考虑实际工程需求,开发一种几何约束条件下海上风电场智能布局优化方法。该方法使用Gaussian模型计算风力机尾流区的速度亏损,并以最大化风电场年发电量为目标采用差分进化算法进行优化,可满足海上风电场布局时的各类几何约束。利用该方法分别在3行、4行、7行几何约束下对中国某海上风电场的风力机排布方式进行优化。结果显示,相比于原始布局方案,在考虑海缆铺设成本增加的情况下布局优化方案可提升风电场年发电量2.13%~2.64%。进一步分析表明,布局优化过程中可行解数量的设置需综合考虑智能算法寻优难度的影响。
Abstract
Considering the actual engineering requirements, this paper proposes an intelligent offshore-wind-farm layout optimization approach under geometrical constraints. The proposed approach adopts the Gaussian wake model to calculate the velocity deficit, and maximizes the wind-farm annual energy production （AEP） with a differential evolution algorithm, which can meet various geometrical constraints in the offshore-wind-farm layout optimization. Then this approach is used to optimize the wind turbine layout for a practical offshore wind farm under 3-, 4-, 7-rows geometrical constraints. The optimized results indicate that compared with the original layout scheme, the layout optimization scheme can increase the AEP of the wind farm by 2.13%-2.64% when considering the increase of cable laying cost. Further analysis suggests that the setup of potential solution number should consider the difficulty of getting the optimized result for this intelligent method.
关键词
海上风电场 /
风力机 /
布局 /
差分进化算法 /
几何约束
Key words
offshore wind farms /
wind turbines /
layout /
differential evolution algorithm /
geometrical constraint
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] PORTÉ-AGL F, BASTANKHAH M, SHAMSODDIN S.Wind-turbine and wind-farm flows: a review[J]. Boundary-layer meteorology, 2020, 174(1): 1-59.
[2] CHENG Y, ZHANG M M, ZHANG Z L, et al.A new analytical model for wind turbine wakes based on Monin-Obukhov similarity theory[J]. Applied energy, 2019, 239: 96-106.
[3] GAO X X, WANG T Y, LI B B, et al.Investigation of wind turbine performance coupling wake and topography effects based on LiDAR measurements and SCADA data[J]. Applied energy, 2019, 255: 113816.
[4] HERBERT-ACERO J F, PROBST O, RÉTHORÉ P E, et al. A review of methodological approaches for the design and optimization of wind farms[J]. Energies, 2014, 7(11): 6930-7016.
[5] GONZÁLEZ J S, PAYÁN M B, SANTOS J M R, et al. A review and recent developments in the optimal wind-turbine micro-siting problem[J]. Renewable and sustainable energy reviews, 2014, 30: 133-144.
[6] 杨祥生, 赵宁, 田琳琳, 等. 基于Park-Gauss模型考虑大气稳定性对布局优化的影响[J]. 太阳能学报, 2021, 42(3): 43-47.
YANG X S, ZHAO N, TIAN L L, et al.Influence of atmospheric stability on layout optimization based on Park-Gauss model[J]. Acta energiae solaris sinica, 2021, 42(3): 43-47.
[7] MOSETTI G, POLONI C, DIVIACCO B.Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm[J]. Journal of wind engineering and industrial aerodynamics, 1994, 51(1): 105-116.
[8] MITTAL A.Optimization of the layout of large wind farms using a genetic algorithm[D]. Cleveland: Case Western Reserve University, 2010.
[9] GRADY S A, HUSSAINI M Y, ABDULLAH M M.Placement of wind turbines using genetic algorithms[J]. Renewable energy, 2005, 30(2): 259-270.
[10] GUO N Z, ZHANG M M, LI B, et al.Influence of atmospheric stability on wind farm layout optimization based on an improved Gaussian wake model[J]. Journal of wind engineering and industrial aerodynamics, 2021, 211: 104548.
[11] TAO S Y, KUENZEL S, XU Q S, et al.Optimal micro-siting of wind turbines in an offshore wind farm using Frandsen-Gaussian wake model[J]. IEEE transactions on power systems, 2019, 34(6): 4944-4954.
[12] ELKINTON C N, MANWELL J F, MCGOWAN J G.Algorithms for offshore wind farm layout optimization[J]. Wind engineering, 2008, 32(1): 67-84.
[13] RODRIGUES S, BAUER P, BOSMAN P A N. Multi-objective optimization of wind farm layouts-complexity, constraint handling and scalability[J]. Renewable and sustainable energy reviews, 2016, 65: 587-609.
[14] BASTANKHAH M, PORTÉ-AGEL F.A new analytical model for wind-turbine wakes[J]. Renewable energy, 2014, 70: 116-123.
[15] ISHIHARA T, QIAN G W.A new Gaussian-based analytical wake model for wind turbines considering ambient turbulence intensities and thrust coefficient effects[J]. Journal of wind engineering and industrial aerodynamics, 2018, 177: 275-292.
[16] GE M W, WU Y, LIU Y Q, et al.A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes[J]. Applied energy, 2019, 233: 975-984.
[17] FUERTES F C, MARKFORT C D, PORTÉ-AGEL F.Wind turbine wake characterization with nacelle-mounted wind lidars for analytical wake model validation[J]. Remote sensing, 2018, 10(5): 668.
[18] PRICE K, STORN R M, LAMPINEN J A.Differential evolution: a practical approach to global optimization[M]. Springer Science & Business Media, 2006.
[19] WANG Y, LIU H, LONG H, et al.Differential evolution with a new encoding mechanism for optimizing wind farm layout[J]. IEEE transactions on industrial informatics, 2017, 14(3): 1040-1054.
基金
中国长江三峡集团有限公司科研项目（WWKY-2020-0703; WWKY-2020-0015）
PDF(2006 KB)
