基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析

doi:10.12082/dqxxkx.2018.170393

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (1): 119-127.doi: 10.12082/dqxxkx.2018.170393

基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析

张乾^1,²(), 辛晓洲^1,^*(), 张海龙¹, 李月³, 李小军^1,², 裔传祥^1,⁴

1. 中国科学院遥感与数字地球研究所遥感科学国家重点实验室,北京100101
2. 中国科学院大学,北京 100049
3. 首都师范大学,北京 100048
4. 南京信息工程大学,南京 210044

收稿日期:2017-08-28 修回日期:2017-11-06 发布日期:2018-02-06
作者简介:
作者简介：张乾（1995-）,男,硕士生,主要从事地表能量平衡定量遥感研究。E-mail: zhangqianskd@126.com
基金资助:
国家自然科学基金项目（41371360、41201352）

Suitability Analysis of Photovoltaic Power Plants in China Using Remote Sensing Data and Multi-criteria Evaluation

ZHANG Qian^1,²(), XIN Xiaozhou^1,^*(), ZHANG Hailong¹, LI Yue³, LI Xiaojun^1,², YI Chuanxiang^1,⁴

1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
2. Chinese Academy of Sciences University, Beijing 100049, China
3. Capital Normal University, Beijing 100048, China
4. Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2017-08-28 Revised:2017-11-06 Published:2018-02-06
Contact: XIN Xiaozhou
Supported by:
National Natural Science Foundation of china, No.41371360, 41201352.

摘要/Abstract

摘要：

中国属于太阳能资源丰富的国家之一,光伏装机量位居世界第一,未来其装机量仍会不断增加。本文拟利用遥感技术获取区域太阳能资源的时空分布,采用多因子评价模型对中国地区大型光伏电站区域适宜性进行评估,以期为光伏电站的选址提供科学依据。结合太阳总辐射、日照时数的稳定程度、离路网的距离、离城镇的距离和坡向5个因子,通过设定海拔以及土地覆盖类型对应的限制区域,利用MuSyQ辐射产品、DEM、道路网数据、VIIRS夜间灯光数据、土地覆盖产品得到因子图层,使用层次分析法确定各因子在模型中的权重,借助GIS进行叠加分析并分为“低适宜”、“较适宜”、“适宜”、“非常适宜”和“限制区”5类,得到光伏电站建设的空间适宜性分布。研究结果表明,西北地区的适宜区占全国的53.0%,“非常适宜”区占全国的47.3%,其累计光伏电站装机量占全国的45.6%。建设光伏电站的“适宜”和“非常适宜”区面积的大小与装机量的多少没有明显的线性关系。“非常适宜”区作为光伏电站的最佳建设场所,光伏发电潜力大于2016年全国发电量的5倍。同时,国家政策制定的装机规模指标以及光电补贴政策对光伏电站的选址也起了一定的指向作用。

关键词: 适宜性, 光伏电站, 多因子评价, 层次分析法, 遥感

Abstract:

Solar energy is recognized as one of the most promising new energy sources because it is abundant, clean, and environmentally friendly. Photovoltaic power generation is one of the main ways to use solar energy resources. Site suitability analysis is necessary to be carried out before constructing a photovoltaic plant. China is one of the most abundant countries in solar energy and has the largest installed photovoltaic capacity in the world. In this paper, we use remote sensing technology to obtain the spatial and temporal distribution of solar energy resources, and adopt the multi-criteria evaluation model to evaluate the site suitability of large-scale photovoltaic power plants in China. We provide scientific basis for site selection of photovoltaic power plants. This study took into account five criteria that affect the suitability, total solar radiation, stability of sunshine hours, distance from major roads, distance from major towns and slope direction, setting constraining conditions for elevation and land cover types to define areas that are not allowed, not suitable for construction of photovoltaic power plants. Multi-source data Synergized Quantitative remote sensing production system (MuSyQ) radiation products, DEM, road network data, nighttime light data and land cover maps were used to get the layers. MuSyQ radiation products were used to derive two indicators to assess solar energy resources according to the meteorological industry standard of the People's Republic of China, i.e., the total solar radiation and sunshine hours. Remotely sensed nighttime light data was used to extract densely populated pixels such as towns where electricity is needed. In this paper, we used the analytic hierarchy process to determine the relative importance of each factor in the analysis model. We used GIS overlay analysis to obtain the site suitability result, which was divided into five categories as "low suitable", "moderate", "suitable", "very suitable" and "constrained areas". The results show that the suitable area of Chinese northwestern region accounts for 53.0% of the national suitable area, 47.3% of very suitable area and the cumulative installed photovoltaic capacity in northwestern region accounts for 45.6% of the country. There is no clear linear relationship between the cumulative installed photovoltaic capacity and the size of the suitable area as well as the very suitable area for constructing photovoltaic power plants. East China is economically developed and has a large demand for electricity. Even if its suitable area accounts for 1.9% of the national suitable area, its installed photovoltaic capacity is more than 12 million kilowatts which accounts for 19.2% of that of the country. Very suitable areas are best places for building photovoltaic power plants and the photovoltaic power generation potential is 5 times more than the national power generation in 2016. Moreover, the installation limit and the subsidy policy made by the government have played a certain point to site selection of photovoltaic power plants. Finally, more work is needed to study the relationship between micro factors and suitability model for the sake of gaining a better evaluation of the spatial suitability of photovoltaic power plants.

Key words: suitability, photovoltaic power plants, multi-criteria evaluation, analytical hierarchy process, remote sensing

张乾, 辛晓洲, 张海龙, 李月, 李小军, 裔传祥. 基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析[J]. 地球信息科学学报, 2018, 20(1): 119-127.DOI:10.12082/dqxxkx.2018.170393

ZHANG Qian,XIN Xiaozhou,ZHANG Hailong,LI Yue,LI Xiaojun,YI Chuanxiang. Suitability Analysis of Photovoltaic Power Plants in China Using Remote Sensing Data and Multi-criteria Evaluation[J]. Journal of Geo-information Science, 2018, 20(1): 119-127.DOI:10.12082/dqxxkx.2018.170393

图/表 12

表1

表2

表3

图1

图2

图3

表4

表5

表6

图4

图5

图6

参考文献 21

[1]	沈义. 我国太阳能的空间分布及地区开发利用综合潜力评价[D].兰州:兰州大学,2014.
	[ Sheng Y.The spatial distribution of solar energy and the comprehensive potential evaluation of regional exploitation and utilization in China[D]. Lanzhou: Lanzhou University, 2014. ]
[2]	申彦波. 近20年卫星遥感资料在我国太阳能资源评估中的应用综述[J].气象,2010,36(9):111-115.
	[ Shen Y B.Review of applications of satellite remote sensing data to solar energy resources assessment in China in Recent 20 Years[J]. Meteorological Monthly, 2010,36(9):111-115. ]
[3]	许洪华. 中国光伏发电技术发展研究[J].电网技术,2007,31(20):77-81.
	[ Xu H H.The Study on development of PV technology in China[J]. Power System Technology, 2007,31(20):77-81. ]
[4]	施渊. 光伏并网发电系统选址及电能质量研究[D].南京:东南大学,2016.
	[ Shi Y.Research on location and power quality of the grid-connected photovoltaic power generation system[D]. Nanjing: Southeast University, 2016. ]
[5]	袁小康,谷晓平,王济.中国太阳能资源评估研究进展[J].贵州气象,2011,35(5):1-4.
	[ Yuan X K, Gu X P, Wang J.Research progress of solar energy resources assessment in China[J]. Journal of Guizhou Meteorology, 2011,35(5):1-4. ]
[6]	肖建华,姚正毅,孙家欢.并网太阳能光伏电站选址研究述评[J].中国沙漠,2011,31(6):1598-1605.
	[ Xiao J H, Yao Z Y, Sun J H.Review on optimal site selection for grid-connected solar photovoltaic plants[J]. Journal of Desert Research, 2011,31(6):1598-1605. ]
[7]	赵明智. 槽式太阳能热发电站微观选址的方法研究[D].呼和浩特:内蒙古工业大学,2009.
	[ Zhao M Z.Study on methods of parabolic trough solar thermal power plant site selection[D]. Hohhot: Inner Mongolia University of Technology, 2009. ]
[8]	Effat H A.Selection of potential sites for solar energy farms in ismailia governorate, Egypt using SRTM and multicriteria analysis[J]. International Journal of Advanced Remote Sensing and GIS, 2013,2(1):205-220.
[9]	Charabi Y, Gastli A.PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation[J]. Renewable Energy, 2011,36(9):2554-2561.
[10]	Lurwan S M, Idrees M O, Ahmed G B, et al.GIS-Based optimal site selection for installation of large-scale smart grid-connected photovoltaic (PV) power plants in selangor, Malaysia[J]. American Journal of Applied Sciences, 2017,14(1):174-183.
[11]	Wang S F, Koch B.Determining proﬁts for solar energy with remote sensing data[J]. Energy, 2010,35:2934-2938.
[12]	周传瑞. 哈尔滨地区太阳能资源评估及开发利用前景分析[D].兰州:兰州大学,2012.
	[ Zhou C R.Solar energy resource assessment of Harbin and analysis of the prospects of development and utilization[D]. Lanzhou: Lanzhou University, 2012. ]
[13]	孙艳伟,王润,肖黎姗,等.中国并网光伏发电系统的经济性与环境效益[J].中国人口·资源与环境,2011,21(4):88-94.
	[ Sun Y W, Wang R, Xiao L S, et al.Economic and environmental analysis of grid-connected photovoltaic systems in China[J]. China Population, Resources and Environment, 2011,21(4):88-94. ]
[14]	陈东坡. 2016-2017年中国光伏回顾与展望[J].电子产品世界,2017(4):9-11.
	[ Chen D P. Review and prospect of China's photovoltaic in 2016 - 2017[J]. Electronic Engineering and Product World, 2017(4):9-11. ]
[15]	国家能源局.太阳能发展“十三五”规划[J].太阳能,2016(12):5-14.
	[ National Energy Administration.The “Thirteen-Five” Development plan of solar energy[J]. Solar Energy, 2016(12):5-14. ]
[16]	QX/T89-2008,太阳能资源评估方法[S].
	[ QX/T89-2008, Assessment Method of Solar Energy Resources[S]. ]
[17]	舒松,余柏蒗,吴健平,等.基于夜间灯光数据的城市建成区提取方法评价与应用[J].遥感技术与应用,2011,26(2):169-176.
	[ Shu S, Yu B L, Wu J P, et al.Method for deriving urban built-up area using night-light data: Assessment and application[J]. Remote Sensing Technology and Application, 2011,26(2):169-176. ]
[18]	辛培裕. 太阳能发电技术的综合评价及应用前景研究[D].北京:华北电力大学,2015.
	[ Xin P Y.Research on comprehensive evaluation and application prospect of solar sower technology[D]. Beijing: North China Electric Power University, 2015. ]
[19]	李柯,何凡能.中国陆地太阳能资源开发潜力区域分析[J].地理科学进展,2010,29(9):1049-1054.
	[ Li K, He F N.Analysis on distribution and potential of solar energy in Chinese mainland[J]. Progress in Geography, 2010,29(9):1049-1054. ]
[20]	Uyan M.GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey[J]. Renewable & Sustainable Energy Reviews, 2013,28(8):11-17.
[21]	李颖.2016年我国光伏装机数据简析[ED/OL]. .
	[ Li Y. A brief analysis of China 's photovoltaic installation data in 2016[ED/OL].. ]

太阳总辐射年总量/MJ·m^-2·a^-1	资源丰富程度
≥6300	最丰富
5040~6300	很丰富
3780~5400	丰富
≤3780	一般

太阳能资源稳定度指标	稳定度
<2	稳定
2-4	较稳定
>4	不稳定

适宜性分数	太阳总辐射/MJ·m^-2	坡向/°
1	0~3780	北（0~22.5）（337.5~360）
2	3780~4200	东北（22.5~67.5）
3	4200~4600	东（67.5~112.5）
4	4600~5040	西北（292.5~337.5）
5	5040~5500	西（247.5~292.5）
6	5500~5900
7	5900~6300
8	6300~6800	东南（112.5~157.5）
9	6800~7300	西南（202.5~247.5）
10	7300~8117	南（157.5~202.5）;平面

适宜性分数	离城镇的距离/km	离路网的距离/km	日照时数的稳定度
10	0~10	0~10	1-1.5
9	10~20	10~20	1.5-2
8	20~40	20~40	2-2.5
7	40~60	40~60	2.5-3
6	60~80	60~80	3-3.5
5	80~100	80~100	3.5-4
4	100~130	100~130	4-6
3	130~160	130~160	6-10
2	160~200	160~200	10-20
1	200~500	200~400	20-31

	太阳总辐射	日照时数的稳定度	离路网的距离	离城镇的距离	坡向
太阳总辐射	1.00	3.00	4.00	5.00	6.00
日照时数的稳定度	0.33	1.00	2.00	2.00	4.00
离路网的距离	0.25	0.50	1.00	2.00	3.00
离城镇的距离	0.20	0.50	0.50	1.00	3.00
坡向	0.17	0.25	0.33	0.33	1.00

基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析

Suitability Analysis of Photovoltaic Power Plants in China Using Remote Sensing Data and Multi-criteria Evaluation

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 21

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	黄宗财, 陆锋, 仇培元, 彭澎. 网络文本蕴含地理信息质量评估框架[J]. 地球信息科学学报, 2023, 25(6): 1121-1134.
[2]	杨颖频, 吴志峰, 黄启厅, 骆剑承, 吴田军, 董文, 胡晓东, 肖文菊. 协同遥感与统计数据的粤西甘蔗种植分布提取及时空分析[J]. 地球信息科学学报, 2023, 25(5): 1012-1026.
[3]	刘潇, 刘智, 林雨准, 王淑香, 左溪冰. 面向遥感影像场景分类的类中心知识蒸馏方法[J]. 地球信息科学学报, 2023, 25(5): 1050-1063.
[4]	衡雪彪, 许捍卫, 唐璐, 汤恒, 许怡蕾. 基于改进全卷积神经网络模型的土地覆盖分类方法研究[J]. 地球信息科学学报, 2023, 25(3): 495-509.
[5]	于方圆, 曹家玮, 李发源, 李思进. 顾及对象特征的地面式光伏电站提取及减碳效益评估[J]. 地球信息科学学报, 2023, 25(3): 529-545.
[6]	高鹏飞, 曹雪峰, 李科, 游雄. 融合多元稀疏特征与阶层深度特征的遥感影像目标检测[J]. 地球信息科学学报, 2023, 25(3): 638-653.
[7]	秦肖伟, 程博, 杨志平, 李林, 董文, 张新, 杨树文, 靳宗义, 薛庆. 基于时序遥感影像的西南山区地块尺度作物类型识别[J]. 地球信息科学学报, 2023, 25(3): 654-668.
[8]	陈凯, 雷少华, 代文, 王春, 刘爱利, 李敏. 基于开源数据和条件生成对抗网络的地形重建方法[J]. 地球信息科学学报, 2023, 25(2): 252-264.
[9]	饶子昱, 卢俊, 郭海涛, 余东行, 侯青峰. 利用视角转换的跨视角影像匹配方法[J]. 地球信息科学学报, 2023, 25(2): 368-379.
[10]	王龙号, 蓝朝桢, 姚富山, 侯慧太, 武蓓蓓. 多源遥感影像深度特征融合匹配算法[J]. 地球信息科学学报, 2023, 25(2): 380-395.
[11]	甘聪聪, 邱炳文, 张建阳, 姚铖鑫, 叶智燕, 黄姮, 黄莹泽, 彭玉凤, 林艺真, 林多多, 苏中豪. 基于Sentinel-1/2动态耦合移栽期特征的水稻种植模式识别[J]. 地球信息科学学报, 2023, 25(1): 153-162.
[12]	于明洋, 陈肖娴, 张文焯, 刘耀辉. 融合网格注意力阀门和特征金字塔结构的高分辨率遥感影像建筑物提取[J]. 地球信息科学学报, 2022, 24(9): 1785-1802.
[13]	吴亚楠, 郭长恩, 于东平, 段爱民, 刘玉, 董士伟, 单东方, 吴耐明, 李西灿. 基于不确定性分析的遥感分类空间分层及评估方法[J]. 地球信息科学学报, 2022, 24(9): 1803-1816.
[14]	吕爱锋, 亓珊珊. 遥感及再分析降水产品在缺资料干旱内陆盆地的适用性评估[J]. 地球信息科学学报, 2022, 24(9): 1817-1834.
[15]	廖周伟, 关燕宁, 郭杉, 蔡丹路, 于敏, 姚武韬, 张春燕, 邓锐. 基于网格的街区尺度城市绿度度量方法[J]. 地球信息科学学报, 2022, 24(8): 1475-1487.