請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23056
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張倉榮 | |
dc.contributor.author | Cheng-Lung Chen | en |
dc.contributor.author | 陳俊龍 | zh_TW |
dc.date.accessioned | 2021-06-08T04:39:47Z | - |
dc.date.copyright | 2009-08-20 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-08-13 | |
dc.identifier.citation | 1. 莊月璇,2001,「台灣地區風速機率分佈之研究」,國立中央大學土木工程研究所碩士論文。
2. 王嘉和,2008,氣候變遷與地層下陷對台灣西南沿海地區淹水之衝擊評估,國立台灣大學生物環境系統工程學系碩士論文。 3. 經濟部能源局,2007,「能源政策白皮書」。(Web site) ( http://www.moeaboe.gov.tw/ ) 4. BP (2002)、World Nuclear Association. 5. Chang, T.J., Wu, Y.T., Hsu, H.Y., Chu, C.R., Liao, C.M. (2003), Assessment of wind characteristics and wind turbine characteristics in Taiwan. Renewable Energy. 28, 851-871. 6. Chang, T.J., Tu, Y.L. (2007), Evaluation of capacity factor of WECS using chronological and probabilistic wind speed data: a case study of Taiwan. Renewable Energy, 32, 1999-2010. 7. Li, G. (2000), Feasibility of large scale offshore wind power for Hong Kong-a preliminary study. Renewable Energy, 21, 387-402. 8. Lu, L., Yang, H., Burnett, J. (2002), Investigation on wind power potential on Hong Kong- an analysis of wind power and wind turbine characteristics. Renewable Energy, 27, 1-12. 9. Lun, I.Y.F., Lam, J.C. (2000), A study of Weibull parameters using long-term wind observations. Renewable Energy, 20, 145-153. 10. Mathew, S., Pandey, K.P., Kumar, A. (2002), Analysis of wind regimes for energy estimation. Renewable Energy, 25, 381-399. 11. Pryor, S.C., Schoof, J.T., Barthelmie, R.J. (2005), Empirical downscaling of wind speed probability distributions. Journal of Geophysical Research, 110, D19109. 12. Rosen, K.R., Van, B.R., Garbesi, K. (1999), Wind energy potential of coastal Eritrea: an analysis of sparse wind data. Solar Energy, 66(3), 72 201-213. 13. Sailor, J.D., Michael, S., Melissa, H. (2008), Climate change implications for wind power resources in the Northwest United States. Renewable Energy, 33, 2393-2406. 14. Segal, M., Pan, Z., Arritt, R.W., Takle, E.S. (2001), On the potential change in wind power over the US due to increases of atmospheric greenhouse gases. Renewable Energy, 24, 235-243. 15. World Wind Energy Association (2007), 'New World Record in Eind Power Capacity: 14,9GW added in 2006 - Worldwide Capacity at 73,9GW' . 16. IPCC -Intergovernmental Panel on Climate Change. ( Web Site )(http://www.ipcc.ch/) | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23056 | - |
dc.description.abstract | 由於人類大量地使用化石燃料,使得溫室氣體大量產生,致使地球溫度不斷上升,再加上幾次的石油危機,迫使人們轉向尋找乾淨、無汙染的再生能源來取代化石燃料,而現有再生能源當中,以風力發電的技術較成熟,且其對環境影響較低,因此風力發電機的裝置數量在近年的成長十分快速,但由於全球溫室效應的增強,氣候發生了異常的改變,因此影響風力發電的主要因素—風速,也可能因為氣候變遷的關係而產生變動,故本文擬對氣候變遷對風速的影響進行評估。
在評估氣候變遷的模式中,目前較為大家所接受之方法為全球環流模式,但其資料的網格太大,無法直接用以評估風力發電廠址之風能潛勢,故必須對全球環流模式所提供的資料進行降尺度。本文所採用的降尺度方式為多變數線性回歸,以韋伯分佈、萊利分佈、時間序列之累積分佈三種機率分佈配合三種全球環流模式進行評估。另外,台灣因地處亞熱帶季風區,每年的風速會有強弱風期之差別,因此不同風期的降尺度方式也將一併討論。 研究結果發現,在機率分佈方式方面,時間序列的累積分佈在低風速時較其餘兩者好,而韋伯分佈則是在高風速時較其餘兩者好。全球環流模式部分,HADCM5模式之誤差較其他兩者要低,分為強弱風期之狀況,強風期之驗證結果誤差較好,但各站誤差變動較大,而弱風期之驗證則是比整年度一起做要差,若以此方式評估未來之風能影響,較大之可能性是會比現代稍為降低。 | zh_TW |
dc.description.abstract | Due to the using of fossil fuel for the past two centuries, it has made greenhouse gas over production and oil depletion. This situation forces people to look for clean and pollution-free renewable energy to replace fossil fuel gradually. Of all kinds of renewable energy, wind power has advantages like mature technology and low environmental impact. Thus, the development of wind energy has rapidly and steadily progressed then other renewable energy for the last decade. However, wind power availability might be affected by climate changes induced by greenhouse gas emissions. To evaluate the trends of wind power production by using GCMs (general circulation models) is necessary for wind energy development.
This research presents approaches to develop empirically downscaled estimates of near-surface wind speed and energy density in Taiwan. These approaches are based on downscaling the Weibull and Rayleigh of wind speed probability distributions and cumulative distribution of time-series parameters. In addition, due to the climate features of Asia monsoon, the differences between strong and weak wind periods in Taiwan are also discussed. The results show that cumulative distribution of time-series is better than other two approaches for the cases of low wind speeds, but the Weibull distribution is the best for the cases of high wind speed. Of the three GCMs, errors estimated by using ECHAM5 model have the lowest values. The error calculated by using the strong wind period data is less IV than the whole year data. Moreover, the error calculated by using the weak wind period data is worse. The results also show that the future situation may be slightly lower than the present. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T04:39:47Z (GMT). No. of bitstreams: 1 ntu-98-R96622030-1.pdf: 4172716 bytes, checksum: daabcd7e2c1b1dbdfa0e682978bf0982 (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 謝 誌 ........................................................................................................ I
摘 要 ...................................................................................................... II ABSTRACT ................................................................................................ III 目 錄 .......................................................................................................... V 表目錄 ....................................................................................................... VII 圖目錄 ......................................................................................................... XI 第一章 緒論 ................................................................................................. 1 1.1 前言 ................................................................................................... 1 1.2 前人研究 ........................................................................................... 3 1.3 研究目的 ........................................................................................... 4 第二章 研究方法 ......................................................................................... 8 2.1 氣候變遷模式 ................................................................................... 8 2.1.1 溫室氣體排放情境 .................................................................... 8 2.1.2 全球環流模式 .......................................................................... 10 2.2 風能評估模式的介紹 ..................................................................... 11 2.2.1 韋伯風速機率分佈 .................................................................. 12 VI 2.2.2 萊利風速機率分佈 .................................................................. 14 2.2.3 時間序列方式 .......................................................................... 15 第三章 降尺度模式 ................................................................................... 16 3.1 氣候變遷資料 ................................................................................. 16 3.2 降尺度方法 ..................................................................................... 17 3.3 實際氣象資料 ................................................................................. 18 第四章 結果與討論 ................................................................................... 24 4.1 有無降尺度之比較 ......................................................................... 24 4.2 不同風速機率分佈之比較 ............................................................. 26 4.3 不同氣候變遷模式之比較 ............................................................. 30 4.4 分為強風期及弱風期之比較 ......................................................... 33 4.5 未來風能評估 ................................................................................. 35 第五章 結論與建議 ................................................................................... 68 參考文獻 ..................................................................................................... 71 附錄A 降尺度後之誤差結果 ................................................................... 73 | |
dc.language.iso | zh-TW | |
dc.title | 氣候變遷對台灣地區風能之影響評估 | zh_TW |
dc.title | Evaluation of the Impact of Climate Change on Wind Power in Taiwan | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 謝正義 | |
dc.contributor.oralexamcommittee | 朱佳仁,曾仁佑,陳明志 | |
dc.subject.keyword | 風力發電,氣候變遷,全球環流模式,降尺度,機率分佈, | zh_TW |
dc.subject.keyword | Wind power,Climate change,GCMs,Downscaling,Probability distribution, | en |
dc.relation.page | 91 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2009-08-14 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-98-1.pdf 目前未授權公開取用 | 4.07 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。