Technical Status and Development Trends of Numerical Modeling for Offshore Wind Resource Assessment

YI Kan, ZHANG Ziliang, ZHANG Hao, WANG Hao

Distributed Energy ›› 2021, Vol. 6 ›› Issue (1) : 1-6.

PDF(972 KB)
PDF(972 KB)
Distributed Energy ›› 2021, Vol. 6 ›› Issue (1) : 1-6. DOI: 10.16513/j.2096-2185.DE.2106004
Review

Technical Status and Development Trends of Numerical Modeling for Offshore Wind Resource Assessment

Author information +
History +

Abstract

Considering the complex and volatile nature of wind energy, numerical models/tools are becoming increasingly important for offshore wind resource assessment. This paper presents an overview on different types of numerical methods/tools used in the offshore wind energy resource assessment. The characteristics and limitations of linear, computational fluid dynamics(CFD), and mesoscale meteorological models have been delineated. The multi-scale modeling framework are attracting more and more attentions in academic and industrial area. Coupling the mesoscale model with a microscale model is proposed to be an efficient method to resolve the limitations of single scale models. The air-sea coupling model has been proved to have better performances in simulating offshore wind, which is expected to be increasingly important for wind resource and ocean environment assessment in the offshore wind farm development. The characteristic of various wake models are thoroughly discussed here. Some challenges existed in an accurate quantification of wake effects are identified in order to guide future research in this area. Overall, further efforts are urgently needed in improving the offshore wind resource assessment.

Key words

offshore wind resource assessment / numerical models / multi-scale modeling framework / air-sea coupling / wake model

Cite this article

Download Citations
Kan YI , Ziliang ZHANG , Hao ZHANG , et al. Technical Status and Development Trends of Numerical Modeling for Offshore Wind Resource Assessment[J]. Distributed Energy Resources. 2021, 6(1): 1-6 https://doi.org/10.16513/j.2096-2185.DE.2106004

References

[1]
张星波. 集约高效开发利用,降低海上风电成本—对海上风电平价时代项目建设的思考[J]. 风能2020(5): 70-72.
[2]
夏云峰. 2019年全球新增风电装机超60 GW[J]. 风能2020(4): 36-41.
[3]
CHANG R, ZHU R, BADGER M, et al. Offshore wind resources assessment from multiple satellite data and WRF modeling over south China sea[J]. Remote Sensing, 2015, 7(1): 467-487.
[4]
JANSEN M, STAFFELL I, KITZING L, et al. Offshore wind competitiveness in mature markets without subsidy[J]. Nature Energy, 2020, 5(8): 1-9.
[5]
DURÁN P, MEINER C, RUTLEDGE K, et al. Meso-microscale coupling for wind resource assessment using averaged atmospheric stability conditions[J]. Meteorologische Zeitschrift, 2019, 28(4): 0937.
[6]
TROEN I. A high resolution spectral model for flow in complex terrain[C]//Ninth symposium on turbulence and diffusion, Roskilde. 1990: 417-420.
[7]
YAMAGUCHI A, ISHIHARA T, FUJINO Y. The applicability of linear and non-linear wind prediction models to wind flow in complex terrain[C]//Proc. Word Wind Energy Conference, 2002: 1-4.
[8]
HRISTOV Y, OXLEY G, ŽAGAR M. Improvement of AEP predictions using diurnal CFD modelling with site-specific stability weightings provided from mesoscale simulation[C]//Journal of Physics: Conference Series. IOP Publishing, 2014, 524(1): 012116.
[9]
SKAMAROCK W C, KLEMP J B, DUDHIA J, et al. A description of the Advanced Research WRF version 3. NCAR Technical note-475+ STR[R]. 2008.
[10]
张德,朱蓉,罗勇等. 风能模拟系统WEST在中国风能数值模拟中的应用[J]. 高原气象2008, 27(1): 202-207.
ZHANG De, ZHU Rong, LUO Yong, et al. Application of Wind Energy Simulation Toolkit (WEST) to Wind Energy Numerical Simulation of China[J]. Plateau Meteorology, 2008, 27(1): 202-207.
[11]
张秀芝,朱蓉,BODDINGTON R. 中国近海风电场开发指南[M]. 北京:气象出版社,2014.
[12]
周荣卫,何晓凤,朱蓉,等. 中国近海风能资源开发潜力数值模拟[J]. 资源科学2010, 32(8): 1434-1443.
ZHOU Rongwei, HE Xiaofeng, ZHU Rong, et al. Numerical simulation of the development potential of wind energy resources over China's offshore areas[J]. Resources Science, 2010, 32(8): 1434-1443.
[13]
VEERS P, DYKES K, LANTZ E, et al. Grand challenges in the science of wind energy[J]. Science, 2019, 366(6464): 443.
[14]
SANZ RODRIGO J, CHAVEZ ARROYO R A, MORIARTY P, et al. Mesoscale to microscale wind farm flow modeling and evaluation[J]. Wiley Interdisciplinary Reviews: Energy and Environment, 2017, 6(2): 214.
[15]
BILAL M, BIRKELUND Y, HOMOLA M, et al. Wind over complex terrain-Microscale modelling with two types of mesoscale winds at Nygårdsfjell[J]. Renewable Energy, 2016, 99: 647-653.
[16]
BILAL M, SOLBAKKEN K, BIRKELUND Y. Wind speed and direction predictions by WRF and WindSim coupling over nygårdsfjell[C]//Journal of Physics: Conference Series. IOP Publishing, 2016, 753(8): 082018.
[17]
LIU Y, WARNER T, LIU Y, et al. Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2011, 99(4): 308-319.
[18]
TAMMELIN B, VIHMA T, ATLASKIN E, et al. Production of the finnish wind atlas[J]. Wind Energy, 2013, 16(1): 19-35.
[19]
WAEWSAK J, LANDRY M, GAGNON Y. Offshore wind power potential of the Gulf of Thailand[J]. Renewable Energy, 2015, 81: 609-626.
[20]
PETERSEN E L, TROEN I, JØRGENSEN H E, et al. The new European wind atlas[J]. Energy Bulletin, 2014, 1(17): 34-39.
[21]
BADGER J, FRANK H, HAHMANN A N, et al. Wind-climate estimation based on mesoscale and microscale modeling: statistical-dynamical downscaling for wind energy applications[J]. Journal of Applied Meteorology and Climatology, 2014, 53(8): 1901-1919.
[22]
LANGE B, LARSEN S, HØJSTRUP J, et al. Importance of thermal effects and sea surface roughness for offshore wind resource assessment[J]. Journal of wind engineering and industrial aerodynamics, 2004, 92(11): 959-988.
[23]
SONG J, FAN W, LI S, et al. Impact of surface waves on the steady near-surface wind profiles over the ocean[J]. Boundary-Layer Meteorology, 2015, 155(1): 111-127.
[24]
SOARES P M M, LIMA D C A, SEMEDO A, et al. Assessing the climate change impact on the North African offshore surface wind and coastal low-level jet using coupled and uncoupled regional climate simulations[J]. Climate dynamics, 2019, 52(11): 7111-7132.
[25]
ALDRIAN E, SEIN D, JACOB D, et al. Modelling Indonesian rainfall with a coupled regional model[J]. Climate Dynamics, 2005, 25(1): 1-17.
[26]
GRÖGER M, DIETERICH C, MEIER M H E, et al. Thermal air-sea coupling in hindcast simulations for the North Sea and Baltic Sea on the NW European shelf[J]. Tellus A: Dynamic Meteorology and Oceanography, 2015, 67(1): 26911.
[27]
PULLEN J, DOYLE J D, SIGNELL R P. Two-way air-sea coupling: A study of the Adriatic[J]. Monthly weather review, 2006, 134(5): 1465-1483.
[28]
LARSEN X G, DU J T, BOLANOS R, et al. Estimation of offshore extreme wind from wind-wave coupled modeling[J]. Wind Energy, 2019, 22(8): 1043-1057.
[29]
WU L, SHAO M, SAHLÉE E. Impact of air-wave-sea coupling on the simulation of offshore wind and wave energy potentials[J]. Atmosphere, 2020, 11(4): 327-347.
[30]
KANG M, KO K, KIM M. Verification of the reliability of offshore wind resource prediction using an atmosphere-ocean coupled model[J]. Energies, 2020, 13(1): 254-268.
[31]
PORTÉ-AGEL F, BASTANKHAH M, SHAMSODDIN S. Wind-turbine and wind-farm flows: a review[J]. Boundary-Layer Meteorology, 2020, 174(1): 1-59.
[32]
GAO X, YANG H, LU L. Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model[J]. Applied Energy, 2016, 174: 192-200.
[33]
TIAN L, ZHU W, SHEN W, et al. Development and validation of a new two-dimensional wake model for wind turbine wakes[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 137: 90-99.
[34]
BASTANKHAH M, PORTÉ-AGEL F. A new analytical model for wind-turbine wakes[J]. Renewable Energy, 2014, 70(oct.): 116-123.
[35]
CHENG Y, ZHANG M, ZHANG Z, et al. A new analytical model for wind turbine wakes based on Monin-Obukhov similarity theory[J]. Applied Energy, 2019, 239: 96-106.
[36]
周洋,许昌,韩星星,等. 基于致动面模型的风力机尾流数值研究[J]. 工程热物理学报2017, 38(3): 535-540.
ZHOU Yang, XU Chang, HAN Xingxing, et al. Numerical study of wind turbine wake modeling based on a actuator surface mode[J]. Journal of Engineering Thermophysics, 2017, 38(3): 535-540.
[37]
QIAN G W, ISHIHARA T. A new analytical wake model for yawed wind turbines[J]. Energies, 2018, 11(3): 665.
[38]
GOIT J, MUNTERS W, MEYERS J. Optimal coordinated control of power extraction in LES of a wind farm with entrance effects[J]. Energies, 2016, 9(1): 29.
[39]
WU Y T, PORTÉ-AGEL F. Large-eddy simulation of wind-turbine wakes: Evaluation of turbine parametrisations[J]. Boundary-layer meteorology, 2011, 138(3): 345-366.
[40]
CHRISTLANSEN M B, HASAGER C B. Wake effects of large offshore wind farms identified from satellite SAR[J]. Remote Sensing of Environment, 2005, 98(2-3): 251-68.
[41]
PLATIS A, SIEDERSLEBEN S K, BANGE J, et al. First in situ evidence of wakes in the far field behind offshore wind farms[J]. Rep, 2018, 8(1): 2163.
[42]
LUNDQUIST J K, DUVIVIER K K, KAFFINE D, et al. Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development[J]. Nature Energy, 2019, 4(1): 26-34.

Funding

Science and Technology Project of China Three Gorges Corporation Ltd.(wwky-2020-0015)
PDF(972 KB)

Accesses

Citation

Detail

Sections
Recommended

/