IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3380-d1066405.html
   My bibliography  Save this article

Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations

Author

Listed:
  • Hou Jiang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Ning Lu

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Xuecheng Wang

    (School of Geography and Planning, Nanning Normal University, Nanning 530001, China)

Abstract

Developing rooftop photovoltaic (PV) has become an important initiative for achieving carbon neutrality in China, but the carbon reduction potential assessment has not properly considered the spatial and temporal variability of PV generation and the curtailment in electricity dispatch. In this study, we propose a technical framework to fill the gap in assessing carbon reduction potential through remote sensing data-driven simulations. The spatio-temporal variations in rooftop PV generations were simulated on an hourly basis, and a dispatch analysis was then performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results showed that the total rooftop PV potential in China reached 6.5 PWh yr −1 , mainly concentrated in the eastern region where PV generation showed high variability. The carbon reduction from 100% flexible grids with 12 h of storage capacity is close to the theoretical maximum, while without storage, the potential may be halved. To maximize the carbon reduction potential, rooftop PV development should consider grid characteristics and regional differences. This study has important implications for the development of rooftop PV and the design of carbon-neutral pathways based on it.

Suggested Citation

  • Hou Jiang & Ning Lu & Xuecheng Wang, 2023. "Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3380-:d:1066405
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3380/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3380/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Taborianski, Vanessa Montoro & Pacca, Sergio Almeida, 2022. "Carbon dioxide emission reduction potential for low income housing units based on photovoltaic systems in distinct climatic regions," Renewable Energy, Elsevier, vol. 198(C), pages 1440-1447.
    2. Knuepfer, K. & Rogalski, N. & Knuepfer, A. & Esteban, M. & Shibayama, T., 2022. "A reliable energy system for Japan with merit order dispatch, high variable renewable share and no nuclear power," Applied Energy, Elsevier, vol. 328(C).
    3. Smriti Mallapaty, 2020. "How China could be carbon neutral by mid-century," Nature, Nature, vol. 586(7830), pages 482-483, October.
    4. Hong, Taehoon & Koo, Choongwan & Park, Joonho & Park, Hyo Seon, 2014. "A GIS (geographic information system)-based optimization model for estimating the electricity generation of the rooftop PV (photovoltaic) system," Energy, Elsevier, vol. 65(C), pages 190-199.
    5. Mohajeri, Nahid & Assouline, Dan & Guiboud, Berenice & Bill, Andreas & Gudmundsson, Agust & Scartezzini, Jean-Louis, 2018. "A city-scale roof shape classification using machine learning for solar energy applications," Renewable Energy, Elsevier, vol. 121(C), pages 81-93.
    6. Cheng, C.L. & Sanchez Jimenez, Charles S. & Lee, Meng-Chieh, 2009. "Research of BIPV optimal tilted angle, use of latitude concept for south orientated plans," Renewable Energy, Elsevier, vol. 34(6), pages 1644-1650.
    7. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    8. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    9. Zhang, Xian & Wang, Jia-Xing & Cao, Zhe & Shen, Shuo & Meng, Shuo & Fan, Jing-Li, 2021. "What is driving the remarkable decline of wind and solar power curtailment in China? Evidence from China and four typical provinces," Renewable Energy, Elsevier, vol. 174(C), pages 31-42.
    10. Jacques, David A. & Gooding, James & Giesekam, Jannik J. & Tomlin, Alison S. & Crook, Rolf, 2014. "Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)," Applied Energy, Elsevier, vol. 124(C), pages 28-34.
    11. Bravo, Ruben & Ortiz, Carlos & Chacartegui, Ricardo & Friedrich, Daniel, 2021. "Multi-objective optimisation and guidelines for the design of dispatchable hybrid solar power plants with thermochemical energy storage," Applied Energy, Elsevier, vol. 282(PB).
    12. Liu, Yujun & Yao, Ling & Jiang, Hou & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2022. "Spatial estimation of the optimum PV tilt angles in China by incorporating ground with satellite data," Renewable Energy, Elsevier, vol. 189(C), pages 1249-1258.
    13. Sánchez de la Nieta, Agustín A. & Paterakis, Nikolaos G. & Gibescu, Madeleine, 2020. "Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping," Applied Energy, Elsevier, vol. 266(C).
    14. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    15. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    16. Wang, Bangjun & Ji, Feng & Zheng, Jie & Xie, Kejia & Feng, Zhaolei, 2021. "Carbon emission reduction of coal-fired power supply chain enterprises under the revenue sharing contract: Perspective of coordination game," Energy Economics, Elsevier, vol. 102(C).
    17. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
    2. Liu, Yujun & Yao, Ling & Jiang, Hou & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2022. "Spatial estimation of the optimum PV tilt angles in China by incorporating ground with satellite data," Renewable Energy, Elsevier, vol. 189(C), pages 1249-1258.
    3. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    4. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    5. Zhixin Zhang & Min Chen & Teng Zhong & Rui Zhu & Zhen Qian & Fan Zhang & Yue Yang & Kai Zhang & Paolo Santi & Kaicun Wang & Yingxia Pu & Lixin Tian & Guonian Lü & Jinyue Yan, 2023. "Carbon mitigation potential afforded by rooftop photovoltaic in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
    7. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    8. Gyanwali, Khem & Komiyama, Ryoichi & Fujii, Yasumasa, 2020. "Representing hydropower in the dynamic power sector model and assessing clean energy deployment in the power generation mix of Nepal," Energy, Elsevier, vol. 202(C).
    9. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    10. Hannes Koch & Stefan Lechner & Sebastian Erdmann & Martin Hofmann, 2022. "Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany," Energies, MDPI, vol. 15(19), pages 1-17, September.
    11. Job Taminiau & John Byrne & Jongkyu Kim & Min‐Hwi Kim & Jeongseok Seo, 2022. "Inferential‐ and measurement‐based methods to estimate rooftop “solar city” potential in megacity Seoul, South Korea," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(5), September.
    12. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    13. Özdemir, Samed & Yavuzdoğan, Ahmet & Bilgilioğlu, Burhan Baha & Akbulut, Zeynep, 2023. "SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data," Renewable Energy, Elsevier, vol. 216(C).
    14. Liao, Xuan & Zhu, Rui & Wong, Man Sing & Heo, Joon & Chan, P.W. & Kwok, Coco Yin Tung, 2023. "Fast and accurate estimation of solar irradiation on building rooftops in Hong Kong: A machine learning-based parameterization approach," Renewable Energy, Elsevier, vol. 216(C).
    15. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    16. Buffat, René & Grassi, Stefano & Raubal, Martin, 2018. "A scalable method for estimating rooftop solar irradiation potential over large regions," Applied Energy, Elsevier, vol. 216(C), pages 389-401.
    17. Elham Fakhraian & Marc Alier & Francesc Valls Dalmau & Alireza Nameni & Maria José Casañ Guerrero, 2021. "The Urban Rooftop Photovoltaic Potential Determination," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    18. Gooding, James & Crook, Rolf & Tomlin, Alison S., 2015. "Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy, Elsevier, vol. 148(C), pages 93-104.
    19. Cárdenas, Bruno & Swinfen-Styles, Lawrie & Rouse, James & Hoskin, Adam & Xu, Weiqing & Garvey, S.D., 2021. "Energy storage capacity vs. renewable penetration: A study for the UK," Renewable Energy, Elsevier, vol. 171(C), pages 849-867.
    20. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3380-:d:1066405. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.