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研究生:曾陳聖
研究生(外文):Zeng, Chen-Sheng
論文名稱:微電網再生能源發電與負載預測及其應用之研究
論文名稱(外文):A Study on Prediction of Renewable Power Generation and Load Demand and their Applications in Microgrids
指導教授:黃維澤
指導教授(外文):Huang, Wei-Ze
口試委員:黃維澤粘孝先姚凱超
口試委員(外文):Huang, Wei-ZeNian, Siao-HsienYao, Kai-Chao
口試日期:2017-06-23
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:工業教育與技術學系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:93
中文關鍵詞:再生能源預測類神經電網重構分散式電源微電網粒子群優演算法
外文關鍵詞:Renewable EnergyForecastingArtificial Neural NetworkNetwork ReconfigurationDistribution Energy ResourcesMicrogridsPSO Algorithm
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本論文旨在運用類神經網路進行再生能源發電及負載需量預測,並利用預測結果求解微電網故障後復電重構最佳化問題。本論文利用溫度、濕度、太陽能發電量及負載需量等歷史資料進行太陽能發電及負載需量之預測,並使用多種指標評估預測能力,如RMSE、MSE、MAPE等。再以粒子群優演算法實現在多目標非線性函數與相關限制條件下,得到滿足微電網故障後復電線路重構的理想配置組合。最後本論文使用測試系統進行一系列模擬分析,藉以驗證本論文所提方法之可行性,模擬結果顯示,其預測精準度將影響後續排程與即時調度,由此可知再生能源發電與負載預測,對於微電網運轉極為重要。本論文所提方法確實可有效地進行再生能源發電及負載之預測,並應用於求解微電網故障後復電重構最佳化問題。
The main purpose of this thesis is to forecast renewable energy generations and load demand using artificial neural network, and the predictions were used to solve the optimal network reconfiguration of microgrids after fault occurred. In this thesis, the historical data of temperature, humidity, power generations of photovoltaic, and load demands were used to forecast the future power generations and load demands, and the indicators, such as RMSE, MSE, and MAPE were used to assess the forecasting capabilities. Furthermore, the particle swarm optimization algorithm was used to solve the multi-objective nonlinear function and related constraints of the optimal reconfiguration problem. In this thesis, a series of simulation analysis was carried out by using the test system to verify the feasibility of the proposed method. The simulation results demonstrated that the prediction accuracy affected the subsequent scheduling and real-time dispatch. Consequently, the accuracy of renewable energy power generation and load demand forecasting were extremely important for microgrids operations. The method proposed in this thesis can effectively predict the renewable energy generation and load demands. Moreover, it can be applied to solve the reconfiguration problem of microgrids after fault occurred.
摘要 I
Abstract II
謝誌 III
圖目錄 VII
表目錄 X
第一章緒論 1
1.1研究動機與背景 1
1.2文獻回顧 4
1.3研究方法與步驟 8
1.4論文架構 10
第二章再生能源發電及負載特性 11
2.1前言 11
2.2太陽光電發展現況 12
2.3負載特性探討與分析 19
2.4結語 33
第三章預測方法及誤差評估指標介紹 34
3.1前言 34
3.2類神經網路 35
3.3迴歸分析法 41
3.4內核法 49
3.5預測能力之評估指標 52
第四章程式模擬結果分析與探討 55
4.1前言 55
4.2資料彙整及格式設定 55
4.3類神經網路輸入因子選擇說明 56
4.4類神經網路流程規劃 57
4.5太陽能發電預測 58
4.6負載需量預測 62
4.7預測能力評估 66
4.8結語 72
第五章以預測結果為基礎之微電網最佳化故障復電模擬分析 73
5.1前言 73
5.2故障恢復策略及多目標非線性函式 75
5.3故障復電求解流程規劃 76
5.4測試系統與模擬情境說明 77
5.5微電網系統併網運轉模式 80
5.6微電網系統孤島運轉模式 84
5.7結語 88
第六章結論與未來研究方向 89
6.1結論 89
6.2未來研究方向 89
參考文獻 90
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