基于计算流体力学(CFD)中的有限体积法,考虑地形、粗糙度等因素的影响建立风场模型,并结合标准k-ε湍流模型与单一线性激励尾流模型,使用RANS方程求解计算域,得到风能资源图和风力机年发电量。以上海嵊山站台附近海域为案例进行完整的风能资源评估。结果显示该地区风向以南北、东南、东向为主,其中北-南方向(Y)速度梯度的变化随着高度增加而增加,冬季风速更高,具有较好的风能开发利用潜力。
Abstract
Based on the finite volume method in computational fluid dynamics (CFD), a wind field model is established considering the effects of terrain, roughness, and other influential factors. The standard k-ε turbulence model and a single linear wake model are combined, and the RANS equations are used to solve the computational domain, resulting in wind resource maps and annual electricity generation of wind turbines. A comprehensive wind resource assessment is conducted in the vicinity of the Shengshan Station in Shanghai as a case study. The results show that the prevailing wind directions in the area are north-south, southeast, and east, with an increasing gradient of velocity in the north-south direction (Y) as the height increases. Wind speeds are higher during winter, indicating significant potential for wind energy development. This study provides a scientific basis and useful reference for wind power planning in similar regions.
关键词
风能 /
海上风电 /
CFD /
风能资源评估 /
线性尾流损失
Key words
wind power /
offshore wind power /
computational fluid dynamics /
wind energy resource assessment /
linear wake loss
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] DE ASSIS TAVARES L F, SHADMAN M, DE FREITAS ASSAD L P, et al. Assessment of the offshore wind technical potential for the Brazilian Southeast and South regions[J]. Energy, 2020, 196: 117097.
[2] 林玉鑫, 张京业. 海上风电的发展现状与前景展望[J]. 分布式能源, 2023, 8(2): 1-10.
LIN Y X, ZHANG J Y.Development status and prospect of offshore wind power[J]. Distributed energy, 2023, 8(2): 1-10.
[3] MURTHY K S R, RAHI O P. A comprehensive review of wind resource assessment[J]. Renewable and sustainable energy reviews, 2017, 72: 1320-1342.
[4] VEERS P, DYKES K, LANTZ E, et al. Grand challenges in the science of wind energy[J]. Science, 2019, 366(6464): eaau2027.
[5] ARGIN M, YERCI V.Offshore wind power potential of the Black Sea region in Turkey[J]. International journal of green energy, 2017, 14(10): 811-818.
[6] KHAN T, THEPPAYA T, TAWEEKUN J.Wind resource assessment of northern part of Thailand[J]. Ain Shams engineering journal, 2023, 14(7): 102025.
[7] DAYAL K K, CATER J E, KINGAN M J, et al.Wind resource assessment and energy potential of selected locations in Fiji[J]. Renewable energy, 2021, 172: 219-237.
[8] 叶瑶, 袁熹, 王逸奇. 基于WRF模式的四川省凉山州地区风能资源可开发区域研究[J]. 太阳能学报, 2024, 45(2): 158-163.
YE Y, YUAN X, WANG Y Q.Study on developable regions of wind energy resources in Liangshan Prefecture, Sichuan Province based on WRF model[J]. Acta energiae solaris sinica, 2024, 45(2): 158-163.
[9] 朱蓉, 徐红, 龚强, 等. 中国风能开发利用的风环境区划[J]. 太阳能学报, 2023, 44(3): 55-66.
ZHU R, XU H, GONG Q, et al.Wind environmental regionalization for development and utilization of wind energy in China[J]. Acta energiae solaris sinica, 2023, 44(3): 55-66.
[10] 易侃, 张子良, 张皓, 等. 海上风能资源评估数值模拟技术现状及发展趋势[J]. 分布式能源, 2021, 6(1): 1-6.
YI K, ZHANG Z L, ZHANG H, et al.Technical status and development trends of numerical modeling for offshore wind resource assessment[J]. Distributed energy, 2021, 6(1): 1-6.
[11] JU B, JENOG J, KO K.Assessment of wind atlas analysis and application program and computational fluid dynamics estimates for power production on a Jeju Island wind farm[J]. Wind engineering, 2016, 40(1): 59-68.
[12] GOMES DA SILVA A F, REGINA DE ANDRASE C, ZAPAROLI E L. Wind power generation prediction in a complex site by comparing different numerical tools[J]. Journal of wind engineering and industrial aerodynamics, 2021, 216: 104728.
[13] TABAS D, FANG J, PORTE-AGEL F.Wind energy prediction in highly complex terrain by computational fluid dynamics[J]. Energies, 2019, 12(7): 1311.
[14] IEA. World Energy Outlook 2023[R]. Paris: IEA, 2023.
基金
上海市浦江人才计划(22PJ1411400)