臺灣博碩士論文加值系統

隨著全球能源短缺、日本福島核災、PM2.5日趨嚴重及京都協議書減碳、巴黎氣候協議抑制溫升及發展智慧電網以利發展分散式能源等前提下，致推升潔淨風能已成為重要之能源發展趨勢，有鑑於此，如何透過精準之預測科技來推估風能，以減少資本支出、發電機組裝置容量及運轉維護費用，已成為各國電業聚焦議題。
本研究旨在運用測量相關推測法(Measure-Correlate-Predict, MCP)、風速機率分佈、風機功率曲線，以建立推估離岸風場之風速、風電產能及收益評估之方法。首先，採 MCP 推測法，係藉由目標風場附近測站之短期風速資料(測量資料Measure)與長期參考測站之風速資料間之統計關係，使用同時或重疊之時段連續風速資料(找出相關Correlate)，建立風場長期風速資料(預測Predict)再據此進行評估分析。惟目標風場附近測站之短期量測風速和參考測站之風速間之相關性，及參考測站風速資料之均勻特性和重疊期間之時間長度係影響預測之關鍵因素。其次，為評估運用參考測站所衍生風速之準確度，將目標風場附近測站之真實風速及所計算得之發電量等視為實績值，將參考測站所衍生風速及所計算得之發電量視為推估值，比較推估值與實績值之間差異，研究結果顯示兩者之均風速誤差率絕對值為0.00983%及發電量之最大誤差率絕對值1.89%。最後，將研究成果推廣應用至某特定離岸風場之財務分析，考量因素包含總建置成本、維護費及未來商轉收益，未來可做為評估開發風場投資效益之參考。

Clean wind energy has become an important energy option, with the Kyoto Protocol carbon reduction, the Paris climate agreement to curb the temperature rise and the PM2.5 issue. Therefore, it is critical to estimate wind energy accurately, in order to reduce capital cost, power capacity and operations & maintenance (O&M) costs. In general, there are more savings on total generation cost with a decrease in estimating errors for wind energy forecasts and with an increase in estimating accuracy for the day-ahead unit commitment schedule.
This study is mainly aimed to establish the method of estimating wind speeds, wind power and the profits for offshore wind farms by using Measure-Correlate-Predict (MCP), wind speed frequency distributions and wind turbine power curves. First of all, the main idea of MCP methods lies essentially in the correlation established between the wind data recorded at the target site and those simultaneously recorded at one or several different nearby weather stations as reference stations and where the long-term data are also estimated. Secondly, in order to upgrade the estimating accuracy for both the long-term wind speeds and the wind power at offshore wind farms, we will check the differences between the real results at the target site and the estimating results at reference stations. The maximum absolute error rates between the real results and the estimating results for the average wind speed and for the wind power are 0.00983% and 1.89% respectively. Finally, the research results will be applied to evaluate the investment efficiency for a specific offshore wind farm.

中文摘要 I
Abstract II
誌 謝 III
目 錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究方法 3
1.3 章節概要 10
第二章 測量相關推測法 11
2.1 方法介紹 11
2.2 方法一：採月風速線性回歸推估風速 14
2.3 方法二：先採梯度後採月風速線性回歸推估風速 15
2.4 方法三：採年均風速倍數關係推估風速 16
2.5 三種推估方法比較 17
第三章 其他研究方法介紹 21
3.1 測站選擇方法 21
3.2 風機功率曲線、風能密度及風速與高度之關係 21
3.3 影響淨發電量之因素 25
3.4 小結 28
第四章 推估風速方法及結果分析 29
4.1 推估風速方法、假設與步驟 29
4.2 參考測站與目標風場風速之相關性 31
4.3 風速機率分佈比較 36
4.4 參考測站原始風速、MCP推估值及目標風場實績風速比較 37
4.5 小結 39
第五章 推估產能收益方法及結果分析 40
5.1 推估產能收益方法、假設與步驟 41
5.2 風機功率曲線比較 42
5.3 單機與風場風機群發電量比較 44
5.4 風場收益分析 49
5.5 小結 53
第六章 結論與未來展望 54
6.1 結論 54
6.2 未來展望 57
參考文獻 59

[1] “Smart grids-best practice fundamentals for a modern energy system,” World Energy Council, 2012. pp.8-26. https://www.worldenergy.org/wp -content/uploads/2012/10/PUB_Smart_grids_best_practice_fundamentals_for_a_modern_energy_system__annexes_2012_WEC.pdf.
[2] “International Energy Outlook 2016,” DOE, EIA, DOE/EIA-0484(2016) , pp.10-84, May 2016. https://www.eia.gov/outlooks/ieo/pdf/0484(2016).pdf
[3] “Insights Series 2017 - Getting Wind and Sun onto the Grid - A Manual for Policy Makers,” The International Energy Agency, IEA Publications, International Energy Agency OECD/IEA, pp. 8-50, 2017. https://www.iea.org/publications/insights/insightpublications/Getting_
Wind_and_Sun.pdf
[4] Ahmad Zahedi, Senior Member, IEEE, “Developing a Method to Analyze the Electricity Cost of Wind Power,” International Conference and Utility Exhibition 2014 on Green Energy for Sustainable Development (ICUE 2014), Pattaya City, Thailand, Mar. 19-21, 2014, pp. 1-3.
[5] Liudong Zhang, Yubo Yuan, Bing Chen, “Cost - benefit Analysis Method for Optimizing Spinning Reserve Requirements with Consideration of Wind Power Generation under Carbon Trading Environment”, First IEEE International Conference on Energy Internet, Apr. 2017, pp. 1-3.
[6] Tiago Soares, Pierre Pinson, Senior Member, IEEE, Tue V. Jensen, and Hugo Morais, Member, IEEE, “Optimal Offering Strategies for Wind Power in Energy and Primary Reserve Markets,” IEEE Transactions on Sustainable Energy, Vol. 7, No. 3, pp. 1036-1045, Jul. 2016.
[7] Kanna Bhaskar, SIEEE, S.N. Singh, SMIEEE, “Wind Power Bidding Strategy in a Day-ahead Electricity Market”, IEEE PES General Meeting / Conference & Exposition, Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur, India, 2014, pp. 1-4.
[8] E. V. M. C. Garrigle, P. G. Leahy, “The value of accuracy in wind energy forecasts,” University College Cork, Ireland, pp.1-5, Proceedings, Renewable Energy in Maritime Island Climates, 2nd Conference, Apr. 26-28, 2006, Dublin, Ireland, pp. 1-3.
[9] “Wind Power Technology Brief,” IRENA, International Renewable Energy Agency, p18, Mar. 2016.
[10] 「中華民國一百零六年度再生能源電能躉購費率及其計算公式」，經濟部能源局2017年7月7日發布。http://web3.moeaboe.gov.tw/ECW/populace/Law/Content.aspx?menu_id=3308
[11] José A. Carta, Sergio Velázquez and Pedro Cabrera, “A review of measure-correlate-predict (MCP) method used to estimate long-term wind characteristics at a target site,” Renewable and Sustainable Energy Reviews, Vol. 27, pp. 362 - 400, Nov. 2013.
[12] Ali Dinler, “A new low-correlation MCP (measure - correlate - predict) method for wind energy forecasting,” Energy, Vol. 63, pp. 152-160, Dec. 2013.
[13] Hui Li, Jingli Guo, Jun Liang, “Application of New Wind Speed Model in Power System Reliability Assessment”, Power Engineering Conference (UPEC), 2015 50th International Universities, Sept. 2015, pp. 1-2.
[14] J. Serrano González, Á.G. González Rodríguez, J. Castro Mora, J. Riquelme Santos and M. Burgos Payán “A New Tool for Wind Farm Optimal Design”, Bucharest, Romania, 2009 IEEE Bucharest Power Tech Conference, Jun. 28.- Jul. 2, 2009, pp. 1-2.
[15] Hagkwen Kim and Chanan Singh, “Comparison of Clustering Approaches for Reliability Simulation of a Wind Farm,” Power System Technology (POWERCON), 2012 IEEE International Conference on Auckland, New Zealand, Jan. 2013, pp. 1-2.
[16] Rabia Shakoor, Mohammad YusriHassan, AbdurRaheem , Yuan-Kang Wu, “Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model,” Renewable and Sustainable Energy Reviews , Vol.58, pp. 1048-1059, May 2016.
[17] Chulsang Hwang, Jin-Hong Jeon, Gyeong-Hun Kim, Eungsang Kim, Minwon Park ,“ Modelling and simulation of the wake effect in a wind farm,” Journal of International Council on Electrical Engineering, Changwon National University Republic of Korea, Vol. 5, 2015 - Issue 1, pp. 74 -77, Dec. 21, 2015.
[18] 陳景林，「離岸風機最佳化佈置分析技術及離岸風機規範對於離岸風機支撐結構在極限風況之因應對策」，台灣電力公司， pp. 4-15，2015年1月。
[19] 「離岸風力發電第一期計畫可行性研究」，台灣電力公司，pp. 4-1-4-22, 2015年1月。
[20] 「離岸風場作業安全評估技術開發計畫」，經濟部，pp. 131-144，2017年3月。
[21] International Standard “Wind Turbines-Part1: Design Requirements”, 189 - IEC 61400-1, 3rd edition.
[22] International Electrotechnical Commission, “Wind Turbines - Part 3: Design Requirements for Offshore Wind Turbines”, IEC 61400-3, 2009.
[23] 黃建日，「澎湖中屯風力發電機運轉資料分析與模擬」，碩士論文，清華大學工程與系統科學系研究所，pp. 10-19, 2006年。
[24] 廖常興，「市電並聯型之光伏風力混成型發電系統之研製」，碩士論文，國立清華大學電機工程研究所，pp. 11-25, 2004年。
[25] G. Hellman收集風力數據及地表情形與摩擦係數對應資料http://windenergy.cycu.edu.tw/knowledge/02/text02.htm
[26] 風速觀測資料與數值模擬料相關性分析，「離岸風場開發作業安全評估技術開發計畫」。