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

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