Electric Engineering, Mechanical Engineering |
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Wind resource assessment of weather research and forecasting model coupled with wind farm parameterization model |
Qiang WANG( ),Kun LUO*( ),Chun-lei WU,Jian-ren FAN |
State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China |
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Abstract To validate the accuracy of evaluating the wind resource characteristics on large-scale wind farms by the weather research and forecasting model coupled wind farm parameterization model (WRF-WFP), the numerical experiments were performed using coupled and uncoupled WFP models respectively, with a gigawatt-scale wind power base in Zhangbei County of Hebei Province as research objective. The wind speed and its probability density function distribution, and wind direction were validated by the observed data from two wind masts on flat and complex terrain regions. Results showed that the WRF models of coupled and uncoupled WFP model both had high accuracy for assessment of wind resources and the simulation accuracy for the flat region was better than that for the mountain region. However, the accuracy of the coupled model was 4.5% higher than that of the uncoupled model due to the consideration of wind farm wake effects. The proposed numerical model can provide reliable technical support for micro-siting of the wind farm with rich wind resources, and assessing the operational characteristics of large-scale wind farms and their atmospheric impacts.
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Received: 09 April 2019
Published: 13 August 2019
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Corresponding Authors:
Kun LUO
E-mail: zjuqw@zju.edu.cn;zjulk@zju.edu.cn
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耦合风电场参数化模型的天气预报模式对风资源的评估和验证
为了验证耦合风电场参数化模型的天气预报模式(WRF-WFP)对大规模风电场风资源特性评估的准确性,以我国河北省张北县百万千瓦风能基地为研究对象,分别采用耦合与未耦合风电场参数化模型的天气预报模式开展相关的模拟试验,利用在平坦及复杂地形场区的测风塔的观测数据对风速及其概率密度函数分布以及风向进行验证分析. 研究结果表明:耦合与未耦合风电场参数化模型的天气预报数值模式对风资源特性的评估均有较高的可靠性,且对平坦地形区域的模拟精确性优于山地地形区域;由于前者考虑了风电场的尾流效应,对风速预测的精确性比后者高约4.5%. 该数值模式可为风能丰富地区的风电场微观选址、大规模风电场的运行特性及其对大气边界层影响的评估提供可靠的技术支撑.
关键词:
大规模风电场,
风资源评估,
天气预报模式,
风电场参数化模型,
精确性
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|
[1] |
REN G, LIU J, WAN J, et al Overview of wind power intermittency: impacts, measurements, and mitigation solutions[J]. Applied Energy, 2017, 204: 47- 65
doi: 10.1016/j.apenergy.2017.06.098
|
|
|
[2] |
祁和生, 胡书举 分布式利用是风能发展的重要方向[J]. 中国科学院院刊, 2016, (2): 173- 181 QI He-sheng, HU Shu-ju Distributed application is an important direction for wind energy development[J]. Bulletin of Chinese Academy of Sciences, 2016, (2): 173- 181
|
|
|
[3] |
LIU J, GAO C, REN J, et al Wind resource potential assessment using a long term tower measurement approach: a case study of Beijing in China[J]. Journal of Cleaner Production, 2018, 174: 917- 926
doi: 10.1016/j.jclepro.2017.10.347
|
|
|
[4] |
GIANNAROS T, MELAS D, ZIOMAS I Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece[J]. Renewable Energy, 2017, 102: 190- 198
doi: 10.1016/j.renene.2016.10.033
|
|
|
[5] |
KHAIN A, LYNN B, SHPUND J High resolution WRF simulations of Hurricane Irene: sensitivity to aerosols and choice of microphysical schemes[J]. Atmospheric Research, 2016, 167: 129- 145
doi: 10.1016/j.atmosres.2015.07.014
|
|
|
[6] |
JIMENEZ B, DURANTE F, LANGE B, et al Offshore wind resource assessment with WAsP and MM5: comparative study for the German Bight[J]. Wind Energy, 2007, 10 (2): 121- 134
doi: 10.1002/(ISSN)1099-1824
|
|
|
[7] |
LU Y, TANG J, WANG Y, et al. Using the MM5 model for wind energy resources evaluation over a fuel-poor region, East China [C]// Asia-Pacific Power and Energy Engineering Conference. [S.l.]: IEEE, 2009: 1-6.
|
|
|
[8] |
LAZIC L, PEJANOVIC G Wind forecasts for wind power generation using the Eta model[J]. Renewable Energy, 2010, 35 (6): 1236- 1243
doi: 10.1016/j.renene.2009.10.028
|
|
|
[9] |
ZHAO P, WANG J, XIA J, et al Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China[J]. Renewable Energy, 2012, 43 (10): 234- 241
|
|
|
[10] |
WANG C, JIN S Error features and their possible causes in simulated low-level winds by WRF at a wind farm[J]. Wind Energy, 2014, 17 (9): 1315- 1325
|
|
|
[11] |
王澄海, 胡菊, 靳双龙, 等 中尺度WRF模式在西北西部地区底层风场模拟中的应用和检验[J]. 干旱气象, 2011, 29 (2): 161- 167 WANG Cheng-hai, HU Ju, JIN Shuang-long, et al Application and test of lower level wind field simulation with meso-scale model WRF in western region of Northwest China[J]. Journal of Arid Meteorology, 2011, 29 (2): 161- 167
doi: 10.3969/j.issn.1006-7639.2011.2.006
|
|
|
[12] |
ZHANG F, YANG Y, WANG C The effects of assimilating conventional and ATOVS data on forecasted near-surface wind with WRF-3DVAR[J]. Monthly Weather Review, 2015, 143: 153- 64
doi: 10.1175/MWR-D-14-00038.1
|
|
|
[13] |
张飞民, 王澄海 利用WRF-3DVAR同化常规观测资料对近地层风速预报的改进试验[J]. 高原气象, 2014, 33 (3): 675- 685 ZHANG Fei-ming, WANG Cheng-hai Experiment of surface-layer wind forecast improvement by assimilating conventional data with WRF-3DVAR[J]. Plateau Meteorology, 2014, 33 (3): 675- 685
doi: 10.7522/j.issn.1000-0534.2012.00198
|
|
|
[14] |
AMJAD M, ZAFAR Q, KHAN F, et al Evaluation of weather research and forecasting model for the assessment of wind resource over Gharo, Pakistan[J]. International Journal of Climatology, 2015, 35 (8): 1821- 1832
doi: 10.1002/joc.2015.35.issue-8
|
|
|
[15] |
DRAXL C, HAHMANN A, PENA A, et al Evaluating winds and vertical wind shear from weather research and forecasting model forecasts using seven planetary boundary layer schemes[J]. Wind Energy, 2014, 17 (1): 39- 55
doi: 10.1002/we.v17.1
|
|
|
[16] |
CARVALHOAABC D A sensitivity study of the WRF model in wind simulation for an area of high wind energy[J]. Environmental Modelling and Software, 2012, 33 (7): 23- 34
|
|
|
[17] |
FITCH A C, OLSON J B, LYBDQYUST J K, et al Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model[J]. Monthly Weather Revie, 2012, 140 (9): 3017- 3038
doi: 10.1175/MWR-D-11-00352.1
|
|
|
[18] |
王勇, 王澄海, 陶健红, 等 甘肃酒泉地区近地层风场特征[J]. 干旱气象, 2012, 30 (3): 393- 403 WANG Yong, WANG Cheng-hai, TAO Jian-hong, et al Characteristics of wind field near ground layer in Jiuquan of Gansu Province[J]. Journal of Arid Meteorology, 2012, 30 (3): 393- 403
|
|
|
[19] |
SKAMAROCK W C, KLEMP J B, DUDHIA J, et al. A description of the advanced research WRF version 3 [R]. Boulder: U.S. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, 2008: 7-25.
|
|
|
[20] |
CARVALHO D, ROCHA A, GOMEZ M, et al Offshore wind energy resource simulation forced by different reanalyses: comparison with observed data in the Iberian Peninsula[J]. Applied Energy, 2014, 134: 57- 64
doi: 10.1016/j.apenergy.2014.08.018
|
|
|
[21] |
JIMENEZ P A, NAVARRO J, PALOMARES A M, et al 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[J]. Wind Energy, 2015, 18 (3): 559- 566
doi: 10.1002/we.v18.3
|
|
|
[22] |
NA J S, KOO E, MUNOZ D, et al Turbulent kinetics of a large wind farm and their impact in the neutral boundary layer[J]. Energy, 2016, 95: 79- 90
doi: 10.1016/j.energy.2015.11.040
|
|
|
[23] |
HONG S Y A new vertical diffusion package with an explicit treatment of entrainment processes[J]. Monthly Weather Review, 2006, 134 (9): 2318
doi: 10.1175/MWR3199.1
|
|
|
[24] |
MLAWER E, TAUBMAN S, BROWN P, et al Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the long-wave[J]. Journal of Geophysical Research Atmospheres, 1997, 102 (D14): 16663- 16682
doi: 10.1029/97JD00237
|
|
|
[25] |
FOUQUART Y, BONNEL B, RAMASWAMY V Intercomparing shortwave radiation codes for climate studies[J]. Journal of Geophysical Research Atmospheres, 1991, 96 (D5): 8955- 8968
doi: 10.1029/90JD00290
|
|
|
[26] |
GRELL G A, DEVENYI D A generalized approach to parameterizing convection combining ensemble and data assimilation techniques[J]. Geophysical Research Letters, 2002, 29 (6): 587- 590
|
|
|
[27] |
NAKAKANISHI M, NIINO H Development of an improved turbulence closure model for the atmospheric boundary layer[J]. Journal of the Meteorological Society of Japan, 2009, 87 (5): 895- 912
|
|
|
[28] |
XIA G, CERVARICH M C, ROY S B, et al Simulating impacts of real-world wind farms on land surface temperature using the WRF model: validation with observations[J]. Monthly Weather Review, 2017, 145: 4813- 36
doi: 10.1175/MWR-D-16-0401.1
|
|
|
[29] |
ABKAR M, PORTE F A new wind-farm parameterization for large-scale atmospheric models[J]. Journal of Renewable and Sustainable Energy, 2015, 7 (1): 013121
|
|
|
[30] |
WANG Q, LUO K, YUAN R, et al Wake and performance interference between adjacent wind farms: case study of Xinjiang in China by means of mesoscale simulations[J]. Energy, 2019, 166: 1168- 1180
|
|
|
[31] |
JIMENEZ P A, DUDHIA J Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model[J]. Journal of Applied Meteorology and Climatology, 2012, 51: 300- 16
doi: 10.1175/JAMC-D-11-084.1
|
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