition can let it express more state. The probability expression characteristic can be expresses the solution state by certain probability. It can raise the ability of optimal solution.Three research cases have been studied and analyzed in the thesis. The optimal commitment for these three cases which involves six, ten and twenty thermal units in the system have been carried out to find the best operation cost over 24-hour period. The result which compare with other methods, i.e. Dynamic Programming and Genetic Algorithm show Quantum Genetic Algorithm more useful and efficient in short-term unit commitment.

中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1研究動機與目的 1
1.2相關文獻探討 2
1.3研究方法 3
1.4論文架構概述 4
第二章 機組排程問題及傳統解法 5
2.1前言 5
2.2目標函數 5
2.3機組限制條件 7
2.4傳統拉格朗日法 11
2.4.1傳統拉格朗日法之流程 11
2.5 優先順序表列法 14
2.6 動態規劃法 16
第三章 本文採用之演算法與模擬軟體介紹 18
3.1模擬軟體介紹 18
3.2基因演算法 18
3.2.1引言 18
3.2.2基因演算法之執行步驟 19
3.2.3基因演算法如何避免陷入區域最佳解 26
3.3量子基因演算法 27
3.3.1引言 27
3.3.2量子基因演算法之執行步驟 28
3.4利用量子基因演算法求解機組排程之應用 36
第四章 模擬結果和比較 39
4.1前言 39
4.2模擬案例一 39
4.3模擬案例二 45
4.4模擬案例三 51
4.5模擬案例總結 58
第五章 結論與未來發展方向 59
5.1結論 59
5.2未來發展方向 60
參考文獻 61

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