臺灣博碩士論文加值系統

解制後的電力市場，對電力市場的各參與者而言創造了更多的競爭機會與更公平的交易機制。在競爭的電力市場中，所有的電力供應業者將在預定的負載需求，透過競價的運作模式謀求最大的利潤。整個市場將由成本為基礎的操作模式轉而為競價投標的運作模式。此時「電力」將成為一種商品，價格將隨買賣雙方、負載變動及電力網路情況而不同，交易型態及交易項目將會更趨於多樣化，也連帶讓各個參與者彼此之間存在更多的商業競爭。發電業者執行機組排程的目的是為了獲取本身最大利潤，是一種價格為導向的機組排程，傳統上滿足負載需求再也不是發電業者的主要目的。另一方面電業市場中，獨立操作員負責供需撮合及系統安全的調度，獨立操作員安全控制的操作，將導引發電業者必需承受各項的風險，諸如電價變動風險、競標風險、壅塞風險、事故風險等等，因此在不確定性電力市場中，發電業者如何在風險控管之下，尋求市場最終交易的排程，已成為一項重要的研究課題。本文首先執行電價預測，電價預測提供重要的資料給發電業者和消費者來發展投標策略以求利潤最大化，而執行精確的風險管理策略，可獲得最大的利潤及提升永續經營的能力。其次，推導以價格為基礎機組排程的模式，並以強化式免疫演算法，求解發電業者最佳的機組排程及發電量。最後，以常態分佈法與共變異矩陣模擬電價波動的模式，並整合於機組排程的數學模式中，模擬分析電價波動機組排程的調度模式，求得不同信賴區間的風險值。

For the power market participators, there are competition and more trade opportunities in the power industry under the deregulation. In the electricity market, the bidding model is adopted instead of the cost model. GenCos try to maximize the profit under bidding model according to the power demand. Electricity becomes commodity and its price varies with power demand, bidding strategy and the grid. GenCos perform the unit commitment in a price volatile environment to reach the maximal profit. In a deregulation environment, Independent System Operator (ISO) is very often responsible for the electricity auction and secured power scheduling. The ISO operation may involve all kinds of risks. These risks include price volatility risk, bidding risk, congestion risk, and so on. For some markets, it is very important how GenCos determine the optimal unit commitment schedule considering risk management. A good risk analysis will help GenCo maximize profit and purse sustainable development. In this study, price forecasting is developed to provide information for power producers to develop bidding strategies to maximize profit. Profit-Based Unit Commitment (PBUC) model was also derived. An Enhanced Immune Algorithm (EIA) is developed to solve the PBUC problem. Finally, the Value-at-Risk (VAR) of GenCos is found with a present confident level. Simulation results provide a risk management rule to find an optimal risk control strategy to maximize profit and raise its compatibility against other players.

誌謝 I
中文摘要 II
英文摘要 III
目錄 IV
圖目錄 VII
表目錄 IX
第一章 緒論 1
1-1 研究動機 1
1-2 研究背景 2
1-2-1 電力市場參與者 2
1-2-2電力市場之電價預測 4
1-2-3 發電機機組排程 5
1-2-4 電力市場風險管理 5
1-3文獻回顧 6
1-4 論文貢獻 9
1-5 論文內容概要 10
第二章 直交實驗設計與田口方法 12
2-1 前言 12
2-2 直交實驗設計 13
2-2-1 直交表 13
2-2-2 因子分析 15
2-3 田口方法 19
2-3-1 田口直交表 20
2-3-2 訊號與雜訊比 21
2-4 結論 22
第三章 電價預測 23
3-1 前言 23
3-2 類神經網路 24
3-2-1 類神經網路發展簡介 25
3-2-2 類神經網路基本架構 26
3-3強化式徑向基函數神經網路 27
3-3-1 徑向基類神經網路RBFN 28
3-3-2 強化式徑向基類神經網路ERBFN 29
3-3-3 ERBFN執行流程 32
3-4 本章結論 36
第四章 最大利潤機組排程 37
4-1 前言 37
4-2 最大利潤機組排程數學模型 38
4-2-1 最大利潤機組排程目標函數 39
4-2-2 機組排程限制式 40
4-3 強化式免疫演算法 42
4-3-1 免疫系統簡介 43
4-3-2 強化式免疫演法流程 47
4-4 本章結論 54
第五章 機組排程風險管理 55
5-1 前言 55
5-2 常態分佈風險模型 56
5-3 共變異風險模型 58
5-4 風險分析流程 62
5-5 本章結論 63
第六章 模擬測試 64
6-1 電價測試範例 64
6-1-1 模擬結果 67
6-1-2 能測試 72
6-2 機組排程測試範例 75
6-2-1 因子分析說明 75
6-2-2 機組排程(26機組系統) 77
6-2-3 機組排程(40、80、100機組系統) 81
6-2-4 最大利潤機組排程(15機組系統) 82
6-3 風險管理測試範例 86
6-3-1 風險分析(常態分佈模型) 86
6-3-2 風險分析(共變異矩陣模型) 87
6-4 本章結論 91
第七章 結論與未來研究方向 92
7-1 結論 92
7-1-1 電價預測 92
7-1-2 最大利潤機組排程 93
7-1-3 風險管理 93
7-2 未來研究方向 94

參考文獻 95
著作目錄 101
圖 目 錄

圖1.1 自由化電力市場架構圖 2
圖1.2 風險管理示意圖 6
圖2.1 直交表符號圖 13
圖2.2 L4(23)直交表示意圖 14
圖3.1 類神經網路基本架圖 26
圖3.2 RBFN架構示意圖 27
圖3.3 高斯函數轉換函數圖 28
圖3.4 ERBFN架構示意圖 29
圖3.5 ERBFN預測架構圖 34
圖3.6 ERBFN流程圖 35
圖4.1 免疫系統示意圖 43
圖4.2 免疫系統功能圖 44
圖4.3 染色體與抗體關係圖 48
圖4.4 對偶基因示意圖 50
圖4.5 EIA流程圖 54
圖5.1 PJM夏月星期二電力價格圖 57
圖5.2 風險值的定義 58
圖6.1 範例1之預測與實際電力價格曲線圖 69
圖6.2 範例2之預測與實際電力價格曲線圖 70
圖6.3 範例3之預測與實際電力價格曲線圖 70
圖6.4 範例4之預測與實際電力價格曲線圖 71
圖6.5 範例5之預測與實際電力價格曲線圖 71
圖6.6 範例6之預測與實際電力價格曲線圖 72
圖6.7 範例3之RMSE曲線圖 73
圖6.8 七機組染色體示意圖 75
圖6.9 26機組系統EIA收斂特性圖 79
圖6.10 10-100機組系統EIA收斂特性圖 82
圖6.11 負載與平均電價 84
圖6.12 EIA與IA收斂比較圖 84
圖6.13 在時段1時95%信賴區間風險值 87
圖6.14 售電利潤與利潤變異數關係圖 90

表 目 錄

表1.1 台電近五年經營績效 1
表2.1 叁因子之完全因子實驗 15
表2.2 兩因子x1、x2實驗 16
表2.3 兩因子A、B 實驗 17
表2.4 兩因子之因子分析 17
表2.5 叁因子兩水準直交表 20
表2.6 直交表L4(23)範例 20
表2.7 訊號與雜訊比說明 22
表3.1 類神經網路發展歷史 25
表3.2 L4(23)直交表水準定義 33
表3.3 L4(23)直交表因子分析 33
表4.1 染色體在田口式直交表中的應用 52
表6.1 訓練資料與測試資料 65
表6.2 訓練與測試資料數目 67
表6.3 ERPFN學習率參數之初始值與收斂值 68
表6.4 測試結果 68
表6.5 範例6之ERBFN、PNN與BPN比較 72
表6.6 各方法之效能比較於範例5 74
表6.7 直交表L4(23)範例 76
表6.8 26機組系統測試結果 77
表6.9 26機組系統機組排程表 78
表6.10 各方法之穩健特性比較 80
表6.11 最佳解之比較 80
表6.12 40、80與100部機組測試結果 81
表6.13 不同方法之最佳值比較 82
表6.14 15機組模擬機組資料 83
表6.15 EIA及IA收斂性測試(一) 85
表6.16 EIA及IA收斂性測試(二) 85
表6.17 不同容忍風險的風險值 86
表6.18 10部發電機組系統之機組數據 88
表6.19 電價預測表 88
表6.20 電價共變異矩陣 89
表6.21 不同風險設定之發電量排程 (Unit 5) 91

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