臺灣博碩士論文加值系統

隨著二氧化碳的排放越來越受國際間的關注，只考慮發電成本最低的系統備轉容量規劃方式已不夠充分，因此，本論文旨在提出考量二氧化碳排放量及再生能源併入時的系統可靠度指標變化下的最佳備轉容量規劃，以供電力調度上的選擇。首先，採用發電成本及碳排放量兩種不同的目標規劃模式，然後以不同的備轉容量值和不同的風電佔比來做機組排程的求解，由於發電排程屬於大型非線性的問題，不利於用一般的數學規劃方式來求解，本文採用具全域搜索能力的粒子群演算法來求解發電排程的問題。另外為了使獲得全域最佳解的機率增高，搭配適當的編碼技術來符合限制條件。其次，依據這些機組排程解，利用蒙地卡羅模擬法中的狀態持續時間取樣法來得出其可靠度評估。此模擬方案的特點在於使決策者能夠在顧及環保、經濟及可靠性因素下擬訂適合的備轉容量值。
最後，以台電公司的27部機組系統進行測試，研究結果顯示本論文所提之模擬規劃方法確實有助於電力業者決定備轉容量值。

With the increasing international concern about carbon dioxide (CO2) emissions, it is no longer adequate to plan spinning reserves taking only the lowest production cost into account. Therefore, the purpose of this thesis was to present an optimal spinning reserve planning scheme considering CO2 emissions and the variations in the system reliability index while incorporating renewable energy in the power dispatch selection. First, we introduced two different objective scheduling modes of production costs and CO2 emissions, and then solved the unit commitment problem in different spinning reserve values and wind penetrations. Owing to the large-scale non-linear programming problem of generation scheduling, the issues were hard to solve by a general mathematical programming method. This study applied a particle swarm algorithm with a global search capability to the generation scheduling problem. Furthermore, in order to obtain a higher probability of the global optimum, the simulation process used appropriate coding techniques to meet the constraints. Secondly, we utilized the state duration sampling of the Monte Carlo simulation to acquire the reliability assessment of these unit commitment solutions. The salient feature of the simulation strategy was that the decision-maker could make a proper spinning reserve level involving environmental, economic and reliability factors simultaneously.
Finally, taking the 27-unit system of the Tai-power as a test example, the simulation results showed that the proposed approach could indeed assist the power industry in setting the value of the spinning reserve.

中文摘要…………. I
Abstract…………. II
致謝…………. III
目錄…………. IV
圖目錄.................. VII
表目錄………. IX
第一章　緒論 1
1.1 研究背景與動機 1
1.2 研究步驟與目的 3
1.3 章節概要 6
第二章　粒子群演算法與蒙地卡羅模擬法 8
2.1 粒子群演算法 8
2.1.1 簡介 8
2.1.2 基本工作原理 8
2.1.3 粒子群演算法流程 9
2.1.4 粒子群演算法範例說明 13
2.1.5 粒子群演算法與其他方法之比較 14
2.2 蒙地卡羅模擬法 16
2.2.1 簡介 16
2.2.2 蒙地卡羅模擬法之特點 16
2.2.3 蒙地卡羅模擬法求解電力系統可靠度指標之模型 19
2.2.4 蒙地卡羅模擬法求解步驟 24
第三章　經濟調度 26
3.1 簡介 26
3.2 數學模型 27
3.2.1 發電成本函數 27
3.2.2 二氧化碳排放模型 28
3.3 目標函數 30
3.4 限制條件 31
3.4.1 電力平衡 31
3.4.2 網路損失 31
3.4.3 升降載率限制及發電量上下限 32
3.4.4 禁止操作區 33
3.5 粒子群演算法的求解過程 35
3.5.1 編碼與解碼流程 35
第四章　機組排程 43
4.1 簡介 43
4.2 目標函數 44
4.3 限制條件 44
4.3.1 電力供需平衡 44
4.3.2 發電量上下限及升降載率限制 45
4.3.3 火力機組最少併聯及最少解聯時間 45
4.3.4 系統備轉容量 45
4.3.5 機組必須運轉 46
4.3.6 機組無法運轉 46
4.4 粒子群演算法的求解過程 47
4.4.1 編碼方式 47
4.4.2 解碼方式 47
4.4.3 發電成本的計算 52
4.4.4 適應函數的計算 53
4.4.5 粒子群的移動與更新 54
第五章 模擬結果與討論 57
5.1 簡介 57
5.2 發電成本最佳化模式 57
5.2.1 模擬結果 62
5.2.2 風電容量佔比分析討論 72
5.3 二氧化碳排放量最佳化模式 74
5.3.1 模擬結果 74
第六章 結論與未來展望 80
6.1 結論 80
6.2 未來研究方向 80
參考文獻……. 82
作者簡述……. 87

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