Impact of utility-scale solar photovoltaic array on the aeolian sediment transport in Hobq Desert, China

doi:10.1007/s40333-021-0096-y

Journal of Arid Land

2021, Vol. 13

Issue (3): 274-289 DOI: 10.1007/s40333-021-0096-y CSTR: 32276.14.s40333-021-0096-y

Research article

Impact of utility-scale solar photovoltaic array on the aeolian sediment transport in Hobq Desert, China

TANG Guodong, MENG Zhongju, GAO Yong^*(

), DANG Xiaohong

College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

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Abstract

Deserts are ideal places to develop ground-mounted large-scale solar photovoltaic (PV) power station. Unfortunately, solar energy production, operation, and maintenance are affected by geomorphological changes caused by surface erosion that may occur after the construction of the solar PV power station. In order to avoid damage to a solar PV power station in sandy areas, it is necessary to investigate the characteristics of wind-sand movement under the interference of solar PV array. The study was undertaken by measuring sediment transport of different wind directions above shifting dunes and three observation sites around the PV panels in the Hobq Desert, China. The results showed that the two-parameter exponential function provides better fit for the measured flux density profiles to the near-surface of solar PV array. However, the saltation height of sand particles changes with the intersection angle between the solar PV array and wind direction exceed 45°. The sediment transport rate above shifting dunes was always the greatest, while that around the test PV panels varied accordingly to the wind direction. Moreover, the aeolian sediment transport on the solar PV array was significantly affected by wind direction. The value of sand inhibition rate ranged from 35.46% to 88.51% at different wind directions. When the intersection angle exceeds 45°, the mean value of sediment transport rate above the solar PV array reduces to 82.58% compared with the shifting dunes. The results of our study expand our understanding of the formation and evolution of aeolian geomorphology at the solar PV footprint. This will facilitate the design and control engineering plans for solar PV array in sandy areas that operate according to the wind regime.

Key words： aeolian sediment transport mass flux density profiles sand-fixation shelter efficacy solar photovoltaic array

Received: 17 August 2020 Published: 10 March 2021

Corresponding Authors:

About author: * GAO Yong (E-mail: 13948815709@163.com)
First author contact:

TANG Guodong and MENG Zhongju contributed equally to this work.

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Cite this article:

TANG Guodong, MENG Zhongju, GAO Yong, DANG Xiaohong. Impact of utility-scale solar photovoltaic array on the aeolian sediment transport in Hobq Desert, China. Journal of Arid Land, 2021, 13(3): 274-289.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0096-y OR http://jal.xjegi.com/Y2021/V13/I3/274

fig. 1 Location of the study area (a) and its satellite image (b)

fig. 2 Meteorological data for period 1980-2018 in Yikewusu Meteorological Station in Hangjin Banner, Ordos City, Inner Mongolia Autonomous Region, China. The grey area in the figure was the best time to investigate the aeolian sand movement in the field due to the frequent occurrence of strong wind, extended drought and limited rain.

fig. 3 Wind speed and direction corresponding to sand transport events. u represents the free-stream wind velocity at 2 m above the shifting dunes. A-H represent the eight prevailing wind directions in Table 1, respectively.

Table 1 field observation data

fig. 4 Surface morphology of the test PV (photovoltaic) panels before the experiment (a), surface morphology after smoothening the surface surrounding the test PV panel (b and c), configuration diagrams of specific measurement instrument (d), and illustration of the test sand sampler (e)

fig. 5 Variation with height in the observed aeolian sediment flux above the shifting dunes and three observation sites of solar PV array at eight wind directions. Note that the x-axis scales differ between observation periods.

WD	OS	Equation 1 ($q(z)=a1{{z}^{-b1}}$)						Equation 2 ($q(z)=a2{{e}^{-z/b2}}$)					Equation 3 ($q(z)=c3+a3{{e}^{-z/b3}}$)
WD	OS	a₁	b₁		R²			a₂		b₂		R²	a₃×100	b₃		c₃		R²
A	CK	1.9360		0.8830		0.9107	2.3689		3.9293		0.9989		0.3580		0.4220		0.7741		0.9989
	TP	1.0245		0.9162		0.9153	1.2771		3.7037		0.9993		-0.2280		0.7833		0.7650		0.9993
	FP	1.0489		0.8385		0.9003	1.2385		4.3745		0.9993		0.0220		0.8074		0.7955		0.9993
	HP	0.2679		0.9632		0.9427	0.3501		3.2383		0.9979		0.3080		2.8458		0.7253		0.9991
B	CK	1.2336		0.9354		0.9185	1.5671		3.5273		0.9985		-0.0578		0.6382		0.7535		0.9985
	TP	0.7061		0.8522		0.9036	0.8462		4.1982		0.9984		0.2040		1.1813		0.7861		0.9984
	FP	0.3265		0.9200		0.9072	0.4131		3.6049		0.9945		-0.0110		2.4207		0.7580		0.9945
	HP	0.3567		0.9568		0.9302	0.4632		3.3179		0.9976		0.2100		2.1556		0.7354		0.9979
C	CK	1.3176		0.9585		0.9524	1.6331		3.4843		0.9723		1.5740		0.6079		0.7397		0.9735
	TP	0.8222		0.7891		0.8799	0.9451		4.8709		0.9982		-0.0484		1.0581		0.8147		0.9982
	FP	0.5839		0.9114		0.9092	0.7291		3.7175		0.9983		-0.1450		1.3721		0.7659		0.9983
	HP	0.5561		0.8788		0.8804	0.6866		3.9526		0.9929		-0.3620		1.4569		0.7805		0.9932
D	CK	0.5498		0.9682		0.9283	0.7105		3.3367		0.9997		-0.1200		1.4083		0.7427		0.9997
	TP	0.3988		0.8776		0.9002	0.4862		4.0000		0.9984		-0.1140		2.0572		0.7807		0.9984
	FP	0.3730		0.9657		0.9036	0.4870		3.3311		0.9943		-0.2820		2.0555		0.7459		0.9948
	HP	0.2483		0.9810		0.9219	0.3265		3.2258		0.9981		-0.0715		3.0647		0.7356		0.9982
E	CK	0.5009		0.9389		0.9183	0.6373		3.5112		0.9988		-0.0851		1.5699		0.7534		0.9988
	TP	0.1745		0.8695		0.8984	0.2141		3.9698		0.9943		-0.0787		4.6685		0.7744		0.9944
	FP	0.2527		0.9921		0.9120	0.3370		3.1437		0.9928		-0.1280		2.9709		0.7312		0.9931
	HP	0.1378		0.9704		0.9276	0.1799		3.2680		0.9983		0.0184		5.5556		0.7354		0.9983
F	CK	0.3628		1.2286		0.9778	0.5717		2.1030		0.9970		0.1020		1.7452		0.6187		0.9971
	TP	0.0314		0.8722		0.9000	0.0391		3.8197		0.9848		0.0449		25.4453		0.7598		0.9863
	FP	0.1174		1.1737		0.9642	0.1785		2.2774		0.9990		0.0150		5.5960		0.6435		0.9991
	HP	0.0289		1.0558		0.9836	0.0417		2.5246		0.9860		0.0913		23.4192		0.6407		0.9968
G	CK	0.4869		1.0464		0.9559	0.6722		2.8058		0.9996		0.2750		1.4839		0.6954		0.9999
	TP	0.0680		0.7121		0.9411	0.0732		5.6786		0.9751		0.4300		13.3333		0.7940		0.9980
	FP	0.1465		1.1075		0.9662	0.2148		2.4582		0.9984		0.1470		4.6318		0.6567		0.9994
	HP	0.0530		0.9343		0.9545	0.0681		3.3580		0.9923		0.1240		14.5349		0.7230		0.9970
H	CK	0.6807		1.2092		0.9831	1.0664		2.1231		0.9969		0.4990		0.9317		0.6166		0.9975
	TP	0.0534		0.7944		0.9171	0.0627		4.4783		0.9796		0.2050		15.7978		0.7727		0.9898
	FP	0.0980		1.1354		0.9597	0.1445		2.4307		0.9994		0.0066		6.9204		0.6621		0.9994
	HP	0.0348		1.0271		0.9586	0.0475		2.8727		0.9968		0.0386		20.9205		0.6964		0.9979

Table 2 Results of the regression analyses for the mass-flux-density profiles above shifting dunes and three observation sites of solar PV array at eight prevailing wind directions calculated using Equations 1-3

fig. 6 Relationship between regression coefficient a and b of the two-parameter exponential function (Equation 2) for the mass-flux-density profiles and the observed wind direction and wind speed

fig. 7 Sediment transport rate (V) at eight prevailing wind directions above the shifting dunes and three observation sites of solar PV array. CK, shifting dunes; TP, between panels; FP, before panels; and HP, behind panels.

fig. 8 Cumulative percentage of sand transported above the shifting dunes and three observation sites of solar PV array at eight prevailing wind directions

fig. 9 Sand inhibition rate above the shifting dunes and three observation sites of solar PV array at eight prevailing wind directions

fig. 10 Relationship above shifting dunes between regression coefficients a and b in Equation 2 and the wind speed

Table 3 Relative acceleration rate (R_u) of wind speed at different heights above the surface in the solar PV array

fig. 11 Relationship between the sand inhibition rate (R_q) and the relative acceleration rate (R_u) at heights of 20, 50, 100 and 200 cm above the surface of solar PV array calculated using Equations 4 and 5, and the result of linear correlation between them.


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