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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
dc.contributor.author | Ping-Liang Chung | en |
dc.contributor.author | 鍾秉良 | zh_TW |
dc.date.accessioned | 2021-06-15T13:23:50Z | - |
dc.date.available | 2022-08-31 | |
dc.date.copyright | 2020-08-24 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-11 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51010 | - |
dc.description.abstract | 近年來民眾環保意識上漲,因此再生能源的相關議題也逐漸浮上檯面,各國政府也極力推展再生能源發展計畫,但其功率輸出受到一天中的時間與氣候條件限制,造成供電間歇性的問題,而這項特性對電力行業提出了巨大的挑戰,因為它們必須維持電力的供應與需求平衡關係,以確保電力系統的穩定性和可靠性,而目前的主流解決方案有兩種,即是精確的再生能源發電預測與併入儲能系統平衡電網功率波動。因此本研究提出了一個基於短期太陽能輻照度預測的儲能系統控制決策,而該策略分為兩個部分,首先本研究提出了三種不同的預測模型比較,分別為Feedforward Neural Networks (FFNN)、Long Short-Term Memory (LSTM) 與 Gated Recurrent Unit (GRU),並將資料集分為5分鐘與10分鐘間隔,用以訓練兩種不同間隔的預測模型,該模型以過去30分鐘的特徵資料做為訓練模型輸入,最後再將性能表現最好的兩種預測模型進行混合權重搭配,提出一個15分鐘的預測模型,並將其預測結果搭配第二階段基於模糊邏輯的儲能系統控制策略,其考慮了儲能系統的充電狀態、微電網的淨功率以及未來輻照度的變化,且策略的主要目標為減少再生能源發電所造成的不穩定性影響,並最大程度地降低與主電網間的功率波動,進而提高光伏系統發電的利用效率,並同時降低運營成本,更重要的是所有的決策控制皆維持在儲能系統之安全充電範圍內,所提出的策略將在實時數位模擬器進行實際模擬驗證以評估其效能與可靠性。 | zh_TW |
dc.description.abstract | This study proposes a fuzzy logic control strategy (FLCS) for an energy storage system (ESS) based on short-term irradiance prediction. The power output of photovoltaic (PV) systems is intermittent, which posts a great challenge to electric power industries, because they have to balance the energy supply and demand to ensure the stability and reliability of a power system. The control strategy is divided into two parts. First, a solar irradiance prediction model is proposed based on FFNN, LSTM, and GRU models, which uses PV model parameters and features of satellite cloud images as the model inputs. The important features of satellite cloud images are selected by the Principal Component Analysis, and the filtered features are used to train the prediction model. Finally, the best prediction model is used to combine with the FLCS which takes a number of factors into consideration, including the state of charge of the ESS, the microgrid net power, and the change of the future irradiance. The goals of the control strategy are to reduce the impact of instability on renewable energy (RE) generation and minimize the grid power profile fluctuations. Finally, irradiance data are simulated by Real Time Digital Simulator (RTDS) to evaluate which control strategies can yield best performance. The results show that the proposed FLCS can control the ESS to balance the power of the grid no matter in the case of sunny and cloudy days, and more importantly, all decisions are maintained within a safe SOC range. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:23:50Z (GMT). No. of bitstreams: 1 U0001-1008202016372100.pdf: 12620120 bytes, checksum: 270230074d9f3f2e0fea791c1eb5c7a0 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 2 摘要 4 Abstract 5 Table of Contents 7 List of Figures 10 List of Tables 18 Chapter 1 Introduction 20 1.1 Background 20 1.2 Motivation and propose 22 1.3 Thesis organization 26 Chapter 2 Literature Review 28 2.1 Microgrid 28 2.2 Solar photovoltaic system 30 2.3 Solar irradiance prediction 35 2.4 Energy storage system 39 2.5 The control strategery for ESS 43 2.6 Real-time digital simulator 45 Chapter 3 Materials and Methods 48 3.1 Experimental framework 48 3.2 Experimental field 50 3.3 Experimental materials and equipment 54 3.3.1 Pyranometer 55 3.3.2 Resistance thermometer 56 3.3.3 Real time digital power system simulator 57 3.4 Feature extraction for the satellite cloud images 58 3.4.1 The process of feature extraction for the satellite cloud images 58 3.4.2 The Format of satellite cloud images 60 3.4.3 Attributes of the satellite cloud images 61 3.5 Principal component analysis 66 3.6 Solar irradiance prediction 68 3.6.1 Framework of an irradiance prediction model 68 3.6.2 Establishment of single timestamp prediction model 73 3.6.3 K-fold cross validation 75 3.6.4 FFNN irradiance model 77 3.6.5 LSTM irradiance model 80 3.6.6 GRU irradiance model 83 3.6.7 Criteria for evaluation 86 3.7 Fuzzy logic control strategy 87 3.7.1 Control strategy 87 3.7.2 Fuzzy logic design 89 3.8 RTDS simulation and data sources 97 3.8.1 Simulation data sources 99 3.8.2 RTDS simulation process 100 3.8.3 Criteria for evaluation 102 Chapter 4 Results and Discussion 104 4.1 Data preprocessing and Principle Component Analysis 104 4.2 The solar irradiance prediction 106 4.2.1 Data sets 106 4.2.2 Finding the best combinations of optimizer and epoch 108 4.2.3 Finding the best combination of neurons and batch size 117 4.2.4 Single timestamp prediction model performance 124 4.2.5 Multi-timestamp prediction model performance 132 4.3 The hybrid solar irradiance prediction method 145 4.4 The simulated results of RTDS 152 Chapter 5 Conclusions and Future Work 164 5.1 Conclusions 164 5.2 Future work 165 References 168 Appendices 174 | |
dc.language.iso | en | |
dc.title | 基於短期輻照度預測之智能太陽光電儲能系統控制策略 | zh_TW |
dc.title | An intelligent control strategy for solar power generation and energy storage systems based on short-term irradiance prediction | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 周呈霙(Chen-Ying Chou),王永鐘(Yung-Chung Wang),蕭瑛東(Ying-Tung Hsiao),李建興(Chien-Hsing Lee) | |
dc.subject.keyword | 太陽能,儲能系統,衛星雲圖,機器學習,深度學習,輻照度預測,模糊邏輯控制, | zh_TW |
dc.subject.keyword | Deep learning,energy storage systems,fuzzy logic control,machine learning,irradiance prediction,renewable energy source,photovoltaic system, | en |
dc.relation.page | 179 | |
dc.identifier.doi | 10.6342/NTU202002834 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-12 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
顯示於系所單位: | 生物機電工程學系 |
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