博碩士論文 104621019 詳細資訊




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姓名 吳炫慶(Syuan-Cing Wu)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 利用WRF模式模擬及光達觀測進行風電場 邊界層風場之模擬校驗研究
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 (2018-9-1以後開放)
摘要(中) 風力發電是一個十分乾淨的綠色能源,台灣目前也在正在推行風力發電,不過很少從氣象的角度來討論風場與發電效益之間的關係。從觀測上我們可以利用光達(Lidar)等儀器來測得風力發電機所在之風場環境條件,同時也可以根據IEC(2005)的規範去評估風機運轉性能,不過觀測所設置的點有限,對於風機與大氣的交互作用可藉由模式了解更多。
本研究針對桃園沿海大潭發電廠附近的8支風力發電機,進行都卜勒光達的風場測量,觀測的時間為2015/12/17~2015/12/30。觀測的目標風機為Vestas V80,其高度為78公尺,葉片直徑為80公尺,Lidar設置的地點位於其西北方約160公尺。模擬的部分則是利用中尺度氣象模式(WRF)來進行模擬,使用大尺度渦流模型(LES)、MYNN邊界層參數化以及風電場參數化(Wind Farm Parameterization)方法來討論風機產生之尾流與大氣邊界層的交互作用,並以Lidar的資料作為風速場的驗證。模擬總共使用六層巢狀網格,在第五以及第六巢狀網格的初始與邊界條件使用one-way nested down製作,同時只在第六層巢狀網格100公尺解析度使用風電場參數化方法,總共植入8支風機。
風電場參數化的概念為因風機拖曳力使得大氣動能的損失,轉為電能以及亂流動能。比較使用風電場參數化(WF)與未使用(Ctrl)的結果,尾流風速在風機後方風速約減少2.0 m/s,同時尾流會具有較大的亂流動能,亂流動能收支主要是來自於垂直傳送、風切作用以及消散速率。邊界層高度在尾流處則略有提高,因MYNN邊界層參數化使用hybrid方法,分別找出虛位溫以及亂流動能所訂之高度,作出權重後定義出邊界層高度,亂流動能在垂直方向上的增加使得高度略微提高。在風速模擬的部分,雖然不同的參數化方法模擬結果相關性高於0.8,但是LES有較大的正偏差,在風速分布亦可見到,整體來說,使用風電場參數化後,風速分布在大於10m/s是較為接近觀測的。在風能評估方面利用風電場參數化所估算出來的風機功率曲線較接近原廠,與觀測差別最大在於高風速區,除了風機老化所造成的差異,還有偏角差異的問題。
摘要(英)
Wind energy is a clean and renewable resource. Taiwan gives impetus to it nowadays. Wind energy is seldom discussed about the relationship between wind field and the power generation performance from the perspective of meteorology. According to the standard of assessing the power generation performance from IEC(2005), Lidar is often used to measure the wind condition. Because of the limitation of the site observation, the model is applied in this study to understand the interaction between the wind turbines and the atmosphere boundary layer.
This study focuses on the eight wind turbines at Taoyuan Datan. The wind speed is measured by Doppler Lidar. There are four fronts passing through from 2015/12/15 to 2015/12/30. The postfrontal cold northeasterlies lead to the maximum wind speed happened. The type of target wind turbine is Vestas V80 with height of 78 meters and rotor diameter of 80 meters. Field measurements are performed using Doppler wind Lidar which lies 160 meters to the northwest of the target wind turbine to verify the model results. On the other hand, Large-Eddy Simulation(LES), MYNN planet boundary layer and Wind Farm Parameterization (WFP), which are based on the WRF model, are applied to discuss the interaction between the wake flow generated from wind turbines and the atmosphere boundary layer. Model design with six nesting domain are used for the multiscale atmospheric simulations. In addition, the initial and boundary conditions of the fifth and the sixth nested domain are made by one-way nested down, and WFP inserted eight wind turbines only is used in domain six.
The idea of the WFP is about energy transition. The kinetic energy loss caused by the turbine drag force is converted into electric power and turbulence kinetic energy (TKE). The difference between WF (with WFP) and Ctrl (without WFP) are apparent behind the wind turbines that the wind speed decreases around 2m/s while TKE increase among the wake flow. The TKE budget of the wake flow is based on the three parts: vertical transport, shear production and dissipation rate. Comparing to Ctrl, the boundary layer height of WF is slightly higher due to the hybrid method in MYNN that consider both the virtual potential temperature and TKE. The simulated wind speed in different parameterizations have high correlation with the observation, but LES has a higher positive bias. Wind speed distribution in WF is closer to the observation, especially for wind speed over 10m/s. The power curve of the WF form WFP is similar to the manufacturer, but far from the observation in high wind speed region because of the attenuated performance and yaw misalignment.
關鍵字(中) ★ 風力發電機
★ 風電場參數化
★ 光達
關鍵字(英)
論文目次
摘要 I
Abstract II
致謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
一、緒論 1
1.1前言 1
1.2文獻回顧 1
1.3 研究目的與動機 4
二、研究工具與方法 6
2.1 資料來源 6
2.1.1 分析場 6
2.1.2 觀測資料與原理 6
2.1.3 風力發電機 (Wind turbine) 7
2.2模式 8
2.2.1模式介紹 8
2.2.2模式設定 9
2.3 實驗設計 10
2.3.1 邊界層參數化特性 10
2.3.2 實驗設計 11
2.4 觀測與模式之比較方法 11
2.5 功率曲線(Power Curve, PC) 12
三、參數化方法 14
3.1 YSU 14
3.2 MYNN 14
3.3 Wind Farm Parameterization(WFP, 風電場參數化) 16
四、觀測與環境場分析 19
4.1 綜觀天氣概況 19
4.2 觀測環境場分析 20
五、模擬結果分析 21
5.1 邊界層參數化之邊界層高度 21
5.2 水平解析度的影響 22
5.2.1 YSU / LES 22
5.2.2 MYNN 23
5.3 風場分析 24
5.4 動力場分析 27
5.5 土地利用型態更新 30
5.6 風能評估 30
六、結論與未來展望 32
七、參考文獻 35
附表 38
附圖 45
參考文獻

曾仁佑,陳景林,劉遠芬,蘇育辰,陳盈臻,吳炫慶,2016:台電現有風機運轉性能評估及改善對策,台電工程月刊,第825期,41~50頁,106年5月。
Banks, R. F., J. Tiana-Alsina, J. M. Baldasano, F. Rocadenbosch, A. Papayannis, S. Solomos, and C. G. Tzanis, 2016: Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign. Atmospheric Research, 176-177, 185-201.
Blahak, U., B. Goretzki, and J. Meis, 2010: A simple parameterization of drag forces induced by large wind farms for numerical weather prediction models. Proceedings of European Wind Energy Conference and Exhibition, 2010, Warsaw, Poland, European Wind Energy Association, 186-189.
Carvalho, D., A. Rocha, M. Gómez-Gesteira, and C. Silva Santos, 2014: Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula. Applied Energy, 135, 234-246.
Cohen, A. E., S. M. Cavallo, M. C. Coniglio, and H. E. Brooks, 2015: A Review of Planetary Boundary Layer Parameterization Schemes and Their Sensitivity in Simulating Southeastern U.S. Cold Season Severe Weather Environments. Weather and Forecasting, 30, 591-612.
Fitch, A. C., J. K. Lundquist, and J. B. Olson, 2013: Mesoscale Influences of Wind Farms throughout a Diurnal Cycle. Monthly Weather Review, 141, 2173-2198.
——, A. C., J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad, 2012: Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Monthly Weather Review, 140, 3017-3038.
Harman, K., 2012: How does the real world performance of wind turbines compare with sales power curves? Presented at the EWEA 2012, Lyon. G.L., Garrard Hassan.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Mon. Wea. Rev. , 134, 2318–2341.
Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113.
IEC, 2005: Annex A: Assessment of obstacles at the test site. Wind turbines—Part 12-1: Power performance measurements of electricity producing wind turbines. 1st ed., IEC 61400-12-1, International Electrical Commission, 33–35.
Jiménez, P. A., and J. Dudhia, 2012: Improving the Representation of Resolved and Unresolved Topographic Effects on Surface Wind in the WRF Model. Journal of Applied Meteorology and Climatology, 51, 300-316.
——, J. Navarro, A. M. Palomares, and J. Dudhia, 2015: Mesoscale modeling of offshore wind turbine wakes at the wind farm resolving scale: a composite-based analysis with the Weather Research and Forecasting model over Horns Rev. Wind Energy, 18, 559-566.
Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170-181.
Llombart, A., JM Fandos, D. Llombart, A. Talayero, and S.J. Watson, 2006: Power curve characterization: stochastic methods.EWEC 2006 Conference, Athens, Greece, Feb. ~ Mar. 2006.
Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary boundary layers. Journal of the Atmospheric Sciences, 31, 1791-1806.
——, 1982: Development of a turbulence closure model for geophysical fluid problems. Reviews of Geophysics, 20, 851-875.
Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada Level-3 model: Its numerical stability and application to a regional prediction of advection fog. Boundary-Layer Meteorology, 119, 397-407.
Porté-Agel, F., Y.-T. Wu, H. Lu, and R. J. Conzemius, 2011: Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms. Journal of Wind Engineering and Industrial Aerodynamics, 99, 154-168.
Rettenmeier, A., D. Schlipf, I. Würth, and P. W. Cheng, 2014: Power Performance Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar Scanner. Journal of Atmospheric and Oceanic Technology, 31, 2029-2034.
Stull, R.B., 1988. Introduction to Boundary-Layer Meteorology. Kluwer Academic Publishers, Dordrecht, 666pp.
Talbot, C., E. Bou-Zeid, and J. Smith, 2012: Nested Mesoscale Large-Eddy Simulations with WRF: Performance in Real Test Cases. Journal of Hydrometeorology, 13, 1421-1441.
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Monthly Weather Review, 136, 5095-5115.
Vanderwende, B. J., B. Kosović, J. K. Lundquist, and J. D. Mirocha, 2016: Simulating effects of a wind-turbine array using LES and RANS. Journal of Advances in Modeling Earth Systems, 8, 1376-1390.
指導教授 林沛練 審核日期 2017-8-21
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