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An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

2022-08-12

天津大学车延博等：基于卫星遥感影像的区域屋顶光伏潜力评估框架

摘要

本文提出了一个评估区域屋顶光伏潜力的综合框架。首先，基于高分7号卫星遥感图像，通过语义分割网络获得屋顶面积信息，并且根据数字表面模型计算得到屋顶方位角和倾斜角信息。此外，为了提高经济性评估的准确性，建筑物被分为工商业建筑以及住宅建筑。之后，依据获得的倾斜角信息，屋顶被分为平屋顶和坡屋顶，并且分别采用最佳倾斜角和平铺的方式安装光伏板，依据面积信息计算得到屋顶光伏的装机容量。基于各向同性天空换位模型计算光伏阵列表面的太阳辐射量，以得到屋顶光伏的发电潜力。最后，采用净现值、动态投资回收期和内部收益率指标评估了屋顶光伏的经济效益。本文提出的框架被应用于北京市大兴区总面积为546.84平方公里的区域。结果显示，屋顶面积和可用的光伏装机容量分别为25.63平方公里和1487.45 MWp，屋顶光伏的年发电量潜力为2832.23GWh，并且具有较高的经济效益。

An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

基于卫星遥感影像的区域屋顶光伏潜力评估框架

Boyu Chen¹, Yanbo Che¹, Jingkai Wang², Hongfeng Li¹, Linjun Yu², Dacheng Wang²

(1. Key Laboratory of Smart Grid of Education Ministry, Tianjin University, Tianjin 300072, P. R. China

2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, P. R. China)

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An estimation framework of regional rooftop photovoltaic po

Abstract

This paper proposes a comprehensive framework for estimating the regional rooftop photovoltaic (PV) potential. The required rooftop information is extracted from Gao Fen-7 satellite images. In particular, the rooftop area is obtained using a semantic segmentation network. The azimuth and inclination angles are calculated based on the digital surface model. In addition, to improve the accuracy of the economic evaluation, buildings are divided into commercial and industrial buildings and residential buildings. Based on the difference in the roof inclination, the rooftops can be divided into flat roofs, on which the PV panels are installed with the optimal inclination angle, and sloped rooftops, on which the PV panels are installed in a lay-flat manner. The solar irradiation on the plane-of-array is calculated using the isotropic sky translocation model. Then, the available installed capacity and generation potential of the rooftop PV is obtained. Finally, the net present value, dynamic payback period, and internal rate of return are used to evaluate the economic efficiency of the rooftop PV project. The proposed framework is applied in the Da Xing district of Beijing, China, with a total area of 546.84 km2. The results show that the rooftop area and available installed capacity of PV are 25.63 km2 and 1487.45 MWp, respectively. The annual rooftop PV generation potential is 2832.23 GWh, with significant economic returns.

Keywords

Rooftop PV, Potential estimation, Economic efficiency evaluation, GF7 satellite image.

Fig.1 The framework for regional rooftop PV potential estimation

Fig.2 GF7 stereo image pair in the experimental area

Fig.3 Prediction map of the building outline

Fig.4 DSM, DEM, and nDSM (left to right)

Fig.5 Pixel point e and its neighboring points

Fig.6 Schematic diagram of the flat rooftop PV array

Fig.7 Scope of the study area

Fig.8 Electricity prices for C&I and residential users

Fig.9 The proportion of different types of sloped rooftops in C&I users

Fig.10 The proportion of different types of sloped rooftops in residential users

Fig.11 Average daily PV generation potential in different months

Fig.12 Results of economic efficiency evaluation under different self-consumption ratio

本文引文信息

Chen BY, Che YB, Wang JK, et al. (2022) An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images. Global Energy Interconnection, 5(3):281-292

陈伯煜，车延博，王靖凯等 (2022) 基于卫星遥感影像的区域屋顶光伏潜力评估框架. 全球能源互联网（英文）, 5(3):281-292

Biographies

Boyu Chen

Boyu Chen received his B.S. and M.S. degrees at Tianjin University, Tianjin, in 2018 and 2021, respectively. He is working towards his Ph.D. degree in electrical engineering at Tianjin University, Tianjin. His research interests include photovoltaic generation and active distribution networks.

Yanbo Che

Yanbo Che received his B.S. degree from Zhejiang University, Hangzhou, China, in 1993. He received his M.S. and Ph.D. degrees from Tianjin University, Tianjin, China, in 1996 and 2002, respectively. Since 1996, he has been engaged in teaching and scientific research on power electronic technology and power systems. He is presently a Professor in the School of Electrical and Information Engineering at Tianjin University. His current research interests include power systems, renewable energy resources, and microgrids.

Jingkai Wang

Jingkai Wang is from Zhengzhou, Henan, and working towards his Master's degree at the Chinese Academy of Sciences. His research interest is cartography and geographical information system.

Hongfeng Li

Hongfeng Li was born in Tianjin. China. in 1979. She received the M.S. degree in intelligent control of electric drive systems and the Ph.D. degree in magnetic field analysis from Tianjin University, Tianjin, China, in 2005 and 2008, respectively. She is currently an Associate Professor in the School of Electrical Engineering and Automation,

Tianjin University, Tianjin. Her current research interests include photovoltaic generation and electrical machines.

Linjun Yu

Linjun Yu is an Assistant Researcher at the National Engineering Research Center of Remote Sensing Application in Aerospace Information Research Institute, Chinese Academy of Sciences. He received his M.S. and Ph.D. degrees from Chia Agriculture University, Beijing, China, in 2004 and 2012, respectively. His research interests include geospatial analysis and simulation.

Dacheng Wang

Dacheng Wang is a Senior Engineer at the National Engineering Research Center of Remote Sensing Application in Aerospace Information Research Institute, Chinese Academy of Sciences. He received his M.S. and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 2004 and 2012, respectively. His research interests include geospatial analysis and smart city projects. Recently, he has been devoted to the research of remote sensing application of low carbon environment in the Daxing functional area of Beijing.

编辑：王彦博

审核：王伟

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An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

天津大学 车延博等：基于卫星遥感影像的区域屋顶光伏潜力评估框架

郑重声明

天津大学车延博等：基于卫星遥感影像的区域屋顶光伏潜力评估框架