數值風場改進及其應用於都市風能評估

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：141

、訪客IP：3.145.206.169

姓名

楊宗燁(Tsung-Yeh Yang) 查詢紙本館藏

畢業系所

能源工程研究所

論文名稱

數值風場改進及其應用於都市風能評估

相關論文

★ 中國塵霾長程傳輸對臺灣細懸浮微粒(PM2.5)濃度的影響	★ 高級中學學生「環境教育認知」評估之研究
★ 以高級氧化程序礦化水中對-硝基苯酚之研究	★ 中南半島生質燃燒於長程傳輸路徑上之光學與化學特性探討
★ Wind Power Assessment for the Bay Islands, Honduras	★ 國小六年級學生實施河川環境教育課程之學習成效探討－以桃園縣某國小為例
★ 評估未來臺灣再生能源發電之供電特性	★ 顯影去墨(膜)廢液前處理之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

鑒於近年風能技術日趨進步以及都市密度快速增加，加上台灣地狹人稠使得陸域大型風場發展受到限制的特性，除了離岸風能外，都市風能有機會成為台灣綠色能源中相當有潛力的一項發展目標。本研究以台中市的都市風能潛力評估為例，首先以模式模擬來得到良好的都市風場，接著根據風場資料評估都市風能，然後討論評估都市風能的經濟效益以及在供電系統上所能扮演的角色。
本研究模擬2017年四季代表月份1月、4月、7月、10月期間的台中市風場，首先針對WRF模式進行敏感度測試，包括都市粗糙度長度、四維資料同化(Four-Dimensional Data Assimilation, FDDA)、Urban Canopy Model(UCM)、Building Effect Parameterization (BEP)、Building Energy Modeling (BEM)三種都市參數化修正，發現整體來說以FDDA加上BEP參數化在台中市模擬風場表現最佳，尤其在市中心建築密度較高區域有明顯的改善，然後將這組模擬風場作為CALMET風場診斷模式的初始猜測場，並結合分布在台中市區域的二十個觀測站資料進行最後修正，由模擬結果可以看到藉由耦合WRF/FDDA/BEP和CALMET能夠將模擬場更加接近觀測場，特別是在地形變化較大的都市郊區。。
本研究接著分析都市風速及風能密度的分布，應用2.5kW屋頂型小型風機架設在清水、梧棲、龍井及沙鹿區4樓以上建築(平均風速2~6 m s-1)，年發電量達5500~12000度，或北屯、西屯、市中心及南屯區120公尺以上大樓(平均風速3~5 m s-1)，年發電量達4500~9000度。15kW地面型小型風機則適合架設在清水、梧棲、龍井及沙鹿區的空曠處(平均風速2~6 m s-1)，年發電量達38000~72000度。
根據目前台灣能源局再生能源躉購政策，本研究所有小型風機地點年發電量皆高於年售電量上限(1650度/kW)，因此投資回收期皆為13年。若在無售電上限的情況下，投資回收期可縮短為6~12年，以經濟投資的立場來說，前述提及的地點皆非常有開發價值。供電方面，以年的角度來看，對服務業、公家機關或大專院校等建築用途，2.5 kW屋頂型小型風機發電量僅能支持部分照明或部分負載項目用電量，較適合做為推廣綠能的用途，而15 kW地面型小型風機可以支持服務業、公家機關或大專院校等照明用電的35%以上，具有發展潛力。小型風機發電量對於供應住宅用電則具有相當大的貢獻，15 kW小型風機的年發電量能夠支持5至10戶住宅用電，2.5 kW屋頂型小型風機則能完全支持1戶住宅年用電量。在季節上，2.5 kW屋頂型小型風機在秋、冬季月份(一月及十月)發電量能完全支持住宅用電，但在春、夏季月份(四月及七月)發電量則較為不足(月發電量為秋、冬季的一半以下)，需要其他供電系統的支持才能穩定供應住宅用電。而從小時來看，一天中發電量與用電量分布相反，即使在本研究中發電量最高的地點，在住宅尖峰用電的18時至凌晨1時之間，亦僅能堪達到完全支持用電，對於供電系統的調節有較大的挑戰，因此調配電力供給、搭配良好的儲能技術以維持供電穩定性會是小型風機開發及運用的關鍵。

摘要(英)

In addition to the large-scale onshore and offshore wind farms, urban wind energy has gradually drawn attention among green energy resources because of creative wind energy technology and rapid increase of urban development in recent years. In order to assess the potential of urban wind energy, this study first compared various wind field correction methods in WRF model and combines CALMET wind diagnostic model to obtain the best final wind field. Then, we assessed urban wind energy based on final wind farm data and discussed the economic benefits and the demand/supply of power system.
This study took Taichung City as an example and simulates wind field during the January, April, April, and October 2017. First, the sensitivity test was conducted for the WRF model, including adjustment of urban roughness length in land use data, Four-Dimensional Data Assimilation. (FDDA), urban parameterization in WRF such as Urban Canopy Model (UCM), Building Effect Parameterization (BEP), and Building Energy Modeling (BEM). We found that the overall performance of FDDA plus BEP parameterization is the best in the test and significantly improved the simulated wind field in areas where building density is high in the city center. Then we used the WRF/BEP derived wind field as the initial guess and combined the observation data for the CALMET diagnosis modeling. The coupling WRF/BEP and CALMET can strongly pull simulations toward observations, especially in the suburban area with relatively high terrain complexity nearby.
This study then analyzed the distribution of urban wind speed and wind energy density. It is found that the 2.5kW rooftop small wind turbine is suitable for building above 4th floor in Chingshuei, Wuchi, Longjing and Shalu districts (Average wind speed 2~6 m s-1, annual power generation 5500~12000 kWh), or the buildings above 120 meters in Bei Tun, Si Tun, Tai Jhong and Nantun districts (Average wind speed is 3~5 m s-1, annual power generation 4500-9000 kWh). The 15 kW ground type small wind turbine is suitable in the open area of Chingshuei, Wuchi, Longjing and Shalu districts (average wind speed is 2~6 m s-1, annual power generation 38000~72000kWh). According to the renewable energy procurement policy of the Bureau of Energy in Taiwan, the annual power generation of all small wind turbine locations in this study is higher than the annual sales cap (1650kWh/kW), so the payback period is 13 years. If there is no electricity sales cap, the payback period can be shortened to 6~12 years. From the standpoint of economic investment, all the locations mentioned above are very valuable for wind energy development.
From the perspective of power supply, 2.5 kW rooftop small wind turbine can only support a small amount of lighting or small-load project power consumption for construction purposes such as service industry, public institutions, or colleges. It implied the 2.5 kW rooftop small wind turbine is suitable for promoting green energy. The use of 15kW ground-type small wind turbines can support more than 35% of lighting power in service industries, public institutions or educational organizations. The power generation of the 15kW ground-type small wind turbines can supply the residential electricity. The annual power generation of 15kW small wind turbines can also support the electricity consumption of 5 to 10 households, as well as the 2.5kW rooftop small wind turbines can fully support the annual electricity consumption of 1 household. From the perspective of seasonal changes, 2.5kW rooftop small wind turbines can fully support residential electricity consumption in the autumn and winter months (January and October). However, in spring and summer months (April and July) the power generation is insufficient (The monthly power generation is less than half of that in autumn and winter). It needs the support of other power supply systems to stabilize the supply of residential electricity. The main challenge of the 2.5kW rooftop small wind turbine is that the trend of its hourly generation is opposite to the electricity consumption. For the spots with the highest power generation, the residential power consumption peaks between 6 p.m. to 1 a.m. and nearly meets the power generation. Therefore, the deployment of power supply and intellectual energy storage technology to maintain power supply stability will help the operation of small wind turbines in urban area.

關鍵字(中)

★ 都市風能
★ WRF
★ UCM
★ BEP/BEM
★ CALMET

關鍵字(英)

★ Urban Wind Energy
★ WRF
★ UCM
★ BEP/BEM
★ CALMET

論文目次

目錄
摘要……………………………………………………………………….I
Abstract………………………………………………………………...III
目錄……………………………………………………………………...V
圖目錄………………………………………………………………......IX
表目錄………………………………………………………………....XV
第一章前言 1
1.1 研究緣起 1
1.2 研究目的 2
第二章文獻回顧 3
2.1 都市風能及小型風機發展及評估 3
2.2 風能評估方法與技術 10
2.2.1 觀測資料風能評估方法 10
2.2.2 數值模式評估方法 11
2.2.2.1 中尺度數值模式模擬方法 11
2.2.2.2 小尺度數值模式及中尺度數值模式耦合技術 12
2.2.2.3 WRF四維資料同化技術FDDA 14
2.2.2.4 WRF模式之都市參數化 15
第三章研究方法 17
3.1 資料蒐集 17
3.1.1 氣象觀測資料蒐集 17
3.1.2 土地利用資料 17
3.1.3 台電及能源局數據 17
3.2 模式模擬方法 19
3.2.1 模擬時間 19
3.2.2 ARW-WRF模式模擬流程介紹 19
3.2.3 WRF模式模擬控制組基本設定 22
3.2.4 WRF模式都市區域粗糙度敏感度測試設定 25
3.2.5 WRF模式FDDA設定 25
3.2.6 WRF模式單層城市冠層Urban Canopy Model(UCM)設定 26
3.2.7 WRF模式多層城市冠層Building Energy Parameterization(BEP)及Building Energy Model (BEM)設定 28
3.2.8 衛星遙測土地利用資料更新 30
3.2.9 CALMET模式介紹及設定 34
3.2.9.1 CALMET第一階段風場 37
3.2.9.2 CALMET第二階段風場 39
3.3 模式模擬資料評估方法 43
3.4 風能評估方法與驗證 46
3.4.1 風能密度 46
3.4.2 風力發電機型號 46
3.4.3 風力發電過程中的能源損耗 49
3.4.4 風速迴歸修正 50
3.4.5 Windographer 軟體風能計算 52
3.5 經濟效益評估-投資回收期法 54
3.6 建築物能耗 56
第四章結果與討論 57
4.1 模式修正結果驗證與比較 57
4.1.1 粗糙度敏感度測試 60
4.1.2 FDDA模擬方法結果評估 67
4.1.3 都市參數化模擬方法結果評估 77
4.1.3.1 單層都市冠層UCM不考慮人造熱源模擬結果分析 77
4.1.3.2 單層都市冠層UCM加入人造熱源的影響 92
4.1.3.3 單層都市冠層UCM不同建築物高度之敏感度 104
4.1.3.4 多層都市冠層參數化BEP與BEM模擬結果分析 114
4.1.4 土地使用資料更新對模式的影響 124
4.1.5 CALMET模擬結果評估 130
4.2 都市風能評估結果 140
4.2.1 平均風速、風能密度分布及潛力區域討論 140
4.2.2 潛力場址分析及風場迴歸分析 149
4.2.3 風能評估結果 159
4.3 都市風能特性分析 176
4.3.1 都市小型風機之經濟效益分析 176
4.3.2 都市風能在供電系統中所扮演的角色 183
4.3.2.1 年發電量與用電量比較 183
4.3.2.2 各季節月發電量及月用電量分析 188
4.3.2.3 小時發電量變化及尖峰用電量 197
第五章結論與建議 203
5.1 結論 203
5.2 未來建議 212
參考文獻 214
附錄一小型風機各地點逐時平均風速及發電功率圖 222
附錄二口試委員意見答覆 225

參考文獻

王京明, 中經院, (1995). 台灣地區住宅與商業部門能源消費調查與研究
王暐晴, (2016).利用WRF/Urban Canopy Model模擬探討台灣北部都市地區之熱島效應
台灣電力公司, (2017). 106年度縣市住商售電資訊, https://www.taipower.com.tw/tc/page.aspx?mid=101
伍见军, 王咏薇, 朱彬, 杜钦, 高阳华, (2013). WRF模式中城市冠层参数化方案在重庆气象环境模拟中的性能比较, 长江流域资源与环境Resources and Environment in the Yangtze Basin, 第22卷第12期
凌拯民, 劉秉勳, 陳卿翊, (2009).台灣地區風速機率分佈函數之建立與特性分析, , 科技期刊第18卷科技類第1期頁23-33
許郁卿, (2011). 土地利用型態對地表能量收支與海陸風模擬的影響, 中央大學大氣物理研究所
郭柏巖, (2005). 住宅耗電實測解析與評估系統之研究,成功大學建築研究所
陳美蘭, (2006). 風能評估技術應用與發展現況, 工研院能環所/ 新能源技術組
經濟部能源局, (2018). 再生能源電能躉購費率及其計算公式公告
鄭景木, 林彥廷, 蘇煒年, 黃金城, (2016). 小型風力機之國際應用趨勢及其國內研發技術現況, 台灣能源期刊第三卷第一期第117-126頁
賴美蓉, 曹瑋玲, (2015). 因應台灣風機抗爭事件之課題與對策分析—以中部區域為例, 台灣能源齊開第二卷第一期第99-112頁
Allwine, K.J., Whiteman, C.D., (1985). MELSAR: A mesoscale air quality model for complex terrain: Volume 1--Overview, Technical Description and User′s Guide. Pacific Northwest Laboratory, Richland, Washington.
Arakawa, C., Fleig, O., Iida, M., Shimooka, M., (2005). Numerical approach for noise reduction of wind turbine blade tip with Earth Simulator. Journal of the Earth Simulator, Volume 2, March 2005, 11-33
AWS Scientific, Inc, (1997). Wind resource assessment handbook. National Renewable Energy Laboratory
Barton, J.P., Infield, D.G., (2004). Energy storage and its use with intermittent renewable energy. IEEE transactions on energy conversion, vol. 19, no. 2, june 2004
Blackadar, A. K., (1976). Modeling the nocturnal boundary layer. Preprints of the third symposium on atmospheric turbulence and air quality, Rayleigh, NC, 19-22 October 1976, Amer. Meteor. Soc., Boston, 46-49
Buttler, A.l., Dinkel, F., Franz, S., Spliethoff, H., (2016). Variability of wind and solar power : An assessment of the currentsituation in the European Union based on the year 2014. Energy 106, 147-161.
Carrasco, J.M., Bialasiewicz, J.T., Guisado, R.C.P., León, J.I., (2006). Power-electronic systems for the grid integration of renewable energy sources: A Survey. IEEE transactions on industrial electronics, vol. 53, no. 4, august 2006
Carrillo, C., Cidrás, J., Díaz-Dorado, E., Obando-Montaño, A.F., (2014). An approach to determine the Weibull parameters for wind energy analysis: The case of Galicia (Spain), Energies 2014, 7, 2676-2700
Carta, J.A., Ramírez, P., Velázquez, S., (2009). A review of wind speed probability distributions used in wind energy analysis: case studies in the Canary Islands. Renew Sustain Energy Rev;13:933–55.
Carvalho, D., Rocha A., Gómez-Gesteira M., Santos C., (2012). A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environmental Modelling & Software 2012;33:23–34.
Carvalho, D., Rocha A., Silva Santos C., Pereira R., (2013). Wind resource modelling in complex terrain using different mesoscale-microscale coupling techniques. Apply Energy;108:493–504.
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
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C.S.B., Grossman-Clarke, S., Loridan, T., Manning, K.W., Martilli, A., Miao, S., Sailor, D., Salamanca, F.P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A.A., Zhang, C., (2011). The Integrated WRF/urban modelling system: Development, Evaluation, and Applications to Urban Environmental Problems, international journal of climatology Int. J. Climatol. 31: 273–288
Counihan J., (1975). Adiabatic atmospheric boundary layers: A review and analysis of data from the period 1880-1972
Deardor, J. W., (1978). Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res., 83, 1889-1903
Deshmukh, M.K., Deshmukh, S.S., (2008). Modeling of hybriod renewable energy systems. Renewable and Sustainable Energy Reviews 12 (2008) 235–249
Douglas, S. and Kessler, R., (1988). User′s Guide to the Diagnostic Wind Field Model (Version 1.0). Systems Applications, Inc., San Rafael, CA, 48 pp.
Draxl, C., Hahmann ,A.N., Peña, A., Giebel, G., (2014). Evaluating winds and vertical wind shear from Weather Research and Forecasting Model forecasts using seven planetary boundary layer schemes. Wind Energy 2014;17(1):39–55.
Dudhia, J., (1996). A Multi-layer Soil Temerature Model for MM5.
Ðurisic, Z., Mikulovic, J., (2012). A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP, Renewable Energy 41 407-411
Dvorak, M.J., Archer, C.L., Jacobson, M.Z., (2010). California offshore wind energy potential, Renewable Energy 35 1244–1254
Emery, W.J., Baldwin, D.J., Schlüssel, P., Reynolds, R.W., (2001). Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measurements. Geophysical Research 106, 148-227.
Fang, H.F., (2014).Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago. Renewable Energy 67, 237-241.
Fyrippis, I., Axaopoulos, P.J., Panayiotou, G., (2010). Wind energy potential assessment in Naxos Island, Greece. Applied Energy 87, 577–586.
Godowitch, J.M., Gilliam, R.C., Roselle, S.J., (2015). Investigating the impact on modeled ozone concentrations using meteorological fields from WRF with an updated four–dimensional data assimilation approach, Atmospheric Pollution Research 6 305-311
Goodin, W.R., McRae, G.J., Seinfeld, J.H., (1980). An objective analysis technique for constructing three-dimensional urban scale wind fields. Journal of Applied Meteorology, Volume 19, 98-108.
Heath, M.A., Walshe, J.D., Watson, S.J., (2007). Estimating the potential yield of small building-mounted wind turbines. Wind Energy 10, 271–287.
Hirsch W, Rindelhardt U, Tetzla G. Saxon, (1996). Wind energy resources: comparison of WAsP and KAMM results. In: Proceedings 1996 European Wind Energy Conference, 20±24 May 1996, Goeteborg, Sweden. 1996. p. 604±7.
Islam, M.R., Saidur, R., Rahim, N.A., (2011). Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function. Energy 36, 985–992.
Karthikeya, B.R., Negi, P.S., Srikanth, N., (2016). Wind resource assessment for urban renewable energy application in Singapore, Renewable Energy 87 403-414
Kusaka, H., Chen, F., Tewari, M., Dudhia, J., Gill, D.O., Duda, M.G., Wang, W., Miya, Y. (2012). Numerical simulation of urban heat island effect by the WRF Model with 4-km grid increment: An inter-comparison study between the Urban Canopy model and Slab Model, Journal of the Meteorological Society of Japan, Vol. 90B, pp, 33-45.
Kusaka, H., Kondo, H., Kikegawa, Y., and Kimura, F., (2001). A simple single-layer urban canopy model for atmospheric models: Comparison with multilayer and slab models. Bound.-Layer Meteor., 101, 329–358.
Lee, S.-H., Kim, S.-W, Amgevine, W.M., Bianco L., McKeen, S.A., Senff, C.J., Trainer, M., Tucker, S.C., Zamora, R.J., (2011). Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign, Atmos. Chem. Phys., 11, 2127–2143,
Leloudas, G., Zhu, W.J., Sørensen, J.N., Shen, W.Z., Hjort, S., (2007). Prediction and reduction of noise from a 2.3mw wind turbine. Journal of Physics: Conference Series 75 (2007) 012083. doi:10.1088/1742-6596/75/1/012083
Li, J.H., Guo Z.H., Wang, H.J., (2014). Analysis of wind power assessment based on the WRF Model. Atmospheric and Oceanic Science Letters 7, 126-131.
Li, M., Zhang, Q., Kurokawa, J., Woo, J.H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D., Carmichael, G., (2015). MIX：A mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects. Atmospherics Chemistry and Physics 15, 34813-34869
Liao, J., Wang, T., Wang, X., Xie, M., Jiang, Z., Huang, X., Zhu, J., (2014). Impacts of different urban canopy schemes in WRF/Chem on regional climate and air quality in Yangtze River Delta, China, Journal of Atmospheric Research 145-146 226-243
Lin, C.-Y., Chen F., Huang J.C., Chen W.-C., Liou Y.-A., Chen W.-N., and Liu S.-C., (2008). Urban heat island effect and its impact on boundary layer development and land–sea circulation over northern Taiwan. Atmos. 29 Environ., 42, 5635–5649.
Lin, C.-Y., Su, C.-J, Kusaka H., Akimoto Y, Sheng Y.-F., Huang J.-C., Hsu H.-H., (2016). Impact of an improved WRF urban canopy model on diurnal air temperature simulation over northern Taiwan, Atmos. Chem. Phys., 16, 1809–1822
Liu, M.K., Yocke, M.A., (1980). Siting of wind turbine generators in complex terrain. J.Energy, 4, 10:16
Liu, S.-Y., Ho, Y.-F., (2016). Wind energy applications for Taiwan buildings: What are the challenges and strategies for small wind energy system exploitation? Renewable and Sustainable Energy Reviews 59 39–55
Liu, Y., Bourgeois, A., Warner, T., Swerdlin, S., Hacker, J., (2005): Implementation of observation-nudging based FDDA into WRF for supporting ATEC test operations. 2005 WRF Users Workshop, Boulder, Colorado, June, 2005.
Liu, Y., Chen, F., Warner, T., and Basara, J., (2006). Veriﬁcation of a mesoscale data-assimilation and forecasting system for the Oklahoma city area during the Joint Urban 2003 Field Project, J. Appl. Meteorol., 45, 912–929
Lu, L., Sun, K., (2014). Wind power evaluation and utilization over a reference high-rise building in urban area, Energy and Buildings 68 339–350
Lu, S.-D., Ho, W.-C., Lu, W.-H., Hu, C.-K., Chen, M.-L., Lien, Y.-S., (2015). A research on the potential energy of offshore wind power and preferable offshore blocks in Taiwan. 中華民國第三十六屆電力工程研討會
Maizi, M., Mohamed, M.H., Dizene, R., Mihoubi, M.C. (2018). Noise Reduction of a Horizontal Wind Turbine Using Different Blade Shapes. Renewable Energy, Volume 117, March 2018, Pages 242-256
Martilli, A., Clappier, A., Rotach, M.W., (2002). An urban surface exchange parameterization for mesoscale models. Bound.-Layer Meteor., 104, 261–304, doi:10.1023/A:1016099921195.
McGowan, J.G., Manwell, J.F., Rogers, A.L., (2002). Wind Energy Explained: Theory, Design and Application.
Mortensen, N.G., Hansen, J.C., Badger, J., Jørgensen, B.H., Hasager, C.B., Paulsen, U.S., Hansen, O.F., Enevoldsen, K., Youssef, L.G., (2006). Wind atlas for Egypt: measurements, micro and mesoscale modelling. In: European Wind Energy Conference EWEC
Nama, P., Nema, R.K., Rangnekar, S., (2009). A current and future state of art development of hybrid energy system using wind and PV-solar: A review. Renewable and Sustainable Energy Reviews 13 (2009) 2096–2103
O′Brien, J.J., (1970). A note on the vertical structure of the eddy exchange coefficient in the planetary boundary layer. J. Atmos. Sci., 27, 1213-1215.
Ozdamar, A., Ozbalta, N., Akin, A., Yildirim, E.D., (2004). An application of a combined wind and solar energy system in Izmir. Renewable and Sustainable Energy Reviews (2004) 1–14
Paz, D. D.-L., Borge, R., Martilli, A., (2016). Assessment of high resolution annual WRF-BEP/CMAQ simulation for the urban area of Madrid (Spain), Atmospheric Environment 144 282-296
Salamanca, F., Martilli, A., Tewari, M., Chen, F., (2011). A Study of the Urban Boundary Layer Using Different Urban Parameterizations and High-Resolution Urban Canopy Parameters with WRF. J. Appl. Meteor. Climatol., 50, 1107–1128.
Schramm, J, Degrazia, F.C., Vilhena, M.T.M.B., Bodmann, B.E., (2016). Comparison of CALMET and WRF/CALMET Coupling for Dispersion of NO2 and SO2 Using CALPUFF Modelling System, American Journal of Environmental Engineering, 6(4A): 50-55
Scire, J.S., Robe, F.R.,(1997). Fine-scale application of the CALMET meteorological model to a complex terrain site. Paper 97-A1313, AWMA 90th Annual Meeting & Exhibition, June 8-13,Toronto, Ontario, Canada.
Simões, T., Estanqueiro, A., (2016).A new methodology for urban wind resource assessment, Renewable Energy 89 598-605
Skamarock, W.C., Klemp, J.B., (2008). A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys. 227, 3465–3485.
Snyder, B., Kaiser, M.J., (2009). Ecological and economic cost-benefit analysis of offshore wind energy
Tursilowati, L., Sumantyo, J.T.S., Kuze, H., Adiningsih, E.S., (2011). The Integrated WRF/Urban Modeling system and its Application to monitoring Urban Heat Island in Jakarta, Indoesia, Journal of Urban and Environmental Engineering, v. 6, n. 1, p01-09
Ucar, A., Figen, B., (2009). Evaluation of wind emergy potential and electricity generation at six location in Turkey, Applied Energy 86 1864–1872
Xie, T., Pejnovic, N., Lees, A.F., Ewing, E., (2008). Wind Energy: A Thorough Examination of Economic Viability
Yim, S.H.L., Fung, J.C.H., Lau, A.K.H., Kot, S.C., (2007). Developing a high-resolution wind map for a complex terrain with a coupled MM5/CALMET system, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, D05106

指導教授

莊銘棟(Ming-Tung Chuang)

審核日期

2019-1-25

推文