臺灣博碩士論文加值系統

針對機組排程問題進行探討，主要將機組排程問題分為兩部分進行討論。探討未考慮電動車併網及風力發電的機組排程問題，機組排程是滿足火力機組與系統限制條件下，調配火力機組的開機或關機狀態，並由經濟調度配置每部機組的發電量，找出最佳的解決方案。其次探討考慮電動車併網及風力發電的電力系統機組排程問題，由於風速的不確定性因素，本文利用機會約束方法，將風力中的不確定性用等效轉換的方式轉換為確定性約束。
在本文中，提出以混合人工蜂群與蜻蜓演算法(Hybridization of Artificial Bee Colony and Dragonfly Algorithm, HAD)計算各機組的開/關機排程及電動車的充/放電量，而風力發電則視作系統約束，建立出確定性模型後，找出最佳的排程方案。提出的方法是以蜻蜓演算法為基礎，將蜻蜓演算法的更新式結合人工蜂群演算法且修改參數，以強化演算法的搜索能力，所提的方法能加快收斂速度且可避免陷入局部解。
為了驗證混合人工蜂群與蜻蜓演算法對於機組排程問題的有效性與可行性，將演算法應用於機組排程問題，並與其它演算法作比較。實驗的結果是使用兩個不同規模的系統以及有無考慮電動車及風力發電方式呈現。模擬時使用10部與54部機組的系統作測試。從測試結果中可以看出，本論文所提出的演算法確實可行且可以獲得不錯的結果。

This thesis investigates the issue of unit commitment problem (UCP) and divides it into two parts. The first part of the thesis discusses the unit commitment problem without considering the grid integration of electric vehicles and wind power generation. The objective of UCP is to decide the on/off status of all thermal units with satisfying the various kinds of constraints in the system, and the power generation of the operating units is allocated by economic dispatch(ED) to find the best solution. The second part is to explore the UCP of the power system considering vehicles to grid (V2G) and wind power generation. Because of the uncertainty of wind speed, this thesis uses the method of chance constraint to convert the uncertainty in wind power into a definite constraint by using the equivalent conversion.
In this thesis, the hybridization of artificial bee colony and dragonfly algorithm (HAD) is proposed to decide the on/off status of each unit and the charging/discharging power of electric vehicles. The wind power generation is considered as the system constraint to establish a deterministic model to find the best scheduling solution. The proposed HAD method based on dragonfly algorithm (DA) combines the updated form of DA with the artificial bee colony algorithm and modifies the parameters to enhance the capability of searching. The proposed algorithm can converge fast, and the solution can avoid trapping in local minimum.
In order to demonstrate the effectiveness and feasibility of the HAD for the UCP, the algorithm is applied to the UCP and compared with other algorithms. The results of the experiments are presented using two systems of different size and with/without considering electric vehicles and wind power generation. The systems with 10 and 54 thermal generating units are adopted for simulation. The results show that the proposed algorithm is feasible and can obtain good results.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究方法與文獻回顧 2
1.3 論文大綱 4
第二章 問題描述 6
2.1 前言 6
2.2 機組排程問題 6
2.2.1 數學模型 7
2.3 考慮併入電動車及風力發電的機組排程問題 9
2.3.1 數學模型 10
2.4 本章結論 14
第三章 研究方法與理論 15
3.1 前言 15
3.2 機會約束 16
3.3 等效轉換 17
3.4 蜻蜓演算法 19
3.4.1 個體的分離參數 20
3.4.2 個體的整合參數 20
3.4.3 與其他個體的凝聚 20
3.4.4 食物來源 21
3.4.5 躲避敵人 21
3.4.6 群體移動 22
3.5 混合人工蜂群與蜻蜓演算法 24
3.6 柏拉圖最佳化 26
3.7 本章結論 27
第四章 不含電動車及風力發電的機組排程 28
4.1 前言 28
4.2 混合人工蜂群與蜻蜓演算法之個體與能量函數的建立 28
4.3 應用混合人工蜂群與蜻蜓演算法作單目標機組排程問題之步驟 30
4.4 應用混合人工蜂群與蜻蜓演算法作雙目標機組排程問題之步驟 34
4.5 實例測試與分析 38
4.5.1 演算法參數設定 39
4.5.2 測試系統一：10部機組系統 41
4.5.3 測試系統二：54部機組系統 49
4.6 本章結論 54
第五章 併入電動車及風力發電的機組排程 55
5.1 前言 55
5.2 混合人工蜂群與蜻蜓演算法於有考慮電動車及風力發電之能量函數的建立 55
5.3 應用混合人工蜂群和蜻蜓演算法於有考慮電動車及風力發電的單目標機組排程問題之步驟 58
5.4 應用混合人工蜂群和蜻蜓演算法於有考慮電動車及風力發電的雙目標機組排程問題之步驟 63
5.5 實例測試與分析 68
5.5.1 測試系統一：10部機組系統 70
5.5.1.1 測試系統一加入電動車及風力發電之變化 70
5.5.1.2 不同電動車滲透率對總成本的影響情形 72
5.5.1.3 不同置信度對總成本的影響情形 75
5.5.1.4 各演算法於考慮電動車及風力發電之測試系統一總成本數據與收斂情形之變化 77
5.5.1.5 混合人工蜂群與蜻蜓演算法於測試系統一所得出單目標最佳解之各時段機組發電量與電動車充/放電量 79
5.5.1.6 混合人工蜂群與蜻蜓演算法於測試系統一所得出雙目標柏拉圖前緣及最佳折衷解之機組發電量與電動車充/放電量 84
5.5.2 測試系統二：54部機組系統 87
5.5.2.1 測試系統二加入電動車及風力發電之變化 87
5.5.2.2 不同電動車滲透率對總成本的影響情形 90
5.5.2.3 各演算法於考慮電動車及風力發電之測試系統二總成本數據與收斂情形之變化 92
5.5.2.4 混合人工蜂群與蜻蜓演算法於測試系統二所得出最佳成本解之各時段機組發電量與電動車充/放電量 94
5.6 本章結論 98
第六章 結論與未來展望 99
6.1 結論 99
6.2 未來展望 100
參考文獻 101

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