隨著綠色能源的進步併聯於電力系統中，顯然可同時降低了傳統能源製造上對環境的各種汙染，也提供了替代能源方案。但供應不穩定的綠色能源增加，勢必將對系統造成影響，例如系統可靠度、電力成本、電力品質、電力穩定度…等等，因此，如何透過輔助服務來維持系統的即時負載變動，將是目前重要的議題。
　　本文研究兩個案例，第一將傳統的火力發電機、風力發電、太陽能發電及電池儲能系統，結合成一個系統，並藉由電池儲能系統來達成需量管理個規劃；第二對於沒有電池儲能系統下，分析電力系統日前市場之輔助服務，其中包含自動發電控制(Automatic Generation Control, AGC)、即時備載容量(Spinning Reserve, SR)以及補充備載容量(Supplemental Reserve, SuppRes)來達到安全調度。以最低發電成本為目標，使用改良型蜂群演算法計算發電機組排程及經濟調度問題；為了改善蜂群演算法陷入局部最佳解與偵查蜂只對於全域最佳解作為解的考量，本文加入了時變性之慣性權重係數於蜂群演算法，並對於偵查蜂之數學模型加以改善，讓解有跳脫全域最佳解得能力，並對於區域最佳解進行空間上的搜索，使問題能更為精確和迅速地獲得整體最佳解。

The advances of renewable energy in power system not only reduced more environmental pollution than using traditional method, but provided alternative programs. As increasing of those unstable supply of green power. It will impact on the system. Such as system reliability, cost of power, power quality, power stability, etc. Therefore, how to stabilize the system while the load keep changing with ancillary service is an important issue currently.
This thesis studies two case, 1th, combined thermal power generator, wind power, solar power, battery storage system to form a system, and reach the goal of security dispatch and the function of demand response by battery storage system. Second, analysis ancillary service of power system day-ahead market without battery storage system, including automatic generation control, spinning reserve, and supplemental reserve. Using improved Bee Swarm Optimization (BSO) to solve unit commitment and economic dispatch problem. This thesis proposed the adaptive inertia weight rule into BSO, and improve the mathematics formula to avoid the local optimality problem and scout bee consider global optimality only, which can quickly reach the optimal solution with a better performance and accuracy.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目 錄 v
圖次 vii
表次 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與貢獻 2
1.3 論文架構 3
第二章 燃料成本曲線與電力市場之輔助服務 5
2.1 燃料成本曲線 5
2.1.1 凸集函數與非凸集函數 5
2.1.2 閥點效應之成本函數 6
2.1.3 含有禁止操作區之函數 7
2.1.4 限制式之控制 8
2.2 電力市場安全調度之輔助服務 12
2.2.1 安全調度輔助服務之性質與種類 12
2.2.2 安全調度輔助服務之訂價 16
第三章 電池應用之描述與系統機組排程架構 18
3.1 電池儲能系統 18
3.1.1 前言 18
3.1.2 電池儲能系統之重要性 18
3.2 系統機組排程問題描述 20
3.2.1 機組排程之目標函數 20
3.2.2 機組排程之等式限制式及不等式限制式 22
第四章 改良型蜂群演算法之設計 28
4.1 前言 28
4.1.1 人工蜂群演算法 28
4.1.2 增強型蜂群演算法 35
4.2 改良型蜂群演算法之設計 38
4.3 利用改良型蜂群演算法之設計求解機組排程 41
第五章 系統模擬與案例分析 44
5.1 改良型蜂群演算法之收斂模擬 54
5.1.1 IEEE 30-bus 結合電池儲能系統夏季負載模擬 54
5.1.2 IEEE 30-bus 結合電池儲能系統冬季負載模擬 59
5.2 考量日前市場規劃安全調度之輔助服務評估 64
5.2.1 夏季負載包含再生能源與10%輔助服務占比之測試 65
5.2.2 冬季負載包含再生能源與10%輔助服務占比之測試 72
5.2.3 夏季負載包含再生能源與13%輔助服務占比之測試 79
第六章 結論與未來發展 87
6.1 結論 87
6.2 未來發展方向 87
參考文獻 89

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