氣候變遷對台灣地區風能之影響評估

Evaluation of the Impact of Climate Change on Wind Power in Taiwan

陳俊龍(Cheng-Lung Chen)

指導教授 ： 張倉榮

共同指導教授 ： 謝正義

國立臺灣大學/生物資源暨農學院/生物環境系統工程學研究所/碩士(2009年)

https://doi.org/10.6342/NTU.2009.10249

摘要

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。

關鍵字

風力發電 ； 氣候變遷 ； 全球環流模式 ； 降尺度 ； 機率分佈

並列摘要

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.

並列關鍵字

Wind power ； Climate change ； GCMs ； Downscaling ； Probability distribution

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