臺灣博碩士論文加值系統

在能源政策和獎勵機制推動下，再生能源發電在各個國家之電力系統中已逐漸有提高其設置占比，期能從中減少傳統石化燃料發電之利用，並促使環境得以永續發展。太陽能發電近年來因技術成熟度提高且設置成本大幅降低，使得其在眾多再生能源類型中程為主流項目之一。然而，隨著配電饋線中日益增加之太陽能併網，為避免太陽能發電之加入對饋線造成衝擊和危害，於系統規劃設置階段需要對饋線所能承受之最大可併網太陽能容量進行評估。
近年來，隨機分析方法被廣泛應用於配電饋線之太陽能發電承載容量分析；然而，相關文獻所採用之隨機分析方法技術有著分析時具有過多的分析場景數(亦即，太陽能發電佈署於饋線之情境數量)之問題，其進而會導致分析時之計算時間過為冗長的缺點，使致遇到較為龐大之系統饋線結構時，分析效能將較顯不足。為克服此項問題，本論文主要提出以最佳化演算法為基礎之隨機分析方法，可用在太陽能發電系統之規劃與安裝前，以決定出在不造成衝擊與危害下，饋線所能承載之適切太陽能安裝容量。本論文所提出之方法，主要將文獻中的改良式隨機分析法加入最佳化運算機制；先建立不同之電壓靈敏度指標，緊接利用粒子群最佳化演算法搭配各項系統限制式，來進行配電饋線之電壓靈敏度分析；最終，透過電壓靈敏度分析結果，來排除饋線中不適合安裝太陽能發電之位置。如此，在執行配電饋線太陽能承載容量分析時，可大幅減少需要被計算之場景數量，進而提高整體評估分析之效能。
本論文所提出之方法與相關系統建模及模擬分析係利用操作於MATLAB與OpenDSS之共同模擬來完成，並且分別利用台灣離島15-Bus、IEEE 37-Bus與IEEE 30-Bus之三個饋線系統來驗證所提出方法之有效性。

Driven by various energy policies and incentives, renewable energy has gradually increased its penetration in power systems among many countries, which can reduce the utilization of traditional fossil fuel power generation and promote the sustainable development of the environment. In recent years, solar photovoltaic (PV) power generation has become one of the mainstreams in many renewable energy types due to its development in technology and the significant reduction in installation costs. However, with the increasing integration of solar power into the distribution feeders, in order to avoid the impact and damage to the feeders caused by the addition of solar power generation, it is necessary and important task to evaluate the maximum allowed solar power installation capacity for the distribution feeders.
Currently, stochastic analysis methods have been widely used in the analysis of PV hosting capacity of distribution feeders; however, the traditional stochastic analysis methods have a drawback that have too many scenarios, i.e. amounts of the PV deployments, needed to analyze in solution process. This drawback then leads to the tedious calculation time is required in analysis, and makes the performance less effective when deal with a large-scale system structure. In order to overcome this problem, this thesis proposes an optimization-based stochastic analysis method for PV hosting capacity assessment; meanwhile, the voltage sensitivity analysis is carried out by Particle Swarm Optimization to overcome the drawback in traditional stochastic analysis methods. The implementation of MATLAB and OpenDSS co-simulation is used to realize the proposed method and the performances are validated by the tests in distribution feeders of islanded 15-Bus, IEEE 37-Bus, and IEEE 30-Bus, respectively.
Driven by various energy policies and incentives, renewable energy has gradually increased its penetration in power systems among many countries, which can reduce the utilization of traditional fossil fuel power generation and promote the sustainable development of the environment. In recent years, solar photovoltaic (PV) power generation has become one of the mainstreams in many renewable energy types due to its development in technology and the significant reduction in installation costs. However, with the increasing integration of solar power into the distribution feeders, in order to avoid the impact and damage to the feeders caused by the addition of solar power generation, it is necessary and important task to evaluate the maximum allowed solar power installation capacity for the distribution feeders.
Currently, stochastic analysis methods have been widely used in the analysis of PV hosting capacity of distribution feeders; however, the traditional stochastic analysis methods have a drawback that have too many scenarios, i.e. amounts of the PV deployments, needed to analyze in solution process. This drawback then leads to the tedious calculation time is required in analysis, and makes the performance less effective when deal with a large-scale system structure. In order to overcome this problem, this thesis proposes an optimization-based stochastic analysis method for PV hosting capacity assessment; meanwhile, the voltage sensitivity analysis is carried out by Particle Swarm Optimization to overcome the drawback in traditional stochastic analysis methods. The implementation of MATLAB and OpenDSS co-simulation is used to realize the proposed method and the performances are validated by the tests in distribution feeders of islanded 15-Bus, IEEE 37-Bus, and IEEE 30-Bus, respectively.

致謝 i
摘要 ii
Abstract iv
目錄 vi
圖目錄 x
表目錄 xiii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 3
1.3 論文架構 6
第二章 太陽能發電併網衝擊與技術規範 8
2.1 太陽能發電併網衝擊問題 8
2.1.1 電壓劇變 8
2.1.2 逆送電流 8
2.1.3 線路元件老化之更新 9
2.1.4 保護設備之衝擊 9
2.1.5 天氣預測 10
2.1.6 鴨子曲線 11
2.2 太陽能併聯技術規範 12
2.2.1 國外相關併網規範 12
2.2.2 國內相關併網規範 13
第三章 太陽能承載容量概念與隨機分析法 14
3.1 太陽能承載容量概念與應用 14
3.2 太陽能承載容量分析方法 16
3.2.1 傳統隨機分析方法 16
3.2.2 改良式隨機分析法 17
3.3 利用變流器控制之太陽能承載容量提升 20
3.3.1 電壓─虛功智慧變流器控制(VVC) 21
3.3.2 電壓─實功智慧變流器控制(VWC) 23
第四章 以最佳化為基礎之隨機分析方法 24
4.1 最佳化隨機分析方法 24
4.2 電壓靈敏度指標 26
4.2.1 過電壓(Overvoltage) 27
4.2.2 電壓變動率(Voltage Deviation) 28
4.2.3 電壓間歇性(Voltage Fluctuation) 29
4.2.4 電壓不平衡(Voltage Unbalance) 30
4.3 最佳化目標函數 32
4.3.1 最佳化概念應用 32
4.3.2 最佳化單目標函數及求解法 33
4.3.3 最佳化多目標函數及求解法 34
4.4 系統限制條件 39
4.5 決策變數 40
第五章 粒子群最佳化演算法架構與共同模擬 42
5.1 傳統型粒子群演算法 42
5.1.1 饋線粒子化 42
5.1.2 世代與粒子間之比較 47
5.1.3 單目標函數粒子群演算法 51
5.1.4 多目標函數粒子群演算法 53
5.1.5 剔除匯流排 55
5.2 改良型粒子群演算法 58
5.2.1 饋線匯流排優劣分區機制 58
5.2.2 世代與粒子間之比較 62
5.2.5 剔除匯流排 65
5.3 MATLAB/OpenDSS共同模擬應用 66
第六章 配電饋線太陽能承載容量案例模擬與分析探討 68
6.1 15-Bus饋線 70
6.1.1單目標函數 71
6.1.1.1 傳統型粒子群演算法 71
6.1.1.2 加入改良式隨機分析法後之結果 73
6.2 IEEE 37-Bus饋線 80
6.2.1 多目標函數 82
6.2.1.1 傳統型粒子群演算法 82
6.2.1.2 改良型粒子群演算法 84
6.2.1.3 加入改良式隨機分析法後之結果 86
6.3 IEEE 30-Bus饋線 94
6.3.1單目標函數 96
6.3.1.1 傳統型粒子群演算法 96
6.3.1.2 改良型粒子群演算法 98
6.3.1.3 加入改良式隨機分析法後之結果 98
6.3.2 多目標函數 102
6.3.2.1 傳統型粒子群演算法 102
6.3.2.2 改良型粒子群演算法 104
6.3.2.3 加入改良式隨機分析法後之結果 107
6.3.2.4 智慧變流器控制 111
第七章 結論與未來工作 114
7.1 結論 114
7.2 未來工作 114
參考文獻 116

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