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Frontiers of Earth Science

Front. Earth Sci.    2018, Vol. 12 Issue (2) : 264-279     https://doi.org/10.1007/s11707-017-0663-y
RESEARCH ARTICLE |
Mapping of wind energy potential over the Gobi Desert in Northwest China based on multiple sources of data
Li LI1,2,3, Xinyuan WANG1,3(), Lei LUO1,2,3, Yanchuang ZHAO1,2,3, Xin ZONG1,2,3, Nabil BACHAGHA1,2,3
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. International Centre on Space Technologies for Natural and Cultural Heritage, Beijing 100094, China
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Abstract

In recent years, wind energy has been a fast-growing alternative source of electrical power due to its sustainability. In this paper, the wind energy potential over the Gobi Desert in Northwest China is assessed at the patch scale using geographic information systems (GIS). Data on land cover, topography, and administrative boundaries and 11 years (2000–2010) of wind speed measurements were collected and used to map and estimate the region’s wind energy potential. Based on the results, it was found that continuous regions of geographical potential (GeoP) are located in the middle of the research area (RA), with scattered areas of similar GeoP found in other regions. The results also show that the technical potential (TecP) levels are about 1.72–2.67 times (2.20 times on average) higher than the actual levels. It was found that the GeoP patches can be divided into four classes: unsuitable regions, suitable regions, more suitable regions, and the most suitable regions. The GeoP estimation shows that 0.41 billion kW of wind energy are potentially available in the RA. The suitable regions account for 25.49%, the more suitable regions 24.45%, and the most suitable regions for more than half of the RA. It is also shown that Xinjiang and Gansu are more suitable for wind power development than Ningxia.

Keywords wind energy      alternative energy      sustainability      Northwest China      Gobi Desert     
Corresponding Authors: Xinyuan WANG   
Just Accepted Date: 25 September 2017   Online First Date: 31 October 2017    Issue Date: 09 May 2018
 Cite this article:   
Li LI,Xinyuan WANG,Lei LUO, et al. Mapping of wind energy potential over the Gobi Desert in Northwest China based on multiple sources of data[J]. Front. Earth Sci., 2018, 12(2): 264-279.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-017-0663-y
http://journal.hep.com.cn/fesci/EN/Y2018/V12/I2/264
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Li LI
Xinyuan WANG
Lei LUO
Yanchuang ZHAO
Xin ZONG
Nabil BACHAGHA
Fig.1  Research area (RA).
Fig.2  Flow chart showing the process for mapping and estimating the wind energy potential.
Topography Wind speed over flat land (m/s)
<6 ≥6
Stable layer Unstable layer Stable layer Unstable layer
Flat land 1 1 1 1
Mountains and hills
1. Hilltop Dha)>50 m 1.4–1.5 1.6–1.8 1.2–1.3 1.4–1.5
2. Gradient:
0–15°,
windward slope
Upper
Dh∈(30–50 m]
1.2–1.3 1.4–1.6 1.1–1.2 1.3–1.5
Middle
Dh∈(10–30 m]
1.0–1.1 1.0–1.1 1.0–1.1 1.1–1.2
Lower
Dh≤10 m
1.0 0.8–0.9 0.9–1.0 1.0
3. Gradient:
0–15°,
Windward to downwind transition slope
Upper
Dh∈(30–50 m]
1.15–1.25 1.35–1.5 1.05–1.15 1.25–1.4
Middle
Dh∈(10–30 m]
0.95–1.05 1.0–1.1 0.9–1.0 1.0–1.1
Lower
Dh≤10 m
0.9–0.95 0.85–0.95 0.8–0.9 0.9–0.95
4. Gradient:
0–15°,
Downwind slope
Upper
Dh∈(30–50 m]
1.1–1.2 1.3–1.4 1.0–1.1 1.2–1.3
Middle
Dh∈(10–30 m]
0.9–1.0 1.0–1.1 0.8–0.9 0.9–1.0
Lower
Dh≤10 m
0.8–0.9 0.9–1.0 0.7–0.8 0.8–0.9
5. Gradient:
0–15°,
Leeward to downwind transition slope
Upper
Dh∈(30–50 m]
0.95–1.05 1.05–1.15 0.85–0.95 0.95–1.05
Middle
Dh∈(10–30 m]
0.85–0.95 0.95–1.05 0.8–0.85 0.9–0.95
Lower
Dh≤10 m
0.75–0.85 0.85–0.95 0.7–0.8 0.8–0.9
6. Gradient:
0–15°,
Leeward slope
Upper
Dh∈(30–50 m]
0.8–0.9 0.8–0.9 0.7–0.8 0.7–0.8
Middle
Dh∈(10–30 m]
0.8–0.9 0.9–1.0 0.8–0.9 0.9–1.0
Lower
Dh≤10 m
0.7–0.8 0.8–0.9 0.7–0.8 0.8–0.9
Tab.1  Ratio of wind speed in different underlying terrain to flat land
Region Elevation range/m Average elevation/m Position name Elevation range/m
Region 1 0–500 367 Hilltop (417,500)
Upper (397,417]
Middle (377,397]
Lower (367,377]
Flat land [0, 367]
Region 2 500–1000 813 Hilltop (863,1000)
Upper (843,863]
Middle (823,843]
Lower (813,823]
Flat land [500, 813]
Region 3 1000–1500 1221 Hilltop (1271,1500]
Upper (1251,1271]
Middle (1231,1251]
Lower (1221,1231]
Flat land [1000, 1221]
Tab.2  Regional divisions based on DEM
Fig.3  (a) Map of RA divisions; (b) Different hill position names.
Fig.4  Division of aspect into different categories according to the direction of the prevailing wind.
Fig.5  Comparison of the simulated data and measured data. The solid line= real measurement data, the dashed line= Kriging interpolation data, dotted line= simulation data (adjusting the interpolation data based on DEM and wind direction according to the model in the paper).
Fig.6  The RA in the Gobi Desert, Northwest China, classified according to the GeoP. The blue circles are the real locations of the wind plants.
Fig.7  Location and layout of the validation sites (wind turbine power plants) as viewed in Google Earth. Region A: Ala Shan Kou wind farm, Xinjiang; Region B: Xiaocaohu wind farm, Turpan, Xinjiang; Region C: Shisanjianfang wind farm, Hami, Xinjiang.
Fig.8  Detailed view of Ala Shan Kou Wind Farm (Hub height: 50 m) (Region A).
Fig.9  Detailed view of Xiaocaohu Wind Farm (Hub height: 60 m) (Region B).
Fig.10  Detailed view of Shisanjianfang Wind Farm (Hub height: 50 m) (Region C).
Wind power potential/(billion kWh·yr–1)
(for a wind power conversion factor of 30%)
Suitable region More suitable region Most suitable region Total wind power potential
Xinjiang 27.15 32.03 94.19 153.37
Gansu 16.70 16.56 56.25 89.51
Ningxia 1.04 0.00 0.01 1.05
Total 44.89 48.59 150.44 243.92
Tab.3  Year mean TecP from 2000 to 2010
Fig.11  Amount of TecP in each county in the RA. (a) Gansu Province: 1 northern Subei Mongolian Autonomous County, 2 Guazhou, 3 Dunhuang, 4 Jinta, 5 Yumen, 6 southern Subei Mongolian Autonomous County, 7 Jiuquan, 8 Jiayuguan, 9 Gaotai, 10 Linze, 11 Minqin, 12 Zhangye, 13 Shandan, 14 Yongchang, 15 Jinchang, 16 Wuwei, 17 Gulang, 18 Jingtai, 19 Jingning, 20 Longxi. (b) Ningxia Hui Autonomous Region: 1 Shizuishan, 2 Huinong, 4 Pingluo, 5 Taole, 6 Helan, 7 Yinchuan, 8 Yongning, 9 Lingwu, 10 Qingtongxia, 11 Yanchi, 12 Wuzhong, 13 Zhongning, 14 Zhongwei, 15 Tongxin, 16 Haiyuan, 17 Guyuan, 18 Pengyang, 19 Xiji, 20 Longde, 21 Jingyuan. (c) Xinjiang Uygur Autonomous Region: 1 Buerjin, 2 Habahe, 3 Aletai, 4 Fuhai, 5 Fuyun, 6 Jimunai, 7 Qinghe, 8 Hoboksar Mongolian Autonomous County, 9 Emin, 10 Yumin, 11 Tuoli, 12 Karamay, 13 Qitai, 14 Bole, 15 Wenquan, 16 Wusu, 17 Jinghe, 18 Mori Kazakh Autonomous County, 19 Barkol Kazakh Autonomous County, 20 Fukang, 21 Jimsar, 22 Yiwu, 23 Urumqi, 24 Hami, 25 Turpan, 26 Piqan, 27 Hejing, 28 Toksun, 29 Heshuo, 30 Artux, 31 Wuqia, 32 Shufu, 33 Akto, 34 Ruoqiang.
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