隨著許多先進國家電力事業市場大都已自由化的趨勢，我國的電力事業也正規劃朝著電力市場自由化發展，然而電力市場自由化也帶來了許多問題，例如：傳輸系統與電力網的壅塞問題、電力代輸的費用分攤（Wheeling Charge, WC）、或線路的損失分攤等等，本論文主要即探討電力系統的「壅塞管理」(Congestion Management, CM)；假設電力系統未預期的發生了緊急事故，此時會導致某些輸電線路有壅塞的現象產生，因此該如何能有效且迅速的對電力系統進行最佳化的調度即為本論文的研究重點。

　　本論文的研究將模擬事故發生時其電力系統的線路壅塞現象，並利用等效電流注入法(Equivalent Current Injection, ECI)來計算電力潮流解並進行潮流追蹤，所使用的方法為電力潮流追蹤法(Power Flow Tracing Method, PFTM)，並以本研究設計的篩選機制挑選出系統中對該壅塞線路的潮流貢獻程度比較大的發電端或供電端，輔以增強型粒子群最佳化演算法(Enhance Particle Swarm Optimization, EPSO)對這些追蹤出來的發電端或供電端進行最佳化調度以解決系統線路壅塞。本研究最後以台電345KV電力系統的實際單線圖進行模擬測試，以作為本論文解決電力系統發生線路壅塞問題的案例研究。

Many countries have advanced their power grid with deregulation. Electric power network in Taiwan is also under pressure to deregulate now. Power deregulation will need some considerations, e.g., congestion problem, wheeling charge, transmission loss allocations and so on. In this thesis, Congestion Management(CM) has been studied, assuming the outages in the power network resulting in some congestions. The power system needs to be re-dispatched efficiently and quickly.

In the thesis, congestion were simulated for power outages and the Power Flow Tracing Method(PFTM) was used, based on the equivalent current model. The generators with high contribution to the congested lines were chosen by a selective mechanism, and these generators were re-dispatched with Enhance Particle Swarm Optimization(EPSO) method to solve the congestion problem. In the thesis, test cases of Taipower 345KV system were built and simulated.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 viii
表次 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 論文目的與方法 2
1.3 論文架構 3
第二章 等效電流注入法與電力潮流追蹤理論 5
2.1 前言 5
2.2 電流注入為基礎的輸電系統負載潮流推導 5
2.2.1 具常數亞可比矩陣之負載匯流排模型推導 6
2.2.2 電壓控制匯流排模型推導 8
2.2.3 具常數亞可比矩陣之假設法則 10
2.3 等效電流為基礎之電力潮流追蹤法 15
2.3.1 逆向潮流追蹤模型 15
2.3.2 順向潮流追蹤模型 21
第三章 增強型粒子群最佳化演算法之設計 26
3.1 粒子群最佳化演算法 26
3.2 改良式具時變加速度係數粒子群最佳化演算法(MPSO-TVAC) 30
3.2.1 粒子群最佳化演算法的變化型 30
3.2.2 改良式具時變加速度係數粒子群最佳化演算法 32
3.3 鄰近擾動粒子群最佳化演算法(NR-PSO) 33
3.4 增強型粒子群最佳化演算法之設計 35
3.4.1 整合與改善最佳化演算法 35
3.4.2 演算法整體流程 39
第四章 設計解決電力網壅塞問題的最佳化模型 41
4.1 壅塞問題描述 41
4.1.1 目標函數 42
4.1.2 等式限制式及不等式限制式 44
4.2 設計解決壅塞問題之模型 46
4.2.1 所有機組重新調度之缺點 46
4.2.2 指定機組重新調度 47
4.2.3 調度量之探討 48
4.3 以貢獻度篩選機組來考慮發電端升降載 49
4.3.1 挑選機制 50
4.3.2 模擬流程與步驟 53
4.4 以貢獻度篩選機組來考慮發電端升降載及負載端降載 55
4.4.1 補償機制 56
4.4.2 模擬流程與步驟 58
第五章 系統測試與案例分析 60
5.1 以IEEE30Bus為測試系統來進行解壅測試 64
5.1.1 增強型粒子群最佳化演算法之強韌性測試 64
5.1.2 解除壅塞測試 67
5.2 以台電345KV為測試系統且不考慮負載端調度的解壅測試 71
5.2.1 增強型粒子群最佳化演算法之強韌性測試 71
5.2.2 案例一 : 單一事故測試 74
5.2.3 案例二 : 雙重事故測試 80
5.3 以台電345KV為測試系統且考慮負載端調度的解壅測試 87
5.3.1 所有發電機組參與調度解決壅塞測試 87
5.3.2 負載端參與調度解決壅塞測試 91
5.4 最小變動量與最小成本的關係探討 95
第六章 結論與未來發展方向 96
6.1 結論 96
6.2 未來發展方向 97
參考文獻 98
附錄 A IEEE30Bus系統線路資料 103
附錄 B 台電345KV系統線路資料 105

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