Case Study of "Wake Effect" of Adjacent Offshore Wind Farms

CUI Donglin; SHA Wei; LIU Shujie; CHEN Qiuyang; WANG Nina

doi:10.16516/j.gedi.issn2095-8676.2023.01.003

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CUI Donglin, SHA Wei, LIU Shujie, CHEN Qiuyang, WANG Nina. Case Study of 'Wake Effect' of Adjacent Offshore Wind Farms[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(1): 21-28. doi: 10.16516/j.gedi.issn2095-8676.2023.01.003

Citation:

CUI Donglin, SHA Wei, LIU Shujie, CHEN Qiuyang, WANG Nina. Case Study of "Wake Effect" of Adjacent Offshore Wind Farms[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(1): 21-28. doi: 10.16516/j.gedi.issn2095-8676.2023.01.003

Case Study of "Wake Effect" of Adjacent Offshore Wind Farms

doi: 10.16516/j.gedi.issn2095-8676.2023.01.003

CUI Donglin^{1
,
,},
SHA Wei^1
,,
LIU Shujie^{2, 3
,},
CHEN Qiuyang^{2, 3
,},
WANG Nina^{2, 3
,}

1.
Xinjiang Goldwind Science & Technology Co., Ltd., Urumqi 830001, Xinjiang, China
2.
Key Laboratory of Far-Shore Wind Power Technology of Zhejiang Province, Hangzhou, China，311122
3.
PowerChina Huadong Engineering Corporation Limited, Hangzhou, China，311122

Received Date: 2021-10-13
Rev Recd Date: 2021-12-30

Available Online: 2022-11-21

Publish Date: 2023-01-11

Abstract

Introduction The purpose of this paper is to study the influence of real "wake effect" of adjacent offshore wind farms on generation loss. Method The method is established with the wake scene classification based on the actual arrangement of wind farms under different wind direction and the real wake power loss of adjacent wind farms (with a spacing of more than 20D) in operation are analyzed, based on the actual SCADA data of wind turbines in large offshore wind farms and the measured wind data of LIDAR in the same period. Result The results show that: for the large-scale offshore wind farms with regular arrangement, the power generation normalization of the actual SCADA data can better reflect the distribution characteristics of offshore wind energy resources and the difference of power generation capacity; Under the condition of highly centralized wind direction, the adjacent wind farms in the downwind are obviously affected by the "wake effect" of the upwind wind farm; The buffer zones with different distances of adjacent wind farms have an obvious effect on the recovery of wind speed which affected the power generating capacity. The power generating capacity can be improved but if the buffer zone can reach enough distance; In different scenes of this case, the buffer zone distance is between 23D and 44D, and the power loss of wake decreases by 27%~4%. Conclusion This work can provide guidance for the planning of offshore wind power base and the optimization design of large offshore wind frams.
- adjacent wind farms,
- wake effect,
- power generation normalization,
- buffer zone,
- layout optimization

References

[1]	ASTARIZ S, IGLESIAS G. Enhancing wave energy competitiveness through co-located wind and wave energy farms. a review on the shadow effect [J]. Energies, 2015(8): 7344-7366. DOI: 10.3390/en8077344.
[2]	HASAGER C B, VINCENT P, BADGER J, et al. Using satellite SAR to characterize the wind flow around offshore wind farms [J]. Energies, 2015(8): 5413-5439. DOI: 10.3390/en8065413.
[3]	FRANDSEN S, BARTHELMIE R, PRYOR S, et al. Analytical modelling of wind speed deficit in large offshore: wind farms [J]. Wind Energy, 2006, 9(1): 39-53. DOI: 10.1002/we.189.
[4]	陈树勇, 戴慧珠, 白晓民, 等. 尾流效应对风电场输出功率的影响 [J]. 中国电力, 1998, 31(11): 28-31. CHEN S Y, DAI H Z, BAI X M, et al. Impact of wind turbine wake on wind power output [J]. Electric Power, 1998, 31(11): 28-31.
[5]	刘沙, 王中权, 蔡彦枫. 海上风电场运行期尾流损失分析 [J]. 南方能源建设, 2019, 6(1): 66-70. DOI: 10.16516/j.gedi.issn2095-8676.2019.01.011. LIU S, WANG Z Q, CAI Y F. Wake loss analysis of offshore wind farm in operation [J]. Southern Energy Construction, 2019, 6(1): 66-70. DOI: 10.16516/j.gedi.issn2095-8676.2019.01.011.
[6]	温建民, 张新旺, 陈锋, 等. 基于风电场实测的风机尾流特征分析 [J]. 机械工程与自动化, 2021(1): 3-6. DOI: 10.3969/j.issn.1672-6413.2021.01.002. WEN J M, ZHANG X W, CHEN F, et al. Analysis of wind turbine wake characteristics based on wind farm field measurement [J]. Mechanical Engineering & Automation, 2021(1): 3-6. DOI: 10.3969/j.issn.1672-6413.2021.01.002.
[7]	李岩, 吴迪, 洪畅, 等. 大型海上风电场风机排布优化策略研究 [J]. 太阳能, 2020(2): 67-74. DOI: 10.3969/j.issn.1003-0417.2020.02.011. LI Y, WU D, HONG C, et al. Optimization of wind turbine layout in large-scale offshore wind farm [J]. Solar Enengy, 2020(2): 67-74. DOI: 10.3969/j.issn.1003-0417.2020.02.011.
[8]	郑建才, 万德成, 王尼娜, 等. 基于尾流叠加模型的风电场数值模拟 [C]// 《水动力学研究与进展》编委会, 中国力学学会流体力学专业委员会水动力学专业组, 集美大学, 等. 第三十一届全国水动力学研讨会, 中国福建厦门, 2020-10-30. 北京: 海洋出版社, 2020: 833-847. DOI: 10.26914/c.cnkihy.2020.037043. ZHEN J C, WAN D C, WANG N, et al. Numerical solution of wind farm wake based on wake interaction model [C]// Editorial Board of Research and Progress in Hydrodynamics, Hydrodynamics Professional Group of Hydromechanics Committee of Chinese Society of Mechanics, Jimei University, et al. Thirty-first National Symposium on Hydrodynamics, Xiamen, Fujian, China, 2020-10-30. Beijing: China Ocean Press, 2020: 833-847. DOI: 10.26914/c.cnkihy.2020.037043.
[9]	TC88. Wind turbines-part 12-1: power performance measurements of electricity producing wind turbines: IEC 61400-12-1 [S]. Genève: International Electrotechnical Commission, 2005.
[10]	杨昆, 姜婷婷, 朱金奎, 等. 大规模风电场实际尾流分析与算法研究 [C]// 中国农机工业协会风力机械分会. 第七届中国风电后市场交流合作大会, 中国江苏无锡, 2020-8-26. 北京: 中国农机工业协会风力机械分会, 2020: 81-86. DOI: 10.26914/c.cnkihy.2020.011484. Y K, J T T, ZHU J K, et al. Research on the actual wake of large scale wind farm and algorithms [C]// China Agricultural Machinery Industry Association Wind Machinery Branch. The 7th China Wind Power Aftermarket Exchange and Cooperation Conference, Wuxi, Jiangsu, China, 2020-8-26. Beijing: China Agricultural Machinery Industry Association Wind Machinery Branch, 2020: 81-86. DOI: 10.26914/c.cnkihy.2020.011484.
[11]	MITTELMEIER N, et al. An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects [J]. Wind Energy Science, 2017(2): 477-490. DOI: 10.5194/wes-2-477-2017.
[12]	许帅, 邓智文, 汪健文, 等. 偏航工况下风电机组尾流模型与风电场尾流叠加研究 [J]. 能源研究与利用, 2015(4): 27-30+49. DOI: 10.16404/j.cnki.issn1001-5523.2018.04.011. XU S, DENG Z W, WANG J W, et al. Study on wind turbine wake model and wake superposition under the yaw condition [J]. Energy Research & Utilization, 2015(4): 27-30+49. DOI: 10.16404/j.cnki.issn1001-5523.2018.04.011.
[13]	杨志超, 高丙团, 叶飞, 等. 基于尾流效应的风电场最大出力优化控制 [J]. 电力建设, 2017, 38(4): 96-102. DOI: 10.3969/j.issn.1000-7229.2017.04.013. YANG Z C, GAO B T, YE F, et al. Maximum output optimal control of wind farm based on wake effect [J]. Electric Power Construction, 2017, 38(4): 96-102. DOI: 10.3969/j.issn.1000-7229.2017.04.013.
[14]	MITTELMEIER N, BLODAU T, KÜHN M. Monitoring offshore wind farm power performance with SCADA data and an advanced wake model [J]. Wind Energy Science, 2017, 2(1): 175-187. DOI: 10.5194/wes-2-175-2017,2017.
[15]	李岩, 罗辛·费伊, 白雪. 基于运营风机scada数据的风电场尾流损失测量方法: CN106321368B [P]. 2019-02-15. LI Y, ROSIN F, BAI X. The method of measuring wake loss in wind farm based on scada data of operating wind turbines: CN106321368B [P]. 2019-02-15.

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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0. 引言

随着全球范围内的风电开发，中国海上风电规模已经跃居第二，而“平价”市场条件下，海上风电开发面临严峻挑战，发电量是影响风电场经济收益的重要指标之一。海上风电较陆上开发规模更大、更集中，同时不受地形干扰，风能资源分布较均匀，风电机组的布置在空间上一般呈现较为规则的几何形态^[1-2]，多台、多排风机尾流相互叠加影响、风速恢复缓慢，造成尾流损失加重。目前针对海上风电场尾流模型的研究，国内外学者已做了大量的研究工作，1982年RisΦ实验室提出的Jensen模型是基于贝茨极限理论和质量守恒定律提出的，适用于平坦地形的尾流模型^[3]；相关研究^[4]表明风机完全处于尾流区运行时，功率损失可达30%～40%；刘沙等^[5]利用华南某海上风电场实际运行SCADA数据，对Jensen/Park模型及其参数设置进行验证，表明Park尾流模型能够较好地模拟近海风场尾流损失，模型参数选择需根据实际项目进行敏感性测算；温建民等^[6]使用激光雷达对陆上某风场单台风机进行尾流观测，分析不同来流风速下尾流区风速恢复速率，为风电场排布优化和发电量提升奠定基础。

如何控制和降低大型海上风电场的尾流电量损失是海上风场评估的关键问题之一，而相邻风电场区间的相互影响作用也是不可忽视的，李岩等^[7]为提升大型海上风电场经济效益，提出一种规则型排布优化策略，以提升大型海上风电场的经济效益；郑建才等^[8]研究不同尾流叠加模型对尾流场模拟精度，为大型风电场尾流叠加形式的选取提供建议；而目前大多海上风场的尾流损失评估主要是基于模型仿真结果，周边相邻风电场之间实际的相互影响与场群间“尾流效应”研究相对比较少，本文基于实际运行风电场SCADA数据分析相邻风电场之间的相互影响造成的真实尾流损失情况，探讨海上相邻风电场间的尾流效应与缓冲带的作用，为后续海上大型风电场项目规划和风场优化排布提供参考。

3. 结论

本文利用海上大型风电场风电机组实际运行SCADA数据结合激光雷达同期实测风数据，基于不同的风向扇区条件和风电场机组实际排布进行尾流效应场景分类，进而开展实际运行相邻风电场间的真实“尾流效应”分析工作。结论如下：

1）对于规则排布的海上大型风电场，基于实际运行SCADA数据选取参照基准机组，对各机组发电量进行归一化，可以较好地反映海上风能资源分布特征及各机组发电能力的差异。

2）单一、高度集中的扇区条件下，相邻风电场处于下风向的场区受 “尾流效应”的影响，发电产能降幅较自由流降幅较大，对于风向高度集中的中低风速区域，需着重考虑周边相邻风电场带来的尾流损失影响。

3）相邻风场间随着缓冲带距离的增加，下风向场区机组尾流电量衰减比随之降低，缓冲带需达到一定的距离，对于风速的恢复有明显的作用，发电产能才能够有所提升，本案例单一扇区条件下，23D～44D缓冲带距离下，尾流损失比在27%～4%之间；缓冲带距离是场群“尾流效应”影响因素之一，距离越大越有利于降低尾流损失，但与周边相邻风电场项目容量、机组数量、机型、排布方案、风向频率分布等多因素相关，需综合考虑。

当然不同区域的风电场如气候条件、风速、风向、风频分布等存在差异，上述研究结论仅代表该风电场项目案例特征，后续可以收集更多不同区域海上风电场项目进行进一步分析与总结。

Reference (15)

[1]	ASTARIZ S, IGLESIAS G. Enhancing wave energy competitiveness through co-located wind and wave energy farms. a review on the shadow effect [J]. Energies, 2015(8): 7344-7366. DOI: 10.3390/en8077344.
[2]	HASAGER C B, VINCENT P, BADGER J, et al. Using satellite SAR to characterize the wind flow around offshore wind farms [J]. Energies, 2015(8): 5413-5439. DOI: 10.3390/en8065413.
[3]	FRANDSEN S, BARTHELMIE R, PRYOR S, et al. Analytical modelling of wind speed deficit in large offshore: wind farms [J]. Wind Energy, 2006, 9(1): 39-53. DOI: 10.1002/we.189.
[4]	陈树勇, 戴慧珠, 白晓民, 等. 尾流效应对风电场输出功率的影响 [J]. 中国电力, 1998, 31(11): 28-31.	CHEN S Y, DAI H Z, BAI X M, et al. Impact of wind turbine wake on wind power output [J]. Electric Power, 1998, 31(11): 28-31.
[5]	刘沙, 王中权, 蔡彦枫. 海上风电场运行期尾流损失分析 [J]. 南方能源建设, 2019, 6(1): 66-70. DOI: 10.16516/j.gedi.issn2095-8676.2019.01.011.	LIU S, WANG Z Q, CAI Y F. Wake loss analysis of offshore wind farm in operation [J]. Southern Energy Construction, 2019, 6(1): 66-70. DOI: 10.16516/j.gedi.issn2095-8676.2019.01.011.
[6]	温建民, 张新旺, 陈锋, 等. 基于风电场实测的风机尾流特征分析 [J]. 机械工程与自动化, 2021(1): 3-6. DOI: 10.3969/j.issn.1672-6413.2021.01.002.	WEN J M, ZHANG X W, CHEN F, et al. Analysis of wind turbine wake characteristics based on wind farm field measurement [J]. Mechanical Engineering & Automation, 2021(1): 3-6. DOI: 10.3969/j.issn.1672-6413.2021.01.002.
[7]	李岩, 吴迪, 洪畅, 等. 大型海上风电场风机排布优化策略研究 [J]. 太阳能, 2020(2): 67-74. DOI: 10.3969/j.issn.1003-0417.2020.02.011.	LI Y, WU D, HONG C, et al. Optimization of wind turbine layout in large-scale offshore wind farm [J]. Solar Enengy, 2020(2): 67-74. DOI: 10.3969/j.issn.1003-0417.2020.02.011.
[8]	郑建才, 万德成, 王尼娜, 等. 基于尾流叠加模型的风电场数值模拟 [C]// 《水动力学研究与进展》编委会, 中国力学学会流体力学专业委员会水动力学专业组, 集美大学, 等. 第三十一届全国水动力学研讨会, 中国福建厦门, 2020-10-30. 北京: 海洋出版社, 2020: 833-847. DOI: 10.26914/c.cnkihy.2020.037043.	ZHEN J C, WAN D C, WANG N, et al. Numerical solution of wind farm wake based on wake interaction model [C]// Editorial Board of Research and Progress in Hydrodynamics, Hydrodynamics Professional Group of Hydromechanics Committee of Chinese Society of Mechanics, Jimei University, et al. Thirty-first National Symposium on Hydrodynamics, Xiamen, Fujian, China, 2020-10-30. Beijing: China Ocean Press, 2020: 833-847. DOI: 10.26914/c.cnkihy.2020.037043.
[9]	TC88. Wind turbines-part 12-1: power performance measurements of electricity producing wind turbines: IEC 61400-12-1 [S]. Genève: International Electrotechnical Commission, 2005.
[10]	杨昆, 姜婷婷, 朱金奎, 等. 大规模风电场实际尾流分析与算法研究 [C]// 中国农机工业协会风力机械分会. 第七届中国风电后市场交流合作大会, 中国江苏无锡, 2020-8-26. 北京: 中国农机工业协会风力机械分会, 2020: 81-86. DOI: 10.26914/c.cnkihy.2020.011484.	Y K, J T T, ZHU J K, et al. Research on the actual wake of large scale wind farm and algorithms [C]// China Agricultural Machinery Industry Association Wind Machinery Branch. The 7th China Wind Power Aftermarket Exchange and Cooperation Conference, Wuxi, Jiangsu, China, 2020-8-26. Beijing: China Agricultural Machinery Industry Association Wind Machinery Branch, 2020: 81-86. DOI: 10.26914/c.cnkihy.2020.011484.
[11]	MITTELMEIER N, et al. An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects [J]. Wind Energy Science, 2017(2): 477-490. DOI: 10.5194/wes-2-477-2017.
[12]	许帅, 邓智文, 汪健文, 等. 偏航工况下风电机组尾流模型与风电场尾流叠加研究 [J]. 能源研究与利用, 2015(4): 27-30+49. DOI: 10.16404/j.cnki.issn1001-5523.2018.04.011.	XU S, DENG Z W, WANG J W, et al. Study on wind turbine wake model and wake superposition under the yaw condition [J]. Energy Research & Utilization, 2015(4): 27-30+49. DOI: 10.16404/j.cnki.issn1001-5523.2018.04.011.
[13]	杨志超, 高丙团, 叶飞, 等. 基于尾流效应的风电场最大出力优化控制 [J]. 电力建设, 2017, 38(4): 96-102. DOI: 10.3969/j.issn.1000-7229.2017.04.013.	YANG Z C, GAO B T, YE F, et al. Maximum output optimal control of wind farm based on wake effect [J]. Electric Power Construction, 2017, 38(4): 96-102. DOI: 10.3969/j.issn.1000-7229.2017.04.013.
[14]	MITTELMEIER N, BLODAU T, KÜHN M. Monitoring offshore wind farm power performance with SCADA data and an advanced wake model [J]. Wind Energy Science, 2017, 2(1): 175-187. DOI: 10.5194/wes-2-175-2017,2017.
[15]	李岩, 罗辛·费伊, 白雪. 基于运营风机scada数据的风电场尾流损失测量方法: CN106321368B [P]. 2019-02-15.	LI Y, ROSIN F, BAI X. The method of measuring wake loss in wind farm based on scada data of operating wind turbines: CN106321368B [P]. 2019-02-15.

场景	扇区筛选	上风向风场	下风向风场	缓冲带距离/km	影响范围	说明
场景1	60°～75°	H7北区	H7南区	5.64 (43.4D)	半影响	北区处于上风向，南区场区部分风机lin5(B43～B47)处于下风向
场景2	30°～45°	H7北区	H7南区	4.3 (33.3D)	全影响	北区处于上风向，南区场区所有机组均处于下风向
场景3	345°～360°	H11	H7	4.3 (33D) &4.05 (31D)	半影响	H7北区部分风机lin1(B1～B8)处于下风向 H7南区部分风机lin5(B51～B55)处于H11和H7北区的下风向
场景3	345°～360°	H12	H7南区	3.05 km(23.4D)	半影响	H7南区部分机组lin5(B43～B47)处于下风向

Case Study of "Wake Effect" of Adjacent Offshore Wind Farms

doi: 10.16516/j.gedi.issn2095-8676.2023.01.003

Abstract

References

通讯作者: 陈斌, bchen63@163.com

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