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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80673完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳文方(Wen-Fang Wu) | |
| dc.contributor.author | Hsin-Hsiang Peng | en |
| dc.contributor.author | 彭新翔 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:12:29Z | - |
| dc.date.available | 2021-11-04 | |
| dc.date.available | 2022-11-24T03:12:29Z | - |
| dc.date.copyright | 2021-11-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-26 | |
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[56] 臺灣電力股份有限公司, “臺灣電力股份有限公司電價表.” [Online]. Available: https://www.taipower.com.tw/upload/238/2018070210412196443.pdf (Accessed on: 2019/12/11). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80673 | - |
| dc.description.abstract | "由於近年來持續成長的電力需求以及民眾環保意識的抬頭，加上傳統集中式電力系統的缺點日漸浮現，各國紛紛開始關注能充分利用再生能源且具有良好環境效益的分散式發電 (Distributed Generation, DG) 技術，也致使虛擬電廠 (Virtual Power Plant, VPP) 概念的生成。本研究以一整合風力電場並以電動汽車做為儲能裝置的虛擬電廠為分析探討對象，以時間電價 (Time-of-Use, TOU) 做為調度規劃基礎，參與電力市場交易。為考量實際系統變化調整控制參數，本研究選擇使用模型預測控制 (Model Predictive Control, MPC)，針對發電數量具動態變化的風力電場，規劃其移動時域 (Receding Horizon) 內的儲能裝置容量以獲得最佳收益；而為鼓勵將電動車併入虛擬電廠參與電力市場交易，本研究提出以電動車額外充電作為紅利，取代對電動車車主直接的金錢補助；針對短期風速的預測，本研究透過小波轉換分解時間序列訊號，並結合傳統時間序列預測與支持向量迴歸預測的一種 ARIMA-SVR 組合預測模型，讓我們得以根據現有風速資料預測風力電場的發電量。在案例模擬中，本研究使用澎湖縣東吉島氣象站 2018 年間的歷史風速資料，經短期風力預測與收益模型分析後，驗證所提最佳化收益分析方法的可行性。分析結果顯示，針對短期風速的預測，採用小波分解 ARIMA-SVR 組合預測模型相較於傳統單一預測模型，在預測能力上有所提升；而整合電動汽車作為儲能裝置的虛擬電廠確能獲得較高收益。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:12:29Z (GMT). No. of bitstreams: 1 U0001-1910202104575400.pdf: 2083556 bytes, checksum: c737cfa0f70f907ec3635e243877d911 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 i 致謝 iii 摘要 v Abstract vii 目錄 ix 圖目錄 xiii 表目錄 xv 第一章 緒論 1 1.1 前言 1 1.2 研究背景 3 1.2.1 電力系統發展 3 1.2.2 再生能源發展 6 1.2.3 電動汽車發展 8 1.2.4 我國能源情勢 10 1.3 研究動機 12 1.4 研究目的 12 1.5 本文架構 13 第二章 文獻回顧 15 2.1 虛擬電廠概念 15 2.2 風電預測評估 17 2.3 電力調度規劃 19 2.4 小結 20 第三章 研究方法 21 3.1 研究流程概述 21 3.2 風力發電預測 21 3.2.1 小波時頻分析 22 3.2.2 時間序列模型 26 3.2.3 支持向量迴歸 33 3.2.4 模型評價方法 38 3.3 收益模型分析 40 3.3.1 自由電力市場 40 3.3.2 風機功率模型 43 3.3.3 電動汽車電池 44 3.3.4 模型預測控制 46 3.4 小結 50 第四章 模型建構 51 4.1 風力電場發電預測 51 4.1.1 時間序列預測建模流程 51 4.1.2 支持向量迴歸建模流程 52 4.1.3 組合預測模型建模流程 52 4.2 虛擬電廠收益分析 55 4.2.1 虛擬電廠架構概述 55 4.2.2 風力電場等效模型 56 4.2.3 電動汽車等效模型 56 4.2.4 電力市場收益模型 57 4.2.5 預測控制求解流程 61 4.3 小結 63 第五章 案例分析 65 5.1 模擬情境概述 65 5.2 風速預測分析 67 5.2.1 風速資料取得 67 5.2.2 歷史風速概述 67 5.2.3 單一預測模型 69 5.2.4 組合預測模型 72 5.2.5 模型誤差比較 76 5.3 電廠收益分析 77 5.3.1 電動汽車儲能影響 77 5.3.2 電動汽車數量需求 78 5.4 小結 80 第六章 結論與建議 81 6.1 研究總結 81 6.2 研究限制 82 參考文獻 85 附錄A — 再生能源場址 93 A.1 臺灣風力發電場址資料 94 A.2 臺灣太陽光電場址資料 96 附錄B — 風力機組規格 99 B.1 Enercon E40/600 Model 99 B.2 Enercon E44/900 Model 100 | |
| dc.language.iso | zh-TW | |
| dc.subject | 風力電場 | zh_TW |
| dc.subject | 時間序列預測 | zh_TW |
| dc.subject | 虛擬電廠 | zh_TW |
| dc.subject | 模型預測控制 | zh_TW |
| dc.subject | 電動車 | zh_TW |
| dc.subject | Virtual Power Plant | en |
| dc.subject | Model Predictive Control | en |
| dc.subject | Time Series Prediction | en |
| dc.subject | Electric Vehicle | en |
| dc.subject | Wind Farm | en |
| dc.title | 整合電動車與風力電場之虛擬電廠收益分析 | zh_TW |
| dc.title | Profit Analysis of a Virtual Power Plant Consisting of a Wind Farm and Electric Vehicles | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃奎隆(Hsin-Tsai Liu),陳湘鳳(Chih-Yang Tseng) | |
| dc.subject.keyword | 虛擬電廠,風力電場,電動車,時間序列預測,模型預測控制, | zh_TW |
| dc.subject.keyword | Virtual Power Plant,Wind Farm,Electric Vehicle,Time Series Prediction,Model Predictive Control, | en |
| dc.relation.page | 101 | |
| dc.identifier.doi | 10.6342/NTU202103859 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-26 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
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