代理模型辅助的复杂地形风电场微观选址多目标优化设计

doi:10.16183/j.cnki.jsjtu.2023.486

摘要/Abstract

摘要：

为解决复杂地形下风电场微观选址优化难度大、耗时长的问题,提出一种基于代理模型辅助的复杂地形风电场微观选址多目标优化方法.首先,考虑复杂地形起伏大的地理特征,计算崎岖度指数,将地面平整度进行数值量化,并对崎岖度过大的点进行约束处理;其次,建立三维多风向下尾流叠加计算发电量的数学模型,构建三维地形集电线路拓扑优化代理模型,检验代理模型的预测精度,表明可替代集电线路拓扑优化的大量计算,有效提高计算效率,然后采用多目标离散状态转移算法,获得复杂地形微观选址多目标优化设计的Pareto前沿;最后,以中国新疆某实际复杂地形风电场为例,实现复杂地形风电场多目标微观选址,并将该算法与单目标优化结果进行比较.仿真结果表明,在考虑复杂地形因素特点的情况下,代理模型辅助的多目标离散状态转移算法能在优化年发电量的前提下减少总电缆铺设长度,降低工程建设投资,为工程实际提供更多布局方案.

关键词: 风电场, 微观选址, 复杂地形, 多目标优化算法, 崎岖度指数, 代理模型

Abstract:

To tackle the challenges of high difficulty and time-consuming micro-site optimization of wind farms in complex terrains, a multi-objective optimization method for micro-site selection is proposed, assisted by proxy model. First, considering the geographical features of complex terrains with significent undulations, the ruggedness index is calculated and the ground flatness is numerically quantified, constraining the points with excessive ruggedness. Then, a mathematical model for three-dimensional windy downward wake superposition calculation of power generation is established, a three-dimensional terrain collector line topology optimization agent model is constructed, and the prediction accuracy of the proxy model is verified, demonstrating the ability to replace numerous calculations in collector line topology optimization and effectively improving the computing efficiency. Finally, taking a real complex terrain wind farm in Xinjiang Uygur Autonomous Region, China as an example, multi-objective micro-site selection of complex terrain wind farm is realized, and the results are compared with those obtained through the single-objective optimization. The simulation results show that the multi-objective discrete state transfer algorithm assisted by the proxy model can reduce the total cable laying length, decrease the construction costs, and provide more feasible layout schemes while optimizing the annual power generation.

Key words: wind farms, microscopic situation, complex terrain, multi-objective optimization algorithm, terrain ruggedness index (TRI), proxy model

中图分类号:

TK81

刘佳惠, 王聪, 张宏立, 马萍, 李新凯, 董颖超. 代理模型辅助的复杂地形风电场微观选址多目标优化设计[J]. 上海交通大学学报, 2025, 59(9): 1315-1326.

LIU Jiahui, WANG Cong, ZHANG Hongli, MA Ping, LI Xinkai, DONG Yingchao. Multi-Objective Optimization Design of Micro-Site Selection of Complex Terrain Wind Farms Assisted by Proxy Model[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1315-1326.

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参考文献 21

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总样本比例区间	训练样本数量
0.7N	0.85S_tot
0.7N~0.8N	S_ave
0.8N~0.85N	0.85S_tot
0.85N~0.95N	S_ave
0.95N~N	S_ave

θ/(°)	k(θ)	c(θ)	θ/(°)
348.75~11.25	1.408 0	3.443 4	168.75~191.25	1.680 8	4.373 5
11.25~33.75	1.487 7	4.565 9	191.25~213.75	2.068 9	5.402 1
33.75~56.25	1.889 4	7.708 3	213.75~236.25	2.075 9	7.570 5
56.25~78.75	1.938 7	9.582 5	236.25~258.75	2.148 1	9.534 0
78.75~101.25	1.650 7	8.176 7	258.75~281.25	2.156 2	9.634 1
101.25~123.75	1.633 6	5.648 9	281.25~303.75	1.783 7	9.853 7
123.75~146.25	1.296 1	5.513 1	303.75~326.25	1.571 9	7.066 4
146.25~168.75	1.360 9	5.789 3	326.25~348.75	1.497 2	3.481 6

参数	取值
轮毂高度/m	110
叶轮直径/m	191
切入风速/(m·s^-1)	2.5
切出风速/(m·s^-1)	20
额定风速/(m·s^-1)	8.9

DSTA	DMOSTA
S_e=10	S'_e=5
M_xit=50	G_ner=20
m_a=1 000	T=5
m_b=1	N=75
m_c=1	m_a=1 000
	m_b=1
	m_c=1

方案	A_EP/(MW·h)	L/m
01	359 385.69	183 673.16
02	359 403.80	196 050.94
03	359 295.61	166 408.48
04	358 277.44	117 050.69
05	359 174.25	152 501.29
06	358 482.79	122 395.84
07	359 093.88	135 843.85
08	358 503.18	128 507.61
09	359 279.34	158 308.29
10	359 316.02	181 074.42